Blog 4 Techies The impact of AI can be felt in almost all businesses. The business sectors experiencing disruption due to AI technologies are the telecommunication industry, banking, insurance industry, oil and gas industry, media and entertainment, healthcare and life sciences industry, etc.
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Machine learning in Artificial Intelligence
AI can be further defined as “narrow AI,” designed to perform tasks within a domain, or “general AI,” which is hypothetical and not domain-specific but can learn and perform tasks anywhere. Advances in narrow AI, algorithms and models in the field of computer science can be referred to as “Machine learning.”
Algorithms are instructions used in a sequence to solve problems. It is developed by programmers to command computers for the tasks. Based on specific rules and instructions, it is the main concept to understand that learning algorithms create rules in machine learning, not computer programmers. It means that computers can be used for new, challenging tasks such as photo recognition applications for the visually impaired, or translation of pictures into speech cannot be programmed manually.
The process of machine learning is to transfer training data to a learning algorithm. Based on inference from the data, the learning algorithm then generates a new set of rules. This is in the resulting new algorithm, which is known as the machine learning model. The different models can be generated through different training data with similar learning algorithms. Similarly, learning algorithms can be used in translation languages and predictions in the stock market.
The machine learning model may apply various techniques, but the methods like supervised learning, unsupervised learning, and reinforcement learning show how a machine learns.
The Turing Test in Artificial Intelligence
The invention and the innovation in the computer will undoubtedly be one of the most far-reaching achievements of the twentieth century. At the beginning of such inventions and innovations, there were three seminal contributions by Alan Mathison Turing:
- The first was theoretical- he developed a mathematical model for computing machines (also known as the Turing machine) to solve major problems of mathematics.
- The second one was practical- he was involved in building the first-ever electronic, programmable, digital computers.
- The last contribution was philosophical- the Imitation game, Proposed in his article in mind in 1950.
The Imitation Game
Alan Mathison Turing begins the mind article “Computing Machinery and Intelligence” with the pronouncement, “I proposed to consider the question, ‘Can machines think?’” in the year 1950. He began by describing an Imitation game. Imagine there are three people, a man A, a woman B, and an interrogator C. They are in a separate room and can communicate with each other through a teleprinter (Instant messenger clients on their desktops, 1950’s equivalent to electronic chat of today). Interrogator C must correctly identify man A and woman B; to do so, he may ask questions capable of being transmitted by teleprinter. Man A tries to convince woman B to interrogate C, while woman B tries to communicate her real identity. During the game, man A is replaced by the machine. Suppose interrogator C remains incapable of identifying or distinguishing the machine from the woman. In that case, the machine will be said to have passed the test, and we will say that machine is intelligent.
The Test is rapidly coming to be described in the literature, and it is also frequently described in terms of a single room containing either a person or machine. The interrogator must identify whether he is communicating with a real person or a machine. These variations in the game differ somewhat from Turing’s original formulation of his imitation game as on a man playing against the woman and the computer which replaces him.
The Turing Test value and validity
Some authors believe that it was precisely the operational definition of intelligence meant by “thinking” and “intelligence.” Also, some authors believe the Turing Test is at best and at worst to progress in artificial intelligence.
Limitations of the Turing Test
The Turing test has been criticized for the nature of questioning a lot. It has to be limited for computers to perform human-like intelligence. The computer might score high if required answers are in “yes” or “no.” When questions need more conversational answers and questions are open-ended, there is less chance of becoming a fool of a questionnaire by computer programs.
In addition to this, for many researchers, the question of whether the computer can pass a Turing test or not has become irrelevant. The real concentration should be on how human-machine interaction can be more intuitive.
Alternatives to Turing Test
Examples of variations to the Turing test are:
- Reverse Turing Test
- Total Turing Test
- Minimum Intelligent signal Test
Examples of alternatives to the Turing Test are The Marcus Test, The Lovelace Test 2.0, Winograd schema Challenge.
Point of View
Artificial intelligence and machine learning are emerging in this digital world. These are mainly used in hardware as well as software for data mining, learning data, etc. As the concept itself is digitally designed to understand better and want to be an expert in gaining knowledge in technology and choosing a career in this competitive world, artificial intelligence can be the right choice.
The Bottom Line
To set up the programs in computers or stipulate the innovative ideas in the digital world, one needs to have a certificate in artificial intelligence. You can get an online artificial intelligence degree by applying for the course available on digital platforms. You can only have expertise in a particular field when the experts teach you and convey to you the purpose of the topic. Why it is to be studied, how it is to be studied. You will get all queries to be answered under one roof. You may also enjoy the learning experience, which will further help you in your career path.
Each concept needs detailed clarification to re-watch all the concepts and clear the doubts in doubt-solving sessions. Courses available digitally can be more convenient compared to offline studies as they can be more time-consuming. The above concept of the Turing test is a brief of Artificial intelligence and machine learning. Considering the vast area of Artificial intelligence, engaging in a relevant course provided by Great Learning can be vital. You can enhance your knowledge while pursuing the course. Being certified and holding a degree in AI can give your career a kick start. This course requires a lot of practice in terms of programming the data. Practice will make you well-versed with the data programming and innovations in your ideas with an enthusiastic approach.
