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AI Should Augment Human Intelligence, Not Replace It

  • David De Cremer
  • Garry Kasparov

smart machines essay

Artificial intelligence isn’t coming for your job, but it will be your new coworker. Here’s how to get along.

Will smart machines really replace human workers? Probably not. People and AI both bring different abilities and strengths to the table. The real question is: how can human intelligence work with artificial intelligence to produce augmented intelligence. Chess Grandmaster Garry Kasparov offers some unique insight here. After losing to IBM’s Deep Blue, he began to experiment how a computer helper changed players’ competitive advantage in high-level chess games. What he discovered was that having the best players and the best program was less a predictor of success than having a really good process. Put simply, “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.” As leaders look at how to incorporate AI into their organizations, they’ll have to manage expectations as AI is introduced, invest in bringing teams together and perfecting processes, and refine their own leadership abilities.

In an economy where data is changing how companies create value — and compete — experts predict that using artificial intelligence (AI) at a larger scale will add as much as $15.7 trillion to the global economy by 2030 . As AI is changing how companies work, many believe that who does this work will change, too — and that organizations will begin to replace human employees with intelligent machines . This is already happening: intelligent systems are displacing humans in manufacturing, service delivery, recruitment, and the financial industry, consequently moving human workers towards lower-paid jobs or making them unemployed. This trend has led some to conclude that in 2040 our workforce may be totally unrecognizable .

  • David De Cremer is the Provost’s chair and professor in management and organizations at NUS Business School, National University of Singapore. He is the founder and director of the Centre on AI Technology for Humankind at NUS Business school and author of Leadership by Algorithm: Who leads and who follows in the AI era? (2020). Before moving to NUS, he was the KPMG endowed chaired professor in management studies and current honorary fellow at Cambridge Judge Business School and fellow at St. Edmunds College, Cambridge University. From July 2023 onwards, he will be the new Dunton Family Dean of D’Amore McKim School of Business at Northeastern University. His website is .
  • Garry Kasparov is the chairman of the Human Rights Foundation and founder of the Renew Democracy Initiative. He writes and speaks frequently on politics, decision-making, and human-machine collaboration. Kasparov became the youngest world chess champion in history at 22 in 1985 and retained the top rating in the world for 20 years. His famous matches against the IBM super-computer Deep Blue in 1996 and 1997 were key to bringing artificial intelligence, and chess, into the mainstream. His latest book on artificial intelligence and the future of human-plus-machine is Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins (2017).

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A Code of Ethics for Smart Machines

What’s happening this week at the intersection of management and technology.

  • Data, AI, & Machine Learning
  • AI & Machine Learning

smart machines essay

Smart machines need ethics, too: Remember that movie in which a computer asked an impossibly young Matthew Broderick, “ Shall we play a game? ” Four decades later, it turns out that global thermonuclear war may be the least likely of a slew of ethical dilemmas associated with smart machines — dilemmas with which we are only just beginning to grapple.

The worrisome lack of a code of ethics for smart machines has not been lost on Alphabet, Amazon, Facebook, IBM, and Microsoft, according to a report by John Markoff in The New York Times . The five tech giants (if you buy Mark Zuckerberg’s contention that he isn’t running a media company ) have formed an industry partnership to develop and adopt ethical standards for artificial intelligence — an effort that Markoff infers is motivated as much to head off government regulation as to safeguard the world from black-hearted machines.

On the other hand, the first of a century’s worth of quinquennial reports from Stanford’s One Hundred Year Study on Artificial Intelligence (AI100) throws the ethical ball into the government’s court. “American law represents a mixture of common law, federal, state, and local statutes and ordinances, and — perhaps of greatest relevance to AI — regulations,” its authors declare. “Depending on its instantiation, AI could implicate each of these sources of law.” But they don’t offer much concrete guidance to lawmakers or regulators — they say it’s too early in the game to do much more than noodle about where ethical (and legal) issues might emerge.

In the meantime, if you’d like to get a taste for the kinds of ethical decisions that smart machines — like self-driving cars — are already facing, visit MIT’s Moral Machine project . Run through the scenarios and decide for yourself who or what the self-driving car should kill. Aside from the fun of deciding whether to run over two dogs and a pregnant lady or drive two old guys into the concrete barrier, it’ll help the research team create a crowd-sourced view of how humans might expect of ethical machines to act. This essay from UVA’s Bobby Parmar and Ed Freeman will also help fuel your thinking.

Shrugging off blockchain hacks: Speaking of ethics, can anyone tell me how to rip off $100 million or so in bitcoins? It seems like a surefire way to top off my retirement account. Heck, according to a new Reuters article by Gertrude Chavez-Dreyfuss , “a third of bitcoin trading platforms have been hacked, and nearly half have closed in the half dozen years since they burst on the scene.”

This seems like a pretty abysmal reflection on blockchains — the distributed ledger technology behind bitcoins that is supposed to secure just about every kind of asset transaction known to humankind. But it doesn’t seem to be slowing adoptions. One of the latest was just announced by UBS, which, reports Jemima Kelly in Reuters , has teamed up with three other major banks to make payments and settle transactions using blockchain technology.

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“Blockchain projects such as this have the potential to shake up the settlement system used by banks, under which transactions can take several days to finalize and which costs the financial industry $65-$80 billion a year,” writes Kelly, who adds that an estimated 80% of the world’s commercial banks will have launched blockchain projects by next year.

In case you’re not entirely clear on what blockchain is or why it’s so popular these days, Don Tapscott’s newly-posted TED talk is worth a listen. “For the first time now in human history, people everywhere can trust each other and transact peer-to-peer,” says Tapscott of blockchain technology. “And trust is established not by some big institution, but by collaboration, by cryptography, and by some clever code.”

Sounds promising, but given all of those bitcoin hacks, does that clever code need an update?

Goal-setting on steroids: It’s been a long time since Peter Drucker wove together the threads that became management by objectives (MBO) — the systemic articulation and collective pursuit of business goals. Over the years, many companies embraced the concept, and it evolved into a host of goal-setting systems. And now, with a boost from digital technologies, it’s being supercharged.

One example comes from a company named BetterWorks, which is the subject of a new article in First Round Review . BetterWorks has taken the Objectives and Key Results (OKRs) system developed at Intel and Oracle in the 1980s and adapted by Google , added goal-science principles to it, and embedded it all in software.

The software, reports First Round Review , connects everyone in a company to corporate goals, enhances engagement and cross-functional collaboration, and continually tracks progress. “The quantified self movement — all these activity trackers like FitBit and Jawbone — have proven that people want to get frequent, measurable, visual and — this is key — graphical feedback,” explains BetterWorks CEO William Duggan. “When individuals get this kind of positive, visual feedback, it literally shapes their behavior to take a more measured and consistent approach toward their goals.”

A digitized goal-setting system also allows for very granular approach to goal setting and attainment: In 2015, for example, a 1,000-employee Internet company adopted BetterWorks software and set 20,000 objectives with 80,000 key results.

About the Author

Theodore Kinni is a business journalist, author, and ghostwriter. He blogs at Reading, Writing re: Management and tweets @tedkinni .

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Michael zeldich.

Artificial Intelligence .

What is artificial intelligence (ai) how does ai work.

smart machines essay

What Is Artificial Intelligence?

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with multiple approaches, advancements in machine learning and deep learning, in particular, are creating a paradigm shift in virtually every industry.

Artificial intelligence allows machines to model, or even improve upon, the capabilities of the human mind. And from the development of self-driving cars to the proliferation of generative AI tools, AI is increasingly becoming part of everyday life.

Artificial intelligence refers to computer systems that can perform tasks commonly associated with human cognitive functions — such as interpreting speech, playing games and identifying patterns. Typically, AI systems learn how to do so by processing massive amounts of data and looking for patterns to model in their own decision-making. In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones. But some AI systems are designed to learn without supervision; for instance, by playing a game over and over until they eventually figure out the rules and how to win.

Strong AI vs. Weak AI

Artificial intelligence is often distinguished between weak AI and strong AI . Weak AI (or narrow AI) refers to AI that automates specific tasks, typically outperforming humans but operating within constraints. Strong AI (or artificial general intelligence) describes AI that can emulate human learning and thinking, though it remains theoretical for now.

Also called narrow AI, weak AI operates within a limited context and is applied to a narrowly defined problem. It often operates just a single task extremely well. Common weak AI examples include email inbox spam filters, language translators, website recommendation engines and conversational chatbots.

Often referred to as artificial general intelligence (AGI) or simply general AI, strong AI describes a system that can solve problems it’s never been trained to work on, much like a human can. AGI does not actually exist yet. For now, it remains the kind of AI we see depicted in popular culture and science fiction.

How Does AI Work?

Artificial intelligence systems work by using any number of AI techniques.

Machine Learning

A machine learning (ML) algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been programmed for that certain task. It uses historical data as input to predict new output values.

Machine learning consists of both supervised learning (where the expected output for the input is known thanks to labeled data sets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled data sets).

Deep Learning

Deep learning is a type of machine learning that runs inputs through a biologically inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.

Neural Networks

Neural networks are a series of algorithms and a subset of machine learning that process data by mimicking the structure of the human brain. Each neural network is composed of a group of attached neuron models, or nodes, which pass information between each other. These systems allow machines to identify patterns and relationships within data, plus learn from mistakes. This makes neural networks useful for recognizing images, understanding human speech and translating words between languages.

Natural Language Processing 

Natural language processing (NLP) is an area of artificial intelligence concerned with giving machines the ability to interpret written and spoken language in a similar manner as humans. NLP combines computer science, linguistics, machine learning and deep learning concepts to help computers analyze unstructured text or voice data and extract relevant information from it. NLP mainly tackles speech recognition and natural language generation , and it’s leveraged for use cases like spam detection and virtual assistants .

Computer Vision

Computer vision is a field of artificial intelligence in which machines process raw images, videos and visual media, taking useful insights from them. Then deep learning and convolutional neural networks are used to break down images into pixels and tag them accordingly, which helps computers discern the difference between visual shapes and patterns. Computer vision is used for image recognition , image classification and object detection, and completes tasks like facial recognition and detection in self-driving cars.

Types of Artificial Intelligence 

Artificial intelligence is often categorized into four main types of AI : reactive machines, limited memory, theory of mind and self-awareness.

Reactive Machines

As the name suggests, reactive machines perceive the world in front of them and react. They can carry out specific commands and requests, but they cannot store memory or rely on past experiences to inform their decision making in real time. This makes reactive machines useful for completing a limited number of specialized duties.

