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Main article: Artificial intelligence

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Why you need a supercomputer to build a house

21:38 | 8 December

When the hell did building a house become so complicated?

Don’t let the folks on HGTV fool you. The process of building a home nowadays is incredibly painful. Just applying for the necessary permits can be a soul-crushing undertaking that’ll have you running around the city, filling out useless forms, and waiting in motionless lines under fluorescent lights at City Hall wondering whether you should have just moved back in with your parents.

Consider this an ongoing discussion about Urban Tech, its intersection with regulation, issues of public service, and other complexities that people have full PHDs on. I’m just a bitter, born-and-bred New Yorker trying to figure out why I’ve been stuck in between subway stops for the last 15 minutes, so please reach out with your take on any of these thoughts: @Arman.Tabatabai@techcrunch.com.

And to actually get approval for those permits, your future home will have to satisfy a set of conditions that is a factorial of complex and conflicting federal, state and city building codes, separate sets of fire and energy requirements, and quasi-legal construction standards set by various independent agencies.

It wasn’t always this hard – remember when you’d hear people say “my grandparents built this house with their bare hands?” These proliferating rules have been among the main causes of the rapidly rising cost of housing in America and other developed nations. The good news is that a new generation of startups is identifying and simplifying these thickets of rules, and the future of housing may be determined as much by machine learning as woodworking.

When directions become deterrents

Photo by Bill Oxford via Getty Images

Cities once solely created the building codes that dictate the requirements for almost every aspect of a building’s design, and they structured those guidelines based on local terrain, climates and risks. Over time, townships, states, federally-recognized organizations and independent groups that sprouted from the insurance industry further created their own “model” building codes.

The complexity starts here. The federal codes and independent agency standards are optional for states, who have their own codes which are optional for cities, who have their own codes that are often inconsistent with the state’s and are optional for individual townships. Thus, local building codes are these ever-changing and constantly-swelling mutant books made up of whichever aspects of these different codes local governments choose to mix together. For instance, New York City’s building code is made up of five sections, 76 chapters and 35 appendices, alongside a separate set of 67 updates (The 2014 edition is available as a book for $155, and it makes a great gift for someone you never want to talk to again).

In short: what a shit show.

Because of the hyper-localized and overlapping nature of building codes, a home in one location can be subject to a completely different set of requirements than one elsewhere. So it’s really freaking difficult to even understand what you’re allowed to build, the conditions you need to satisfy, and how to best meet those conditions.

There are certain levels of complexity in housing codes that are hard to avoid. The structural integrity of a home is dependent on everything from walls to erosion and wind-flow. There are countless types of material and technology used in buildings, all of which are constantly evolving.

Thus, each thousand-page codebook from the various federal, state, city, township and independent agencies – all dictating interconnecting, location and structure-dependent needs – lead to an incredibly expansive decision tree that requires an endless set of simulations to fully understand all the options you have to reach compliance, and their respective cost-effectiveness and efficiency.

So homebuilders are often forced to turn to costly consultants or settle on designs that satisfy code but aren’t cost-efficient. And if construction issues cause you to fall short of the outcomes you expected, you could face hefty fines, delays or gigantic cost overruns from redesigns and rebuilds. All these costs flow through the lifecycle of a building, ultimately impacting affordability and access for homeowners and renters.

Startups are helping people crack the code

Photo by Caiaimage/Rafal Rodzoch via Getty Images

Strap on your hard hat – there may be hope for your dream home after all.

The friction, inefficiencies, and pure agony caused by our increasingly convoluted building codes have given rise to a growing set of companies that are helping people make sense of the home-building process by incorporating regulations directly into their software.

Using machine learning, their platforms run advanced scenario-analysis around interweaving building codes and inter-dependent structural variables, allowing users to create compliant designs and regulatory-informed decisions without having to ever encounter the regulations themselves.

For example, the prefab housing startup Cover is helping people figure out what kind of backyard homes they can design and build on their properties based on local zoning and permitting regulations.

