Perspectives on ai and machine learning Understanding AI and machine learning :-
Artificial Intelligence (AI): . AI refers to artificial intelligence of humans, enabling them to perform tasks that normally require human intelligence, such as understanding natural language, recognizing patterns and making decisions
Machine Learning (ML): . ML is a subset of AI that involves training algorithms on data in order to explicitly make predictions or random decisions. It relies on statistical methods to learn from data.
Domains of AI
Artificial intelligence has the following domains:
-Machine Learning
-Deep Learning
-Robotics
-Expert systems
-Fuzzy logic
-Natural Language Processing
History of Artificial Intelligence:-
History of Intellectual Property Going back to the time of the Romans and Greeks, their mythology contains many stories of mechanical men equivalent to modern robots one such famous name Talos. In Greek mythology, Talos was a massive bronze castle designed to protect the Greek city of Europe from pirates and invaders Fast forward to the last century, and our movies and books are filled with these thought-provoking devices. Needless to say, humans have long had the idea of human-like entities with minds of their own.
Beginnings and Developments in Artificial Intelligence:-
The origin and development of artificial intelligence For many years philosophers have thought of the human brain as a ‘sign system’. Whether on intergalactic missions or fighting aliens, human robotic companions have been a staple of science fiction pop culture. From TARS from Interstellar, from Passengers to Arthur to Wall-E to cult films like The Matrix, machines that think, talk and perform actions for humans have long been embedded in the minds of many
But when did the development of artificial intelligence really begin?
It wasn’t until 1956 that artificial intelligence was officially launched. Alan Turing, a British mathematician who is also considered the father of theoretical computer science, made a proposal that became the basis for AI. The proposal was simple – why can’t machines, like the human brain, use information and logic to solve problems and make choices and decisions based on some given information? In the 1950s, Turing wrote a paper entitled ‘Computing Machinery and Intelligence’. This paper described intelligent machines that can make decisions and perform actions, and how to test their intelligence. however, the ideas of this paper were not immediately realized. This is because, to be intelligent, any machine must be able to store commands. This was a major obstacle in the post-Turing AI paper, as computers in the 1950s were not modern enough to store commands; They could only obey orders given to them.
Future of Artificial Intelligence:-
Fast forward two years later, and the term 'artificial intelligence' was officially introduced by a computer scientist named John McCarthy at a conference at Dartmouth College. He later became known as the father of artificial intelligence. McCarthy, along with Marvin Minsky, Nathaniel Rochester, and Claude Shannon, presented a proposal at the Dartmouth Conference, proposing a 2-month course of ten men at Dartmouth College The basis of the theory is "...the theory that in principle any aspect of learning or any other characteristic of intelligence can be described with such accuracy that a machine can be constructed to simulate it." McCarthy suggested that research into understanding AI would serve to uncover "how machines are made to use languages, make abstractions and concepts, solve problems currently reserved for humans, and make themselves look good." effective.
Common machine learning algorithms:-
A number of machine learning algorithms are commonly used. These include:
-Neural networks
-Linear regression
-Logistic regression
-Clustering
-Decision trees
-Random forests
AI impact on jobs:-
As machine learning technology has advanced, our lives have definitely gotten easier. However, the implementation of machine learning in industry has also raised ethical concerns about AI technology. These include: Uniform Technology While the topic is attracting a lot of attention, many researchers are not worried about the idea of AI surpassing human intelligence in the near future. Technical latency is also known as basic AI or superintelligence. Philosopher Nick Bostrum defines superintelligence as “outperforming the best human brains in almost everything, including scientific creativity, general intelligence, and life skills .” .” Despite the fact that superintelligence cannot develop in society, his idea raises some interesting questions. It’s unrealistic to think that a driverless car would never crash, but who is responsible and liable in those situations? Should we still build autonomous cars, or limit this technology to semi-autonomous vehicles that help people drive safely? The jury is still out on this one, but these are the ethical debates that arise when developing new, innovative AI technologies. Impact of AI on industry While much public opinion on artificial intelligence has focused on job loss, perhaps this concern needs to be reframed. With each disruptive new technology, we see the market demand for specific job functions change. Looking at the auto industry, for example, more companies like GM need to focus on producing electric vehicles to keep up with green policies.
Accountability:-
responsible Since there is no central law to govern AI practices, there is no real enforcement mechanism to ensure proper AI practices. The current incentive for companies to be ethical is the negative impact of unethical AI at stake. To bridge the gap, ethics policies have emerged as part of ethicists and researchers working together to regulate the development and dissemination of AI models in society.
How to choose the right AI platform for machine learning :-
Choosing a platform can be a complicated process, as incorrect configuration can drive up costs, or prevent the use of other valuable tools or technologies. When reviewing multiple vendors to choose an AI platform, there is often a tendency to think that more features = better systems. Maybe so, but critics should start by considering what an AI platform would do for their organization. What machine learning capabilities need to be delivered, and what resources are needed to achieve them? One missing item can ruin the implementation of the entire system. Here are some things to consider.