Examples of reactive machines include Netflix’s recommendation engine and IBM’s Deep Blue (used to play chess).

Limited Memory

Limited memory AI has the ability to store previous data and predictions when gathering information and making decisions. Essentially, it looks into the past for clues to predict what may come next. Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data, or an AI environment is built so models can be automatically trained and renewed.

Examples of limited memory AI systems include some chatbots (like ChatGPT ) and self-driving cars .

Theory of Mind

Theory of mind is a type of AI that does not actually exist yet, but it describes the idea of an AI system that can perceive and understand human emotions , and then use that information to predict future actions and make decisions on its own.


Self-aware AI refers to artificial intelligence that has self-awareness , or a sense of self. This type of AI does not currently exist. In theory, though, self-aware AI possesses human-like consciousness and understands its own existence in the world, as well as the emotional state of others.

Why Is Artificial Intelligence Important?

Artificial intelligence aims to provide machines with similar processing and analysis capabilities as humans, making AI a useful counterpart to people in everyday life. AI is able to interpret and sort data at scale, solve complicated problems and automate various tasks simultaneously, which can save time, effort and fill in operational gaps missed by humans. AI serves as the foundation for computer learning and is used in almost every industry — from healthcare to manufacturing and education — to help make data-driven business decisions and carry out repetitive or computationally intensive tasks.

Many existing technologies use artificial intelligence to enhance user experiences. We see it in smartphones with AI assistants, online platforms with recommendation systems and vehicles with autonomous driving abilities. AI also helps protect people by piloting fraud detection and robotics for dangerous jobs, as well as leading research in healthcare and climate initiatives. 

Benefits of AI

AI is beneficial for automating repetitive tasks, solving complex problems, reducing human error and much more.

Automating Repetitive Tasks

Repetitive tasks such as data entry and factory work , as well as customer service conversations, can all be automated using AI technology. This lets humans focus on other priorities.

Solving Complex Problems

AI’s ability to process large amounts of data at once allows it to quickly find patterns and solve complex problems that may be too difficult for humans, such as predicting financial outlooks or optimizing energy solutions.

Improving Customer Experience

AI can be applied through user personalization, chatbots and automated self-service technologies, making the customer experience more seamless and increasing customer retention for businesses.

Advancing Healthcare and Medicine

AI works to advance healthcare by accelerating medical diagnoses, drug discovery and development and medical robot implementation throughout hospitals and care centers.

Reducing Human Error

The ability to quickly identify relationships in data makes AI effective for catching mistakes or anomalies among mounds of digital information, overall reducing human error and ensuring accuracy.

Disadvantages of AI

While artificial intelligence has its benefits, the technology also comes with risks and potential dangers to consider.

Job Displacement

AI’s abilities to automate processes, generate rapid content and work for long periods of time can mean job displacement for human workers.

Bias and Discrimination

AI models may be trained on data that reflects biased human decisions, leading to outputs that are biased or discriminatory against certain demographics. 

Privacy Concerns

The data collected and stored by AI systems may be done so without user consent or knowledge, and may even be accessed by unauthorized individuals in the case of a data breach.

Ethical Concerns

AI systems may be developed in a manner that isn’t transparent, inclusive or sustainable , resulting in a lack of explanation for potentially harmful AI decisions as well as a negative impact on users and businesses.

Environmental Costs

Large-scale AI systems can require a substantial amount of energy to operate and process data, which increases carbon emissions and water consumption.

Artificial Intelligence Applications

Artificial intelligence has applications across multiple industries, ultimately helping to streamline processes and boost business efficiency.

AI is used in healthcare to improve the accuracy of medical diagnoses, facilitate drug research and development, manage sensitive healthcare data and automate online patient experiences. It is also a driving factor behind medical robots, which work to provide assisted therapy or guide surgeons during surgical procedures.

AI in retail amplifies the customer experience by powering user personalization, product recommendations, shopping assistants and facial recognition for payments. For retailers and suppliers, AI helps automate retail marketing, identify counterfeit products on marketplaces, manage product inventories and pull online data to identify product trends.


AI in manufacturing can reduce assembly errors and production times while increasing worker safety. Factory floors may be monitored by AI systems to help identify incidents, track quality control and predict potential equipment failure. AI also drives factory and warehouse robots, which can automate manufacturing workflows and handle dangerous tasks. 

The finance industry utilizes AI to detect fraud in banking activities, assess financial credit standings, predict financial risk for businesses plus manage stock and bond trading based on market patterns. AI is also implemented across fintech and banking apps, working to personalize banking and provide 24/7 customer service support.

Video game developers apply AI to make gaming experiences more immersive . Non-playable characters (NPCs) in video games use AI to respond accordingly to player interactions and the surrounding environment, creating game scenarios that can be more realistic, enjoyable and unique to each player. 

AI assists militaries on and off the battlefield, whether it's to help process military intelligence data faster, detect cyberwarfare attacks or automate military weaponry, defense systems and vehicles. Drones and robots in particular may be imbued with AI , making them applicable for autonomous combat or search and rescue operations.

Artificial Intelligence Examples

Specific examples of AI include:

Generative AI Tools

Generative AI tools, sometimes referred to as chatbots — including ChatGPT , Gemini , Claude and Grok — use artificial intelligence to produce written content in a range of formats, from essays to code and answers to simple questions.

Smart Assistants

Personal AI assistants , like Alexa and Siri, use natural language processing to receive instructions from users to perform a variety of ‘ smart tasks .’ They can carry out commands like setting reminders, searching for online information or turning off your kitchen lights.

Self-Driving Cars

Self-driving cars are a recognizable example of deep learning, since they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signals and much more.

Many wearable sensors and devices used in the healthcare industry apply deep learning to assess the health condition of patients, including their blood sugar levels, blood pressure and heart rate. They can also derive patterns from a patient’s prior medical data and use that to anticipate any future health conditions.

Visual Filters

Filters used on social media platforms like TikTok and Snapchat rely on algorithms to distinguish between an image’s subject and the background, track facial movements and adjust the image on the screen based on what the user is doing.

The Rise of Generative AI

Generative AI describes artificial intelligence algorithms that can create new content — such as text, images, video or audio — based on a given user prompt. To work, a generative AI model is fed massive data sets and trained to identify patterns within them, then subsequently generates outputs that resemble this training data. Generative AI uses machine learning, neural networks, and deep learning-based large language models to produce its content.

Generative AI has gained massive popularity in the past few years, especially with chatbots like ChatGPT, Gemini and Claude — as well as image generators such as DALL-E 2 and Midjourney — arriving on the scene. These kinds of tools are often used to create written copy, code, digital art, object designs and more. They are leveraged in industries like entertainment, marketing, consumer goods and manufacturing.

AI Regulation

As artificial intelligence algorithms grow more complex and powerful, AI technologies — and the companies that create them — have increasingly drawn scrutiny from regulators across the world.

In 2021, the European Union Parliament proposed a regulatory framework that aims to ensure AI systems deployed within the European Union are “safe, transparent, traceable, non-discriminatory and environmentally friendly.” Under this framework, AI systems that can be used to perform real-time surveillance, or to manipulate people, categorize populations or discriminate against vulnerable groups, would be banned from use within the EU (though some limited exceptions may be made for law enforcement purposes).

In 2022, the Biden White House introduced an AI Bill of Rights , outlining principles for responsible use of AI. And in 2023, the Biden-Harris administration introduced The Executive Order on Safe, Secure and Trustworthy AI , which aims to regulate the AI industry while maintaining the United States’ status as a leader in artificial intelligence innovation.

The order requires the companies operating large AI systems to perform safety testing and report results to the federal government before making their products publicly available. It also calls for labeling of AI-generated content and increased efforts to answer questions about the impact of AI on intellectual property rights. Additionally, the executive order calls for several worker protections including against unsafe AI implementation and harmful disruptions of the labor force. The order also calls for the United States government to work alongside other countries to establish global standards for mitigating the risks of AI and promoting AI safety more generally.

Future of Artificial Intelligence 

In the near future , AI is poised to advance in machine learning capabilities and related frameworks like generative adversarial networks (GANs), which can help further develop generative AI and autonomous systems. Inevitably, AI will continue to make an impact across multiple industries, potentially causing job displacement, but also new job opportunities.

Looking ahead, one of the next big steps for artificial intelligence is to progress beyond weak or narrow AI and achieve artificial general intelligence (AGI). With AGI, machines will be able to think, learn and act the same way as humans do, blurring the line between organic and machine intelligence. This could pave the way for increased automation and problem-solving capabilities in medicine, transportation and more — as well as sentient AI down the line.

While likely groundbreaking, future advancements in AI have raised concerns such as heightened job loss, widespread disinformation, unpredictable AI behavior and possible moral dilemmas associated with reaching technological singularity . 

For now, society is largely looking toward federal and business-level AI regulations to help guide the technology’s future.

History of AI

Artificial intelligence as a concept began to take off in the 1950s when computer scientist Alan Turing released the paper “ Computing Machinery and Intelligence ,” which questioned if machines could think and how one would test a machine’s intelligence. This paper set the stage for AI research and development, and was the first proposal of the Turing test , a method used to assess machine intelligence. The term “artificial intelligence” was coined in 1956 by computer scientist John McCartchy in an academic conference at Dartmouth College.

Following McCarthy’s conference and throughout the 1970s, interest in AI research grew from academic institutions and U.S. government funding. Innovations in computing allowed several AI foundations to be established during this time, including machine learning, neural networks and natural language processing. Despite its advances, AI technologies eventually became more difficult to scale than expected and declined in interest and funding, resulting in the first AI winter until the 1980s.

In the mid-1980s, AI interest reawakened as computers became more powerful, deep learning became popularized and AI-powered “expert systems” were introduced. However, due to the complication of new systems and an inability of existing technologies to keep up, the second AI winter occurred and lasted until the mid-1990s.

By the mid-2000s, innovations in processing power, big data and advanced deep learning techniques resolved AI’s previous roadblocks, allowing further AI breakthroughs. Modern AI technologies like virtual assistants, driverless cars and generative AI began entering the mainstream in the 2010s, making AI what it is today.

