Major League Baseball has teamed with creative software developer Adobe to offer dozens of business school students access to data on fan behavior as part of the software giant's yearly analytics competition. For a chance at $60,000 in cash and prizes, the students will analyze the information, which includes stats like in-game purchases, web traffic and customer drop-off tallies, and distill it into recommendations for how the league can better expand its in-person stadium and retail experience to its digital properties. This year's contest will be the first in the decade-old Adobe Analytics Challenge to include machine learning software among the tools to which students have access, namely Adobe Sensei, the artificial intelligence engine that powers much of the creative software giant's customer targeting and predictive analytics suite. Specifically, students will look for anomalies and behavioral patterns in the data that might point to elements of the MLB's digital user experience that are driving people away, or particularly successful features upon which the league's developers should expand. The data is segmented by customer demographics and spans the MLB's flagship website, mobile apps and other digital properties.

Facebook has developed software to make people invisible to facial recognition technology. Its'de-identification' program is intended to protect people from'deepfake' style videos in which their faces can be edited onto videos of other people. These convincing clips are becoming so advanced it can be difficult to tell which videos are real and which ones are fake. And there are concerns that, in future, people will be able to make footage of others doing or saying things that they never actually did. But Facebook AI Research now says it has a way of fooling the artificial intelligence used to make these videos while still keeping the original video lifelike.

The latest industrial robots look like petting zoo versions of the big machines found in many modern factories – small, cute and you can play with them. But don't be deceived by their cuddly appearance. They have the potential to change the way humans work with machines and disrupt the existing market for industrial robots. The big difference with this new generation of robots is that they don't have to operate in closed-off areas. Instead they can safely operate alongside and even collaborate with human workers.

WIRE)--RiskSense, Inc., pioneering risk-based vulnerability management and prioritization, today announced that its CEO, Dr. Srinivas Mukkamala will lead an expert panel at the SINET Showcase conference in Washington, DC on November 7, 2019 on the impact of bias in AI-driven security systems. Dr. Srinivas Mukkamala, co-founder and CEO of RiskSense, is a recognized expert on artificial intelligence (AI) and neural networks. He was part of a think tank that collaborated with the U.S. Department of Defense and U.S. Intelligence Community to apply these concepts against cybersecurity problems. Dr. Mukkamala was also a lead researcher for CACTUS (Computational Analysis of Cyber Terrorism against the U.S.) and holds a patent on Intelligent Agents for Distributed Intrusion Detection System and Method of Practicing. Artificial intelligence and machine learning are increasingly being used and trusted by organizations to automate security threat detection.

When we hear technology we think of electronic gadgets and a hundred types of software. But the problems of the future are going to be more basic. Food, water, and shelter are important to talk about. They're essential to sustain human life and limited in availability. Moreover, the increasing population and concentration of population in major cities will possibly lead to scarcity unless we take due action.

The Machinery Directive is the core piece of EU legislation for the mechanical engineering industry: it promotes the free movement of machinery within the Internal Market, while setting out the'Essential Health and Safety Requirements' to be observed when placing a machine on the market for the first time. The mechanical engineering companies Orgalim represents really value this Directive: in addition to ensuring a high level of safety for employees and end users, it has provided a stable legal environment for businesses since it came into force over a decade ago. Particularly for small and medium sized enterprises (SMEs), this stability is invaluable. In the last year or two, however, the European Commission has been exploring the possibility of reviewing the Directive – prompted primarily by new developments in technologies like artificial intelligence (AI). AI has climbed up the political agenda in recent years, reflecting public concerns that this technology is somehow different; that, in the most dramatic scenarios, systems deploying AI could start to act autonomously and provoke new, unmanageable dangers.

Garry Kasparov is considered by many to be the greatest chess player of all time. From 1986 until his retirement in 2005, he dominated the chess world, ranking world number 1 for most of those 19 years. While he has many historic matches against human chess players, in the long arc of history he may be remembered for his match again a machine, IBM's Deep Blue. His initial victories and eventual loss to Deep Blue captivated the imagination of the world of what role Artificial Intelligence systems may play in our civilization's future. That excitement inspired an entire generation of AI researchers, including myself, to get into the field.

Data security threats from malicious insiders have already been recognised as a big problem for businesses – but an IBM Australia-built proof of concept could go a long way towards solving it with an artificial intelligence (AI) based solution that can spot disgruntled workers before they have acted. The tool grew out of an AI-themed internal hackathon run at IBM's Gold Coast-based Australian Security Development Lab, where developers are encouraged to come up with novel solutions. A team of IBM Security engineers realised that businesses are collecting masses of data about network performance and user behaviour, QRadar flows product owner Holly Wright told CSO Australia, and set about looking for ways this information could be meaningfully paired with other data and analysed to give greater insight about users' state of mind. "QRadar gives us deep visibility into the messages, views and emails going across the network," explained Wright, who shared details of the project with attendees at AISA's recent Australian Cyber Conference. "We decided to look at users from a risk perspective. We're essentially leveraging that information that's on the network, that nobody has really done anything with."

Vectra delivers a new class of real-time threat detection and advanced analysis of active network intrusions. Vectra picks up where perimeter security leaves off using AI to provide deep, continuous analysis of both internal and Internet-facing network traffic for all phases of the attack progression as attackers attempt to breach, spy, spread, and steal within networks. Vectra directly analyzes network traffic in real time using combination of patent-pending data science, machine learning, and behavioral analysis to detect attacker behaviors and user anomalies in the network. All detections are algorithmically correlated and prioritized to show an attack in context, and Vectra Networks' machine learning adapts as attacks evolve. Apply cutting edge machine learning, artificial intelligence, and modeling techniques to stop hackers in real time as they attack networks.