Tesla and SpaceX CEO Elon Musk and Alibaba founder and Chairman Jack Ma kicked off the 2019 World Artificial Intelligence Conference in Shanghai, China, with an informal debate about AI and its implications to humanity. Throughout their conversation, Musk and Ma touched on several topics, from jobs, the need for educational reform, moving to Mars, and how humans' way of life can improve in the future. The two billionaires have vastly differing points of view concerning artificial intelligence. While Musk is cautious about AI considering the dangers it may pose to humanity, Ma is far more optimistic. "I don't think AI is a threat," Ma said, responding to the Tesla CEO's introductory points.

Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founding father of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast. OUTLINE: 0:00 - Introduction 1:11 - HAL 9000 and Space Odyssey 2001 7:49 - The surprising thing about deep learning 10:40 - What is learning?

NEW YORK–(BUSINESS WIRE)–The World Artificial Intelligence Conference 2019 (WAIC 2019) opened grandly in Shanghai on August 29.

NEW YORK--(BUSINESS WIRE)--The World Artificial Intelligence Conference 2019 (WAIC 2019) opened grandly in Shanghai on August 29. With the theme of "Intelligent Connectivity, Infinite Possibilities", it gathers the world top minds for the highest-level AI academic discussion. Government officials and most influential AI scientists, entrepreneurs and investors across the world witnessed the grand opening of WAIC 2019. The most eye-catching dialogue between Jack Ma and Elon Musk talked about the impact of AI on human life, employment, life, consciousness and environment and inspired people's imagination on the future AI world, marking the climax of the opening ceremony. There are dozens of key sessions where industry leaders will focus on frontline algorithms, brain-inspired intelligence and AI chips and share latest research results and practice of key technologies; focus on autonomous driving, AI 5G and smart robots and analyze AI industry trends.

The traditional finance sector as we know it is going through a process of change. As new technologies disrupt the conventions and dogmas, whole industries are transformed, keeping pace with the rapidly-changing world. Finance is no exception to this rule, and, as a sphere that lives and breathes quantitative data (lots and lots of quantitative data!), it has been particularly sensitive to the rise of the artificial intelligence, a technology driven by the computers' newfound ability to crunch massive troves of data. And while it may or may not be too early to speak of the financial industry as we are about to know it, it is already clear that AI in finance is very much a part of it. But before we delve into all the exciting ways that AI is reshaping the financial sector in, it may be worth saying a few words about the technology itself, especially given all the buzz and hype around it.

In April 2019 our partner Schneider Electric launched EcoStruxure Workplace Advisor, a smart building application aiming to increase the efficiency of managed office facilities. In this posting I want to outline the general architecture of this application which is based on Trinity RDF: our enterprise .NET API which enables developers without RDF experience to build knowledge graph applications. For anyone interested in increasing the productivity and flexibility of knowledge graph development teams I would like to advertise my talk on Tuesday where I will share more details about the case. The industry use case I will be presenting is Schneider Electric's EcoStruxure Workplace Advisor. Using this service one can derive actionable insights about a building through intuitive dashboards that analyse and integrate data from numerable IoT sensors and systems.

It admittedly sounds a little like Big Brother, that a robot can tell significant things about your personality, merely by looking into your eyes. Yet, that is the hiring territory that we are fast approaching – although we may not be sitting across from androids in interviews anytime soon. The use of artificial intelligence in making HR decisions is, while fraught with peril, not without its promising aspects. In an era when it is increasingly difficult for businesses to unearth the best job candidates, we may yet see the day when technology makes it possible to separate good from bad in the blink of an eye. Despite caveats about security and privacy, relying on AI would appear to be a method far superior to digging through a pile of resumes or asking ice-breaking questions like, "What's the last book you read?" Hiring good people – people who are talented, agreeable and work well with their coworkers – goes a long way toward nipping workplace conflicts in the bud.

As the world becomes increasingly connected through advancements in technology, ensuring the safety and security of automobiles, drones, electronic devices and our cities is a top priority. Artificial intelligence (AI) is a powerful tool that's being used to improve nearly every industry. From digital farming tools used to help growers optimize and sustain their crops, to driverless shuttles that aim to improve mobility solutions in cities worldwide, AI technology is rapidly transforming business models across the globe. Although the industries may be different, the goal remains the same: to use machine learning to create efficiencies and improve operations to produce safer, more effective products for consumers. If the end game is increased safety, cybersecurity needs to be a large part of the conversation.

Data is quickly becoming the most valuable asset in the insurance sector, given its tremendous volume in our digital era. Simultaneously, Artificial Intelligence (AI), harnessing big data and complex structures with Machine Learning (ML) and other methods, is becoming more powerful. Insurers expect more efficient processes, new product categories, more personalized pricing, and increasingly real-time service delivery and risk management from this development. Given the many leverage points in insurance, it surprises that AI-driven digitization is not evolving more rapidly. When according to a recent Gartner[2] study, 85 % of data science projects fail, how can insurance companies make sure that their projects are among the successful ones[3]?