Some startups are trying to provide similar services to developers of larger scale buildings as well. Just this past week, I covered the seed round for a startup called Cove.Tool, which analyzes local building energy codes – based on location and project-level characteristics specified by the developer – and spits out the most cost-effective and energy-efficient resource mix that can be built to hit local energy requirements.

And startups aren’t just simplifying the regulatory pains of the housing process through building codes. Envelope is helping developers make sense of our equally tortuous zoning codes, while Cover and companies like Camino are helping steer home and business-owners through arduous and analog permitting processes.

Look, I’m not saying codes are bad. In fact, I think building codes are good and necessary – no one wants to live in a home that might cave in on itself the next time it snows. But I still can’t help but ask myself why the hell does it take AI to figure out how to build a house? Why do we have building codes that take a supercomputer to figure out?

Ultimately, it would probably help to have more standardized building codes that we actually clean-up from time-to-time. More regional standardization would greatly reduce the number of conditional branches that exist. And if there was one set of accepted overarching codes that could still set precise requirements for all components of a building, there would still only be one path of regulations to follow, greatly reducing the knowledge and analysis necessary to efficiently build a home.

But housing’s inherent ties to geography make standardization unlikely. Each region has different land conditions, climates, priorities and political motivations that cause governments to want their own set of rules.

Instead, governments seem to be fine with sidestepping the issues caused by hyper-regional building codes and leaving it up to startups to help people wade through the ridiculousness that paves the home-building process, in the same way Concur aids employee with infuriating corporate expensing policies.

For now, we can count on startups that are unlocking value and making housing more accessible, simpler and cheaper just by making the rules easier to understand. And maybe one day my grandkids can tell their friends how their grandpa built his house with his own supercomputer.

And lastly, some reading while in transit:

 


0

AI desperately needs regulation and public accountability, experts say

00:44 | 8 December

Artificial intelligence systems and creators are in dire need of direct intervention by governments and human rights watchdogs, according to a new report from researchers at Google, Microsoft, and others at AI Now. Surprisingly, it looks like the tech industry just isn’t that good at regulating itself.

In the 40-page report (PDF) published this week, the New York University-based organization (with Microsoft Research and Google-associated members) shows that AI-based tools have been deployed with little regard for potential ill effects or even documentation of good ones. While this would be one thing if it was happening in controlled trials here and there, instead these untested, undocumented AI systems are being put to work in places where they can deeply affect thousands or millions of people.

I won’t go into the examples here, but think border patrol, entire school districts and police departments, and so on. These systems are causing real harm, and not only are there no systems in place to stop them, but few to even track and quantify that harm.

“The frameworks presently governing AI are not capable of ensuring accountability,” the researchers write in the paper. “As the pervasiveness, complexity, and scale of these systems grow, the lack of meaningful accountability and oversight – including basic safeguards of responsibility, liability, and due process – is an increasingly urgent concern.”

Right now companies are creating AI-based solutions to everything from grading students to assessing immigrants for criminality. And the companies creating these programs are bound by little more than a few ethical statements they decided on themselves.

Google, for instance, recently made a big deal about setting some “AI principles” after that uproar about its work for the Defense Department. It said its AI tools would be socially beneficial, accountable, and won’t contravene widely accepted principles human rights.

Naturally, it turned out the company has the whole time been working on a prototype censored search engine for China. Great job!

So now we know exactly how far that company can be trusted to set its own boundaries. We may as well assume that’s the case for the likes of Facebook, which is using AI-based tools to moderate; Amazon, which is openly pursuing AI for surveillance purposes; and Microsoft, which yesterday published a good piece on AI ethics — but as good as its intentions seem to be, a “code of ethics” is nothing but promises a company is free to break at any time.

The AI Now report has a number of recommendations, which I’ve summarized below but really are worth reading in their entirety. It’s quite readable and a good review as well as smart analysis.

