Branding for the AI field doesn't need to use the same old science - learn how top studios have bucked the trend in marketing machine learning to the masses. "We didn't want the brand to feel cold or technocratic, and didn't want to rehash common visual tropes, like amorphous networks of dots and lines or weird Jude Law-ian robots." Ritik Dholakia is talking to Digital Arts about the common visuals associated with the branding of companies in the artificial intelligence and machine learning field. Managing partner and founder of New York's Studio Rodrigo, Ritik had a chance to buck the trend with a recent branding project for Spell, a cloud-based platform offering individuals and organisations access to the AI and deep learning capabilities usually reserved for big corporations. Working with Spell CEO Serkan Piantino, Ritik and team wanted to create a visual system that balanced technical and trustworthy qualities with approachability, all the while communicating the potential of machine learning to the uninitiated.
DeepCube, the award-winning deep learning pioneer, today announced that its software-based deep learning acceleration platform has been recognized as a winner in several recent, prominent AI awards programs. These awards celebrate the top AI innovations and leaders across the globe. DeepCube's inclusion validates the immense potential of its patented deep learning acceleration platform that dramatically improves performance, latency and usability of deep learning on intelligent edge devices and in data centers. "Enterprises across industries are enticed by the potential for AI to unlock business impact and efficiencies; however, real-world, edge and data center deployments of deep learning remain out of reach, due to the immense size, processing power and memory requirements of these models," said Dr. Eli David, Co-Founder and Chief Technology Officer, DeepCube. "It's a difficult technical challenge, but it's one we're committed to solving at DeepCube. In 2020, we've made significant strides – both for our business and for the industry as a whole."
DeepCube announced the launch of a new suite of products and services to help drive enterprise adoption of deep learning, at scale, on intelligent edge devices and in data centers. The offerings build on DeepCube's patented platform, which is the industry's first software-based deep learning accelerator that drastically improves performance on any existing hardware. Now, DeepCube will offer solutions for neural network training and inference, allowing users to leverage DeepCube's technology to address challenges in … More The post DeepCube's suite of products drives enterprise adoption of deep learning appeared first on Help Net Security. Become a supporter of IT Security News and help us remove the ads.
A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms combined with self-play have achieved superhuman proficiency in Go, Chess, and Shogi without human data or domain knowledge. In these environments, a reward is always received at the end of the game, however, for many combinatorial optimization environments, rewards are sparse and episodes are not guaranteed to terminate. We introduce Autodidactic Iteration: a novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik's Cube with no human assistance. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves -- less than or equal to solvers that employ human domain knowledge.
A self-taught artificial intelligence (AI) system called DeepCube has mastered solving the Rubik's Cube puzzle in just 44 hours without human intervention. The system's inventors have detailed their design in a paper titled'Solving the Rubik's Cube Without Human Knowledge'. "A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision," write the paper's authors. "Indeed, if we're ever going to achieve a general, human-like machine intelligence, we'll have to develop systems that can learn and then apply those learnings to real-world applications." While many AI systems have been taught to play games, mastering the complexity of a Rubik's Cube posed a unique set of challenges.