Shows that went on way too long
"Californication" (seven seasons)
Facebook is set to get an even better understanding of the 700 million people who share details of their personal lives using the social network each day.
A new research group within the company is working on an emerging and powerful approach to artificial intelligence known as deep learning, which uses simulated networks of brain cells to process data. Applying this method to data shared on Facebook could allow for novel features, and perhaps boost the company’s ad targeting.
Deep learning has shown potential to enable software to do things such as work out the emotions or events described in text even if they aren’t explicitly referenced, recognize objects in photos, and make sophisticated predictions about people’s likely future behavior.
The eight-strong group, known internally as the AI team, only recently started work, and details of its experiments are still secret. But Facebook’s chief technology officer, Mike Schroepfer, will say that one obvious place to use deep learning is to improve the news feed, the personalized list of recent updates he calls Facebook’s “killer app.” The company already uses conventional machine learning techniques to prune the 1,500 updates that average Facebook users could possibly see down to 30 to 60 that are judged to be most likely to be important to them. Schroepfer says Facebook needs to get better at picking the best updates due to the growing volume of data its users generate and changes in how people use the social network.
“The data set is increasing in size, people are getting more friends, and with the advent of mobile, people are online more frequently,” Schroepfer told MIT Technology Review. “It’s not that I look at my news feed once at the end of the day; I constantly pull out my phone while I’m waiting for my friend, or I’m at the coffee shop. We have five minutes to really delight you.”
Shroepfer says deep learning could also be used to help people organize their photos, or choose which is the best one to share on Facebook.
Facebook’s foray into deep learning sees it following its competitors Google and Microsoft, which have used the approach to impressive effect in the past year. Google has hired and acquired leading talent in the field (see “10 Breakthrough Technologies 2013: Deep Learning”), and last year created software that taught itself to recognize cats and other objects by reviewing stills from YouTube videos. The underlying deep learning technology was later used to slash the error rate of Google’s voice recognition services (see “Google’s Virtual Brain Goes to Work”).
Researchers at Microsoft have used deep learning to build a system that translates speech from English to Mandarin Chinese in real time (see “Microsoft Brings Star Trek’s Voice Translator to Life”). Chinese Web giant Baidu also recently established a Silicon Valley research lab to work on deep learning.
Less complex forms of machine learning have underpinned some of the most useful features developed by major technology companies in recent years, such as spam detection systems and facial recognition in images. The largest companies have now begun investing heavily in deep learning because it can deliver significant gains over those more established techniques, says Elliot Turner, founder and CEO of AlchemyAPI, which rents access to its own deep learning software for text and images.
“Research into understanding images, text, and language has been going on for decades, but the typical improvement a new technique might offer was a fraction of a percent,” he says. “In tasks like vision or speech, we’re seeing 30 percent-plus improvements with deep learning.” The newer technique also allows much faster progress in training a new piece of software, says Turner.
Conventional forms of machine learning are slower because before data can be fed into learning software, experts must manually choose which features of it the software should pay attention to, and they must label the data to signify, for example, that certain images contain cars.
Deep learning systems can learn with much less human intervention because they can figure out for themselves which features of the raw data are most useful to understanding it. They can even work on data that hasn’t been labeled, as Google’s cat recognizing software did. Systems able to do that typically use software that simulates networks of brain cells, known as neural nets, to process data, and require more powerful collections of computers to run.
Facebook’s AI group will work on both applications that can help the company’s products and on more general research on the topic that will be made public, says Srinivas Narayanan, an engineering manager at Facebook helping to assemble the new group. He says one way Facebook can help advance deep learning is by drawing on its recent work creating new types of hardware and software to handle large data sets (see “Inside Facebook’s Not-So-Secret New Data Center”). “It’s both a software and a hardware problem together; the way you scale these networks requires very deep integration of the two,” he says.
View “Facebook Launches Advanced AI Effort to Find Meaning in Your Posts” and find more technology news from MIT Technology Review.
