Five years is an awful lot of time in the tech industry. Darling startups find ways to crash and burn. Trends that seem unstoppable sputter-out. In the field of artificial intelligence, the past five years have been nothing short of transformative.
Facebook’s AI Research lab (FAIR) turns five years old this month, and just as the social media giant has left an indelible mark on the broader culture — for better or worse — the work coming out of FAIR has seen some major impact in the AI research community and entrenched itself in the way Facebook operates.
“You wouldn’t be able to run Facebook without deep learning,” Facebook Chief AI Scientist Yann LeCun tells TechCrunch. “It’s very, very deep in every aspect of the operation.”
Reflecting on the formation of his team, LeCun recalls his central task in initially creating the research group was “inventing what it meant to do research at Facebook.”
“Facebook didn’t have any research lab before FAIR, it was the first one, until then the company was very much focused on short-term engineering projects with six-month deadlines, if not less,” he says.
Five years after its formation, FAIR’s influence permeates the company. The group has labs in Menlo Park, New York, Paris, Montreal, Tel Aviv, Seattle, Pittsburgh and London. They’ve partnered with academic institutions and published countless papers and studies, many of which the group has enumerated in this handy five-year anniversary timeline here.
“I said ‘No’ to creating a research lab for my first five years at Facebook,” CTO Mike Schroepfer wrote in a Facebook post. “In 2013, it became clear AI would be critical to the long-term future of Facebook. So we had to figure this out.”
The research group’s genesis came shortly after LeCun stopped by Mark Zuckerberg’s house for dinner. “I told [Zuckerberg] how research labs should be organized, particularly the idea of practicing open research.” LeCun said. “What I heard from him, I liked a lot, because he said openness is really in the DNA of the company.”
FAIR has the benefit of longer timelines that allow it to be more focused in maintaining its ethos. There is no “War Room” in the AI labs, and much of the group’s most substantial research ends up as published work that benefits the broader AI community. Nevertheless, in many ways, AI is very much an arms race for Silicon Valley tech companies. The separation between FAIR and Facebook’s Applied Machine Learning (AML) team, which focuses more on imminent product needs, gives the group a “huge, huge amount of leeway to really think about the long term,” LeCun says.
I chatted with LeCun about some of these long-term visions for the company, which evolved into him spitballing about what he’s working on now and where he’d like to see improvements. “First, there’s going to be considerable progress in things that we already have quite a good handle on…”
A big trend for LeCun seems to be FAIR doubling down on work that impacts how people can more seamlessly interact with data systems and get meaningful feedback.
“We’ve had this project that is a question-and-answer system that basically can answer any question if the information is somewhere in Wikipedia. It’s not yet able to answer really complicated questions that require extracting information from multiple Wikipedia articles and cross-referencing them,” LeCun says. “There’s probably some progress there that will make the next generation of virtual assistants and data systems considerably less frustrating to talk to.”
Some of the biggest strides in machine learning over the past five years have taken place in the vision space, where machines are able to parse out what’s happening in an image frame. LeCun predicts greater contextual understanding is on its way.
“You’re going to see systems that can not just recognize the main object in an image but basically will outline every object and give you a textual description of what’s happening in the image, kind of a different, more abstract understanding of what’s happening.”
FAIR has found itself tackling disparate and fundamental problems that have wide impact on how the rest of the company functions, but a lot of these points of progress sit deeper in the five-year timeline.
FAIR has already made some progress in unsupervised learning, and the company has published work on how they are utilizing some of these techniques to translate between languages for which they lack sufficient training data so that, in practical terms, users needing translations from something like Icelandic to Swahili aren’t left out in the cold.
As FAIR looks to its next five years, LeCun contends there are some much bigger challenges looming on the horizon that the AI community is just beginning to grapple with.
“Those are all relatively predictable improvements,” he says. “The big prize we are really after is this idea of self-supervised learning — getting machines to learn more like humans and animals and requiring that they have some sort of common sense.”
