THE RISE OF ARTIFICIAL intelligence has inspired both fascination and fear of the world to come. Some tech prophets envision a "singularity," in which advances in AI trigger drastic technological growth, while others imagine that autonomous machines will someday turn on their creators and destroy us. But when you're engaged in the science of machine intelligence, you understand that this is a false set of choices shaped by a misleading phrase. The term "artificial intelligence" was coined in 1955 to convey the concept of general intelligence: the notion that all human cognition stems from one or more underlying algorithms, and that by programming computers to think in the same way, we could create autonomous systems modeled on the human brain. At the same time, other researchers were taking a different approach.

Researchers at Sony's computer science laboratory in Paris recently put out a set of pop songs composed by an AI system, which scans songs from a database to compose entirely new pieces in certain musical styles. The final results were, unexpectedly, fairly catchy tunes. Expanding that idea, Sony CSL now wants to use its system of machine-learning algorithms--which is named Flow Machines, and has been in development since 2012--to write and publish entire albums. Using an algorithm to write songs "makes all the elements of experimentation easier, which otherwise would have been too time-consuming or meticulous," says the lab's director and artificial intelligence expert François Pashet. Does a music-making AI violate the intimacy and emotion of the songwriting process?

Back row, left to right: Wayne Xiong, Geoffrey Zweig, Frank Seide. In December 2015, Microsoft Chief Scientist of Speech Xuedong Huang told Business Insider that "in the next four to five years, computers will be as good as humans" at understanding the words that come out of your mouth. Less than a year later, and Microsoft just set a record with the announcement of a system that can transcribe the contents of a phone call with "the same or fewer errors" than real actual human professionals trained in transcription. It's a huge milestone for speech recognition, even as gadgets like Amazon Echo and Apple's Airpods prove that voice is going to play a big role in the future of technology. And by Huang's standard, that's mission accomplished.

Position yourself for growth in 2017--join us live at the Entrepreneur 360 Conference in Long Beach, Calif. on Nov. 16. We're all growing familiar with the ways in which automation, big data, AI and machine learning are slowly replacing human jobs. While the general tone up until now has been somewhat negative toward this sort of change, there is a growing number of entrepreneurs who have looked at the landscape and been prompted to think differently about the issue. Will Lee, founder and CEO of Verlocal, an online marketplace for freelancers, says, "AI is going to take most of our jobs... but it won't be the end of the world." Lee, an Artificial Intelligence expert who studied at Stanford, created Verlocal to teach and assist people in monetizing their passions to prepare for this shift in employment.

ADM: Tell us a bit about Affectiva, and the company's SDKsPitre: We are an MIT Media Lab spin-off and consider ourselves the pioneers of Emotion AI. We coined the term and are now defining this category, which is essentially the next frontier of artificial intelligence.We live in a world full of hyper-connected devices, smart technology, advanced AI systems -- lots of IQ, but no EQ -- these technologies are not able to adapt to human emotion. I think that is a problem: it not only affects how we interact with technology, but also how humans communicate with each other as more and more of these interactions take place in a digital context. Consequently, we are on a mission to bring emotional intelligence to the digital world, thereby humanizing technology. Our software measures emotions through facial expressions, unobtrusively, using a standard webcam or device camera.

Photographs of nearly half of all U.S. adults -- 117 million people -- are collected in police facial recognition databases across the country with little regulation over how the networks are searched and used, according to a new study. Along with a lack of regulation, critics question the accuracy of facial recognition algorithms. Meanwhile, state, city, and federal facial recognition databases include 48 percent of U.S. adults, said the report from the Center on Privacy & Technology at Georgetown Law. The search of facial recognition databases is largely unregulated, the report said. "A few agencies have instituted meaningful protections to prevent the misuse of the technology," its authors wrote.

I write about gadgets, which means everyone asks me what laptop/ phone/ dishwasher to buy. I struggle with this, because the answer often starts, "It depends." Unless you ask about a phone. In that case, I usually say get an iPhone. But the phones can be … frustrating.

There's a good chance information about you is available for sale to advertisers and other businesses on Vertical Mass. The nearly 3-year-old West Hollywood start-up provides user data storage and analysis software for companies in music, sports, video games and Hollywood. Those companies also can list some of their user information for sale, with Vertical Mass collecting a portion of the proceeds. Fees from the software service and the data marketplace have brought Vertical Mass seven figures in revenue for two consecutive years, Chief Executive Mark Shedletsky said, declining to provide specific figures. But investors think the company is only getting started as a key information broker in the entertainment universe.

THE RISE OF ARTIFICIAL intelligence has inspired both fascination and fear of the world to come. Some tech prophets envision a "singularity," in which advances in AI trigger drastic technological growth, while others imagine that autonomous machines will someday turn on their creators and destroy us. But when you're engaged in the science of machine intelligence, you understand that this is a false set of choices shaped by a misleading phrase. The term "artificial intelligence" was coined in 1955 to convey the concept of general intelligence: the notion that all human cognition stems from one or more underlying algorithms, and that by programming computers to think in the same way, we could create autonomous systems modeled on the human brain. At the same time, other researchers were taking a different approach.

On the Data Technology Automation team, we know that extracting data with automation is the best path to speed and accuracy. And, we know that machine learning is the best path to automation. We develop the machine learning models and infrastructure to automate the processing of all types of financial documents. We extract data from these documents and use it to build intelligent models using information retrieval, machine learning, and natural language processing. The models we build enable our customers to get the right answers – fast.