Alex Zhavoronkov, CEO of Insilico Medicine, a startup that generates potential drugs using artificial intelligence, was recently given a challenge by one of his pharma company partners. His team would see how quickly Insilico's AI could identify new molecules that bind with a protein associated with tissue scarring. Then they'd put the molecules to the test, synthesizing a few of them in the lab to see if the AI was onto something, or only dreaming. It now costs $2.6 billion, by one estimate, to get a new drug to market, and pipelines are only getting slower and more expensive. There's hope--and hype--that AI could help chip away at that figure by reducing the time and labor before a drug starts clinical trials.

September 2nd, 2019, 4 PM, London, Insilico Medicine, a global leader in artificial intelligence for drug discovery, today announced the publication of a paper titled, "Deep learning enables rapid identification of potent DDR1 kinase inhibitors," in Nature Biotechnology. The paper describes a timed challenge, where the new artificial intelligence system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of DDR1, a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice. The traditional drug discovery starts with the testing of thousands of small molecules in order to get to just a few lead-like molecules and only about one in ten of these molecules pass clinical trials in human patients.

A team of Italian mathematicians, including one who is also a neuroscientist from the Champalimaud Centre for the Unknown (CCU), in Lisbon, Portugal, has shown that artificial vision machines can learn to recognize complex images spectacularly faster by using a mathematical theory that was developed 25 years ago by one of this new study's co-authors. Their results have been published in the journal Nature Machine Intelligence. During the last decades, machine vision performance has exploded. For example, these artificial systems can now learn to recognise virtually any human face - or to identify any individual fish moving in a tank, in the midst of a large number of other almost identical fish which are also moving. The machines we're talking about are, in fact, electronic models of networks of biological neurons, and their aim is to simulate the functioning of our brain, which is as good as it gets at performing these visual tasks - and this, without any conscious effort on our part.

Open GI is one of the leading technology partners to the General Insurance industry. Open GI provides a range of configurable insurance software to insurance brokers, underwriting agencies, insurers and MGAs in the UK and Ireland. Its digital insurance solutions, which includes Mobius and Core, provide multi-line, multi-channel, multi-brand trading capability complemented by innovative eCommerce and mobile technologies. Open GI is part of the Open International Group and has 550 staff across offices in Worcester, Dublin, London, Winchester, Milton Keynes, Skopje and Krakow. Machine Learning Programs offers a range of AI and machine learning services to the general insurance market and financial services sector.

Innovative machine learning may seem light years away from first class test cricket, but it was the introduction of machine learning which enabled experts at Bangor University to reveal to the England & Wales Cricket Board (ECB) the factors which can lead to developing county or international world-class cricketers. Having gathered detailed and multifaceted information on over 1000 factors believed to be important predictors within a player's journey to elite or super elite cricket; whether they become a County or international player, experts at Bangor University's Institute for Psychology of Elite Performance worked with computer scientists at the University to number-crunch the vast data-set. The results revealed that a crucial combination of only 18 factors can determine talent development. "Anecdotal and scientific evidence suggests that it takes 10,000 hours of deliberate practice to reach expertise. Unfortunately, what this evidence can't tell us is exactly what type of practice do we need to undertake and when? As a result, there could be hundreds or even thousands of aspiring cricketers practicing the wrong stuff at the wrong time. "Until now, science has not been able to answer these questions.

The US, China, Russia and the UK are among a growing number of countries that are turning to artificial intelligence (AI) and machine learning as they look to develop a new generation of advanced weapons system. The Pentagon, in the US, has made a commitment to spend $2 billion over the next five years through the Defense Advanced Research Projects Agency (DARPA). Its OFFSET programme, for example, is looking to develop drone swarms comprising of up to 250 unmanned aircraft systems (UASs) and/or unmanned ground systems (UGSs) for deployment across a number of diverse and complex environments. In China, there are a growing number of collaborations between defence and academic institutions in the development of AI and machine learning and Tsinghua University has launched the Military-Civil Fusion National Defense Peak Technologies Laboratory to create "a platform for the pursuit of dual-use applications of emerging technologies, particularly artificial intelligence." Russia has gone one step further and is creating a new city named Era, which is devoted entirely to military innovation.

Sprint (NYSE: S) today announced the launch of Curiosity Smart Video Analytics. The highly adaptable solution delivers automated alerts and advanced video analytics technology, helping to make smarter decisions, respond faster to potential emergencies, and improve efficiency while keeping people and property safe. "Together with technologies from Hitachi Vantara and Ericsson, our Curiosity Smart Video Analytics technology is enabling the transformation of critical security operations – from government to enterprises – with the power of AI and IoT to generate intelligence that can be acted upon faster and with more accuracy," said Ivo Rook, senior vice president of IoT and product development at Sprint. "According to research, humans miss out on more than 90 percent of video activities after manual monitoring. We're automating workflow and enabling the visualization of distinct events with unprecedented precision to improve public safety and overall security for a variety of entities."

Surely and steadily, Saudi Arabia has been taking powerful technical strides. A couple of years down the line, young men and women have been focussing on a variety of modern day topics such as artificial intelligence, deep education (blockchain) and the Internet maze. The focus on such non mundane things, according to Al-Arabiya.net, has become crucial and a touchable reality and it, of course, is a source of pride, self respect and dignity. The talk about Hackathons or the exhibitions and creativity workshops is no longer weird or strange; it is now a next window phenomenon. Families and educational institutions are increasingly encouraging the youth to focus on technical fields.

Tesla CEO Elon Musk spoke at the World Conference on Artificial Intelligence in Shanghai, talking about AI and its impact on humanity. Musk believes that most people are already cyborgs, as they are too connected with their smartphones.

Andrew Weinert and his colleagues were deeply frustrated. After Hurricane Maria struck Puerto Rico, the researchers from MIT's Lincoln Laboratory were hard at work trying to help the Federal Emergency Management Agency (FEMA) assess the damage. In hand they had the perfect data set: 80,000 aerial shots of the region taken by the Civil Air Patrol right after the disaster. But there was an issue: there were too many images to sort through manually, and commercial image recognition systems were failing to identify anything meaningful. In one particularly egregious example, ImageNet, the golden standard for image classification, recommended labeling an image of a major flooding zone as a toilet.