Artificial intelligence is intelligence shown by machines and not human intelligence. AI Research is been defined as studying intelligent agents that perceive their environment and take the necessary actions to maximize their chances to achieve their objectives. The term artificial intelligence was originally used for the use of computers to mimic human cognitive abilities associated with the human body like “learning” or “problem-solving”.
What is artificial intelligence (AI)?
Artificial intelligence (AI) is a broad field of computer science associated with designing intelligent systems able to do tasks that usually require human intelligence. The two important qualities possessed by artificial intelligence are ‘learning’ and ‘problem-solving’. However, as the AI research progressed the AI is defined more in terms of ‘Rationality’.
Tell me the history of AI?
The belief that certain artificial objects may have had or taught consciousness was held since the beginning of time. However, modern scientific research on artificial intelligence only began in the mid-20th Century.
Artificial intelligence research was established as an academic study in 1956. Alan Turing presented a seminal study in 1950 in which he theorized on the feasibility of devising machines that thought. Alan Turing also introduced the famous ‘Turing Test’ in 1950. He called it the ‘Imitation Game’.
Allen Newell and Herbert A. Simon developed the “Logic Theorist” AI in 1955. The algorithm ended up proving 38 of the first 52 theorems in Principia Mathematica by Russel and Whitehead.
In the 1950s and 60s, AI had gained enough power to play human-level games such as Checkers and Chess.
There were a lot of breathtaking developments. AI proved mathematical theorems and learnt the languages. A lot of funding went into AI during this time. Various types of AI were designed and created.
They included AI that can perform logic and reasoning, process natural language, and control simple robots. However, researchers underestimated the level of complexity of the problem of creating AI. This led to insufficient progress and funding dried out.
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Artificial Intelligence can be very beneficial to the society.
Many tasks which are routinely done by machines now can be made very efficient by Artificial Intelligence.
The existing system of working can be made much more efficient and smoothly running by the Artificial Intelligence.
The connection between the employers and the workers can also be managed very efficiently by AI.
The most significant being the most optimum use of resources in performing any number of complex tasks, thus making very efficient use of time.
Since AI can work for itself, it can execute tasks in ways so efficiently that no humans can think of.
What are the Goals of AI development?
The major goals of AI can be broadly classified into two categories.
1. One is to develop expert systems that display intelligent behavior, learn, teach, educate, and assist humans.
2. The other is to design systems that comprehend, think, learn, and behave like humans to incorporate human intelligence into machines.
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What are the applications of AI?
AI has been continuously evolving and in the past decade has been applied to plenty of fields.
Commercial – AI is used in commercial activities such as personalized shopping, recommendations, advertisements, etc.
Educational – In the educational field, they are used in personalized learning, voice assistants, generating smart content, administrative tasks, etc.
Medical – They have also found use in healthcare in diagnosing diseases and finding cancer cells, analyzing medical data, discovering new drugs, etc.
Others – They are used in navigation, autonomous vehicles, machines, spacecraft, facial recognition, gaming, solving complex tasks, etc.
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From expert systems to machine learning
Expert systems and machine learning are two different approaches to the functioning of AI.
In an expert system approach, the AI tries to perform the task like how a human expert would do, following the same processes and getting the same solutions.
However, in a machine learning scenario, the AI learns how to solve the problem using the data given and finds its own solutions.
Obviously, the machine learning AI is more complex and sophisticated compared to the expert systems AI.
While both the expert systems approach and the machine learning approaches have their own merits and disadvantages, machine learning is much more capable to handle different situations and act accordingly, while learning from its mistakes.
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Search and optimization
Most internet users search for their content. The search engines use AI and provide them with various search results based on hundreds of parameters. These parameters include their device type, location, past behaviors, etc. Search engines and other apps utilize advanced AI to provide recommendations and advertising to the users, an example being YouTube.
Since the search engines use AI, to make the websites optimized, AI is being utilized in various Search Engine Optimization (SEO) techniques. AI is used in designing content, optimizing web pages, and identifying necessary keywords, traffic opportunities, etc.
Classifiers and statistical learning methods
Classification is identifying, analyzing, and categorizing topics and things into specified groupings. Machine learning systems classify the datasets into categories using training datasets and a range of techniques. In machine learning, classification algorithms utilize input training data to determine the chance that the following data will fall through one of the established categories.
AI uses statistical learning methods to learn the data. Statistical learning can be broadly classified into two types; supervised learning and unsupervised learning. In supervised learning, the results are due to one or more inputs given. Whereas in unsupervised learning, the AI finds the solutions using the data sets given, without getting any supervision inputs.
Knowledge representation and reasoning is a field of AI which involves providing AI with the information that it can later use to solve complicated and sophisticated tasks. It uses human psychology to apply the same principles to AI. It focuses on making the AI perform tasks at human levels, rather than being a general problem solver.
