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Demoting Outdated 'Truth' With Machine Learning

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Sometimes the truth has an expiry date. When a time-limited claim (such as'masks are obligatory on public transport') emerges in search engine rankings, its apparent'authoritative' solution can outstay its welcome even by many years, outranking later and more accurate content on the same topic. This is a by-product of search engine algorithms' determination to identify and promote'long-term' definitive solutions, and of their proclivity to prioritize well-linked content that maintains traffic over time – and of an increasingly circumspect attitude to newer content in the emerging age of fake news. Alternately, devaluing valuable web content simply because the timestamp associated with it has passed an arbitrary'validity window' risks that a generation of genuinely useful content will be automatically demoted in favor of subsequent material that may be of a lower standard. Towards redressing this syndrome, a new paper from researchers in Italy, Belgium and Denmark has used a variety of machine learning techniques to develop a methodology for time-aware evidence ranking.


What is Artificial Intelligence? -- Suffixtree

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Artificial Intelligence (AI) is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Artificial Intelligence, often abbreviated as "AI", may connote robotics or futuristic scenes, AI goes well beyond the automatons of science fiction, into the non-fiction of modern day advanced computer science. Professor Pedro Domingos, a prominent researcher in this field, describes "five tribes" of machine learning, comprised of symbolists, with origins in logic and philosophy; connectionists, stemming from neuroscience; evolutionaries, relating to evolutionary biology; Bayesians, engaged with statistics and probability; and analogizers with origins in psychology. Recently, advances in the efficiency of statistical computation have led to Bayesians being successful at furthering the field in a number of areas, under the name "machine learning". Similarly, advances in network computation have led to connectionists furthering a subfield under the name "deep learning".


The Machine Learning Schools Championed by the Biggest AI Labs in the World

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I recently started an AI-focused educational newsletter, that already has over 80,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Recently, one of my students asked me a question as of whether DeepMind was solely working in reinforcement learning applications. The answer is obviously no but the question is still valid as it rooted in the fact that most of DeepMind's highly publicized work such as AlphaGo, MuZero or AlphaFold are based in reinforcement learning.


GPU-Powered Data Science (NOT Deep Learning) with RAPIDS - KDnuggets

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You do a lot of data wrangling, cleaning, statistical tests, visualizations on a regular basis. You also tinker with a lot of linear models fitting data and occasionally venture into RandomForest. You are also into clustering large datasets. However, given the nature of the datasets you work on (mostly tabular and structured), you don't venture into deep learning that much. You would rather put all the hardware resources you have into the things that you actually do on a day-to-day basis, than spending on some fancy deep learning model.


Which is Better For Your Machine Learning Task, OpenCV or TensorFlow?

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I like to stay up-to-date with what's happening in the field of ML because this is a field that can surprise you almost everyday! Which is better OpenCV or Tensorflow? To some, this is not a valid question. To others, this is a question worth thinking about. The simplest answer is that Tensorflow is better than OpenCV and OpenCV is better than Tensorflow!


Is DeepMind's new reinforcement learning system a step toward general AI?

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One of the key challenges of deep reinforcement learning models--the kind of AI systems that have mastered Go, StarCraft 2, and other games--is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the "first steps to train an agent capable of playing many different games without needing human interaction data," according to a blog post about their new "open-ended learning" initiative. Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges. The new system, according to DeepMind's AI researchers, is an "important step toward creating more general agents with the flexibility to adapt rapidly within constantly changing environments."


Hike hiring ML Intern (Remote) in Delhi, Delhi, India

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Description A NEW SOCIAL FUTURE How is it that in a world that's evolving so quickly that social products still feel the same? Strangely enough, we're still using products that were invented in the 2G era. There seems to be an emptiness with the current experience and today's products are built to force humanity to be superficial. We'd like to change that. With the advancements in technology, so much more is possible today that wasn't even possible, just a few years ago.


Deep Learning System Learns Better When Distracted

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Computer scientists from the Netherlands and Spain have determined how a deep learning system learns better when distracted. The artificial intelligence (AI) is aimed at image recognition and can learn to recognize its surroundings. The team was able to simplify the learning process after forcing the system to focus on secondary characteristics.


DeepMind's Vibrant New Virtual World Trains Flexible AI With Endless Play

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Last year, DeepMind researchers wrote that future AI developers may spend less time programming algorithms and more time generating rich virtual worlds in which to train them. In a new paper released this week on the preprint server arXiv, it would seem they're taking the latter part of that prediction very seriously. The paper's authors said they've created an endlessly challenging virtual playground for AI. The world, called XLand, is a vibrant video game managed by an AI overlord and populated by algorithms that must learn the skills to navigate it. The game-managing AI keeps an eye on what the game-playing algorithms are learning and automatically generates new worlds, games, and tasks to continuously confront them with new experiences.


DeepMind's Vibrant New Virtual World Trains Flexible AI With Endless Play

#artificialintelligence

Last year, DeepMind researchers wrote that future AI developers may spend less time programming algorithms and more time generating rich virtual worlds in which to train them. In a new paper released this week on the preprint server arXiv, it would seem they're taking the latter part of that prediction very seriously. The paper's authors said they've created an endlessly challenging virtual playground for AI. The world, called XLand, is a vibrant video game managed by an AI overlord and populated by algorithms that must learn the skills to navigate it. The game-managing AI keeps an eye on what the game-playing algorithms are learning and automatically generates new worlds, games, and tasks to continuously confront them with new experiences.