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Kuzma Vladimirov
Kuzma Vladimirov

's Artificial Girl 3 Trainer

This introductory course introduces students to the concepts and terminology of artificial intelligence (AI) and machine learning (ML). By the end of this course, students will be able to select and apply ML services to resolve business problems. They will also be able to label, build, train, and deploy a custom ML model. This course contains approximately 20 hours of content delivered through lectures, hands-on labs, and project work.

's Artificial Girl 3 Trainer

A focus on nurturing unique human skills that artificial intelligence (AI) and machines seem unable to replicate: Many of these experts discussed in their responses the human talents they believe machines and automation may not be able to duplicate, noting that these should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI. These respondents suggest that workers of the future will learn to deeply cultivate and exploit creativity, collaborative activity, abstract and systems thinking, complex communication, and the ability to thrive in diverse environments.

Lasers and GPS have been incorporated into various aspects of the sports training world. Instead of relying on times and splits, trainers can measure the exact position, distance, velocity and acceleration of athletes to better understand where they can improve. Identifying more intricate data leads to improved performance with less stress and chance for injury.

Swimmers and divers participate in an extremely technical sport and have adapted sensors into their practices as well. When swimming or diving, the sensors measure more than the usual time and effort metrics. They map movements like rotational speed, dive angle, leg movement and hydrodynamics. Observing movements like this is groundbreaking, and allows trainers to help athletes perfect their movements. They may only shave milliseconds off their performance, but a millisecond in a race can be all the difference.

Applications such as YouTube have also enhanced communication during training. Countless hours of workouts and game plays can be found by anyone and shared just as quickly through YouTube. To bolster education through watching film or discussing plays, athletes and trainers can upload and watch the necessary videos during practice or on their own time.

Training management software can assist coaches and trainers in monitoring all aspects of training: diet, energy, sleep, etc. When coaches and trainers can define individual practice for optimum results, they are preventing fatigue and self-created injuries. Besides outside variables that cannot be accounted for, the future may some day see injury-free athletics.

The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".[b]This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity.[13] Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards beneficial goals.[c]

Artificial beings with intelligence appeared as storytelling devices in antiquity,[14]and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R.[15] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[16]

By the 1950s, two visions for how to achieve machine intelligence emerged. One vision, known as Symbolic AI or GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included Allen Newell, Herbert A. Simon, and Marvin Minsky. Closely associated with this approach was the "heuristic search" approach, which likened intelligence to a problem of exploring a space of possibilities for answers. The second vision, known as the connectionist approach, sought to achieve intelligence through learning. Proponents of this approach, most prominently Frank Rosenblatt, sought to connect Perceptron in ways inspired by connections of neurons.[20] James Manyika and others have compared the two approaches to the mind (Symbolic AI) and the brain (connectionist). Manyika argues that symbolic approaches dominated the push for artificial intelligence in this period, due in part to its connection to intellectual traditions of Descartes, Boole, Gottlob Frege, Bertrand Russell, and others. Connectionist approaches based on cybernetics or artificial neural networks were pushed to the background but have gained new prominence in recent decades.[21]

Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.[29]Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do".[30]Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".[31] They had failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill[32]and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.[7]

AI gradually restored its reputation in the late 1990s and early 21st century by finding specific solutions to specific problems. The narrow focus allowed researchers to produce verifiable results, exploit more mathematical methods, and collaborate with other fields (such as statistics, economics and mathematics).[40]By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".[10]

Numerous academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Much of current research involves statistical AI, which is overwhelmingly used to solve specific problems, even highly successful techniques such as deep learning. This concern has led to the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.[11]

A machine with general intelligence can solve a wide variety of problems with breadth and versatility similar to human intelligence. There are several competing ideas about how to develop artificial general intelligence. Hans Moravec and Marvin Minsky argue that work in different individual domains can be incorporated into an advanced multi-agent system or cognitive architecture with general intelligence.[80]Pedro Domingos hopes that there is a conceptually straightforward, but mathematically difficult, "master algorithm" that could lead to AGI.[81]Others believe that anthropomorphic features like an artificial brain[82]or simulated child development[l]will someday reach a critical point where general intelligence emerges.

Deep learning[124]uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[125] Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification[126] and others.

AI is relevant to any intellectual task.[134]Modern artificial intelligence techniques are pervasive and are too numerous to list here.[135]Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.[136]

Game playing has been a test of AI's strength since the 1950s. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.[143] In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.[144] 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.[145] Other programs handle imperfect-information games; such as for poker at a superhuman level, Pluribus[o] and Cepheus.[147] DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own.[148]

By 2020, Natural Language Processing systems such as the enormous GPT-3 (then by far the largest artificial neural network) were matching human performance on pre-existing benchmarks, albeit without the system attaining a commonsense understanding of the contents of the benchmarks.[149]DeepMind's AlphaFold 2 (2020) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein.[150]Other applications predict the result of judicial decisions,[151] create art (such as poetry or painting) and prove mathematical theorems.

No established unifying theory or paradigm has guided AI research for most of its history.[q] The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, neat, soft and narrow (see below). Critics argue that these questions may have to be revisited by future generations of AI researchers.


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