Artificial intelligence research has a long history, but most of the investment community s views of AI are shaped by the media. The robot in Lost in Space, HAL 9000 in 2001: A Space Odyssey, Skynet in The Terminator these media tropes subconsciously shape our perceptions of what AI is and what it could be capable of. We dream of (or maybe dread) a HAL 9000 computer sitting next to us as we trade. As we move our mouse to buy some shares, it will sonorously intone, I don t think you should do that, Dave, or maybe it will just lock us out of the trading room when we are about to make a mistake. Robert Harris s 2011 novel The Fear Index develops this theme near the dramatic conclusion.

If there was any doubt that AI has officially arrived in the world of enterprise software, Salesforce just put it to rest. The CRM giant on Sunday announced Einstein, a set of artificial intelligence capabilities it says will help users of its platform serve their customers better. AI's potential to augment human capabilities has already been proven in multiple areas, but tapping it for a specific business purpose isn't always straightforward. "AI is out of reach for the vast majority of companies because it's really hard," John Ball, general manager for Salesforce Einstein, said in a press conference last week. With Einstein, Salesforce aims to change all that.

Lamp lighters once performed a vital service for Londoners. Every evening as dusk fell they lit the gas lamps that illuminated the city's shadowy streets, returning just before dawn to extinguish the flames. It was a respectable job, often passed from father to son. But, apart from the small band of British Gas engineers who maintain the 1,500 gas lamps still clustered around the royal parks, Westminster and Covent Garden, lamp lighters are now a thing of the past, their jobs snuffed out by automated timers and electricity. The lamp lighters are part of a wider narrative that shapes every city: technological change. Scientific advances and new technologies often enable dramatic improvements in public services and urban life, eradicating some jobs while creating new types of employment.

Startup Drive.ai is revealing its product and strategy for the first time, and the autonomous driving tech company is looking not only to create the best hardware and software to enable self-driving cars, but also to make sure those cars communicate with people outside of the car in the most effective way possible. Core to Drive.ai's approach is using deep learning across the board in its autonomous driving system, which means they're teaching their self-driving cars somewhat like how you'd teach a human. That involves providing a host of examples of situations, objects and scenarios and then letting the system extrapolate how the rules it learns there might apply to novel or unexpected experiences. It still means logging a huge number of driving hours to provide the system with basic information, but Carol Reiley, co-founder and president of Drive.ai, "We are using deep learning for more of an end-to-end approach. We're using it not just for object detection, but for making decisions, and for really asking the question'Is this safe or not given this sensor input' on the road,'" Reiley explained.

SAP is committed to the principles of Equal Employment Opportunity and to providing reasonable accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please contact us at EMEA: 49 6227 7 48599 and careers@sap.com, Requests for reasonable accommodation will be considered on a case-by-case basis.

Even though only 15% of the respondents think artificial intelligence is having a large impact on their business today. Forty-six percent believe AI will be critical to them within the next 40 months - that's a 207% predicted increase in business impact. Only 18% of digital leaders in our survey report bots are having a "large to very large" impact on their businesses today, but by year 2020, it jumps to 41%. Without the implementation of an OILS, your IT infrastructure will be too slow to operate today in an increasingly mobile world, and too slow to add the additional layers of AI, bot and predictive functionality for tomorrow.

The Yomiuri Shimbun Next fiscal year the government plans to start developing a system for medical information and communication using artificial intelligence (AI, see below) to minimize the damage to humans in the event of disasters, The Yomiuri Shimbun has learned. If a large-scale disaster occurs, the AI technology will analyze the equipment and transportation routes that are best suited for the treatment of injured people and notify concerned authorities of the information. The government expects that high-level information-processing capabilities through AI will help reduce the damage of future disasters, and it aims to put the system into practical use within three to five years. To prepare for earthquakes and other natural disasters whose scales of damage have been increasingly large, the government intends to regard the disaster management field as a priority issue in AI development. The Internal Affairs and Communications Ministry and the National Institute of Information and Communications Technology -- a state-run research and development institute -- will begin studies for the development in April next year jointly with private companies.

The media has declared chatbots the digital version of the little black dress: a technology staple that every brand must now have and every payment type must now commerce-enable. Technology enthusiasts draw pictures on whiteboards showing bots as the new king of the stack, out-stacking the last great stack -- apps, which sit on top of the next-to-last great stack -- mobile operating systems, which sit on top of the next-to-next-to-last great stack -- the web. VCs tout bots as commerce's next big frontier, one that can make the notion of contextual, conversational commerce real inside of the app where consumers now spend 75 percent of their time: messaging. The big bet is that bots will make it possible for brands to indulge the every digital whim of this group of consumers without them ever having to leave their digital home away from home. Facebook Messenger's announcement last week that 30,000 bots would soon be payment-enabled for the 900 million people who hang inside of its messaging ecosystem only stoked that fire.

HopData is a machine learning service that makes it easy for developers of all skill levels to integrate machine learning technology in their apps. HopData provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Each dataset is optimized automatically to achieve better accuracy and then trained on multiple algorithms. Once your model is ready, HopData makes it easy to obtain predictions using simple API.

There's no doubt that 2016 has been a sensationally hot year for auto tech. This week's acquisition of Chariot by auto manufacturer Ford marked just another indication that big auto makers are seriously entering ride-sharing services. Other M&A activity this year such as GM's acquisition of Cruise or Volvo's partnership with Uber has firmly pointed to big auto certainly also starting to secure paths for monetizing on the future fleets of autonomous vehicles. These recent energitic developments, as well as Elon Musk's Master Plan Deux, makes one thing clear; the transition to autonomous autos will be a reality sooner than later. Consequently, the debate on unemployment for drivers is a pressing matter.