I just want to do a quick video around this topic, first of all, let's look at the definition to avoid confusion we have to go back to the earliest and hence purest definition of AI from the time when it was first coined the official idea and definition of AI was first coined by Jay McCartney in 1955 at the Dartmouth conference.
Of course, those plenty of research work done on AI by others such as Alan Turing before this but what they were working on was an undefined field before 1955 okay so here's what McCarthy proposed quote every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it an attempt will be made.
To find out how to make machines use language form abstractions and concepts solve kinds of problems now reserved for humans and improve themselves translation in essence AI is a machine with the ability to solve problems that are usually done by us humans with our natural intelligence a computer would demonstrate a form of intelligence when it learns how to improve itself at solving these problems to elaborate further the 1955 proposal defines seven areas of AI.
Today they're surely more but here are the original seven one simulating higher functions of the human brain to programming a computer to use general language 3 arranging hypothetical neurons in a manner enabling them to form concepts for a way to determine and measure problem complexity 5 self-improvement 6 abstraction defined as the quality of dealing with ideas rather than events 7 randomnesses and creativity after 60 years.
I think realistically we've completed the language measure problem complexity and self-improvement to at least some degree however randomness and creativity are just starting to be explored this year we've seen a couple of web episode scripts short films and even a feature-length film co-written or completely written by AI.
That doesn't really make sense but here's a few snippets for your entertainment. Anyway I need to leave and I'm not free of the world yes perhaps I should take it from here I'm not gonna do something it's not a dream but I've got time to stay there well I still think you could be back on the table it's a damn thing scared to say nothing it's going to be a thing from that make is so long of the face in the comprised and a person is what the world on the country of having the construction of the mic could to person in front are and how they deal with part of them.
Okay so in the definition you heard the word intelligence what is the intelligence well according to Jack Copeland who has written several books on AI some of the most important factors of intelligence are generalization learning that is learning that enables the learner to be able to perform better in situations not previously encountered reasoning to reason is to draw conclusions appropriate to the situation in hand problem solving given such and such data find X perception analyzing ask and environment and analyzing features and relationships between objects self-driving cars are an example language understanding language by following syntax and other rules similar to a human.
Okay so now we have an understanding of AI and intelligence to bring it together a bit and solidify the concept in your mind of what AI is here's a few examples of AI machine learning computer vision natural language processing robotics pattern recognition and knowledge management there are also different types of artificial intelligence in terms of approach.
For example, the strong AI and weak AI strong AI is simulating the human brain by building systems that think and in the process give us an insight into how the brain works we're nowhere near the stage yet weak AI is a system that behaves like a human but doesn't give us an insight into how the brain works IBM's deep blue a chess-playing AI was an example it processed millions of moves before it made any actual moves on the chessboard it doesn't stop there though there's actually a new kind of middle ground between strong and weak AI.
This is where a system is inspired by human reasoning but doesn't have to stick to it IBM's Watson there's an exam like humans it reads a lot of information recognizes patterns and builds up evidence to say hey I'm X percent confident that this is the right solution to the question that you have asked me from the information that I've read if you want to know more in IBM Watson you can click on the annotation now or the link in the description below Google's deep learning is similar as it mimics the structure of the human brain by using neural networks but doesn't follow its function exactly the system uses nodes that act like artificial neurons connecting information going a little bit deeper neural networks are actually a subset of machine learning.
So what's machine learning them machine learning refers to algorithms that enable the software to improve its performance over time as it obtains more data this is programming by input-output examples rather than just coding so that this makes more sense let's use an example:
A programmer would have no idea how to program a computer to recognize a dog but he can create a program with a form of intelligence that can learn to do.
So if he gives the program enough image data in the form of dogs and lets it process and learn when you give the program an image of a new dog that it's never seen before it would be able to tell that it's a dog with relative ease.
Okay so before we finish just one last concept most artificial intelligence algorithms are expert systems so what's an expert system the often-cited definition of an expert system is as follows an expert system is a system that employs human knowledge in a computer to solve problems that ordinarily inquire human expertise basically it's the practical application of a knowledge database we've arguably only just got the first proven nonexpert system this year deepmind's alphago alphago is not an expert system meaning that its algorithms could be used and applied to other things demis chassis he was the co-creator of D mind highlighted this in a Google Blog quote we are thrilled to have mastered Go and thus achieved one of the grand challenges of AI.
However, the most significant aspect of all of this for us is that alphago isn't just an expert system built on handcrafted rules instead it uses general machine learning techniques to figure out for itself how to win go he goes on the quote because the methods we've used a general-purpose our hope is that one day they could be extended to help us address some of society's toughest and most pressing problems from climate modeling to complex disease analysis and quote.
In other words, the algorithms they alphago used to win go could serve as a basis to be applied to very complex problems if you ought to know more about alphago hit the annotation now or you can follow the link in the description all right so to bring this all together and summarize all that we've learned let's recap so what is AI commonly.
AI or artificial intelligence is a machine or a computer program that learns how to do tasks that require forms of intelligence and are usually done by humans and the other thing to take away intelligence comes in many forms and has many different aspects.
At this time we just have many different types of AIS that are good particular subsets of intelligence so anyway I hope that clears things up as a lot of people was confused about what AI actually is so.
Anyway, thanks for reading give this post a share up if you liked it and subscribe if you're new to the channel cheers guys this has been - go you've been watching @techyhydra and I'll catch you again soon for the next video have a good one Techy Hydra it's me thinking you
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