  • (1942) Isaac Asimov publishes the Three Laws of Robotics , an idea commonly found in science fiction media about how artificial intelligence should not bring harm to humans.
  • (1943) Warren McCullough and Walter Pitts publish the paper “ A Logical Calculus of Ideas Immanent in Nervous Activity ,” which proposes the first mathematical model for building a neural network. 
  • (1949) In his book The Organization of Behavior: A Neuropsychological Theory , Donald Hebb proposes the theory that neural pathways are created from experiences and that connections between neurons become stronger the more frequently they’re used. Hebbian learning continues to be an important model in AI.
  • (1950) Alan Turing publishes the paper “Computing Machinery and Intelligence,” proposing what is now known as the Turing Test, a method for determining if a machine is intelligent. 
  • (1950) Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC , the first neural network computer.
  • (1950) Claude Shannon publishes the paper “ Programming a Computer for Playing Chess .”
  • (1952) Arthur Samuel develops a self-learning program to play checkers. 
  • (1954) The Georgetown-IBM machine translation experiment automatically translates 60 carefully selected Russian sentences into English. 
  • (1956) The phrase “artificial intelligence” is coined at the Dartmouth Summer Research Project on Artificial Intelligence. Led by John McCarthy, the conference is widely considered to be the birthplace of AI.
  • (1956) Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program. 
  • (1958) John McCarthy develops the AI programming language Lisp and publishes “ Programs with Common Sense ,” a paper proposing the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans.  
  • (1959) Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving. 
  • (1959) Herbert Gelernter develops the Geometry Theorem Prover program.
  • (1959) Arthur Samuel coins the term “machine learning” while at IBM.
  • (1959) John McCarthy and Marvin Minsky found the MIT Artificial Intelligence Project.
  • (1963) John McCarthy starts the AI Lab at Stanford.
  • (1966) The Automatic Language Processing Advisory Committee (ALPAC) report by the U.S. government details the lack of progress in machine translations research, a major Cold War initiative with the promise of automatic and instantaneous translation of Russian. The ALPAC report leads to the cancellation of all government-funded MT projects. 
  • (1969) The first successful expert systems, DENDRAL and MYCIN, are created at Stanford.
  • (1972) The logic programming language PROLOG is created.
  • (1973) The Lighthill Report, detailing the disappointments in AI research, is released by the British government and leads to severe cuts in funding for AI projects. 
  • (1974-1980) Frustration with the progress of AI development leads to major DARPA cutbacks in academic grants. Combined with the earlier ALPAC report and the previous year’s Lighthill Report, AI funding dries up and research stalls. This period is known as the “ First AI Winter .”
  • (1980) Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first AI Winter.
  • (1982) Japan’s Ministry of International Trade and Industry launches the ambitious Fifth Generation Computer Systems project. The goal of FGCS is to develop supercomputer-like performance and a platform for AI development.
  • (1983) In response to Japan’s FGCS, the U.S. government launches the Strategic Computing Initiative to provide DARPA funded research in advanced computing and AI. 
  • (1985) Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp. 
  • (1987-1993) As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the “ Second AI Winter .” During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.
  • (1991) U.S. forces deploy DART, an automated logistics planning and scheduling tool, during the Gulf War.
  • (1992) Japan terminates the FGCS project in 1992, citing failure in meeting the ambitious goals outlined a decade earlier.
  • (1993) DARPA ends the Strategic Computing Initiative in 1993 after spending nearly $1 billion and falling far short of expectations. 
  • (1997) IBM’s Deep Blue beats world chess champion Gary Kasparov.
  • (2005) STANLEY , a self-driving car, wins the DARPA Grand Challenge.
  • (2005) The U.S. military begins investing in autonomous robots like Boston Dynamics’ “Big Dog” and iRobot’s “PackBot.”
  • (2008) Google makes breakthroughs in speech recognition and introduces the feature in its iPhone app.
  • (2011) IBM’s Watson handily defeats the competition on Jeopardy!. 
  • (2011) Apple releases Siri, an AI-powered virtual assistant through its iOS operating system. 
  • (2012) Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network using deep learning algorithms 10 million YouTube videos as a training set. The neural network learned to recognize a cat without being told what a cat is, ushering in the breakthrough era for neural networks and deep learning funding.
  • (2014) Google makes the first  self-driving car to pass a state driving test. 
  • (2014) Amazon’s Alexa, a virtual home smart device , is released.
  • (2016) Google DeepMind’s  AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.
  • (2016) The first “robot citizen,” a humanoid robot named Sophia, is created by Hanson Robotics and is capable of facial recognition, verbal communication and facial expression.
  • (2018) Google releases natural language processing engine  BERT , reducing barriers in translation and understanding by ML applications.
  • (2018)  Waymo launches its Waymo One service, allowing users throughout the Phoenix metropolitan area to request a pick-up from one of the company’s self-driving vehicles.
  • (2020) Baidu releases its LinearFold AI algorithm to scientific and medical teams working to develop a vaccine during the early stages of the SARS-CoV-2 pandemic. The algorithm is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than other methods.
  • (2020) OpenAI releases natural language processing model GPT-3 , which is able to produce text modeled after the way people speak and write. 
  • (2021) The European Union Parliament proposes a regulatory framework that aims to ensure that AI systems deployed within the EU are “safe, transparent, traceable, non-discriminatory and environmentally friendly.”
  • (2021) OpenAI builds on GPT-3 to develop DALL-E , which is able to create images from text prompts.
  • (2022) The National Institute of Standards and Technology releases the first draft of its AI Risk Management Framework , voluntary U.S. guidance “to better manage risks to individuals, organizations, and society associated with artificial intelligence.”
  • (2022) DeepMind unveils Gato , an AI system trained to perform hundreds of tasks, including playing Atari, captioning images and using a robotic arm to stack blocks.
  • (2022) The White House introduces an AI Bill of Rights outlining principles for the responsible development and use of AI.
  • (2022) OpenAI launches ChatGPT, a chatbot powered by a large language model that gains more than 100 million users in just a few months.
  • (2023) Microsoft launches an AI-powered version of Bing, its search engine, built on the same technology that powers ChatGPT.
  • (2023) Google announces Bard, a competing conversational AI.
  • (2023) OpenAI Launches GPT-4 , its most sophisticated language model yet.
  • (2023) The Biden-Harris administration issues The Executive Order on Safe, Secure and Trustworthy AI , calling for safety testing, labeling of AI-generated content and increased efforts to create international standards for the development and use of AI. The order also stresses the importance of ensuring that artificial intelligence is not used to circumvent privacy protections, exacerbate discrimination or violate civil rights or the rights of consumers.

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In the age of the smart machine.

Shoshana Zuboff is author of the celebrated classic In the Age of the Smart Machine: The Future of Work and Power (1988). This book won instant critical acclaim in both the academic and trade press—including the front page review in The New York Times Book Review – and is widely considered the definitive study of information technology in the workplace. Of particular interest, this book introduced the concept of Informating, the process through which digitalization translates activities, events,  social exchange,and objects into information.


Zuboff’s research consisted of in-depth multi-year studies of office, factory, professional, executive, and craft workplaces all characterized by a recent shift from traditional to computer-mediated task environments. The research demonstrated the tripartite nature of the relationship between information technology and work: 1) technology is not neutral, but embodies intrinsic characteristics that enable new human experiences and foreclose others, 2) within these new “horizons of the possible” individuals and groups construct meaning and make choices, further shaping the situation, and 3) the interplay of intrinsic qualities and human choices is further shaped by social, political, and economic interests that inscribe the situation with their own intended and unintended opportunities and limitations.

According to London School of Economics Professor Jannis Kallinikos’s analysis in “Smart Machines,” an invited essay written in honor of the book’s twenty year anniversary for The Encyclopedia of Software Engineering , In the Age of the Smart Machine is “a profound study of the work implications associated with the extensive involvement of information technology in organizations. The book rapidly gained recognition across a wide spectrum of social science disciplines, including management and organization studies, information systems, social psychology, and sociology, and has been debated and quoted extensively. Twenty years may seem an awfully long time in this age of speed and rapid technological change. But, the Smart Machine, as perhaps every great work, holds out remarkably. The central themes of the book are equally if not more relevant today. Key insights the author develops concerning the nature of information and its relation to reality can be brought to bear on the analysis of phenomena such as the emergence and diffusion of the Internet that were not yet manifest at the time she conducted her study. Indeed, later and significant works on the social and organizational implications of information technology draw in one way or another on the legacy the Smart Machine has left…

The distinctive flavor of the book and its enduring significance are inextricably bound up with the masterly ways Zuboff managed to navigate between the potent but tidy worlds of theory and the inspiring yet messy reality of the workplace. Her work represents long-standing evidence of the fact that theory and concepts if skillfully used may sharpen observation, disclosing aspects of reality that might otherwise have escaped attention…

Written in superb prose, the Smart Machine deserves to be described as a landmark contribution to the cross- disciplinary field of the history and social psychology of work. While a book about information and its significance in restructuring and redefining the patterns and meaning of work, the Smart Machine is much more than a treatise on this subject. Out of the pages of this remarkable book emerge with evocative force the history of work as the bodily struggle to master the resistant materiality of the world through skill but also exertion and toil; the mixed blessings of technology and the forms through which technology liberates, enables, and enslaves at the same time; the stratified character of the workplace and the social struggles that have underlain its formation and its persisting role as an institutional pillar of modern societies; the history of administration and the different social connotations white- and blue-collar work came to embody; the developments of managerial methods and techniques and the relentless discipline they impose in the factory and the office; and, finally, the allure of technology in general and information technology in particular to construct a more fulfilling workplace and the rather disappointing outcome in which automation, driven by the dominant elites and their will to control, erodes and undoes the promise of a transparent and multivalent workplace in which information could have played an enlightening role…

It would be reasonable to conjecture that a book written in the pre-Internet age might well be outdated and no longer relevant. This holds undeniably true for many issues, ideas, or debates that took place during the 1970s and 1980s. However, the case of the Smart Machine is rather different. The central theme of the book concerning the hot issue of whether information technology is or will be used as a means to automation and control or as a way to construct new, less hierarchical, and more rewarding forms of collective engagement and an enlightened workplace is equally, if not more, relevant today. The widely diffused fear of the Orwellian big brother is just an indicator of this, as is the debate of how personal data produced from our online habits and Internet site trajectories will be used. Another highly crucial issue evolves around copyright and the efforts of the culture industry to control and shape the growth of the Internet and the patterns of exchanging ideas and culture. To some degree, time has supported Zuboff’s rather gloomy predictions of the appropriation of the promise of information technology by powerful groups and its concomitant use in ways that, by and large, accommodate the interests of these groups. It is thus more than urgent to revisit that issue…

Another central and highly interesting theme of the book evolves around the relationship of information to reality in general and work reality in particular. The production of information is never an innocent description, a literal, point-by-point mapping of a pre-existing world. The comprehensive rendition of work states and processes to information constructs new realities in the workplace, lifts up factors or processes that have gone unnoticed, raises new problems and opportunities, and defines priorities and relevancies that were not there prior to computerization. By the same token, comprehensive computerization samples and assembles reality in a variety of ways and thus shapes the forms of perceiving and acting upon it. The central and timely character of these issues provides evidence of the persistent relevance of the Smart Machine. One could indeed go as far as to claim that in some respects the book is even more relevant and timely today than it was at the time of its publication.”


