There is still no Wikipedia explanation on Social Data Science, not that it would make it established as a field, but it is more of a side note in these beginnings. The last few days I have been considering how to put together a program that would unite people from different backgrounds to explore the topic of artificial intelligence. Doing so led me into the path of exploring firstly four different modules relating to: AI, science and technology studies (STS) and computer science ethics. To begin with I would say there does not seem to be very fruitful to mesh them together without a thought or regard for each field and their historical connotations, literature, personas and so on. However new practitioners or researchers who increasingly combine an understanding or expertise in social science with the performative skills in programming is edging into the conceived emerging field of social data science. I find this worthy of further exploration.

Artificial intelligence will create new liabilities for organizations, but it can also be harnessed as a risk management tool, a panel of experts said. By processing high volumes of data, risk managers can get a better grasp of the risks they face, spend less time on repetitive tasks and use connected devices to enhance their risk management processes, they said. Companies are already implementing AI, and risk managers should ensure they are aware of the risks and liabilities that the technology creates, said Philippe Cotelle, head of insurance and risk management at Airbus Defence & Space, a division of Airbus SE in Toulouse, France. He was speaking during a session of the Federation of European Risk Management Associations' biennial forum in Berlin on Monday. Risk managers should make sure they are "going to capture what is within AI both in terms of risks and in terms of opportunity, and it becomes a tool for the risk manager," Mr. Cotelle said.

According to a recent study conducted by Forrester Research, waiting in the checkout line is the top complaint among U.S. grocery, mass-merchandise, and convenience store shoppers. Mega-retailer Amazon and a quartet of well-funded retail technology startups -- Zippin, Standard Cognition, Grabango and Trigo -- believe they have the solution to the problem: Checkout-free stores powered by various technologies that enable shoppers to walk into the store, grab what they want off the shelves and just walk out. Autonomous checkout, another term for checkout-free, is becoming one of the hottest areas of retail investment today. It comes as the convenience expectations of today's Amazon-shopping, Grubhub-ordering, Uber-hailing consumers are ever-increasing, and informing their in-real-life (IRL) shopping demands. Brands are responding in kind, delivering digital services aimed at automating mundane tasks -- in this case, the checkout process -- so much so that the result is meant to feel "automagical," according to trend forecasting firm TrendWatching. Checkout-free retail has the potential to make shopping even more convenient, retail technology consultant Richard Crone said at this summer's National Retail Federation's NRF Tech 2019 conference in San Francisco.

The other streaming war – bringing video games to the cloud – is heating up. Google flips the switch Tuesday on its Stadia cloud gaming service, which lets you stream games to your television, computer, tablet and Android phone. Stadia lets you play marquee video games without the need of a game console or PC to house the game. Instead your game resides on Google's expansive array of data servers. As part of the monthly subscription fee, Stadia stores your games and connects you with other gamers in games that support multiplayer gaming.

Ad blockers aren't going to be useful for much longer. Major entertainment companies including NBC Universal and 20th Century Fox are now using artificial intelligence to digitally insert advertisements and products into movies and TV shows after they have been filmed. The firms are using technology developed by UK company Mirriad to insert flat posters on buildings, walls and buses in already-filmed scenes and even to add 3D objects. It has the potential to make advertising more targeted and ubiquitous than ever before, and also virtually impossible to avoid.

These days it seems like nearly every tech startup is touting the use of AI in their products or business processes. They release press and marketing materials advertising smart, new features that "look" like artificial intelligence, all under the guise that this rebrand will serve the end user better. "We're doing it to save you money …. Unfortunately, recent data shows that companies are less than honest about their use of artificial intelligence, advertising AI product features that are really just basic automation technology features. There is value -- cash value -- associated with a company's ability to appear "tech-savvy". The UK investment firm MMC Ventures says that startups with some type of AI component can attract as much as 50 percent more funding than other software companies. Nevermind that the Wall Street Journal suspects 40 percent or more of those companies don't use any form of real AI at all. "Artificial Intelligence" is the ultimate marketing buzzword.

It is only a matter of time before driverless cars take us to work and our children to school, according to James Peng, CEO and co-founder of Pony.ai, a California-based self-driving car start-up. "If I have to give a number, I'll say probably in five years," Peng told CNBC's Deirdre Bosa at the East Tech West conference in the Nansha district of Guangzhou, China. "We'll definitely see a wide adoption of autonomous driving vehicles -- fully autonomous driving vehicles -- on the open roads." That could happen in any part of the world, but Pony.ai has been focusing on the U.S. and China, where the start-up has been testing autonomous vehicles. It recently partnered with Hyundai to introduce an on-demand vehicle service for residents in Irvine, California, where passengers can share autonomous cabs using an app.

Our mission is to revolutionize the future of transportation by building the safest and most reliable technology for autonomous vehicles. Armed with the latest breakthroughs in artificial intelligence, we aim to deliver our technology at a global scale. We believe our work has the potential to transform lives and industries for the better. When it comes to our technology, quality and reliability are hallmark attributes; we don't believe in taking shortcuts. Our emphasis on craftsmanship enables us to deliver an autonomous driving solution that is highly sophisticated and best-in-class.

In this post we are going to learn about Venus, my deep learning computer, and how I built it. Along the way, I will explain at a high-level what each hardware component of a computer does and how I navigated the landscape of selecting parts for a functional build. I'll also describe how I installed relevant software for the machine and include some benchmarks showing the superior performance of a GPU system over a pure CPU system. WARNING: this is a pretty long post that functions as a complete tutorial for building a deep learning computer literally from scratch, no assumptions made. But…since it's long I highly encourage you to peruse and skip any sections depending on your interest. While there are numerous build descriptions out there showing how people constructed their own deep learning rigs, as I went about consulting some of them, I often felt there was some crucial component missing. As you start on your build journey, it's easy to get mired in the weeds of hardware terminology. Should I pick an M.2 SSD or will SATA suffice? Can I get away with HDD? How many PCIe x16 slots do I need? Should I pick DDR4-3000 or DDR4-2400 memory? All this lingo can be very overwhelming especially for newcomers to hardware. But before we start shamelessly name-dropping so that we sound smart, let's go back to the fundamentals.

Companies have historically used focus groups and surveys to understand how people felt. Now, emotional AI technology can help businesses capture the emotional reactions of both employees and consumers in real time -- by decoding facial expressions, analyzing voice patterns, monitoring eye movements, and measuring neurological immersion levels, for example. The ultimate outcome is a much better understanding both of workers and customers. But, because of the subjective nature of emotions, emotional AI is especially prone to bias. AI is often also not sophisticated enough to understand cultural differences in expressing and reading emotions, making it harder to draw accurate conclusions.