We All know about the artificial intelligence.
DEFINITION OF ARTIFICIAL INTELLIGENCE :
DEFINITION OF ARTIFICIAL INTELLIGENCE :
In Computer technology , artificial intelligence(AI), sometimes called machine intelligence, is intelligence demonstrated by mashing , in contrast to the natural intelligence displayed by humans and other animals
. Computer science defines AI research as the study of "intelligence agent ": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”.Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds such as "learning" and "problem solving".
History of AI
Here is the history of AI during 20th century −
YearMilestone / Innovation1923
Karel Čapek play named “Rossum's Universal Robots” (RUR) opens in London, first use of the word "robot" in English.
1943
Foundations for neural networks laid.
1945
Isaac Asimov, a Columbia University alumni, coined the term Robotics.
1950
Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence.Claude Shannon published Detailed Analysis of Chess Playing as a search.
1956
John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.
1958
John McCarthy invents LISP programming language for AI.
1964
Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.
1965
Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English.
1969
Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving.
1973
The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.
1979
The first computer-controlled autonomous vehicle, Stanford Cart, was built.
1985
Harold Cohen created and demonstrated the drawing program, Aaron.
1990
Major advances in all areas of AI −
Significant demonstrations in machine learning
Case-based reasoning
Multi-agent planning
Scheduling
Data mining, Web Crawler
natural language understanding and translation
Vision, Virtual Reality
Games
1997
The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.
2000
Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomadexplores remote regions of Antarctica and locates meteorites.
Applications of artificial intelligence
ARTIFICIAL intelligence , defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is weak ai, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities diagnosis, electronic traindind platform , robot con , and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.
Types of artificial intelligence
Weak AI (narrow AI) – non-sentient machine intelligence, typically focused on a narrow task (narrow AI).
STRONG AI(AGI) – (hypothetical) machine with the ability to apply intelligence to any problem, rather than just one specific problem, typically meaning "at least as smart as a typical human". It's future potential creation is referred to as a TECHNOLOGICAL SINGULARITY , and constitutes a GLOBAL (see Superintelligence, below).
SUPERINTELLIGENCE – (hypothetical) artificial intelligence far surpassing that of the brightest and most gifted human minds. Due to RECURSIVE SELF-IMPROVEMENT , superintelligence is expected to be a rapid outcome of creating artificial general intelligence.
Applications of AI:
AI has been dominant in various fields such as −
Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.
Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans.
Expert Systems -There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.
Vision Systems These systems understand, interpret, and comprehend visual input on the computer. For example,
A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.
Doctors use clinical expert system to diagnose the patient.
Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.
Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.
Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.
Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.
Problems
The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.
Reasoning, problem solving
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probabilities and economics .
These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger. In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.
Knowledge representation
Knowledge development Among the most difficult problems in knowledge representation are:Default reasoning and the qualifications problem Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. Scientists identified this problem in 1969as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.The breadth of commonsense knowledgeThe number of atomic facts that the average person knows is very large. Research projects that attempt to build a complete knowledge base knowledge (e.g., ) require enormous amounts of laborious ontological engineering —they must be built, by hand, one complicated concept at a time.The subsymbolic form of some commonsense knowledgeMuch of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"or an art critic can take one look at a statue and realize that it is a fake.These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated Ai, computational intelligence , or statistical Ai will provide ways to represent this kind of knowledge.
and knowledge engineering are central to classical AI research. Some "expert systems" attempt to gather together explicit ontology language .The most general are called default ontology, which attempt to provide a foundation for all other knowledge by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval, scene interpretation,clinical decision support,knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas.
possibly other systems in the future.Organized and manages records.Interact with humans for entertainment or a task as avatars or robots.An example of this is AI for playing many videogames.Robotic pets can interact with humans. Can help w/ depression and inactivity.Can fulfill sexual pleasure.They can think logically without emotions, making rational decisions with less or no mistakes.Can assess people.
This can be for medical purposes, such as health risks and emotional state. Can simulate medical procedures and give info on side effects.
Robotic radiosurgery, and other types of surgery in the future, can achieve precision that humans can't.They don't need to sleep, rest, take breaks, or get entertained, as they don't get bored or tired.
Now let's go and see the disadvantages:
Disadvantages:
Can cost a lot of money and time to build, rebuild, and repair. Robotic repair can occur to reduce time and humans needing to fix it, but that'll cost more money and resources.
It's questionable: is it ethically and morally correct to have androids, human-like robots, or recreate intelligence, a gift of nature that shouldn't be recreated? This is a discussion about AI that's popular in the days.
Storage is expansive, but access and retrieval may not lead to connections in memory as well as humans could.
They can learn and get better with tasks if coded to, but it's questionable as to if this can ever become as good as humans can do such.
They cannot work outside of what they were programmed for.
They could never, or, at least, seemingly never with our technological perceptions, recieve creativity that humans have.
This can prevent sympathizing with emotions for human contact, such as in being nurses. This can also reduce wisdom can understanding.
This can prevent common sense occuring. Even if coded with common sense and to learn, it seems hard for them to get as much common sense that humans could.
Robots, with them replacing jobs, can lead to severe unemployment, unless if humans can fix the unemployment with jobs AI can't do or severly change the government to communism.
As seen partially with smartphones and other technology already, humans can become too dependent on AI and lose their mental capacities.
Machines can easily lead to destruction, if put in the wrong hands. That is, at least a fear of many humans.
AI as robots can supercede humans, enslaving us.
Friend or foe?
With AI continuing to be a prominent buzzword in 2019, businesses need to realize that self-learning and black-box capabilities are not the panacea. Many organisations are already beginning to see the incredible capabilities of AI, using these advantages to enhance human intelligence and gain real value from their data.
As there is increasing evidence demonstrating the benefits of intelligent systems, more decision-makers in the boardroom are gaining a better understanding of what AI can really offer. Research connected by EY explains “organizations enabling AI at the enterprise level are increasing operational efficiency, making faster, more informed decisions and innovating new products and services.”
The first companies employing AI systems across the board will gain competitive advantage, reduce cost of operations and remove head counts. Whilst this may be a positive from a business perspective, it is obvious why this a worry for those working in roles at risk of displacement. The introduction of these technologies will likely trigger an issue with unions and job security due to the substantial operational changes.
Although AI will affect every sector in some way, not every job is at equal risk. PwC predictions a relatively low displacement of jobs (around 3%) in the first wave of automation, but this could dramatically increase up to 30% by the mid-2030’s. Occupations within the transport industry could potentially be at much greater risk, whereas jobs requiring social, emotional and literary abilities are at the lowest risk of displacement.
A positive future with artificial intelligence
Many businesses and individuals are optimistic that this AI-driven shift in the workplace will result in more jobs being created than lost. As we develop innovative technologies, AI will have a positive impact on our economy by creating jobs that require the skill set to implement new systems. 80% of respondents in the EY survey said it was the lack of these skills that was the biggest challenge when employing AI programs.
It is likely that artificial intelligence will soon replace jobs involving repetitive or basic problem-solving tasks, and even go beyond current human capability. AI systems will be making decisions instead of humans in industrial settings, customer service roles and within financial institutions. Automated decisioning will be responsible for tasks such as approving loans, deciding whether a customer should be onboarded or identifying corruption and financial crime.
Organisations will benefit from an increase in productivity as a result of greater automation, meaning more revenue will generated. This thus provides additional money to spend on supporting jobs in the services sector.
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