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Articles related to "competition"


The Minecraft test that stumped AIs

  • It takes minutes for most new Minecraft players to work out how to dig up the diamonds that are key to the game, but training artificial intelligence to do it has proved harder than expected.
  • A relatively small Minecraft dataset, with 60 million frames of recorded human player data, was also made available to entrants to train their systems.
  • It contrasts with relying solely on "reinforcement learning", in which an agent is effectively trained to find the best solution via a process of trial and error, without drawing on past knowledge.
  • For instance, DeepMind's AlphaGo Zero program trumped one of the research hub's earlier efforts, which used both reinforcement learning and the study of labelled data from human play to learn the board game Go. But this "pure" approach typically requires much more computing power, making it too expensive for researchers other than large organisations or governments.

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Valve Cancels Half-Life: Alyx Preview, Proving the Game Is Nowhere Near Ready

  • In a mad dash to save face, Valve decided at the last minute to pull the much-anticipated preview of Half-Life: Alyx from this year’s Game Awards.
  • Only three hours before the scheduled gameplay preview, Valve announced via Twitter that they would be delaying the sneak peek until March of next year.
  • Developer Stress Level Zero launched the game on 10 December and you can’t help but argue that they did it to front-run Valve’s preview at this year’s Game Awards.
  • Ultimately this all boils down to the likely fact that Valve was going to be showing teleport-only gameplay at The Game Awards.
  • Boneworks is already light-years ahead showcasing its own “smooth” locomotion physics in a practically complete game.
  • But given the competition right now, they’re really going to have to up their game (for lack of a better pun) in 2020.

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How 2020 Democrats think about breaking up Big Tech

  • Pete Buttigieg: As president, I will hold online platforms accountable, demand comprehensive privacy protections, set standards of accountability and transparency for online political ads, elevate ongoing antitrust enforcement reviews, and ensure we keep market power in check for the benefit of consumers.
  • I support the ongoing antitrust probes of online platforms by the Justice Department, FTC, and state attorneys general and will double antitrust enforcement budgets so the Justice Department can prioritize the scrutiny of large online platforms such as Facebook, Google, Apple, and Amazon under my administration.
  • We either need to break up some of these big tech companies or regulate them so they don’t continue to stifle innovation and competition, and harm the American consumer.
  • I also believe that we need to evaluate whether current antitrust standards and practices are working effectively to address the competition concerns raised by large internet platforms.

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Takeaways from the World’s largest Kaggle Grandmaster Panel

  • More than often, you’d team up and you’d end up working with a team of people that you wouldn’t have met and remotely contributing, GM Pavel says teaming up remotely and pushing a team to it’s best was one of his favorite takeaways.
  • Dmitry and Mark agreed on the point that deep learning could you help with modeling but in terms of automatically creating features, validating ideas, specifically to Kaggle validating an idea and thinking critically if the feature will reflect on Kaggle’s Private Leaderboard- Deep Learning may not be able to do that.
  • Kim would spend a lot of the time initially on feature engineering and focus on modeling towards the end of the competition.
  • At this point, every Grandmaster picked up the mic in sync and mentioned the Chai Time Data Science Podcast!
  • At the end of the day, Kaggle is the home of Data Science and it has to be one of the greatest learning platforms on there.

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Facebook Stock Got Hammered Because the FTC Plans App Crackdown

  • The agency claims that Facebook’s efforts to regulate apps have stifled competition and given unfair advantage to Facebook’s own products.
  • In particular, the FTC wants to stop Facebook from integrating its messaging products such as WhatsApp further into its primary platform.
  • Regulators may seek to break-up Facebook, forcing it to jettison past acquisitions such as WhatsApp and Instagram.
  • Meanwhile, regulators and political opponents wouldn’t be able to bash the company for stifling competition.
  • Facebook now claims that America needs larger tech giants to remain competitive against foreign rivals such as TikTok. Critics find this argument unconvincing, given that Zuckerberg was trying to acquire TikTok’s previous owner.
  • It seems unlikely that Facebook will be able to find political refuge from these antitrust concerns, as it’s not just the Trump administration going after Facebook.

