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.

The world's leading non-profit artificial intelligence research team needs the world's fastest AI system. "I thought it was incredibly appropriate that the world's first supercomputer dedicated to artificial intelligence would go to the laboratory that was dedicated to open artificial intelligence," Huang said. OpenAI's researchers will put the first production DGX-1 -- packing 170 teraflops of computing power, equal to 250 conventional servers -- to work on artificial intelligence's toughest problems. OpenAI's team is working at the cutting-edge of a field that promises incredible advances. Imagine artificial personal assistants that can coordinate our digital lives and autonomous cars and robots that are accessible to everyone.

Fluenty is an AI-driven messaging platform that is well positioned to deliver in the midst of this much hyped and growing wave. The company released its technology and published its patents before Smart Reply or Google Allo were even on the market. It is simple to integrate with other messaging apps and quick at acquiring users' taste and personal style. No need to open Fluenty when you need it. Unlike Google, it has the capacity to combine with other apps such as Facebook Messenger, WhatsApp, Google Hangout or KakaoTalk, as well as text messaging apps. By suggesting pre-written replies to messages, Fluenty does not interfere with the flow.

Robert Gordon has written a magnificent book on the economic history of the United States over the last one and a half centuries. His study focuses on what he calls the "special century" from 1870 to 1970--in which living standards increased more rapidly than at any time before or after. The book is without peer in providing a statistical analysis of the uneven pace of growth and technological change, in describing the technologies that led to the remarkable progress during the special century, and in concluding with a provocative hypothesis that the future is unlikely to bring anything approaching the economic gains of the earlier period. The message of Rise and Fall is this. For most of human history, economic progress moved at a crawl. According to the economic historian Bradford DeLong, from the first rock tools used by humanoids three million years ago, to the earliest cities ten thousand years ago, through the Middle Ages, to the beginning of the Industrial Revolution around 1800, living standards doubled (with a growth of 0.00002 percent per year). Another doubling took place over the subsequent period to 1870. Then, according to standard calculations, the world economy took off. Gordon focuses on growth in the United States.

The new machine, called a DGX-1, is optimized for the form of machine learning known as deep learning, which involves feeding data to a large network of crudely simulated neurons and has resulted in great strides in artificial intelligence in recent years. Language remains a very tricky problem for artificial intelligence, but in recent years researchers have made progress in applying deep learning to the problem (see "AI's Language Problem"). "This will allow us to train models on larger data sets, which we have found leads to progress in AI." OpenAI hopes to use reinforcement learning to build robots capable of performing useful chores around the home, although this may prove a time-consuming challenge (see "This Is the Robot Maid Elon Musk Is Funding" and "The Robot You Want Most Is Far from Reality").

Is it possible that the secret to building machine intelligence lies in spending endless hours reading Reddit? That's one question a team of researchers at OpenAI, a nonprofit backed by several Silicon Valley luminaries, hopes to answer with a new kind of supercomputer developed by chipmaker Nvidia. The researchers are also training robots do the dishes through experimentation, and they are building algorithms capable of learning to play a wide variety of different computer games. The new machine, called a DGX-1, is optimized for the form of machine learning known as deep learning, which involves feeding data to a large network of crudely simulated neurons and has resulted in great strides in artificial intelligence in recent years. The DGX-1 will let AI researchers train deep-learning systems more quickly using more data.