The Pentagon wants to incorporate artificial intelligence into more systems but first needs to ensure its employees fully understand what drives AI, a new broad agency announcement suggests. Potential applications for defense are endless--autonomous aerial and undersea war-fighting or surveillance, among others--but humans won't make full use of AI until they trust it won't fail, according to the Defense Advanced Research Projects Agency. A new DARPA effort aims to nurture communication between machines and humans by investing in AI that can explain itself as it works. An intelligence analyst who receives recommendations from algorithms about what to investigate further "needs to understand why the algorithm has recommended certain activity," the BAA said. And the personnel overseeing a new autonomous system needs to know why it makes decisions so they "can decide how to use it in future missions."

The future of defense technology will be driven by artificial intelligence (AI), but robotic weapons, vehicles, and soldiers won't be much use if human service members don't trust or understand their automated counterparts. In order to build human confidence in machines, Pentagon researchers want to develop systems that explain exactly what they're doing. Last week, the United States Defense Advanced Research Projects Agency (DARPA) announced their Explainable AI (XAI) program, an initiative that will ensure people won't be confused by emerging battlefield tech. The agency will begin development in May 2017 and work on the project for four years. The human-robot trust gap is a growing concern for the Department of Defense.

In 1991, Microsoft founded Microsoft Research, a division for the company's army of PhDs and scientists to pursue the coolest, wildest, most science-fictional ideas they possibly could, with the very non-corporate goal of expanding human knowledge. They came up with some incredible stuff. Over the years, Microsoft Research contributed to a whole mess of inventions including Google Maps-style global mapping, voice recognition software, and the smartwatch. The problem was, very few of those cool academic discoveries were actually making their way back to Microsoft's actual commercial products. Microsoft may have invented the tablet, but the Apple iPad ran away with the market.

The Internet of Things (IoT) has received massive coverage and widespread adoption. What few people have stopped to consider thus far is where these connections will take us. As chatbots become more popular, we're bearing witness to a move toward further machine learning. As the natural progression from smart objects to learning objects occurs, this new wave will encompass the globe. At the heart of IoT is a desire to connect items we already own into one cohesive network.

Bots are redefining how consumers interact with products and services. Conversational interfaces may already seem groundbreaking, but we are only seeing the tip of the iceberg. The real disruption will take place in the business world. Even though business bots are just gearing up, they will soon be influential enough that we can expect them to disrupt competition and turn power dynamics upside down. They will shake up entire markets, and it all starts with employees.

In June 2016 Ravi Mody and Tim Schmeier gave a presentation at the NYC Machine Learning meetup to discuss their work on the data science team at iHeartRadio. This is the first in a three part article complementing the presentation. Like almost everyone who grew up in the 80s/90s, my music discovery followed a familiar pattern: I'd listen to the radio, talk to my friends about what they're listening to, then buy a CD/tape to play on repeat until everyone around me was sick of it. This worked fine for most people, but as a music lover I'm thankful it seems so foreign today -- the internet has completely transformed the way I interact with music. While I still often listen to the radio, I now use my phone to tune into any of thousands of stations from across the country.

Apple and Intel acquired machine learning startups. Palantir purchased a data visualization startup. AWS rolled out an analytics service for streaming real-time data. We've got all this and more in our Big Data Roundup for the week ending August 14, 2016. The company acquired Turi, an artificial intelligence (AI) and machine learning startup, in a deal reportedly worth 200 million.

This article was co-authored by Andrew Flachner, the CEO at RealScout. RealScout is a collaborative home search platform that empowers real estate agents to close more deals faster. By teaching computers how to interpret the visual world, multiple listing service photos are starting to unlock a treasure trove of listing data previously thought to be unavailable. While, historically, the amount of bedrooms, bathrooms, and square footage primarily guides real estate searches, these numbers may soon just be a starting point. What if other criteria of selection could be the size of the backyard, the quantity of natural light, or the vastness of the view?

In the 1980s and 1990s, the term "artificial intelligence" became synonymous with The Terminator movies. And it was a bleak look at technology, too – capped off by a future where humankind struggled to survive after Skynet went rogue. Today's tech startups are a little more optimistic. They argue that AI technology will make life easier and more efficient in any number of industries – including agriculture. And "the future" may arrive sooner than you might think.

Machine learning companies are being snapped up in droves by tech giants cognizant that these startups represent a new wave of technology innovation. This month alone, Intel announced plans to acquire deep learning startup Nervana Systems. Apple confirmed it would acquire Turi. Earlier this year, Twitter acquired Magic Pony Technology, Salesforce acquired PredictionIO, ESI Group acquired Mineset, and Apple acquired Emotient, among other deals. PricewaterhouseCoopers said 29 machine learning companies have been acquired so far this year by companies large and small, and total deals in 2016 will likely exceed the 37 such buyouts made last year.