And so, you want to give different treatment to those. And in classic econometrics you just kind of put everything in a regression, and the econometrics were the same--like, you'd put in a dummy variable for whether there was a higher minimum wage, and you would put in all these other covariates; but you'd use the same statistical analysis for both. And so I think the starting point for doing causal inference, which is mostly what we're interested in, in economics, is that you treat these things differently. So, I'm going to use machine learning methods, or sort of data mining techniques, to understand the effects of covariates, like, you know, all the characteristics of the patients; but I'm going to tell my model to do something different about the treatment effect. That's the variable I'm concerned about.

Capital One Europe CIO Rob Harding is leading a huge transformation at the company's UK business which involves becoming more digital, agile and self-sufficient, as he told CIO UK recently when he described his six-point master plan of fusing financial services with technology. As part of this transition, Harding has been keeping his eye on the biggest IT trends. In fact, he keeps a graph that he updates periodically of all the emerging technologies and buzzwords. The x-axis of the graph is Harding's opinion on the importance to CapitalOne UK's business strategy and the y-axis is the firm's current fluency with the technology. Below we run down his thoughts on several of these trends.

New York, NY: Datorama, a marketing analytics innovator, announced it has closed 32 million in Series C funding led by Lightspeed Venture Partners. In addition, Marker LLC and Eric Schmidt's Innovation Endeavors also participated in the round. This series brings Datorama's total funding to 50 million. Datorama will primarily use the new funding to accelerate the company's research and development in the artificial intelligence field, which will further elevate its machine learning capabilities. In addition, the company will continue to expand its global footprint, and is in the process of recruiting and hiring exceptional talent to further support its significant customer adoption and rapid growth. "This is a very pivotal moment for Datorama," said Ran Sarig, co-founder and CEO, Datorama.

As one of the leading research hubs for Natural Language Understanding (NLU) worldwide, Macquarie University and US-based VoiceBox Technologies has announced an international research and development partnership. VoiceBox Technologies, is an award-winning pioneer of Contextual Natural Language Understanding (CNLU) and the leading innovator of next gen Voice Artificial Intelligence (Voice AI). VoiceBox is taking voice interaction NLU based systems to the next generation by employing AI techniques in advanced Semantic Language Understanding, large-scale data mining, Deep Neural Networks and advanced machine learning. Macquarie professor Dr. Mark Johnson has been named the Chief Scientific Officer of VoiceBox Australia and will lead the VoiceBox office as a Center of Excellence for Voice AI. Dr. Johnson is overseeing the advanced labs located on the university's campus in the heart of the Macquarie Park Innovation District (MPID).

Data volumes are soaring to previously unimaginable heights. More data has been created in the past two years than in the entire history of humanity. It is predicted that, by 2020, each person on the planet will account for the creation of an average of 1.7 megabytes of new data every second. Scalable AI solutions could help address humanity's biggest challenges. Drawing meaningful insight from such vast amounts data is beyond our capabilities as humans, but perhaps not those of machines.

Yesterday, Tesla released details regarding the latest updates to its Autopilot software, and in the wake of the Model S crash that took place while the autopilot feature was enabled (and which resulted in the death of Joshua Brown), and last month's non-fatal crash in China, Version 8 is set to put an increased premium on safety. The upgrades will go live in one to two weeks, and the most significant involves the role of radar within the Autopilot sensor system. Initially, radar was designed to supplement the primary camera and image processing system, and confirmation from the camera was necessary to avoid false positives from radar (and subsequent unnecessary braking events). With new upgrades to the signal-processing tech, however, radar can now be used as a primary control sensor capable of initiating braking events without confirmation from the cameras. On top of this, the upgraded software can access six times as many radar objects as the previous version without requiring any upgrades to hardware.

It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. He added: "Unlike in the original Turing test, however, our interrogators are not human but rather computer programs that learn by themselves. They would not simply copy the observed behaviour, but rather reveal what makes human players distinctive from the rest." So far, Dr Gross and his team have tested Turing Learning in robot swarms but the next step is to reveal the workings of some animal collectives such as schools of fish or colonies of bees.

It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. This could mean advances in the world of technology with machines able to predict, among other things, human behaviour. The discovery takes inspiration from the work of pioneering computer scientist Alan Turing, who proposed a test, which a machine could pass if it behaved indistinguishably from a human. The interrogator has to find out which of the two players is human. If they consistently fail to do so - meaning that they are no more successful than if they had chosen one player at random - the machine has passed the test, and is considered to have human-level intelligence.

Google has been offering public cloud services for several years now, but the company has continued to lag behind Amazon and Microsoft in customer growth. Under the leadership of VMware co-founder Diane Greene, who serves as the executive vice president of Google Cloud Enterprise, the tech titan has focused harder on forging partnerships and developing products to appeal to large customers. It has added a number of key customers under Greene's tenure, including Spotify. One such win is Evernote, which announced Tuesday it would be migrating its service away from its private data centers and to Google's public cloud. When Evernote was looking for a public cloud provider, the company was interested in not only the base level infrastructure available, but also high-level machine learning services and services for building machine learning-driven systems, said Anirban Kundu, Evernote's CTO.

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