  • Regulation is desperately needed. But a “national AI safety body” or something like that is impractical. Instead, AI experts within industries like health or transportation should be looking at modernizing domain-specific rules to include provisions limiting and defining the role of machine learning tools. We don’t need a Department of AI, but the FAA should be ready to assess the legality of, say, a machine learning-assisted air traffic control system.
  • Facial recognition, in particular questionable applications of it like emotion and criminality detection, need to be closely examined and subjected to the kind of restrictions that false advertising and fraudulent medicine are.
  • Public accountability and documentation need to the be rule, including how a system’s internal operations, from datasets to decision-making processes. These are necessary not just for basic auditing and justification for using a given system, but for legal purposes should such a decision be challenged by a person that system has classified or affected. Companies need to swallow their pride and document these things even if they’d rather keep them as trade secrets — which seems to me the biggest ask in the report.
  • More funding and more precedents need to be established in the process of AI accountability; it’s not enough for the ACLU to write a post about a municipal “automated decision-making system” that deprives certain classes of people of their rights. These things need to be taken to court and the people affected need mechanisms of feedback.
  • The entire industry of AI needs to escape its engineering and computer science cradle — the new tools and capabilities cut across boundaries and disciplines and should be considered in research not just by the technical side. “Expanding the disciplinary orientation of AI research will ensure deeper attention to social contexts, and more focus on potential hazards when these systems are applied to human populations,” write the researchers.

They’re good recommendations, but not the kind that can be made on short notice, so expect 2019 to be another morass of missteps and misrepresentations. And as usual, never trust what a company says, only what it does — and even then, don’t trust it to say what it does.

 


0

TikTok parent ByteDance said to raise $1.45 billion for AI and content

14:43 | 7 December

ByteDance, the Chinese company behind the immensely popular video app TikTok, is in talks to raise $1.45 billion for a new fund, The Information reported on Friday citing sources.

The fresh vehicle will help power artificial intelligence and media content for the $75 billion startup that has leapfrogged Uber’s valuation, the report said. ByteDance declined to comment on the matter.

The Chinese startup has set off an aggressive global expansion that sees it merge teen video app Musical.ly into TikTok, which has 100 million and 500 million users, respectively. The upstart has compelled Tencent to up the ante in short videos and Facebook to make a clone.

By 2021, ByteDance aims to count at least 50 percent of its total users from overseas, its founder and chief executive officer Zhang Yiming said during a speech in June.

In China, a fold of ByteDance’s media products — ranging from short-video platforms, a news portal to a humor app — have been in hot water with media watchdogs who are tightening control over online content. The harshest punishment arrived when the government shuttered Neihan Duanzi, literally meaning “implied jokes” in Chinese, for charges of propagating “vulgar content”.

The Beijing-based media company is seeking capital from government-led funds and state-owned investment banks for its new venture fund, according to The Information.

The gesture could help six-year-old ByteDance navigate relationships with local authorities. Meanwhile, it has already hired thousands of censors to ensure its content does not deviate from China’s official guidelines, though the startup has long prided itself on its AI prowess to make personalized recommendations to users.

 


0

Artie aims to bring you closer to your digital idols with autonomous AR avatars

23:18 | 6 December

If you spend enough time scrolling through manicured photos of manicured lives on social media, you might come to the realization that maybe the fakeness of the online world has started to look too real.

This might be why so many investors are starting to stare headlong into the world of avatars and digital influencers that can learn from their audiences in real time. Earlier this week, I chatted with a pair of interesting founders from the startup Artie. The team is basically trying to create an interaction engine for digital avatars to sit in the real world and have some sort of meaningful interaction with users through phone-based AR.

The startup’s backers include Founders Fund and YouTube co-founder Chad Hurley. Co-founders Armando Kirwin and Ryan Horrigan both come from some top startups in the VR media space.

The Artie team

Artie’s sort of autonomous storytelling platform really focuses in on a couple emerging trends.

One is this big idea of digital influencers revving up in Japan and Korea that’s basically leveraging all of these new face-tracking capabilities of smartphones to allow users to craft 3D avatars that are sort of animated, abstracted online personalities. It’s started to make waves stateside, but it’s a slower grind.  Artie isn’t necessarily looking at user-generated content at this moment, but the company’s work in more branded moments with already leveraged IP is an interesting first step towards something bigger.

