Phase 2: Experiment: We help customers test the waters with A.I. by applying pre-determined experiments to a companys unique problems both within our Centers of Excellence, designed to assist IT departments and data scientists who are looking to accelerate their deep learning applications and realize better ROI from their deep learning deployments in the near term, and eventually within a company. With HPE Pointnext, scaling deep learning and A.I. is as easy as turning the dial for more capacity, but without losing control of the underlying architecture. We offer customers a flexible consumption services for HPE infrastructure, which avoids over-provisioning, increases cost savings and scales up and down as needed to accommodate the needs of deep learning deployments.

The healthcare industry has always been a leader in innovation. The constant mutating of diseases and viruses makes it difficult to stay ahead of the curve, but with the help of artificial intelligence and machine learning algorithms, it continues to advance, creating new treatments and helping people live longer and healthier lives. A study published this week by The Lancet Digital Health compared the performance of deep learning--a form of artificial intelligence (AI)--in detecting diseases from medical imaging versus that of healthcare professionals, using a sample of studies carried out between 2012 and 2019. The study found that, in the past few years, AI has become more accurate of identifying disease diagnosis in these images and has become a more viable source of diagnostic information. According to the researchers, out of 14 studies that compared deep learning models and healthcare professionals within the same sample, the diagnostic performances were found to be equivalent.

For newbies, starting to learn machine learning can be painful if they don't have right resources to learn from. Most of the machine learning libraries are difficult to understand and learning curve can be a bit frustrating. I am creating a repository on Github(cheatsheets-ai) containing cheatsheets for different machine learning frameworks, gathered from different sources. Do visit the Github repository, also, contribute cheat sheets if you have any.

This blog post was co-authored by Parashar Shah, Senior Program Manager, Applied AI Developer COGS. When data scientists work on building a machine learning model, their experimentation often produces lots of metadata: metrics of models you tested, actual model files, as well as artifacts such as plots or log files. They often try different models and parameters, for example random forests of varying depth, linear models with different regularization rates, or deep learning models with different architectures trained using different learning rates. With all the bookkeeping involved, it is easy to miss a test case, or waste time by repeating an experiment unnecessarily. After they finalize the model that they want to use for predictions, they have to do multiple things in order to create a deployment environment and then create a webservice (http endpoint) from their model.

Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32 pretrained models in 100 languages and deep interoperability between TensorFlow 2.0 and PyTorch. Choose the right framework for every part of a model's lifetime This repo is tested on Python 2.7 and 3.5 (examples are tested only on python 3.5), PyTorch 1.0.0 and TensorFlow 2.0.0-rc1 First you need to install one of, or both, TensorFlow 2.0 and PyTorch. Please refere to TensorFlow installation page and/or PyTorch installation page regarding the specific install command for your platform. When TensorFlow 2.0 and/or PyTorch has been installed, Transformers can be installed using pip as follows: Here also, you first need to install one of, or both, TensorFlow 2.0 and PyTorch.

AWS pre-trained AI Services provide ready-made intelligence for your applications and workflows. AI Services easily integrate with your applications to address common use cases such as personalized recommendations, modernizing your contact center, improving safety and security, and increasing customer engagement. Because we use the same deep learning technology that powers Amazon.com and our ML Services, you get quality and accuracy from continuously-learning APIs. And best of all, AI Services on AWS don't require machine learning experience.

To power AI applications and research across engineering, science, and medicine, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory Supercomputing Center has just installed a new GPU-accelerated supercomputer, powered by 896 NVIDIA Tensor Core V100 GPUs. According to MIT, the new system named TX-GAIA for Green AI Accelerator was ranked by TOP500 as the most powerful AI supercomputer at any university in the world. "We are thrilled by the opportunity to enable researchers across Lincoln and MIT to achieve incredible scientific and engineering breakthroughs," said Jeremy Kepner, a Lincoln Laboratory Fellow who heads the Lincoln Laboratory Supercomputing Center. "TX-GAIA will play a large role in supporting AI, physical simulation, and data analysis across all Laboratory missions," he added. The new supercomputer has a peak performance of 100 AI petaFLOPs, as measured by the computing speed required to perform mixed-precision floating-point operations commonly used in building deep neural networks.

Researchers from Google AI (formerly Google Research) and Toyota Technological Institute of Chicago have created ALBERT, an AI model that achieves state-of-the-art results that exceed human performance. ALBERT now claims first place on major NLP performance leaderboards for benchmarks like GLUE and SQuAD 2.0, and high RACE performance score. On the Stanford Question Answering Dataset benchmark (SQUAD), ALBERT achieves a score of 92.2, on General Language Understanding Evaluation (GLUE) benchmark, ALBERT achieves a score of 89.4, and on ReAding Comprehension from English Examinations (RACE) benchmark, ALBERT gets a score of 89.4%. ALBERT is a version of Transformer-based BERT that "uses parameter reduction techniques to lower memory consumption and increase the training speed of BERT," according to a paper published on OpenReview.net The paper was published alongside other papers being considered for publication as part of the International Conference of Learning Representations, which will take place in April 2020 in Addis Ababa, Ethiopia.

Modern artificial intelligence lacks a strong theoretical basis, and so it's often a shrug of the shoulders why it works at all (or, oftentimes, doesn't entirely work). One of the deepest mysteries of deep learning is one of its most brilliant successes, what's known as stochastic gradient descent. Stochasticity, the process of randomly picking out examples of data, has yielded breakthroughs in image recognition and other deep learning tasks. And now, one computer chip company thinks they may have a kind of machine for stochasticity, a chip whose power comes from randomness. It might not lead to a theory of why machine learning works, but it might lead to knew breakthroughs in what stochasticity can achieve.

At the heart of artificial intelligence is the idea that one day we'll be able to build a machine that's as smart as a human. That type of AI is called Artificial General Intelligence or AGI. The most prominent AI experts today think (including DeepMind CEO Demis Hassabis, Google AI Chief Jeff Dean, and Stanford AI director Fei-Fei Li) this would take quite a few years before it is built. The years ranged from 2029 to 2200, with the average estimate being 2099 -- 81 years from now.