In July of this year, we sent out a series of survey questions to past guests who have been featured on the TechEmergence podcast, including academic researchers, founders, and executives who are experts in the machine learning domain. "What industries do you believe to be most poised to take advantage of machine learning in a business context?" We received 58 total responses from 30 researchers and executives (the survey structure allowed respondents to choose from one to four relevant response categories, with an average individual response rate of 1.93 chosen categories). On the whole, the trend in responses aligns with what we might have predicted, based on previous proprietary editorials/research and external published media on the topic. For example, the apparent optimistic bent towards healthcare & pharmaceuticals, followed by eCommerce, aligns with CB Insights' tracking of popular areas for artificial intelligence venture capital deals in 2016 (an exception is robotics, though this may be a domain "on the horizon").

Facebook's chatbots are learning the art of the deal, bartering and deceiving their way to better terms in negotiations with humans and other bots. Artificial intelligences that can negotiate effectively would make useful virtual assistants, says Mike Lewis at Facebook's research lab. Bots could be left to arrange appointments for people, sorting out calendar clashes by themselves. Or they could negotiate with several agents at once to book a holiday or make a purchase on your behalf. Most existing bots – such as Apple's Siri or those built into chat apps like Facebook Messenger – may be able to get you a taxi or order a pizza but they can't engage in complex negotiations, says Lewis. If we want bots to help us with more complex tasks they need to become dealmakers, especially if the task involves cooperation or compromise, like negotiating the purchase or sale of a property, for example.

Something quite spectacular happened during the week: Students have achieved an astounding level of score improvement on a highly complicated machine learning problem - in just three afternoons. They achieved scores that improved more than 70% over the initial solution that were built by a team of experienced domain specialists and senior data scientists (figure 1). Considering that roughly half of the students had no prior exposure to machine learning, and that the other half were mostly beginners, these improvements are impressive. In fact, this is not the first time we observed this kind of results: every time we ran a data challenge using RAMP (rapid analytics and model prototyping) platform, major improvements have been made over the initial solution. So, how does this happen?

Randstad Sourceright, a global talent solutions company, has released the finding of its research titled "Talent Trends," which reveals that 36% of the US companies have increased the use of robotics and artificial intelligence (AI) in the past 12 months, compared to 18% in Q4 2016. When asked about performance, 26% of the US companies reported growths that surpassed expectations in the past 12 months, compared to 20% in Q4 2016. In the coming 12 months, 28% of the respondents said that they were expecting significant growth, compared to only 10% in Q4 2016. The optimistic political climate is also expected to have a positive impact on business. So, how does all this positive growth impact human resources?

More than a third of U.S. jobs could be at "high risk" of automation by the early 2030s, a percentage that's greater than in Britain, Germany and Japan, according to a report released Friday. The analysis, by accounting and consulting firm PwC, emphasized that its estimates are based on the anticipated capabilities of robotics and artificial intelligence, and that the pace and direction of technological progress are "uncertain." It said that in the U.S., 38% of jobs could be at risk of automation, compared with 30% in Britain, 35% in Germany and 21% in Japan. The main reason is not that the U.S. has more jobs in sectors that are universally ripe for automation, the report says; rather, it's that more U.S. jobs in certain sectors are potentially vulnerable than, say, British jobs in the same sectors. For example, the report says the financial and insurance sector has much higher possibility of automation in the U.S. than in Britain.

Everyone who gets going in Machine Learning (and Deep Learning) gets overwhelmed by the plethora of MOOCs available. Here, I try to give a comprehensive survey of such courses available freely on the internet. You can take this post as an complementary to this and this previous posts. I will try to highlight some important pointers such as the difficulty of the courses, the correct order in which these should to be completed, the right audience for these courses. You will get a feel of how these courses give you a stack of skills in your arsenal and how you can use them to develop practical machine learning systems.

The belief that there is a link between talent and left-handedness has a long history. Leonardo da Vinci was left-handed. So were Mark Twain, Mozart, Marie Curie, Nicola Tesla and Aristotle. It's no different today – former US president Barack Obama is a left-hander, as is business leader Bill Gates and footballer Lionel Messi. But is it really true that left-handers are more likely to be geniuses?

Chatbots can help you order pizza, accept payments and be super racist, but their usefulness has been pretty limited. However, Facebook announced today that it has created a much more capable bot by giving it the ability to negotiate, strategize, and plan ahead in a conversation. Getting computers to understand conversation at a human level has been a pretty unsuccessful venture thus far. It requires not only a large amount of knowledge but rapid and accurate adaptability as well. But researchers at Facebook Artificial Intelligence Research (FAIR) have developed a new technique that lets bots successfully navigate a very human type of dialogue -- negotiations.

Element AI, an artificial intelligence company that delivers groundbreaking AI solutions, announced today it has raised $102M USD, representing the largest Series A funding round for an artificial intelligence company in history. With this funding, Element AI will accelerate its capabilities and invest in large-scale AI projects internationally, solidifying its position as the largest global AI company in Canada and creating 250 jobs in the high tech sector by January 2018. Element AI solves impossible problems for global organizations that urgently need to use AI in combination with their proprietary and valuable data to leap ahead of their competitors. Serial entrepreneurs Jean-François Gagné and Nicolas Chapados, Real Ventures, and Yoshua Bengio, a co-father of deep learning technology, co-founded Element AI in October 2016 to empower industry with the massive scale of academic AI innovation Bengio was driving at the world-leading Montreal Institute of Learning Algorithms (MILA). Together with MILA, one of the three leading centers of AI research in the world, Element AI pioneered a unique, non-exploitative model of academic cooperation they have now replicated to many other institutes.

Deep learning AI could one day work as an early warning system to allow earlier medical intervention to patients. This AI will tell people when they're likely to die -- and that's a good thing. That's because scientists from the University of Adelaide in Australia have used deep learning technology to analyze the computerized tomography (CT) scans of patient organs, in what could one day serve as an early warning system to catch heart disease, cancer, and other diseases early so that intervention can take place. Using a dataset of historical CT scans, and excluding other predictive factors like age, the system developed by the team was able to predict whether patients would die within five years around 70 percent of the time. The work was described in an article published in the journal Scientific Reports.