Experts are putting machine learning systems through their paces using IQ tests, which measure a number of reasoning skillsResearchers at Google's DeepMind project in London used puzzles known as 'Raven's Progressive Matrices'.Developed by John C Raven in 1936, the Matrices measure participants' ability to make sense and meaning out of complex or confusing data.They also test their ability to perceive new patterns and relationships, and to forge largely non-verbal constructs that make it easy to handle complexity. The intelligent system, developed as part of Google's DeepMind AI program, has taught itself to visualise any angle on a space in a static photographAI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.ANNs can be trained to recognise patterns in information - including speech, text data, or visual images - and are the basis for a large number of the developments in AI over recent years.Conventional AI uses input to 'teach' an algorithm about a particular subject by feeding it massive amounts of information. Google Newcomers to the tests would often fail to answer all the questions.The full findings are awaiting peer-review but can be viewed on the pre-print repository In recent weeks, it was revealed Google's artificial Intelligence can now dream an entire world based on a single photo.Dubbed Generative Query Network, it gives the machine a 'human-like imagination'.This allows the algorithm to generate three-dimensional impressions of spaces it has only ever seen in flat, two-dimensional images.The AI breakthrough was announced by DeepMind CEO Demis Hassabis.With Generative Query Network, Dr Hassabis and his team tried to replicate the way a living brain learns about its environment simply by looking around.This is a very different approach to most projects, in which researchers manually label data and slowly feed it to the AI.To train the DeepMind neural network, the team showed the AI a carousel of static images taken from different viewpoints on the same scene.By using these images, the algorithm was able to teach itself to predict how something would appear in a new viewpoint not included in the images.DeepMind soon learnt to imagine complete three-dimensional representations of the scene.The intelligent machine is even able to move around its imagined space.As it moves, the algorithm must constantly make predictions about where the objects initially seen in the photos should be, and what they look like from its ever-changing perspective.Artificial Intelligence can now dream an entire world based on a single photo.
They're kind of bohemian.
知能指数(IQ) 少年期 青年期 ライプニッツ 195 205 ゲーテ 190 210 デカルト 170 180 ガリレオ 160 185 レオナルド ダ ヴンチ 155 180 ニュートン 150 190 ネルソン 130 150
This allows the algorithm to generate three-dimensional impressions of spaces it has only ever seen in flat, two-dimensional images (pictured) The second part, the 'generative' component, uses these vectors to imagine what a different viewpoint in that scene – not included in the original images – would be able to see.Using the data gathered from the initial photos, DeepMind is able to ascertain spatial relationships within the scene.Ali Eslami explained: 'Imagine you're looking at Mt.
The algorithms are created by blending research and expertise from neuroscience and machine learning.So far, the algorithms have been used to defeat the best human player of Chinese board game Go and to help Google slash its enormous electricity bill.
Much of Legg's time is dedicated to hiring and deciding where DeepMind should focus its efforts next.
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