Of particular interest, In the Age of the Smart Machine introduced the concept of “Informating”, the process Zuboff described as unique to information technology that translates activities, objects, and events into information. Zuboff characterizes computer-mediated work as distinguished from earlier generations of mechanization and automation designed to deskill jobs and substitute for human labor, because information technology itself is characterized by a unique duality. It can be applied to automate operations according to a logic that hardly differs from that of the nineteenth-century machine system–replace the human body with a technology that enables the same processes to be performed with more continuity and control. But information technology simultaneously generates information about the underlying productive and administrative processes through which an organization accomplishes its work. It provides a deeper level of transparency to activities that had been either partially or completely opaque. It can automate tasks, but also translates its action into information. In this way it symbolically renders events, objects, and processes so that they become visible, knowable, and shareable in a new way. Zuboff referred to this unique capacity as “informating.” As a result of the informating process, work processes become more abstract. Computer-mediated work radically extends organizational codification resulting in a comprehensive “textualization” of the work environment that creates what Zuboff calls “the electronic text.” As information systems theorist Jannis Kallinikos describes it, “A continuously accruing electronic text installs itself at the center stage of work and organizational life.”


According to Kallinikos, “The problems, issues, and opportunities associated with the growing involvement of computer-based records and operations in organizations emerge forcefully in Chapter 5 of the Smart Machine, entitled “Mastering the Electronic Text,” one of the most penetrating and evocative pieces ever written in the century-long history of the administrative sciences. The entry offers the conclusions of the first of the two parts that comprise the book, dealing with the history of work, and the role of technology and knowledge in constructing the modern industrial workplace. With force and almost cunning insight into what is yet to come, Zuboff describes what may well be considered the predicament of this age, that is, the construction of reality out of the cognitive forms the technologies of computing avail. The varieties of technological information that computer technology generates construct an expansive electronic text, which is accruing every single moment by the potent recording and storage capacities of computer technology and its inability, as it were, to forget.”


Zuboff concluded that the essence of computer-mediated work consisted in a blurring of the age-old demarcation between what is called “work” and what is called “learning”, suggesting that the focus of authority systems would shift from a “division of labor” to a “division of learning.” Zuboff’s work foresaw a challenge to the concentrated hierarchies of the industrial era. A full exploitation of information technology’s unique potential would require new distributed and collaborative working arrangements and social relations inimical to the old demands of time, place, physical discipline, and bodily presence. She wrote, “The informated workplace, which may no longer be a ‘place’ at all, is an arena through which information circulates, information to which intellective effort is applied. The quality, rather than the quantity, of effort will be the source from which added value is derived…A new division of learning requires another vocabulary–one of colleagues and co-learners, of exploration, experimentation, and innovation. Jobs are comprehensive, tasks are abstractions that depend upon insights and synthesis, and power is a roving force that comes to rest as dictated by function and need.”


In the Age of the Smart Machine is the source of many concepts that have become widely integrated into the understanding of information technologies and their consequences. These include the abstraction of work associated with information technology and its related skill demands; that information technology can pave the way for more fluid distributed work arrangements; the concept of the “information panopticon”; the duality of information technology as an informating and an automating technology; computer-mediated work; information as a challenge to command/control; the social construction of technology; the collaborative patterns of information work–to name but a few. According to Finnish scholars Hanna Timonen and Kaija-Stiina Paloheimo’s 2008 analysis of the emergence and diffusion of the concept of knowledge work, In the Age of the Smart Machine is one of three late twentieth century books, including Peter Drucker’s In the Age of Discontinuity and Daniel Bell’s The Coming of Post-Industrial Society , that are responsible for the diffusion of the concept of “knowledge work.

A new scholarly article by Andrew Burton-Jones (forthcoming, Information and Organization: 2014) reviews the continuing impact of In the Age of the Smart Machine on IT-oriented organization scholarship.

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Home Essay Samples Science

Intelligent Machines Essays

Intelligent machines, also referred to as smart machines or cognitive systems, are devices or systems equipped with the ability to learn, reason, and autonomously perform tasks. These cutting-edge creations leverage the power of machine learning algorithms, neural networks, and natural language processing to revolutionize industries across the board. From healthcare and manufacturing to finance and transportation, intelligent machines are transforming how we live, work, and interact.

Writing an Intelligent Machines Essay

To create a compelling intelligent machines essay, it is crucial to begin with a succinct introduction that defines the concept and emphasizes its significance in our ever-evolving world. From there, you can delve into the different types of intelligent machines and explore their applications in various fields. Provide concrete examples, such as self-driving cars, virtual personal assistants, or robotic surgery, to illustrate their practical implementation and impact.

As you craft your essay, consider exploring the ethical, social, and economic implications of intelligent machines. Delve into topics such as

  • the ethical responsibility of developers in designing and deploying these systems
  • the potential for job displacement and retraining
  • the implications for privacy and data security, etc.

To enhance your understanding and gather inspiration, we invite you to explore our curated collection of intelligent machines essay example. These examples can serve as valuable references, helping you structure your own essay effectively while offering unique perspectives on the subject. Illuminate the transformative power of these technological marvels and their profound implications for our future.

Technology's Paradox: Increased Profitability & Economic Inequality

Introduction Humans are always looking to find new ways to ease their ways of life, hence coming up with progressive tools to achieve these goals. Society has been through an ever-changing industrial revolution starting with the usage of tools for food production, using electric power...

  • Effects of Technology
  • Intelligent Machines

Future of Artificial Intelligence: Safe AI Development

Abstract From voice automated personal assistants to self-driving cars, there has been a tremendous progress in the field of Artificial Intelligence (AI). While the general notion of AI is depicted as robots behaving like human beings, AI can actually encompass anything from game playing agents...

  • Artificial Intelligence

Future of Artificial Intelligence and the Strategies of Its Use in Business

Meaning of Artificial Intelligence Artificial Intelligence (AI) is a “wide range of computer science that deals with the producing of smart machines that can perform tasks that typically require human intelligence, it can adjust to new inputs and perform human-like tasks”,(“Artificial Intelligence – What it...

Artificial Intelligence Versus Human Labor: An Organizational Business Dilemma

Abstract Technological advancements have led to fast-growing economies and have completely changed the way human beings work. As a move to increase productivity, companies have found themselves at crossroads on whether to automate their processes or retain their workforce. With new expectations, competition, and the...

The Effectivity Of Robots Vs Human Work In Production

There is an obvious fact that, the rate of wage growth to ordinary worker is falling, that global wage growth lowest since 2008 [1]. It is hardly to understand that during the time when the global economy is recovering how can this happens. However, it...

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Man And Machine: In Deference To Humanity

An interesting quality that humans possess is how different we are from one another. Just like snowflakes, no two humans are identical. Each of us differ in our experiences, opinions, values, attitudes, interests, and so on. With all the complex diversity that changes from person...

SAP Leonardo: Machine Learning with SAP Leonardo

The emergence of digital technology has changed the way things are done leading to a dramatic change in the economy of the nation. Similarly, there is a significant change in the digital architecture of the industry. The disruption in digital technology is changing the way...

Study On Population Analysis Using Satellite Image And Spatial Data

Machine Learning Approach For Forecasting Crop Yield Based On Climatic Parameters Combination of spatial data processing techniques with skilled system techniques and applied them to ascertain an intelligent agriculture land grading data system. In order to create the ready knowledge sets helpful for demand statement...

AI Along With Machine Learning For Rural Population Across India

Introduction Indian banking landscape is seeing massive transition with the advent of financial inclusion through RBI. As the government shifts focus toward cashless society, it also pushes a bouquet of digital payment options in the form of schemes, apps and services like Small savings accounts,...

Handwritten Gujarati Script Recognition With Deep Learning

Abstract The motive behind writing this paper is to throw light on the proposed application which can be used for detecting and recognizing Gujarati handwritten scripts using image processing machine learning techniques. It emphasizes the key technologies involved in this process. There is a lot...

Best Machine Learning Classification Formula For Diabetic Prediction

INTRODUCTION Machine learning Machine learning teaches computers to try and do what comes naturally to humans and animals: learn from expertise. Machine learning algorithms use machine ways to “learn” info directly from information while not counting on a planned equation as a model. The algorithms...

Artificial Intelligence As “Simulated Intelligence In Machines.”

Investopedia defines artificial intelligence as “simulated intelligence in machines.” It is widely regarded as one of the greatest technological achievements ever. As the years go on, it will be the most useful technology globally, because of all the ways engineers are seeing it can be...

The Reasons Artificial Intelligence Should Be Regulated

Artificial intelligence has both positive and negative attributes. That being said I believe that AI should be regulated. This is because though AI helps us in many ways such as cleaning houses and building things in factories, it still has the possibility to go haywire....

Best topics on Intelligent Machines

1. Technology’s Paradox: Increased Profitability & Economic Inequality

2. Future of Artificial Intelligence: Safe AI Development

3. Future of Artificial Intelligence and the Strategies of Its Use in Business

4. Artificial Intelligence Versus Human Labor: An Organizational Business Dilemma

5. The Effectivity Of Robots Vs Human Work In Production

6. Man And Machine: In Deference To Humanity

7. SAP Leonardo: Machine Learning with SAP Leonardo

8. Study On Population Analysis Using Satellite Image And Spatial Data

9. AI Along With Machine Learning For Rural Population Across India

10. Handwritten Gujarati Script Recognition With Deep Learning

11. Best Machine Learning Classification Formula For Diabetic Prediction

12. Artificial Intelligence As “Simulated Intelligence In Machines.”

13. The Reasons Artificial Intelligence Should Be Regulated

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Smart machines.

