Last week, we witnessed DeepMind AlphaGo's stunning victories over Go legend and world champion Lee Se-dol. This landmark event has stoked excitement over AI's potential in every aspect of life more than any other in recent memory. DeepMind (an AI system of neural networks) was acquired by Google in 2014 and uses games as a testing ground for AI algorithms that could have real-world applications. 'I think what we've done with AlphaGo is to introduce with the neural networks this aspect of intuition, if you want to call it that, and that's really the thing that separates out top Go players: their intuition.' The same could be said about top lawyers, lawyering and legal service provision.

Matt Harrigan is a longtime professional in the information security industry, building products that challenge traditional security models. I know, right now you're saying "But wait Matt, All I hear is that every hot new company in the space is based on machine learning and that VCs are funding machine learning companies in record numbers, even with the economy in question." And you're right, they are – usually for very good reasons. I am not advocating against an entire field of study, only against its recent inappropriate application in analyzing the entirety of your network. Algorithmic learning theory, clustering, self-organizing maps and all that other neat sounding stuff –could- potentially be very useful in specific areas of security, under the right circumstances.

A big data analytics platform is being deployed by the Ministry of Defence as it looks to take the weight of military analysts and improve threat identification. The analytics platform, which incorporates machine learning technology, will be used for operational support and comes from a recently signed contract by the Defence Science and Technology Laboratory's Centre for Defence Enterprise (CDE) and Massive Analytic, a London based company. The idea behind the platform from Massive Analytic is that it will help make it possible to examine outcomes of decisions before they are put into effect, effectively acting as a trusted advisor. Under the deployment the MoD will use the system to bring together surveillance data from static and dynamic sources which will feed an interactive dashboard. The dashboard will be capable of providing insights that will help to identify threats in advance and can be used for planning different scenarios. Military analysts are currently the main source for sifting through large volumes of data, it is hoped that this platform will help to remove some of that burden.

One day in the not-so-distant future, an app might make a dinner reservation for you before you realize you even want to go out, or your smartphone might suggest tourist sights you'd enjoy when you land in a new city. It's possible -- and it's really not so far away, say analysts, who were encouraged today by Google's announcement that it's open sourcing an enhanced machine learning system. The system, dubbed TensorFlow, is smarter, faster and more flexible machine-learning software than Google has ever had before, according to Sundar Pichai, Google's CEO, in a blog post . "Just a couple of years ago, you couldn't talk to the Google app through the noise of a city sidewalk, or read a sign in Russian using Google Translate, or instantly find pictures of your Labradoodle in Google Photos," wrote Pichai. But in a short amount of time they've gotten much, much smarter. Now, thanks to machine learning, you can do all those things pretty easily, and a lot more."

Machine learning techniques may have been used for years, but recently there has been an explosion in their applications. In fact, in a recent Q3 earnings call, Google CEO Sundar Pichai said "Machine learning is a core, transformative way by which we're re-thinking how we're doing everything." In the past, successful use of machine learning algorithms required bespoke algorithms and huge R&D budgets, but all that is changing. IBM Watson, Microsoft Azure, Amazon and Alibaba all launched turnkey cloud based machine learning SaaS solutions in 2015. At the same time startups like Idibon, MetaMind, Dato and MonkeyLearn have built machine learning products that companies can take advantage of.

Between cross-platform politics (Apple vs. Amazon vs. Google) and the fact that voice control in consumer electronics is still nascent, the promise of a truly automated home is still sadly unrealized for the most part. However, there are plenty of easter eggs you can ask your Echo to entertain you with while you wait for Jeff Bezos to decide to play nice with Apple and Google (spoiler: he probably won't). I think it's neat that you can turn on pretty much anything throughout a house just by saying so out loud. I also think that voice should and will be the de facto method of interaction in the household by the end of this decade. In that regard, I have to thank Apple, Google, and Amazon for advancing the state of responsive AI.

Harry Stebbings is the founder and host of The Twenty Minute VC, an independent podcast focused on venture capital. Next week at YC Demo Day, venture capitalists and angel investors eager to speak with the founders of tomorrow's technology companies today, will present an array of startups from the fields of artificial intelligence, virtual reality, consumer healthcare (just to name a few). As the 126 entrepreneurs in the latest batch prepare for their big day, we thought we'd catch up with one of these founders before their nervous 3 minute pitch to the world. Latching on to the current excitement surrounding the rise of AI, in the wake of the dominant performance of AlphaGo, we chose Msg.ai and their founder Puneet Mehta as the startup to speak to from this year's batch. The company works in the field of AI for conversational commerce and in the discussion with Mehta we reveal how YC prepares founders for the 3 minute pitch that could change the trajectory of their company.

With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?

The year 2015 might be seen as the year that "artificial intelligence risk" or "artificial intelligence danger" went mainstream (or close to it). With the founding of Elon Musk's Open AI and The Leverhulme Centre for the Future of Intelligence, the increased attention on the Future of Life Institute and Oxford's Future of Humanity Institute, and a flurry of attention around celebrity comments around AI dangers (including the now well-known statements of Bill Gates and Elon Musk), it's safe to say that AI risk has embedded itself as a topic of pop-culture discourse – even if it's not a very serious one at present. Recently, we interviewed and reached out to a total of over 30 artificial intelligence researchers (all except one of whom hold a PhD) to ask them which AI risks they believe to be the most likely – if any – in the next 20, and in the next 100 years, respectively. Below you can see a list of all of our respondents, clicking on a respondent will bring up their answer to the 20-year risk question. Interestingly enough, automation and economic impact topped this list – coinciding with the massive media attention on autonomous vehicles, improved robotic manufacturing, and more.

The sandbox world of Minecraft allows you build practically anything. Soon though, it won't be used simply to create spatial objects, but artificial intelligence systems. At least, that's Microsoft's plan, taking an project aiming to teach AI to make its own decisions and launching it as an open-source testbed. Currently, Microsoft's internal study sees a small team of computer scientists trying to teach a virtual player how to navigate Mojang's blocky world. The AI has only the same available resources a flesh and blood player would have access to, and must learn from its mistakes.