Even in traditional industries, aspirations are high. If there was a single overarching theme in our conversations with executives, it was the desire to succeed in digital and AI by developing new offerings and changing their companies' business models. For example, RBL Bank has launched India's first profitable open banking initiative. By opening its APIs to the public, RBL is allowing other companies to develop innovative services for the bank's customers. We see this trend in other industries, too.

AI has truly been a far-flung goal ever since the conception of computing, and every day we seem to be getting closer and closer to that goal with new cognitive computing models. Coming from the amalgamation of cognitive science and based on the basic premise of simulating the human thought process, the concept, as well as applications of cognitive computing, are bound to have far-reaching impacts on not just our private lives, but also industries like healthcare, insurance and more. The advantages of cognitive technology are well and truly a step beyond the conventional AI systems. According to David Kenny, General Manager, IBM Watson -- the most advanced cognitive computing framework, "AI can only be as smart as the people teaching it." The same is not true for the latest cognitive revolution.

ABBYY, a global leader in Content IQ technologies and solutions, today announced it has signed an agreement to acquire Philadelphia, Pennsylvania-based TimelinePI. TimelinePI provides a comprehensive process intelligence platform designed to empower users to understand, monitor and optimize any business process. The global process analytics market size is expected to grow to USD 1,421.7 million by 2023 according to Research and Markets. The acquisition of TimelinePI is a strategic investment by ABBYY into the emerging process intelligence market which is critical to truly understanding the impact and effectiveness of business processes and opportunities for productivity gains from digital transformation investments. TimelinePI's vision of combining the most versatile process mining and operational monitoring with cutting-edge, process-centric AI and machine learning will serve as a critical cornerstone to ABBYY's Digital IQ strategy.

From treating chronic diseases and reducing fatality rates in traffic accidents, to fighting climate change and anticipating cybersecurity threats, Artificial Intelligence (AI) is no longer considered a futuristic construct – it is already a reality and is helping humanity solve pressing global challenges. It significantly improves peoples' lives, helps with day-to-day tasks and benefits society and the economy. Nevertheless, AI applications should not only be consistent with the law, but also adhere to ethical principles. The ethical dimension of AI is not a luxury feature or an add-on: it needs to be an integral part of AI development. The European Commission recognises AI as one of the 21st century's most strategic technologies and is therefore increasing its annual investment in AI by 70% as part of the research and innovation programme Horizon 2020, reaching €1.5 billion for the period 2018-2020.

Facebook has publicly spoken about its interest in robotics in the past, but on Monday, the company finally shared details regarding the specific projects it's working on. The social media giant unveiled three robotics projects that it hopes will contribute to solving the ongoing challenge of building artificial intelligence systems that don't have to rely on large quantities of labeled data to learn new information. To do so, the company is conducting research aimed at teaching robots how to learn about the world, similar to the way that humans do. "The real world is messy, it's difficult," Roberto Calandra, a research scientist in Facebook's AI division said when speaking to Business Insider. "The world is not a perfect place; it's not neat. So the fact that we are trying to develop algorithms that work on real robots [will] help to create [AI] algorithms that, generally speaking, are going to be more reliable, more robust, and that are going to learn faster."

This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. AI might be a hot topic but you'll still need to justify those projects.

If cyber is the ultimate team sport, as many in the U.S. Defense Department like to say, then artificial intelligence (AI) would likely be the number one draft pick for the Defense Information Systems Agency (DISA). Anthony "Tony" Montemarano, DISA's executive deputy director, stressed the importance of AI during a luncheon plenary on the final day of the AFCEA TechNet Cyber conference in Baltimore. "We've heard about it time and again. Artificial intelligence is probably the most significant technology we have to come to grips with." The younger generation, he noted, does not want to be burdened with security barriers when using technology.

We have come to the fifth month of the year, and technology especially the disruptive one that includes Artificial Intelligence (AI) is gaining strong-hold more than ever. Understanding its disruptive factors is important as it enables more accurate forecasting and better planning for civil society, policymakers and businesses. Identifying the main levers that drive the growth of AI applications can help to expedite the many positive use cases in the pipeline; like optimised renewable energy distribution at scale and Machine Learning disease diagnosis systems in healthcare. So how are the disruptive technologies redefining businesses sphere? Over the years, it is been seen that AI adaptability is increasing.

Allied Market Research recently published a report, titled, "Automotive Artificial Intelligence Market by Component (Hardware, Software, and Service), Technology (Machine Learning & Deep Learning, Computer Vision, and Natural Language Processing), and Application (Semi-Autonomous and Autonomous): Global Opportunity Analysis and Industry Forecast, 2017 – 2025." The report offers a detailed analysis of top investment pockets, top winning strategies, drivers & opportunities, market size & estimations, competitive landscape, key segments, and changing market trends. According to the report, the automotive AI market was pegged at $445.81 million in 2017 and is anticipated to hit $8.89 billion by 2025, registering a CAGR of 45.0% from 2018 to 2025. Rise in demand for enhanced user experience as well as convenience features and growing demand for autonomous vehicle have fueled the growth of the global automotive AI market. On the other hand, rise in various security & privacy concerns hamper the growth to certain extent.

F. Allen et al., "Predicting the mutations generated by repair of Cas9-induced double-strand breaks," Nat Biotechnol, 37:64–72, 2019. During gene editing with CRISPR technology, the Cas9 scissors that cut DNA home in on the right spot to snip with the help of guide RNA. The way the genetic material is stitched back together afterward isn't terribly precise, though; in fact, scientists have long thought that without a template, the process is random. However, "there's been anecdotal evidence that cells don't repair DNA randomly," geneticist Richard Sherwood of Brigham and Women's Hospital tells The Scientist. A 2016 paper also suggested patterns in the repairs. Sherwood wondered if artificial intelligence could predict these outcomes.