Meet The Man Who Makes Facebook’s Machines Think

Meet The Man Who Makes Facebook’s Machines Think

Nearly 3,000 miles away from Facebook’s Menlo Park headquarters, in an old, beige office building in downtown Manhattan, a group of company employees is working on projects that seem better suited for science fiction than social networking. The team, Facebook Artificial Intelligence Research — known internally as FAIR — is focused on a singular goal: to create computers with intelligence on par with humans. While still far from its finish line, the group is making the sort of progress few believed possible at the turn of the decade. Its AI programs are drawing pictures almost indistinguishable from those by human artists and taking quizzes on subject matter culled from Wikipedia. They’re playing advanced video games like Starcraft. Slowly, they’re getting smarter. And someday, they could change Facebook from something that facilitates interaction between friends into something that could be your friend.

For these reasons and others, FAIR isn’t your typical Facebook team. Its members do not work directly on the $410 billion company’s collection of mega popular products: Instagram, WhatsApp, Messenger, and Facebook proper. Its ultimate goal is likely decades off, and may never be reached. And it’s led not by your typical polished Silicon Valley overachiever but by Yann LeCun, a 56-year-old academic who’s experienced real failure in his life and managed to come back. His once-rejected theories about artificial intelligence are today considered world-class, and his vindication is Facebook’s bounty.

“Your interaction with the digital world, your phone, your computer, is going to be transformed,” LeCun told BuzzFeed News of what may be.

FAIR is improving computers’ ability to see, hear, and communicate on their own, and its findings now permeate Facebook’s products, touching everything from the News Feed ranking to cameras and photo filters. And Facebook is investing, big time — not simply because artificial intelligence is interesting, but because it’s necessary. In all corners of tech today, companies are competing on the basis of their AI. Uber’s AI-powered autonomous cars are core to its ride-hailing strategy. Google’s AI-reliant Google Home smart speaker is answering queries users once typed in the search bar (and, long before that, looked up in the encyclopedia). Amazon is building convenience stores with artificially intelligent cashiers in an effort to crack the $674 billion edible grocery market.

And at Facebook, AI is everywhere. Its AI-powered photo filters, for instance, are helping it fend off a challenge from Snapchat. Its AI’s ability to look at pictures, see what’s inside them, and decide what to show you in its feeds that is helping the company provide a compelling experience keeps you coming back. And similar technology is monitoring harassing, terroristic, and pornographic content and flagging it for removal.

“The experiences people have on the whole family of Facebook products depend critically on AI,” said Joaquin Candela, the head of Facebook’s Applied Machine Learning group, or AML, which puts research into action on the platform itself. “Today, Facebook could not exist without AI. Period.”

As the field becomes more advanced, Facebook will rely on LeCun and his team to help it stay ahead of competitors, new or current, who are likely to embrace the science.

After years of criticism and marginalization, LeCun finally has it all: 80 researchers, the backing of Facebook’s vast financial resources, and mainstream faith in his work. All he has to do now is deliver.

Sight

From an early age, LeCun believed he could get computers to see. Facial recognition and image detection may be standard today, but when LeCun was a university student in Paris in the early 1980s, computers were effectively blind, unable to make sense of anything within images or to figure out what was appearing inside their cameras’ lenses. It was in college that LeCun came across an approach to the field that had remained largely unexplored since the 1960s, but that he thought could potentially “allow machines to learn many tasks, including perception.”

The approach, called an artificial neural network, takes systems of small, interconnected sensors and has them break down content like images into tiny parts, then identify patterns and decide what they're seeing based on their collective inputs. After reading the arguments against neural nets — namely that they were hard to train and not particularly powerful— LeCun decided to press ahead anyway, pursuing a PhD where he’d focus on them despite the doubts. “I just didn't believe it,” he said of the criticism.

Hard times in the artificial intelligence field occur with such frequency and intensity that they have their own special name: AI Winter. These periods come about largely when researchers’ results don’t live up to their boasts, making it seem like the science doesn’t work, causing funding and interest to dry up as a result, and technological progress along with it.

LeCun has seen his fair share of AI Winter. After settling into an AI research job at Bell Labs in the mid ‘90s, internal strife at AT&T caused his team there to come apart just as it was rolling out check-reading ATMs — neural-net-powered technology that’s still in use today — right as LeCun believed it was making clear progress. “The whole project was disbanded essentially on the day that it was becoming really successful,” LeCun said. “This was really depressing.”

At the same time, other methods were gaining favor with mainstream researchers. These methods would later fall back out of favor, but their rise was enough to push neural nets — and LeCun, their longtime champion — to the margins of the field. In the early 2000s, other academics wouldn’t even allow him to present his papers at their conferences. “The computer-vision community basically rejected him,” Geoff Hinton, a neural net pioneer who’s currently an engineering fellow at Google and a professor at the University of Toronto, told BuzzFeed News. “The view was that he was carrying on doing things that had been promising in the ‘80s but he should have got over it by now,” Hinton explained.

“That’s not the view anymore,” he added.

Other neural net researchers encountered similar problems at the time. Yoshua Bengio, a professor at the University of Montreal and head of Montreal Institute for Learning Algorithms, had a hard time finding grad students willing to work with him. “I had to twist the arms of my students to work in this area because they were scared of not having a job when they would finish their PhD,” he told BuzzFeed News.

