strength


Biasing MCTS with Features for General Games

arXiv.org Artificial Intelligence

This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games. This is unlikely to match the potential raw playing strength of DNNs, but has advantages in terms of generality, interpretability and resources (time and hardware) required for training. Features describing local patterns are used as inputs. The features are formulated in such a way that they are easily interpretable and applicable to a wide range of general games, and might encode simple local strategies. We gradually create new features during the same self-play training process used to learn feature weights. We evaluate the playing strength of an MCTS player biased by learnt features against a standard upper confidence bounds for trees (UCT) player in multiple different board games, and demonstrate significantly improved playing strength in the majority of them after a small number of self-play training games.


How Human Intelligence Differs From Artificial Intelligence

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Through my Twitter and on LinkedIn feeds I see a lot of postings about technology. Many (primarily technology experts) write about the massive potential of technologies, for example Artificial Intelligence (AI), Blockchain, Cloud, Internet of Things (IoT), mobile and other technologies. In the current blog I will refer specifically to AI, not to other technologies. Other people write about AI in a way that implies that they fear AI; that AI is a risk, maybe more than an opportunity. Articles with titles like "Robots will take our jobs. We'd better plan now, before it's too late" can create fear, especially when non-tech-experts read the title on Twitter, absorb the connotation "robots danger for my job", without reading the full article and doing additional research on the topic.


How to Automatically Determine the Number of Clusters in your Data - and more

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Determining the number of clusters when performing unsupervised clustering is a tricky problem. Many data sets don't exhibit well separated clusters, and two human beings asked to visually tell the number of clusters by looking at a chart, are likely to provide two different answers. Sometimes clusters overlap with each other, and large clusters contain sub-clusters, making a decision not easy. For instance, how many clusters do you see in the picture below? What is the optimum number of clusters?


How to Automatically Determine the Number of Clusters in your Data - and more

#artificialintelligence

Determining the number of clusters when performing unsupervised clustering is a tricky problem. Many data sets don't exhibit well separated clusters, and two human beings asked to visually tell the number of clusters by looking at a chart, are likely to provide two different answers. Sometimes clusters overlap with each other, and large clusters contain sub-clusters, making a decision not easy. For instance, how many clusters do you see in the picture below? What is the optimum number of clusters?


The Creativity Code by Marcus du Sautoy – review

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Marcus du Sautoy is the kind of science writer who cares more about questions than answers. In his books he tackles "unsolved problems", "number mysteries" and "the great unknown", topics at the edge of human understanding. They are subtitled with words such as "odyssey", "exploration" and "journey". But Du Sautoy is a flaneur: his trips are not motivated by destinations. This is both the main strength and flaw of The Creativity Code, a wide-ranging and fact-packed tour d'horizon of current applications of artificial intelligence in mathematics and the arts.


Welcome! You are invited to join a webinar: Webinar: Validating Success as an S&C Coach Using AI and Data for Sports Medicine with Tracy Zimmer. After registering, you will receive a confirmation email about joining the webinar.

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The role of a strength and conditioning coach remains ambiguous and undefined. From one university to the next, there are rarely clearly set metrics to measure success. Tracy Zimmer, S&C coach from the University of Pennsylvania discusses how the use of AI and data through objective movement assessments and technology has helped Penn Athletics improve performance and reduce injury. She also shares how Sparta data aligns the organization from sport coaches, to athletic trainers and strength & conditioning coaches like herself, keeping everyone accountable to building better athletes who are resilient against injury and perform better at their sport. Speaker Bio: Tracy Zimmer has been at Penn since 2010 and serves an Assistant Strength and Conditioning Coach.


Only one Canadian company places on list of top 100 AI companies in the world

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Despite Canada's rising reputation as a global leader in the artificial intelligence community, it appears Canadian companies are not making as much of an impact on the international AI scene. CB Insights has released its third annual list of promising AI startups to watch around the world, and only one Canadian company, Montreal-based Element AI, made the cut. In addition to Element AI, a German startup with Canadian roots, Twenty Billion Neurons (TwentyBN) was also named to CB Insights' AI 100. Canadian companies leveraging this technology may still only have a minor presence on the global stage. CB Insights selected the 100 startups from a pool of over 3,000 companies from startups to unicorns, based on: patent activity, investor profile, news sentiment analysis, proprietary Mosaic scores, market potential, partnerships, competitive landscape, team strength, and tech novelty.


BMW, Daimler team up to develop self-driving cars DW 28.02.2019

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In their latest strategic partnership agreement, BMW and Daimler announced Thursday that they have joined forces to develop automated driving technology. The German automakers have agreed to a "long-term, strategic cooperation" to more quickly develop advanced driver assistance systems, automated driving on closed spaces and automated parking, they said in a statement. They hope "to make next-level technologies widely available" by 2025. "Combining the strength of our two companies will boost our innovative strength and speed up the spread of this technology," said Klaus Fröhlich, BMW's head of development. BMW and Daimler, the parent company of Mercedes-Benz and Smart, have launched multiple collaborative efforts as they try to fend off competition from tech companies like Uber and Waymo.


In Real Life: How Will AI Impact Workplace Learning?

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Will AI impact workplace learning? At least, that's the wrong question to be asking right now. Well, remember when offices had typists? I don't, but I've seen it on TV so it must have been real. Imagine if L&D had asked, "How will the desktop computer help us better train our typists?"


Engineers create wonder material with the strength of metal and the elasticity of rubber

Daily Mail

Scientists have developed a fibre that combines the elasticity of rubber with the strength of a metal. Researchers at North Carolina State University are behind the innovation, which has created a tougher material that could be incorporated into soft robotics, packaging materials or next-generation textiles. The team made fibres consisting of a gallium metal core surrounded by an elastic polymer sheath. When placed under stress, the fibre has the strength of the metal core. But whereas the metal eventually breaks, the fiber doesn't fail - the polymer sheath absorbs the strain between the breaks in the metal and transfers the stress back to the metal core.