At the time of writing these lines running PyTorch code on TPUs is not a well-trodden path. Naturally, TPUs have been optimized for and mainly used with TensorFlow. But Kaggle and Google distribute free TPU time on some of its competitions, and one doesn't simply change his favorite framework, so this is a memo on my (mostly successful) experience of training PyTorch models with TPU on GCP. PyTorch/XLA is the project that allows doing it. It is still in active development, issues get fixed.

Nvidia's GPU-powered platform for developing and running conversational AI that understands and responds to natural language requests has achieved some key milestones and broken some records that have big implications for anyone building on their tech -- which includes companies large and small, as much of the code they've used to achieve these advancements is open source, written in PyTorch and easy to run. The biggest achievements Nvidia announced today include its breaking the hour mark in training BERT, one of the world's most advanced AI language models and a state-of-the-art model widely considered a good standard for natural language processing. Nvidia's AI platform was able to train the model in less than an hour, a record-breaking achievement at just 53 minutes, and the trained model could then successfully infer (i.e. Nvidia's breakthroughs aren't just cause for bragging rights -- these advances scale and provide real-world benefits for anyone working with their NLP conversational AI and GPU hardware. Nvidia achieved its record-setting times for training on one of its SuperPOD systems, which is made up of 92 Nvidia DGX-2H systems runnings 1,472 V100 GPUs, and managed the inference on Nvidia T4 GPUs running Nvidia TensorRT -- which beat the performance of even highly optimized CPUs by many orders of magnitude.

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Thanks for the article... which doesn't tell us why (and begs the question). At least it wasn't a mile long like some celebrity deaths which go on and on but never mention the cause of death (which everyone is fucking wondering about). Don't bother writing articles like this until you actually have something worth writing about.

When humans and machines collaborate, we can produce things neither would create on their own. The intersection of art and AI is an area that I find really exciting, but with all the business impact AI can have, I personally feel it doesn't always get enough attention. In this spirit, I recently set out on a personal quest to learn more about PyTorch, the machine learning library that's been creating a lot of buzz since its 1.0 release late last year, and I was pleasantly surprised by what I found. For me, PyTorch turned out to be more than just an interesting alternative to TensorFlow, with dynamic graphs and an imperative coding style. One of the examples from their official docs inspired me to track down some academic papers and take a closer look at the inner-workings of a deep learning technique that's fascinated me for quite some time -- 'Neural Style Transfer'.

After a year of development, Huawei today announced the commercial availability of the Huawei Ascend 910 AI computing chip and the machine learning framework MindSpore. Huawei says the Ascend 910 is the world's fastest AI processor, packing twice the performance of rival Nvidia's Tesla v100. Billed as the single chip with the greatest computing density, Ascend 910 delivers performance of up to 256 teraFLOPS under FP16 and 512 teraOPS under IN8 with declared max power consumption of 310W. In comparison, the GPU Tesla V100 delivers up to 125 teraFLOPS with a max power consumption of 300W, while Google's TPU 2.0 with four ASICs can reach 180 teraFLOPS. Ascend 910 is designed for deep learning training, and targets users such data scientists, researchers and engineers.

They also demand increased flexibility with hardware that allows them to program with mainstream languages at a higher abstraction level along with libraries. The data science community is looking for a complete solution stack that abstracts away the hardware specifics, allowing them the ease to crunch parallel workloads more efficiently.

"We are in the age of Artificial Intelligence, Internet of Things, Machine Learning, Deep Learning and Robotics," he said.

This talk proposes that the future of artificial intelligence is smart networks that have intelligence "baked in" in the form of Blockchain Distributed Ledgers for confirming authenticity and transferring value, and Deep Learning Algorithms for predictive identification. Smart networks are not a far-off possibility but already needed as deep learning systems are going online in connected apps for Autonomous Driving and Drone Delivery, and Human-Robot Interaction. Two high-impact contemporary emerging technologies for the future of AI are Blockchain Distributed Ledgers and Deep Learning Algorithms, and discusses their implications for the future of artificial intelligence.

SAN FRANCISCO – Applications of Artificial Intelligence, machine learning, and deep learning are relatively useless without a lucid understanding of how the outputs of their predictive models are derived. Explainable AI hinges on explainability--a clear verbalizing of how the various weights and measures of machine learning models generate their outputs. Those explanations, in turn, are determined by interpretability: the statistical or mathematical understanding of the numerical outputs of decisions made by predictive models. Interpretability is foundational to unraveling some of the more consistent issues plaguing AI today. Facilitating interpretability--and using it as the impetus for refining machine learning models and the data on which they're trained--is indispensable for overcoming the threat of biased models once and for all.