The most promising 150 private digital health startups working to transform the healthcare industry with new models of primary care to emerging tech solutions for providers. CB Insights' first ever annual cohort of Digital Health 150 startups is a list of 150 of the most promising private companies creating innovative products and services in the $5T healthcare industry, according to CB Insights' Industry Analyst Consensus. Our research team selected the 150 startups from a pool of 5K companies based on several factors, including patent activity, investor profile, news sentiment analysis, proprietary Mosaic scores, market potential, partnerships, competitive landscape, team strength, and tech novelty. For the purposes of this report, digital health is defined as companies in the healthcare space that use technology/software as a key differentiator from their competition. This includes everything from disease diagnostics to tech-driven health insurance platforms to AI tools for drug discovery, and more.

"The JAIC is working to bring critical AI detection technology to the first responders who bravely battle wildfires. Increased use of AI will reduce response timelines, increase situational awareness, and save more American lives." Last year's California wildfire season was the deadliest and most destructive in United States history. More than 8,500 fires burned across nearly 1.9 million acres in the state of California and resulted in more than $16.5 billion in damage. Cumulatively, the wildfires were the costliest natural disaster of 2018, as well as one of the deadliest.

Generative Adversarial Networks (GAN) has changed the way we observe deep learning field. Up until that point, generative algorithms were a one-side ally, and the engineers were focused more on regression and classification tasks. Different approaches and applications were used for generating data. However, Ian Goodfellow presented GAN back in 2014 and shook up the entire field. Little did he knew the idea that he got while drinking with his friends would make him famous (well, science famous, not Rihanna famous).

The need to use wavefunction or density functional theory (DFT) calculations to determine electron densities has been bypassed by a machine learning model. It will allow chemists to quickly determine properties that depend on the electron density of large systems such as van der Waals forces, halogen bonding and C-H–π interactions. These non-covalent interactions can hold insight into the binding of host–guest systems or favoured enantiomers within reaction pathways where intermediates and transition states may be stabilised by subtle attractions. The electron density distribution is one of the most powerful tools at the disposal of a computational chemist. From the electron density, properties such as charges, dipoles and electrostatic interaction energies can be determined.

Warangal: The students of SR Engineering College (SREC) have put up a fine performance at the National level Artificial Intelligence Hackathon organised by the TATA Motors at Symbiosis Institute of Technology (SIT) in Pune. The two-member team of Krishna Teja Jillelamudi and Rahul Bayya, both studying third year CSE- led by mentor Dr D Kothandaraman (Team CAIDL) won the second runner-up prize. The team worked for 72 uninterrupted hours on their solution, which is based on Deep Learning and Natural Language Processing (Technologies of AI). The team was awarded with a cash prize of Rs 50,000, besides gaining attention from industry & academia circles. The round 02 is a 72-hour AI Hackathon which started at 10 am on September 27 and concluded at 10 am on September 30.

A few weeks ago, I told you I'd let you know more about the behind-the-scenes action and the technical writers who are going to be working with NumPy and SciPy during Google Season of Docs. It's time to meet Maja! Maja has done some knockout research, which you can find here. She has not only had significant experience with SciPy, but she's well aware of what a difference great documentation and guides can make. Because it's so easy for technical writers to get lost in the background of a project, I wanted to take this space to let you know what she's working on in her own words. If you aren't familiar with what we're doing with NumPy and SciPy through Google Season of Docs, you can read all about it here: While I'm building a new beginner-oriented technical documentation section with NumPy, Maja is working with SciPy to restructure its existing documentation.

The convergence of AI and HPC has created a fertile venue that is ripe for imaginative researchers -- versed in AI technology -- to make a big impact in a variety of scientific fields. From new hardware to new computational approaches, the true impact of deep- and machine learning on HPC is, in a word, "everywhere". Just as technology changes in the personal computer market brought about a revolution in the design and implementation of the systems and algorithms used in high performance computing (HPC), so are recent technology changes in machine learning bringing about an AI revolution in the HPC community. Expect new HPC analytic techniques including the use of GANs (Generative Adversarial Networks) in physics-based modeling and simulation, as well as reduced precision math libraries such as NLAFET and HiCMA to revolutionise many fields of research. Other benefits of the convergence of AI and HPC include the physical instantiation of data flow architectures in FPGAs and ASICs, plus the development of powerful data analytic services.

An artificially intelligent computer program can now diagnose skin cancer more accurately than a board-certified dermatologist.1 Better yet, the program can do it faster and more efficiently, requiring a training data set rather than a decade of expensive and labor-intensive medical education. While it might appear that it is only a matter of time before physicians are rendered obsolete by this type of technology, a closer look at the role this technology can play in the delivery of health care is warranted to appreciate its current strengths, limitations, and ethical complexities. Artificial intelligence (AI), which includes the fields of machine learning, natural language processing, and robotics, can be applied to almost any field in medicine,2 and its potential contributions to biomedical research, medical education, and delivery of health care seem limitless. With its robust ability to integrate and learn from large sets of clinical data, AI can serve roles in diagnosis,3 clinical decision making,4 and personalized medicine.5 For example, AI-based diagnostic algorithms applied to mammograms are assisting in the detection of breast cancer, serving as a "second opinion" for radiologists.6

Japan's Hayabusa2 on Thursday released a robotic explorer bound for the surface of an asteroid in the probe's final task before returning to earth, the nation's space agency said. The Minerva-II2, a small rover attached to Hayabusa2, began its descent toward the surface of the Ryugu asteroid at around 1 a.m. Its primary task will be to research the asteroid's gravity. Previous plans for surface observations were scrapped due to glitches, according to the Japan Aerospace Exploration Agency (JAXA). Observing the explorer's descent to the surface will be the last mission for the probe before it leaves the asteroid in November or December, the Japan Aerospace Exploration Agency (JAXA) said.

In 2010, the small community of specialists who pay attention to US road safety statistics picked up the first signs of a troubling trend: more and more pedestrians were being killed on American roads. That year, 4,302 American pedestrians died, an increase of almost 5% from 2009. The tally has increased almost every year since, with particularly sharp spikes in 2015 and 2016. Last year, 41% more US pedestrians were killed than in 2008. During this same period, overall non-pedestrian road fatalities moved in the opposite direction, decreasing by more than 7%. For drivers, roads are as safe as they have ever been; for people on foot, roads keep getting deadlier. Through the 90s and 00s, the pedestrian death count had declined almost every year. No one would have confused the US for a walkers' paradise – at least part of the reason fewer pedestrians died in this period was that people were driving more and walking less, which meant that there were fewer opportunities to be struck. But at least the death toll was shrinking. The fact that, globally, pedestrian fatalities were much more common in poorer countries made it possible to view pedestrian death as part of an unfortunate, but temporary, stage of development: growing pains on the road to modernity, destined to decrease eventually as a matter of course. The US road death statistics of the last decade have blasted a hole in that theory.