Machine Learning in Bookmaking – FansUnite – Medium

Let's talk about machine learning in bookmaking for a minute. #fansunitetoken

  • Smart bettors quickly take advantage and the bookmaker shifts the line to equivalate betting volume on either side of a matchup.Similarly, high variance in opinion when the data between two teams is very similar can often lead to poor lines.
  • By polling the crowd with low limits to start, Pinnacle can often limit exposure on early lines and avoid getting picked off on markets by sharp bettors.This novel method of polling the crowd drives lines globally, and it’s no surprise that the default action for almost every sportsbook is to…
  • To produce lines we will use an ensemble of best in class Deep Learning networks, alongside other more common approaches to shape a line up to 24 hours before current markets take shape.At Fansunite.io, the world’s preeminent social token betting platform, we have been actively shaping our risk management strategy…
  • We offer an industry leading 1% margin and will maintain a winners welcome philosophy.The Value to the Betting CustomerOur automated machine approach to setting lines offers the following core value to our customers.Savings we can pass on to our bettors.
  • By using Machine Learning, we can offer real time In-Play betting markets to our amazing customers.Stable Currency: Solid lines offer big rewards to currency and token holders by ensuring that the coin base is not drained by sophisticated traders and demand remains strong for our low margin lines.

Machine Learning is becoming a standard tool of the sports betting industry. At fansunite.io we are keenly aware of this technology and actively incorporating it into our risk management strategy…
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Azure Machine Learning Documentation (Preview)

📣 Deploying your machine learning model - from start to finish
 #Dev

  • Learn how to prepare data, and build and deploy models with our quickstarts and tutorials.
  • The preview features of Azure Machine Learning enable building, deploying, and managing ML and AI models using any Python tools and libraries.
  • You can use a wide variety of data and compute services in Azure in order to store and process your data.

Learn how to prepare data, and build and deploy models with our quickstarts and tutorials. The preview features of Azure Machine Learning enable building, deploying, and managing ML and AI models using any Python tools and libraries. You can use a wide variety of data and compute services in Azure in order to store and process your data.
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Top Trends in the Gartner Hype Cycle for Emerging Technologies, 2017

#AI will be the most disruptive class of technologies over the next 10 years. —Gartner #DF17

  • While human augmentation is just at the beginning of the innovation trigger phase of the Hype Cycle, complementary emerging technologies such as machine learning, blockchain, drones (commercial UAVs), software-defined security and brain-computer interfaces have moved significantly along the Hype Cycle since 2016.
  • The Gartner Hype Cycle for Emerging Technologies, 2017 focuses on three emerging technology mega-trends: Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms.
  • “Our 2017 Hype Cycle reveals three distinct technology trends that profoundly create new experiences with unrivaled intelligence, and offer platforms that propel organizations to connect with new business ecosystems in order to become competitive over the next five to 10 years.”
  • “When autonomous vehicles, AI, IoT and other emerging technologies are combined with economic trends like the sharing economy, we truly see different business designs that profoundly disrupt the market,” Walker says.
  • Also in the realm of AI, machine learning, one of the hottest concepts in technology, has the potential to benefit industries from supply chain to drug research.

Enterprises should explain the business potential of blockchain, artificial intelligence and augmented reality.
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GitHub

Awesome list for machine learning in cybersecurity

  • A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security.
  • Please read CONTRIBUTING if you wish to add tools or resources.

awesome-ml-for-cybersecurity – :octocat: Machine Learning for Cyber Security
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IBM is teaching AI to behave more like the human brain

Can a machine make memories? How IBM is exploring neural network learning in #AI:  @engadget

  • Deep learning neural networks — the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems — are the best machine learning systems we’ve developed to date.
  • While neurons use their various connections with each other to recognize patterns, “We are explicitly forcing the network to discover the relationships that exist” between pairs of objects in a given scenario, Timothy Lillicrap, a computer scientist at DeepMind told Science Magazine.When subsequently tasked in June with answering complex questions…
  • In a pair of research papers presented at the 2017 International Joint Conference on Artificial Intelligence held in Melbourne, Australia last week, IBM submitted two studies: one looking into how to grant AI an “attention span”, the other examining how to apply the biological process of neurogenesis — that is,…
  • It’s the same way that your doctor doesn’t tap your knees with that weird little hammer thing when you come in complaining of chest pain and shortness of breath.While the attention system is handy for ensuring that the network stays on task, IBM’s work into neural plasticity (how well memories…
  • Basically the attention model will cover the short term, active, thought process while the memory portion will enable the network to streamline its function depending on the current situation.But don’t expect to see AIs rivalling the depth of human consciousness anytime soon, Rish warns.

Since the days of Da Vinci’s “Ornithoper”, mankind’s greatest minds have sought inspiration from the natural world for their technological creations. It’s no di…
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Alphabet chair praises Canada’s AI innovations at Google’s Go North

If there’s any country that’s at the forefront of #AI innovation, it’s Canada. #GoNorth »

  • The head of Google’s parent company contrasted divisive U.S. politics against Canada’s innovation and immigration-friendly policies Thursday, adding his company owes this country a favour – one the Prime Minister said he’d be sure to call in.
  • Google is among the backers of the Vector Institute, a Toronto-based artificial intelligence research lab which is part of Ottawa’s strategy to drive innovation in Canada.
  • The U.S., Canada and Mexico have been in prolonged negotiations over the North American Free Trade Agreement, a deal Schmidt said has been “enormously successful.”
  • Schmidt added that he was worried about the impact that U.S. identity politics under Donald Trump – in contrast to Canada’s focus on inclusivity and diversity – would have on the countries’ dealings.
  • Story continues below advertisement Story continues below advertisement Schmidt noted that the U.S. is “critically dependent” on supply chains from Canada, “including the back and forth that we have in the tech industry.”

Alphabet Inc. chairman Eric Schmidt said during an onstage chat with Justin Trudeau in Toronto that his company is ‘enormously thankful to Canadians’ for the country’s artificial intelligence innovations
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The AI that can make grainy images razor sharp: Experts hail sci-fi breakthrough that could revolutionise everything from holiday snaps to crime fighting

The #AI that can make grainy images razor sharp.  @_cheymac

  • Researchers have unveiled a Blade Runner-style AI that can enhance pixelated images.
  • Dubbed EnhanceNet, the system relies on neural networks to boost the image quality, creating high-resolution images from a low-resolution input.
  • Dubbed EnhanceNet, the system relies on neural networks to boost the image quality, creating high-resolution images from a low-resolution input – – The system uses adversarial training, which pits the networks against each other in competing goals so they ultimately learn together.
  • ‘By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios,’ the researchers explain in the study.
  • Examples from the study show just how good the new AI is, far surpassing the quality of images enhanced using a method known as peak signal-to-noise ratio (PSNR).

Researchers from the Max Planck Institute for Intelligent Systems have unveiled a Blade Runner-style AI dubbed EnhanceNet, that can enhance pixelated images.
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