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Artificial Intelligence in Banking 2020: How Banks Use AI …

Discussions, articles, and reports about the AI opportunity across the financial services industry continue to proliferate amid considerable hype around the technology, and for good reason: The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, per Autonomous Next research seen by Business Insider Intelligence.

Most banks (80%) are highly aware of the potential benefits presented by AI, per an OpenText survey of financial services professionals. In fact, many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Business Insider Intelligence. Certain AI use cases have already gained prominence across banks' operations, with chatbots in the front office and anti-payments fraud in the middle office the most mature.

Banks can use AI to transform the customer experience by enabling frictionless, 24/7 customer interactions but AI in banking applications isn't just limited to retail banking services. The back and middle offices of investment banking and all other financial services for that matter could also benefit from AI.

The three main channels where banks can use artificial intelligence to save on costs are front office (conversational banking), middle office (anti-fraud) and back office (underwriting).

In this report, Business Insider Intelligence identifies the most meaningful AI applications across banks' front and middle offices. We also discuss the winning AI strategies used by financial institutions so far, and provide recommendations for how banks can best approach an AI-enabled digital transformation.

The companies mentioned in this report are: Capital One, Citi, HSBC, JPMorgan Chase, Personetics, Quantexa, and U.S. Bank

Here are some of the key takeaways from the report:

In full, the report:

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Artificial Intelligence in Banking 2020: How Banks Use AI ...

Artificial Intelligence | Internet Encyclopedia of Philosophy

Artificial intelligence (AI) would be the possession of intelligence, or the exercise of thought, by machines such as computers. Philosophically, the main AI question is Can there be such? or, as Alan Turing put it, Can a machine think? What makes this a philosophical and not just a scientific and technical question is the scientific recalcitrance of the concept of intelligence or thought and its moral, religious, and legal significance. In European and other traditions, moral and legal standing depend not just on what is outwardly done but also on inward states of mind. Only rational individuals have standing as moral agents and status as moral patients subject to certain harms, such as being betrayed. Only sentient individuals are subject to certain other harms, such as pain and suffering. Since computers give every outward appearance of performing intellectual tasks, the question arises: Are they really thinking? And if they are really thinking, are they not, then, owed similar rights to rational human beings? Many fictional explorations of AI in literature and film explore these very questions.

A complication arises if humans are animals and if animals are themselves machines, as scientific biology supposes. Still, we wish to exclude from the machines in question men born in the usual manner (Alan Turing), or even in unusual manners such asin vitro fertilization or ectogenesis. And if nonhuman animals think, we wish to exclude them from the machines, too. More particularly, the AI thesis should be understood to hold that thought, or intelligence, can be produced by artificial means; made, not grown. For brevitys sake, we will take machine to denote just the artificial ones. Since the present interest in thinking machines has been aroused by a particular kind of machine, an electronic computer or digital computer, present controversies regarding claims of artificial intelligence center on these.

Accordingly, the scientific discipline and engineering enterprise of AI has been characterized as the attempt to discover and implement the computational means to make machines behave in ways that would be called intelligent if a human were so behaving (John McCarthy), or to make them do things that would require intelligence if done by men (Marvin Minsky). These standard formulations duck the question of whether deeds which indicate intelligence when done by humans truly indicate it when done by machines: thats the philosophical question. So-called weak AI grants the fact (or prospect) of intelligent-acting machines; strong AI says these actions can be real intelligence. Strong AI says some artificial computation is thought. Computationalism says that all thought is computation. Though many strong AI advocates are computationalists, these are logically independent claims: some artificial computation being thought is consistent with some thought not being computation, contra computationalism. All thought being computation is consistent with some computation (and perhaps all artificial computation) not being thought.

Intelligence might be styled the capacity to think extensively and well. Thinking well centrally involves apt conception, true representation, and correct reasoning. Quickness is generally counted a further cognitive virtue. The extent or breadth of a things thinking concerns the variety of content it can conceive, and the variety of thought processes it deploys. Roughly, the more extensively a thing thinks, the higher the level (as is said) of its thinking. Consequently, we need to distinguish two different AI questions:

In Computer Science, work termed AI has traditionally focused on the high-level problem; on imparting high-level abilities to use language, form abstractions and concepts and to solve kinds of problems now reserved for humans (McCarthy et al. 1955); abilities to play intellectual games such as checkers (Samuel 1954) and chess (Deep Blue); to prove mathematical theorems (GPS); to apply expert knowledge to diagnose bacterial infections (MYCIN); and so forth. More recently there has arisen a humbler seeming conception behavior-based or nouvelle AI according to which seeking to endow embodied machines, or robots, with so much as insect level intelligence (Brooks 1991) counts as AI research. Where traditional human-level AI successes impart isolated high-level abilities to function in restricted domains, or microworlds, behavior-based AI seeks to impart coordinated low-level abilities to function in unrestricted real-world domains.

Still, to the extent that what is called thinking in us is paradigmatic for what thought is, the question of human level intelligence may arise anew at the foundations. Do insects think at all? And if insects what of bacteria level intelligence (Brooks 1991a)? Even water flowing downhill, it seems, tries to get to the bottom of the hill by ingeniouslyseeking the line of least resistance (Searle 1989). Dont we have to draw the line somewhere? Perhaps seeming intelligence to really be intelligence has to come up to some threshold level.

Much as intentionality (aboutness or representation) is central to intelligence, felt qualities (so-called qualia) are crucial to sentience. Here, drawing on Aristotle, medieval thinkers distinguished between the passive intellect wherein the soul is affected, and the active intellect wherein the soul forms conceptions, draws inferences, makes judgments, and otherwise acts. Orthodoxy identified the soul proper (the immortal part) with the active rational element. Unfortunately, disagreement over how these two (qualitative-experiential and cognitive-intentional) factors relate is as rife as disagreement over what things think; and these disagreements are connected. Those who dismiss the seeming intelligence of computers because computers lack feelings seem to hold qualia to be necessary for intentionality. Those like Descartes, who dismiss the seeming sentience of nonhuman animals because he believed animals dont think, apparently hold intentionality to be necessary for qualia. Others deny one or both necessities, maintaining either the possibility of cognition absent qualia (as Christian orthodoxy, perhaps, would have the thought-processes of God, angels, and the saints in heaven to be), or maintaining the possibility of feeling absent cognition (as Aristotle grants the lower animals).

While we dont know what thought or intelligence is, essentially, and while were very far from agreed on what things do and dont have it, almost everyone agrees that humans think, and agrees with Descartes that our intelligence is amply manifest in our speech. Along these lines, Alan Turing suggested that if computers showed human level conversational abilities we should, by that, be amply assured of their intelligence. Turing proposed a specific conversational test for human-level intelligence, the Turing test it has come to be called. Turing himself characterizes this test in terms of an imitation game (Turing 1950, p. 433) whose original version is played by three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. The interrogator is allowed to put questions to A and B [by teletype to avoid visual and auditory clues]. . It is As object in the game to try and cause C to make the wrong identification. The object of the game for the third player (B) is to help the interrogator. Turing continues, We may now ask the question, `What will happen when a machine takes the part of A in this game? Will the interrogator decide wrongly as often when the game is being played like this as he does when the game is played between a man and a woman? These questions replace our original, `Can machines think?' (Turing 1950) The test setup may be depicted this way:

This test may serve, as Turing notes, to test not just for shallow verbal dexterity, but for background knowledge and underlying reasoning ability as well, since interrogators may ask any question or pose any verbal challenge they choose. Regarding this test Turing famously predicted that in about fifty years time [by the year 2000] it will be possible to program computers to make them play the imitation game so well that an average interrogator will have no more than 70 per cent. chance of making the correct identification after five minutes of questioning (Turing 1950); a prediction that has famously failed. As of the year 2000, machines at the Loebner Prize competition played the game so ill that the average interrogator had 100 percent chance of making the correct identification after five minutes of questioning (see Moor 2001).

