'League of Legends' maker Riot Games has new legends in the works

USATODAY - Tech Top Stories

This video covers the action of the 2018 League of Legends World Championship and previews the 2019 event. Riot Games, publishers of "League of Legends," is looking to expand its lore. For starters, there are some new features coming to the super-popular online video game, which turns 10 this month. Beyond that, Riot Games announced Tuesday it is working on several other projects including new shooter and strategy games, as well as a trio of new video games set in the "League of Legends" universe. The game publisher announced these developments as part of its 10th anniversary livestream Tuesday night.


The best robot vacuums of 2019

USATODAY - Tech Top Stories

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Whether you just like the idea of letting a robot handle cleaning up your floors or you just don't like to vacuum, a robot vacuum cleaner can be a real help. But with so many companies making robot vacuums, how do you know if any of them are actually worth the money? Luckily, we've done the hard work for you. We have a specially built obstacle course in our labs that tests how well robot vacuums pick up dirt, navigate around ytour furniture, and deal with floor types from hardwood floors to low- and high-pile carpets.


c-bata/goptuna

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Bayesian optimization framework for black-box functions, inspired by Optuna. This library is not only for hyperparameter tuning of machine learning models. Everything will be able to optimized if you can define the objective function (e.g. See the blog post for more details: Practical bayesian optimization using Goptuna. You can integrate Goptuna in wide variety of Go projects because of its portability of pure Go.


Machine learning system may offer warnings about negative side effects of drug-drug interactions

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The more medications a patient takes, the greater the likelihood that interactions between those drugs could trigger negative side effects, including long-term organ damage and even death. Now, researchers at Penn State have developed a machine learning system that may be able to warn doctors and patients about possible negative side effects that might occur when drugs are mixed. In a study, researchers designed an algorithm that analyzes data on drug-drug interactions listed in reports -- compiled by the Food and Drug Administration and other organizations -- for use in a possible alert system that would let patients know when a drug combination could prompt dangerous side effects. Let's say I'm taking a popular over-the-counter pain reliever and then I'm put on blood pressure medicine, and these medications have an interaction with each other that, in turn, affects my liver. Essentially, what we have done, in this study, is to collect all of the data on all the diseases related to the liver and see what drugs interact with each other to affect the liver." Drug-drug interaction problems are significant because patients are frequently prescribed multiple drugs and they take over-the-counter medicine on their own, added Kumara, who also is an affiliate of the Institute for CyberScience, which provides supercomputing resources for Penn State researchers. "This study is of very high importance," said Kumara. "Most patients are not on one single drug.


Duke researchers use machine learning to defend personal information

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Two Duke researchers have found a way to confuse machine learning systems, potentially revealing a new way to protect online privacy. Neil Gong, assistant professor of electrical and computer engineering, and Jinyuan Jia, a Ph.D. candidate in electrical and computer engineering, have displayed the potential for so-called "adversarial examples," or deliberately altered data, to confuse machine learning systems. This research could be used to fool attackers who use these systems to analyze user data. "We found that, since attackers are using machine learning to perform automated large-scale inference attacks, and the machine learning is vulnerable to those adversarial examples, we can leverage those adversarial examples to protect our privacy," Gong said. Machine learning systems are tools for statistical analysis.


DRIVE Labs: Eliminating Collisions with Safety Force Field - NVIDIA Developer News Center

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Safety Force Field (SFF) vehicle software is designed specifically for collision avoidance. It acts as an independent supervisor on the actions of the vehicle's primary planning and control system, which could be either human-driven or autonomous. Specifically, SFF performs real-time double-checks of the controls that were chosen by the primary system. If SFF deems the controls to be unsafe, it will veto and correct the primary system's decision. SFF is provably safe, in the sense that, if all road participants comply with SFF and the perception and vehicle controls are within expected design margins, then it can be mathematically proven that no collisions can occur.


Seven helpful chatbot building tips for your brand

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Make sure you understand the value a chatbot will bring to your business. Massive returns are being created for companies like Coca-Cola and Bank of America because of chatbots. Countless companies are being won over, and you should consider using chatbots for your brand, as well. Here are seven helpful ideas for you to use when building a chatbot for your brand. Make sure that when you are creating your chatbot icon, it shows the characteristics of your brand.


AI: Understanding bias and opportunities in financial services

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It is undeniable that our lives have been made better by artificial intelligence (AI). AI technology allow us to get almost anything, anytime, anywhere in the world at the click of a button; prevent disease epidemics and keep them from spiralling out of control, and generally just make day-to-day life a bit easier by helping us to save energy, book a babysitter, manage our cash and our health all at a very low cost. AI's penetration into systems and processes in virtually all sectors of business and life has been rapid and global. The speed and scale at which AI is proliferating does however raise the question of how at-risk we may be that the AI we are building for good can also be introducing damaging bias at scale. In this two-part series, I explore the issues with AI constructs, the good bad and the ugly and how we can think about shaping a future through AI in financial services that helps lift people up rather than scaling problems up.


Diversity Now!

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As an introvert, it was overwhelming and yet very empowering to be surrounded by hundreds of extremely talented women from all over the world who had convened at the 100 Brilliant Women in AI Ethics summit at the Lady Margaret Hall in Oxford on September 16, 2019. The historic significance of this venue, one of the first women's colleges at Oxford, can't be understated. The immense energy of this event could almost make one believe that all was well in the world and women couldn't possibly be underrepresented, given the breadth and depth of clearly visible talent in the room. But the very next day, I had to sit through a series of manels (men-only panels) at the prestigious AI@Oxford conference, which diminished much of the glow from the previous day's event. While there were many amazing women who spoke at the conference like Gina Neff, Safiya Noble and one could claim that it just one elite university but research shows that manels are still the norm and despite all the talk, the number of women speakers at conferences continues to be low.


Merck turning to machine learning to prevent drug shortages: Merck plans to use a cloud-based software platform to better predict and prevent drug shortages, according to The Wall Street Journal.

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Merck plans to use a cloud-based software platform to better predict and prevent drug shortages, according to The Wall Street Journal. The platform, developed by healthcare software company TraceLink, will analyze in real time data from pharmacies, hospitals and wholesale distributors. By using analytics and machine learning, the software can improve predictions and help drugmakers better match drug demand. The software could also save drugmakers hundreds of millions of dollars annually by reducing waste and avoiding costs like expedited shipments, because it can track a drug's status at every step in the supply chain. The platform currently holds data on more than 6 billion drugs.