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.

"There's no one-size-fits-all answer to finding the right supply chain for a company, because it's so dependent on strategic business choices & focus" - Kevin Overdulve, Director in Logistics & Distribution, Deloitte Belgium

Siliconrepublic.com spoke to Veritone's Aaron Edell to learn how data scientists can harness AI and machine learning. Data science, artificial intelligence (AI) and machine learning (ML) are all massive areas undergoing further growth and attracting increasing amounts of attention. But what about the jobs of the future that will combine all three? Edell's prevailing advice for data scientists working with AI and machine learning technologies is to always maintain sight of the problem they're tackling. With machine learning, business process scalability has made leaps and bounds, but it's important not to get side-tracked by that.