Artie is also an AR company. The phone AR market really seems to have a number of usage obstacles to overcome. Despite the excitement coming from Apple and Google, platforms like ARKit and ARCore have mostly arrived with a thud. There are a few companies trying to build out some more fundamental backend capabilities to enable shared experiences that adjust to their surroundings, but it’s unclear where the missing link really is in getting people to use a feature that’s really just sitting dormant on their smartphone.

The company is working with WebXR standards that will basically allow anyone to tap a link on their phone and plunge straight into an experience where the avatar is inside their physical space. The video below gives some early insight into what their platform is going to offer.

As niche as this market sounds, Artie isn’t totally alone here, Google has actually flirted with this in its Playground release on Pixel phones where users can jump into photos with 3D characters who are somewhat aware of their environments. For Artie, the deeper interactions between the avatar and characters is really where they hope the magic comes into view. Their platform carries out emotion tracking and object detection to give Unity developers some freedom to let users interrupt the avatars and send them on tangents, all while learning from the user in how they interact with the character and want them to act.

“Think of it like how YouTube, back in the day, established this notion where content creators could for the first time get closer to their audiences through the comments, but it always happens post-mortem after the video was published and would inform what would happen next week,” Horrigan told TechCrunch. “So the difference here is that we’re actually bringing that intimacy between audience and content creator in real time.”

The co-founders both share some big ideas for the direction of storytelling that leverages deep learning to tell the content creators more about the world and audience they’re building for. Artie is at the forefront of some interesting but deeply odd market trends, ones that are probably driven as much by the state of pop culture as they are by tech capabilities, though it’s all still early tech coming from a small team.

The founders say they’ll start working with some early “power users” like media companies and celebrities in the first quarter of next year to start building out the first experiences for Artie on their “Wonderfriend” engine.

 


0

The trust dilemma of continuous background checks

19:15 | 6 December

First, background checks at startups, then Huawei’s finance chief is arrested, SoftBank’s IPO is subscribed, and I am about to record our next edition of TechCrunch Equity. It’s Thursday, December 6, 2018.

TechCrunch is experimenting with new content forms. This is a rough draft of something new – provide your feedback directly to the author (Danny at danny@techcrunch.com) if you like or hate something here.

The dilemma of continuous background checks

My colleague John Biggs covered the Series A round for Israel-based Intelligo, a startup that provides “Ongoing Monitoring” — essentially a continuous background check that can detect if (when?) an employee has suddenly become a criminal or other deviant. That’s a slight pivot from the company’s previous focus of using AI/ML to conduct background checks more efficiently.

Background checks are a huge business. San Francisco-based Checkr, perhaps the most well-known startup in the space, has raised $149 million according to Crunchbase, driven early on by the need to on-board thousands of contingent workers at companies like Uber. Checkr launched what it calls “Continuous Check” which also actively monitors all employees for potential problems, back in July.

Now consider a piece written a few weeks ago by Olivia Carville at Bloomberg that explored the rise of “algorithmic auditors” that actively monitor employee expenses and flags ones it feels are likely to be fraudulent:

U.S. companies, fearing damage to their reputations, are loath to acknowledge publicly how much money they lose each year on fraudulent expenses. But in a report released in April, the Association of Certified Fraud Examiners said it had analyzed 2,700 fraud cases from January 2016 to October 2017 that resulted in losses of $7 billion.

Here’s a question that bugs me though: we have continuous criminal monitoring and expense monitoring. Most corporations monitor web traffic and email/Slack/communications. Everything we do at work is poked and prodded to make sure it meets “policy.”

And yet, we see vituperative attacks on China’s social credit system, which …. monitors criminal records, looks for financial frauds, and sanctions people based on their scores. How long will we have to wait before employers give us “good employee behavior” scores and attach it to our profiles in Slack?

The conundrum of course is that no startup or company wants (or can) avoid background checks. And it probably makes sense to continually monitor your employees for changes and fraud. If Bob murders someone over the weekend, it’s probably good to know that when you meet Bob at Monday’s standup meeting.

But let’s not pretend that this continuous monitoring isn’t ruinous to something else required from employees: trust. The more heavily monitored every single activity is in the workplace, the more that employees feel that if the system allows them to get away with something, it must be approved. Without any checks, you rely on trust. With hundreds of checks, policy is essentially etched into action — if I can do it, it must meet policy.

