In today’s technological era, machines are able to do things that were once considered the sole province of human beings. A classic example of this trend is the chess-playing computer, which bested grandmaster Garry Kasparov in 1997—a development with enormous implications for cognitive science and philosophy. Even more impressively, IBM’s Watson machine recently proved itself to be the champion of Jeopardy!, a game that requires enormous amounts of knowledge and reasoning to play well.
In 2001 IBM’s Deep Blue became one of the first computer programs to beat a grandmaster in chess when it defeated Garry Kasparov. At that time, most people assumed that, with time, computers would soon be able to do more than just play games — including complex tasks such as diagnosing diseases or driving cars. They believed that AI technology would widen and deepen (Wagner-Dobler 2007) our everyday experiences. However, many experts now believe we are still far from achieving advanced forms of artificial intelligence.
General Goals of Machine Learning
Machine learning (ML) is a subfield of computer science that, according to Arthur Samuel in 1959, provides computers with the ability to learn without being explicitly programmed. This is done by building an artificial neural network that can take a set of inputs and turn them into outputs (which can be more complicated than simple numbers), allowing it to recognize patterns between those inputs and outputs. The goal of most ML techniques does not just pattern recognition but also prediction. Using millions or billions of previous examples, ML algorithms can be trained on a specific task so they can then predict outcomes without having been told which examples are more relevant than others.
The Neural Network Approach
Rather than defining what intelligence is, it’s simpler to simply consider whether or not computers can display intelligent behavior. If you’re looking for that goalpost, researchers have often used artificial neural networks as an example of intelligent computing. Neural networks are computer systems designed to mimic human thought processes. In a sense, they are extremely complicated forms of AI.
Where does Artificial Intelligence go from here?
While it might seem as if we’re nowhere near creating true artificial intelligence, or even thinking computers that are indistinguishable from humans, in reality, we’ve already made huge strides in some of those areas. Right now, for example, Siri can talk to you—in fact, she can do a whole lot more than that. In many ways, then, she is fairly advanced; however, well-designed her software may be.for more info visit
Conclusions on Artificial Intelligence
One of these days, artificial intelligence will prove it can really think and that its actions are not predetermined. Then we’ll begin building even more-complex systems because there’s a sense in which we’ve only just begun thinking about thinking. And when you do think about thinking — what is it, how does it work, where does it come from — pretty soon you find yourself talking about consciousness as well. What is consciousness anyway? If I’m conscious but my laptop isn’t, what makes me so special? My mind is inside my skull; how do I know that yours isn’t inside your computer or cellphone or iPhone (which many people carry around all day long)? Where in space does consciousness reside? Do microbes have inner lives with hopes and dreams and emotions?