Military hierarchies are, by necessity, rigid structures. DARPA’s ‘Mosaic Warfare’ project aims for something much more fluid and adaptable, with AI doing the logistical grunt work so human commanders can get creative.
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We have over a decade of experience in developing industrial-strength custom AI solutions. We specialize in Machine Learning & Natural Language Processing (NLP) solutions...
Have you ever wondered how will the machine learning frameworks of the '20s look like? In this essay, I examine the directions AI research might take and the requirements they impose on the tools at our disposal, concluding with an overview of what I believe to be the two strong candidates: `JAX` and `S4TF`.
Ever since graduation, people have been asking me: “What’s now?” My answer has been an unequivocal: “I don’t know.” I used to think that by the time I finish...
allainews.com aggregates all of the top news in the field of AI, Machine Learning, Deep Learning, Computer Vision, NLP and Big Data into one place.
The goal is to provide a quick and clean overview of the global news landscape regarding all things Artificial Intelligence.
I’ve been engrossed in a few recent academic pre-prints which are skeptical of AI. These are not just from a business / hype-train perspective, but digging deeper into how machine learning research…
This post discusses the benefits of full-stack data science generalists over narrow functional specialists. The later will help you execute and bring process...
A group of experienced lawyers pitted their skills and knowledge against an algorithm, and the results reveal much about how artificial intelligence is shaking up industries.
The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and
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A Key Challenge In Complex Visuomotor Control Is Learning Abstract Representations That Are Effective For Specifying Goals, Planning, And Generalization. To This End, We Introduce Universal Planning Networks (upn). Upns Embed Differentiable Planning Within A Goal-directed Policy. This Planning Computation Unrolls A Forward Model In A Latent Space And Infers An Optimal Action Plan Through Gradient Descent Trajectory Optimization. The Plan-by-gradient-descent Process And Its Underlying Representations Are Learned End-to-end To Directly Optimize A Supervised Imitation Learning Objective. We Find That The Representations Learned Are Not Only Effective For Goal-directed Visual Imitation Via Gradient-based Trajectory Optimization, But Can Also Provide A Metric For Specifying Goals Using Images. The Learned Representations Can Be Leveraged To Specify Distance-based Rewards To Reach New Target States For Model-free Reinforcement Learning, Resulting In Substantially More Effective Learning When Solving New Tasks Described Via Image-based Goals. We Were Able To Achieve Successful Transfer Of Visuomotor Planning Strategies Across Robots With Significantly Different Morphologies And Actuation Capabilities.
Next time you’re at King’s Cross station, take a moment to think about this. Just yards from where you’re standing, the world’s most advanced artificial intelligence (AI) technology is being developed — by a London company called DeepMind.
MIT and SenseTime today announced that SenseTime, a leading artificial intelligence (AI) company, is joining MIT's efforts to define the next frontier of human and machine intelligence.
Just about every AI advance you’ve heard of depends on a breakthrough that’s three decades old. Keeping up the pace of progress will require confronting AI’s serious limitations.
S. Albrecht, and P. Stone. (2017)cite arxiv:1709.08071Comment: 42 pages, submitted for review to Artificial Intelligence Journal. Keywords: multiagent systems, agent modelling, opponent modelling, survey, open problems.