artificial intelligence ai

For some years we have been hearing about AI, while some are still wondering what is this AI all about, it is already transforming different areas of our lives. So, what then is AI

What is Artificial Intelligence – AI?

Artificial intelligence also called (AI) is a sub‐field of computer science aimed at the development of computers capable of doing things that are normally done by people — in particular, things associated with people acting intelligently. Some of the activities artificial intelligence are designed for include:

  • Speech recognition
  • Learning
  • Planning
  • Problem-solving etc.

Any program is an AI system simply by the fact that it does something that we would normally think of as intelligent in humans. How it does so is not the issue; just that it is able to do it at all is the key point.

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons. Artificial intelligence (AI) is already creating a great deal of hype, excitement, and fear. From robots who may take over the world, to supercomputers competing on Jeopardy!, to Siri telling us where we can find the closest restaurant, the AI landscape is vast and the potential uses are many.

Today, we are confronted with an emerging suite of intelligent systems that do things in a way that we do not quite understand. What is actually frightening is that we might not know enough about these systems to be able to evaluate them appropriately. What is evident today is, AI is gradually transforming the way we work and live.

Weak & Strong

For some researchers and developers, the goal is to build systems that can act (or just think) intelligently in the same way that people do. Others simply don’t care if the systems they build have human-like functionality, just so long as those systems do the right thing. Alongside these two schools of thought are others somewhere in between, using human reasoning as a model to help inform how we can get computers to do similar things.

Strong AI

The work aimed at genuinely simulating human reasoning tends to be called strong AI in that any result can be used to not only build systems that think but also explain how humans think as well. Genuine models of strong AI or systems that are actual simulations of human cognition have yet to be built.

Weak AI

The work in the second school of thought, aimed at just getting systems to work, is usually called weak AI in that while we might be able to build systems that can behave like humans, the results tell us nothing about how humans think. One of the prime examples of this was IBM’s Deep Blue (a chess-playing computer developed by IBM), a system that was a master chess player but certainly did not play in the same way that humans do and told us very little about cognition in general.

Everything in between

Balanced between strong and weak AI are those systems that are informed by human reasoning but not slaves to it. This tends to be where most of the more powerful work in AI is happening today. This work uses human reasoning as a guide but is not driven by the goal to perfectly model it.

Now if we could only think of a catchy name for this school of thought! I don’t know, maybe Practical AI? A good example is advanced natural language generation (NLG). Advanced NLG platforms transform data into language. Where basic NLG platforms simply turn data into text, advanced platforms turn this data into language indistinguishable from the way a human would write.

By analysing the context of what is being said and deciding what are the most interesting and important things to say, these platforms communicate to us through intelligent narratives. The important takeaway is that in order for a system to be AI, it doesn’t have to be smart in the same way that people are. It just needs to be smart.

Narrow AI, broad AI, and is that AI at all?

Some AI systems are designed around specific tasks (often called narrow AI) and some are designed around the ability to reason in general (referred to as broad AI or general AI). As with strong and weak AI, the most visible work tends to focus on specific problems and falls into the category of narrow AI.

The major exceptions to this are found in emerging work such as Google’s deep learning (aimed at a general model of automatically learning categories from examples) and IBM’s Watson (designed to draw conclusions from masses of textual evidence). But in both of these cases, the commercial impact of these systems has yet to be completely played out.

The power of narrow AI systems is that they are focused on specific tasks. The weakness is that these systems tend to be very good at what they do and absolutely useless for things that they don’t do. Different systems use different techniques and are aimed at different kinds of inference. So there’s a difference between systems that recommend things to you based on your past behaviour, systems that learn to recognise images from examples, and systems that make decisions based on the synthesis of evidence.

Consider these differences when looking at systems. You probably don’t want a system that is really good at finding the nearest gas station to do your medical diagnostics. Many systems fall under the definition of narrow AI even though some people don’t think of them as all-encompassing AI.

When you use Google search engine and you are about typing something and you saw some other suggestions you don’t realise that an AI system is behind the recommendation. A system collects information about you and your search behaviour, figures out who you are and how you are similar to other people and uses that information to suggest search query based on what similar people searched. You don’t need to understand how the system works. Google’s ability to look at your search pattern and figure out what else you might like is pretty smart.



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