WEB 3.0: Artificial intelligence Wiki

 Welcome to the !

To examine variety functions of Artificial Interlligence and brief introduction

Our blog: https://web-30-artificial-intelligence.fandom.com/wiki/Blog:Recent_posts

Contact us with any question!

What is Artificial Intelligence?
The short answer to What is Artificial Intelligence is that it depends on who you ask.

A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own.

If you ask about artificial intelligence to an AI researcher, (s)he would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So, to summarize, Artificial  Intelligence meaning is:


 * 1) An intelligent entity created by humans.
 * 2) Capable of performing tasks intelligently without being explicitly instructed.
 * 3) Capable of thinking and acting rationally and humanely.

History


Thought-capable artificial beings appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.

 Origin 

The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis.

Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour". The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".

 AI research 

The field of AI research was born at a workshop at Dartmouth College in 1956, where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener. 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. 1954) (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.

 Create AI By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI's founders were optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".

They 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 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.

 Expert systems 

In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. 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.

The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) transistor technology, enabled the development of practical artificial neural network (ANN) technology in the 1980s. A landmark publication in the field was the 1989 book Analog VLSI Implementation of Neural Systems by Carver A. Mead and Mohammed Ismail.

Expand AI usage

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 and transistor count), 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.

 Project use 

According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks. 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". Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower".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 commonsense understanding of the contents of the benchmarks. DeepMind's AlphaFold 2 (2020) demonstrated the ability to determine, in hours rather than months, the 3D structure of a protein. Facial recognition advanced to where, under some circumstances, some systems claim to have a 99% accuracy rate.

Reactive Machines
These are the oldest forms of AI systems that have extremely limited capability. They emulate the human mind’s ability to respond to different kinds of stimuli. These machines do not have memory-based functionality. This means such machines cannot use previously gained experiences to inform their present actions, i.e., these machines do not have the ability to “learn.” These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same. A popular example of a reactive AI machine is IBM’s Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997.

Limited Memory
Limited Memory Limited memory machines are machines that, in addition to having the capabilities of purely reactive machines, are also capable of learning from historical data to make decisions. Nearly all existing applications that we know of come under this category of AI. All present-day AI systems, such as those using deep learning, are trained by large volumes of training data that they store in their memory to form a reference model for solving future problems. For instance, an image recognition AI is trained using thousands of pictures and their labels to teach it to name objects it scans. When an image is scanned by such an AI, it uses the training images as references to understand the contents of the image presented to it, and based on its “learning experience” it labels new images with increasing accuracy.

Theory of Mind
While the previous two types of AI have been and are found in abundance, the next two types of AI exist, for now, either as a concept or a work in progress. Theory of mind AI is the next level of AI systems that researchers are currently engaged in innovating. A theory of mind level AI will be able to better understand the entities it is interacting with by discerning their needs, emotions, beliefs, and thought processes. While artificial emotional intelligence is already a budding industry and an area of interest for leading AI researchers, achieving Theory of mind level of AI will require development in other branches of AI as well. This is because to truly understand human needs, AI machines will have to perceive humans as individuals whose minds can be shaped by multiple factors, essentially “understanding” humans.

Self-aware
This is the final stage of AI development which currently exists only hypothetically. Self-aware AI, which, self explanatorily, is an AI that has evolved to be so akin to the human brain that it has developed self-awareness. Creating this type of AI, which is decades, if not centuries away from materializing, is and will always be the ultimate objective of all AI research. This type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, beliefs, and potentially desires of its own. And this is the type of AI those doomsayers of the technology are wary of. Although the development of self-aware can potentially boost our progress as a civilization by leaps and bounds, it can also potentially lead to catastrophe. This is because once self-aware, the AI would be capable of having ideas like self-preservation which may directly or indirectly spell the end for humanity, as such an entity could easily outmaneuver the intellect of any human being and plot elaborate schemes to take over humanity.

Artificial Narrow Intelligence (ANI)
This type of artificial intelligence represents all the existing AI, including even the most complicated and capable AI that has ever been created to date. Artificial narrow intelligence refers to AI systems that can only perform a specific task autonomously using human-like capabilities. These machines can do nothing more than what they are programmed to do, and thus have a very limited or narrow range of competencies. According to the aforementioned system of classification, these systems correspond to all the reactive and limited memory AI. Even the most complex AI that uses machine learning and deep learning to teach itself falls under ANI.

Artificial General Intelligence (AGI)
Artificial General Intelligence is the ability of an AI agent to learn, perceive, understand, and function completely like a human being. These systems will be able to independently build multiple competencies and form connections and generalizations across domains, massively cutting down on time needed for training. This will make AI systems just as capable as humans by replicating our multi-functional capabilities.

Artificial Superintelligence (ASI)
The development of Artificial Superintelligence will probably mark the pinnacle of AI research, as AGI will become by far the most capable forms of intelligence on earth. ASI, in addition to replicating the multi-faceted intelligence of human beings, will be exceedingly better at everything they do because of overwhelmingly greater memory, faster data processing and analysis, and decision-making capabilities. The development of AGI and ASI will lead to a scenario most popularly referred to as the singularity. And while the potential of having such powerful machines at our disposal seems appealing, these machines may also threaten our existence or at the very least, our way of life.

Function
Machine Learning: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision. Deep Learning: Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome. Neural Networks: Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does. Natural Language Processing: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly. Computer Vision: Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations. Cognitive Computing: Cognitive computing algorithms try to mimic a human brain by analyzing text/speech/images/objects in a manner that a human does and tries to give the desired output.

Impact
As humans, we have always been fascinated by technological changes and fiction, right now, we are living amidst the greatest advancements in our history. Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Artificial intelligence is not only impacting the future of every industry and every human being but has also acted as the main driver of emerging technologies like big data, robotics and IoT. Considering its growth rate, it will continue to act as a technological innovator for the foreseeable future. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

Getting certified in AI will give you an edge over the other aspirants in this industry. With advancements such as Facial Recognition, AI in Healthcare, Chat-bots, and more, now is the time to build a path to a successful career in Artificial Intelligence. Virtual assistants have already made their way into everyday life, helping us save time and energy. Self-driving cars by Tech giants like Tesla have already shown us the first step to the future. AI can help reduce and predict the risks of climate change, allowing us to make a difference before it’s too late. And all of these advancements are only the beginning, there’s so much more to come. 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022.

Reference List
https://en.wikipedia.org/wiki/Artificial_intelligence#History

https://www.forbes.com/sites/cognitiveworld/2019/06/19/7-types-of-artificial-intelligence/?sh=f3c7216233ee



<mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn />

Need help building out this community?


 * Rules of this wiki
 * Getting Started
 * How to Contribute
 * Managing your new community
 * Guides
 * All Help articles

<mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn /><mainpage-endcolumn />
 * <mainpage-endcolumn />