Apple May Bring Major Design Changes To Entry-Level iPad
The changes aren’t skin deep, of course, and the next base iPad is expected to sport changes that may make it more appealing to the casual consumer. At the top of that list is the anticipated switch from the Lightning connector to USB-C, something that all other iPad models have already received. This would not only open up the entry-level iPad to more use cases like hooking up external displays but would also break compatibility with plenty of accessories, particularly the first-gen Apple Pencil.
The first Apple Pencil charges using a Lightning port, but with this connector gone from the upcoming iPad, what would no longer be possible. Given its expected switch to flat edges, it’s likely that the iPad 10 will support the second-gen Apple Pencil. That, in turn, means the days of the original Apple Pencil are numbered, and it wouldn’t be surprising if Apple immediately halts its production.
With the changes to the design and Lightning port would also come a change to the one other legacy connector that has been present since the first iPad: the 3.5mm headphone jack, which will supposedly be making its exit from the iPad this year. If that rumor proves true, Apple’s transition away from wired headphones — at least as far as a direct connection goes — will be complete. These changes also mean that accessory makers will have to alter their designs, as well, especially case manufacturers. The magnetic Smart Cover’s design, for example, no longer has a place in this flat-edged world.
BMW Is Testing Electric Cars With Four Motors For Its Fiercest M EVs
The company’s M xDrive four-wheel drive system is currently in the testing phase, but has already produced some very promising results. The system gives each wheel its own electric motor and runs through a “highly integrated control unit” that takes action based on the driving conditions and the driver’s choices. Along with the driving surface, several other factors are taken into consideration, including accelerator pedal position, steering angle, longitudinal and lateral acceleration, and wheel speeds. All of this is continually monitored and the optimal amount of power and torque is given to each wheel. The decisions the control unit makes are put into action within milliseconds.
BMW has already tested this technology and claims it delivered a number of benefits, including “significantly higher cornering speeds” even in tough conditions, like rain-soaked or snow-covered roads. A specific example the company gave involved the control unit eliminating understeer by temporarily giving more power to the rear outside wheel. The motors also recoup energy when braking. This has been a common feature on many EVs and hybrids for several years, but BMW’s experimental drive train may be the first to optimize energy recovery on all four wheels.
The concept is being tested out on a modified BMW i4 M50 with the front end based around an adapted body strut concept taken from an M3/M4 chassis, and a radiator unit configuration modeled on current high-performance sports cars. The test car is designed to have high torsional rigidity during dynamic driving situations.
The Truth About Porsche’s Complicated Model Number System
Why did it start with the number seven? According to the book “Porsche, Excellence Was Expected” by Karl Ludvigsen, the designers didn’t want Wanderer to “think they were a bunch of novices.” And if you want to get really technical, the very first car Ferdinand built was the Egger-Lohner C2 Phaeton (designated P1) in 1898. Remember, literally every project the company worked on received a successively higher number, from axles to suspensions, gearboxes, and even tractors. Yes, Porsche designed an even slower vehicle than the Volkswagen Thing.
In 1932 came type 22, its first Grand Prix car, the 16-cylinder Auto Union race car. For Porsche, the race was indeed on as figuratively as it was literally. Dr. Ing. h.c. F. Porsche GmbH worked on all sorts of things, from steering components for Citroën and Fiat to axels, plane and motorcycle engines, and yes… the type 60 KdF-Wagen for Volkswagen (and Hitler), which would go on to fame as the VW Bug. However, the system got a little wonky during World War II, when many numbers in the 200 range were simply skipped over (via Ingenieurbüro Kukuk).
By 1948, its internal numbering system had gotten up into the mid-300s. In June of that year, the first vehicle that displayed the official Porsche name rolled into existence with the now iconic Porsche 356, according to the automaker. But it came with a new wrinkle: as the 356 evolved with the latest technological advances, each subsequent model was designated with letters (A, B, C). Alphabet soup with your zip codes, anyone?
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