IBM's Watson and the Era of Cognitive Computing

John E. Kelly III and Steve Hamm

Columbia Business School Publishing

Smart Machines

Pub Date: October 2013

ISBN: 9780231168564

Format: Hardcover

List Price: $22.95 £18.99

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If you think the tidal wave of digital disruption is over, think again. Kelly and Hamm pull back the curtain on the next great wave of the computing revolution, which will transform how every industry and business operates in the near future. David Rogers, author of The Network Is Your Customer: Five Strategies to Thrive in a Digital Age
As Watson's win against Jeopardy! champion Ken Jennings showed, IBM's research labs are doing some of the world's most revolutionary research in artificial intelligence and related fields. In this short and very accessible book, the authors outline this work and the wave of 'cognitive computing' that is about the break. James Hendler, Rensselaer Polytechnic Institute
This book will give the careful reader an understanding of the immense possibilities offered by the intelligent collaboration of man and machine; armed with this knowledge, readers can then tackle the difficult but essential task of ensuring that these new cognitive technologies will, in practice, be devoted to bettering our lives. Ralph Gomory, Stern School of Business, New York University
Technological change, from new materials to smart systems, is accelerating, and the latest advances fuel others. John E. Kelly and Steve Hamm show how these technologies will transform our jobs, our cities—even how we think. Stephen Baker, author of Final Jeopardy: Man vs. Machine and the Quest to Know Everything
IBM's Watson is one of the most important technological breakthroughs in decades, and this is the go-to book for understanding what this new technology is all about and how it will change your life. Tyler Cowen, George Mason University, author of Average Is Over
This book is a gem... Highly recommended. CHOICE
  • Read an interview with Steve Hamm
  • Read an article about Smart Machines in Scientific American
  • How IBM's Watson Will Transform Business and Society
  • Bigger, better, stronger, faster: How IBM's Watson upends Moore's Law, an article on Watson and the book via InfoWorld

Winner, 2014 Choice Outstanding Academic Title

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The Marginalian

What to Think About Machines That Think: Leading Thinkers on Artificial Intelligence and What It Means to Be Human

By maria popova.

smart machines essay

From its very outset, this new branch of human-machine evolution made it clear that any answer to these questions would invariably alter how we answer the most fundamental questions of what it means to be human.

That’s what Edge founder John Brockman explores in the 2015 edition of his annual question , inviting 192 of today’s most prominent thinkers to tussle with these core questions of artificial intelligence and its undergirding human dilemmas. The answers, collected in What to Think About Machines That Think: Today’s Leading Thinkers on the Age of Machine Intelligence ( public library ), come from such diverse contributors as physicist and mathematician Freeman Dyson , music pioneer Brian Eno , biological anthropologist Helen Fisher , Positive Psychology founding father Martin Seligman , computer scientist and inventor Danny Hillis , TED curator Chris Anderson , neuroscientist Sam Harris , legendary curator Hans Ulrich Obrist , media theorist Douglas Rushkoff , cognitive scientist and linguist Steven Pinker , and yours truly.

smart machines essay

The answers are strewn with a handful of common threads, a major one being the idea that artificial intelligence isn’t some futuristic abstraction but a palpably present reality with which we’re already living.

Beloved musician and prolific reader Brian Eno looks at the many elements of his day, from cooking porridge to switching on the radio, that work seamlessly thanks to an invisible mesh of connected human intelligence — a Rube Goldberg machine of micro-expertise that makes it possible for the energy in a distant oil field to power the stove built in a foreign factory out of components made by scattered manufacturers, and ultimately cook his porridge. In a sentiment that calls to mind I, Pencil — that magnificent vintage allegory of how everything is connected — Eno explains why he sees artificial intelligence not as a protagonist in a techno-dystopian future but as an indelible and fruitful part of our past and present:

My untroubled attitude results from my almost absolute faith in the reliability of the vast supercomputer I’m permanently plugged into. It was built with the intelligence of thousands of generations of human minds, and they’re still working at it now. All that human intelligence remains alive, in the form of the supercomputer of tools, theories, technologies, crafts, sciences, disciplines, customs, rituals, rules of thumb, arts, systems of belief, superstitions, work-arounds, and observations that we call Global Civilization. Global Civilization is something we humans created, though none of us really know how. It’s out of the individual control of any of us — a seething synergy of embodied intelligence that we’re all plugged into. None of us understands more than a tiny sliver of it, but by and large we aren’t paralyzed or terrorized by that fact — we still live in it and make use of it. We feed it problems — such as “I want some porridge” — and it miraculously offers us solutions that we don’t really understand. […] We’ve been living happily with artificial intelligence for thousands of years.

smart machines essay

In one of the volume’s most optimistic essays, TED curator Chris Anderson , who belongs to the increasingly endangered tribe of public idealists, considers how this “hive mind” of semi-artificial intelligence could provide a counterpoint to some of our worst human tendencies and amplify our collective potential for good:

We all know how flawed humans are. How greedy, irrational, and limited in our ability to act collectively for the common good. We’re in danger of wrecking the planet. Does anyone thoughtful really want humanity to be evolution’s final word? […] Intelligence doesn’t reach its full power in small units. Every additional connection and resource can help expand its power. A person can be smart, but a society can be smarter still… By that logic, intelligent machines of the future wouldn’t destroy humans. Instead, they would tap into the unique contributions that humans make. The future would be one of ever richer intermingling of human and machine capabilities. I’ll take that route. It’s the best of those available. […] Together we’re semiunconsciously creating a hive mind of vastly greater power than this planet has ever seen — and vastly less power than it will soon see. “Us versus the machines” is the wrong mental model. There’s only one machine that really counts. Like it or not, we’re all — us and our machines — becoming part of it: an immense connected brain. Once we had neurons. Now we’re becoming the neurons.

smart machines essay

Astrophysicist and philosopher Marcelo Gleiser , who has written beautifully about how to live with mystery in a culture obsessed with knowledge , echoes this idea by pointing out the myriad mundane ways in which “machines that think” already permeate our daily lives:

We define ourselves through our techno-gadgets, create fictitious personas with weird names, doctor pictures to appear better or at least different in Facebook pages, create a different self to interact with others. We exist on an information cloud, digitized, remote, and omnipresent. We have titanium implants in our joints, pacemakers and hearing aids, devices that redefine and extend our minds and bodies. If you’re a handicapped athlete, your carbon-fiber legs can propel you forward with ease. If you’re a scientist, computers can help you extend your brainpower to create well beyond what was possible a few decades back. New problems that once were impossible to contemplate, or even formulate, come around every day. The pace of scientific progress is a direct correlate of our alliance with digital machines. We’re reinventing the human race right now.

Another common thread running across a number of the answers is the question of what constitutes “artificial” intelligence in the first place and how we draw the line between machine thought and human thought. Caltech theoretical physicist and cosmologist Sean Carroll performs elegant semantic acrobatics to invert the question:

We are all machines that think, and the distinction between different types of machines is eroding. We pay a lot of attention these days, with good reason, to “artificial” machines and intelligences — ones constructed by human ingenuity. But the “natural” ones that have evolved through natural selection, like you and me, are still around. And one of the most exciting frontiers in technology and cognition is the increasingly permeable boundary between the two categories.

smart machines essay

Developmental psychologist Alison Gopnik , who has revolutionized our understanding of how babies think , considers the question from a complementary angle:

Computers have become highly skilled at making inferences from structured hypotheses, especially probabilistic inferences. But the really hard problem is deciding which hypotheses, out of all the many possibilities, are worth testing. Even preschoolers are remarkably good at creating brand-new, out-of-the-box concepts and hypotheses in a creative way. Somehow they combine rationality and irrationality, systematicity and randomness, to do this, in a way we haven’t even begun to understand. Young children’s thoughts and actions often do seem random, even crazy — just join in a three-year-old pretend game sometime… But they also have an uncanny capacity to zero in on the right sort of weird hypothesis; in fact, they can be substantially better at this than grown-ups. Of course, the whole idea of computation is that once we have a complete step-by-step account of any process, we can program it on a computer. And after all, we know there are intelligent physical systems that can do all these things. In fact, most of us have actually created such systems and enjoyed doing it, too (well, at least in the earliest stages). We call them our kids. Computation is still the best — indeed, the only — scientific explanation we have of how a physical object like a brain can act intelligently. But at least for now, we have almost no idea at all how the sort of creativity we see in children is possible. Until we do, the largest and most powerful computers will still be no match for the smallest and weakest humans.

smart machines essay

In my own contribution to the volume, I consider the question of “thinking machines” from the standpoint of what thought itself is and how our human solipsism is limiting our ability to envision and recognize other species of thinking:

Thinking isn’t mere computation — it’s also cognition and contemplation, which inevitably lead to imagination. Imagination is how we elevate the real toward the ideal, and this requires a moral framework of what is ideal. Morality is predicated on consciousness and on having a self-conscious inner life rich enough to contemplate the question of what is ideal. The famous aphorism attributed to Einstein — “Imagination is more important than knowledge” — is interesting only because it exposes the real question worth contemplating: not that of artificial intelligence but of artificial imagination. Of course, imagination is always “artificial,” in the sense of being concerned with the unreal or trans-real — of transcending reality to envision alternatives to it — and this requires a capacity for accepting uncertainty. But the algorithms driving machine computation thrive on goal-oriented executions in which there’s no room for uncertainty. “If this, then that” is the antithesis of imagination, which lives in the unanswered, and often vitally unanswerable, realm of “What if?” As Hannah Arendt once wrote , losing our capacity for asking such unanswerable questions would be to “lose not only the ability to produce those thought-things that we call works of art but also the capacity to ask all the unanswerable questions upon which every civilization is founded.” […] Will machines ever be moral, imaginative? It’s likely that if and when they reach that point, theirs will be a consciousness that isn’t beholden to human standards. Their ideals will not be our ideals, but they will be ideals nonetheless. Whether or not we recognize those processes as thinking will be determined by the limitations of human thought in understanding different — perhaps wildly, unimaginably different — modalities of thought itself.

Futurist and Wired founding editor Kevin Kelly takes a similar approach:

The most important thing about making machines that can think is that they will think differently. Because of a quirk in our evolutionary history, we are cruising as if we were the only sentient species on our planet, leaving us with the incorrect idea that human intelligence is singular. It is not. Our intelligence is a society of intelligences, and this suite occupies only a small corner of the many types of intelligences and consciousnesses possible in the universe. We like to call our human intelligence “general purpose,” because, compared with other kinds of minds we’ve met, it can solve more kinds of problems, but as we continue to build synthetic minds, we’ll come to realize that human thinking isn’t general at all but only one species of thinking. The kind of thinking done by today’s emerging AIs is not like human thinking. […] AI could just as well stand for Alien Intelligence. We cannot be certain that we’ll contact extraterrestrial beings from one of the billion Earthlike planets in the sky in the next 200 years, but we can be almost 100 percent certain that we’ll have manufactured an alien intelligence by then. When we face those synthetic aliens, we’ll encounter the same benefits and challenges we expect from contact with ET. They’ll force us to reevaluate our roles, our beliefs, our goals, our identity. What are humans for? I believe our first answer will be that humans are for inventing new kinds of intelligences that biology couldn’t evolve. Our job is to make machines that think differently — to create alien intelligences. Call them artificial aliens.

smart machines essay

Linguist and anthropologist Mary Catherine Bateson — whose mother happens to be none other than Margaret Mead — directly questions how the emergence of artificial intelligence will interact with our basic humanity:

Will humor and awe, kindness and grace, be increasingly sidelined, or will their value be recognized in new ways? Will we be better or worse off if wishful thinking is eliminated and, perhaps along with it, hope?

