LONDON – Scientists at Britain's national synchrotron facility have harnessed powerful light beams to virtually unwrap and decipher fragile scrolls dating back some 2,000 years in a process they hope will provide new insights into the ancient world. The two complete scrolls and four fragments -- from the so-called Herculaneum library, the only one surviving from antiquity -- were buried and carbonized by the deadly eruption of Mount Vesuvius in the year 79 and are too fragile to be opened. The items were examined at the Diamond Light Source facility in Oxfordshire, home to Britain's synchrotron, a particle accelerator in which beams travel around a closed-loop path to produce light many times brighter than the sun. "The idea is essentially like a CT scanner where you would take an image of a person, a three-dimensional image of a person and you can slice through it to see the different organs," said Laurent Chapon, physical science director of Diamond Light Source. "We … shine very intense light through (the scroll) and then detect on the other side a number of two-dimensional images. From that we reconstruct a three-dimensional volume of the object … to actually read the text in a nondestructive manner," Chapon said.

AI and machine learning are changing global consumption habits, and companies are playing catch-up. Good salespersonship is a species of street smarts. It's about quickly sizing up your customers and pitching your wares in terms that reverberate with their unspoken needs and desires. As AI and machine learning increasingly intersect with e-commerce, these priceless human skills are finding algorithmic analogues – not just at point of sale, but throughout the customer journey. The results will be familiar to online shoppers everywhere.

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