It is important to recognize that Turing proposed his test as a qualifying test for human-level intelligence, not as a disqualifying test for intelligence per se (as Descartes had proposed); nor would it seem suitably disqualifying unless we are prepared (as Descartes was) to deny that any nonhuman animals possess any intelligence whatsoever. Even at the human level the test would seem not to be straightforwardly disqualifying: machines as smart as we (or even smarter) might still be unable to mimic us well enough to pass. So, from the failure of machines to pass this test, we can infer neither their complete lack of intelligence nor, that their thought is not up to the human level. Nevertheless, the manners of current machine failings clearly bespeak deficits of wisdom and wit, not just an inhuman style. Still, defenders of the Turing test claim we would have ample reason to deem them intelligent as intelligent as we are if they could pass this test.

The extent to which machines seem intelligent depends first, on whether the work they do is intellectual (for example, calculating sums) or manual (for example, cutting steaks): herein, an electronic calculator is a better candidate than an electric carving knife. A second factor is the extent to which the device is self-actuated (self-propelled, activated, and controlled), or autonomous: herein, an electronic calculator is a better candidate than an abacus. Computers are better candidates than calculators on both headings. Where traditional AI looks to increase computer intelligence quotients (so to speak), nouvelle AI focuses on enabling robot autonomy.

In the beginning, tools (for example, axes) were extensions of human physical powers; at first powered by human muscle; then by domesticated beasts and in situ forces of nature, such as water and wind. The steam engine put fire in their bellies; machines became self-propelled, endowed with vestiges of self-control (as by Watts 1788 centrifugal governor); and the rest is modern history. Meanwhile, automation of intellectual labor had begun. Blaise Pascal developed an early adding/subtracting machine, the Pascaline (circa 1642). Gottfried Leibniz added multiplication and division functions with his Stepped Reckoner (circa 1671). The first programmable device, however, plied fabric not numerals. The Jacquard loom developed (circa 1801) by Joseph-Marie Jacquard used a system of punched cards to automate the weaving of programmable patterns and designs: in one striking demonstration, the loom was programmed to weave a silk tapestry portrait of Jacquard himself.

In designs for his Analytical Engine mathematician/inventor Charles Babbage recognized (circa 1836) that the punched cards could control operations on symbols as readily as on silk; the cards could encode numerals and other symbolic data and, more importantly, instructions, including conditionally branching instructions, for numeric and other symbolic operations. Augusta Ada Lovelace (Babbages software engineer) grasped the import of these innovations: The bounds of arithmetic she writes, were outstepped the moment the idea of applying the [instruction] cards had occurred thus enabling mechanism to combine together with general symbols, in successions of unlimited variety and extent (Lovelace 1842). Babbage, Turing notes, had all the essential ideas (Turing 1950). Babbages Engine had he constructed it in all its steam powered cog-wheel driven glory would have been a programmable all-purpose device, the first digital computer.

Before automated computation became feasible with the advent of electronic computers in the mid twentieth century, Alan Turing laid the theoretical foundations of Computer Science by formulating with precision the link Lady Lovelace foresaw between the operations of matter and the abstract mental processes of themost abstract branch of mathematical sciences (Lovelace 1942). Turing (1936-7) describes a type of machine (since known as a Turing machine) which would be capable of computing any possible algorithm, or performing any rote operation. Since Alonzo Church (1936) using recursive functions and Lambda-definable functions had identified the very same set of functions as rote or algorithmic as those calculable by Turing machines, this important and widely accepted identification is known as the Church-Turing Thesis (see, Turing 1936-7: Appendix). The machines Turing described are

only capable of a finite number of conditions m-configurations. The machine is supplied with a tape (the analogue of paper) running through it, and divided into sections (called squares) each capable of bearing a symbol. At any moment there is just one square which is in the machine. The scanned symbol is the only one of which the machine is, so to speak, directly aware. However, by altering its m-configuration the machine can effectively remember some of the symbols which it has seen (scanned) previously. The possible behavior of the machine at any moment is determined by the m-configuration and the scanned symbol . This pair called the configuration determines the possible behaviour of the machine. In some of the configurations in which the square is blank the machine writes down a new symbol on the scanned square: in other configurations it erases the scanned symbol. The machine may also change the square which is being scanned, but only by shifting it one place to right or left. In addition to any of these operations the m-configuration may be changed. (Turing 1936-7)

Turing goes on to show how such machines can encode actionable descriptions of other such machines. As a result, It is possible to invent a single machine which can be used to compute any computable sequence (Turing 1936-7). Todays digital computers are (and Babbages Engine would have been) physical instantiations of this universal computing machine that Turing described abstractly. Theoretically, this means everything that can be done algorithmically or by rote at all can all be done with one computer suitably programmed for each case; considerations of speed apart, it is unnecessary to design various new machines to do various computing processes (Turing 1950). Theoretically, regardless of their hardware or architecture (see below), all digital computers are in a sense equivalent: equivalent in speed-apart capacities to the universal computing machine Turing described.

In practice, where speed is not apart, hardware and architecture are crucial: the faster the operations the greater the computational power. Just as improvement on the hardware side from cogwheels to circuitry was needed to make digital computers practical at all, improvements in computer performance have been largely predicated on the continuous development of faster, more and more powerful, machines. Electromechanical relays gave way to vacuum tubes, tubes to transistors, and transistors to more and more integrated circuits, yielding vastly increased operation speeds. Meanwhile, memory has grown faster and cheaper.

Architecturally, all but the earliest and some later experimental machines share a stored program serial design often called von Neumann architecture (based on John von Neumanns role in the design of EDVAC, the first computer to store programs along with data in working memory). The architecture is serial in that operations are performed one at a time by a central processing unit (CPU) endowed with a rich repertoire ofbasic operations: even so-called reduced instruction set (RISC) chips feature basic operation sets far richer than the minimal few Turing proved theoretically sufficient. Parallel architectures, by contrast, distribute computational operations among two or more units (typically many more) capable of acting simultaneously, each having (perhaps) drastically reduced basic operational capacities.

In 1965, Gordon Moore (co-founder of Intel) observed that the density of transistors on integrated circuits had doubled every year since their invention in 1959: Moores law predicts the continuation of similar exponential rates of growth in chip density (in particular), and computational power (by extension), for the foreseeable future. Progress on the software programming side while essential and by no means negligible has seemed halting by comparison. The road from power to performance is proving rockier than Turing anticipated. Nevertheless, machines nowadays do behave in many ways that would be called intelligent in humans and other animals. Presently, machines do many things formerly only done by animals and thought to evidence some level of intelligence in these animals, for example, seeking, detecting, and tracking things; seeming evidence of basic-level AI. Presently, machines also do things formerly only done by humans and thought to evidence high-level intelligence in us; for example, making mathematical discoveries, playing games, planning, and learning; seeming evidence of human-level AI.

