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Data preprocessing is a fundamental part of any machine learning project and often more time is spent on the data preparation than on the actual machine learning. While some preprocessing tasks are problem specific many others such as partitioning data into training and test folds, stratifying samples or building mini-batches are generic. The following Canonical Pipeline shows the processing steps common for deep-learning in vision. A Reader reads sample data stored in text files, Excel or Pandas tables. The Splitter then partitions data into training, validation and test folds and performs stratification if needed.

The OLCF's recently deployed DGX-1 artificial intelligence supercomputer by NVIDIA, featuring eight NVIDIA Tesla GPUs and NVLink technology, will offer scientists and researchers new opportunities to delve into deep learning technologies. The Oak Ridge Leadership Computing Facility (OLCF) recently deployed a new NVIDIA DGX‑1 artificial intelligence supercomputer to offer scientists and researchers opportunities to delve into deep learning technologies with more vigor than ever before. Deep learning uses neural networks to classify data or predict outcomes by training models on large data sets and by abstracting high-level features or patterns from lower level data. The OLCF is a DOE Office of Science User Facility located at ORNL. Scientists and researchers at the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) are using deep learning because of its potential to leverage big data analytics to automate and accelerate the scientific discovery process.

While AI designers are creating powerful machines that can beat humans at many complex cognitive endeavors, they're still envious of the human brain's facility with certain seemingly simple tasks: such as recognizing a face after seeing it only once or when it's partially obscured. The U.S. intelligence agency IARPA is particularly interested in developing AI deep learning programs with visual recognition skills, so last year it launched a US $100 million program called Microns. Under Microns, three teams of researchers are looking for answers on the micro scale and in rodent brains. Each team is studying 1 cubic millimeter of brain tissue from a rodent's visual cortex, using precision instruments to map the 50,000 neurons and 500 million neural connections within that chunk. The researchers hope to discover patterns of neural activation that can be translated to architectures for AI programs known as deep neural networks.

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Doxel is a startup that came out of stealth this week with a US $4.5 million funding round. Their business is making construction cheaper, and their secret (as with so many startups now) is combining massive amounts of data with deep-learning techniques. Using lidar-equipped robots, Doxel scans construction sites every day to monitor how things are progressing, tracking what gets installed and whether it's the right thing at the right time in the right place. You'd think that construction sites would be doing this by themselves anyway, but it turns out that they really don't, and in a recent pilot study on a medical office building, Doxel says it managed to increase labor productivity on the project by a staggering 38 percent. The concept behind Doxel is straightforward enough: Construction projects have plans and budgets and timelines.

In March 2016, Deepmind's AlphaGo beat 18 times world champion Go player Lee Sedol 4–1 in a series watched by over 200 million people. A machine had learnt a super-human strategy for playing Go, a feat previously thought impossible, or at the very least, at least a decade away from being accomplished. This in itself, was a remarkable achievement. However, on 18th October 2017, DeepMind took a giant leap further. The paper'Mastering the Game of Go without Human Knowledge' unveiled a new variant of the algorithm, AlphaGo Zero, that had defeated AlphaGo 100–0.

Theoretical physics now firmly resides within an Age wherein new physics, new mathematics and new data coexist in a symbiosis which transcends interdisciplinary boundaries and wherein concepts and developments in one field are evermore rapidly enriching another. String theory has spearheaded this vision for the past few decades and has, perhaps consequently, become a paragon of the theoretical sciences. That she engenders the cross-fertilization between physics and mathematics is without dispute: interactions on an unprecedented scale have commingled fields as diverse as quantum field theory, general relativity, condensed matter physics, algebraic and differential geometry, number theory, representation theory, category theory, etc. With the advent of increasingly powerful computers, from this fruitful dialogue has also arisen a plethora of data, ripe for mathematical experimentation. This emergence of data in some sense began with the incipience of string phenomenology [1] where compactification of the heterotic string on Calabi-Yau threefolds (CY3) was widely believed to hold the ultimate geometric unification.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. ANYmal was at the World Economic Forum in Davos, where it got cold feet. Robot arm maintenance in space is much more difficult than robot arm maintenance on Earth, but you get quite the view.

Elon Musk has put a lot of thought into the harsh realities and wild possibilities of artificial intelligence (AI). These considerations have left him convinced that we need to merge with machines if we're to survive, and he's even created a startup dedicated to developing the brain-computer interface (BCI) technology needed to make that happen. But despite the fact that his very own lab, OpenAI, has created an AI capable of teaching itself, Musk recently said that efforts to make AI safe only have "a five to 10 percent chance of success." Musk shared these less-than-stellar odds with the staff at Neuralink, the aforementioned BCI startup, according to recent Rolling Stone article. Despite Musk's heavy involvement in the advancement of AI, he's openly acknowledged that the technology brings with it not only the potential for, but the promise of serious problems.