AI a revolutionary invention
The concept of intelligent beings has been around for a long time. The ancient Greeks, in fact, had myths about robots as the Chinese and Egyptian engineers built automatons. However, the beginnings of modern AI has been traced back to the time where classical philosophers’ attempted to describe human thinking as a symbolic system. Between the 1940s and 50s, a handful of scientists from various fields discussed the possibility of creating an artificial brain. This led to the rise of the field of AI research — which was founded as an academic discipline in 1956 — at a conference at Dartmouth College, in Hanover, New Hampshire. The word was coined by John McCarthy, who is now considered as father of Artificial Intelligence.
Despite a well-funded global effort over numerous decades, scientists found it extremely difficult to create intelligence in machines. Between the mid 1970s and 1990s, scientists had to deal with an acute shortage of funding for AI research. These years came to be known as the ‘AI Winters’. However, by the late 1990, American corporations once again were interested in AI. Furthermore, the Japanese government too, came up with plans to develop a fifth generation computer for the advancement of AI. Finally, In 1997, IBM’s Deep Blue defeated became the first computer to beat a world Chess champion, Garry Kasparov.
As AI and its technology continued to march — largely due to improvements in computer hardware, corporations and governments too began to successfully use its methods in other narrow domains. The last 15 years, Amazon, Google, Baidu, and many others, have managed to leverage AI technology to a huge commercial advantage. AI, today, is embedded in many of the online services we use. As a result, the technology has managed to not only play a role in every sector, but also drive a large part of the stock market too.
How do we measure if the Artificial Intelligence is acting like a human?
Even if we reach that state where an AI can behave like a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:
- Turing Test
- The Cognitive Modelling Approach
- The Law of Thought Approach
- The Rational Agent Approach
What is the Turing Test in Artificial Intelligence?
The basis of the Turing Test is that the Artificial Intelligence entity should be able to hold a conversation with a human agent. The human agent ideally should not able to conclude that they are talking to an Artificial Intelligence. To achieve these ends, the AI needs to possess these qualities:
- Natural Language Processing to communicate successfully.
- Knowledge Representation to act as its memory.
- Automated Reasoning to use the stored information to answer questions and draw new conclusions.
- Machine Learning to detect patterns and adapt to new circumstances.
Cognitive Modelling Approach
As the name suggests, this approach tries to build an Artificial Intelligence model-based on Human Cognition. To distill the essence of the human mind, there are 3 approaches:
- Introspection: observing our thoughts, and building a model based on that
- Psychological Experiments: conducting experiments on humans and observing their behavior
- Brain Imaging: Using MRI to observe how the brain functions in different scenarios and replicating that through code.
The Laws of Thought Approach
The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issues with this approach, because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.
The Rational Agent Approach
A rational agent acts to achieve the best possible outcome in its present circumstances.
According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that it’s a much more dynamic and adaptable agent.
Now that we understand how Artificial Intelligence can be designed to act like a human, let’s take a look at how these systems are built.
How Artificial Intelligence (AI) works?
Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using it’s computational prowess to surpass what we are capable of.
To understand How Artificial Intelligence actually works, one needs to deep dive into the various sub domains of Artificial Intelligence and and understand how those domains could be applied into the various fields of the industry.
- Machine Learning : ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.
- Deep Learning : Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
- Neural Networks : Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does.
- Natural Language Processing: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
- Computer Vision : Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
- Cognitive Computing : Cognitive computing algorithms try to mimic a human brain by analysing text/speech/images/objects in a manner that a human does and tries to give the desired output.
Comments
Post a Comment