The Wizard of Oz movie is filled with classic quotes. One of them is simply, "Pay no attention to that man behind the curtain!" That quote, of course, occurs when Dorothy's dog, Toto, pulls back a curtain to reveal a man at a control panel and microphone. Rather than the sought-after magical Wizard of Oz, the man is, at best, a wizard of special effects. Today, nearly 80 years after the first release of the movie version of Wizard of Oz, we live with a wizard of our own making who is now often behind the curtain in video games, movies and sporting events.

The Wizard of Oz movie is filled with classic quotes. One of them is simply, "Pay no attention to that man behind the curtain!" That quote, of course, occurs when Dorothy's dog, Toto, pulls back a curtain to reveal a man at a control panel and microphone. Rather than the sought-after magical Wizard of Oz, the man is, at best, a wizard of special effects. Today, nearly 80 years after the first release of the movie version of Wizard of Oz, we live with a wizard of our own making who is now often behind the curtain in video games, movies and sporting events.

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years. Since early 2013, IBM's Watson has been used in the medical field, and after winning an astounding series of games against with world's best living Go player, Google DeepMind's team decided to throw their weight behind the medical opportunities of their technologies as well. Many of the machine learning (ML) industry's hottest young startups are knuckling down significant portions of their efforts to healthcare, including Nervanasys(recently acquired by Intel), Ayasdi (raised $94MM as of 02/16), Sentient.ai With all the excitement in the investor and research communities, we at TechEmergence have found most machine learning executives have a hard time putting a finger on where machine learning is making its mark on healthcare today. We've written this article, not to be a complete catalogue of possible applications, but to highlight a number of current and future uses of machine learning in the medical field, with relevant links to external sources and related TechEmergence interviews. The list below is by no means complete, but provides a useful lay-of-the-land of some of ML's impact in the healthcare industry.

Machine learning offers an opportunity to leverage competition and new forms of collaboration in order to yield new products, services, and entire business models… but machine learning misconceptions run rampant. In such a nascent niche, it's helpful to gather expert consensus in how best to apply machine learning in business. As a company, it's tempting to see other businesses yielding tangible results from applying'machine learning' technologies and to want to immediately jump on the boat. But good business sense also dictates not getting involved in a new venture before having done due diligence in understanding what a technology is – the potential capabilities and limitations, risks and rewards, and relevance in context to what a particular company produces or provides. "What do you believe to be the biggest misconception that executives and businesspeople have in applying machine learning to business opportunities?"

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years. Since early 2013, IBM's Watson has been used in the medical field, and after winning an astounding series of games against with world's best living Go player, Google DeepMind's team decided to throw their weight behind the medical opportunities of their technologies as well. Many of the machine learning (ML) industry's hottest young startups are knuckling down significant portions of their efforts to healthcare, including Nervanasys (recently acquired by Intel), Ayasdi (raised $94MM as of 02/16), Sentient.ai With all the excitement in the investor and research communities, we at TechEmergence have found most machine learning executives have a hard time putting a finger on where machine learning is making its mark on healthcare today. We've written this article, not to be a complete catalogue of possible applications, but to highlight a number of current and future uses of machine learning in the medical field, with relevant links to external sources and related TechEmergence interviews. The list below is by no means complete, but provides a useful lay-of-the-land of some of ML's impact in the healthcare industry.

My most recent TEDx is titled "Can AI Make the World a Better Place?" – but this title is somewhat misleading. How the transition beyond humanity will take place. Those who've followed TechEmergence since the early days are aware of the broader moral vision behind the company: "To proliferate the conversation about determining and moving towards the most beneficial transition beyond humanity." I have never identified as a transhumanist, I see the transition beyond humanity as literally inevitable, and I believe we should guide this transition rather than be taken for a ride inadvertently. Because the TEDx format is so short, I'm never permitted the kind of time I wish I was permitted to fully flesh out my ideas, and to reference the sources and people I have drawn from in putting the ideas together.

We interview hundreds of AI executives and researchers each year We conduct public and private research on the applications of AI in important industries, including healthcare / finance @danfaggella!

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This article was originally published on http://techemergence.com/ and it s the result of a collaboration between BootstrapLabs and Techemergence. Job automation predictions from an individual expert typically draw from years of academic research experience, or time "in the trenches" of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks. We wanted to know – what work functions have the most potential for near-term automation? In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks – on which business functions have the greatest potential for automation today, and in the coming five years ahead.

Virtual agents and chatbots will be the top consumer applications of artificial intelligence over the next five years, according to a consensus poll released today by TechEmergence, a marketing research firm for AI and machine learning. The emphasis on virtual agents and chatbots is in many ways not surprising. After all, the tech industry's 800-pound gorillas have all made big bets: Apple with Siri, Amazon with Alexa, Facebook with M and Messenger, Google with Google Assistant, Microsoft with Cortana and Tay. However, the poll's data also suggests that chatbots may soon be viewed as a horizontal enabling technology for many industries. "The most unexpected result was that so many founders who were not directly involved in the chatbot space or smart home/device space were very excited about these areas," wrote Daniel Faggella, founder of TechEmergence, in an email interview.