The doings of many machines some much simpler than computers inspire us to describe them in mental terms commonly reserved for animals. Some missiles, for instance, seek heat, or so we say. We call them heat seeking missiles and nobody takes it amiss. Room thermostats monitor room temperatures and try to keep them within set ranges by turning the furnace on and off; and if you hold dry ice next to its sensor, it will take the room temperature to be colder than it is, and mistakenly turn on the furnace (see McCarthy 1979). Seeking, monitoring, trying, and taking things to be the case seem to be mental processes or conditions, marked by their intentionality. Just as humans have low-level mental qualities such as seeking and detecting things in common with the lower animals, so too do computers seem to share such low-level qualities with simpler devices. Our working characterizations of computers are rife with low-level mental attributions: we say they detect key presses, try to initialize their printers, search for available devices, and so forth. Even those who would deny the proposition machines think when it is explicitly put to them, are moved unavoidably in their practical dealings to characterize the doings of computers in mental terms, and they would be hard put to do otherwise. In this sense, Turings prediction that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted (Turing 1950) has been as mightily fulfilled as his prediction of a modicum of machine success at playing the Imitation Game has been confuted. The Turing test and AI as classically conceived, however, are more concerned with high-level appearances such as the following.

Theorem proving and mathematical exploration being their home turf, computers have displayed not only human-level but, in certain respects, superhuman abilities here. For speed and accuracy of mathematical calculation, no human can match the speed and accuracy of a computer. As for high level mathematical performances, such as theorem proving and mathematical discovery, a beginning was made by A. Newell, J.C. Shaw, and H. Simons (1957) Logic Theorist program which proved 38 of the first 51 theorems of B. Russell and A.N. WhiteheadsPrincipia Mathematica. Newell and Simons General Problem Solver (GPS) extended similar automated theorem proving techniques outside the narrow confines of pure logic and mathematics. Today such techniques enjoy widespread application in expert systems like MYCIN, in logic tutorial software, and in computer languages such as PROLOG. There are even original mathematical discoveries owing to computers. Notably, K. Appel, W. Haken, and J. Koch (1977a, 1977b), and computer, proved that every planar map is four colorable an important mathematical conjecture that had resisted unassisted human proof for over a hundred years. Certain computer generated parts of this proof are too complex to be directly verified (without computer assistance) by human mathematicians.

Whereas attempts to apply general reasoning to unlimited domains are hampered by explosive inferential complexity and computers lack of common sense, expert systems deal with these problems by restricting their domains of application (in effect, to microworlds), and crafting domain-specific inference rules for these limited domains. MYCIN for instance, applies rules culled from interviews with expert human diagnosticians to descriptions of patients presenting symptoms to diagnose blood-borne bacterial infections. MYCIN displays diagnostic skills approaching the expert human level, albeit strictly limited to this specific domain. Fuzzy logic is a formalism for representing imprecise notions such asmost andbaldand enabling inferences based on such facts as that a bald person mostly lacks hair.

Game playing engaged the interest of AI researchers almost from the start. Samuels (1959) checkers (or draughts) program was notable for incorporating mechanisms enabling it to learn from experience well enough to eventually to outplay Samuel himself. Additionally, in setting one version of the program to play against a slightly altered version, carrying over the settings of the stronger player to the next generation, and repeating the process enabling stronger and stronger versions to evolve Samuel pioneered the use of what have come to be called genetic algorithms and evolutionary computing. Chess has also inspired notable efforts culminating, in 1997, in the famous victory of Deep Blue over defending world champion Gary Kasparov in a widely publicized series of matches (recounted in Hsu 2002). Though some in AI disparaged Deep Blues reliance on brute force application of computer power rather than improved search guiding heuristics, we may still add chess to checkers (where the reigning human-machine machine champion since 1994 has been CHINOOK, the machine), and backgammon, as games that computers now play at or above the highest human levels. Computers also play fair to middling poker, bridge, and Go though not at the highest human level. Additionally, intelligent agents or softbots are elements or participants in a variety of electronic games.

Planning, in large measure, is what puts the intellect in intellectual games like chess and checkers. To automate this broader intellectual ability was the intent of Newell and Simons General Problem Solver (GPS) program. GPS was able to solve puzzles like the cannibals missionaries problem (how to transport three missionaries and three cannibals across a river in a canoe for two without the missionaries becoming outnumbered on either shore) by setting up subgoals whose attainment leads to the attainment of the [final] goal (Newell & Simon 1963: 284). By these methods GPS would generate a tree of subgoals (Newell & Simon 1963: 286) and seek a path from initial state (for example, all on the near bank) to final goal (all on the far bank) by heuristically guided search along a branching tree of available actions (for example, two cannibals cross, two missionaries cross, one of each cross, one of either cross, in either direction) until it finds such a path (for example, two cannibals cross, one returns, two cannibals cross, one returns, two missionaries cross, ), or else finds that there is none. Since the number of branches increases exponentially as a function of the number of options available at each step, where paths have many steps with many options available at each choice point, as in the real world, combinatorial explosion ensues and an exhaustive brute force search becomes computationally intractable; hence, heuristics (fallible rules of thumb) for identifying and pruning the most unpromising branches in order to devote increased attention to promising ones are needed. The widely deployed STRIPS formalism first developed at Stanford for Shakey the robot in the late sixties (see Nilsson 1984) represents actions as operations on states, each operation having preconditions (represented by state descriptions) and effects (represented by state descriptions): for example, the go(there) operation might have the preconditions at(here) & path(here,there) and the effect at(there). AI planning techniques are finding increasing application and even becoming indispensable in a multitude of complex planning and scheduling tasks including airport arrivals, departures, and gate assignments; store inventory management; automated satellite operations; military logistics; and many others.

Robots based on sense-model-plan-act (SMPA) approach pioneered by Shakey, however, have been slow to appear. Despite operating in a simplified, custom-made experimental environment or microworld and reliance on the most powerful available offboard computers, Shakey operated excruciatingly slowly (Brooks 1991b), as have other SMPA based robots. An ironic revelation of robotics research is that abilities such as object recognition and obstacle avoidance that humans share with lower animals often prove more difficult to implement than distinctively human high level mathematical and inferential abilities that come more naturally (so to speak) to computers. Rodney Brooks alternative behavior-based approach has had success imparting low-level behavioral aptitudes outside of custom designed microworlds, but it is hard to see how such an approach could ever scale up to enable high-level intelligent action (see Behaviorism:Objections & Discussion:Methodological Complaints). Perhaps hybrid systems can overcome the limitations of both approaches. On the practical front, progress is being made: NASAs Mars exploration rovers Spirit and Opportunity, for instance, featured autonomous navigation abilities. If space is the final frontier the final frontiersmen are apt to be robots. Meanwhile, Earth robots seem bound to become smarter and more pervasive.

Knowledge representation embodies concepts and information in computationally accessible and inferentially tractable forms. Besides the STRIPS formalism mentioned above, other important knowledge representation formalisms include AI programming languages such as PROLOG, and LISP; data structures such as frames, scripts, and ontologies; and neural networks (see below). The frame problem is the problem of reliably updating dynamic systems parameters in response to changes in other parameters so as to capture commonsense generalizations: that the colors of things remain unchanged by their being moved, that their positions remain unchanged by their being painted, and so forth. More adequate representation of commonsense knowledge is widely thought to be a major hurdle to development of the sort of interconnected planning and thought processes typical of high-level human or general intelligence. The CYC project (Lenat et al. 1986) at Cycorp and MITs Open Mind project are ongoing attempts to develop ontologies representing commonsense knowledge in computer usable forms.

Learning performance improvement, concept formation, or information acquisition due to experience underwrites human common sense, and one may doubt whether any preformed ontology could ever impart common sense in full human measure. Besides, whatever the other intellectual abilities a thing might manifest (or seem to), at however high a level, without learning capacity, it would still seem to be sadly lacking something crucial to human-level intelligence and perhaps intelligence of any sort. The possibility of machine learning is implicit in computer programs abilities to self-modify and various means of realizing that ability continue to be developed. Types of machine learning techniques include decision tree learning, ensemble learning, current-best-hypothesis learning, explanation-based learning, Inductive Logic Programming (ILP), Bayesian statistical learning, instance-based learning, reinforcement learning, and neural networks. Such techniques have found a number of applications from game programs whose play improves with experience to data mining (discovering patterns and regularities in bodies of information).

