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?"

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.

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."

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.

Are you into Machine Learning OR are you "just" a Statistician? Have you been asked this question yet? If you are in a career or looking to get into one that has anything to do with deriving insights out of data, you probably know what I am talking about. The year 2016 has seen over three dozen machine learning startups being acquired by tech giants; another several dozen machine learning startups raked up a aggregate funding to the tune of $4 Billion worldwide. Is it a blip or a bubble?

Suppose you work at a company. You want to use machine learning to improve marketing or operations or the product. Everyone agrees that this is a good idea because machine learning is the future. There are data scientists and engineers. The data scientists say that they know machine learning and will build you a model.

Here's one proof that artificial intelligence is moving closer to replacing more human jobs. Search giant Google this week introduced AI Duet. It's an experiment that uses machine learning to respond to your music. In a video (which I embedded below), Mann explains that the project is using neural networks and machine learning to create a map for responding to musical notes. Apparently, mapping for musical notes would have been created by a computer developer with a degree or musical background.

SAP is intent on delivering S/4HANA ERP in the cloud and will infuse it with machine learning and artificial intelligence. The company debuted the "next-generation intelligent" SAP S/4HANA Cloud at its Capital Markets Day event held at the New York Stock Exchange on Feb. 9. SAP S/4HANA Cloud is designed for enterprises of more than 1,500 employees and meets the increasing need for ERP delivered in the cloud, Sven Denecken, SAP's senior vice president of product marketing for S/4HANA, said after the event. SAP S/4HANA Cloud enables customers to meet their digital transformation needs and provides a platform to act on insights in real time, he said. "The benefits of having real-time data accessible from an in-memory platform has been there for a while, but we see the market shifting more and accepting cloud more in certain industries and in certain regions," Denecken said. "The consumption model of the cloud and the benefits of the cloud are now combining with this modern suite."

Already established as a major player in artificial intelligence, Microsoft Corp.. is aiming to make the technology a foundation of the healthcare industry with the launch Thursday of a new partnership program called Healthcare NExT. The program brings together Microsoft's latest innovations in AI with its Azure cloud computing capabilities in an effort to advance medical research and improve patient care, said Peter Lee, corporate vice president of Microsoft Research NExT. He added that the new program will "deeply integrate greenfield research and health technology product development," while also helping the company establish a new model for strategic health industry partnerships. "Our goal is to enable a new wave of innovation and impact using Microsoft's deep AI expertise and global-scale cloud," Lee wrote in a blog post announcing the program. As part of the initiative, Microsoft is teaming up with the University of Pittsburgh Medical Center on the first Healthcare NExT strategic research partnership.