In China, where social trust is extremely low, it likely makes sense to have some sort of scoring mechanism to substitute. But for startups and tech companies, building a culture of trust — of doing the right thing even when not monitored — seems crucial to me for success. So before signing up for one of these continuous services, I’d do a double take and consider the potentially deleterious consequences.

If I was a startup employee, I would think twice (maybe thrice?) before traveling to China

Photo by VCG/VCG via Getty Images

Last weekend, Trump and Xi agreed to delay the implementation of tariffs on Chinese goods, which led to buoyant Chinese (tech) stocks Monday in Asia time zones. I wrote about how that doesn’t make any sense, since delaying tariffs doesn’t do anything to solve the structural issues in the US/China conflict:

To me the market is deeply misjudging not only the Chinese economy, but also the American leadership as well.

And specifically, I wrote about constraints on Huawei and ZTE:

In what world do these prohibitions disappear? The U.S. national security agencies aren’t going to allow Huawei and ZTE to deploy their equipment in America. Like ever. Quite frankly, if the choice was getting rid of all of China’s non-tariff barriers and allowing Huawei back into America, I think the U.S. negotiators would walk out.

So it was nice to learn (for me, not for her) that the head of finance of Huawei was arrested last night in Canada at the United States’ request. From my colleague Kate Clark:

Meng Wanzhou, the chief financial officer of Huawei, the world’s largest telecom equipment manufacturer and second-largest smartphone maker, has been arrested in Vancouver, Canada on suspicion she violated U.S. trade sanctions against Iran, as first reported by The Globe and Mail.

Huawei confirmed the news with TechCrunch, adding that Meng, the daughter of Huawei founder Ren Zhengfei, faces unspecified charges in the Eastern District of New York, where she had transferred flights on her way to Canada.

If you wanted to know how the Trump administration was going to continue to fight the trade war outside of tariffs, you now have your answer. This is a bold move by the administration, targeting not just one of China’s most prominent tech companies, but the daughter of the founder of the company to boot.

China has since demanded her return.

Here is how this is going to play out. China is preventing the two American children of Liu Changming from leaving the country, essentially holding them hostage until their father returns to the mainland to face a criminal justice process related to an alleged fraud case. America now has a prominent daughter of a major Chinese company executive in their hands. That’s some nice tit-for-tat.

For startup founders and tech executives migrating between the two countries, I don’t think one has to literally worry about exit visas or extradition.

But, I do think the travel security operations centers at companies that regularly have employees moving between these countries need to keep very keen and cautious eyes on these developments. It’s entirely possible that these one-off “soft hostages” could flare to much higher numbers, making it much more complicated to conduct cross-border work.

Quick Bites

SoftBank’s IPO raises a lot of dollars

KAZUHIRO NOGI/AFP/Getty Images

Takahiko Hyuga at Bloomberg reports that SoftBank has sold its entire book of shares for its whopping $23.5 billion IPO. The shares will officially price on Monday and then will trade on December 19. This is a critical and important win for Masayoshi Son, who needs the IPO of his telecom unit to deleverage some of the risk from SoftBank’s massive debt pile (and also to continue funding his startup dreams through Vision Fund, etc.)

SoftBank Vision Fund math, part 2

Arman and I talked yesterday about the complicated math behind just how many dollars are in SoftBank’s Vision Fund. More details, as Jason Rowley pointed out at Crunchbase News:

In an annual Form D disclosure filed with the Securities and Exchange Commission this morning, SBVF disclosed that it has raised a total of approximately $98.58 billion from 14 investors since the date of first sale on May 20, 2017. The annual filing from last year said there was roughly $93.15 billion raised from 8 investors, meaning that the Vision Fund has raised $5.43 billion in the past year and added six new investors to its limited partner base.

I said yesterday that the fund size should be “$97 billion or $96.7 billion with precision, assuming this $5 billion reaches a final close.” So let’s revise this number again to $99 billion or $98.6 billion with precision, since it seems the $5 billion did indeed close.

