Startup Grind is a global startup community designed to educate, inspire, and connect entrepreneurs. It is powered by Google for Entrepreneurs. We host monthly events in 400 cities and 120 countries featuring successful local founders, innovators, educators and investors who share personal stories and lessons learned on the road to building great companies. Startup Grind is a connected online and offline network of vibrant startup communities to help fuel innovation, economic growth and prosperity at the local level. While Startup Grind was founded in Palo Alto, California, our extended network of Startup Grind chapters are located around the world.

So just about once a day, we hear some new story about advances in AI engines and the terrifying speed at which they are approaching Skynet-level self awareness (and potentially self interest! Just this week it was "Bots creating their own language", "DeepMind bots outperforming and learning faster than humans", and "Ray Kurzweil predicting AI passing a Turing test in 2029 with The Singularity (the point at which our conscious minds transcend matter and merge with the cloud) occurring somewhere around 2045ish". If you work in physical labor, this has probably already happened to some extent. If you work in thought labor, it's happening right now. If you work in thought labor that's chiefly creative, it's probably up in the air right now because we don't know how quickly the bots will learn to be decent comedians or mumble rappers or kitty cats that are pretty.

The most recent World Economic Forum took place in Davos, Switzerland, in January 2016. The theme was the premise that we're now moving into the Fourth Industrial Revolution. Some of the world's most innovative companies are already moving into the digital age. They're learning to utilize cloud computing and machine learning to revolutionize different aspect of their businesses. This is an enormous opportunity for resourceful businesses to tap into the potential offered by digital listening, or using data in new ways.

It has never been easier to build AI or machine learning-based systems than it is today. The ubiquity of cutting edge open-source tools such as TensorFlow, Torch, and Spark, coupled with the availability of massive amounts of computation power through AWS, Google Cloud, or other cloud providers, means that you can train cutting-edge models from your laptop over an afternoon coffee. Though not at the forefront of the AI hype train, the unsung hero of the AI revolution is data -- lots and lots of labeled and annotated data, curated with the elbow grease of great research groups and companies who recognize that the democratization of data is a necessary step towards accelerating AI. However, most products involving machine learning or AI rely heavily on proprietary datasets that are often not released, as this provides implicit defensibility. With that said, it can be hard to piece through what public datasets are useful to look at, which are viable for a proof of concept, and what datasets can be useful as a potential product or feature validation step before you collect your own proprietary data. It's important to remember that good performance on data set doesn't guarantee a machine learning system will perform well in real product scenarios.

"Artificial Intelligence": this term has become so popular/hyped/*add an adjective of your choice* in this decade, that we're talking about it more than ever. So much so that anything about AI becomes front page news. Tech media must be having a crush on AI for sure. Popular voices in the ecosystem, are so polarizing that you're left scared or excited. Mr. Singularity Ray Kurzweil says: But wait, even before we get started on what's missing in the whole picture, let me show you counter arguments too.

Most college students relax over their winter break, eating good food and de-stressing from the previous semester. But when I first talked with Joshua Browder, a Stanford University sophomore, he was busy finishing up schoolwork. I was lucky he had time to talk in the middle of his busy schedule. He's the founder of DoNotPay, a chatbot that helps overturn traffic tickets, and in a few days would be flying to London to meet with government officials about using his technology. Then off to Munich to speak at an international design and innovation conference.

I had the pleasure of joining an intimate dinner hosted by Fast Company (not pictured above) this week and for the 3rd time in as many days found myself embroiled in a conversation about AI and its impact on industry. It was a room full of smart people talking passionately about the robot apocalypse; in this case, through the lens of self-driving cars and its impact on the humans in the Taxi and Trucking industries. There were tons of great points around the table about timelines, and augmenting vs. replacing but the one forgone conclusion seemed to be that the continued rise of ever-more sophisticated industry means… And while folks brought up the possibility of a Universal Basic Income Tax, there was one thing I didn't hear from anyone. In fact, one thing that I haven't heard anywhere in the discussion around what we're going to do when AI really gets cranking in all of its commercial and industrial: Can we retrain all 3.5mm truck drivers and 250K taxi drivers in the US if their jobs largely go away over the next 10–20 years? Can we make some of them Road Performance quality control experts?

Artificial intelligence is, of course, all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants, often early in the life of their startups. As always, the reality of building a startup is different, especially when one aims to build a self-standing company for the long term. The path to success in AI requires not just technical prowess but also careful thinking and execution through a range of strategic and tactical questions that are specific to this domain and market. One possible framework to think through these topics is this "5P"list: Positioning (finding blue ocean), Product, Petabytes (data), Process (social engineering) and People. This week at the excellent O'Reilly Artificial Intelligence Conference, I covered those topics (as much as a 40 minute presentation allows), with the help of Peter Brodsky, CEO of HyperScience.

Customers buy from people who help them make an informed decision without all the headaches and in today's business environment that requires you to shift from analogue to a social selling mindset. So you are not bouncing along with random acts of social you will need buy-in from the top. Tim Hughes will go into detail about what that needs to look like for you and your team and how you can ensure you you are using the right drivers to gain your leadership's yes. Social selling requires building relationships, communities and influencers, and educating buyers, both of which take time. Therefore we have to change the way we think about the costs associated with social sales.

Startup Grind is a global startup community designed to educate, inspire, and connect entrepreneurs. It is powered by Google for Entrepreneurs. We host monthly events in 150 cities and 65 countries featuring successful local founders, innovators, educators and investors who share personal stories and lessons learned on the road to building great companies. Startup Grind is a connected online and offline network of vibrant startup communities to help fuel innovation, economic growth and prosperity at the local level. While Startup Grind was founded in Palo Alto, California, our extended network of Startup Grind chapters are located around the world.