The growth of machine learning and its ability to provide deep insights using big data continues to be a hot topic. Many C-level executives are developing deliberate ML initiatives to see how their companies can benefit, and cybersecurity is no exception. Most information security vendors have adopted some form of ML, however it's clear that it isn't the silver bullet some have made it out to be. While ML solutions for cybersecurity can and will provide a significant return on investment, they do face some challenges today. Organizations should be aware of a few potential setbacks and set realistic goals to realize ML's full potential.

Symbolic AI algorithms have played an important role in AI's history, but they face challenges in learning on their own. After IBM Watson used symbolic reasoning to beat Brad Rutter and Ken Jennings at Jeopardy in 2011, the technology has been eclipsed by neural networks trained by deep learning. The power of neural networks is that they help automate the process of generating models of the world. This has led to several significant milestones in artificial intelligence, giving rise to deep learning models that, for example, could beat humans in progressively complex games, including Go and StarCraft. But it can be challenging to reuse these deep learning models or extend them to new domains. Now researchers and enterprises are looking for ways to bring neural networks and symbolic AI techniques together.

It does not access other files in your codebase and does not access other resources on your machine.

In 2004, the U.S. Department of Defense issued a challenge: $1 million to the first team of engineers to develop an autonomous vehicle to race across the Mojave Desert. Though the prize went unclaimed, the challenge publicized an idea that once belonged to science fiction -- the driverless car. It caught the attention of Google co-founders Sergey Brin and Larry Page, who convened a team of engineers to buy cars from dealership lots and retrofit them with off-the-shelf sensors. But making the cars drive on their own wasn't a simple task. At the time, the technology was new, leaving designers for Google's Self-Driving Car Project without a lot of direction. YooJung Ahn, who joined the project in 2012, says it was a challenge to know where to start.

Trinamix, a Platinum-level member of Oracle PartnerNetwork (OPN), announced the launch of Industry 4.0 Lab to help enterprises enable smart manufacturing using Oracle Internet of Things (IoT), Oracle Adaptive Intelligence, and Oracle Blockchain applications. Hosted at Trinamix's San Jose office, the lab will offer visiting customers a miniature experience that highlights how Oracle solutions will work in an industrial scenario, connected with machines through devices and gateways. Trinamix has been working on Industry 4.0 (I4.0) solutions for the last two years and has undertaken various initiatives to provide an end-to-end offering to its customers. Trinamix offers design thinking, creating quick proof of concept, road map and implementation services on I4.0. This lab drives Trinamix's focus on the newest and the next wave of disruptive technologies including Artificial Intelligence, blockchain, data science and factory automation.

Artificial intelligence (AI) can revolutionise business, but it should not be deployed unless there is a solid business case for it. This is according to Johan Steyn, chair of the Institute of Information Technology Professionals South Africa's (IITPSA) AI and robotics special interest group (SIG), who was addressing the Institute's first Tabling Tech Webinar this week. Steyn said AI could improve business in areas ranging from HR, sales and finance through to R&D and customer service. In fields such as human capital management, AI could enhance recruitment by scanning candidates' social media feeds and monitoring their micro-expressions during interviews; it could help personalise training and development programmes and pre-empt the loss of key skills. In sales, AI could ensure products and services met customer needs and wants, and predict market changes.

Artificial intelligence is the future. If "Westworld" or "Black Mirror" are to be believed, there will soon come a day when the computers rule us all. But for now, an AI's power ends at the US Patent Office. The USPTO has denied a pair of patents filed on behalf of DABUS, an artificial intelligence system, and published a ruling that says US patents can only be granted to "natural persons." The two patents were for a food container and a flashlight, and were filed by Stephen Thaler, an AI researcher and DABUS' creator. According to the filing from the USPTO, Thaler calls DABUS a "creativity machine" and wanted the AI to get full credit for the inventions.

Manufacturing is a robust industry that requires high levels of accuracy, continuous improvements in the production quality, and the best possible maintenance processes. Artificial intelligence has been steadily supported these processes, giving manufacturers the opportunity to increase productivity as well as profits. The benefits of applying artificial intelligence solutions to the manufacturing industry are numerous, and here we will show several cases to see how far AI has come. Maintenance is one of the most critical parts of the manufacturing industry. Instead of guessing and causing potential issues in the production process, predicting when machines need maintenance will prevent unplanned downtime.

About 110 U.S. service members were diagnosed with traumatic brain injuries after the Iranian ballistic missile attack at al-Asad Air Base in Iraq on Jan. 8. More than a dozen missiles struck the base in an attack that Iran carried out as retaliation for a U.S. drone strike in Baghdad that killed Tehran's most powerful general, Qassem Soleimani, on Jan. 3. Troops at al-Asad were warned of an incoming attack, and most were in bunkers scattered around the base.

Sun Jifa is a Chinese veteran who has created his own bionic arms and is now helping other amputees in his country. At age 27, Sun lost his hands in a fishing-related explosion. Unfortunately, he soon realised he could not afford the prosthetic hands recommended by the local hospital. After eight years of creating prototypes, he says he finally developed a working model that would allow him to grip, hold, and mimic other necessary movements via a system of pulleys and wires. "Up to now, my artificial limbs are beautiful and can be designed with the help of the disabled person's own motivation," Sun explains.