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Showing posts with label ARTIFICIAL INTELLIGENCE. Show all posts
Showing posts with label ARTIFICIAL INTELLIGENCE. Show all posts

Friday, September 6, 2019

This Company Is Betting the Future Is Personal AI Avatars, So It Made Me One

In January 2019, when China Central Television, the largest broadcast network in the most populous nation in the world, aired a special to celebrate the Lunar New Year, the hosts welcomed four life-sized “personal artificial intelligences” to share the stage with them. Called PAIs, they were three-dimensional holographic replicas of the presenters that moved, spoke, and sang to the delight of the cheering live audience. The program was viewed some 1.8 billion times. One of the most-watched TV shows in the world had been hosted by AI avatars.
The company behind those avatars is the Pasadena-based ObEN. This startup, with its 100 plus employees, is betting that in the future, everyone will want their own PAIs—to digitally try on clothes, to interact with friends, to keep the kids company while you’re away on a business trip. In that future, celebrities will create PAIs to interact with fans to promote their latest films and albums. Teachers and doctors will have PAIs that offer personalized services to their students and patients. When you go to the mall, PAIs will pop up on the interactive screens there, enticing you to buy stuff.
ObEN describes its ambitious vision of the future as “personal AI for all.” And ObEN is far from alone, of course. Investors, tech giants, and even governments are betting big on lifelike digital avatars—between Facebook’s push to port your likeness into VR, the eerily lifelike AI news anchors put on the air by Xinhua, China’s state-run news agency, and the burgeoning CGI celebrity simulacra scene in Hollywood, there’s a newfangled interest in the (potentially vastly profitable) art of porting people’s digital likeness to our screens.
Cyberculture has revolved around avatars for decades, but the avi-to-avi future pursued by ObEN and others promise a level of representation saturation hitherto unimagined by even the most fervent cyberpunks. Would a world filled with PAIs really beget more convenience and entertainment? Or would it further accelerate already ascendent trendlines of the crowding and hyper-commercialization of our digital spaces?
To better understand this new frontier of companion AI, and both its utopian and dystopian implications, I headed to Pasadena, to ObEN’s HQ, to become the first non-celebrity civilian to get my own PAI.

AI-generated avatars are a growing trend, especially in China. What sets ObEN apart from the competition, the company says, is the lengths to which it goes to personalize its product. In order to experience this firsthand, I sat for my personal PAI-ification in Pasadena one long afternoon. The 3D face scan was fairly painless—I essentially sat in a chair and made faces when prompted, and 3D mapping technology rendered a digital model of my mug.
The audio session was the grueling part. Sitting in a recording studio in the ObEN office space, I intoned line after varyingly comprehensible line, like I was recording a spoken word poetry album of prose poems written by Siri. But I got through it (pretty quickly, apparently) and at the end ObEN had a sample of my voice.
I returned to the studio a week or so later so they could photograph me wearing another shirt, and that was it—my raw PAI materials were ready for processing.
“The creation of a PAI is based on the concept of ‘models,’” ObEN’s chief engineer, Dr. Mark Harvilla, tells me. There are just two major components of a PAI—its appearance and its voice.
“The appearance of a PAI is comprised mainly of a user-specific face model,” Harvilla explains, adding that “for now, a general body shape is used across all PAIs, but things like height, weight, body type, and clothing can be customized by the end user.” The PAI’s speech, meanwhile, is generated from a “user-specific voice model.”
Both models are built using a machine learning process called “adaptation,” or “fine tuning,” Harvilla says. “To create the 3D head of a PAI, a so-called base model is chosen, which most closely represents the given user’s appearance. This base model is then adapted to the specific user; the adaptation process captures nuances of the facial structure and appearance, closing the gap between the base model and the fully representative PAI.”
“Voice model creation follows a similar pipeline,” he says, “wherein a language-specific base model is chosen, and from the relatively small sample of audio recordings of the user, fine-tuned to reflect idiosyncrasies of the user’s voice.” ObEN’s neural nets can currently give voice to PAIs speaking English, Chinese, Japanese, and Korean. “Both technology suites,” Harvilla assures me, “utilize state-of-the-art deep learning algorithms.”
For me, the process of bringing my AI-ified avatar took a couple of weeks, though ObEN was busy finishing various projects, and I’m not sure just how much of a priority making a wacky singing high-res avatar for a journalist was. Did I not mention my PAI was going to sing?