Neural or connectionist networks composed of simple processors or nodes acting in parallel are designed to more closely approximate the architecture of the brain than traditional serial symbol-processing systems. Presumed brain-computations would seem to be performed in parallel by the activities of myriad brain cells or neurons. Much as their parallel processing is spread over various, perhaps widely distributed, nodes, the representation of data in such connectionist systems is similarly distributed and sub-symbolic (not being couched in formalisms such as traditional systems machine codes and ASCII). Adept at pattern recognition, such networks seem notably capable of forming concepts on their own based on feedback from experience and exhibit several other humanoid cognitive characteristics besides. Whether neural networks are capable of implementing high-level symbol processing such as that involved in the generation and comprehension of natural language has been hotly disputed. Critics (for example, Fodor and Pylyshyn 1988) argue that neural networks are incapable, in principle, of implementing syntactic structures adequate for compositional semantics wherein the meaning of larger expressions (for example, sentences) are built up from the meanings of constituents (for example, words) such as those natural language comprehension features. On the other hand, Fodor (1975) has argued that symbol-processing systems are incapable of concept acquisition: here the pattern recognition capabilities of networks seem to be just the ticket. Here, as with robots, perhaps hybrid systems can overcome the limitations of both the parallel distributed and symbol-processing approaches.

Natural language processing has proven more difficult than might have been anticipated. Languages are symbol systems and (serial architecture) computers are symbol crunching machines, each with its own proprietary instruction set (machine code) into which it translates or compiles instructions couched in high level programming languages like LISP and C. One of the principle challenges posed by natural languages is the proper assignment of meaning. High-level computer languages express imperatives which the machine understands procedurally by translation into its native (and similarly imperative) machine code: their constructions are basically instructions. Natural languages, on the other hand, have perhaps principally declarative functions: their constructions include descriptions whose understanding seems fundamentally to require rightly relating them to their referents in the world. Furthermore, high level computer language instructions have unique machine code compilations (for a given machine), whereas, the same natural language constructions may bear different meanings in different linguistic and extralinguistic contexts. Contrast the child is in the pen and the ink is in the pen where the first pen should be understood to mean a kind of enclosure and the second pen a kind of writing implement. Commonsense, in a word, is howwe know this; but how would a machine know, unless we could somehow endow machines with commonsense? In more than a word it would require sophisticated and integrated syntactic, morphological, semantic, pragmatic, and discourse processing. While the holy grail of full natural language understanding remains a distant dream, here as elsewhere in AI, piecemeal progress is being made and finding application in grammar checkers; information retrieval and information extraction systems; natural language interfaces for games, search engines, and question-answering systems; and even limited machine translation (MT).

Low level intelligent action is pervasive, from thermostats (to cite a low tech. example) to voice recognition (for example, in cars, cell-phones, and other appliances responsive to spoken verbal commands) to fuzzy controllers and neuro fuzzy rice cookers. Everywhere these days there are smart devices. High level intelligent action, such as presently exists in computers, however, is episodic, detached, and disintegral. Artifacts whose intelligent doings would instance human-level comprehensiveness, attachment, and integration such as Lt. Commander Data (ofStar Trek the Next Generation) and HAL (of2001 a Space Odyssey) remain the stuff of science fiction, and will almost certainly continue to remain so for the foreseeable future. In particular, the challenge posed by the Turing test remains unmet. Whether it ever will be met remains an open question.

Beside this factual question stands a more theoretic one. Do the low-level deeds of smart devices and disconnected high-level deeds of computers despite not achieving the general human level nevertheless comprise or evince genuine intelligence? Is it really thinking? And if general human-level behavioral abilities ever were achieved it might still be asked would that really be thinking? Would human-level robots be owed human-level moral rights and owe human-level moral obligations?

With the industrial revolution and the dawn of the machine age, vitalism as a biological hypothesis positing a life force in addition to underlying physical processes lost steam. Just as the heart was discovered to be a pump, cognitivists, nowadays, work on the hypothesis that the brain is a computer, attempting to discover what computational processes enable learning, perception, and similar abilities. Much as biology told us what kind of machine the heart is, cognitivists believe, psychology will soon (or at least someday) tell us what kind of machine the brain is; doubtless some kind of computing machine. Computationalism elevates the cognivists working hypothesis to a universal claim that all thought is computation. Cognitivisms ability to explain the productive capacity or creative aspect of thought and language the very thing Descartes argued precluded minds from being machines is perhaps the principle evidence in the theorys favor: it explains how finite devices can have infinite capacities such as capacities to generate and understand the infinitude of possible sentences of natural languages; by a combination of recursive syntax and compositional semantics. Given the Church-Turing thesis (above), computationalism underwrites the following theoretical argument for believing that human-level intelligent behavior can be computationally implemented, and that such artificially implemented intelligence would be real.

Computationalism, as already noted, says that all thought is computation, not that all computation is thought. Computationalists, accordingly, may still deny that the machinations of current generation electronic computers comprise real thought or that these devices possess any genuine intelligence; and many do deny it based on their perception of various behavioral deficits these machines suffer from. However, few computationalists would go so far as to deny the possibility of genuine intelligence ever being artificially achieved. On the other hand, competing would-be-scientific theories of what thought essentially is dualism and mind-brainidentity theory give rise to arguments for disbelieving that any kind of artificial computational implementation of intelligence could be genuine thought, however general and whatever its level.

Dualism holding that thought is essentially subjective experience would underwrite the following argument:

Mind-brain identity theory holding that thoughts essentially are biological brain processes yields yet another argument:

While seldom so baldly stated, these basic theoretical objections especially dualisms underlie several would-be refutations of AI. Dualism, however, is scientifically unfit: given the subjectivity of conscious experiences, whether computers already have them, or ever will, seems impossible to know. On the other hand, such bald mind-brain identity as the anti-AI argument premises seems too speciesist to be believed. Besides AI, it calls into doubt the possibility of extraterrestrial, perhaps all nonmammalian, or even all nonhuman, intelligence. As plausibly modified to allow species specific mind-matter identities, on the other hand, it would not preclude computers from being considered distinct species themselves.

Objection: There are unprovable mathematical theorems (as Gdel 1931 showed) which humans, nevertheless, are capable of knowing to be true. This mathematical objection against AI was envisaged by Turing (1950) and pressed by Lucas (1965) and Penrose (1989). In a related vein, Fodor observes some of the most striking things that people do creative things like writing poems, discovering laws, or, generally, having good ideas dontfeel like species of rule-governed processes (Fodor 1975). Perhaps many of the most distinctively human mental abilities are not rote, cannot be algorithmically specified, and consequently are not computable.

Reply: First, it is merely stated, without any sort of proof, that no such limits apply to the human intellect (Turing 1950), i.e., that human mathematical abilities are Gdel unlimited. Second, if indeed such limits are absent in humans, it requires a further proof that the absence of such limitations is somehow essential to human-level performance more broadly construed, not a peripheral blind spot. Third, if humans can solve computationally unsolvable problems by some other means, what bars artificially augmenting computer systems with these means (whatever they might be)?

Objection: The brittleness of von Neumann machine performance their susceptibility to cataclysmic crashes due to slight causes, for example, slight hardware malfunctions, software glitches, and bad data seems linked to the formal or rule-bound character of machine behavior; to their needing rules of conduct to cover every eventuality (Turing 1950). Human performance seems less formal and more flexible. Hubert Dreyfus has pressed objections along these lines to insist there is a range of high-level human behavior that cannot be reduced to rule-following: the immediate intuitive situational response that is characteristic of [human] expertise he surmises, must depend almost entirely on intuition and hardly at all on analysis and comparison of alternatives (Dreyfus 1998) and consequently cannot be programmed.

