There were some headlines the other day about the "first AI-discovered drug", so that should send us to the work in question to see what's going on. The company in question is called Deep Genomics, and here's what its founder has to say: "Making drugs has traditionally been a gambling game. Big Pharma is throwing a stick into the tree and seeing what happens," Frey told FierceBiotech. "It's like the Big Pharma companies come into a casino, put a million-dollar coin into a slot machine and with some probability like 10% or something, they get a win." Instead of gambling to get at the fruit higher up on the tree, Frey built Deep Genomics, a company using artificial intelligence to discover new disease targets as well as the best compounds to drug them.

Wherever you look these days, you can find AI affecting your life in one way or another. Whether it's the Netflix recommendation system or self driving cars, the use of deep learning is becoming ever more prevalent throughout our lives and is starting to make increasingly more crucial decisions. Since AI is becoming ingrained in our lives, you'd expect it to be safe and fool proof, right? The potential exists for bad actors to trick deep learning systems into misinterpreting the input on purpose causing it to give a wrong answer. We present a method for preventing these intentional misclassifications to help maintain trust in complex AI systems. Shortly after Alexnet brought neural networks back into the main stream in 2012 [Krizhevsky et al. 2012 ], people were immediately beginning to find ways to manipulate the fundamental structure of these tools.

This is the age of data! As soon as you open your Facebook account, you are inundated with huge amount of data. You get to see posts from your friends, which could be in the format of text, pictures and videos. Now, just imagine if you could tap into this data and use it gain insights, that would be just wonderful, wouldn't it? And this is exactly where data science comes in.

As if the internet isn't already a complicated cesspool full of trolls, AI engineers have gone one step further to build a machine learning model that can generate fake comments for news articles. The eyebrow-raising creation, known as DeepCom, was developed by a group of engineers at Beihang University and Microsoft, China. "Automatic news comment generation is beneficial for real applications but has not attracted enough attention from the research community," they said in a paper released on arXiv. Allowing readers to post comments under articles keeps them engaged, they argued. Open dialogue allows people to discuss their opinions and share new information.

In this May 6, 2019 file photo, Microsoft CEO Satya Nadella delivers the keynote address at Build, the company's annual conference for software developers in Seattle. Microsoft and other tech giants have been competing to strike lucrative partnerships with ExxonMobil, Chevron, Shell, BP and other energy firms. One employee stood up to ask Microsoft CEO Nadella about the ethics of the company's oil and gas contracts at an all-staff meeting in Sept. 2019, and Nadella defended the partnerships. Employee activism and outside pressure have pushed big tech companies like Amazon, Microsoft and Google promising to slash their carbon emissions. When Microsoft held an all-staff meeting in September, an employee asked CEO Satya Nadella if it was ethical for the company to be selling its cloud computing services to fossil fuel companies, according to two other Microsoft employees who described the exchange on condition they not be named.

I just won 1st place out of 4,500 teams in the LANL Earthquake Research competition, sponsored by Los Alamos National Laboratory. Yea, that's the place where they invented the nuclear bomb! This awarded me my third gold medal. Our team's writeup can be found here. You can also see all of the code needed to get first place by clicking here. I learned how to deal with signal data and also how to exploit the Kolmogorov-Smirnov test for regression tasks.

This summer I was invited to take part in the 2018 NASA Frontier Development Lab, along with a small team including Michele Sasdelli (University of Adelaide), and a pair of planetary scientists, Megan Ansdel (University of California at Berkeley) and Hugh Osborn (Laboratoire d'Astrophysique de Marseille). Our team composed of both machine learning and planetary scientists, was challenged over the course of 8 weeks to combine our expert knowledge in order to improve the methods behind one of the most exciting frontiers of science: exoplanet discovery. Here I discuss some of the challenges of applying machine learning to real-world scientific data, in particular noisy and sparse periodic time-series data. Our knowledge of exoplanets, or planets that exist outside our Solar System, has advanced drastically over the last few decades. In fact, until relatively recently one could have called exoplanets a theoretical concept.

What would you do if you saw a sharp, sudden spike in demand among for sauerkraut? Wrong, says Alon Chen, the CEO and founder of Tastewise, a start-up that uses AI and machine learning to provide real-time insight into people's food tastes. Far better to identify the trend behind the trend. "Sauerkraut is fermented food, and fermentation is highly associated today with gut health and brain health, " Chen explained. So sauerkraut is an example of a demand for a traditional ingredient being driven by a new concept.

Tesla has acquired DeepScale, a Silicon Valley startup that uses low-wattage processors to power more accurate computer vision, in a bid to improve its Autopilot driver assistance system and deliver on CEO Elon Musk's vision to turn its electric vehicles into robotaxis. CNBC was the first to report the acquisition. TechCrunch independently confirmed the deal with two unnamed sources, although neither one would provide more information on the financial terms of the deal. Tesla vehicles are not considered fully autonomous, or Level 4, a designation by SAE that means the car can handle all aspects of driving in certain conditions without human intervention. Instead, Tesla vehicles are "Level 2," and its Autopilot feature is a more advanced driver assistance system than most other vehicles on the road today.

I t wasn't long ago that technology was a topic only discussed among techies. In fact, technology was an elective course in many graduate school programs until very recently. Today, technology is part of our daily lives so it's not surprising that technology is very much a part of any industry. It's also not surprising to see the direction technology has taken. It has evolved from a way to communicate with each other and store important information, to a way to interact with each other, express ourselves and manage our lives. The drive to monetize our personal information for the purpose of creating the latest and greatest target marketing algorithm has paved the way for artificial intelligence or AI. Google was a pioneer and early adopter of this type of AI, gathering information about our interest based on our searches and pairing businesses and products we would likely use. It is this type of AI that brings customers to businesses like an arranged marriage. Collection of data through cloud-based applications originally created for business solutions slowly evolved for consumer convenience for everything from banking to entertainment. Amassing raw data to create solutions for everyday activities helped to speed the process of AI for the birth of AI. Had we not partaken in taking information once only saved on our desktops and placing it on cloud servers, AI may not have evolved into the presence of daily life today. Years ago, reluctance and lack of understanding of how digital information is used kept many people who are not computer savvy from partaking in this community. Today, thanks to companies like Facebook and Amazon, people readily share their information with companies with a basic trust that the information will only be used for the purpose intended. This is why, even though the information is occasionally breached, we are so willing to join communities like Citizens app and Waze which use crowd sourcing for the collective purpose of helping each of its participants. Crowd sourcing applications can then place ads as a form of revenue, though not all do. This rather invasive, though passive, business model hones in on our inherent need to share information in order to benefit from the information shared by others.