What’s next

I am still obsessing about next-gen semiconductors. If you have thoughts there, give me a ring: danny@techcrunch.com.

Thoughts on Articles

Hopefully more reading time tomorrow.

Reading docket

What I’m reading (or at least, trying to read)

  • Huge long list of articles on next-gen semiconductors. More to come shortly.

 


0

Amazon is crowdsourcing Alexa’s answers to tough questions

18:34 | 6 December

If you’ve got an Echo device at home, you’ve almost certainly played a few rounds of “stump Alexa,” asking Amazon’s assistant increasingly arcane questions in an attempt to elicit some baffled response. Likely you found it was all that difficult to confuse the AI. After all, Amazon doesn’t have the advantage of Google’s deep Knowledge Graph.

One quick workaround is going the Wikipedia route, drawing upon the knowledge of users to help build a deeper base of knowledge. Amazon’s just opened that door, with an invite-only program that asks customers to submit answers to Alexa’s more difficult questions.

Amazon has been testing Alexa Answers internally, adding more than 100,000 responses in the past month alone. Now it’s opening the program up to a small cross section of the public, via email invites. Those who get asked to join can answer questions for Alexa via a website hosting a broad range of different topics.

For example, Amazon’s offered up the following (admittedly bizarre) suggestions. “Where was Barbara Bush buried?” or “Who wrote the score for Lord of the Rings?” or “What’s cork made out of?” or “Where do bats go in the winter?” I’d like to image they were all asked by the same weirdo in quick succession.

Once responses are added, Alexa will have access to them, noting that they’re from “an Amazon customer” — one assumes to take a bit of the heat off of the assistant. But what could possibly go wrong?

 


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Farmstead is an ambitious grocery delivery startup with plans to defeat Instacart

17:00 | 6 December

In its 3,000-square-foot warehouse in San Francisco’s Mission District, Farmstead founders Pradeep Elankumaran and Kevin Li, a pair of former Yahoo product managers, plot the future of grocery shopping.

“Think of us as if Whole Foods was rebuilt from scratch by tech founders,” Elankumaran, Farmstead’s chief executive officer, told TechCrunch. “Of course we do delivery because it’s 2018 and no one wants to go to the store anymore.”

Elankumaran launched San Francisco-based Farmstead in 2016 after Amazon and Instacart’s food delivery services repeatedly disappointed him. The startup leverages artificial intelligence-powered predictive analytics and machine learning to accurately predict supply and demand of its inventory, a move Elankumaran says has helped the company significantly reduce waste, as well as complete deliveries to Bay Area residents in less than an hour.

“I had a lot of trouble getting food delivered consistently,” he said. “My daughter had just turned two and she started drinking a lot of milk and I found myself going to the grocery store three to four times a week to buy the same things.”

“So I posted on Nextdoor asking if anyone was interested in a milk, eggs and bread delivery service and in two days, 200 people said yes.”

Two-plus years later, the company is today announcing an additional seed round of $2.2 million, bringing its total raised to date to $7.5 million. ARTIS Labs, Resolute Ventures and Red Dog Capital participated in the round, along with Y Combinator . Farmstead completed the Silicon Valley accelerator program in 2016 shortly before its initial launch, similar to Instacart, which graduated from Y Combinator in 2012. Elankumaran said the company plans to use the capital to hire aggressively and expand beyond the Bay Area in 2019. 

Farmstead’s business may sound a lot like Instacart, a very well-funded grocery delivery service worth an astounding $7.6 billion, but the startup says the differences are notable. Instacart is a tech layer on top of a supermarket that provides delivery, whereas Farmstead is the supermarket and the delivery service. Elankumaran says this — storing groceries in large, centralized warehouses and making the deliveries — is a highly scalable model destined to defeat Instacart.

Resolute Ventures general partner Mike Hirshland said in a statement that Farmstead could “become a monster company.”

“To replace a trip to the grocery store, so many things have to go right, from ordering the right inventory to last-mile delivery. Farmstead has cracked the code on making grocery delivery profitable and rapidly scalable,” he said.