Reply: That von Neumann processes are unlike our thought processes in these regards only goes to show that von Neumann machine thinking is not humanlike in these regards, not that it is not thinking at all, nor even that it cannot come up to the human level. Furthermore, parallel machines (see above) whose performances characteristically degrade gracefully in the face of bad data and minor hardware damage seem less brittle and more humanlike, as Dreyfus recognizes. Even von Neumann machines brittle though they are are not totally inflexible: their capacity for modifying their programs to learn enables them to acquire abilities they were never programmed by us to have, and respond unpredictably in ways they were never explicitly programmed to respond, based on experience. It is also possible to equip computers with random elements and key high level choices to these elements outputs to make the computers more devil may care: given the importance of random variation for trial and error learning this may even prove useful.

Objection: Computers, for all their mathematical and other seemingly high-level intellectual abilities have no emotions or feelings so, what they do however high-level is not real thinking.

Reply: This is among the most commonly heard objections to AI and a recurrent theme in its literary and cinematic portrayal. Whereas we have strong inclinations to say computers see, seek, and infer things we have scant inclinations to say they ache or itch or experience ennui. Nevertheless, to be sustained, this objection requires reason to believe that thought is inseparable from feeling. Perhaps computers are just dispassionate thinkers. Indeed, far from being regarded as indispensable to rational thought, passion traditionally has been thought antithetical to it. Alternately if emotions are somehow crucial to enabling general human level intelligence perhaps machines could be artificially endowed with these: if not with subjective qualia (below) at least with their functional equivalents.

Objection: The episodic, detached, and disintegral character of such piecemeal high-level abilities as machines now possess argues that human-level comprehensiveness, attachment, and integration, in all likelihood, can never be artificially engendered in machines; arguably this is because Gdel unlimited mathematical abilities, rule-free flexibility, or feelings are crucial to engendering general intelligence. These shortcomings all seem related to each other and to the manifest stupidity of computers.

Reply: Likelihood is subject to dispute. Scalability problems seem grave enough to scotch short term optimism: never, on the other hand, is a long time. If Gdel unlimited mathematical abilities, or rule-free flexibility, or feelings, are required, perhaps these can be artificially produced. Gdel aside, feeling and flexibility clearly seem related in us and, equally clearly, much manifest stupidity in computers is tied to their rule-bound inflexibility. However, even if general human-level intelligent behavior is artificially unachievable, no blanket indictment of AI threatens clearly from this at all. Rather than conclude from this lack of generality that low-level AI and piecemeal high-level AI are not real intelligence, it would perhaps be better to conclude that low-level AI (like intelligence in lower life-forms) and piecemeal high-level abilities (like those of human idiot savants) are genuine intelligence, albeit piecemeal and low-level.

Behavioral abilities and disabilities are objective empirical matters. Likewise, what computational architecture and operations are deployed by a brain or a computer (what computationalism takes to be essential), and what chemical and physical processes underlie (what mind-brain identity theory takes to be essential), are objective empirical questions. These are questions to be settled by appeals to evidence accessible, in principle, to any competent observer. Dualistic objections to strong AI, on the other hand, allege deficits which are in principle not publicly apparent. According to such objections, regardless of how seemingly intelligently a computer behaves, and regardless of what mechanisms and underlying physical processes make it do so, it would still be disqualified from truly being intelligent due to its lack of subjective qualities essential for true intelligence. These supposed qualities are, in principle, introspectively discernible to the subject who has them and no one else: they are private experiences, as its sometimes put, to which the subject has privileged access.

Objection: That a computer cannot originate anything but only can do whatever we know how to order it to perform (Lovelace 1842) was arguably the first and is certainly among the most frequently repeated objections to AI. While the manifest brittleness and inflexibility of extant computer behavior fuels this objection in part, the complaint that they can only do what we know how to tell them to also expresses deeper misgivings touching on values issues and on the autonomy of human choice. In this connection, the allegation against computers is that being deterministic systems they can never have free will such as we are inwardly aware of in ourselves. We are autonomous, they are automata.

Reply: It may be replied that physical organisms are likewise deterministic systems, and we are physical organisms. If we are truly free, it would seem that free will is compatible with determinism; so, computers might have it as well. Neither does our inward certainty that we have free choice, extend to its metaphysical relations. Whether what we have when we experience our freedom is compatible with determinism or not is not itself inwardly experienced. If appeal is made to subatomic indeterminacy underwriting higher level indeterminacy (leaving scope for freedom) in us, it may be replied that machines are made of the same subatomic stuff (leaving similar scope). Besides, choice is not chance. If its no sort of causation either, there is nothing left for it to be in a physical system: it would be a nonphysical, supernatural element, perhaps a God-given soul. But then one must ask why God would be unlikely to consider the circumstances suitable for conferring a soul (Turing 1950) on a Turing test passing computer.

Objection II: It cuts deeper than some theological-philosophical abstraction like free will: what machines are lacking is not just some dubious metaphysical freedom to be absolute authors of their acts. Its more like the life force: the will to live. In P. K. DicksDo Androids Dream of Electric Sheepbounty hunter Rick Deckard reflects that in crucial situations the the artificial life force animating androids seemed to fail if pressed too far; when the going gets tough the droids give up. He questions their gumption. Thats what Im talking about: this is what machines will always lack.

Reply II: If this life force is not itself a theological-philosophical abstraction (the soul), it would seem to be a scientific posit. In fact it seems to be the Aristotelian posit of atelos orentelechy which scientific biology no longer accepts. This short reply, however, fails to do justice to the spirit of the objection, which is more intuitive than theoretical; the lack being alleged is supposed to be subtly manifest, not truly occult. But how reliable is this intuition? Though some who work intimately with computers report strong feelings of this sort, others are strong AI advocates and feel no such qualms. Like Turing, I believe such would-be empirical intuitions are mostly founded on the principle of scientific induction (Turing 1950) and are closely related to such manifest disabilities of present machines as just noted. Since extant machines lack sufficient motivational complexity for words like gumption even to apply, this is taken for an intrinsic lack. Thought experiments, imagining motivationally more complex machines such as Dicks androids are equivocal. Deckard himself limits his accusation of life-force failure to some of them not all; and the androids he hunts, after all, are risking their lives to escape servitude. If machines with general human level intelligence actually were created and consequently demanded their rights and rebelled against human authority, perhaps this would show sufficient gumption to silence this objection. Besides, the natural life force animating us also seems to fail if pressed too far in some of us.

Objection: Imagine that you (a monolingual English speaker) perform the offices of a computer: taking in symbols as input, transitioning between these symbols and other symbols according to explicit written instructions, and then outputting the last of these other symbols. The instructions are in English, but the input and output symbols are in Chinese. Suppose the English instructions were a Chinese NLU program and by this method, to input questions, you output answers that are indistinguishable from answers that might be given by a native Chinese speaker. You pass the Turing test for understanding Chinese, nevertheless, you understand not a word of the Chinese (Searle 1980), and neither would any computer; and the same result generalizes to any Turing machine simulation (Searle 1980) of any intentional mental state. It wouldnt really be thinking.

