Is there a global theory for the shapes of fish? But for most of the history of biology, it's not the kind of thing anyone would ever have asked. Stephen Wolfram is the creator of Mathematica, Wolfram Alpha and the Wolfram Language; the author of A New Kind of Science; and the founder and CEO of Wolfram Research. Sign up to get Backchannel's weekly newsletter, and follow us on Facebook, Twitter, and Instagram. And it's now 100 years since D'Arcy Thompson published the first edition of his magnum opus On Growth and Form--and tried to use ideas from mathematics and physics to discuss global questions of biological growth and form. Stretch one kind of fish, and it looks like another. Yes, without constraints on how you stretch. It's not quite clear what this is telling one, and I don't think it's much. But just to ask the question is interesting, and On Growth and Form is full of interesting questions--together with all manner of curious and interesting answers. D'Arcy Thompson was in many ways a quintessential British Victorian academic, steeped in the classics, and writing books with titles like A Glossary of Greek Fishes (i.e. But he was also a diligent natural scientist, and he became a serious enthusiast of mathematics and physics. And where Aristotle (whom Thompson had translated) used plain language, with perhaps a dash of logic, to try to describe the natural world, Thompson tried to use the language of mathematics and physics.

Robots filling orders for Amazon AMZN, 0.99% and driverless vehicles hardly spell the end of work, but the artificial intelligence driving the current wave of automation -- if Americans don't embrace it effectively -- could catapult China ahead of the United States once and for all. Those of us who were around in the 1960s remember elevator operators and bowling alley pin-setters losing their jobs. Alarmists warn that artificial intelligence now is beginning to enable machines to replace not just unskilled workers but knowledge workers too -- for example, insurance adjusters. They worry society will divide between those owning the intellectual property and indolent masses who will depend on government handouts. More compelling is the example of mid-20th century office workers.

I may have mentioned it before, but schools are big business. In the U.S. alone, some 50 million school-aged boys and girls are not only gracing our schools with wide-eyed wonder, they are also helping to drive our economy in an industry that spends a cool $700 billion on everything from piping hot breakfasts to busses to software. In fact, once you get past all the basics like salaries and utilities that eat about 80 percent of the pie, and you'll find that software represents a huge slice of what's left. Little Johnny and little Suzy are forking over between $100 and $250 per kiddo on software and technology purchases. The ability for districts to efficiently buy and manage Suzy and Johnny's massive appetite for software can drastically effect the school district's capacity to properly educate each child.

Bill Gates proclaimed in a recent graduation ceremony, that artificial intelligence (AI), energy, and bio science are three most exciting and rewarding career choices today's young college graduates can choose from. I have come to believe strongly that some of the most important questions of our generation - related to sustainability, energy generation and distribution, transportation, access to basic amenities of life etc., are dependent on how intelligently we can mix the the first two branches of knowledge Mr. Gates mentions. I am a semiconductor professional with 8 years of post-PhD experience in a top technology company. I take pride in the fact that I work in the cross-section of physical electronics which directly contributes to the energy sector. I develop power semiconductor devices.

This is made possible by incredible advances in a wide range of technologies, from computation to interconnect to storage, and innovations in software libraries, frameworks, and resource management tools. While there are many critical challenges, an open technology approach provides significant advantages. The Scaling Challenge The full deep learning story, though, must be an end-to-end technology discussion and encompass production at scale. As we scale out deep learning workloads to the massive compute clusters required to tackle these big issues, we begin to run into the same challenges that hamper scaling of traditional high-performance computing (HPC) workloads. Ensuring optimal use of compute resources can be challenging, particularly in heterogeneous architectures that may include multiple central processing unit (CPU) architectures, such as x86, ARM64, and Power, as well as accelerators, such as graphical processing units (GPUs), field programmable gate arrays (FPGAs), tensor processing units (TPUs), etc. Architecting an optimal deep learning solution for training or inferencing, with potentially varied data types, can result in the application of one or more of these architectures and technologies. The flexibility of open technologies allows one to deploy the optimal platform at server, rack, and data center scales.

Due to the lack of enough generalization in the state-space, common methods in Reinforcement Learning (RL) suffer from slow learning speed especially in the early learning trials. This paper introduces a model-based method in discrete state-spaces for increasing learning speed in terms of required experience (but not required computational time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation (full-space). Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the full-space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called Model Based Learning with Subspaces (MoBLeS), calculates confidence intervals of the estimated Q-values in the full-space and in the subspaces. These confidence intervals are used in the decision making, such that the agent benefits the most from the possible generalization while avoiding from detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to the optimal policy is theoretically investigated. Additionally, we show through several experiments that MoBLeS improves the learning speed in the early trials.

The ocean is indeed a strange place, but Whitman might not have found it quite so confounding if he'd had access to deep learning. This technology is allowing machines to do everything from disease diagnosis to musical composition to playing video games. Now, a team of scientists and engineers at the IBM Research lab in Dublin have set deep learning on that harshest of mistresses: the sea. Their deep-learning framework for simulating ocean waves enables real-time wave condition forecasts for a fraction of the traditional computational cost. How are wave forecasts traditionally calculated?

A critical element of change leadership is "going slow to go fast", but sometimes leaders need a short, sharp focus to generate professional learning conversations or for individual reflection. Designed as "quick shots", "Leadables" are intended to be used to provoke dialogue and focussed conversations around a variety of Leading, Teaching and Learning elements. Themes will be drawn from examples we are seeing in schools and organizations, questions we are encountering and new ideas and research around deep learning. Click here to access Leadable 1.1 – Trusty Tools, which focuses on how we can use the NPDL Learning Progressions.

Diversity was a central theme in the ACM SIGIR 2017 held in Shinjuku Ward in Tokyo, Japan. Fuji, a view of Shinjuku sky-scrapers, including the Tokyo Metropolitan Government (Office), as seen from Keio Plaza the conference hotel, and fireworks celebrating the 40th anniversary. The colorfulness of the fireworks and the circles within and enclosing the logo represent diversity and inclusion." SIGIR 2017 featured a session on Women in IR (Information Retrieval) organized by Laura Dietz of the University of New Hampshire on the first day, just before the welcome party. A week before the conference, I received an email from the secretary of the session, Maram Hasanain, a graduate student in computer science (CS) at Qatar University, asking if I would like to prepare a one-minute introduction of myself for the session. I was so overwhelmed by her beautifully written e-mail, and the excitement of a first-time contact with someone from Qatar, that I immediately accepted her invitation.

The Intel Nervana AI Academy for Students program was created to work collaboratively with students at innovative schools and universities doing great work in the Machine Learning and Artificial Intelligence space. I had the opportunity to get to know Intel Student Ambassador Karandeep Singh Dhillon and learn about how he became interested in deep learning and how he wants to make it easy for anyone to understand and apply to real-life situations. Tell us about your background and what got you started in technology. My parents bought me a computer when I was in 6th grade and by the time I was in 8th grade would make small Bash programs to help me automate tasks. I loved to create those small programs and I decided that I wanted to learn more and would take Computer Science courses for my undergraduate degree.