In 2003, LeCun laid the foundation for his redemption. That year, he joined New York University’s faculty and also got together with Hinton and Bengio in a largely informal coalition to revive neural nets. “We started what I've been calling the Deep Learning Conspiracy,” LeCun said with a smile.

The Deep Learning Conspiracy played a critical role in the field, mostly by virtue of sticking to its belief that of instead building individual, specialized neural nets for each type of object you wanted to detect, you could use the same template to build one neural that could detect image, video, and speech. So instead of building one neural net to detect penguins and another for cats, you could build a single neural net that could detect both, and tell the difference. These new neural nets could also be modified for other tasks, such as looking at audio waves to detect the patterns of speech.

The Conspiracy’s research was buoyed by two important outside factors: Increases in computing power, which helped its neural nets work fast enough to be practical, and an exponential increase in available data (pictures, text, etc.) created thanks to the widespread adoption of the internet, which could be churned through the networks to make them smarter. The result eventually became a nimble, fast, accurate approach that opened up new possibilities for the field.

With the fundamentals set in place by LeCun and his compatriots, computer vision exploded in the early 2010s. Computers began recognizing objects in images, then in videos, and then live in the camera. Now, you can point a camera at a basketball and AI can understand what it’s looking at. LeCun quickly went from guy on the sidelines to a leader in the field. “It went from nobody working on it to everybody working on it within a year,” LeCun said. “It's just insane — it's completely insane.”

In December 2013, LeCun joined Facebook, an ideal environment for someone interested in applying AI research to photos. Facebook’s platform is packed with billions of images, giving LeCun and his researchers a big broad canvas to implement new ideas. FAIR regularly collaborates with AML, to put its research into action on Facebook proper. The two groups build new systems that make the advances available across the company. AML is using FAIR’s research to help determine what content is shown to you in the News Feed, to translate content inside Facebook; it's also deploying it inside Facebook’s camera to create special effects that react to your motion.

Thought

Teaching computers to see is an elemental step toward teaching them how the world works. Humans understand how the world operates because we watch scenarios repeated over and over, and develop an understanding of how they’ll play out. When a car comes speeding down a road we’re standing in, for instance, we predict it might hit us, so we get out of the way. When it gets dark, we predict flipping a light switch will make it light again, so we flip it.

FAIR is trying to teach computers to predict outcomes, just like humans do, using a similar method. The team, LeCun explained, is showing its AI lots of related videos, then pausing them at a certain point, and asking the machine to predict what happens next. If you repeatedly show an AI system videos of water bottles being turned over people's heads, for instance, it could potentially predict the action will get someone wet.

“The essence of intelligence, to some extent, is the ability to predict,” LeCun explained. “If you can predict what's going to happen as a consequence of your actions then you can plan. You can plan a sequence of actions that will reach a particular goal.”

Teaching artificial intelligence to predict is one of the most vexing challenges in the field today, largely because there are many situations in which multiple possible outcomes are theoretically correct.

Imagine, LeCun said, holding a pen vertically above a table and letting go. If you ask a computer where the pen will be in one second, there is no correct answer — the machine knows the pen will fall, but it can’t know exactly where it will land. So you need to tell the system that there are multiple correct answers “and that what actually occurs is but one representative of a whole set of alternatives. That’s the problem of learning to predict under uncertainty.”

Helping AI understand and embrace uncertainty is part of an AI discipline called “unsupervised learning,” currently the field’s cutting edge. When AI has observed enough to know how the world works and predict what’s going to happen next, it can start thinking a bit more like humans, gaining a kind of common sense, which, LeCun believes, is key to making machines more intelligent.

LeCun and his researchers allow that it will most likely take years before AI will fully appreciate the gray areas, but they’re confident they’ll get it there. “It’ll happen,” said Larry Zitnick, a research manager who works under LeCun. “But I would say that’s more of the 10-year horizon-ish.”


Speech

Back in December, Mark Zuckerberg published a splashy video demoing his “AI butler,” Jarvis. Coded by the Facebook founder himself, Jarvis made Zuckerberg toast, allowed his parents into his house after recognizing their faces, and even taught his baby daughter, Max, a lesson in Mandarin.

Jarvis was cool. But to LeCun, it was nothing special. “It’s mostly scripted, and it’s relatively simple, and the intelligence is kind of shallow, in a way,” LeCun said. His sights are set higher.

LeCun wants to build assistants, but ones that really understand what you tell them. "Machines that can hold a conversation,” he said. “Machines that can plan ahead. Machines you don't get annoyed at because they are stupid.”

There’s no blueprint for that, but FAIR is working on what may be the building blocks. Giving AI a rudimentary understanding of the world and training it to predict what might happen within it is one component. So is teaching it to read and write, which FAIR is using neural nets for, too. To a computer, an image is an array of numbers — but a spoken sentence can also be represented as an array of numbers, as can text. Thus, people like LeCun can use neural networks architectures to identify the object in images, the words in spoken sentences, or the topics in text.

AI still can’t comprehend words the way it comprehends images, but LeCun already has a vision for what the ultimate Jarvis might look like. His ideal assistant will possess common sense and an ability to communicate with other assistants. If you want to go to a concert with a friend, for instance, you’d tell your assistants to coordinate, and they would compare your musical tastes, calendars, and available acts to suggest something that works.