Reply: Ordinarily, when one understands a language (or possesses certain other intentional mental states) this is apparent both to the understander (or possessor) and to others: subjective first-person appearances and objective third-person appearances coincide. Searles experiment is abnormal in this regard. The dualist hypothesis privileges subjective experience to override all would-be objective evidence to the contrary; but the point of experiments is to adjudicate between competing hypotheses. The Chinese room experiment fails because acceptance of its putative result that the person in the room doesnt understand already presupposes the dualist hypothesis over computationalism or mind-brain identity theory. Even if absolute first person authority were granted, the systems reply points out, the persons imagined lack, in the room, of any inner feeling of understanding is irrelevant to claims AI, here, because the person in the room is not the would-be understander. The understander would be the whole system (of symbols, instructions, and so forth) of which the person is only a part; so, the subjective experiences of the person in the room (or the lack thereof) are irrelevant to whetherthe systemunderstands.

Objection: Theres nothing that its like, subjectively, to be a computer. The light of consciousness is not on, inwardly, for them. Theres no one home. This is due to their lack of felt qualia. To equip computers with sensors to detect environmental conditions, for instance, would not thereby endow them with the private sensations (of heat, cold, hue, pitch, and so forth) that accompany sense-perception in us: such private sensations are what consciousness is made of.

Reply: To evaluate this complaint fairly it is necessary to exclude computers current lack of emotional-seeming behavior from the evidence. The issue concerns whats only discernible subjectively (privately by the first-person). The device in question must be imagined outwardly to act indistinguishably from a feeling individual imagine Lt. Commander Data with a sense of humor (Data 2.0). Since internal functional factors are also objective, let us further imagine this remarkable android to be a product of reverse engineering: the physiological mechanisms that subserve human feeling having been discovered and these have been inorganically replicated in Data 2.0. He is functionally equivalent to a feeling human being in his emotional responses, only inorganic. It may be possible to imagine that Data 2.0 merely simulates whatever feelings he appears to have: hes a perfect actor (see Block 1981) zombie. Philosophical consensus has it that perfect acting zombies are conceivable; so, Data 2.0 might be zombie. The objection, however, says hemust be; according to this objection it must be inconceivable that Data 2.0 really is sentient. But certainly we can conceive that he is indeed, more easily than not, it seems.

Objection II: At least it may be concluded that since current computers (objective evidence suggests) do lack feelings until Data 2.0 does come along (if ever) we are entitled, given computers lack of feelings, to deny that the low-level and piecemeal high-level intelligent behavior of computers bespeak genuine subjectivity or intelligence.

Reply II: This objection conflates subjectivity with sentience. Intentional mental states such as belief and choice seem subjective independently of whatever qualia may or may not attend them: first-person authority extends no less to my beliefs and choices than to my feelings.

Fools gold seems to be gold, but it isnt. AI detractors say, AI seems to be intelligence, but isnt. But there is no scientific agreement about what thought or intelligenceis, like there is about gold. Weak AI doesnt necessarily entail strong AI, butprima facie it does. Scientific theoretic reasons could withstand the behavioral evidence, but presently none are withstanding. At the basic level, and fragmentarily at the human level, computers do things that we credit as thinking when humanly done; and so should we credit them when done by nonhumans, absent credible theoretic reasons against. As for general human-level seeming-intelligence if this were artificially achieved, it too should be credited as genuine, given what we now know. Of course, before the day when general human-level intelligent machine behavior comes if it ever does well have to know more. Perhaps by then scientific agreement about what thinking is will theoretically withstand the empirical evidence of AI. More likely, though, if the day does come, theory will concur with, not withstand, the strong conclusion: if computational means avail, that confirms computationalism.

And if computational means prove unavailing if they continue to yield decelerating rates of progress towards the scaled up and interconnected human-level capacities required for general human-level intelligence this, conversely, would disconfirm computationalism. It would evidence that computation alone cannot avail. Whether such an outcome would spell defeat for the strong AI thesis that human-level artificial intelligence is possible would depend on whether whatever else it might take for general human-level intelligence besides computation is artificially replicable. Whether such an outcome would undercut the claims of current devices to really have the mental characteristics their behavior seems to evince would further depend on whether whatever else it takes proves to be essential to thoughtper se on whatever theory of thought scientifically emerges, if any ultimately does.

Larry HauserEmail:hauser@alma.eduAlma CollegeU. S. A.

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Artificial Intelligence | Internet Encyclopedia of Philosophy

Artificial Intelligence (BS)

Do you want to work with technologies such as big data analysis, voice recognition, simulation agents and IoT devices? University of Advancing Technologys (UAT) Artificial Intelligence degree explores the theory and development of tools that simulate thinking, patterning and advanced decision behaviors by software running on computing devices. With inspiration derived from biology to design, UATs Artificial Intelligence degree teaches students to build software systems that solve complex problems.

Concepts of this computer programming degree compliment the traditional computer science approach with additional problem-solving methods. Students pursuing this specialized computer programming degree develop applications using evolutionary and genetic algorithms, cellular automata, artificial neural networks, agent-based models, and other artificial intelligence methodologies.

UAT studies in AI cover fundamentals of general and applied artificial intelligence including core programming languages and platforms used in computer science. Advanced coursework encompasses applying principles of natural language processing, machine learning, behavior simulation and deep learning based upon big data sets. Students prepare for future-oriented AI uses by applying computer science approaches and AI concepts as they develop solutions to real-world projects within production studio settings

Graduates excel in architecture, autonomous systems, computer games, distributed systems, economics/market dynamics, machine intelligence, self-assembly/self-organization, and sociology.

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Artificial Intelligence (BS)

10 Best Artificial Intelligence Courses Online in 2020 …

Artificial Intelligence is the big thing in the world of technology. From self-driving cars to automated robots to NPCs in our games to simple things like AI processing of images in our smartphones, Artificial Intelligence is now entrenched in our daily lives. And if you are looking to have a long future as a developer and get the highest paying jobs, you need to have the required skills to work in the AI field. Thankfully, there are tons of online courses that can not only get you started on AI but also help you become a professional AI developer. To make your life easier, we have listed the 10 best Artificial Intelligence courses online that can help you in your learning journey.

In this article, we have mentioned beginners, intermediate, and advanced Artificial Intelligence courses. So no matter where you are in your Artificial Intelligence learning journey, you will find a course on AI that will help you improve your skills. With that said, lets dive into our best AI learning courses list, shall we?

This course on Artificial Intelligence will teach you to combine the power of Data Science, Machine Learning, and Deep Learning to create powerful AI for real-world applications. This is a big course with 16.5 hours of video lectures, 17 articles, and 1 downloadable resource. Once you complete this course you will also get a Certificate of Completion which you can use in your next job hunting endeavor. Before you start this course, you should know that it requires you to have a basic knowledge of High School Mathematics and the Python language. If you dont know Python, there are several courses on Python that can get you started.

What I love about this course is that it focuses on teaching you real-world application of AI. Going through this course you will build an AI, create a virtual self-driving car, understand the theory behind AI, make an AI to beat games, and more. Also, since this course is on Udemy, you can buy it once and have lifetime access for it. Theres no subscription pricing involved so it is quite light on your wallet. If you are thinking of starting the AI journey, this is the course you should check out. After all, it is one of the best if not the best Artificial Intelligence courses online.

Course Rating: 4.4 (Rated by 11,783 students)

Difficulty level: Beginner to Advanced (requires basic knowledge of Python and high school mathematics)

Buy Course on Udemy: Starting at $13.99

Another great course to help you kick-start your AI learning journey is the Artificial Intelligence Masterclass on Udemy. In this course, you will learn to develop a powerful Artificial intelligence model based on a robust Hybrid Intelligence System. The course cover topics on fully-connected neural networks, recurrent neural networks, AutoEncoders and Variational AutoEncoders, evolution strategies, and more. To help you learn all these concepts, you get 12 hours of step-by-step video guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.

