AI News, Research for Beneficial Artificial Intelligence

Research for Beneficial Artificial Intelligence

There’s a scene in Jurassic Park, in which Jeff Goldblum’s character laments that the scientists who created the dinosaurs “were so preoccupied with whether or not they could that they didn’t stop to think if they should.” Until recently, AI researchers have also focused primarily on figuring out what they could accomplish, without longer-term considerations, and for good reason: scientists were just trying to get their AI programs to work at all, and the results were far too limited to pose any kind of threat.

It’s not okay to say, ‘I just make tools and someone else decides whether they’re used for good or ill.’ If you’re participating in the process of making these enormously powerful tools, you have a responsibility to do what you can to make sure that this is being pushed in a generally beneficial direction.

With AI, everyone who’s involved has a responsibility to be pushing it in a positive direction, because if it’s always somebody else’s problem, that’s a recipe for letting things take the path of least resistance, which is to put the power in the hands of the already powerful so that they can become even more powerful and benefit themselves.” Other AI experts I spoke with agreed with the general idea of the Principle, but didn’t see quite eye-to-eye on how it was worded.

“Yes, I agree with that,” Craw said, but adds, “I think it’s a little strange the way it’s worded, because of ‘undirected.’ It might even be better the other way around, which is, it would be better to create beneficial research, because that’s a more well-defined thing.” Roman Yampolskiy, an AI researcher at the University of Louisville, brings the discussion back to the issues of most concern for FLI: “The universe of possible intelligent agents is infinite with respect to both architectures and goals.

It is only by aligning future superintelligence with our true goals, that we can get significant benefit out of our intellectual heirs and avoid existential catastrophe.” And with that in mind, we’re excited to announce we’ve launched a new round of grants!

Artificial intelligence

In computer science AI research is defined as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term 'artificial intelligence' is applied when a machine mimics 'cognitive' functions that humans associate with other human minds, such as 'learning' and 'problem solving'.[2] The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring 'intelligence' are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, 'AI is whatever hasn't been done yet.'[3] For instance, optical character recognition is frequently excluded from 'artificial intelligence', having become a routine technology.[4] Capabilities generally classified as AI as of 2017[update] include successfully understanding human speech,[5] competing at the highest level in strategic game systems (such as chess and Go[6]), autonomous cars, intelligent routing in content delivery network and military simulations.

'robotics' or 'machine learning'),[13] the use of particular tools ('logic' or artificial neural networks), or deep philosophical differences.[14][15][16] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[12] The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[13] General intelligence is among the field's long-term goals.[17] Approaches include statistical methods, computational intelligence, and traditional symbolic AI.

The field was founded on the claim that human intelligence 'can be so precisely described that a machine can be made to simulate it'.[18] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[19] Some people also consider AI to be a danger to humanity if it progresses unabatedly.[20] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[21] In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding;

Turing proposed that 'if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent'.[26] The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete 'artificial neurons'.[27] The field of AI research was born at a workshop at Dartmouth College in 1956.[28] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[29] They and their students produced programs that the press described as 'astonishing':[30] computers were learning checkers strategies (c.

At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research.[8] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[10] In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[22] The success was due to increasing computational power (see Moore's law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[38] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov on 11 May 1997.[39] In 2011, a Jeopardy!

data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[41] The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One use algorithms that emerged from lengthy AI research[42] as do intelligent personal assistants in smartphones.[43] In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.[6][44] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[45] who at the time continuously held the world No.

Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[48] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[11] Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.[48] In a 2017 survey, one in five companies reported they had 'incorporated AI in some offerings or processes'.[49][50] A

The traits described below have received the most attention.[13] Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[73] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[74] These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a 'combinatorial explosion': they became exponentially slower as the problems grew larger.[55] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model.

Such formal knowledge representations can be used in content-based indexing and retrieval,[84] scene interpretation,[85] clinical decision support,[86] knowledge discovery (mining 'interesting' and actionable inferences from large databases),[87] and other areas.[88] Among the most difficult problems in knowledge representation are: Intelligent agents must be able to set goals and achieve them.[95] They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or 'value') of available choices.[96] In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[97] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty.

a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its 'object model' to assess that fifty-meter pedestrians do not exist.[112] AI is heavily used in robotics.[113] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[114] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment;

the paradox is named after Hans Moravec, who stated in 1988 that 'it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility'.[118][119] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[120] Moravec's paradox can be extended to many forms of social intelligence.[122][123] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[124] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects.[125][126][127] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis, wherein AI classifies the affects displayed by a videotaped subject.[128] In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent.

Nowadays, the vast majority of current AI researchers work instead on tractable 'narrow AI' applications (such as medical diagnosis or automobile navigation).[131] Many researchers predict that such 'narrow AI' work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[17][132] Many advances have general, cross-domain significance.

One high-profile example is that DeepMind in the 2010s developed a 'generalized artificial intelligence' that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[133][134][135] Besides transfer learning,[136] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to 'slurp up' a comprehensive knowledge base from the entire unstructured Web.[5] Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, 'Master Algorithm' could lead to AGI.[137] Finally, a few 'emergent' approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[138][139] Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do.

This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[144][145] Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms.[14] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[146] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[147] Researchers at MIT (such as Marvin Minsky and Seymour Papert)[148] found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior.

Roger Schank described their 'anti-logic' approaches as 'scruffy' (as opposed to the 'neat' paradigms at CMU and Stanford).[15] Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of 'scruffy' AI, since they must be built by hand, one complicated concept at a time.[149] When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[150] This 'knowledge revolution' led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[37] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[152][153][154][155] Interest in neural networks and 'connectionism' was revived by David Rumelhart and others in the middle of the 1980s.[156] Artificial neural networks are an example of soft computing --- they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient.

