AI News, The Future of Artificial Intelligence: Is Your Job Under Threat?
- On Wednesday, June 6, 2018
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The Future of Artificial Intelligence: Is Your Job Under Threat?
Since the dawn of machinery and the first flickerings of computer technology, humanity has been obsessed with the idea of artificial intelligence - the concept that machines could one day interact, respond and think for themselves as if they were truly alive.
According to experts across the globe, machines will soon be capable of replacing a variety of jobs - from writing bestsellers, to composing Top 40 pop songs and even performing your open-heart surgery!
Despite some base model machines showing promise, from the “first robotic person” Shakey the Robot in 1966, to anthropomorphic androids WABOT-1 and WABOT-2 from Waseda University - the field of AI started to plateau in the 1980’s.
Instead of creating machines that could carry out ever-more advanced singular “top-down” tasks - from playing the piano to calculating maths problems - AI should be a machine-based relationship with the world around it, or “bottom-up”.
It might sound obvious to us now, thanks to a lifetime rooted in the advances of AI - but back in the early 90s, the suggestion that artificial intelligence should be reactive to its surroundings was revolutionary.
The historical Chinese board game had long been seen as one of AI's greatest challenges, the sheer variety of possible moves demanding players evaluate and react in countless different ways to each turn.
Perhaps the biggest challenge will be ensuring “artificial intelligence” does not lead to the mass wipeout of several job sectors - almost certainly requiring new legislation to be passed, as well as a re-think of the employment market overall.
Commenting on the risk of artificial intelligence on the labour market, James Tweddle, AI Specialist at AI vs Humanity, said: “The risk to the labour market from artificial intelligence is a growing one, particularly given the rapid rate at which AI seems to be developing.
One of the biggest challenges for any artificial intelligence is the idea of ‘bottom up’ learning - the ability for a machine mind to react in a situational manner rather than simply following algorithms.
Artificial general intelligence
The most difficult problems for computers are informally known as 'AI-complete' or 'AI-hard', implying that solving them is equivalent to the general aptitude of human intelligence, or strong AI, beyond the capabilities of a purpose-specific algorithm. AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem. AI-complete problems cannot be solved with current computer technology alone, and also require human computation.
Funding agencies became skeptical of strong AI (ASI) and put researchers under increasing pressure to produce useful 'applied AI'. As the 1980s began, Japan's Fifth Generation Computer Project revived interest in strong AI (ASI), setting out a ten-year timeline that included strong AI (ASI) goals like 'carry on a casual conversation'. In response to this and the success of expert systems, both industry and government pumped money back into the field. However, confidence in AI spectacularly collapsed in the late 1980s, and the goals of the Fifth Generation Computer Project were never fulfilled. For the second time in 20 years, AI researchers who had predicted the imminent achievement of strong AI (ASI) had been shown to be fundamentally mistaken.
They became reluctant to make predictions at all and to avoid any mention of 'human level' artificial intelligence for fear of being labeled 'wild-eyed dreamer[s].' In the 1990s and early 21st century, mainstream AI has achieved far greater commercial success and academic respectability by focusing on specific sub-problems where they can produce verifiable results and commercial applications, such as artificial neural networks, computer vision or data mining. These 'applied AI' systems are now used extensively throughout the technology industry, and research in this vein is very heavily funded in both academia and industry.
A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer).' Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action.
Estimates vary for an adult, ranging from 1014 to 5×1014 synapses (100 to 500 trillion). An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). In 1997 Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps). (For comparison, if a 'computation' was equivalent to one 'floating point operation' – a measure used to rate current supercomputers – then 1016 'computations' would be equivalent to 10 petaFLOPS, achieved in 2011).
It took 50 days on a cluster of 27 processors to simulate 1 second of a model. The Blue Brain project used one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 108 synapses in 2006. A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: 'It is not impossible to build a human brain and we can do it in 10 years,' Henry Markram, director of the Blue Brain Project said in 2009 at the TED conference in Oxford. There have also been controversial claims to have simulated a cat brain.
