Source: analyticsinsight.net Today machines can teach themselves based upon the results of their own actions. This advancement in Artificial Intelligence seems like a promising technology through which we can explore more innovative potentials of AI. The process is termed as deep reinforcement learning. Deep reinforcement learning, as defined by Bernard Marr, a well-known AI Influencer, is Read More

Read More

Source: venturebeat.com In a technical paper published on Arxiv.org this week, researchers at Facebook and Arizona State University lifted the hood on AutoScale, which shares a name with Facebook’s energy-sensitive load balancer. AutoScale, which could theoretically be used by any company were the code to be made publicly available, leverages AI to enable energy-efficient inference on smartphones and other edge Read More

Read More

Source: analyticsindiamag.com One of the popular AI research labs, OpenAI has been working tremendously in the domain of artificial intelligence, particularly on the grounds of neural networks, reinforcement learning, among others. Just a few days back, the AI lab introduced Microscope for AI enthusiasts who are interested in exploring how neural network work. And now the audio team of OpenAI Read More

Read More

Source: analyticsindiamag.com As most workers in India make a harried transition to remote working amid Covid-19 lockdown, they are still getting acclimated to the modus operandi of freelancers. With only a handful of tools in their arsenal, freelance professionals master those tools, as a rule, to compete with full-time professionals as well as other gig Read More

Read More

Source: Along with unsupervised machine learning and supervised learning, another common form of AI creation is reinforcement learning. Beyond regular reinforcement learning, deep reinforcement learning can lead to astonishingly impressive results, thanks to the fact that it combines the best aspects of both deep learning and reinforcement learning. Let’s take a look at precisely how deep reinforcement learning operates. Note that this article won’t Read More

Read More

Source: devclass.com Google’s AI teams used the comparatively quiet post-easter days to get ML practitioners up to speed with their latest research in reinforcement learning, natural language processing, and computer vision. In “An optimistic perspective on offline reinforcement learning”, a team of researchers has looked into ways to use a fixed offline dataset of logged Read More

Read More

Source: theburnin.com Google is in the process of developing its own Pixel and Chromebook processors, reports Axios. The search engine has teamed with Samsung on a project called Whitechapel to make chipsets for its smartphones. The tech giant intends to release a handset powered by its new mobile CPU as soon as 2021, but its redesigned laptops won’t Read More

Read More

Source: asianscientist.com In a study published in IEEE Robotics and Automation Letters, researchers have shown that walking robots spontaneously developed coordinated limb control when trained using deep learning. Human motor control can execute complex movements naturally, efficiently and without much thought involved. This is because of motor synergy in the central nervous system (CNS), which Read More

Read More

Source: ibtimes.sg Artificial intelligence (AI) seems to be taking over the world and is even helping us combat the ongoing coronavirus pandemic, but so far it has been a product of human supervision – we teach computers to see patterns, just like we teach children to read. However, researchers believe the future of AI depends on systems Read More

Read More

Source: chess.com Making an appearance in Lex Fridman’s Artificial Intelligence Podcast, DeepMind’s David Silver gave lots of insights into the history of AlphaGo and AlphaZero and deep reinforcement learning in general. Today, the finals of the Chess.com Computer Chess Championship (CCC) start between Stockfish and Lc0 (Leela Chess Zero). It’s a clash between a conventional chess engine that implements an advanced Read More

Read More

Source: unite.ai Creating an Artificial General Intelligence (AGI) is the ultimate endpoint for many AI specialists.  An AGI agent could be leveraged to tackle a myriad of the world’s problems. For instance, you could introduce a problem to an AGI agent and the AGI could use deep reinforcement learning combined with its newly introduced emergent consciousness to Read More

Read More

Source: infoq.com Uber andOpenAI have open-sourced Fiber, a new library which aims to empower users in implementing large-scale machine learning computation on computer clusters. The main objectives of the library are to leverage heterogeneous computing hardware, dynamically scale algorithms, and reduce the burden on engineers implementing complex algorithms on clusters. It’s a challenge for machine learning Read More

Read More

Source:inverse.com You might think you’re good at Atari games, but a new artificial intelligence system from Alphabet subsidiary DeepMind called Agent57 is probably better. The company claims its A.I. can beat the average person on all 57 Atari 2600 games. Agent57 uses a type of machine learning called deep reinforcement learning to learn from its Read More

Read More

Source: middleeastheadlines.com Often it feels like every internet site, application, or maybe efficiency device is mentioning Artificial Intelligence 101 (AI) as the top-secret component for their financial success. What’s even less frequent is a breakdown of what Artificial Intelligence is, the reason that it’s so great, and even the way service providers are taking advantage Read More

Read More

Source: venturebeat.com In a preprint paper published this week by DeepMind, Google parent company Alphabet’s U.K.-based research division, a team of scientists describe Agent57, which they say is the first system that outperforms humans on all 57 Atari games in the Arcade Learning Environment data set. Assuming the claim holds water, Agent57 could lay the groundwork Read More

Read More

Source: engineering.com A new simulation system allows driverless vehicles to be trained in an environment with infinite steering possibilities. While autonomous vehicles typically rely on datasets from real-world human drivers, simulated testing opens up opportunities for cars to encounter and navigate through a variety of worst-case scenarios before they’re even released out on the streets. Read More

Read More

Source: somagnews.com Artificial intelligence is used in many areas from games to smart phones today. That’s why developers are more focused on new AI designs. In fact, Google is now used to make artificial intelligence algorithms for artificial intelligence algorithms. The artificial intelligence silicon design handled by Google is defined as a subset of the Read More

Read More

Source: itprotoday.com When it comes to machine learning, supervised learning has long been the superstar. But recent advancements and emerging enterprise applications are putting new attention on reinforcement learning. In an analysis of more than 16,000 artificial intelligence (AI) research papers undertaken by MIT’s Technology Review, reinforcement learning emerged as one of the leading trends in the past Read More

Read More

Source: technologynetworks.com A simulation system invented at MIT to train driverless cars creates a photorealistic world with infinite steering possibilities, helping the cars learn to navigate a host of worse-case scenarios before cruising down real streets. Control systems, or “controllers,” for autonomous vehicles largely rely on real-world datasets of driving trajectories from human drivers. From Read More

Read More
Artificial Intelligence