“The machine has to come up with some sort of representation of the state of the world,” LeCun said, describing the challenge. “People can’t be in two places at the same time, people can’t go from New York to San Francisco in a certain number of hours, factor in the cost of traveling — there’s a lot of things you have to know to organize someone’s life.”

Facebook is currently experimenting with a simple version of these digital assistant called M, being run by its Messenger team and relying on some FAIR research. Facebook Messenger recently released “M suggestions,” where M chimes into conversations in moments it thinks it can help. When someone asks “Where are you?” for instance, M can pop into the conversation and give you the option to share your location with a tap. It’s likely the company will expand this functionality into more advanced uses.

M is one application of Facebook’s efforts use AI to understand meaning, but the company is considering uses for that type of technology. It may even put it to work in an effort to break down barriers it was recently accused of helping put up.

Even before the 2016 election drew attention to polarization and fake news on Facebook, Y-Lan Boureau, a member of LeCun’s team, was working to use AI to create more constructive conversations on Facebook. Boureau, who studied neurology as well as AI, decided to pursue this project after spending a summer watching her friends fight on Facebook with little interest in hearing opposing views. “If we could better understand what drives people in terms of their state of mind,” Boureau explained, “and how opinions are formed and how they get ossified and crystallized, and how you can end up with two people being unable to talk to each other, this would be a very good thing.”

Boureau wants to create a world where we see as many opinions as we can handle — up until the point at which we start tuning them out. AI can help with this by mapping out patterns in text, understanding where something goes off the rails and potentially figure out a way to alter the conversation flow to stem the bad turn. “If we knew more about that learning process and how these beliefs get in people’s heads from the data then it might be easier to understand how to get more constructive conversations in general,” Boureau said.

In the aftermath of the 2016 election, LeCun publicly suggested Facebook had the technical capabilities to use AI to filter out fake news. Some saw his statement as a solution to a problem many blamed for the widespread polarization in the US, but LeCun said that the task was best left to third parties, instead of machines capable of introducing bias. “There’s a role AI can play there, but it’s a very difficult product design issue rather than a technological issue,” he said. “You don’t want to lead people into particular opinions. You kind of want to be neutral in that respect.”

Reality

Hype cycles can be dangerous for the AI field, as LeCun knows well. And today, it certainly seems like we’re in one. In the first quarter of 2013, six companies mentioned AI on earnings calls. In the first quarter of 2017, 244 did, according to Bloomberg.

LeCun is careful to couch his statements when discussing the future. “This is still very far from where we want it to be,” he’ll say. “The stuff doesn't work nearly as well as we'd like it to,” he’ll caveat. Indeed, as LeCun cautions, AI is still far from reaching human level intelligence, or General AI as it's known.

Still, sometimes LeCun can’t restrain his enthusiasm. He’s particularly excited about adversarial training, a relatively new form of AI research that could help solve the prediction and uncertainty challenges facing the field today. Adversarial training pits two AI systems against each other in an attempt to get them to teach themselves about the real world. In one FAIR experiment, for instance, a researcher has one AI system draw pictures in an attempt to trick another into guessing they were drawn by humans. The first uses feedback from the second to learn to draw better.

At a conference earlier this year, LeCun showed something even more advanced: One AI's attempt to convince a second AI that a few frames of video it created part of a video the second AI had already viewed. Adversarial training, LeCun said, “is the best, coolest idea in machine learning in the last 10 or 20 years.”

And so LeCun will move keep playing around with adversarial training, once again pushing the field to its boundaries. He’s come a long way from the man who couldn’t even get his ideas heard 20 years ago. Though LeCun will be the first one to tell you the work is far from over, and that the success is far from his alone, he’s not one to let the moment pass without a small bit of appreciation. “I can't say it feels bad,” he said. “It feels great.”●

Alex Kantrowitz is a senior technology reporter for BuzzFeed News and is based in San Francisco. He reports on social and communications.

Contact Alex Kantrowitz at alex.kantrowitz@buzzfeed.com.

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6 upgrades for your next email campaign

6 upgrades for your next email campaign


Social media marketing and other marketing platforms might be growing at a phenomenal rate, but email marketing is still very much alive and well in today’s world.


The question to ask yourself is this: are your emails actually getting the response you want? If not, then perhaps it’s time to take a new direction. Check out these six actionable upgrades that you can implement next time you create a campaign.

1. Be clear and define your email’s purpose


Clarity is critical to the success of any email blast. This might not seem like an upgrade, yet you might be surprised at how often businesses fail to clearly define the goals of their emails.

In fact, the name itself—email blast—implies something that doesn’t have a lot of thought behind it. ‘Who am I speaking to?’ is the most important question any marketer should ask themselves before they begin writing an email. If you don’t know who your audience is, it’s almost impossible to create content that’ll resonate with them.

2. Use better images


Staged stock photos just aren’t personal enough in these days of social media marketing. If you’re going to use images in your email blast—and you should—then use something unique; something that shows the personality of your brand and the message you are trying to send.

You can create something yourself, have a professional photographer do it, or leverage user-generated content. If you do use stock photos, try to go beyond the overused ones to find something that feels more real.

3. Make it personal


Firstly, no one wants an email from a no-reply email address; it feels far too impersonal. Ideally, the email should come from a real person in your organization, such as you, your CEO or a member of your customer service team, giving the reader the feeling that they can have a two-way conversation if they wish.

