Machine Learning: offloading optimisation

Machine Learning: offloading optimisation

Machine learning (ML) is a subset of AI. All machine learning is AI, but not all AI is machine learning. Interest in ‘AI’ today reflects enthusiasm for machine learning, where advances are rapid and significant.

The goal of most machine learning is to develop a prediction engine for a particular use case. Machine learning algorithms learn through training.

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The fourth industrial revolution: a primer on Artificial Intelligence (AI)

medium.com

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Deep learning involves using an artificial ‘neural network’ — a collection of ‘neurons’ connected together.

An artificial neuron has one or more inputs. It performs a mathematical calculation based on these to deliver an output. The input-output function can vary. A neuron may be:

  • a linear unit (the output is proportional to the total weighted input;
  • a threshold unit (the output is set to one of two levels, depending on whether the total input is above a specified value); or a
  • sigmoid unit (the output varies continuously, but not linearly as the input changes).

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Example applications of AI include the following; there are many more.

  1. Reasoning: Legal assessment; financial application processing; games; autonomous weapons systems.
  2. Knowledge: Medical diagnosis; media recommendation; purchase prediction; financial market trading; fraud prevention.
  3. Planning: Logistics; scheduling; navigation; physical and digital network optimization.
  4. Communication: Voice control; assistants, and customer support; real-time translation of written and spoken languages; real-time transcription.
  5. Perception: Autonomous vehicles; medical diagnosis; surveillance.

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Some of the most effective machine learning algorithms beyond deep learning include:

  • random forests’ that create multitudes of decision trees to optimise a prediction;
  • Bayesian networks’ that use a probabilistic approach to analyse variables and the relationships between them; and
  • support vector machines that are fed categorised examples and create models to assign new inputs to one of the categories. 

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Coined in 1956 by Dartmouth Assistant Professor John McCarthy, ‘Artificial Intelligence (AI) is a general term that refers to hardware or software that exhibits behavior that appears intelligent.

Basic ‘AI’ has existed for decades, via rules-based programs that deliver rudimentary displays of ‘intelligence’ in specific contexts. Progress, however, has been limited — because algorithms to tackle many real-world problems are too complex for people to program by hand.

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What is Artificial Intelligence? Who knows. It’ s an ever-moving target to define what is or isn’t AI. So, I’d like to dive into a science that’s a little more concrete — Computational Intelligence (CI). CI is a three-branched set of theories along with their design and applications. They are more mathematically rigorous and can separate you from the pack by adding to your Data Science toolbox. You may be familiar with these branches — — Neural Networks , Evolutionary Computation , and Fuzzy Systems . Diving into CI, we can talk about sophisticated algorithms that solve more complex problem

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Although AI researchers can train systems to win at Space Invaders, it couldn’t play games like Montezuma Revenge where rewards could only be collected after completing a series of actions (for example, climb down ladder, get down rope, get down another ladder, jump over skull and climb up a third ladder).

For these types of games, the algorithms can’t learn because they require an understanding of the concept of ladders, ropes and keys. Something us humans have built in to our cognitive model of the world & that can’t be learnt by the reinforcement learning approach of DeepMind.

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Neuroevolution is a form of artificial intelligence. It is a meta-algorithm, an algorithm for designing algorithms. It adopts the principles of biological evolution in order to design smarter algorithms. Eventually, the algorithms get pretty good at their job.

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Computers Evolve a New Path Toward Human Intelligence | Quanta Magazine

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