As we've reported, NYU Tandon is making a bid for New York City to become the capital city of artificial intelligence. One graduate of its Future Labs incubator, Geometric Intelligence, has already made a big splash in the field: last year, it was acquired by Uber, where its founder, Gary Marcus, launched the ride-hailing company's R&D lab for artificial intelligence. Based upon that accomplishment, you might think Marcus, a professor of psychology at NYU, would be one of the biggest cheerleaders for the potential of AI. Instead, he's consistently thrown cold water on the grandiose notions that are commonly disseminated in the media. IBM's Watson might have beaten Ken Jennings at Jeopardy!, but in Marcus's view, the average AI system isn't smarter than a fifth-grader: it can't make abstractions, and it can't converse naturally.

Last summer, NYU Tandon and ff Venture Capital announced the launch of their jointly run artificial intelligence incubator, AI NexusLab. The stated goal then, as ffVC founding partner John Frankel put it, was to make New York City a prime destination for technologists pursuing one of the hottest areas in the industry. Frankel reiterated that mission Wednesday at the Future Labs AI Summit, held at NYU's Skirball Center, where the incubator's inaugural class of five startups (which we'll be covering in a separate post) unveiled their work. "We had a thesis coming into this that New York should be an artificial intelligence hub," Frankel said, rattling off a series of data points about the city's tech cred. Among those data bytes: there are some 2,400 open positions in data science in the metro area right now.

The advent of easily accessible, mondo computing power could open up lots of possibilities in the arts, not just in the startup and tech world. Brooklyn startup Paperspace has a new post out about how to use its product (which is sort of like AWS for the computational power required of machine learning) to make art. The post centers on "style transfer," which is taking the style of one image and imparting it onto the content of another. "The central problem of style transfer revolves around our ability to come up with a clear way of computing the'content' of an image as distinct from computing the'style' of an image," the post reads. "Before deep learning arrived at the scene, researchers had been handcrafting methods to extract the content and texture of images, merge them and see if the results were interesting or garbage."

If you haven't yet finished holiday shopping, here's a bonus suggestion for your tech-inspired gift list: chocolates made using a recipe that is generated by a computerized chef. Nunu Chocolates, which has shops in Boerum Hill and Park Slope, enlisted IBM's Watson to create recipes for several of its latest confections. Among the artificial intelligence platform's many applications, it turns out, is coming up with novel combinations of ingredients. For Nunu, Chef Watson (yes, that's its actual name) came up with three varieties: a brown ale, espresso, yogurt and lime zest truffle (ambitious!); a strawberry and cranberry truffle and a lemon and Earl Grey caramel. Watson earned its chef's hat by analyzing some 100,000 recipes sourced from Bon Appétit as well as the chemical profiles of hundreds of ingredients.

Dumbo startup Paperspace announced on Thursday that it is launching a service to allow startups and software developers to run Linux for cheap in their virtual desktop. Erb's company is a bit like Airbnb, or many other of the sharing economy startups, but for processing power. It pays for an asset, in this case super heavy duty GPUs and processing power, and rents it out to individual users as they need it. "I've been impressed with the Paperspace team since Dillon and Dan showed me their first demo at YC…," Reddit cofounder Alex Ohanion wrote on the company's Product Hunt launch. "I think their take on machine learning will open up the tech to an even broader audience. I can see this really taking off the developer community."

Do you know that many of the artificially-intelligent things we use in our everyday, quotidian lives "learned" how to "think" to varying degrees by studying the emails of some of the most craven and degraded capitalists in our deeply weird corporate history? Brooklyn's Sam Lavigne and Tega Brain have a new piece of internet art out called The Good Life (Enron Simulator). We first told you about it back in August, right after it won a Rhizome Net Art Microgrant. You input your email into a very Windows 95-looking website and the site sends you each of the 500,000 publicly-available emails from the Enron archives in the order they were sent. You can choose to receive these emails over the course of seven days, 30 days, one year or seven years. Depending on your choice, you'll receive somewhere between 100,000 and 196 emails per day.

It's something we've been thinking about over at Technical.ly Brooklyn HQ for a bit and it's come up in the news recently a few times, most notably last month with the Facebook Trending Topic Imbroglio of 2016. In the end, the big deal was that people thought that the trending topics that show up on the sidebar on Facebook were value-neutral and reflected only what people were talking about on FB. Well, turns out, no, people have an editorial role, too, and there were serious claims of liberal bias. But to take a step back, liberal bias, sensational bias, or otherwise, maybe we shouldn't have expected that the stories on Facebook or machine-generated content to be some magical, neutral, mechanical thing, anyway.

If this year's selection of electable decisionmakers feels like it leaves something to be desired, then here's a story for you. Imagine a world without politicians, or, really, politics. Policy options are instead plugged into a sophisticated program and 1,000 or 100,000 simulations are made. Likely outcomes of the policy change are analyzed and unintended consequences are made visible. Politics are reduced to an argument over what inputs should take precedence in the model, not over what decision to make, and the arguments have to contend with data, which takes precedence over all.