You also want to make the most of the data you have to personalize your emails. You can do basic things like including the subscriber’s name, or you can go much further by tailoring the content of each email to reflect that subscriber’s interests, geographic location, birthday, past purchases, and customer behavior and profile. Customer data is the most powerful tool that will allow you to segment your contacts and deliver meaningful information to them.


4. Make your call-to-action clear


Make it big, make it bold, and make it obvious. No matter what your call-to-action is, you need to get it out there in a very obvious way. Anyone who looks at the email should know what they are supposed to do within a matter of seconds. This is especially important when it comes to those who are viewing emails on mobile devices, because it needs to be easy to tap, too.


5. Send it out at the right day/time


The day and time you send out your email will vary depending on your target audience. For example, stay-at-home parents with small children will have a different best-time than an executive working for a large corporation. This is where dotmailer’s Send Time Optimization tool comes into its own; it remembers when subscribers open their emails and delivers future sends at the times they’re most likely to be read.


6. Promote in-store


If you have a bricks-and-mortar location for your business, you have the perfect way to gather email addresses. You can ask for email addresses at the cash register or you can run an in-store contest or raffle. E-receipts are also becoming more popular and provide a mechanism to collect customers’ email addresses, connecting valuable offline and online purchase data.

These are just a few upgrades you can give your email campaigns to increase their effectiveness and get the results you are looking for. Check out our resources library for heaps of free content downloads that will help you to advance your email marketing.


By Isabel Stewart, Marketing Executive at Dotmailer


George Hotz is giving away the code behind his self-driving car project

George Hotz is giving away the code behind his self-driving car project

Famed iPhone and PlayStation cracker George Hotz is resurrecting the DIY autonomous car project he canceled in October. But this time, there’s a twist: instead of selling a physical product, Hotz’s Comma.ai is releasing the company’s self-driving software, as well as the plans for the necessary hardware, which Hotz calls Comma Neo. All of this code will be available for free — in fact, it is already on Github.

Hotz framed the self-driving software, called Open Pilot, as an “open source alternative to [Tesla’s] Autopilot” during a press event that was held in a San Francisco house that serves as Comma.ai’s headquarters. He claimed that the Open Pilot and Comma Neo combination “provides almost all the same functionality as Autopilot 7,” which is the second-most-recent version of Tesla’s self-driving software.

People who want to tinker with Comma Neo and Open Pilot will need much more than the Github code, though. The Neo side of the project requires a special OS (called NeOS), and the only Android phone that can run it is the OnePlus 3. Hotz says that this is because it’s the only phone that’s open enough (it’s bootloader unlockable) and that has the specs (particularly the Snapdragon 820 processor) and cameras capable of running Comma.ai’s software. They’ll also need to 3D-print the Comma Neo housing.

“We’re not shipping a product,” Hotz said. “We’re shipping alpha software really for research purposes only. We do not provide any guarantees.”

Earlier this year, Hotz announced ambitions to create and sell a $999 after-market kit called “Comma One” that could add semi-autonomous capabilities to Honda Civics and some Acura cars. Over the summer, Comma.ai demoed this tech to various outlets, including The Verge. Most were impressed by the idea, but were careful to point out apparent shortcomings, like how the system handled itself poorly in city driving situations.

Comma One faced increased scrutiny as Comma.ai got closer to releasing the product. In response, Hotz talked down some of the autonomous vehicle hype in a blog post, likening Comma One to the existing features like “lane keep assist” and saying that it “will not turn your car into an autonomous vehicle. It is an advanced driver assistance system.” Hotz also stated that Comma One “does not remove any of the driver's responsibilities from the task of driving.”

One week later, the National Highway Traffic Safety Administration sent Comma.ai a letter regarding concern that Comma One “would put the safety of [Comma.ai’s] customers and other road users at risk.”

“We strongly encourage you to delay selling or deploying your product on the public roadways unless and until you can ensure it is safe,” Paul A. Hemmersbaugh, NHTSA’s chief counsel, wrote in the letter. Hemmersbaugh also took umbrage with Hotz’s claim about driver responsibility, and subtly referenced the trend of Tesla customers pushing their cars’ semi-autonomous features in unsafe ways as evidence. Hotz responded by canceling Comma One, tweeting that he’d “much rather spend my life building amazing tech than dealing with regulators and lawyers. It isn't worth it.”

During today’s press conference, Hotz said that Comma.ai decided to go open source in an effort to sidestep NHTSA as well as the California DMV, the latter of which he said showed up to his house on three separate occasions. “NHTSA only regulates physical products that are sold,” Hotz said. “They do not regulate open source software, which is a whole lot more like speech.” He went on to say that “if the US government doesn't like this [project], I’m sure there are plenty of countries that will.”

Hotz compared Open Pilot to Android, and said that it’s really aimed at “hobbyists and researchers and people who love” self-driving technology. “It’s for people who want to push the future forward,” he said. When asked how or if Comma.ai plans to make any money off of this project, Hotz responded: “How does anybody make money? Our goal is to basically own the network. We want to own the network of self driving cars that is out there.”

Developing...