© 2013 MIT Technology Review
"Californication" (seven seasons)
"Entourage" (eight seasons)
Much like “Californication,” this man-centric show started strong and buzzy -- a perpetual nominee at the Golden Globes and Emmys, and a perceived gender-swapped “Sex and the City.” Then it ground on and on, and what might once have been read as a sophisticated satire of Hollywood materialism became a grinding conveyor belt of self-congratulatory guest-star appearances.
"Will & Grace" (eight seasons)
Hey, did someone say “self-congratulatory guest-star appearances?” Look -- it’s Jennifer Lopez, and Cher, and Janet Jackson, and Madonna! The latter seasons of “Will & Grace” effectively ruined the fun of watching the show in syndication now -- will it be a fun and jaunty early episode, or a later episode in which title characters enact an Ibsen play about having a baby together (really) while Jack and Karen meet one pop star or another? The fact that the show hastened a widespread acceptance of gay people that, then, made the show something of a throwback by the time it ended is one thing; the fact that the show itself seemed uninterested in relying on its actors’ sharp comic timing is quite another.
"The King of Queens" (nine seasons)
This CBS stalwart just kind of kept going, exactly as long as was needed to launch Kevin James’ film career. In the show’s final minutes, a formulaic sitcom became a mile-a-minute soap, with the central characters considering divorce and then having two children.
"Frasier" (11 seasons)
Though it ended strong, "Frasier" had something of the opposite problem as “The King of Queens”: While the CBS comedy chucked a whole bunch of plot at viewers toward the end, NBC’s Emmy magnet stayed stuck in familiar ruts, with Frasier questing endlessly for love and Daphne and Niles in fairly unthrilling domestic bliss. The jokes stayed good, but this maybe could have gone one or two years shorter.
"Weeds" (eight seasons)
As “Homeland” viewers may be learning, Showtime isn’t particularly good at keeping its shows coherent over time. (Maybe this is “Californication”’s issue -- we wouldn’t know!) This show changed settings and, effectively, organizing conceits so many times that by the end, it had few earnest defenders.
"Nip/Tuck" (six seasons)
This FX series, too, changed settings midway through, moving from Miami to Los Angeles four seasons in for no compelling reason. The show’s most gripping subplots had a way of petering out (remember the anticlimactic solution to the mystery of the Carver?), and its bizarre tendencies overtook any sense of fun.
"Glee" (five seasons and counting)
The series has, like its sibling show “Nip/Tuck” (Ryan Murphy created them both), switched locations, moving in large part to New York once its core cast graduated high school. But what’s the point of a high school series when the stars graduate? Despite some lovely moments, the show’s heat seems gone, and attempts to get back into the conversation (the school shooting episode, for instance) have been more desperate and tone-deaf than effective.
"Grey's Anatomy" (10 seasons and counting)
Here’s the thing: By all accounts, “Grey’s Anatomy” is not a creative failure. And it’s still widely watched. But when you begin your life as a world-beating hit, anything else seems somewhat marginal. “Grey’s Anatomy” has shed more regular viewers than many shows will ever hope to get in the first place (same’s true of “Survivor” and latter-day “ER,” to name just a few). Those who stopped watching once the Golden Globe nominations petered out may wonder why the show is still on; loyal viewers know better.
"The Simpsons" (25 seasons and counting)
Like the “Grey’s” doctors, the Springfield clan and their neighbors still draw a crowd. But “The Simpsons” is so omnipresent in syndication and in pop culture that the first-run series seems besides the point (not least because, though there are good episodes here and there, the show’s best days are universally agreed to be behind it -- like way behind it, in the 1990s).
"The Office" (nine seasons)
There was a natural break for this show, where it ought to have ended -- with the departure of lead actor Steve Carell in Season 7. The latter years were a creative fugue state, and as NBC’s Thursday night lineup continued to flatline in the ratings, one-time fans could be forgiven at their surprise that the adventures of Jim and Pam kept on unfolding.
"The X-Files" (nine seasons)
Once one of the show’s leads departs and has to be replaced -- as Steve Carell did on “The Office,” or David Duchovny did here -- the show faces a reckoning; if the lead is so central to the show’s plot as to make people wonder how the show could possibly go on, maybe the show shouldn’t. And even “X-Files” superfans might have been happier with fewer seasons of drawing out the conspiracy string toward a famously unsatisfying ending.