What is Artificial Intelligence (Ai)?

Artificial intelligence, or Ai for short, is an area of computer science that emphasises the creation of intelligent machines that work and react like humans. Currently, some of the activities computers with Artificial Intelligence are designed for include: Speech recognition, Learning, Planning and Problem-solving.

Artificial intelligence is not based on a static formula, instead, it is a constantly evolving system designed to identify, sort, and present the data that is most likely to meet the needs of users at that specific time, based on a multitude of variables.

Artificial intelligence falls into five paradigms – Evolutionary (e.g. genetic algorithms, artificial life), Connectionism (e.g neural networks), Symbolism (e.g Logic/Rules), Probabilistic (Bayesian and related approaches), and Analogy-based reasoning.

The key requirement to “train” an Artificial intelligence powered system are lots of datasets. That’s also where Machine Learning gives us some really fascinating opportunities, as it’s an iterative process that improves over time as you feed it more and more data.

Classifying Ai

We have a unique way of planning and classifying Ai. We define as programmes in the following four ways;

  • Supportive Ai: Systems that are able to intelligently retreive information on request. Such as search, chatbots or expert systems. These systems are also able to learn and evolve over-time when more questions are asked, and more data is a assimilated.
  • Service Ai: Systems that are able to take requests, and perform actions on behalf of someone. For instance, a system that is able to retrieve someones bank-balance on request (supportive) and then give proactive advice on how to better manage money, and budget, based on the data is observes. Taking this one step further, the machine can also make the changes required to help the person get better.
  • Predictive Ai: A predictive analytics model can look at a specific set of defined data inputs, and support some kind of decision using machine learning. In business, it may lead to higher sales of a product or increased lift on a promotional effort, or simply help someone make a better decision by predicting various outputs.
  • Perceptual Ai: An autonomous agent that is able to operate on an owner’s behalf but without any interference of that ownership entity. An Intelligent agent will carry out some set of operations on behalf of a business or person, or another program, with a degree of independence or autonomy, and in so doing, employ some knowledge or representation of the desired goals and outcomes.

Machine Learning

Machine learning is a subset of AI. The theory is simple, machines take data and ‘learn’ for themselves. It is currently the most promising tool in the AI pool for businesses. Machine learning systems can quickly apply knowledge and training from large datasets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks. Machine learning allows a system to learn to recognise patterns on its own and make predictions, contrary to hand-coding a software program with specific instructions to complete a task.

While Deep Blue and DeepMind are both types of AI, Deep Blue was rule-based, dependent on programming — so it was not a form of machine learning. DeepMind, on the other hand — beat the world champion in Go by training itself on a large data set of expert moves.

That is, all machine learning counts as AI, but not all AI counts as machine learning.

Deep Learning

Deep learning is a subset of machine learning. Deep artificial neural networks are a set of algorithms reaching new levels of accuracy for many important problems, such as image recognition, sound recognition, recommender systems, etc.

It uses some machine learning techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be costly and requires huge datasets to train itself. This is because there are a huge number of parameters that need to be understood by a learning algorithm, which can primarily yield a lot of false-positives. For example, a deep learning algorithm could be trained to ‘learn’ how a dog looks like. It would take an enormous dataset of images for it to understand the minor details that distinguish a dog from a wolf or a fox.

Deep learning is part of DeepMind’s notorious AlphaGo algorithm, which beat the former world champion Lee Sedol in 4 out of 5 games of Go using deep learning in early 2016. Google said, “the way the deep learning system worked was by combining Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.”

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