Since the launch of AI research in 1956, the growth of this field has slowed down over time and has stalled the aims of creating machines skilled with intelligent action at the human level. A possible explanation for this delay is that computers lack a sufficient scope of memory or processing power. In addition, the level of complexity that connects to the process of AI research may also limit the progress of AI research. While most AI researchers believe that strong AI can be achieved in the future, there are some individuals like Hubert Dreyfus and Roger Penrose that deny the possibility of achieving AI. John McCarthy was one of various computer scientists who believe human-level AI will be accomplished, but a date cannot accurately be predicted. Conceptual limitations are another possible reason for the slowness in AI research. AI researchers may need to modify the conceptual framework of their discipline in order to provide a stronger base and contribution to the quest of achieving strong AI.
As William Clocksin wrote in 2003: 'the framework starts from Weizenbaum’s observation that intelligence manifests itself only relative to specific social and cultural contexts'. Furthermore, AI researchers have been able to create computers that can perform jobs that are complicated for people to do, but conversely they have struggled to develop a computer that is capable of carrying out tasks that are simple for humans to do [example needed]. A problem that is described by David Gelernter is that some people assume that thinking and reasoning are equivalent. However, the idea of whether thoughts and the creator of those thoughts are isolated individually has intrigued AI researchers. The problems that have been encountered in AI research over the past decades have further impeded the progress of AI.
The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers from emulating the function of the human brain into a computer hardware. Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously can people then overlook solutions to problematic questions. Clocksin says that a conceptual limitation that may impede the progress of AI research is that people may be using the wrong techniques for computer programs and implementation of equipment. When AI researchers first began to aim for the goal of artificial intelligence, a main interest was human reasoning. Researchers hoped to establish computational models of human knowledge through reasoning and to find out how to design a computer with a specific cognitive task. The practice of abstraction, which people tend to redefine when working with a particular context in research, provides researchers with a concentration on just a few concepts. The most productive use of abstraction in AI research comes from planning and problem solving. Although the aim is to increase the speed of a computation, the role of abstraction has posed questions about the involvement of abstraction operators. A
There are no emotions in typical models of AI and some researchers say programming emotions into machines allows them to have a mind of their own. Emotion sums up the experiences of humans because it allows them to remember those experiences. David Gelernter writes, 'No computer will be creative unless it can simulate all the nuances of human emotion.' This concern about emotion has posed problems for AI researchers and it connects to the concept of strong AI as its research progresses into the future.
Microsoft co-founder Paul Allen believes that such intelligence is unlikely in the 21st century because it would require 'unforeseeable and fundamentally unpredictable breakthroughs' and a 'scientifically deep understanding of cognition'. Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level artificial intelligence is as wide as the gulf between current space flight and practical faster-than-light spaceflight. Optimism that AGI is feasible waxes and wanes, and may have seen a resurgence in the 2010s.
The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning
We’re all familiar with the term “Artificial Intelligence.” After all, it’s been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine).
Arthur Samuel coined the phrase not too long after AI, in 1959, defining it as, “the ability to learn without being explicitly programmed.” You see, you can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.
So instead of hard coding software routines with specific instructions to accomplish a particular task, machine learning is a way of “training” an algorithm so that it can learnhow.
To give an example, machine learning has been used to make drastic improvements to computer vision (the ability of a machine to recognize an object in an image or video).
Other approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others.
It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer.
Our brains take that data and makes sense of it, turning light into recognizable objects and turning sounds into understandable speech.
As mentioned above, machine learning and deep learning require massive amounts of data to work, and this data is being collected by the billions of sensors that are continuing to come online in the Internet of Things.
On the industrial side, AI can be applied to predict when machines will need maintenance or analyze manufacturing processes to make big efficiency gains, saving millions of dollars.
We might ask for information like the weather or for an action like preparing the house for bedtime (turning down the thermostat, locking the doors, turning off the lights, etc.).
Wireless connectivity, driven by the advent of smartphones, means that data can be sent in high volume at cheap rates, allowing all those sensors to send data to the cloud.
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. 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'. 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.' For instance, optical character recognition is frequently excluded from 'artificial intelligence', having become a routine technology. Capabilities generally classified as AI as of 2017[update] include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery network and military simulations.
