Dato, creator of the popular machine learning platform GraphLab Create, announced tools to give scientists, developers and users confidence in machine learning models and predictions. Dato is the first machine learning company to address the industry need for confidence in models and predictions. Demand for machine learning has spread to large enterprise organizations," said Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. "We have more than 80 commercial customers. The need for trust in models and predictions is an indicator of market adoption among established companies." Dato introduced tools within GraphLab Create and Dato Predictive Services to build trust and confidence in machine learning by making it easy to evaluate, explore, and explain models and predictions. With Dato's machine learning platform, companies can gain trust and confidence in the models and predictions behind their core business applications. At Capital One, we exhaustively work on model robustness and validation," said Brendan Herger, Capital One Data Innovation Lab Data Scientist.

The numbers are mind-boggling: 11.5 million documents in total, comprising 4.8 million emails, 2.1 million PDFs, 1.1 million images and 320,000 text files. To put it in context, the amount of data in the Panama Papers leak was 2,000 times the amount in the WikiLeaks State Department cables in 2010. Trying to sift through data like this manually would be a Sisyphean task, so technology was required. Enter the little-known Australian company Nuix. The software company has worked with the D.C.-based International Consortium of Investigative Journalists (ICIJ) for over four years, giving them free access to their software that can take huge troves of unstructured data and turn it into an indexed and searchable database.

The growing capabilities and widening use of artificial intelligence applications (AI apps) in mainstream consumer devices (eg Siri on the iPhone 6S and Amazon's Alexa being just two examples, plus the whole conversations-as-a-platform development) are converging to poise interesting intellectual property challenges. While currently the most sophisticated of these apps are, at best, in an advanced-alpha or early-beta version, this technology is fueled by innovation moving at an exponential rate. About six months ago I became involved in an intellectual property infringement case involving artificial intelligence applications. But – finally – my academic work in AI was bearing fruit. The case involves what are called Level B apps, part of a computational capability-continuum first proposed by Eran Kahana who is a technology and intellectual property attorney with extensive IP experience and a senior Fellow at Stanford Law School.

If Facebook announces the "Messenger Bot Store" at F8, as many predict, it would be arguably the most consequential event for the tech industry since Apple announced the App Store and iPhone SDK in March 2008. Even Steve Jobs could not have foreseen the impact of what he described as "a new application that lets users browse, search, purchase and download third party applications directly onto their iPhone". By the time the App Store opened for business in July 2008, approximately 6 million people worldwide owned an iPhone. By the end of the year, the number of iPhone owners had more than doubled, and in each of the following years iPhone sales doubled and then doubled again. The App Store ecosystem – which now has more than 1.5 million iOS apps – heralded the arrival of a new "mobile" era.

Both are good examples of machine learning. Microsoft added a natural language processing layer, arguably pushing Tay into cognitive computing territory. But as far as true AI goes, neither comes close. In 1956 Herbert Simon, a Carnegie Mellon researcher who's considered to be one of the founding fathers of computer AI systems, said "Machines will be capable, within 20 years, of doing any work a man can do." Leaving aside the wildly inaccurate timescale of his prediction, the quote serves as a good guide to what AI has always been about -- replacing human brain power with machine brain power.

The roots of modern Artificial Intelligence, or AI, can be traced back to the classical philosophers of Greece, and their efforts to model human thinking as a system of symbols. More recently, in the 1940s, a school of thought called "Connectionism" was developed to study the process of thinking. In 1950, a man named Alan Turing wrote a paper suggesting how to test a "thinking" machine. He believed if a machine could carry on a conversation by way of a teleprinter, imitating a human with no noticeable differences, the machine could be described as thinking. His paper was followed in 1952 by the Hodgkin-Huxley model of the brain as neurons forming an electrical network, with individual neurons firing in all-or-nothing (on/off) pulses.

This week's episode is dedicated entirely to Tay, the Microsoft chat bot who started her 1-day life making adorable meme jokes, and ended it praising Hitler. Tay's demise is an ominous warning about a future dominated by amoral robot overlords, and also comedy gold! All of our listeners get a 10% discount on your first month. So go sign up now and get human help with your data projects (at scale)! Last but not least, we'll be at the Austin Data Science Popup on April 13th.

Confirm your place early to avoid disappointment. For enquiries on Startup/Academic ticket availability and eligibility please contact: pcurtis@re-work.co Student/Academic passes are only available to those in full-time positions. Current Student/Academic ID must be shown at registration. Startups must be less than 3 years old and have raised less than 3m in funding.

To test whether his invention is indistinguishable from a human being, he helicopters-in a young engineer to see if he falls in love with the robot. Today, making machines and humans indistinguishable from each other is no longer science fiction, it's good business. In fact, a wave of startups are part of a new trend that promises to radically simplify our lives by making it harder to determine whether we're communicating with a person or computer code. In my last post I discussed how I use some of these services and in this post, I'll go deeper into what this trend is all about. I'll look into how pairing new technologies with human assistants will result in tremendous new products, which promise to enhance our lives -- that is, until the robots completely take over and destroy us all.

Artificial Intelligence is what enables any digital device to see and recognize objects, understand and reply to recognizable messages, make decisions, and even learn to change its thinking and behavior as it analyzes of data points in the distributed memory known as the cloud. IBM's Watson project is a good example of this last point especially. AI in all of these facets is already in use in the world of healthcare at a fairly basic level but what more extended role is Artificial Intelligence likely to play in healthcare in the future? Although we can't be certain about the future, we can look just a few years ahead and make several predictions upon which many experts in the healthcare technology space can agree. This includes smart individuals such as Dr. Eric Topol, Dr, Berci Meskó, and Dr. Bob Wachter, all of which have written about this. Most of the above are already starting to happen in healthcare.