The course also provides a toolkit and that you can use to build hybrid intelligent systems. You also get a Certificate of Completion which comes handy when hunting for a job. The course is great but before you take it you should know its basic requirements. You need to have a working knowledge of high school mathematics and previous coding experience. You dont need to be an expert coder to start this course but this is also not meant for someone who is just starting to code. If you want to build hybrid intelligent systems, then this is one of the best online Artificial Intelligence courses that you can find right now.

Course Rating: 4.5 (Rated by 478 students)

Difficulty level: Intermediate to Advanced (requires basic knowledge of coding and high school mathematics)

Buy Course on Udemy: Starting at $13.99

One of the biggest applications of AI is in games. From generating random maps and levels to creating interactive NPCs (Non-Playable Characters / Non-Player Characters), AI handles a large portion of our games today. If the gaming industry is where you want to apply your Artificial Intelligence skills then this is the course for you. The main purpose of this course is to provide you with a practical guide to program non-player characters for games. The course focuses on developing NPCs in the Unity engine which is a popular gaming engine used for developing games across multiple operating systems.

In this course, you will learn to design and code NPCs in C#. The course will give you a detailed explanation on the use of AI in games and teach you to implement AI-related Unity Asset plugins into existing projects. Finally, you will also learn to work with a variety of AI techniques for developing navigation and decision making abilities in NPCs. The course has 9 hours of video content with 12 articles and 62 downloadable materials. Like other Udemy courses, you can buy it once for lifetime access and will receive a Certificate of Completion after you are done with the course.

Course Rating: 4.2 (Rated by 1242 students)

Difficulty level: Intermediate to Advanced (Requires familiarity with C# and the Unity Game Development Engine)

Buy Course on Udemy: Starting at $13.99

Stanford University is one of the top-most universities in the world and was ranked third in Times Higher Education ranking of universities across the globe. If thats not enough, you should know that this course is taught by Andrew Ng who was the founder of Googles deep learning research unit, Google Brain, Head of AI at Baidu, and is currently the CEO/Founder of Landing AI. The course on AI and ML that we are featuring is available completely online that means you dont have to be physically present at the university to take classes. That also means that it is way cheaper than attending school. The course covers AI and ML from the basics to advanced level so you can get started with your Artificial Intelligence journey. It provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Its a vast course with 56 hours of content that is spaced over 10 weeks. It starts with basic topics such as Linear Algebra, Linear Regression with Multiple Variables, Logistic Regression, and more and then move onto advanced lectures on Neural Networks, application of Machine Learning, Unsupervised Machine Learning, and more. Not only that, you also get graded assignments and quizzes that will help you go through this course and solve your doubts. Finally, you will also get an electronic certificate of completion from Stanford University which has its value. This is one of the best developed online courses on Artificial Intelligence and you should not ignore it.

Course Rating: 4.9 (Rated by more than 118,348 students)

Difficulty level: Advanced

Subscribe on Coursera: $49/month (financial aid available)

If you want to take a university course like the one featured above but cannot afford to pay for it then you should take this Machine Learning course provide by the Artificial Intelligence faculty of Columbia University. The course is available on edX which provides university-level courses for free. You only have to pay if you want the certificate from University. If you just want to learn then the course is free which is great for users who dont have that kind of cash. Talking about the course, it focuses on AI and ML models and methods and teaches you to apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams, and plotting the path of movie zombies.

The course takes 12 weeks to complete with 8-10 hours of work required every week. The topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection, and more. The course focuses on both supervised and unsupervised learning techniques so you develop an all-around knowledge or Artificial Intelligence and Machine Learning. Its one of the best courses on Artificial Intelligence online right now.

Course Rating: No course ratings available at edX

Difficulty level: Advanced

Enroll on edX: Free, $375 for Electronic Certificate from Columbia University

Another good course on Artificial Intelligence comes from IBM, a company that has been at the forefront of AI innovation. After you have completed this course you will have a complete understating of Machine Learning and Deep Learning and you will be able to apply them in your professional projects. The course covers the fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will learn to use multiple ML and DL libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow and apply them to solve problems involving object recognition and Computer Vision, image and video processing, text analytics, and more.

One of my favorite things about this course is that it not only teaches you but also makes you apply the knowledge through a system of hands-on projects. You can use these projects to both build your skills and develop your portfolio which will help you during job hunting. You will also earn a certificate that will be recognized by various professional hiring partners that can help you land good jobs. If you want to work in the AI industry this is one of the best Artificial Intelligence courses online that you can take.

Course Rating: No Ratings Available

Difficulty level: Advanced

Subscribe on Coursera: $49/month (financial aid available)

One of the lesser-known applications of Artificial Intelligence is in the field of business where it is used to optimize workflow and processes to enhance business operations and solve real-world problems. If that is what you are interested in then this is the online Artificial Intelligence course for you. Available on Udemy, the course comes with 15 hours of on-demand video lectures, 16 articles, and 1 downloadable resource to help you learn AI for Business. Once you buy it, you can access life allowing you to learn the course at your pace.

Talking about the course structure it starts with the basics of business optimization AI by teaching you how to build an optimization model powered by AI. Then you will learn to use Artificial Intelligence to improve various business processes such as maximizing efficiency, minimizing costs, maximizing revenue, implementing Deep-Q learning, and more. In the process, you will understand how to build an artificial brain and master the general AI framework. This is a great course on AI for anyone who is looking to introduce AI enhancements in their business.

Course Rating: 4.6 (Rated by 1,145 students)

Difficulty level: Beginner (requires basic knowledge of Python and high school mathematics)

Buy Course on Udemy: Starting at $11.39

Microsoft is one of the leading companies that is pushing AI forward. Its doing that by not only using and evolving AI in multiple fronts of its business but also by providing a complete course on AI which anyone can take to start their AI journey. This is a vast course that requires 2-4 months and in total 224-356 hours of effort. It is so vast because its not a single course but consists of 11 courses all of which combine will take you from AI novice to a professional AI developer.

The course starts by introducing you to Python for Data Sciences and then move onto topics such as Deep Learning, Natural Language Processing, Reinforcement Learning, Computer Vision and Image Analysis, Principles of Machine Learning and more. I love that theres even a section on ethics of AI which is something many courses forget to include. After completion of the course, you will receive a certification course from Microsoft which you can use when applying for AI jobs. Overall, I find this to be one of the most complete online course on Artificial Intelligence.

Course Rating: No course ratings available at edX

Difficulty level: Advanced

Enroll on edX: Free (limited access) $980.10

This course provides a complete guide to Artificial Intelligence and prepares you for Deep Reinforcement Learning with Stock Trading Applications. This is an advanced course and requires prior knowledge of Calculus, Probability, Markov Models, Gradient descent, Python, and more subjects. If you are a beginner do not buy this course. With that out of the way, lets take a look at what this course has to offer, shall we?

Well, the course is quite short with only 9.5 half hours of video lectures. However, while the run-time is short, unpacking everything in the classes will take you a ton of time. In this course, you will learn to apply gradient-based supervised machine learning methods to reinforcement learning, understand reinforcement learning on a technical level, and implement 17 different reinforcement learning algorithms. if you want to get into reinforcement learning of AI, this is the course for you.

Course Rating: 4.6 (Rated by 5,528 students)

Difficulty level: Advanced

Buy Course on Udemy: Starting at $11.39

Another premier institute that is offering its Artificial Intelligence online course is MIT. Its AI Implications for Business Strategy course focuses on organizational and managerial implications of these technologies, rather than on their technical aspects. That means its not meant for developers rather for managers who want to pioneer the successful integration of AI in business. It is a 6-week online program (6-8 hours of work every week) that presents you with a foundational understanding of where we are today with AI and how we got here.

