In classification problems (i.e. when the labels Y are discrete), the scaling of the test error with respect to n is determined by characterizations of the VC-dimension [2]/Rademacher complexity [3] of the function class, which in the worst case increases with its number of parameters. In regression (i.e. when the labels Y are continuous), the mean-squared error of the ordinary least-squares estimate is characterized by the condition number of the regression matrix, which is reasonable for appropriate ratios of d/n but tends to increase astronomically as d approaches n. The qualitative fear is the same: if the function class is too complex, it starts to overfit noise and can generalize poorly to unseen test data. But there is a gap between "can" and "will" -- and indeed this conventional wisdom has been challenged by the recent advent of deeper and deeper neural networks. In particular, a thought-provoking paper [4] noted that several deep neural networks generalize well despite achieving zero or close to zero training error, and being so expressive that they even have the ability to fit pure noise. As they put it, "understanding deep learning requires rethinking generalization". How can we reconcile the fact that good interpolative solutions exist with the classical bias-variance tradeoff? These phenomena are being actively investigated in a statistical sense [5,6] and a computational sense [7-9] in classification problems and/or noiseless models.

A new host of liquid metals that have applications towards soft robotics are making movies like'The Terminator' transcend make-believe. According to researchers, experimental liquid metals like gallium and other alloys, when supplemented with nickel or iron, are able to flex and mold into shapes with the use of magnets, much like the iconic movie villain, T-1000 from'The Terminator 2: Judgement Day.' While other such metals have been developed, they contended with two major drawbacks. A new host of liquid metals that have applications towards soft robotics are making movies like'The Terminator' transcend make-believe toward real life. Researchers say experimental liquid metals like gallium and other alloys, when supplemented with nickel or iron, are able to flex and mold into shapes with the use of magnets. A new material revealed by the American Chemical Society solves to major problems experienced by similar substances.

In just 30 years' time, it is forecasted that the human population of our planet will be close to 10 billion. Producing enough food to feed these hungry mouths will be a challenge, and demographic trends such as urbanization, particularly in developing countries, will only add to that. To meet that challenge, agricultural businesses are pinning their hopes on technology, and that idea that increasingly sophisticated data and analytics tools will help to drive efficiencies and cut waste in agriculture and food production. Leading the way is John Deere – the 180-year-old manufacturer of farming and industrial machinery which has spent the past decade transforming itself into an artificial intelligence (AI) and data-driven business. I have covered John Deere before here.

Machine learning embodies the principles of data mining, but can also make automatic correlations and learn from them to apply to new algorithms. It's the technology behind self-driving cars that can quickly adjust to new conditions while driving. Machine learning also provides instant recommendations when a buyer purchases a product from Amazon. These algorithms and analytics are constantly meant to be improving, so the result will only get more accurate over time. Machine learning isn't artificial intelligence, but the ability to learn and improve is still an impressive feat.

HADRIAN X, a robot developed by Australian company FBR (formerly known as Fastbrick Robotics), has successfully completed its first full-scale test by building a 180 square metre house with three bedrooms and two bathrooms. Initially, Hadrian X was made to pass Factory Acceptance Testing (FAT), which focused on its ability to work with bricks of different sizes and cuts, building from a CAD model and building tall or "from slab to cap". Above: Hadrian X laying blocks to complete Factory Acceptance Testing (image courtesy of FBR). After completing the trial, Hadrian X completed its first full home structure in less than three days. The structure was verified by independent civil and structural engineers as having met the relevant building standards.

Toronto, Ontario--(Newsfile Corp. - March 11, 2019) - Deveron UAS Corp. ("Deveron" or the "Company") and Deveron's wholly owned data analytics subsidiary, Veritas Farm Management ()Veritas") have been awarded an AI for Earth grant from Microsoft to help further our efforts in artificial intelligence ()AI") and making recommendations and predictions using agricultural data. This new grant will provide Deveron with Microsoft Azure computing resources and AI tools to accelerate our work on utilizing in-season imagery and AI to apply nitrogen fertilizer to corn. Deveron will help growers more fully utilize the nitrogen credit produced when cover crops are introduced into crop rotation. Additional nitrogen can then be applied as needed using variable rate applications around these credits, insuring that the nitrogen needs of the crop is met in an efficient way across the field. "We are excited to be chosen by Microsoft to participate in this transformational opportunity" reported David Macmillan, President and CEO of Deveron.

In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules - in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly reducing resources spent downstream synthesizing and characterizing bad leads in the lab. In this review we survey the increasingly complex landscape of models and representation schemes that have been proposed. The four classes of techniques we describe are recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning. After first discussing some of the mathematical fundamentals of each technique, we draw high level connections and comparisons with other techniques and expose the pros and cons of each. Several important high level themes emerge as a result of this work, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training.

It wasn't that long ago that talking to computers was the preserve of movies and science fiction. Slowly, voice recognition improved, and these days it's getting to be pretty usable. The technology has moved beyond basic keywords, and can now parse sentences in natural language. The device is built around Google's AIY Voice Kit, which consists of a Raspberry Pi with some additional hardware and software to enable it to process voice queries. This allows WhatIsThat to respond to users asking questions by taking a photo, and then identifying what it sees in the frame.

Adaptive behaviors ranging from self-assembly to self-healing showcase the ability of such systems to sense and adapt to dynamic environments based on signaling between living cells. This signaling takes on many forms--biochemical, mechanical, and electrical--and uncovering it has become as much the purview of regenerative medicine as of fundamental biology. We cannot reverse-engineer native tissues if we do not understand the fundamental design rules and principles that govern their assembly from the bottom up (1). Movement is fundamental to many living systems and driven primarily by skeletal muscle in human bodies. Disease or damage that limits the functionality of skeletal muscle severely affects human health, mobility, and quality of life.

There's a good chance your smartphone contains tainted cobalt. The metal is a crucial ingredient in most of the lithium-ion batteries that power our devices, and 70% of it is mined in war-torn Democratic Republic of Congo (DRC), where children are often deployed to work in toxic environments. Though global brands like Apple and Samsung are keen to clean up their supply chain, DRC's dominance of the cobalt market makes the task difficult. These brands are also pressured by growing demand for cobalt, which Citigroup estimates will outstrip supply by 2023. That's because lithium-ion batteries also power electric cars, and every car battery needs as much as 1,000 times the amount of cobalt of a smartphone battery.