The Internet Archive is building a Canadian copy to protect itself from Trump

The Internet Archive is building a Canadian copy to protect itself from Trump

The Internet Archive, a digital library nonprofit that preserves billions of webpages for the historical record, is building a backup archive in Canada after the election of Donald Trump. Today, it began collecting donations for the Internet Archive of Canada, intended to create a copy of the archive outside the United States.

“On November 9th in America, we woke up to a new administration promising radical change,” writes founder Brewster Kahle. “It was a firm reminder that institutions like ours, built for the long-term, need to design for change. For us, it means keeping our cultural materials safe, private and perpetually accessible. It means preparing for a web that may face greater restrictions. It means serving patrons in a world in which government surveillance is not going away; indeed it looks like it will increase.”

The Internet Archive provides some of the most comprehensive preservation of our digital ephemera, for both intellectual study and practical use — including journalistic fact checking. Kahle estimates it will cost “millions” of dollars to host a copy of the Internet Archive in Canada, but it would shield its data from some American legal action.

The future of privacy and surveillance under the Trump administration remains unpredictable, but the president-elect has shown support for greater law enforcement surveillance powers and legal censorship, including “closing that internet up in some ways” to fight terrorism. “Somebody will say, 'Oh freedom of speech, freedom of speech.' These are foolish people,” he said in a 2015 speech. (This morning, he also suggested that burning the American flag — a constitutionally protected action — should be punished by loss of citizenship.)

Kahle notes that moving the internet archive would both insulate it from efforts to take down specific content, and make it harder to request data on user activity — something that more traditional librarians fought when American surveillance powers expanded under George W. Bush. And whatever happens, a Canadian copy would create more redundancy for data that can be seemingly ubiquitous but deceptively fragile. “The history of libraries is one of loss,” writes Kahle. “The Library of Alexandria is best known for is disappearance.”

Twitter brings ranked conversations to mobile devices

Twitter brings ranked conversations to mobile devices

Twitter introduced a new design for replies today that ranks conversations in your timeline according to signals including whether you follow the person who replied and whether the author replied. The personalized ranking means that different people will see a different set of replies by default, although you can still expand the tweet to see every reply. The feature is new to mobile, devices, though Twitter previously brought this design to the web in ... June 2015? Huh.

It's now easier than ever to see the buzz around your Tweet with reply counts and organized conversations: https://t.co/GM7DBEZbJr. pic.twitter.com/FgaH2I4nsI

— Twitter Support (@Support) November 29, 2016

The sort-of-new take on replies ranks more popular replies higher than others, so if you deliver a sick burn to a brand, more people will see it by default. The new design also includes conversation counts, so you can see which tweets are generating the most abuse and harassment at a glance. (Also fun and productive conversations, sometimes!)

Twitter is a free app and you can download it on Android and iOS, if you want.

Exclusive: Mike Allen and Jim VandeHei Reveal Their Plan for Media Domination

Exclusive: Mike Allen and Jim VandeHei Reveal Their Plan for Media Domination

New Media

Exclusive: Mike Allen and Jim VandeHei Reveal Their Plan for Media Domination

For starters, their new company will be called Axios, which means worthy in Greek. And Allen will return with a newsletter that “cuts across our topic areas.”
November 30, 2016 5:00 am
Left, by T.J. Kirkpatrick/Redux; Right, by Alex Wong/Getty Images.

The much-anticipated new media venture from Politico co-founder and former C.E.O. Jim VandeHei and Mike Allen, the founding father of its Playbook newsletter (the lifeblood of the enterprise for years), has been shrouded in mystery since the duo departed earlier this year. VandeHei seemed to suggest its broad contours through various well-placed hints and intimations. Now, he and his partners are unveiling the company name and its mission statement, neatly rolled out ahead of VandeHei’s appearance at a Recode conference later today.

The name: Axios. The mission statement: “Media is broken—and too often a scam.”

If you added an emphatic “Sad!” at the end of that sentence it might credibly pass as the latest tweet from President-Elect Donald Trump. But, as recent polling has shown the public’s trust in journalists to be at rock bottom, VandeHei and company certainly see opportunity there. Axios stands for “worthy” in Greek. As such, the new company promises to deliver content deserving of its readers’ attention. And that starts with the articulation of a seemingly new approach to media strategy.

As VandeHei sees it, the current news-media landscape can be bifurcated into two major categories. There are the existing big-media companies, such as The New York Times and The Wall Street Journal, among others, who are “sitting on a hyper-expensive and extensive blob of technology that is outdated, that is filled with archives and so much information about your customers that you can’t get rid of it.” These companies, he added, “are reliant on a type of advertising that you know is going to die, but you can’t leave it because it’s short-term revenue.” He continued: “They are in a very difficult and painful transition. The reality of it is just setting in, and the amount of upheaval is going to be profound, and it drains not just resources but emotions and enthusiasm.”

Then, there are the news and media start-ups that have been built during the last 10 years, such as BuzzFeed and Business Insider, which rely on a model that VandeHei describes as “just give me a lot of traffic, I swear to God I will find a business model.” As it happens, when one news outlet gets a lot of traffic, other companies follow the model, and then, eventually, “the law of supply and demand kicks in,” which means that the price those outlets can charge for advertising “goes down, down, down,” according to VandeHei. In that scenario, such companies will either go out of business or combine. We’ve seen some of that with the Huffington Post, the granddaddy of the start-ups being purchased by AOL, which was subsequently bought by Verizon, who later bought Yahoo (not without some regrets). Vox bought Recode. Business Insider sold to Axel Springer. Bill Simmons’s Ringer has failed to gain traction, proving that individual brands are not ironclad guarantees that an audience will follow. (Simmons’s show on HBO premiered on June 22, and was canceled in November.)