The company has also recently partnered with Udelv, an autonomous vehicle startup, to make deliveries via the company’s modified GEM eL XD electric trucks.

 


0

Still a year away from launch, Meg Whitman and Jeffrey Katzenberg’s Quibi keeps adding talent

00:25 | 6 December

Video won’t start rolling on Meg Whitman and Jeffrey Katzenberg’s new bite-sized streaming service with the billion dollar backing until the end of 2019, but talent keeps signing up to come along for their ride into the future of serialization.

The latest marquee director to sign on the dotted line with Quibi is Catherine Hardwicke, who will be helming a story around the creation of an artificial intelligence with the working title “How They Made Her” according to an announcement from Katzenberg onstage at the Variety Innovate summit.

Hardwicke, who directed ThirteenLords of Dogtown, and, most famously, Twilight, is joining Antoine Fuqua, Guillermo del Toro, Sam Raimi and Lena Waithe, in an attempt to answer the question of whether Whitman and Katzenberg’s gamble on premium (up to $6 million per episode) short-form storytelling is a quixotic quest or a quintessential viewing experience for a new generation of media consumers.

In some ways, the big adventure backed by Katzenberg, the former chairman of Walt Disney Studios and founder of WndrCo, and every major Hollywood studio including Disney, 21st Century Fox, Entertainment One, NBCUniversal, Sony Pictures Entertainment, Alibaba Goldman Sachs, is the latest in an everything old is new again refrain.

If blogs reinvented printed media, and podcasts and music streaming reinvented radio, why can’t Quibi reinvent serialized storytelling.

Again and again, Whitman and Katzenberg returned to an analogy from the early days of the cable revolution. “We’re not short form, we’re Quibi,” said Whitman, echoing the tagline that HBO made famous in its early advertising blitzes. That Whitman and Katzenberg’s project to take what HBO did for premium television and apply that to mobile media is ambitious. Now industry-watchers will have to wait until 2019 at the earliest to see if it’s also successful.

In the interview onstage at a Variety event on artificial intelligence in media, Katzenberg cited Dan Brown’s DaVinci Code as something of an inspiration — noting that the book had over one hundred chapters for its five hundred pages of text. But Katzenberg could have gone back even further to the days of Dickens and his serialized entertainments.

And right now for the entertainment business it really is the best of times and the worst of times. Traditional Hollywood studios are seeing new players like Netflix, Amazon, Apple, and others all trying to drink their milkshake. And, for the most part, these studios and their new telecom owners are woefully ill-equipped to fight these big technology platforms at their own game. 

Taking the long view of entertainment history, Katzenberg is hoping to win networks with not just a new skin for the old ceremony of watching entertainment but with a throwback to old style deal-making. The term serialization here takes on greater meaning. 

Quibi is offering its production partners a sweetheart deal. After seven years the production company behind the Quibi shows will own their intellectual property, and after two years those producers will be able to repackage the Quibi content back into long form series and pitch them for distribution to other platforms. Not only that but Quibi is fronting the money for over 100% of the production.

Katzenberg said that it “will create the most powerful syndicated marketplace” Hollywood has seen in decades. It’s a sort of anti-Netflix model where Katzenberg and Whitman view Quibi as a platform where creators and talent will want to come. “We are betting on the success of the platform — and by the way it worked brilliantly in the 60s, and 70s and 80s.” Katzenberg said. “Hundreds of TV shows were tremendous successes and [like the networks then] we don’t want to compete with our suppliers.”

In addition to the business model innovations (or throwbacks, depending on how one looks at it), Quibi is being built from the ground up with a technology stack that will leverage new technologies like 5G broadband, and big data and analytics, according to Whitman.

Indeed, launching the first platform built without an existing stable of content means that Quibi is preparing 5,000 unique pieces of content to go up when it pulls the curtains back on its service in late 2019 or early 2020, Whitman said.

And the company is looking to big telecommunications companies like Verizon (my corporate overlord’s corporate overlord) and AT&T as partners to help it get to market. Since those networks need something to do with all the 5G capacity they’re building out, high quality streaming content that’s replete with meta-tags to monitor and manage how an audience is spending their time is a compelling proposition.

