Technology makes some types of jobs obsolete and creates other types of jobs -- that's been true since the stone age. While in the past, machines have replaced people in jobs that require physical labor, we're increasingly seeing traditionally white collar jobs augmented by machines: financial analysts, online marketers, and financial reporters, just to name a few. Of course, these advances also create new jobs. The electronic computers that we know today, for example, replaced human beings performing the actual calculations, but in the process created all kinds of new types of work. Artificial intelligence seems like it might work the same way, creating jobs for artificial intelligence researchers and slowly displacing all other kinds of knowledge work.

'Machine learning' is a term that has been used in our industry since the early days of programmatic, and more recently'artificial intelligence' is being used, partly because it is much-discussed term in other parts of the tech world, but also because it accurately describes what some ad technology companies in the industry are doing. However, the two terms shouldn't be used interchangeably, as John Trenkle, TubeMogul's Chief Scientist, explains here. Trenkle himself has 30 years of experience in machine learning, during which time he has worked on everything from developing advanced data mining capabilities for NASA right through to developing systems that can recognise license plates in Arabic.

Real-world artificial-intelligence applications are popping up in unexpected places--and much sooner than you might think. While winning a game of Go might be impressive, machine intelligence is also evolving to the point where it can be used by more people to do more things. That's how four engineers with almost zero knowledge of Japanese were able to create software, in just a few months, that can decipher handwriting in the language. The programmers at Reactive Inc. came up with an application that recognizes scrawled-out Japanese with 98.66 percent accuracy. The 18-month-old startup in Tokyo is part of a growing global community of coders and investors who are harnessing the power of neural networks to put AI to far more practical purposes than answering trivia or winning board games.

The source image on the left was used to generate the one on the right. A British startup is using the unique abilities of convolutional neural networks to do a sort of scaled-up version of Adobe's content-aware fill -- but instead of filling in the gaps in a picture, it's imagining a whole new picture, larger and more detailed than the original. Kind of hard to believe without seeing it, right? That's why they call their company Magic Pony. Just emerging from semi-stealth mode (and even then, only barely), Magic Pony Technology's researchers have trained their system by exposing it to high- and low-resolution versions of images and video, letting it learn the differences between the two.

The European Union's digital chief wants search engines such as Alphabet Inc's Google and Microsoft's Bing to be more transparent about advertising in web search results but ruled out a separate law for web platforms. European Commission vice-president Andrus Ansip, who is overseeing a wide-ranging inquiry into how web platforms conduct their business, said on Friday the EU executive would not take a horizontal approach to regulating online services. "We will take a problem-driven approach," Ansip said. "It's practically impossible to regulate all the platforms with one really good single solution." Related: Do Google's'unprofessional hair' results show it is racist?

I have not yet read James Joyce's Ulysses. The majority of the population also has not. However, of those who have, it seems that most laud it. It is so universally praised that even people who have never read the book reference it as an archetypal masterpiece. Presumably, reading it doesn't require a special skill set beyond reasonable literacy.

There has recently been an explosion of cynicism in the Twitterverse (and generally in the tech community) about machine learning and AI. This was taken up a notch after Facebook's F8 conference earlier this week and the splashy launch of their bot platform on Messenger. When you dig in, many of the companies claiming to be leveraging machine learning are doing nothing of the sort. It's easy to find companies that are building weak ML-driven user experiences, turning off early adopters. Others are building products centered around gimmicky ML features that won't create any sort of long term barrier to competitors, frustrating would-be investors.

There has recently been an explosion of cynicism in the Twitterverse (and generally in the tech community) about machine learning and AI. This was taken up a notch after Facebook's F8 conference earlier this week and the splashy launch of their bot platform on Messenger. When you dig in, many of the companies claiming to be leveraging machine learning are doing nothing of the sort. It's easy to find companies that are building weak ML-driven user experiences, turning off early adopters. Others are building products centered around gimmicky ML features that won't create any sort of long term barrier to competitors, frustrating would-be investors.

Rodney Brooks wasn't willing in 2002 to hazard a guess about when the challenges of robotics would be overcome. "Not in two years or three years," Brooks asserted during an IndustryWeek interview that year. "But is it going to be here in 30 years or 40 years? I'm not quite prepared [to make a prediction]." Still in a book published then and the interview, he gave us a peak at what it would look like when it arrived: "robots that take instruction easily rather than ones that require hours of programming time; robots that can help in small-batch operations rather than ones that only make financial sense in continuous or nearly continuous fixed operation settings with long runs; robots with social interaction skills;" and robots that reduce costs on the factory floor.

Much research and development has gone to analyzing human language and messages, from voice recognition software like Siri to supercomputers analyzing text to determine the writer's attitude and tone. While this has many applications in many fields, one stands out in particular: Marketing. With this in mind, it seems that Goldman Sachs is making a bigger push towards developing tech and software for marketing, with a 30 million dollar investment in "cognitive content," or automated copywriting. A Series C round for software company Persado was led by Goldman Sachs and includes all of Persado's previous investors: Bain Capital Ventures, StarVest Ventures, American Express Ventures, and Citi Ventures. It increases the New York-based company's outside funding to 66 million since its creation.