For VandeHei, too many media companies have fallen into the traffic trap, or as he has eloquently put it, the “crap trap.” What Axios is trying to do is occupy the space that VandeHei feels The New York Times and The Economist could have commanded if they weren’t tethered to their old print roots. He has joked with potential investors that Axios is best described as what you get if the “Economist mated with Twitter,” and “smartly narrated all the good stuff its own reporters missed,” according to someone familiar with the conversation. In late summer, Axios secured $10 million in financing. The round was led by Lerer Hippeau Ventures, the venture-capital firm integral to the launch of the Huffington Post and BuzzFeed. Also backing the venture is NBC News, which is a media partner and whose president, Andy Lack, will sit on the board. Other funders include Laurene Powell Jobs’s Emerson Collective, Greycroft Partners, and David and Katherine Bradley, owners of Atlantic Media.

In particular, Axios wants to focus on business, technology, politics, and media trends through hiring people “who are authentically wired and smart in those topic areas,” as Allen was uniquely suited when he began Playbook. (Dan Primack, who authored the daily Term Sheet newsletter for Fortune, just joined Axios. Primack’s Term Sheet was so popular, in fact, that it hosted its own N.C.A.A.-tournament-bracket pool for its readers.) VandeHei and his co-founders have talked openly with job candidates and investors about the eye-opening lessons of “smart brevity” that he has learned from We the People, a news service on Snapchat that VandeHei has produced in partnership with NowThis News.

VandeHei, who declined to go into details about exactly how the organization would be structured, appears focused on creating good content that this is specifically designed to live on other platforms, such a Snapchat and Facebook. While the Axios site will not fully launch until late January, closer to the presidential inauguration, the site, with sign-ups for e-mails and news alerts, goes live later today. Primack will likely launch his tech-deals newsletter before the launch, probably around December. There will subsequently be another newsletter on the business and politics of health care, written by David Nather. Allen will reprise his newsletter writing gig with one that “cuts across our topic areas,” VandeHei said.

Meanwhile, VandeHei seems to be mostly defining Axios by what it is not. After talking to VandeHei about why anyone would want to launch a new media venture in 2017, with mass layoffs hitting major news organizations and Vice’s co-founder Shane Smith calling for a “bloodbath” in the industry, he e-mailed me: “If we look anything like everyone else a year from now, we are screwed. That is why we plan innovative twists on content, platform, audience and monetization.”

The most significant problem facing news producers, VandeHei told me, is that “there is more good information out there than at any point in humanity, but it’s harder than ever to get to it.” The key to Axios, he elaborated, will be exploring the “collision” between tech and areas such as bureaucracy, health care, energy, and the transportation infrastructure.” He added, “Google and Facebook are gobbling up the media business—they now control two-thirds of the ad market—and conventional publishers are tanking. The media business needs radical overhaul, not knock-offs or wishful thinking.”

Though VandeHei hopes that Axios will eventually garner half of its revenue through subscriptions—presumably through selling high-priced packages to corporations and professionals in specialized fields—he acknowledged that this part of the business has not yet been finalized. “We won’t have a subscription on day one,” he said, adding that Axios would build a model “very quickly.” “What will differentiate us is a very unique twist on how we go about getting content and the platform we built to disseminate that content and how we go after very specific audiences with a level of precision.”

But some of the mystery that shrouded the company in its earliest days of development seems likely to persist, at least for a little bit longer. VandeHei won’t say what the unique twist is, but he lays out the key points in the manifesto, which describes a media company that values short, specialized, high-quality news items that are easily shareable. VandeHei notes that all the 27 people listed in his memo “left cool, safe jobs to start a new company.” VandeHei has said he hopes the company will have 50 employees at launch time. We at the Hive are among those watching closely to see what, in the putative year of the bloodbath, they have joined.

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Why This CEO Pumped The Brakes On Growth Before Stepping On The Gas

Why This CEO Pumped The Brakes On Growth Before Stepping On The Gas

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Why This CEO Pumped The Brakes On Growth Before Stepping On The Gas

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Photo Courtesy of SendGrid

This month SendGrid, a cloud-based customer communication platform, announced the opening of its new global headquarters in downtown Denver. A leader in email deliverability, SendGrid has 40,000 paying customers and delivers over 30 billion emails each month for internet and mobile-based customers like Airbnb, Pandora, HubSpot, Spotify, Uber and FourSquare and more traditional enterprises like Intuit and Costco.

This move consolidated three separate offices while more than doubling the capacity of the company’s previous Colorado footprint. Currently they have just over 300 employees spread across Colorado and California and plan to grow to 900 by 2020.

Rapid growth at this scale doesn’t happen responsibly unless the financials are solid. SendGrid projects crossing $100 million in revenue next year and has already booked two profitable quarters. IPO preparations are underway.

SendGrid CEO Sameer Dholakia joined in 2014 when the company had roughly 200 employees. Despite an eagerness to capitalize on the upside potential, his first move as CEO was to throttle back the investment in growth.

