A programming technique that works on the same principle as disease-preventing vaccinations could safeguard machine learning systems from malicious cyber-attacks. The technique was developed by the digital specialist arm of Australia's national science agency, the CSIRO, and presented recently at an international conference on machine learning, held in Long Beach, California, US. Machine learning systems, or neural networks, are becoming increasingly prevalent in modern society, where they are pressed into service across a wide range of areas, including traffic management, medical diagnosis, and agriculture. They are also critical components in autonomous vehicles. They operate from an initial training phase, in which they are fed tens of thousands of possible iterations of a given task.

Beyond the classroom curriculum, many law schools are designing experiential modes of introducing law students to artificial intelligence. At Georgia State University School of Law, for instance, the Legal Analytics and Innovation Initiative gives law students a chance to collaborate closely with computer science and business students at the same university to design complex technologies that solve previously unsolvable legal problems (such as predicting to a high degree of accuracy how a particular judge will rule in cases defined by a large set of parameters). This kind of work not only has the potential to be a flow-through to the legal practitioner space, but could over time become a mechanism for law schools to "spin out" the kinds of revenue-generating start-up businesses that are a common facet of life science departments at research universities. These programs have also been shown (according to the programs' own statistics) to help law students land jobs at higher rates than the overall student body, no doubt because the intersection of technology and law is a rare and valuable skillset in the eyes of employers.

Northside Hospital in Atlanta is adopting machine learning technology to enable the organization to predict when insurance companies will end payments. The new technology it's using is from The SSI Group, which is providing technology that aggregates all remittance data coming through its clearinghouse to make the predictions. The goal is to enable providers that manually build their own spreadsheets to predict payments to use the SSI technology to determine when they can expect to get paid, down to the day and time, according to the vendor. "Without predictive analytics, hospitals and other providers are left guessing when they will receive payments," says Eric Nilsson, chief technology officer at SSI. Using analytics, SSI can give greater visibility on the payment of institutional, professional, in-patient and out-patient claims.

The story of Artificial Intelligence (AI) and Machine Learning (ML) is all about hope and hype. On the one hand, there's a technology that promises to revolutionize fields as diverse as agriculture, manufacturing, education, and healthcare. On the other, there's so much media attention that it gets impossible to cut through the hype and, proverbially speaking, separate the wheat from the chaff. And though making heads or tails of it all is difficult, DevOps is for sure poised to capitalize on the opportunities that AI and ML offer, such as automation of tasks, data analysis, and improvement of efficiency. DevOps generates tons of data.

Today, there are a large number of online discussion fora on the internet which are meant for users to express, discuss and exchange their views and opinions on various topics. In such fora, it has been often observed that user conversations sometimes quickly derail and become inappropriate such as hurling abuses, passing rude and discourteous comments on individuals or certain groups/communities. Similarly, some virtual agents or bots have also been found to respond back to users with inappropriate messages. As a result, inappropriate messages or comments are turning into an online menace slowly degrading the effectiveness of user experiences. Hence, automatic detection and filtering of such inappropriate language has become an important problem for improving the quality of conversations with users as well as virtual agents.

Zendesk, customer service and engagement solutions provider, recently introduced the expansion of its Answer Bot product. Answer Bot is a machine learning tool that helps customers find answers for themselves. It pulls data from the Zendesk Guide knowledge base and suggests articles to help customers solve problems on their own. Answer Bot has been around for a few years. However, Zendesk is expanding the service through integration capabilities through API, SDK, Web Widget, and forms (both email and web).

Across industries, Big Data and Artificial Intelligence (AI) have proven to be powerful tools when it comes to informing companies about their target customers. Gartner predicts that by 2019, more than 50% of organizations will redirect their investments to customer experience innovations. As a result, many organizations have built teams to collect and analyze data on every step of the customer journey – taking into account where, why and how customers interact with their channels. By analyzing this data in real time, companies are able to keep up with evolving customer demands. Dissecting every interaction to understand what drives customer behavior may seem like a gargantuan task for many.

The 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) is an annual international conference dedicated to emerging and challenging topics in intelligent data analysis, data mining and their associated learning systems and paradigms. The conference provides a unique opportunity and stimulating forum for presenting and discussing the latest theoretical advances and real-world applications in Computational Intelligence and Intelligent Data Analysis.

Walmart is using computer vision technology to monitor checkouts and deter potential theft in more than 1,000 stores, the company confirmed to Business Insider. The surveillance program, which Walmart refers to internally as Missed Scan Detection, uses cameras to help identify checkout scanning errors and failures. The cameras track and analyze activities at both self-checkout registers and those manned by Walmart cashiers. When a potential issue arises, such as an item moving past a checkout scanner without getting scanned, the technology notifies checkout attendants so they can intervene. The program is designed to reduce shrinkage, which is the term retailers use to define losses due to theft, scanning errors, fraud, and other causes.

It's not like academic researchers have the time or money to command a fleet of self-driving mapping vehicles, which is why Argoverse is so important to that community. A self-driving car is only as good as its maps. Automakers around the world have made efforts to create high-definition maps of as many roads as possible as they ramp up AV development, so that their cars can have the best idea possible of the surrounding world. But while most groups don't seem too keen on the idea of giving those maps away, Ford's Argo AI is taking a different approach. Argo AI announced on Wednesday that it has created a public repository for its self-driving-car development data, including high-definition maps.