Artificial neural networks
Artificial Neural Networks are the techniques used in AI that resemble or try to resemble the human brain. Artificial Neural Networks enable AI to make complex decisions without much input data or human input. This is because they try to execute tasks just like how a human brain would. Using Artificial Neural Networks, an AI will try to find patterns and relationships in data.
It will learn and improve next time using the results. AI using artificial neural networks have wide applications in marketing, healthcare, chemical industry, energy sector, financial markets, etc. They also have a lot of applications in computing and speech recognition.
There have been some big controversies regarding the AIs which have been developed.
In June 2022, there was a claim by a Google engineer working on the LaMDA AI model that the AI had become sentient. However, the company put him on administrative leave and denied the claims.
In 2016, Microsoft has unveiled Tay, a Twitter chatbot that talks to people based on the data from the people. Tay was found to speak in the racist and misogynistic language. Microsoft quickly apologized and removed the Chatbot.
There was also a controversy with Amazon‘s ‘Rekognition‘ facial recognition program. When identifying people with criminal backgrounds it was found to be biased towards people of color.
Amazon‘s recruiting AI was also found to be biased towards women.
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Myths surrounding AI
Top myths about advanced AI
There are a lot of myths circulating about advanced artificial intelligence systems. A common myth is that an AI can make sense of any data and find solutions to it instantly. But this is actually not true. AI needs the right data. If AI got fed by wrong data, AI will give the wrong solutions. If an AI is using machine learning, it will have to go through the data or create simulations by doing different trials and come up with the best solutions.
Another important myth is that some AI can do tasks just like a human brain does. This however is not true. The AI can do tasks that a human being can do; for example voice recognition and image recognition. But it cannot mimic the human brain.
Myths About the Risks of Superhuman AI
A lot of people throughout the world fear that the development of advanced artificial intelligence could lead to Superhuman AI. But at this stage of AI research, it is just not possible to actually decide whether Superhuman AI is possible. The most competent researchers are divided on this topic.
While some of the myths are not a big concern, the reasons there are some myths are due to actual scientific questioning. The actual question is how long will it take for AI research to catch up to the level where machines become conscious.
The greatest concern about the development of Superhuman AI is the timeline for its development. When the first articles about AI were published, they thought that the development of human-level AI would be much faster. However, historically the development of AI has always been slower than expected due to a variety of reasons.
Some of the major aims of early AI research such as simulating the human brain could not be achieved. But the AI research is constantly progressing. There will always be some new development in the field of AI every year. There are also some myths that are highly skeptical about the possibility of AI. They argue that conscious AI can never be achieved within this Century.
How can AI be dangerous?
The risks of AI technology have been pointed out by many eminent scientists and technologists, such as Elon Musk and Stephen Hawking. They worry that AI could be the worst thing that could happen to human civilization. As AI sophistication and usage are growing, the concerns and risks about AI are also increasing.
The most obvious examples are the automation of jobs, gender and racial biases in some technologies, or bombing strikes carried out by autonomous drones. The risks due to the exponential growth of AI could be especially dangerous. If an AI-powered car goes wrong and crashes suddenly, it would be of very high risk.
Why research AI safety?
When developing AI, it is extremely important to make sure the AI we develop works for the benefit of humanity. Instead of worrying after the AI has become conscious and uncontrollable, when we create AI, we can solve the problems of AI safety by doing careful research in the development stage.
The inventions made in the past few centuries have dramatically transformed human society and civilization. With AI being extremely powerful since it can think for itself, a lot of careful work has to be done to foresee the effects of AI development.
Why the recent interest in AI safety?
Given the inclusion of AI into various technologies, there has been a rise in concern about the safety due to AI. If an AI causes your passwords to be leaked, it can cause concern. But it is of low risk as compared to AI used in autonomous cars, planes, medical equipment, financial decisions, etc. If an AI went wrong with high-risk systems, it can cause significant damage to society.
The most frightening among those is using AI in Nuclear weapons and missile systems. Another reason why there has been an interest in AI safety is due to big names in tech speaking about the concerns due to AI. Many prominent people are worried about the risks posed by AI.
Ethical use of artificial intelligence
There is a widespread cry for AI to be designed in such a way that it works ethically. At present, some of the ethical concerns regarding the use of AI are unemployment due to AI taking over the jobs of humans, income inequality due to more automation, manipulation of humans to do something using human language, mistakes done by AI in critical tasks that could make a lot of impacts, bias caused, AI going into wrong hands, AI used in warfare, AI turning evil, AI outsmarting humans and being the dominant species, etc. The points of concern are also the decisions made by AI for a company, whether they are in an ethical way towards the society and employees.
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