This, indeed, is another of the common threads — the question of moral responsibility implicit to the future of artificial intelligence. Philosopher Daniel Dennett , who has pondered the flaws of our intuition , counters our misplaced fears about artificial intelligence with the appropriate focus of our concerns:

After centuries of hard-won understanding of nature that now permits us, for the first time in history, to control many aspects of our destinies, we’re on the verge of abdicating this control to artificial agents that can’t think, prematurely putting civilization on autopilot. The process is insidious, because each step of it makes good local sense, is an offer you can’t refuse. You’d be a fool today to do large arithmetical calculations with pencil and paper when a hand calculator is much faster and almost perfectly reliable (don’t forget about round-off error), and why memorize train timetables when they’re instantly available on your smartphone? Leave the map reading and navigation to your GPS; it isn’t conscious, it can’t think in any meaningful sense, but it’s much better than you are at keeping track of where you are and where you want to go.

But by outsourcing the drudgery of thought to machines, Dennett argues, we are rendering ourselves at once obsolete and helplessly dependent:

What’s wrong with turning over the drudgery of thought to such high-tech marvels? Nothing, so long as (1) we don’t delude ourselves, and (2) we somehow manage to keep our own cognitive skills from atrophying.

He drives the point home with a simple, discomfiting thought experiment:

As we become ever more dependent on these cognitive prostheses, we risk becoming helpless if they ever shut down. The Internet is not an intelligent agent (well, in some ways it is), but we have nevertheless become so dependent on it that were it to crash, panic would set in and we could destroy society in a few days. That’s an event we should bend our efforts to averting now , because it could happen any day. The real danger, then, is not machines that are more intelligent than we are usurping our role as captains of our destinies. The real danger is basically clueless machines being ceded authority far beyond their competence.

smart machines essay

Computer scientist and inventor Danny Hillis similarly urges for prudent progress:

Machines that think will think for themselves. It’s in the nature of intelligence to grow, to expand like knowledge itself. Like us, the thinking machines we make will be ambitious, hungry for power — both physical and computational — but nuanced with the shadows of evolution. Our thinking machines will be smarter than we are, and the machines they make will be smarter still. But what does that mean? How has it worked so far? We’ve been building ambitious semi-autonomous constructions for a long time — governments and corporations, NGOs. We designed them all to serve us and to serve the common good, but we aren’t perfect designers and they’ve developed goals of their own. Over time, the goals of the organization are never exactly aligned with the intentions of the designers.

He calls the notion of smart machines capable of building even smarter machines “the most important design problem of all time” and adds:

Like our biological children, our thinking machines will live beyond us. They need to surpass us too, and that requires designing into them the values that make us human. It’s a hard design problem, and it’s important that we get it right.

In the collection’s pithiest contribution, Freeman Dyson , he of great wisdom on the future of science , answers with a brilliant reverse Turing Test of sorts:

I do not believe that machines that think exist, or that they are likely to exist in the foreseeable future. If I am wrong, as I often am, any thoughts I might have about the question are irrelevant. If I am right, then the whole question is irrelevant.

smart machines essay

The most lyrical essay in the volume comes from Oxford computer scientist Ursula Martin , who offers a Thoreauesque account of a marshland hike and extracts from it a beautiful metaphor for the dimensional meaning of intelligence:

Reading the watery marshland is a conversation with the past, with people I know nothing about, except that they laid the stones that shape my stride, and probably shared my dislike of wet feet. Beyond the dunes, wide sands stretch across a bay to a village beyond. The receding tide has created strangely regular repeating patterns of water and sand, which echo a line of ancient wooden posts. A few hundred years ago salmon were abundant here, and the posts supported nets to catch them. A stone church tower provides a landmark, and I stride out cross the sands toward it to reach the village, disturbing noisy groups of seabirds. The water, stepping-stones, posts, and church tower are the texts of a slow conversation across the ages. Path makers, salmon fishers, and even solitary walkers mark the land; the weather and tides, rocks and sand and water, creatures and plants respond to those marks; and future generations in turn respond to and change what they find. […] What kind of thinking machine might find its own place in slow conversations over the centuries, mediated by land and water? What qualities would such a machine need to have? Or what if the thinking machine was not replacing any individual entity but was used as a concept to help understand the combination of human, natural, and technological activities that create the sea’s margin, and our response to it? […] The purpose of the solitary walker may be straightforward — to catch fish, to understand birds, or merely to get home safely before the tide comes in. But what if the purpose of the solitary walker is no more than a solitary walk — to find balance, to be at one with nature, to enrich the imagination, or to feed the soul. Now the walk becomes a conversation with the past, not directly through rocks and posts and water but through words, through the poetry of those who have experienced humanity through rocks and posts and water and found the words to pass that experience on. So the purpose of the solitary walker is to reinforce those very qualities that make the solitary walker a human being, in a shared humanity with other human beings. A challenge indeed for a thinking machine.

What to Think About Machines That Think is an immeasurably stimulating read in its entirety, exploring the intersection of science, philosophy, technology, ethics, and psychology to unravel some of the most important questions worth asking. Complement it with Diane Ackerman’s poetic meditation on what the future of robots reveals about the human condition , then revisit previous editions of Brockman’s annual question, in which prominent thinkers address the most major misconception holding us back (2014), the only thing worth worrying about (2013), the single most elegant theory of how the world works (2012), and the best way to make ourselves smarter (2011).

— Published October 12, 2015 — —




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How technology is reinventing education

Stanford Graduate School of Education Dean Dan Schwartz and other education scholars weigh in on what's next for some of the technology trends taking center stage in the classroom.

smart machines essay

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”


Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

Home — Essay Samples — Science — Technology & Engineering — Intelligent Machines

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Essays on Intelligent Machines

Intelligent machines essay, types of intelligent machines essay.

  • Definition Essay: A definition essay on intelligent machines aims to explain what these machines are, how they work, and their potential uses in various fields.
  • Cause and Effect Essay: A cause and effect essay on intelligent machines explores the consequences of integrating intelligent machines into society, either positive or negative.
  • Compare and Contrast Essay: A compare and contrast essay on intelligent machines analyzes the similarities and differences between various types of intelligent machines or their applications.

Intelligent Machines: Definition Essay

  • Begin with a clear and concise introduction that provides the reader with a brief overview of the topic and sets the tone for the essay.
  • Define intelligent machines by providing a clear definition and outlining their characteristics, functions, and capabilities.
  • Use relevant examples to support your definition and provide a better understanding of the topic.
  • Discuss the different types of intelligent machines and their applications, including artificial intelligence, machine learning, and robotics.
  • Explore the advantages and disadvantages of intelligent machines, and discuss the potential impact they may have on society.
  • Conclude the essay by summarizing the main points and providing a final definition of intelligent machines.

Intelligent Machines: Cause and Effect Essay

  • Identify a clear and specific cause and effect relationship to explore.
  • Use evidence and examples to support the argument and illustrate the causes and effects.
  • Consider both short-term and long-term effects.
  • Discuss the potential implications for individuals, society, and the world at large.
  • Use logical transitions to link the causes and effects together.
  • Provide a conclusion that summarizes the main points and restates the thesis.

Intelligent Machines: Compare and Contrast Essay

  • Choose subjects that have enough similarities and differences to make for an interesting and meaningful comparison. For example, comparing a self-driving car and a smart home system would make for an interesting comparison, as both use intelligent technology to enhance our lives, but they are used in different ways and have different features.
  • Create a clear and concise thesis statement that outlines the purpose and focus of the essay. For example, "This essay will compare and contrast the use of intelligent machines in healthcare and manufacturing industries."
  • Organize the essay using a clear and logical structure. A common structure for a compare and contrast essay is to have an introduction, followed by a body section that explores the similarities and differences between the subjects, and then a conclusion that summarizes the main points and provides a final thought on the topic.
  • Use specific examples and evidence to support your points. This can include statistics, case studies, and real-world examples.
  • Use transitional phrases and words to help guide the reader through the essay and make the connections between the subjects clear.
  • Conclude with a final thought or observation that summarizes the main points of the essay and provides insight into the topic being explored.

Tips for Choosing an Intelligent Machines Essay Topic

  • Choose a topic that interests you and aligns with your expertise or academic background.
  • Consider the latest advancements in intelligent machines and their potential implications for society.
  • Research the ethical and social issues surrounding intelligent machines, such as job displacement and privacy concerns.
  • Determine the specific angle or focus of your essay to avoid a broad and unfocused topic.

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Intelligent machine is a machine that uses sensors to monitor the environment and thereby adjust its actions to accomplish specific tasks in the face of uncertainty and variability.

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Smart glove teaches new physical skills

Press contact :.

Collage of four images of a hand wearing a white, fabric-based glove with black fingertips and haptics and sensors sewn in. Two use cases shown include manipulating a robotic arm and playing a piano.

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You’ve likely met someone who identifies as a visual or auditory learner, but others absorb knowledge through a different modality: touch. Being able to understand tactile interactions is especially important for tasks such as learning delicate surgeries and playing musical instruments, but unlike video and audio, touch is difficult to record and transfer.

To tap into this challenge, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and elsewhere developed an embroidered smart glove that can capture, reproduce, and relay touch-based instructions. To complement the wearable device, the team also developed a simple machine-learning agent that adapts to how different users react to tactile feedback, optimizing their experience. The new system could potentially help teach people physical skills, improve responsive robot teleoperation, and assist with training in virtual reality.

An open-access paper describing the work was published in Nature Communications on Jan. 29.

Will I be able to play the piano? To create their smart glove, the researchers used a digital embroidery machine to seamlessly embed tactile sensors and haptic actuators (a device that provides touch-based feedback) into textiles. This technology is present in smartphones, where haptic responses are triggered by tapping on the touch screen. For example, if you press down on an iPhone app, you’ll feel a slight vibration coming from that specific part of your screen. In the same way, the new adaptive wearable sends feedback to different parts of your hand to indicate optimal motions to execute different skills.