'robotics' or 'machine learning'), the use of particular tools ('logic' or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers). 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. General intelligence is among the field's long-term goals. 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'. 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. Some people also consider AI to be a danger to humanity if it progresses unabatedly. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment. 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'. The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete 'artificial neurons'. The field of AI research was born at a workshop at Dartmouth College in 1956. 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. They and their students produced programs that the press described as 'astonishing': 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. 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. In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. 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. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov on 11 May 1997. In 2011, a Jeopardy!
data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. 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 as do intelligent personal assistants in smartphones. 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. In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie, 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. 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. 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. In a 2017 survey, one in five companies reported they had 'incorporated AI in some offerings or processes'. A
The traits described below have received the most attention. Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. 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. 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, scene interpretation, clinical decision support, knowledge discovery (mining 'interesting' and actionable inferences from large databases), and other areas. Among the most difficult problems in knowledge representation are: Intelligent agents must be able to set goals and achieve them. 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. 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. 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. AI is heavily used in robotics. 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. 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'. This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years. Moravec's paradox can be extended to many forms of social intelligence. Distributed multi-agent coordination of autonomous vehicles remains a difficult problem. Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. 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. 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). 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. 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. Besides transfer learning, 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. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, 'Master Algorithm' could lead to AGI. 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. 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. 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. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. 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. Researchers at MIT (such as Marvin Minsky and Seymour Papert) 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). 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. When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This 'knowledge revolution' led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. 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.). Interest in neural networks and 'connectionism' was revived by David Rumelhart and others in the middle of the 1980s. 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. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. 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. Bayesian networks are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm),[f] planning (using decision networks) and perception (using dynamic Bayesian networks). 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). 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, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design. 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 is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network, k-nearest neighbor algorithm,[g] kernel methods such as the support vector machine (SVM),[h] Gaussian mixture model and the extremely popular naive Bayes classifier.[i] 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. 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. 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, and was introduced to neural networks by Paul Werbos. Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex. 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. Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others. According to one overview, the expression 'Deep Learning' was introduced to the Machine Learning community by Rina Dechter in 1986 and gained traction after Igor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000. 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. Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. 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. Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions. 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. Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs) which are in theory Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.
thus, an RNN is an example of deep learning. RNNs can be trained by gradient descent but suffer from the vanishing gradient problem. 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. 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. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. 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.' Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well. Games provide a well-publicized benchmark for assessing rates of progress.
this phenomenon is described as the AI effect. 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 and targeting online advertisements. 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, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic. 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. 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. 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. 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. 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. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies. The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. 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, and Elon Musk. 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. 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. 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'. 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. 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). 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' and 'Can (Ro)bots Really Be Moral'. 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. 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. 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.' 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. 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. 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. You awake one morning to find your brain has another lobe functioning.
Experts Predict When Artificial Intelligence Will Exceed Human Performance
Artificial intelligence is changing the world and doing it at breakneck speed.
They surveyed the world’s leading researchers in artificial intelligence by asking them when they think intelligent machines will better humans in a wide range of tasks.
Grace and co asked them all—1,634 of them—to fill in a survey about when artificial intelligence would be better and cheaper than humans at a variety of tasks.
Grave and co then calculated their median responses The experts predict that AI will outperform humans in the next 10years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027).
AI won’t be better than humans at working in retail until 2031, able to write a bestselling book until 2049, or capable of workingas a surgeon until 2053.
(This was in 2015, remember.) In fact, Google’s DeepMind subsidiary has already developed an artificial intelligence capable of beating the best humans.
So any predicted change that is further away than that means the change will happen beyond the working lifetime of everyone who is working today.
To find out if different groups made different predictions, Grace and co looked at how the predictions changed with the age of the researchers, the number of their citations (i.e., their expertise), and their region of origin.
Humans Vs Robots: Don’t Give Advanced Machines Rights, AI Experts Warn
Despite how human-like they may act and appear, giving rights to robots may not be the best move.
team of 150 experts in robotics, artificial intelligence, law, medical science and ethics wrote an open letter to the European Union advising that robots not be given special legal status as 'electric persons,' CNN reported.
“From an ethical and legal perspective, creating a legal personality for a robot is inappropriate whatever the legal status model,” the letter states.
The experts go on to claim that public perception of a robot is distorted by “Science-Fiction and a few recent sensational press announcements.” One of the reasons listed for denying robots these rights are that machines currently cannot take part in society without a human operator, and therefore cannot have their own rights.
- On Tuesday, March 19, 2019
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