ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

• HENRY HARVIN EDUCATION

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Artificial intelligence

• AI brings with it a promise of genuine
  human-to-machine interaction. Computer science
  defines AI research as the study of intelligent
  agent, a device that perceives its environment
  and takes actions that maximize its chance of
  successfully achieving its goals.
• We always think we can solve problems on our own,
  but in recent studies, we have come across that
  computer has become more versatile and humane in
  terms of solving problems, both emotionally and
  manually with an advance in AI.

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• AI was first used in 1956, at a computer science
  conference in Dartmouth. AI described an attempt
  to model how the human brain works and, based on
  this knowledge create more advanced computers.
  Since then, more than 60 years, the scientist and
  researchers have taken many brainstorming
  sessions. They collected the brightest minds of
  the centuries and fed the computers with that.
  Though it turned out to be complicated, but they
  came out with the understanding and developed a
  protocol of learning, natural language processing
  and creativity through AI, and thus liberated
  humanity from many of its troubles.

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• Though AI is ubiquitous computer is still far
  from perfection. But, never give up attitude of
  the programmers is leading us to achieve and
  activate human brain into the computer memory.

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Machine learning

• Machine learning as the Wikipedia says is a
  subset of AI that provides systems the ability to
  automatically learn and improve from experience
  without being explicitly programmed. It uses
  different algorithms that help to solve problems.
  
• HAs we all know, various machines nowadays work
  on verbal commands, scan pictures, drive
  automated cars and play games. It wont be far
  when these will start walking among us. To make
  it easier to understand, I would like to place an
  example for you from daily life-choosing a song
  to play(imusic)it will ask you your liking of
  genre, find various options will filter and sort
  and finally play your choice.

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• All this happens with the help of machine
  learning, i. e. computer learns on its own and
  provide you with a solution.

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Types of AI
Weak or narrow AI
Strong AI
Super intelligent

• Weak or narrow AI
• You must have heard of Deep Blue, the first
  computer to defeat a human in chess, and that
  too, Gary Kasporav in 1996.how did this happen?
  Deep Blue was capable of generating and
  evaluating about 200 million chess positions per
  second and is an early example of weak AI.it is
  widely used nowadays in science, business and
  healthcare. Another example is AlphaGo.

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• Strong AI
• Who doesnt like to be independent? Strong AI is
  the same. It is the point in the future where
  machines become human like. They decide and learn
  without any human inputs. They are competent,
  solve logical tasks and have emotions too.
• Now the question arises how to build a living
  machine? You must have watched many robotic
  movies, in which the machines show emotions but
  at the end, they have their limitations and
  ultimately fail the human emotions.

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• The advancement of technology has given us
  chatbots and virtual assistants that are good at
  maintaining a conversation. Experiments are still
  in action to make them humane but reproducing
  emotional reactions doesnt make them emotional,
  will they?

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• Super intelligence
• It is the near future of machines that are par
  excellence? A big question
• Machines can be smart, wise, creative with social
  skills, and the goal may be to better the lives
  of human but creating autonomous emotional
  machines like the bicentennial Man, can be a
  dream come true for any of the scientists.

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• Right now, to make super intelligent machines
  they are focusing more on
• Machine reasoning information on database or
  library, includes SEO planning.
• Robotics building, development and controlling
  robots.
• Machine learning study of algorithms.

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Components of machine learning

• Datasets the special collection of samples are
  called datasets. The samples can be numbers,
  images, texts or any other kind of data.
• Features it is the key to the solution of the
  task. They demonstrate to the machine what to pay
  attention to.
• Algorithm it is an ensemble learning, where we
  use different algorithms to achieve better
  performance. Depending on the algorithm, the
  accuracy or speed of getting the results can be
  different.

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• Any software using machine learning is
  independent than manually encoded instructions
  for performing specific tasks. If the quality of
  the dataset was high, and the features were
  chosen right, a machine learning powered system
  can become better at a given task than humans.
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