Most of my other projects are concerned with large-scale virtual screening applications: In collaboration with labs that do experimental biology, we develop and apply methods to predict candidate molecules that either inhibit (or activate, depending on the project) an individual protein, in absence or presence of a protein crystal structure. The interesting part about it is the interplay between predictions and feedback: I get to make predictions and (at some point), I get the experimental results to see whether I was right or wrong and to analyze why certain predictions worked better than others. Another exciting challenge in such projects is that one has to find ways to make this all computationally feasible -- if you have 15 million molecules, selecting 100 candidate molecules for experimental testing is a bit like searching for the needle in the haystack. Usually, it comes down to formulating specific hypotheses upfront as "filtering" steps since a brute-force docking, which computationally not feasible (since we also have time constraints). My projects require a certain amount of creativity and technical skills to put the ideas into action, but eventually, the approach (the hypotheses) also have to make sense to our collaborators (and the funding agencies).

Turns out that DJI's Mavik wasn't the company's only new airframe for the year. On Tuesday, a pair of updated drone models joined the family: the Phantom 4 Pro and the Inspire 2. The Phantom 4 Pro (P4P) is the upgraded version of the Phantom 4 (P4), which debuted earlier this year. The P4, if you recall, integrated a suite of obstacle avoidance systems: a pair of stereoscopic sensors mounted on the front of the drone. The P4P builds on that system, adding a second pair of stereoscopic sensors on the rear of the frame as well as an infrared sensor on either flank. These IR sensors have a 7m range -- shorter than the 30m range of the front and rear stereos but still plenty farsighted to avoid sideswiping trees and fences.

Microsoft is teaming up with Elon Musk's OpenAI, the non-profit artificial intelligence research company. OpenAi announced on Tuesday it will work with Microsoft to start running the cloud platform Azure, making it the primary cloud system that OpenAI uses for deep learning and artificial intelligence, a move Microsoft said it was "excited" about. "OpenAI chose Microsoft due to our deep learning research and ongoing commitment to AI, along with Azure's support for open source technologies and its unique combination of high performance computing, big data and intelligence capabilities such as Azure Batch, Azure Machine Learning and the Microsoft Cognitive Toolkit (formerly CNTK)," said Microsoft in a statement. The partnership will work on "democratizing AI and making it accessible to everyone," Microsoft says. The company plans to make this happen by taking that intelligence and infusing it into everyday devices and apps.

Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a four year old can do, there's still a pretty enormous gap." Babies and children construct theories about the world around them using the same approach scientists use to construct scientific theories.

Intel has moved to a new architecture called Kaby Lake for its PC chips, but it isn't done with the previous generation Skylake yet. The company will release new Xeon server chips based on Skylake in mid-2017, and they will boast big performance increases, said Barry Davis, general manager for the accelerated workload group at Intel. The Skylake Xeon chips will go into mainstream servers and could spark a big round of hardware upgrades, Davis said. Xeon chips aren't as visible as Intel's PC chips but remain extremely popular. Companies like Google, Facebook, and Amazon buy thousands of servers loaded with Xeon chips to power their search, social networking, and artificial intelligence tasks.

There's an arms race among public cloud providers to provide businesses with the best machine learning capabilities. Enterprises are increasingly interested in creating intelligent applications, and companies like Amazon, Microsoft and Google are rushing to help meet their needs. Google fired its latest salvo on Tuesday, announcing a set of enhancements to its existing suite of cloud machine-learning capabilities. The first was a new Jobs API aimed at helping match job applicants with the right openings. In addition, the company is slashing the prices on its Cloud Vision API and launching an enhanced version of its translation API.

Microsoft said Tuesday that a forthcoming Azure cloud service will help developers build and deploy bots or chatbots easily. Bots, a hot, much-hyped topic of late, are pieces of software that work under the covers to let consumers perform tasks, such as ordering pizza, using their favorite chat or texting app. Microsoft's Azure Bot Service, now in preview, builds on the company's much touted Bot Framework, which aims to support all the popular chat and texting applications in the universe. "This is a full application, a managed platform that does all you need to host a bot," Microsoft executive vice president Scott Guthrie told Fortune. The service relies on Azure Functions, which like the Lambda service from Microsoft msft rival Amazon Web Services, lets developers quickly build in capabilities that are triggered by a user action or some sort of software trigger or event.

The U.S. State Department generates some two billion e-mails every year. A significant fraction of these contain sensitive or secret information and so have to be classified, a process that is time-consuming and costly. In 2015 alone, it spent $16 billion to protect classified information. But the reliability of this process of classification is unclear. Nobody knows whether the rules for classifying information are applied consistently and reliably.

REDWOOD CITY, CA - November 14, 2016-- Numerous proposals have been offered for how intelligent machines might learn sequences of patterns, which is believed to be an essential component of any intelligent system. Researchers at Numenta Inc. have published a new study, "Continuous Online Sequence Learning with an Unsupervised Neural Network Model," which compares their biologically-derived HTM sequence memory to traditional machine learning algorithms. The paper has been published in MIT Press Journal's Neural Computation 28, 2474-2504 (2016). You can read and download the paper here. Authored by Numenta researchers Yuwei Cui, Subutai Ahmad, and Jeff Hawkins, the new paper serves as a companion piece to Numenta's breakthrough research offered in "Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex," which appeared in Frontiers in Neural Circuits, in March 2016.

Alphabet's Google announced the formation of an artificial intelligence group for Google Cloud, the tech company's latest gambit to increase its market share in the lucrative cloud computing business. Diane Greene, who leads Google's cloud business, announced the team at an event at the company's facilities in San Francisco. The group will be led by Fei-Fei Li, an artificial intelligence professor at Stanford University, and researcher Jia Li. "What really attracted these two people to come and be in Google Cloud is a chance to democratize machine learning and artificial intelligence," Greene said.