“We want to work to have video that good on mobile [and] ramp up content in terms of quantity and quality,” Whitman said. That quality extends to things like the user interface, search features and analytics.

“We have to have a different search and find metaphor,” Whitman said. “It takes 8 minutes to find what you’re looking for on Netflix… We will be able to instrument this with data on what people are watching and using that in our recommendation engine.”

Questions remain about the service’s viability. Like what role will the telcos actually play in distribution and development? Can Quibi avoid the Hulu problem where the various investors are able to overcome their own entrenched interests to work for the viability of the platform? And do consumers even want a premium experience on mobile given the new kinds of stars that are made through the immediacy and accessibility that technology platforms like YouTube, Instagram, and Snap offer?

“Where the fish are today is a phenomenal environment,” Katzenberg said of the current short-form content market. “But it is an ocean. We need to find a place where there are these premium services.”

 


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Where Facebook AI research moves next

00:21 | 6 December

Five years is an awful lot of time in the tech industry. Darling startups find ways to crash and burn. Trends that seem unstoppable putter-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 6 month deadlines if not less,” he says.

LeCun

Five years after its formation, FAIR’s influence permeates the company. The group has labs in 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 5-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 “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, the company has published work on how they are utilizing some of these techniques to translate in between languages they lack sufficient training data for so that, in practical terms, users needing translations from something like Icelandic to Swahili aren’t left in the cold.

As FAIR looks to its next five years, LeCun contends that 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.”

 


0

Workato raises $25M for its integration platform

20:27 | 5 December

Workato, a startup that offers an integration and automation platform for businesses that competes with the likes of MuleSoft, SnapLogic and Microsoft’s Logic Apps, today announced that it has raised a $25 million Series B funding round from Battery Ventures, Storm Ventures, ServiceNow and Workday Ventures. Combined with its previous rounds, the company has now received investments from some of the largest SaaS players, including Salesforce, which participated in an earlier round.

At its core, Workato’s service isn’t that different from other integration services (you can think of them as IFTTT for the enterprise) in that it helps you to connect disparate systems and services, set up triggers to kick of certain actions (if somebody signs a contract on Docusign, send a message to Slack and create an invoice). Like its competitors, it connects to virtually any SaaS tool that a company would use, no matter whether that’s Marketo and Salesforce, or Slack and Twitter. And like some of its competitors, all of this can be done with a drag-and-drop interface.

What’s different, Workato founder and CEO Vijay Tella tells me, is that the service was built for business users, not IT admins. “Other enterprise integration platforms require people who are technical to build and manage them,” he said. “With the explosion in SaaS with lines of business buying them – the IT team gets backlogged with the various integration needs. Further, they are not able to handle all the workflow automation needs that businesses require to streamline and innovate on the operations.”

Battery Ventures’ general partner Neeraj Agrawal also echoed this. “As we’ve all seen, the number of SaaS applications run by companies is growing at a very rapid clip,” he said. “This has created a huge need to engage team members with less technical skill-sets in integrating all these applications. These types of users are closer to the actual business workflows that are ripe for automation, and we found Workato’s ability to empower everyday business users super compelling.”

Tella also stressed that Workato makes extensive use of AI/ML to make building integrations and automations easier. The company calls this Recipe Q. ” Leveraging the tens of billions of events processed, hundreds of millions of metadata elements inspected, and hundreds of thousands of automations that people have built on our platform – we leverage ML to guide users to build the most effective integration/automation by recommending next steps as they build these automations,” he explained. “It recommends the next set of actions to take, fields to map, auto-validates mappings, etc. The great thing with this is that as people build more automations – it learns from them and continues to make the automation smarter.”

The AI/ML system also handles errors and offers features like sentiment analysis to analyze emails and detect their intent, with the ability to route them depending on the results of that analysis.

As part of today’s announcement, the company is also launching a new AI-enabled feature: Automation Editions for sales, marketing and HR (with editions for finance and support coming in the future). The idea here is to give those departments a kit with pre-built workflows that helps them to get started with the service without having to bring in IT.

 


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