Follow the latest updates of the outbreak on our timeline. Artificial intelligence (AI) technology provider Iktos and research centre SRI International have partnered to discover and develop drugs to treat various viruses, including the novel coronavirus that causes Covid-19 and influenza. Iktos will combine its generative modelling technology with SRI's fully automated synthetic chemistry platform called SynFini to design compounds and speed-up the identification of drug candidates. The Iktos AI technology leverages deep generative models for the accelerated drug discovery process, made possible via the automatic design of virtual molecules with the required characteristics of a new drug candidate. Iktos co-founder and CEO Yann Gaston-Mathé said: "Iktos generative AI technology has proven its value and potential to accelerate drug discovery programs in multiple collaborations with renowned pharmaceutical companies.

The promise of deep learning in the field of computer vision is better performance by models that may require more data however, less digital sign preparing ability to train and work. There is a ton of promotion and large claims around deep learning methods, however, past the hype, deep learning techniques are accomplishing cutting edge results on challenging issues. Outstandingly, on computer vision tasks, for example, image classification, object recognition, and face detection. Deep learning strategies are well known, principally in light of the fact that they are delivering on their promise. This isn't to imply that there is no publicity around the innovation, however, that the hype depends on genuine outcomes that are being exhibited over a suite of challenging artificial intelligence issues from computer vision and natural language processing.

In a study published today in the journal JAMA Network Open, researchers demonstrated that machine-learning algorithms could help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists. The study was based on results from the Digital Mammography (DM) DREAM Challenge, a crowd-sourced competition that kicked off in 2016 to engage a broad, international scientific community to assess whether artificial intelligence (AI) algorithms could meet or beat radiologist interpretive accuracy. New study in @JAMANetworkOpen shows #AI may help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists. "This DREAM Challenge allowed for a rigorous, apples-to-apples assessment of dozens of state-of-the-art deep learning algorithms in two independent datasets," said Dr. Justin Guinney, VP of Computational Oncology at Sage Bionetworks and Chair of DREAM Challenges. "This is a much-needed comparison effort given the importance and activity of AI research in this field."

Over the past few years, we've seen computer programs winning games which we believe humans were unbeatable. This belief held considering this games had so many possible moves for a given position that would be impossible to computer programs calculate all of then and choose the best ones. However, in 1997 the world witnessed what otherwise was considered impossible: the IBM Deep Blue supercomputer won a six game chess match against Gary Kasparov, the world champion of that time, by 3.5 – 2.5. Such victory would only be achieved again when DeepMind's AlphaGo won a five game Go match against Lee Sedol, 18 times world champion, by a 4-1 score. The IBM Deep Blue team relied mostly in brute force and computation power as their strategy to win the matches.

Deep AI or General AI has been one of the biggest fascinations of much of the tech world. Many kinds of research are being carried out to achieve it. This is simply because it is the highest level that the Artificial Intelligence (AI) technology can achieve. In fact, it is the closest that technology can get towards human intelligence. This is to say it will be able to almost do all the mental tasks that humans are able to perform. There are both people who are welcoming of the idea of a deep AI, and those who are apprehensive about it.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the early days of artificial intelligence, computer scientists have been dreaming of creating machines that can see and understand the world as we do. The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data. In recent years, computer vision has taken great leaps thanks to advances in deep learning and artificial neural networks. Deep learning is a branch of AI that is especially good at processing unstructured data such as images and videos.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the early days of artificial intelligence, computer scientists have been dreaming of creating machines that can see and understand the world as we do. The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data. In recent years, computer vision has taken great leaps thanks to advances in deep learning and artificial neural networks. Deep learning is a branch of AI that is especially good at processing unstructured data such as images and videos.

So you've built your machine learning or deep learning model. That final stage – the crucial cog in your machine learning or deep learning project – is model deployment. You need to be able to get the model to the end user, right? And yet you'll face a ton of questions about model deployment when you sit for data scientist interviews! What are the different tools for model deployment?

A new data set reveals just how bad AI is at reasoning--and suggests that a new hybrid approach might be the best way forward. Questions, questions: Known as CLEVRER, the data set consists of 20,000 short synthetic video clips and more than 300,000 question and answer pairings that reason about the events in the videos. Each video shows a simple world of toy objects that collide with one another following simulated physics. In one, a red rubber ball hits a blue rubber cylinder, which continues on to hit a metal cylinder. The questions fall into four categories: descriptive (e.g., "What shape is the object that collides with the cyan cylinder?"),

Let's imagine one morning you decide to go for a run after several years of procrastination and being naive you didn't stretch or warm up before the run. You started running like Phoebe (F.R.I.E.N.D.S reference alert!!) and after running for approx 15 minutes on an uneven ground you suddenly lost your balance and fell down, hurting your ankle. After going back home you notice that your ankle is swollen and you have sharp pain there. You want to know if you got a sprain or fracture but don't want to visit the doctor, because you don't want to catch the deadly virus out there! Suddenly, you remember a conversation with your friend about an app that uses Convolutional Neural Networks (CNN from now on) to detect the type of injury with the help of pictures. You open that app and take picture of your ankle and hocus-pocus, it shows that it is a sprain and you just have to rest, compress and elevate to be healthy again.