Until six or seven years ago, econometric models offered the best way to measure multi-touch attribution. These methodologies, like MMM (marketing mix modeling), turned statistical analyses into predictions and answers to high-level questions: How much revenue is generated from each channel? How much do I need to spend in each channel to optimize my mix? Econometric models rely on complex information and assumptions by human experts, and these models did (and still do) provide valuable insight into big-picture forecasts. Two recent shifts, however, have necessitated a new way to address multi-touch attribution: big data and user-level analysis.

DAILY VIDEO: Verizon negotiates down to $4.55B for Yahoo transaction; Congressional staffers see Russian hacking, FISA vote as priorities; IBM launches machine learning for z System mainframes; and there's more. DAILY VIDEO: White House withholds cyber-security order for further revision; Cortana to help Windows... DAILY VIDEO: Kaspersky discovers new malware designed to stealthily steal data; Microsoft to shield... DAILY VIDEO: Federal court says Google must turn over data in foreign servers; Cisco report: mobile... DAILY VIDEO: Windows 10 Cloud leak points to potential Chrome OS fighter; TiVo's analytics pinpoint... DAILY VIDEO: Google drops hands free mobile payment app; Microsoft Outlook on iOS welcomes Evernote... DAILY VIDEO: Snap Inc. makes it official, will go public next month; Microsoft sharpens Edge browser... DAILY VIDEO: Japan's supreme court backs Google in'right to be forgotten' case; HPE acquires... DAILY VIDEO: Flock adds "fake news" detector to collaboration platform; Google upgrades security... Dell's latest Intel-based PowerEdge servers bring new levels of operational efficiency and... The Dell PowerEdge R630 is a mainstream 2S/1U rack server that delivers incredible density across a... With the introduction of the Dell PowerEdge FM 120x4, Dell and Intel are bringing to market a server... The Dell PowerEdge R730xd, also based on Intel Xeon processors, is one of the world's densest...

Microsoft Corp. is trying again in health care, betting its prowess in cloud services and artificial-intelligence can help it expand in a market that's been notoriously hard for technology companies. A new initiative called Healthcare NExT will combine work from existing industry players and Microsoft's Research and AI units to help doctors reduce data entry tasks, triage sick patients more efficiently and ease outpatient care. "I want to bring our research capabilities and our hyper-scale cloud to bear so our partners can have huge success in the health-care world," said Peter Lee, a Microsoft Research vice president who heads Healthcare NExT. Microsoft has tried to expand in health care before, with mixed results. It had a Health Solutions Group for many years, but combined that into a joint venture with General Electric Co.

Andrew Yan-Tak Ng (Chinese: 吴恩达; born 1976) is a Chinese American computer scientist. He is the chief scientist at Baidu Research in Silicon Valley. In addition, he is an adjunct professor (formerly associate professor) at Stanford University. Ng is also the co-founder and chairman of Coursera, an online education platform. Ng researches primarily in machine learning and deep learning.

Autonomous vehicles, their placement is shown to have shifted from pre-peak 2014 to peak 2015 of the Hype Cycle. According to Gartner, "while autonomous vehicles(like driverless cars) are still embryonic, this movement still represents a significant advancement, with all major automotive companies putting autonomous vehicles on their near-term roadmaps". Internet of Things (Network of Intelligent objects around us coordinating activities) remains consistently almost at the Peak in both years. It is thought of as the most disruptive technology in decades once widely deployed. Natural Language Process Question Answering, last year's winner, is on its slide down to the Trough.

Automation and Artificial Intelligence: Threat or Help? There are over 5 million search results when you Google, "Will artificial intelligence replace humans?". The doomsday scenario and visceral fear created by the potential "rise of the machines" has people like Elon Musk describing artificial intelligence as "summoning the demon," and the biggest threat facing the world. Advances in technology can absolutely simplify certain processes, buying behaviors and reduce operational costs. Just look at what Amazon has done to the retail sector, and not the least of which at where it's taking consumer goods purchasing, with its newly released Amazon Go.

Thanks to neural networks - digital approximations of the way that the human brain learns - artificial intelligence has made enormous breakthroughs in everything from creating machines that can recognise faces with more accuracy than a human, to building cars capable of driving themselves, to recently, a computer "Turing test for sound" which can watch silent videos and predict the sounds that should accompany them. But it very nearly didn't happen like this. Forty years ago, research into neural networks almost stopped altogether. Budgets were slashed, plugs were pulled and students were advised by their teachers that researching neural networks was a bit like dating the loser in school: they'd never amount to anything and you'd just get hurt in the process. Certainly there were things neural networks weren't capable of at the time, but it's equally true that a large amount of the backlash the field suffered came down to the massive amount of hype it had received. Researchers, particularly in the rival, more established field of symbolic AI, were perturbed by articles like the one Science magazine published in 1958 about neural nets, entitled "Human Brains Replaced?"

Babak Hodjat believes humans are too emotional for the stock market. So he's started one of the first hedge funds run completely by artificial intelligence. "Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped lay the groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

Top scientists and theoreticians are expressing their concerns about how badly wrong human experimentation with highly advanced artificial intelligence programs could turn out. The renowned physicist Stephen Hawking is one of the most prominent scientists to express his disquiet about the potential of the technology, describing artificial intelligence "the best, or the worst thing, ever to happen to humanity". In the best case scenario, this technology could improve the world in unimaginable ways. But in the worst case scenario, super-intelligent robots capable of thinking for themselves could effectively take over as this planet's most dominant'species', something which might pose an existential threat to humanity itself. While the worst case scenario might seem like the stuff of science-fiction dystopic fantasy, Google's new DeepMind AI system might suggest that this terrifying story might well become a reality.

Machine learning is exciting, but the work is complex and difficult. It typically involves a lot of manual lifting -- assembling workflows and pipelines, setting up data sources, and shunting back and forth between on-prem and cloud-deployed resources. The more tools you have in your belt to ease that job, the better. Thankfully, Python is a giant tool belt of a language that's widely used in big data and machine learning. Here are five Python libraries that help relieve the heavy lifting for those trades.