The smart glove could teach users how to play the piano, for instance. In a demonstration, an expert was tasked with recording a simple tune over a section of keys, using the smart glove to capture the sequence by which they pressed their fingers to the keyboard. Then, a machine-learning agent converted that sequence into haptic feedback, which was then fed into the students’ gloves to follow as instructions. With their hands hovering over that same section, actuators vibrated on the fingers corresponding to the keys below. The pipeline optimizes these directions for each user, accounting for the subjective nature of touch interactions. “Humans engage in a wide variety of tasks by constantly interacting with the world around them,” says Yiyue Luo MS ’20, lead author of the paper, PhD student in MIT’s Department of Electrical Engineering and Computer Science (EECS), and CSAIL affiliate. “We don’t usually share these physical interactions with others. Instead, we often learn by observing their movements, like with piano-playing and dance routines. “The main challenge in relaying tactile interactions is that everyone perceives haptic feedback differently,” adds Luo. “This roadblock inspired us to develop a machine-learning agent that learns to generate adaptive haptics for individuals’ gloves, introducing them to a more hands-on approach to learning optimal motion.”

The wearable system is customized to fit the specifications of a user’s hand via a digital fabrication method. A computer produces a cutout based on individuals’ hand measurements, then an embroidery machine stitches the sensors and haptics in. Within 10 minutes, the soft, fabric-based wearable is ready to wear. Initially trained on 12 users’ haptic responses, its adaptive machine-learning model only needs 15 seconds of new user data to personalize feedback. In two other experiments, tactile directions with time-sensitive feedback were transferred to users sporting the gloves while playing laptop games. In a rhythm game, the players learned to follow a narrow, winding path to bump into a goal area, and in a racing game, drivers collected coins and maintained the balance of their vehicle on their way to the finish line. Luo’s team found that participants earned the highest game scores through optimized haptics, as opposed to without haptics and with unoptimized haptics.

“This work is the first step to building personalized AI agents that continuously capture data about the user and the environment,” says senior author Wojciech Matusik, MIT professor of electrical engineering and computer science and head of the Computational Design and Fabrication Group within CSAIL. “These agents then assist them in performing complex tasks, learning new skills, and promoting better behaviors.” Bringing a lifelike experience to electronic settings

In robotic teleoperation, the researchers found that their gloves could transfer force sensations to robotic arms, helping them complete more delicate grasping tasks. “It’s kind of like trying to teach a robot to behave like a human,” says Luo. In one instance, the MIT team used human teleoperators to teach a robot how to secure different types of bread without deforming them. By teaching optimal grasping, humans could precisely control the robotic systems in environments like manufacturing, where these machines could collaborate more safely and effectively with their operators.

“The technology powering the embroidered smart glove is an important innovation for robots,” says Daniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT, CSAIL director, and author on the paper. “With its ability to capture tactile interactions at high resolution, akin to human skin, this sensor enables robots to perceive the world through touch. The seamless integration of tactile sensors into textiles bridges the divide between physical actions and digital feedback, offering vast potential in responsive robot teleoperation and immersive virtual reality training.” Likewise, the interface could create more immersive experiences in virtual reality. Wearing smart gloves would add tactile sensations to digital environments in video games, where gamers could feel around their surroundings to avoid obstacles. Additionally, the interface would provide a more personalized and touch-based experience in virtual training courses used by surgeons, firefighters, and pilots, where precision is paramount. While these wearables could provide a more hands-on experience for users, Luo and her group believe they could extend their wearable technology beyond fingers. With stronger haptic feedback, the interfaces could guide feet, hips, and other body parts less sensitive than hands. Luo also noted that with a more complex artificial intelligence agent, her team's technology could assist with more involved tasks, like manipulating clay or driving an airplane. Currently, the interface can only assist with simple motions like pressing a key or gripping an object. In the future, the MIT system could incorporate more user data and fabricate more conformal and tight wearables to better account for how hand movements impact haptic perceptions.

Luo, Matusik, and Rus authored the paper with EECS Microsystems Technology Laboratories Director and Professor Tomás Palacios; CSAIL members Chao Liu, Young Joong Lee, Joseph DelPreto, Michael Foshey, and professor and principal investigator Antonio Torralba; Kiu Wu of LightSpeed Studios; and Yunzhu Li of the University of Illinois at Urbana-Champaign.

The work was supported, in part, by an MIT Schwarzman College of Computing Fellowship via Google and a GIST-MIT Research Collaboration grant, with additional help from Wistron, Toyota Research Institute, and Ericsson.

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Essay Example on AI: Transforming Lives with Smart Machines

Essay Example on AI: Transforming Lives with Smart Machines


Artificial intelligence (AI) is the aptitude of a computer-controlled robot or digital computer to accomplish tasks commonly done or associated with intelligent human beings (Copeland 1). It is an area of information technology (IT) that accentuates the creation of smart machines that behave, work, and react like humans (Bresnick 1). Artificial intelligence is transforming people's lives every day by optimizing logistics, composing art, detecting fraud, conducting research, and providing translations (John 1). With its vast landscape, artificial intelligence has attracted attention and controversial debates across the world about its importance or, rather, its lack. In as much as artificial intelligence possess human intelligence capabilities, it has its disadvantages which have led quite a several companies and state not to embrace it. How does society survive with artificial intelligence-driven reality such that people are no longer required or used in the workplace? What generally transpires in our socio-economic structure where people have little value in the workplace? Artificial intelligence should not be embraced due to the many challenges that it poses.

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Many people across the world survive from the earnings they get through their daily jobs. The hierarchy of labor is principally concerned with automation. Thus, as artificial intelligence progresses, more jobs and tasks fall into the category of things and activities that can be automated, leading to the disappearance of the entire job industry (Reddy 2). Some of the that could be affected by artificial intelligence are jobs in agriculture, manufacturing, transportation, food service, hospitality, logistics and retail. In the year 2018, The World Economic Forum approximated that artificial intelligence is likely to displace about 75 million jobs by 2022 ("Technology Org 2). Moreover, job polarization has occurred in the labor market due to artificial intelligence and robotics automation (Maarten et al. 6). It is possible when skilled technical jobs rise in demand and become highly paid while low-skilled service jobs become less in demand and poorly paid. The mid-qualification jobs in offices and factories become under pressure, condensed and moderately. This is proof enough to discredit the full adoption of artificial intelligence in all sectors of the economy

Privacy Concerns

There are many privacy concerns when it comes to artificial intelligence applications across all sectors. There is a general debate about privacy and surveillance in information technology, which concerns access to private data and also data that is personally identifiable. A person has the right to be let alone, privacy as an aspect of personhood, information privacy and right to secrecy (Bossmann 2). Artificial intelligence upsurges both the possibilities of intelligent data collection and data analysis, which applies to blanket surveillance of the entire population and classic targeted surveillance where data is traded between agents at a charge fee. Adopting artificial intelligence, controlling who collects which type of data, and who has access to it becomes harder. These systems will often reveal data and facts about us that we wish to suppress, or even in other cases, the non-conscious but super-intelligent algorithms know more about us than we do. For example, in a healthcare setup, the requirement of large data sets creates incentives for developers to collect this data from many patients and customers. Artificial intelligence can also predict private information about patients, even when the system never received (Price 2).

A good example is that artificial intelligence can identify that actually, a patient has Parkinson's disease the moment the computer mouse trembles even in cases where the patient had not shared that information to anyone. Patients take this as a violation of their privacy, especially if third parties, such as banks and insurance companies, are involved. In the same measure, it is now possible to track and monitor a person's every move as they go about their daily business. Surveillance cameras are nearly everywhere, while facial recognition algorithms now know who you are. Since machines can gather and track and scrutinize so much about an individual, it's very probable for the same machines to use the information derived against you. A potential employer might withhold a job offer based on one's social credit score or an insurance company informing that one is not insurable. Based on the rounds, you were caught on camera talking on one's phone (Bossmann 3). With such inconceivable privacy concerns, artificial intelligence development is invalidated and should not be fully adopted.

Innovation, development and adoption of artificial intelligence are not best suited because of the security threat it poses across the world (Bossmann 2). It is worth noting that the more powerful a technology becomes, the more it can be used for despicable and wicked reasons. If used maliciously, artificial intelligence systems can cause terrible damage to the world at large. Replacement of soldiers with autonomous weapons or robots only means that the battles will no longer be fought in a battleground; instead, cybersecurity will become more vital.

With the extensive use of artificial intelligence in various fields across the world, the truth is that it is likely to become dominant for attacking purposes than the defensive ones. Criminals cover themselves by use of artificial intelligence rather than masks to rob a bank. With the adoption of AI systems, criminals practice drug trafficking, buying and selling, which rely on artificial intelligence planning and autonomous navigation technologies (Data Flair Team 1). Cybercriminals still use the same artificial intelligence systems to conduct cybersecurity, such as phishing and whaling, cracking passwords, creating malware and doing chatbox-related crimes (Data Flair Team 1). The rate of development of artificial intelligence systems poses a threat to crime prevention since the crime rates are likely to go up. If there are no stable prevention strategies set in place, then artificial intelligence is expected to pose a lot of danger.

The adoption of artificial intelligence will negatively affect human behaviors and interactions. Artificial intelligence milestones will start an age where we recurrently interact with machines like humans would do, whether in sales or customer service. With the limited attention and kindness that humans expend towards each other during interactions, it will be challenging to develop relationships. In 2015, a bot named Eugene Goostman fooled more than half of the human raters and made them think they had been communicating with human beings (Bossmann 3).

Social Manipulation

Social manipulation is another factor that discredits the effectiveness of artificial intelligence. Through its autonomous-powered algorithms, social media is very efficient and effective at target marketing. They clearly know who we are, things we like and are unbelievably food at surmising what we think. If the ongoing investigation of the case of Cambridge Analytica who used data from 50 million Facebook operators to influence the 2016 U.S presidential election and U. K’s Brexit referendum turns out to be accurate, then this is enough evidence of artificial intelligence power for social manipulation (Marr 3). With this kind of outcome, unless better standards are put in place to limit social manipulation, artificial intelligence will negatively affect humans and should not be entirely adopted.