“On the financial side, I wanted us to increase our rate of spending at no rate faster than revenue growth,” says Dholakia, who has 20 years of experience in bringing high growth, disruptive cloud and enterprise software solutions to market. “We weren’t doing that in 2014.”

He started by slowing hiring, he says, because talent acquisition is the single most important decision made every week . Then he installed new leaders over every major function of the business who could convey a clear direction and future focus. “New talent doesn’t appreciate a lack of organization or a lack of clarity on priorities,” Dholakia explains. “They want to know how they’ll develop new skills or they’ll bounce out quickly.”

With this new layer of management in place, he commenced a major cultural shift regarding recruiting. He taught hiring managers to view attracting, developing, and retaining talent as central to their roles and insisted that they always make the trade-off that benefits the long-term.

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6 ways to maximize your impact on Giving Tuesday

6 ways to maximize your impact on Giving Tuesday

6 ways to maximize your impact on Giving Tuesday

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2016%2f06%2f30%2fea%2f201503270cheadshot_20.ff394.2e0b1By Matt Petronzio2015-11-30 23:00:28 UTC

'Tis the season to make a difference.

Since its inaugural year in 2012, Giving Tuesday has seamlessly joined Black Friday and Cyber Monday as one of the biggest events kicking off the holiday shopping season — but with a charitable twist. The first Tuesday after Thanksgiving moves beyond the year-end's typical consumerism and reminds us the holidays also call for giving back.

But as Giving Tuesday grows in popularity, and more organizations and businesses alike join the fray, it can be difficult to wade through the options and figure out how to do the most with your charitable dollars.

To help you maximize your philanthropic potential this Giving Tuesday, we've rounded up six things to think about before you click that "donate" button.

1. Find organizations where your donation really counts.

Just released our updated charity recommendations. Details at https://t.co/boNExVSuDl

— GiveWell (@GiveWell) November 20, 2015

The charities and nonprofits that can benefit the most from Giving Tuesday donations are small, underfunded and ones often implementing hyperlocal solutions to big issues. But these organizations usually can't afford big marketing efforts — which can make them hard to find.

Luckily, there are resources to help you surface them. GiveWell, a charity evaluator that thoroughly researches and vets nonprofits based on a range of criteria, rounds up an annual "top charities" list. The list is short, but impactful — you can be sure your donations to these organizations will help greatly.

Other charity watchdogs are your friends during the giving season. These sites navigate the nonprofit terrain for you, and organize clear stats surrounding a charity's real impact. Try Charity Navigator, CharityWatch, and ProPublica's Nonprofit Explorer, among various others.

2. Research what amount would make the biggest difference.



Once you find a worthy organization, don't just check off a random amount on a donation form — look into how much money is needed to make a tangible impact.

For example, if a $2.50 net protects two people from malaria in the developing world, know that a $50 donation could directly impact 40 people. If it costs 50 cents to provide a meal to a Syrian refugee child for a day, know that your donation of $182.50 can feed that same child for a year.

This is where the "other" field on donation forms comes especially in handy.

3. Beware gimmicky campaigns.

You can't fake it til you make it in #causemarketing. It has to be in your org's DNA. #DS2015

— Ad Council (@AdCouncil) February 5, 2015

Just like Black Friday and Cyber Monday before it, Giving Tuesday has become prey for businesses hoping to turn a profit — except this time, they capitalize primarily on a consumer's good nature.

Be wary of Giving Tuesday campaigns from big companies and chains. While the intentions may very well be good, keep an eye out for causewashing — or when businesses claim your purchase contributes to a tangible social impact, but doesn't make nearly as much of a difference as it claims (think Breast Cancer Awareness Month).

Be critical of all campaigns, and put in the necessary research to confirm your entire donation actually contributes to a good cause.

4. Make sure you're not being scammed.

Today is #GivingTuesday: but don't let scammers trick you into a fake "charity" scam. Tips from @FTC: http://t.co/p0tZ4lM35g

— Scam Awareness (@EndScamming) December 2, 2014

There's causewashing, and then there are straight-up scams. If there's one thing May's big cancer charity scam taught us, it's that even the most worthy-sounding charities could be using your well-intentioned donations for personal luxuries.

It also reinforced that we need to be diligent in making sure charities are legitimate before we donate. Look for red flags, Google the organization's financials, make sure you don't donate over the phone and consult those convenient charity watchdogs. These precautions are worth the extra effort.

5. Remember it's not just about money.

People everywhere are #GivingHours for #GivingTuesday! Pledge & learn more https://t.co/9hE76oyGiC @plus_socialgood pic.twitter.com/pnqPi1Ypfz

— #GivingTuesday (@GivingTues) November 25, 2015

In many cases, donations are the best way to support a charity, especially when it's underfunded and can barely afford overhead costs. But you should also find out whether there are other ways you can help, like donating your time and skills.

Use Mashable's guide on how to find volunteer work online, which includes resources that help you find both in-person and remote opportunities.

6. Think beyond Giving Tuesday.

It's Wednesday, but #GivingTuesday doesn't have to end! If you can, please support your favorite nonprofit during this giving season!

— Equal Voice News (@EqualVoiceNews) December 4, 2013

It's important to remember your charitable efforts shouldn't begin and end on Giving Tuesday. Consider your donation an investment in an organization — hold it accountable, track its progress, sign up for updates and consider showing support throughout the year, if you have the means.