For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[168] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[169] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[114] Many learning algorithms use search algorithms based on optimization.

AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[187] Bayesian networks[188] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[189] learning (using the expectation-maximization algorithm),[f][191] planning (using decision networks)[192] and perception (using dynamic Bayesian networks).[193] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[193] Compared with symbolic logic, formal Bayesian inference is computationally expensive.

Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[195] and information value theory.[96] These tools include models such as Markov decision processes,[196] dynamic decision networks,[193] game theory and mechanism design.[197] The simplest AI applications can be divided into two types: classifiers ('if shiny then diamond') and controllers ('if shiny then pick up').

The decision tree[199] is perhaps the most widely used machine learning algorithm.[200] Other widely used classifiers are the neural network,[201] k-nearest neighbor algorithm,[g][203] kernel methods such as the support vector machine (SVM),[h][205] Gaussian mixture model[206] and the extremely popular naive Bayes classifier.[i][208] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, the dimensionality, and the level of noise.

Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.[214] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ('fire together, wire together'), GMDH or competitive learning.[215] Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[216][217] and was introduced to neural networks by Paul Werbos.[218][219][220] Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[221] In short, most neural networks use some form of gradient descent on a hand-created neural topology.

Many deep learning systems need to be able to learn chains ten or more causal links in length.[223] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[224][225][223] According to one overview,[226] the expression 'Deep Learning' was introduced to the Machine Learning community by Rina Dechter in 1986[227] and gained traction after Igor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[228] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V.

Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[232] Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[233] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture.

In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.[234] Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions.[223] CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's 'AlphaGo Lee', the program that beat a top Go champion in 2016.[235] Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[236] which are in theory Turing complete[237] and can run arbitrary programs to process arbitrary sequences of inputs.

thus, an RNN is an example of deep learning.[223] RNNs can be trained by gradient descent[238][239][240] but suffer from the vanishing gradient problem.[224][241] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[242] Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter &

There is no consensus on how to characterize which tasks AI tends to excel at.[253] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[254][255] Researcher Andrew Ng has suggested, as a 'highly imperfect rule of thumb', that 'almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.'[256] Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[120] Games provide a well-publicized benchmark for assessing rates of progress.

this phenomenon is described as the AI effect.[266] High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, prediction of judicial decisions[267] and targeting online advertisements.[265][268][269] With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[270] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[271] Artificial intelligence is breaking into the healthcare industry by assisting doctors.

Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[273] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[274] According to CNN, a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot.

However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[282] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[283] Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger.

AI can react to changes overnight or when business is not taking place.[285] In August 2001, robots beat humans in a simulated financial trading competition.[286] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[287] The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[288] For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing.

This concern has recently gained attention after mentions by celebrities including the late Stephen Hawking, Bill Gates,[309] and Elon Musk.[310] A group of prominent tech titans including Peter Thiel, Amazon Web Services and Musk have committed $1billion to OpenAI a nonprofit company aimed at championing responsible AI development.[311] The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI.[312] In his book Superintelligence, Nick Bostrom provides an argument that artificial intelligence will pose a threat to mankind.

for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at 'high risk' of potential automation, while an OECD report classifies only 9% of U.S. jobs as 'high risk'.[324][325][326] Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.[327] Author Martin Ford and others go further and argue that a large number of jobs are routine, repetitive and (to an AI) predictable;

This issue was addressed by Wendell Wallach in his book titled Moral Machines in which he introduced the concept of artificial moral agents (AMA).[328] For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as 'Does Humanity Want Computers Making Moral Decisions'[329] and 'Can (Ro)bots Really Be Moral'.[330] For Wallach the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs.[331] The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.[332] The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: 'Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines.

The philosophical position that John Searle has named 'strong AI' states: 'The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.'[338] Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the 'mind' might be.[339] Mary Shelley's Frankenstein considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel?

Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.[344][132] Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.[344] You awake one morning to find your brain has another lobe functioning.

Asilomar AI Principles

6) Safety: AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.

8) Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.

9) Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.

17) Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.

Artificial intelligence

AI research follows two distinct, and to some extent competing, methods, the symbolic (or “top-down”) approach, and the connectionist (or “bottom-up”) approach.

The top-down approach seeks to replicate intelligence by analyzing cognition independent of the biological structure of the brain, in terms of the processing of symbols—whence the symbolic label.

(Tuning adjusts the responsiveness of different neural pathways to different stimuli.) In contrast, a top-down approach typically involves writing a computer program that compares each letter with geometric descriptions.

In The Organization of Behavior (1949), Donald Hebb, a psychologist at McGill University, Montreal, Canada, suggested that learning specifically involves strengthening certain patterns of neural activity by increasing the probability (weight) of induced neuron firing between the associated connections.

This hypothesis states that processing structures of symbols is sufficient, in principle, to produce artificial intelligence in a digital computer and that, moreover, human intelligence is the result of the same type of symbolic manipulations.

China announces goal of leadership in artificial intelligence by 2030

BEIJING -- China's government has announced a goal of becoming a global leader in artificial intelligence in just over a decade, putting political muscle behind growing investment by Chinese companies in developing self-driving cars and other advances.

Artificial intelligence is one of the emerging fields along with renewable energy, robotics and electric cars where communist leaders hope to take an early lead and help transform China from a nation of factory workers and farmers into a technology pioneer.

'By 2030, our country will reach a world leading level in artificial intelligence theory, technology and application and become a principal world center for artificial intelligence innovation,' the statement said.

The announcement follows a sweeping plan issued in 2015, dubbed 'Made in China 2025,' that calls for this country to supply its own high-tech components and materials in 10 industries from information technology and aerospace to pharmaceuticals.

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