As someone on Twitter said, if "bots" was on your Build 2016 drinking game card, you'd be long dead. But while Microsoft is all about getting developers to create intelligent app companions to make our lives easier, is there any impressive machine learning the Redmond firm is ready to show off right now? The answer, surprise, is yes. Besides ordering Domino's pizzas, Microsoft has been tinkering with its Azure-based tools to recognize age, gender, emotion and individuals by name. Remember the great/awful How Old Do I Look? website introduced at last year's Build?

The sheer volume and unstructured nature of the data generated by billions of connected devices and systems presents significant challenges for those in search of turning this data into insight. For many, machine learning holds the promise of not only structuring this vast amount of data but also to create true business intelligence that can be monetized and leveraged to guide decisions. In the past, it wasn't possible or practical to implement machine learning at such a large scale for a variety of reasons. Machine learning, generally speaking, refers to a class of algorithms that learn from data, uncover insights, and predict behavior without being explicitly programmed. Machine learning algorithms vary greatly depending on the goal of the enterprise and can include various algorithms targeting classification or anomaly detection, clustering of information, time series prediction such as video and speech and even state-action learning and decision making through the use of reinforcement learning.

When should you use bandit tests, and when is A/B/n testing best? Though there are some strong proponents (and opponents) of bandit testing, there are certain use cases where bandit testing may be optimal. First, let's dive into bandit testing and talk a bit about the history of the N-Armed Bandit Problem. The multi-armed bandit problem is a classic thought experiment. There are many different slot machines (known as'one-armed bandits', as they're known for robbing you), each with a lever (and arm, if you will). You think that some slot machines payout more frequently than others do, so you'd like to maximize this.

One year ago, we launched On This Day to make it easier to relive and share memories on Facebook. Since launch, On This Day has been used by hundreds of millions of people globally. Because memories are so personal and unique, we wanted to make sure On This Day shows people the memories they most likely want to see and share, especially when it comes to the memories they see in News Feed. Specifically, over the past year, we focused on three areas to optimize the product experience: user experience research, filtering, and ranking. To better understand what types of memories are best to show people in News Feed, we listened to feedback from people through UX research. In these sessions, we learned just how complex memories can be.

The bot, created by Joshua Browder, a 19-year-old British programmer who is studying computer science at Stanford University, uses a chat interface powered by machine learning to suss out the details of each user's situation and then file an appeal. The traditional option--hiring a lawyer to handle the appeal--is prohibitively expensive. It can cost hundreds of dollars, in some cases more than it costs to pay the fine, which helps explain why New Yorkers pay more than 600 million a year in parking tickets.

Artificial intelligence may seem like a futuristic concept, but we're already experiencing it in real ways in our lives, whether we know it or not -- in areas including speech recognition, spam filters and even loan processing. And AI is only going to get more sophisticated from here. That was one of the messages from Oren Etzioni, CEO of the Seattle-based Allen Institute for Artificial Intelligence (AI2), founded by Microsoft co-founder Paul Allen. Etzioni spoke with us for this week's episode of the GeekWire radio show and podcast. Our conversation comes amid a boom in everyday AI, from self-driving cars to a computer that has mastered the game of Go. Microsoft put its stake in the ground with an AI-driven vision that CEO Satya Nadella calls "Conversation as a Platform," with virtual agents working on our behalf. Etzioni takes a much more optimistic view of AI than some of his peers. "The existential risk is just way overblown," he says. "It's much more likely that an asteroid will strike the Earth and annihilate life as we know it than AI will turn evil. Listen to the show below, download the MP3 here, and continue reading for an edited transcript of this week's show. Todd Bishop: Oren, in your current position, you really have a sense for the state of artificial intelligence. I think a lot of people out there see it in their daily lives in a very primitive form. They're watching Google's DeepMind beat a world champion Go player. The potential of artificial intelligence is there in a rudimentary form. Where are we now today in terms of the state of artificial intelligence, and where do you think we'll go over the next three to five years? Oren Etzioni: I do actually think that people are using it more than they realize. In addition to something like Siri, Google Search algorithm uses AI and machine learning all the time. Speech dictation on our phones whether it's Android or iPhone has gotten tremendously better and that's using deep learning behind the scenes to improve what's called a speech recognition. Loan processing these days is often done in a highly automated fashion using machine learning. As a matter of fact, AI is becoming more invisible and integrated into our lives. Of course, that can be a little bit scary to people. They say, "Wait a minute.

X2AIX2AI founders Eugene Bann (left) and Michiel Rauws (right) intrigue school children with Karim's automatic responses at Jusoor school, located within a Syrian refugee community in Al Marj, Lebanon. According to the UN, over 3 million Syrian refugees are now in neighboring Turkey, Lebanon, Jordan and Iraq, with millions more displaced within Syria. To help with this crisis, artificial intelligence startup X2AI is in the middle of a two week stay in Beirut, Lebanon, where it's piloting the use of artificial intelligence as a psychotherapy treatment for refugees. Partnering with Singularity University and the Field Innovation Team, X2AI is pitching the psychotherapy bot (named Karim) to aid workers and refugee communities. X2AIX2AI founder and CTO Eugene Bann watches on as a student from Jusoor school has a conversation with Karim in Arabic, and his first interaction with an AI.

Weaponizing Machine Learning Against ISIS Will Tangle Military Chains of Command Everyone on the Internet had a great time with Tay, Microsoft's Twitter robot that became a racist Holocaust denier in a matter of a few hours (then came back and did it again). The company had created a public relations flap -- more incident than a disaster -- while giving the public an object lesson on the pros and cons of machine learning: Automation can harness patterns to fascinating effect at speed, but the results will be predictably hard to predict. As is often the case, the military is an early adopter of automation technology. It is -- at one time -- leading the charge toward machine learning and also trying desperately to keep up.

Go looks simple, deceptively so. The Chinese board game is played on a board with a grid of 19x19 lines. The object is for two players to alternately place black and white markers on vacant intersections of those lines. And now, this nearly 3,000-year-old board game is a frontier of Artificial Intelligence development. At the time of writing, Google's DeepMind AI's AlphaGo program has played four games of a five game series against Go world champion, South Korea's Lee se-Dol.

Optimizing agent performance and progression is critical for call centers as phone agents are faced with increasingly evolving and difficult scenarios. Traditionally, this process has been achieved by employing a Quality Assurance team where QA representatives review a sample of randomly selected phone calls across call agents and score them on knowledge and "soft skills"--for example, employee enthusiasm, helpfulness, empathy, etc. This method becomes very costly and limits the integrity of analysis. But what if there was a new, more efficient way of analysis? Using advanced artificial intelligence technology call centers are improving the QA process through automation.