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Takeaways from the World’s largest Kaggle Grandmaster Panel

  • More than often, you’d team up and you’d end up working with a team of people that you wouldn’t have met and remotely contributing, GM Pavel says teaming up remotely and pushing a team to it’s best was one of his favorite takeaways.
  • Dmitry and Mark agreed on the point that deep learning could you help with modeling but in terms of automatically creating features, validating ideas, specifically to Kaggle validating an idea and thinking critically if the feature will reflect on Kaggle’s Private Leaderboard- Deep Learning may not be able to do that.
  • Kim would spend a lot of the time initially on feature engineering and focus on modeling towards the end of the competition.
  • At this point, every Grandmaster picked up the mic in sync and mentioned the Chai Time Data Science Podcast!
  • At the end of the day, Kaggle is the home of Data Science and it has to be one of the greatest learning platforms on there.

save | comments | report | share on


Takeaways from the World’s largest Kaggle Grandmaster Panel

  • More than often, you’d team up and you’d end up working with a team of people that you wouldn’t have met and remotely contributing, GM Pavel says teaming up remotely and pushing a team to it’s best was one of his favorite takeaways.
  • Dmitry and Mark agreed on the point that deep learning could you help with modeling but in terms of automatically creating features, validating ideas, specifically to Kaggle validating an idea and thinking critically if the feature will reflect on Kaggle’s Private Leaderboard- Deep Learning may not be able to do that.
  • Kim would spend a lot of the time initially on feature engineering and focus on modeling towards the end of the competition.
  • At this point, every Grandmaster picked up the mic in sync and mentioned the Chai Time Data Science Podcast!
  • At the end of the day, Kaggle is the home of Data Science and it has to be one of the greatest learning platforms on there.

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THE GLOBAL NEOBANKS REPORT: How 26 upstarts are winning customers and pivoting from hyper-growth to profitability in a $27 billion market

  • Neobanks — digital-only banks with industry-leading capabilities that don't operate physical branches or rely on legacy back-ends — have exploded onto the global scene in recent years.
  • Increased consumer interest in neobanks is stimulating competition globally, creating an increasingly competitive landscape which has driven neobanks to roll out extravagant features, like overdraft protection and sign-up incentives.
  • Beyond scaling rapidly by user count, neobanks are navigating the best route to profitability.
  • Today, the average neobank loses $11 per user, per Accenture, and though neobanks' expenses are partially offset by not operating costly branch networks, they still need to find sustainable business models.
  • In The Global Neobanks report, Business Insider Intelligence explores how the neobank market has grown rapidly, and what's in store as the industry pivots from hyper-growth to sustainability.
  • We discuss how 26 neobanks in key global markets are prioritizing scale versus profitability, identifying best practices to emulate and pitfalls to avoid.

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Takeaways from the World’s largest Kaggle Grandmaster Panel

  • More than often, you’d team up and you’d end up working with a team of people that you wouldn’t have met and remotely contributing, GM Pavel says teaming up remotely and pushing a team to it’s best was one of his favorite takeaways.
  • Dmitry and Mark agreed on the point that deep learning could you help with modeling but in terms of automatically creating features, validating ideas, specifically to Kaggle validating an idea and thinking critically if the feature will reflect on Kaggle’s Private Leaderboard- Deep Learning may not be able to do that.
  • Kim would spend a lot of the time initially on feature engineering and focus on modeling towards the end of the competition.
  • At this point, every Grandmaster picked up the mic in sync and mentioned the Chai Time Data Science Podcast!
  • At the end of the day, Kaggle is the home of Data Science and it has to be one of the greatest learning platforms on there.

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And the winner of Startup Battlefield at Disrupt Berlin 2019 is… Scaled Robotics

  • They all presented in front of multiple groups of VCs and tech leaders serving as judges for a chance to win $50,000 and the coveted Disrupt Cup. After hours of deliberations, TechCrunch editors pored over the judges’ notes and narrowed the list down to five finalists: Gmelius, Hawa Dawa, Inovat, Scaled Robotics and Stable.
  • These startups made their way to the finale to demo in front of our final panel of judges, which included: Andrei Brasoveanu (Accel), Andrew Reed (Sequoia Capital), Carolina Brochado (SoftBank Vision Fund), Lila Preston (Generation Investment Management) and Mike Butcher (TechCrunch).
  • Read more about Scaled Robotics in our separate post.
  • Stable offers a solution as simple as car insurance, designed to protect farmers around the world from pricing volatility.
  • Read more about Stable in our separate post.

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