A couple of weeks ago, I pointed out three compelling Artificial Intelligence (AI) stocks you may not have even known about. There are certainly more than three such artificial intelligence prospects though. So, today I'd like to introduce you to three more possibilities we just didn't have the time or room to explore with the first go-around. Like last time, the goal isn't to state the obvious. Most investors fully understand that Nvidia Corporation (NASDAQ:NVDA) makes the hardware that powers most AI applications while International Business Machines Corporation (NYSE:IBM) is arguably doing the most to put artificial intelligence to practical use.

President Emmanuel Macron together with many Silicon Valley CEOs will kick off the VivaTech conference in Paris this week with the aim of showcasing the "good" side of technology. Our research highlights some of those benefits, especially the productivity growth and performance gains that automation and artificial intelligence can bring to the economy -- and to society more broadly, if these technologies are used to tackle major issues such as fighting disease and tackling climate change. But we also note some critical challenges that need to be overcome. To see just how big those shifts could be, our latest research analyzed skill requirements for individual work activities in more than 800 occupations to examine the number of hours that the workforce spends on 25 core skills today. We then estimated the extent to which these skill requirements could change by 2030, as automation and artificial technologies are deployed in the workplace, and backed up our findings with a detailed survey of more than 3,000 business leaders in seven countries, who largely confirmed our quantitative findings.

As world population grows, crop production needs to keep up. Can we use Artificial Intelligence for Agriculture? Right now, AI is being (and will be) used for so many things. If you follow journals, blogs, publications, and more, you can see people solving problems from speech recognition to breast cancer detection and much more. So, why not try to solve a problem for the agricultural space. I know you may be thinking, "What?", but actually, AI -- and more specifically, Deep Learning -- can be used for this purpose as well.

When former Google CEO Eric Schmidt was asked about Elon Musk's warnings about AI, he had a succinct answer: "I think Elon is exactly wrong." "He doesn't understand the benefits that this technology will provide to making every human being smarter," Schmidt said. "The fact of the matter is that AI and machine learning are so fundamentally good for humanity." He acknowledged that there are risks around how the technology might be misused, but he said they're outweighed by the benefits: "The example I would offer is, would you not invent the telephone because of the possible misuse of the telephone by evil people? No, you would build the telephone and you would try to find a way to police the misuse of the telephone."

Developers using the edge computing platform may be interested in a new piece of hardware created by UP based in the Netherlands called UP AI Edge. As the name suggests it adds artificial intelligence and hardware acceleration'ON' the Edge of the Internet of Things. Watch the demonstration video below to learn more about the UP hardware and its specifications. The UP AI Edge platform offers developers and users and ultra-compact form factor, combining three powerful Intel technologies together into one device providing an Intel Apollo Lake SoC, Intel Movidius Myriad 2 VPU and Intel Cyclone 10GX FPGA. "Not long ago the trend has changed from the Internet of Things to Artificial Intelligence. At UP, we didn't only stop at talking about this shift but we began to actually make it! Not forgetting the post-it sized computing platform, UP Core, we've been launching each exciting project successively. With refueled excitement, we bring you UP AI Edge. The first embedded, ultra compact, high performance, low power consumption, Artificial Intelligence platform for Edge computing."

Renault is letting machine learning power the decisions it makes about influencer marketing. The automaker is testing an AI social listening tool in a number of European markets which allows it to easily segment audiences and identify influencers they trust. It's part of a wider shift by the brand to move away from mass reach and towards one-to-one communication on social, a strategy being led by Francois-Xavier Pierrel, corporate director of data, CRM and social. Pierrel was poached from Facebook last year to sit in the newly-created role, and admitted it was "scary" that the car marque didn't have anyone in this position with a firm grip on its data until just last year. However, now that he's settled in, it's clear he's trying to break down internal data silos and realign the company's social strategy to both engage and retain customers. As part of this, the brand has combined eight of its internal databases – which comprise everything from dealerships to connectivity and battery usage – with a fresh product from Socialbakers that allows it access more detailed and actionable insights: including the social content Renault's online audience responds to and the influencers they like.

Robots that train themselves in battle tactics by playing video games could be used to mount cyber-attacks, the UK military fears. The warning is in a Ministry of Defence report on artificial intelligence. Researchers in Silicon Valley are using strategy games, such as Starcraft II, to teach systems how to solve complex problems on their own. But artificial intelligence (AI) programs can then "be readily adapted" to wage cyber-warfare, the MoD says. Officials are particularly concerned about the ability of rogue states and terrorists to mount advanced persistent threat attacks, which can disable critical infrastructure and steal sensitive information.

The deployment of machine learning tools across different sectors, be it for eligibility for a loan or scoring candidates during a recruitment process, is becoming more and more common. The main privacy issues relating to machine learning (ML) tools come from the collection of large amounts of information coupled with the tool's ability to make autonomous decisions and actions aimed at maximising success. As the GDPR comes into force tomorrow (25 May 2018), the recently published'GDPR Article 29 Working Party' guidelines in automated individual decision making and profiling is likely to turn any ML activities into hurdles. Let's start by quoting the GDPR itself: 'data subjects shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her'. At first sight, this seems to read that only decisions that are based solely on automated processes trigger a right for the data subject to opt out.

Digital Transformation continues to gain velocity. But even as technology takes over many tasks, it's important that a Human touch is retained. This thought-provoking article by Bryan Kramer s another in our "Great Articles You may have missed" series. A few years ago, experts were trumpeting that the future is mobile, and they weren't wrong. Some of the world's most successful new apps and business models are mobile-based -- just look at Uber, Instagram, and Snapchat.

Qualcomm announced a new division that would unify all its fundamental artificial intelligence research. Qualcomm A.I. Research gives form to what was largely an amorphous effort inside the company, which is focused on moving inference out of the cloud and into devices installed with its mobile chips, such as smartphones, warehouse robots, cars and security cameras. The reorganization reflects Qualcomm's doubling down on embedded artificial intelligence, which it argues can improve privacy for applications like voice-controlled speakers and save energy wasted sending information to the cloud. Taking artificial intelligence – a blanket term that includes machine learning – out of the cloud would also lower latency, which is important in mission-critical devices that require fast reaction times, like driverless cars. Accordingly, the company is focused on model compression and efficient hardware to squeeze as much processing as possible from embedded devices constrained by power and heat. Qualcomm A.I. Research is also targeting more data efficient models in machine learning, as well as system architecture problems – like sensor fusion and multimodal learning – and device personalization.