One of the first key takeaways from Vanderbilt Law School's conference on Thursday about artificial intelligence is that the term doesn't carry much value in the scientific community. "A.I. is whatever we can't do this year," David Lewis, a speaker who holds a PhD in computer science, said in between panel sessions. Lewis estimated we're currently experiencing the second or third wave of "A.I. hype," in which everyone uses the term to describe their technology. That's happened before, he said, and then it went out of style as a marketing buzzword. "By 2020, it'll have a negative connotation again," he predicted.

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All these terms and more get thrown around when it comes to marketing to today's consumer, and for good reason. They're all used in an attempt to address the core problem faced by every brand competing for shoppers' increasingly divided attentions: building real, one-to-one relationships with customers -- at scale. Authentic, sustainable customer relationships are key to long-term business success. According to a recent survey by Oracle, 34 percent of shoppers reported "breaking up" with a brand because of "poor, disruptive or irrelevant marketing messages." Of them, over half (53 percent) attributed parting ways to irrelevant messages sent via multiple channels, and one-third (33 percent) said they did so because the messages were generic and obviously sent en masse.

AI experts believe that computers will write as well as humans within the next 15 years. This means that any student will be able to input a poorly-written essay into a software program, which will analyze the text and reconstruct it as well-written, grammatically correct text. Since we use calculators as an extension of our minds, shouldn't we also use AI software to become better writers? This is not a hypothetical question. Across the world, teams of computer scientists are racing at a breakneck speed to construct advanced artificial intelligence that can automate thinking and writing. Last month, AlphaGo, the artificial intelligence program created by Google, beat the world-champion Lee Sodel in Go, a game that is so complex that there are more choices available in a single game than there are atoms in the entire universe.

If you find a new dinosaur the next time you stick a shovel in the dirt, you'll be famous. But pity the paleobotanists -- they find new leaf fossils every time they dig. Lack of fame is the least of their problems, though. A central obstacle botanists face is the inability to identify all those fossils. Leaves are naturally complex, with an astounding variety of vein and shape patterns. Comprehensive knoweldge and identification are virtually impossible.

Humans routinely solve problems of immense computational complexity by intuitively forming simple, low-dimensional heuristic strategies. Citizen science (or crowd sourcing) is a way of exploiting this ability by presenting scientific research problems to non-experts. 'Gamification'--the application of game elements in a non-game context--is an effective tool with which to enable citizen scientists to provide solutions to research problems. The citizen science games Foldit3, EteRNA and EyeWire have been used successfully to study protein and RNA folding and neuron mapping, but so far gamification has not been applied to problems in quantum physics. Here we report on Quantum Moves, an online platform gamifying optimization problems in quantum physics.

The debate over how robots could affect employment has been going on for more than a century. Those who rage against the machine say robots will steal our jobs, make us their slaves, and then kill us. Others believe robots are the key to ultimate freedom from work that humans find dull or dangerous. Some robots could climb stairs, others could pick up and place objects, while others could drive you around the sidewalk without you exerting any effort. The majority of executives at the conference explained how robots are here to rescue us from manual labor and will help to make our companies leaner, more profitable, more consistent, and more competitive.

Many companies including Google, GoDaddy, Yahoo!, and LinkedIn use what's known as HITL, or Human-In-The-Loop, to improve the accuracy of everything from maps, matching business listings, ranking top search results and referring relevant job postings. Why are we still at this point? Because many times humans are better at labeling content than machines. However, when we combine human knowledge with machine learning, we can create truly robust data flows. With that being said, what's the best way to go about it?

Deep learning has been fantastically successful in recent years, and is responsible for better-than-human performance in image classification, face recognition and playing Go. Not everyone thinks that deep learning is the bee's knees -- because the conclusions it reaches can't be explained easily (they're not'interpretable'), and it tends to require a LOT of data and compute power. Combinations of deep and other learning methods may be far more powerful than one alone. How does machine learning relate to Artificial Intelligence (and Artificial General Intelligence)? AI refers to systems that can act intelligently, even in a very narrow scope.