AI

What white-collar jobs will machine learning take first? The answers may surprise you. Shelly Palmer talks with Teresa Priolo and Antwan Lewis on Fox 5. Original Airdate: February 28, 2017 Continue Reading →

Oxford University researchers have estimated that 47 percent of U.S. jobs could be automated within the next two decades. But which ones will robots take first? Continue Reading →

The phrase “digital transformation” is so overused that it may be on the brink of its own transformation from a business imperative to a hackneyed refrain. Clichés aside, digital transformation "is" a business imperative and time is the enemy. So, let’s have a look at seven brain-busting steps that will enable you to create value in your organization through digital transformation. Continue Reading →

If you let your imagination run wild in the world of automatic speech recognition (ASR) and natural language understanding (NLU), and you throw in a little fear, uncertainty and doubt (FUD), you can come up with several illegal uses for systems such as Alexa Voice Service, Google Home, Siri, and Cortana that will give you pause. Just for fun, let’s play pretend in our world of infinite possibilities. Continue Reading →

The superstar of CES® 2017 was not a car, or a robot, or even a TV; it was Alexa Voice Service (AVS), the software that allows you to control compatible devices with your voice. Various reports estimated there were 700–1,100 Alexa-controllable products at the show. I can't verify the number, but "and it works with Alexa" was the running gag at CES. The familiar Amazon/Alexa logo seemed to be everywhere. Continue Reading →

Unless you turn off the microphones and use a button or a remote, Alexa Voice Service and other IVCSs are always listening. Let me be the first to scream … "Look out!" Continue Reading →

Opining about the future of AI at the recent Brilliant Minds event at Symposium Stockholm, Google Executive Chairman Eric Schmidt rejected warnings from Elon Musk and Stephen Hawking about the dangers of AI, saying, "In the case of Stephen Hawking, although a brilliant man, he's not a computer scientist. Elon is also a brilliant man, though he too is a physicist, not a computer scientist." This absurd dismissal of Musk and Hawking was in response to an absurd question about "the possibility of an artificial superintelligence trying to destroy mankind in the near future." However, in Commander #1's immortal words, "We've analyzed their attack, sir, and there is a danger." Continue Reading →

Sometimes I walk into a room and say, “Alexa, what’s the temperature outside?” She answers by speaking the current temperature followed by an abbreviated weather report. She’s so human-like, I have to resist the temptation to say “Thank you” when she finishes. Importantly, Alexa is not a she; it is a component of Amazon's Echo natural language processing system. Amazon has anthropomorphized Echo with a female voice and a feminine name, which makes it easy to call Alexa a “she.” Should we be polite when we speak to it, or is it OK to be abrupt or even abusive? The device won’t care. It doesn't have feelings; but how will we teach our children to differentiate between machines that sound and act like people, and other disembodied voices that actually are people? Continue Reading →

Generally speaking, there are two kinds of companies in the world: data rich and data poor. The richest of the data rich (Google, Facebook, Amazon, Apple, etc.) are easy to name. But you don't need to be at the top of this list to use data to create value. You need to have the tools in place to turn information (data) into action -- that's what the data rich do that the data poor and the data middle class do not. Continue Reading →

Because the velocity of data is increasing and will always increase, the need for data literacy is increasing and will always increase. This does not mean that to be successful executive you have to become a data scientist -- quite the contrary. It means that in order to be a successful executive, you need to understand how data is turned into action, be familiar with the methods of data science and data scientific research, and be able to think strategically about how to use data to create value for your business. All other things being equal, there is a significant difference between being literate and being fluent. Continue Reading →