Use Giving Tuesday as a jumping-off point to establish a relationship with an organization and its cause. You could be making a lasting difference.

Topics: Business, Giving Tuesday, nonprofits, philanthropy, Social Good
An overview of the bot landscape

An overview of the bot landscape

Bots landscape. Bots landscape. (source: Lev Mass).

Bots are a growing segment of software that acts as an agent on a human’s behalf. These tasks range from ordering online, to making dinner reservations, to handling customer service requests, to helping employees be more productive in the workplace.

Historically, most bots have used simple rules-based approaches to present an output for a given input (such as presenting the weather). But today, with advances in server-side processing power and improvements in implementing artificial intelligence (AI) and machine learning (ML), bots are starting to provide real value to consumers. The tide has finally turned and bots are entering the mainstream consciousness, especially after the recent announcements at Facebook’s annual conference F8.

This is the year of the bot, with many companies looking to automate boring, repetitive, or manual tasks. Imagine not having to dial a 1-800 number but instead texting your wishes to your airline (or better yet the airline anticipating your needs and texting you). This kind of service can be powered by bots, and we’re sure to see more applications in the near future. Just about every venture firm and corporation is trying to wrap their heads around where they can leverage bots and how to plan a bot strategy.

Evolution

The idea behind bots dates back to Alan Turing’s early research on computing machinery and intelligence in the 1950s. Turing laid out the idea behind what is now known as the Turing test, whereby a human and a computer would interact entirely by written messages. Turing posited that if the human recipient couldn’t tell the human and the computer apart, then the computer should be labeled as intelligent.

The first wave of bots were based on rules programmed into the software and were used to automate simple, repetitive tasks. However, bots have advanced to the point where they are streamlining support cases, explaining frequently asked questions, scheduling appointments, and completing orders. Originally, these tasks required multiple inputs from a human to answer rule-based logic questions. But with advancements in AI and leveraging deep learning, bots are now able to perform more complex tasks and write their own commands on the fly using massive data sets to answer even more complex queries.

Transactional bots

The potential uses for bots are tremendous given the breadth of potential applications. Over the past few years, most bots just delivered information to the user in a question/answer format and were not truly conversational. A user interacted with the bot, and it pulled data from its own database and surfaced it to the user. An example of this kind of bot is the North Face bot, which helps customers find the right jacket based on inputs that are specific to their plans—in other words, location, climate, projected activities, and so forth.

However, the real value of bots is in their agency to act on your behalf. Bots with agency can help consumers save time by interacting with services on their behalf. A perfect example would be a Slackbot that files tickets for a software development team or completes a customer service request. Because there are so many possible uses, especially for businesses, expect to see a massive increase in these kinds of transactional bots that pass data back and forth between separate platforms.

A move toward the consumer

Currently, most of the focus on bots is on messaging apps. Users’ time spent in messaging apps has recently surpassed time spent in social networks. More than 2.5 billion people have at least one messaging app on their phones.

This new medium of interacting with intelligent computers has spawned the term “chatbot.” Slack and Facebook (and others) are working to create a whole new ecosystem based on this premise as a more efficient medium of communication and productivity.

The market for bot applications is huge and will parallel the pool of users already using chat as a channel to interact with friends and family. Why not use it to complete tasks in your daily life? When you have a channel that gives you access to more than a billion people, developers start to take notice.

The future of bots

Most business models for chatbots are still emerging, but as we look to Asia—where WeChat has hundreds of millions of users—we find applications that let you transact within WeChat. Even though it is still early for ecommerce in chat applications, we are seeing interesting use cases, such as selling children’s books and apparel. In these use cases the brand is making a personal appeal to their consumers in chat. In the modern economy, we are all presented with the paradox of choice; bots are a great application to help surface actionable insights or refine our options and take action.

Amazon is leveraging this power with Echo, to help people seamlessly interact with commerce and their world. In doing so, it also nicely helps people transact on Amazon. By removing the need to look at prices when you order by voice, Echo abstracts the desire for the product from the cost, even as—in some cases—the cost is slightly elevated to compensate for the ease of ordering.The more seamless integration of voice ordering also increases velocity of commerce done through the Echo. As with all services, there is no free lunch.

Imagine using the agency power of chatbots to save your most precious resource: time.

Because time is money, people will pay to automate many tedious aspects of their lives, and thus the potential for customer-facing businesses to provide new services and means of services is very promising. Saving the consumer time is just one of the compelling use cases of bots.

The future is not just one of building standalone bots or bots on platforms. The big question is: What will the inflection point be with bots? When the Apple App Store launched, it catapulted apps to the mainstream—now we can’t imagine a smartphone without apps. It remains to be be seen what the dominant distribution mechanism will be for bots. This is quite literally a billion dollar question.

The first step in building the future of bots is understanding the landscape today. The below is one way to visualize the developing landscape.

Diagram of Bot Space Figure 1. Diagram of Bot Space. Source: Lev Mass.
Article image: Bots landscape. (source: Lev Mass).
Lev Mass

Lev Mass

Lev Mass is a Venture Partner at XSeed Capital. Prior to joining XSeed, Lev ran operations and business development for the Cloud Group at Yahoo!. While at Yahoo!, Lev helped start what is now Hortonworks, a leading big data company.