Scientists have been researching how a protein folds into a unique 3D shape for approximately 50 years. Now, thanks to the use of artificial intelligence (AI), U.K.-based AI lab, DeepMind, has helped to solve this scientific mystery, as the organizers of a scientific challenge, CASP (Critical Assessment of protein Structure Prediction) said, which kicked off in the early 1990s, reports Science Alert. Understanding a protein shape could lead to major scientific advancements, as well as environmental ones, per the BBC. The full findings have not yet been published, explains Science Alert, however, the study's abstract can be read over on CASP14, here. SEE ALSO: GOOGLE'S DEEPMIND AI BETTER AT DETECTING BREAST CANCER THAN EXPERTS Proteins are integral as they are present in all living things.

Artificial intelligence (AI) provides a wide range of current society applications, including predicting, classifying, and solving both social and scientific problems. As one of the oldest and most traditional engineering disciplines, civil engineering covers various aspects of the built environment, from design and construction to maintenance. Civil engineering offers ample practical scope for applications of AI. In turn, AI can improve human life quality and originate novel approaches to solving engineering problems. AI methods and techniques, including neural networks, evolutionary computation, fuzzy logic systems, and deep learning, have rapidly evolved over the past few years.

Artificial Intelligence (AI) has been a top trend in many industries lately, attracting massive media attention and investments. Over the last decade, this complex area of research has rapidly progressed from being a "resurrected cool technology from the past" to a full-blown driver of nothing less than a new industrial revolution -- a digital one. As of today, AI is widely commercialized in such applications as manufacturing robots, smart assistants (e.g. Siri), automated financial investing systems, virtual travel booking agents, social media monitoring tools, conversational bots, surveillance systems, online security systems, language translators, self-driving cars, and much more. In some industries, AI (including its many technologies and sub-disciplines, such as deep learning, recommender systems, and natural language processing), is becoming a standardized component rather than a cutting-edge innovation it once was. This rapid progress in AI adoption is also seen in the pharmaceutical industry -- not without caveats, however. Unlike "mainstream" use cases, like image recognition or spam email filtering, drug discovery research appears to be a much harder case for several reasons.

This is the second edition of my weekly update on deep learning. Every Thursday, I’ll release a new batch of research papers, blog posts, Github repos, etc. that I liked over the past week. Links are…

TensorFlow is one of the greatest gifts to the machine learning community by Google. An end-to-end open-source framework for machine learning with a comprehensive ecosystem of tools, libraries and community resources, TensorFlow lets researchers push the state-of-the-art in ML and developers can easily build and deploy ML-powered applications. Ever since its release to the public back in November 2015, TensorFlow has grown to become one of the most popular deep learning frameworks. This month, TensorFlow turned five, and in this article, we take a look at its popular libraries. TensorFlow Lattice library implements constrained and interpretable lattice-based models that enable users to inject domain knowledge into the learning process through common-sense.

Amazon today debuted AWS Trainium, a chip custom-designed to deliver what the company describes as cost-effective machine learning model training in the cloud. It comes ahead of the availability of new Habana Gaudi-based Amazon Elastic Compute Cloud (EC2) instances built specifically for machine learning training, powered by Intel's new Habana Gaudi processors. "We know that we want to keep pushing the price performance on machine learning training, so we're going to have to invest in our own chips," AWS CEO Andy Jassy said during a keynote address at Amazon's re:Invent conference this morning. "You have an unmatched array of instances in AWS, coupled with innovation in chips." Amazon claims that Trainium will offer the most teraflops of any machine learning instance in the cloud, where a teraflop translates to a chip being able to process 1 trillion calculations a second.

Deep Learning Prerequisites: Logistic Regression in Python, Data science, machine learning, and artificial intelligence in Python for students and professionals Created by Lazy Programmer Inc. English [Auto], Portuguese [Auto]Preview this Course - GET COUPON CODE This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Earlier this week, the Google-owned AI development company DeepMind announced with great fanfare that it had built an algorithm capable of predicting how proteins would fold based on their molecular composition. If it holds up, it's a stunning achievement that's eluded scientists for decades. But Business Insider reports that many experts in the field remain unimpressed, instead calling DeepMind's announcement hype. While critical scientists don't diminish the importance of DeepMind's achievement, they do question whether AlphaFold 2 will actually provide a useful tool to researchers like DeepMind claims. DeepMinds' AlphaFold 2 algorithm scored higher at the Critical Assessment of Structure Prediction (CASP) competition, which tests potential solutions to the protein folding problem, than any other team in history.

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