Formula E Racing, like its Formula 1 counterpart, relies on speed and strategy to win. But how do you crunch through the reams of data that you can get from an electric race car and analyze it in a way that would help your driver and your racing team beat the competition? And that's why he has partnered with Sanjay Srivastava, Chief Digital Officer of Genpact, to leverage data analytics and artificial intelligence (AI) to build a multi-layer platform that turns a mountain of data into actionable analysis. Formula E racing produces different types of data across many fronts. There's a set of telemetry data from the cars, a stream of large data sets that cars produce while they are on the road, and data from competing drivers and their vehicles. Then there's data gleaned from weather, satellite, traffic, and road patterns. All that needs a data analytics system that can interpolate the information as it comes in from all these sources and analyze it in real-time in a way that the driver and the racing team can absorb and act upon instantaneously. But, as Sylvain points out, that's easier said than done, especially since a Formula E race happens in just one day, and every second counts. As Sylvain and Sanjay explain, it starts with knowing how to structure the incoming information so that the driver and engineers can act upon it quickly. That means setting up the correct algorithms, developing an analytical infrastructure that--with the help of AI--integrates all of the different types of data, and synchronizing it to give the driver and engineers the whole picture and predict the likeliest outcomes in any given scenario in order to make the right decisions during the race. That also means creating a user interface for the data that's both comprehensive and instantly comprehensible to the driver. The work that Sylvain and Sanjay are doing has notable implications for business that goes beyond racing. The technologies they are developing will trickle down to make electric cars and sustainable energy better. The analytics tools they are creating can potentially be utilized by other companies to make better sense of data coming from multiple sources in order to make well-informed business and digital transformation decisions and do so quickly, and to manage their resources more efficiently. This transcript has been edited for length and clarity. Michael Krigsman: Formula E Racing involves cars, speed, data, and advanced technologies such as AI and machine learning.

WASHINGTON, D.C.--(BUSINESS WIRE)--At the Association for the United States Army (AUSA) conference today, General Micro Systems (GMS) announced that its new S422-SW and X422 combination has been chosen for two new military development programs. The system pair brings a massive amount of server processing power, 10/40/100 Gigabit networking ports for sensors, and general-purpose graphics processing unit (GPGPU) artificial intelligence (AI) onto the battlefield for the first time in two small "shoebox-sized" rugged chassis designed to survive the harshest conditions where regular rackmount servers cannot. The two programs that selected the S422-SW "Thunder" and X422 "Lightning" combo will deploy it in mobile platforms to move IP-based sensor data instantaneously over multi-sensor LANs into the server and AI processor. Once processed, the server reports out to operators information that can help maneuver a vehicle or UAS in real-time, calculate a fire control solution for a weapon, or identify threats such as stationary IEDs or incoming objects such as projectiles. "The tremendous processing power of this combo makes it a highly attractive option for these two development programs as well as others creating autonomous, self-driving or self-piloting vehicles," said Ben Sharfi, chief architect and CEO, General Micro Systems.

I am the co-founder and CEO of Neurala Inc., a Boston-based company building Artificial Intelligence emulating brain function in software. Neurala's deep learning tech makes robots, drones, cars, consumer electronics, toys and smart devices more useful, engaging and autonomous. Neurala stems out of 10 years of research at Boston University Neuromorphics Lab, where as AI Professor I have pioneered the research and fielding of brain-inspired (also called Deep Learning, or Artificial Neural Networks) algorithms that allow robots and drones to perceive, navigate, interact and learn real-time in complex environments. Over my academic and industrial career, I have lectured and spoken at dozens of events and venues, including TEDx, keynote at Mobile World Congress Drone Summit, NASA, the Pentagon, GTC, InterDrone, Los Alamo National Lab, GE, Air Force Research Labs, HP, iRobot, Samsung, LG, Qualcomm, Huawei, Ericsson, BAE Systems, AI World, Mitsubishi, ABB and Accenture, among many others. My work has been featured in TIME, IEEE Spectrum, Fortune, CNBC, The Boston Globe, Xconomy, The Chicago Tribune, TechCrunch, VentureBeat, Nasdaq, Associated Press and many other media.

Everyone had died – not that you'd know it, from how they were laughing about their poor choices and bad rolls of the dice. As a social anthropologist, I study how people understand artificial intelligence (AI) and our efforts towards attaining it; I'm also a life-long fan of Dungeons and Dragons (D&D), the inventive fantasy roleplaying game. During a recent quest, when I was playing an elf ranger, the trainee paladin (or holy knight) acted according to his noble character, and announced our presence at the mouth of a dragon's lair. But while success in D&D means'beating the bad guy', the game is also a creative sandbox, where failure can count as collective triumph so long as you tell a great tale. What does this have to do with AI?

Futurist Ray Kurzweil famously predicted that "By 2029, computers will have emotional intelligence and be convincing as people." We don't know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they're conscious, does that mean they actually are? Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don't understand it?

Human-level intelligence is familiar in biological hardware – you're using it now. Science and technology seem to be converging, from several directions, on the possibility of similar intelligence in non-biological systems. It is difficult to predict when this might happen, but most artificial intelligence (AI) specialists estimate that it is more likely than not within this century. Freed of biological constraints, such as a brain that needs to fit through a human birth canal (and that runs on the power of a mere 20W lightbulb), non-biological machines might be much more intelligent than we are. What would this mean for us?

We, as humans, have the tendency to anthropomorphize objects around us. That is to say we attribute human characteristics, such as emotions or even intent to non-human things. To do so is literally within our psychological nature. However, it is one thing where a home vacuum, like a Roomba, can be seen as an endearing pet. It is a completely different scenario to give an AI a pronoun (Project Debater is referred to as a "she") and proceed to debate with it as if it were another human with actual stake in the issues being argued about.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists, and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying use of the phrase. However, this is not the classical case of the public not understanding the scientists--here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us, enthralling us and frightening us in equal measure. There is a different narrative that one can tell about the current era.

Meet Foodvisor, a startup that has built a mobile app that helps you log everything you eat in order to lose weight, follow a diet or get healthier. You can add data by capturing a photo of your plate before you eat. "We've spent a little over two years doing research and development before we launched the app in 2018 in France," co-founder and CMO Aurore Tran told me. Foodvisor has raised $1.5 million so far (€1.4 million). The company is using deep learning to enable image recognition and detect what you're about to eat. In addition to identifying the type of food, the app tries to estimate the weight of each item.

We dedicate this countdown post to all the early-stage startup founders who hunger for an opportunity to break new ground. This is the final week you can apply to be a TC Top Pick and exhibit your startup -- for free -- in Startup Alley at Disrupt Berlin 2019 on 11-12 December. The application window remains open until this Friday, 18 October at 12 p.m. (PT). Don't miss your shot at media attention, investor interest and plenty of exposure to potential customers and collaborators -- apply to be a TC Top Pick right now. If you haven't heard about our Top Picks program, here's a brief rundown.