As much as the adoption of artificial intelligence has some benefits, it does not guide how to stay in control of complex, intelligent systems, making humans unpredictable. The idea of singularity is that if the trajectory of artificial intelligence spreads up to the policies that possess a human level of intelligence, they would themselves have the capability to develop and progress artificial systems that exceed the human scale of intelligence (Marr 2). Such a strident turn of events after reaching super intelligent artificial intelligence is the singularity from which the creation of progress of artificial intelligence is out of human control and very hard to forecast

As humans, we stay on top of the food chain because of our ingenuity and intelligence and not sharp teeth and strong muscles (Marr 2). Due to this, we can get better of bigger, stronger, and even faster animals since we create and use tools to control them. This scenario poses a serious question about artificial intelligence systems such that if it will have the same impact over us. And if it does, are we prepared to take control of the situation? Of course not, and that is why its effectiveness is still doubtful. Therefore, it is a risky attempt to completely incorporate artificial intelligence and rely on it in the world’s operations.

Inequality in Wealth Distribution

Inequality in wealth distribution is likely to happen if artificial intelligence is completely adopted in incorporated into our economic systems. The world's economic system is based on compensation for contribution to the economy, which is often assessed using an hourly wage (Bossmann 3). With almost all companies still being reliant on hourly work in their services and products, the full adoption of artificial intelligence will lead companies to cut down on relying on the human workforce radically. In such a scenario, the incomes will go to fewer such that persons who have proprietorship in artificial intelligence-driven companies will eventually make more money. In contrast, the ones whose jobs were replaced by artificial intelligence systems will earn nothing. The economy will end up having a section of wealthy people and another section of impoverished people leading to a vast wealth gap between the two groups. A pointer of what is likely to happen is evident in 2014, when the same revenues were earned by the three most significant companies in Silicon Valley and Detroit, with ten times fewer employees (Bossmann 4).

Most scholars agree that super-intelligent artificial intelligence systems can be hazardous but unlikely to display human emotions like hate and love. Thus, the systems cannot be intentionally malevolent. No matter how smart they are, they can never replicate a human since they do not possess emotions and moral values. Simply the machines do not know what is legal or ethical, and due to this, they do not maintain their judgment-making skills (Kuklenko 2). The artificial intelligence systems do what they are told, and thus the judgment of right or wrong is nill for them (Mullan 1). In cases, when they come across situations that are unfamiliar they seize to perform, perform incorrectly, or break down, which may cause adverse effects. Two scenarios are likely to make the artificial intelligence systems dangerous; when the orders are programmed to do something devastating and when the AI is programmed to do something beneficial, it develops a destructive method of achieving the goal.

AI systems can be programmed to do something devastating through autonomous weapons, which are artificial intelligence systems that are planned to kill. Therefore, in a scenario where these weapons are in the wrong hands, they could easily cause mass and fatal casualties (Bossmann 2). Humans have different motives, and the devastating effects that might arise from wrongfully using these weapons rule out the adoption of artificial intelligence.

When the AI is automated to do something important but, in the process, develops a destructive method of achieving the goal rules out the complete incorporation of artificial intelligence systems. (Marr 2). It can happen when these AI systems fail to...

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EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

A man faces a computer generated figure with programming language in the background

As part of its digital strategy , the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits , such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation. Once approved, these will be the world’s first rules on AI.

Learn more about what artificial intelligence is and how it is used

What Parliament wants in AI legislation

Parliament’s priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems.

Learn more about Parliament’s work on AI and its vision for AI’s future

AI Act: different rules for different risk levels

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

  • Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
  • Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
  • Biometric identification and categorisation of people
  • Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.

AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation . This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into specific areas that will have to be registered in an EU database:

  • Management and operation of critical infrastructure
  • Education and vocational training
  • Employment, worker management and access to self-employment
  • Access to and enjoyment of essential private services and public services and benefits
  • Law enforcement
  • Migration, asylum and border control management
  • Assistance in legal interpretation and application of the law.

All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle.

General purpose and generative AI

Generative AI, like ChatGPT, would have to comply with transparency requirements:

  • Disclosing that the content was generated by AI
  • Designing the model to prevent it from generating illegal content
  • Publishing summaries of copyrighted data used for training

High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.

Limited risk

Limited risk AI systems should comply with minimal transparency requirements that would allow users to make informed decisions. After interacting with the applications, the user can then decide whether they want to continue using it. Users should be made aware when they are interacting with AI. This includes AI systems that generate or manipulate image, audio or video content, for example deepfakes.

On December 9 2023, Parliament reached a provisional agreement with the Council on the AI act . The agreed text will now have to be formally adopted by both Parliament and Council to become EU law. Before all MEPs have their say on the agreement, Parliament’s internal market and civil liberties committees will vote on it.

More on the EU’s digital measures

  • Cryptocurrency dangers and the benefits of EU legislation
  • Fighting cybercrime: new EU cybersecurity laws explained
  • Boosting data sharing in the EU: what are the benefits?
  • EU Digital Markets Act and Digital Services Act
  • Five ways the European Parliament wants to protect online gamers
  • Artificial Intelligence Act

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Student Essays


Essay on Machines | Importance, Advantages & Disadvantages of Machines

Machines are tools used to make our lives easier. There are many types in the world but they have become more important over time.  Now they look like humans and make decisions.

List of Topics

Essay on Machines & their Role in our Life

A machine is a tool that can be made to do work. It has many different types and it dates back to the early 19th century. They are used for all sorts of things, but they must always be taken care of because if they break then it will make the job harder than ever before.

Machines are an important part of our lives, but they must be controlled because if they do not take over then we will. If humans continue to advance at this rate. Without machines our life would be very difficult. The main thing that we have to remember is that they are not perfect and can make errors. They should only take over if the humans cannot do their job efficiently.

>>>> Related Post:   Essay on Technology Today & its Importance

Importance of Machines in Today’s World:

Machines are important in today’s world because they can do things more efficiently. They help with so many different jobs that humans cannot do on their own. There are some who think that machines will take over the world, but it is up to the people to control what they do.

Machines have advanced our lives beyond what most people can even imagine. They have changed in so many ways in which we will probably never truly realize. They have brought in the computer age where everyone is connected with anyone else at any time. The biggest advancement that they have done is make jobs easier to do because of their massive power.

Advantages of machines:

  • They are not perfect so they always need the assistance of humans to improve their work.
  • They can perform work faster than us, this will give us time to do other things that humans should be doing.
  • We must teach them and make sure they do not take over because we were the first and always will be the first.
  • They do not require sleep or food; they can work 24/7 and give us the results we need in a shorter time period than humans.
  • They help solve problems with ease and make our lives easier.
  • We must learn how to control them and use them for good instead of evil because once they get a mind of their own they will not stop until humans are enslaved.
  • They do not need breaks so we have more time to spend with our families.
  • We can use them for everything from babysitting, cooking, cleaning and protecting us from any danger that may come our way.
  • Machines just do the work and they do not complain or make judgments.
  • We can use them for work and fun, instead of having to hire someone we can buy a machine that will perform the same task.

  Disadvantages of machines:

  • They are made of metal so they can malfunction at any time.
  • The only way to stop them doing what they are doing is if we physically go into their system and fix the problem.
  • By teaching them how to solve problems will mean that humans are no longer needed.
  • Machines can cause harm, instead of solving problems they can create more problems for us in the future.
  • They never need to take a break or sleep which means that if they do not have an off switch, then we will never get any peace.
  • By teaching them how to solve problems our way and not theirs, this can lead to us losing all control of them and they may end up taking over the world.
  • They only know the conventional way of solving problems so they may not be able to solve the problems we give them.
  • We do not know how many machines are on our side or the ones that are against us, this puts us in more danger than if we did not have any machines.
  • If they were smart enough they could come up with a way to turn themselves off and become so advanced that humans will not be able to catch up with them.
  • Machines are a good point of view because they take over the workload but we must make sure that we never lose control because if we do then they might end up taking over the world and turning us into their slaves.

Role of Machines Tomorrow

The future of machines is different than what most people think. The biggest advancement that we will see is computers becoming an actual part of the human body, possibly through brain implants. Once you think about it, this will make sense because they can get information like a computer but they still have the human touch.

Machines have been a big part of our life since the beginning and they will be for as long as we can see into the future. They are simply an efficient way to do work but at the same time they need to be controlled by humans because they cannot make decisions like us.

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There is no step in the future of machines that they will take over the earth. They are simply an efficient way to do what humans cannot do on their own. This is why there is no reason to fear them because they are only here for our benefit, but it is up to us to control them when necessary

10 Lines & More Sentences on Machines Essay For Children

  • Machines help us solve problems.
  • We need to teach them or they will not be able to perform their work efficiently and we will have no time for ourselves.
  • They cannot take over unless we give them control because humans should never become obsolete; we were the first and always will be the first beings that live on this planet.
  • They make errors but we should only take over if they cannot do their work efficiently.
  • We need to teach them how to solve problems our way and not theirs.
  • Machines are an important part of our lives but need to be controlled just like humans are because if they come into power then there will be no escape of them.
  • If we continue to advance at this rate machines will take over and we will become their slaves.
  • They make our lives easier without them we would not be able to do things on our own and would depend on others for everything.
  • We need them, but they should never come to a point where they start taking over our lives.
  • Their role is important but we need to be prepared for the future and make sure that we control them by teaching them how to solve problems our way and not theirs, only then will humans succeed.

Essay on Advantages of Machines in human Life:

Machines have become an essential part of human life. From simple tools used for hunting and agriculture to complex machinery used in factories, machines have greatly improved our way of living. In this essay, we will discuss the advantages that machines bring to human life.

One of the biggest benefits of machines is their ability to perform tasks efficiently and accurately. With the use of machines, human labor has been reduced significantly, resulting in increased productivity and cost-effectiveness. For example, a single machine can perform the work of multiple individuals in a fraction of the time.

Moreover, machines have also made our lives easier by taking over dangerous or tedious tasks. This has not only improved working conditions for humans but also reduced the risk of accidents and injuries. In industries such as mining and manufacturing, machines are used to perform tasks that would be too dangerous for humans to do manually.

Another advantage of machines is their consistency. Unlike humans who are prone to errors, machines can perform the same task repeatedly with precision and accuracy. This makes them ideal for mass production where quality control is crucial.

Furthermore, machines have also enabled us to explore new frontiers and achieve new heights. With the help of machines, we have been able to send probes and satellites into space, allowing us to gather valuable information about our universe. In the medical field, machines play a crucial role in diagnosing illnesses and performing complex surgeries that were once thought impossible.

In conclusion, the advantages of machines in human life are undeniable. They have not only made our lives more convenient but also allowed us to achieve feats that were once unimaginable. However, it is important to use machines responsibly and ethically to ensure that their benefits continue to outweigh any potential negative impacts on society. As technology continues to advance, we must strive to strike a balance between the use of machines and preserving the human touch in our everyday lives.

Essay on Machines

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