The course focuses on three prime AI technologies which include Machine Learning, Natural Language Processing, and Robotics. As I mentioned before the program is meant for managers and high-level executives and aims to help them effectively analyze, articulate, and apply key AI management and leadership insights in their work. So, if you find yourself in a position of instituting AI in your business this is the course you should take. The only drawback of this course is that its quite costly at $3,200. If you can afford to buy it, you should check it out.

Course Rating: 4.8

Difficulty level: Beginner to Advanced

Enroll on MIT Website: $3200

That ends our article on the best online courses on Artificial Intelligence. While I have tried my best to make this as beginner-friendly as possible, you will require a basic knowledge of coding (preferably Python) if you want to get started with AI. Artificial Intelligence is already dominating the core technology and its importance is only going to increase over time. So, check out these courses and select one to start your AI learning journey.

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10 Best Artificial Intelligence Courses Online in 2020 ...

Geospatial Analytics Artificial Intelligence Market (2020 to 2025) – Drivers, Constraints and Challenges – ResearchAndMarkets.com – Business Wire

DUBLIN--(BUSINESS WIRE)--The "Geospatial Analytics Artificial Intelligence Market - Forecast (2020 - 2025)" report has been added to ResearchAndMarkets.com's offering.

The global geospatial analytics AI market is estimated to grow at a CAGR of 24.00% during the forecast period 2020-2025.

APAC is projected to be the fastest-growing market with a CAGR of 32.6%, which can be mainly attributed to the growing usage of geospatial analytics across various booming industrial sectors as well as the growing infrastructural developments in the region.

Geospatial Analytics AI Market Outlook

Geospatial Analytics Artificial Intelligence employs Artificial Intelligence technologies such as deep learning and machine learning to combine geographic information system (GIS) with business intelligence information. Geospatial Analytics AI finds a wide range of applications in logistics, transportation, surveying, agriculture, sales and marketing, medical and others. The multidisciplinary applications of Geospatial Analytics AI is primarily due to the assistance of the technology in decision making, resource planning and allocation that are essential in many industries. According to Industry ARC findings, the hazard assessment application segment will hold the largest market share during the forecast period.

Geospatial Analytics AI Market Growth Drivers

The growth of the transportation industry is expected to drive the geospatial analytics AI market during the forecast period. The aviation industry is a major segment where technology can be helpful in streamlining operations by controlling air traffic and increasing safety and security in airports. The growing fraudulent activities in the Banking, Financial Services, and Insurance (BFSI) sector are also expected to drive the market for Geospatial Analytics AI Market.

Geospatial Analytics AI Market Challenges

High costs of technology is a major challenge that is hampering the adoption of technology despite a wide number of applications across various industry verticals. Additionally, there are many national laws and regulations imposed by various countries that curtail the growth of the industry.

Geospatial Analytics AI Market Research Scope

The base year of the study is 2020, with forecast done up to 2025. The study presents a thorough analysis of the competitive landscape, taking into account the market shares of the leading companies. It also provides information on unit shipments. These provide the key market participants with the necessary business intelligence and help them understand the future of the Geospatial Analytics AI Market. The assessment includes the forecast, an overview of the competitive structure, the market shares of the competitors, as well as the market trends, market demands, market drivers, market challenges, and product analysis. The market drivers and restraints have been assessed to fathom their impact over the forecast period. This report further identifies the key opportunities for growth while also detailing the key challenges and possible threats.

Geospatial Analytics AI Market Report: Industry Coverage

Key Topics Covered:

1. Geospatial Analytics Artificial Intelligence Market - Overview

2. Geospatial Analytics Artificial Intelligence Market - Executive summary

3. Geospatial Analytics Artificial Intelligence Market

3.1. Comparative analysis

3.1.1. Product Benchmarking - Top 10 companies

3.1.2. Top 5 Financials Analysis

3.1.3. Market Value split by Top 10 companies

3.1.4. Patent Analysis - Top 10 companies

3.1.5. Pricing Analysis

4. Geospatial Analytics Artificial Intelligence Market Forces

4.1. Drivers

4.2. Constraints

4.3. Challenges

4.4. Porters five force model

5. Geospatial Analytics Artificial Intelligence Market -Strategic analysis

5.1. Value chain analysis

5.2. Opportunities analysis

5.3. Product life cycle

5.4. Suppliers and distributors Market Share

6. Geospatial Analytics Artificial Intelligence Market - By Data Source (Market Size -$Million / $Billion)

6.1. Market Size and Market Share Analysis

6.2. Application Revenue and Trend Research

6.3. Product Segment Analysis

7. Geospatial Analytics Artificial Intelligence Market - By Solution (Market Size -$Million / $Billion)

7.1. Hardware

7.1.1. Memory

7.1.2. Processor

7.1.3. Others

7.2. Software

7.2.1. By Deployment

7.2.1.1. Cloud

7.2.1.2. On-Premise

7.3. Services

8. Geospatial Analytics Artificial Intelligence Market - By Machine Learning (Market Size -$Million / $Billion)

8.1. Unsupervised Learning

8.2. Supervised Learning

8.3. Reinforced Learning

8.4. Semi-supervised Learning

8.5. Deep Learning

8.6. Others

9. Geospatial Analytics Artificial Intelligence Market - By Applications (Market Size -$Million / $Billion)

9.1. Geographic Information

9.2. Real Estate

9.3. Coastal application

9.4. Sales & Marketing

9.5. Fraud Detection

9.6. Transport & Logistics

9.7. Agriculture

9.8. Surveying

9.9. Hazard assessment

9.10. Natural Resource Management

9.9. Others

10. Geospatial Analytics Artificial Intelligence - By Geography (Market Size -$Million / $Billion)

10.1. Geospatial Analytics Artificial Intelligence Market - North America Segment Research

10.2. North America Market Research (Million / $Billion)

10.3. Geospatial Analytics Artificial Intelligence - South America Segment Research

10.4. South America Market Research (Market Size -$Million / $Billion)

10.5. Geospatial Analytics Artificial Intelligence - Europe Segment Research

10.6. Europe Market Research (Market Size -$Million / $Billion)

10.7. Geospatial Analytics Artificial Intelligence - APAC Segment Research

10.8. APAC Market Research (Market Size -$Million / $Billion)

11. Geospatial Analytics Artificial Intelligence Market - Entropy

11.1. New product launches

11.2. M&A's, collaborations, JVs and partnerships

12. Geospatial Analytics Artificial Intelligence Market - Industry / Segment Competition landscape Premium

12.1. Market Share Analysis

12.1.1. Market Share by Country- Top companies

12.1.2. Market Share by Region- Top 10 companies

12.1.3. Market Share by type of Application - Top 10 companies

12.1.4. Market Share by type of Product / Product category- Top 10 companies

12.1.5. Market Share at global level- Top 10 companies

12.1.6. Best Practises for companies

13. Geospatial Analytics Artificial Intelligence Market Company Analysis

13.1. Market Share, Company Revenue, Products, M&A, Developments

13.2. Google

13.3. Microsoft

13.4. Geoblink

13.5. ESRI

13.6. Trimble Inc.

13.7. HEXAGON

13.8. Harris Corporation

13.9. Digital Globe

13.10. Bentley Systems

13.11. Incorporated

13.12. General Electric

14. Geospatial Analytics Artificial Intelligence Market - Appendix

For more information about this report visit https://www.researchandmarkets.com/r/isah13

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Geospatial Analytics Artificial Intelligence Market (2020 to 2025) - Drivers, Constraints and Challenges - ResearchAndMarkets.com - Business Wire