DeepMind and Machine Learning

By now you have probably seen this:

About Watson

Watson is a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.

Craig Rhinehart

Watson can process 500 gigabytes, the equivalent of a million books, per second. The sources of information for Watson include encyclopedias, dictionaries, thesauri, newswire articles, and literary works. Watson also used databases, taxonomies, and ontologies. Specifically, DBPedia, WordNet, and Yago were used. For the Jeopardy contest, Watson had access to the full text of Wikipedia.

In healthcare, Watson’s natural language, hypothesis generation, and evidence-based learning capabilities allow it to function as a clinical decision support system for use by medical professionals. To aid physicians in the treatment of their patients, once a physician has posed a query to the system describing symptoms and other related factors, Watson first parses the input to identify the most important pieces of information; then mines patient data to find facts relevant to the patient’s medical and hereditary history; then examines available data sources to form and test hypotheses; and finally provides a list of individualized, confidence-scored recommendations. The sources of data that Watson uses for analysis can include treatment guidelines, electronic medical record data, notes from physicians and nurses, research materials, clinical studies, journal articles, and patient information

About Deepmind

Google DeepMind was founded in 2011 by Demis Hassabis, Shane Legg and Mustafa Suleyman.The team is based in London and was supported by some of the most iconic technology entrepreneurs and investors of the past decade. DeepMind combines the best techniques from machine learning and systems neuroscience to build powerful general‑purpose learning algorithms. This video from child prodigy Demis Hassabis explains their latest innovations:

About Machine Learning

Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning “signal” or “feedback” available to a learning system:

Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.

Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end.

Reinforcement learning: Software interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent.

And now for something almost completely different

A little more Watson is fun:

Ray Kurzweil

No discussion of machine learning would be complete without some mention of the contributions of Ray Kurzweil. His focus for several years has been on pattern recognition. He is involved in fields such as futurology, optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic keyboard instruments. He has written books on health, artificial intelligence (AI), transhumanism, the technological singularity, and futurism. Kurzweil proposed “The Law of Accelerating Returns”, according to which the rate of change in a wide variety of evolutionary systems (including the growth of technologies) tends to increase exponentially.