Bono and The Edge are excited by Nuritas' potential for impact and profitability. Try to imagine walking into your local grocery store ten years from now. You head towards the back, as you've done countless times before, to snatch a handful of your favorite cereal bars. Only this time, the packaging looks a bit different. You notice that there are two new versions, each with a label below the brand name.

I met Aman Naimat, Senior Vice President of Technology at Demandbase at their headquarters in San Francisco on my recent visit to California for Social Media Strategies Summit. Aman is working on leveraging the latest developments in Artificial Intelligence (AI) and data science for marketing and sales platforms. On this podcast, episode 150, Aman and I discuss how AI functions in account-based marketing (ABM). Before working with Demandbase Aman was previously founder and CTO of Spiderbook, a data-driven sales engine for account-based targeting. Aman has been building CRM systems since he was 19 and was the architect for the Oracle CRM Applications.

Business leaders want to understand the return they are getting from social media marketing investments, just like all other marketing channels. Additionally, they want to understand how their brand's efforts are doing compared to the competition and overall industry. Today, brands can use Artificial Intelligence (AI) to see real-time performance of their social media efforts and to develop a social media competitor analysis. Most social networks offer analytics and insights for the profiles you manage. Plus, there is no shortage of social media management tools that import data into a centralized dashboard.

Can artificial intelligence, or machine learning, be deployed to predict earthquakes, potentially saving thousands of lives around the world? Some seismologists are working to find out. But they know such efforts are eyed with suspicion in the field. "You're viewed as a nutcase if you say you think you're going to make progress on predicting earthquakes," Paul Johnson, a geophysicist at Los Alamos National Laboratory, told Scientific American. In the past, scientists have used various criteria to try to predict earthquakes, including foreshocks, electromagnetic disturbances, changes in groundwater chemistry.

In October 2014, speaking to students at MIT, Elon Musk (CEO of SpaceX and Tesla) proclaimed that A.I. was the greatest threat to mankind, saying "I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it's probably that. So we need to be very careful."[i] Then, in December 2014, renowned physicist Stephen Hawking added fuel to the fire, saying "The development of full artificial intelligence could spell the end of the human race … It [A.I.] would take off on its own, and re-design itself at an ever-increasing rate," he said. "Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded."[ii]

Under the watchful eyes of the public and lawmakers, several traditional automakers and tech companies have been racing to the finish line of "fully autonomous driving." Companies are already testing their cars in real-world conditions on public streets, and to date, some have driven millions of miles. Though opinions vary, it is hypothesized that we will be safer in autonomous vehicles (AKA self-driving cars). And while we would really need significantly more data to determine this as a statistic, I can imagine the experience of riding in full autopilot will be somewhat like flying: though much scarier than driving (for most), you are still safer, statistically-speaking. Regardless, it will be some time before the majority of average consumers have replaced their conventional vehicles.

Machine learning and artificial intelligence are terms that any carbon-based humanoid operating in 2017 has probably heard or discussed in one way or another. What was the stuff of science fiction two decades ago is now science, and it is present everywhere. Machine Learning – type of artificial intelligence enabling computers to learn without the explicit programming to do so. It is essentially the evolution of programming, whereby millions of formulae and functions are converged to work in synchronicity and analyse masses of data. Almost like adding a trend line to a data set, on top of a data set, on top of another data set, ad infinitum.

Like all hardware device makers eager to meet the newest market opportunity, Intel is placing multiple bets on the future of machine learning hardware. The chipmaker has already cast its Xeon Phi and future integrated Nervana Systems chips into the deep learning pool while touting regular Xeons to do the heavy lifting on the inference side. However, a recent conversation we had with Intel turned up a surprising new addition to the machine learning conversation--an emphasis on neuromorphic devices and what Intel is openly calling "cognitive computing" (a term used primarily--and heavily--for IBM's Watson-driven AI technologies). This is the first time to date we've heard the company make any definitive claims about where neuromorphic chips might fit into a strategy to capture machine learning, and marks a bold grab for the term "cognitive computing" which has been an umbrella term for Big Blue's AI business. Intel has been developing neuromorphic devices for some time, with one of the first prototypes that was well known in 2012.

A machine-learning'breakthrough'on Alphabet's Loon project to connect the unconnected in the world's underdeveloped and remote areas could accelerate progress on global Internet connectivity. Project Loon has "now exceeded even their own expectations for how well their smart software algorithms can help their balloons navigate the globe, and in the process they've leapt much closer to a day when balloon-powered Internet could become a reality for people in rural and remote regions of the globe," wrote Astro Teller, the head of X, Alphabet's self-described'moonshot factory' that houses Project Loon and has produced such innovations as Waymo, Alphabet's self-driving car project. Project Loon is a network of free-flying, high-altitude balloons that beam Internet to earth. And the machine-learning breakthrough announced yesterday comes at an important time, because the project has struggled with several issues that have brought its viability into question, including renegade balloons that floated off-course or crashed. "Although our navigation algorithms can get even better, and we need to test them in many other parts of the world, this is a positive sign for Loon's economic and operational viability," Teller wrote.

SAN FRANCISCO – Alphabet chairman Eric Schmidt says artificial intelligence is key to advances in diverse areas such as healthcare and datacenter design and that security concerns related to it are somewhat misguided. In a wide-ranging on-stage conversation here at the RSA Security conference with Gideon Lewis-Kraus, author of The Great A.I. Awakening, Schmidt shared his insights from decades of work related to AI (he studied AI as a PhD student 40 years ago) and why the technology seems to finally be hitting its stride. In fact, last year Google CEO Sundar Pichai said AI is what helps the search giant build better products over time. "We will move from a mobile-first to an AI-first world," he said. Asked about that, Schmidt said that Google is still very much focused on mobile advances.