“I started this company,” ObEN’s CEO Nikhil Jain says, “when I realized I was traveling a lot and my kids were missing me back home.”
Jain and ObEN co-founder Adam Zheng, ObEN’s COO, were both veterans of the tech industry, and they’d often find themselves talking about how they have to travel frequently for work, leaving too little time to spend with their children. The pair also frequently found themselves discussing the emergence of AI technology, and the increasing personalization of AI, and apparently, the topics converged, and the idea of PAIs materialized.
“I thought—what if my kids had my PAI back home?” Jain says. That way, his kids could interact with an avatar that looked, sounded, and behaved like their father while he was away for days or weeks at a time.
As Jain and Zheng set out looking for funding, though, investors pointed them towards the entertainment space. Chinese tech conglomerate Tencent, which provided a significant portion of ObEN’s $23.7 million in funding, seemed particularly interested in driving ObEN towards celebrity PAIs. So ObEN started out targeting the entertainment industry.
“When we created technology,” Jain says, “we thought, ‘how do we get this out to a newer audience?’” Jain says. “And we felt like the best way to get that out was using the power of celebrities. Thanks to these investors, we were able to team up with the Spice Girls of China.”
Jain is referring to SNH48, a massive ‘idol group’ in the j- or k-pop mold, filled with rotating members, where 20-year-old singers join on and get voted off. (SNH48 itself is essentially a massive startup; according to Quartz, investors have dumped more than $150 million in the venture, which really took off after China banned Korean entertainment imports after the neighboring nation began constructing a missile defense shield. The world is a weird place.)
Regardless, Tencent brokered a deal that saw ObEN create PAIs for one of SNH48’s new songs and the accompanying music video. It was heralded as “the world’s first commercially released song co-starring human singers and their 3D AI avatars,” when it aired in December 2018.
ObEN developed PAIs for the pop stars that could both speak (in multiple languages, no less) and sing—and, so they did. “Our AI takes the regular speaking voice and converts it into the singing voice,” Jain says. “We want to make it like a Turing test—is it AI singing, or you singing?”
The PAIs show up in the video above, and their voices were recorded in the chorus of the single—another ostensible first in the realm of entertainment-focused AI.
“Our personal AI technology has great potential in the entertainment industry,” Zheng said at the time. “Let’s say you have an actor who stars in a movie. A producer may want him to sing the ending theme song, which he is not good at. We can make his AI avatar sing for him.”
Next, ObEN teamed up with CCTV for the Spring Gala Festival, to celebrate the Lunar New Year, and that’s where the PAI-ified TV hosts came in. So ObEN has designed PAIs for two of China’s major cultural institutions, and kicked off the year with a bang, earning a nice amount of exposure for their signature PAI sidekicks. Now, the company is trying to figure out what else people might want to do with their PAIs.

My PAI, it turns out, has a better singing voice than I ever will.
Personally, it’s hard for me to place where, exactly, this lands in regards to the uncanny valley.
I found it deeply unsettling at first, then just a little weird, then sort of funny, then ridiculous, then a little unsettling again. So it’s kind of the effect of staring into the mirror while you’re stoned for too long, if the mirror were a screen undertaking the realtime rendering of your face in triple A PS4 game-caliber graphics. I found it a little hard to look at, and as of now, I’m not sure how much more I want to see of PAI-me. But it’s also only a limited clip, with no interactivity, so I can’t really engage with it or see what else it can do.
“It certainly doesn’t seem human,” Kelly Bourdet, the editor in chief of Gizmodo said, “It looks like an uncanny valley creep show avi but one that looks closer to you than to any other human on earth.”
“Your eyes are freaking me out,” a friend said. “Sound is all off, to me at least,” said another, “And you look like you tried but failed to audition for a Korean boy band.”
“Wow .... very cool... (and a little unsettling at the same time.),” my mom texted when I sent her the video. “Pretty close... when I first looked at the picture I wasn’t sure If it was you or someone else…until you started talking and said who you were.”
Strangely, it was my wife who was the most sold on the rendering. “Wow!” she said. “The teeth are weird, but other than that…”
Which was sort of what I was expecting—most agreed it looked well enough like me, and most got a kick out of the thing, which is precisely what it was designed to do, I think. But I wonder if one challenge these PAIs will face is that the uncanny valley will inevitably seem wider when you’re measuring your own likeness across it.
“For these avatars to be useful the uncanny valley has to be passed,” Jain says. “The voice has to have some emotion to it. The four different expressions we are able to create—joy anger sadness—to have the corneas move in conjunction with how your lips are moving, these are things that will reduce the uncanny valley.”
But renderings of our own faces come bundled with more insecurities and hang-ups than any other, I’d imagine, and we judge them and engage them more skeptically, more harshly.
That’s why, perhaps, some of ObEN’s competition, like SoGo, chooses to render personal avatars more cartoonishly. “The difference between us,” Jain says, “is that it’s an extremely personalized version of you.”
My PAI was decidedly not an example of what a typical version will look like—for one thing, I can’t interact with it; it’s basically a demo built using the company’s AI and my training inputs. For another, it’s something of a premium model. Later this year, ObEN says, it will release an app that will let users create their own lower-resolution PAIs by taking smartphone photos of their face. These PAIs will mainly be toys; you can your digitized mini-self in different outfits and make yourself dance. It’s a way, Jain says, to get people familiarized with the idea of having a constant AI companion on hand. Now, for celebrity PAIs, and the PAI ObEN made for me, an in-depth, sometimes multiple-appointment process is necessary to get a good audio sample.
“For celebrities the requirement is to have an extremely high resolution, Jain says, “first they go through a 3D scan, then full voice recordings. We outsource that work to a studio of their choice.” Once the ObEN team is satisfied with the sample, it then takes another few days to generate the PAI.
For regular users though, smartphone resolution will have to do. “From one selfie you can make your PAI,” Jain says. “It may not be as high resolution as Taylor Swift’s, but it’s going to be looking like you. Singing in a voice like yours. We believe that to make your PAI should always be free.”
It’s early yet, but the cyberpunk would point out two potentially diverging classes of PAIs here—high res, glamorized versions for the rich, fremium PAI models for the plebes, but I digress. Speaking of cyberpunk, ObEN imagines users bringing those PAIs to the mall, where they might interact with mall-PAIs, the digital concierges there. Your favorite celebrity might pop up when you enter the mall, and tell you where the jacket is looking for,” Jain says, describing what could be a scene out of Minority Report. “Any consumer can have a PAI, get the full shopping experience.”
Beyond commerce, ObEN is also experimenting with healthcare PAIs, and has entered into a partnership with Georgetown-Howard Universities Center for Clinical and Translational Studies (GHUCCTS), and MedStar Health Research Institute to create Tara, an avatar designed to interact with patients with heart disease on their tablets. The institutes are carrying out a 5-year study to see if interacting with Tara will help improve patients’ recovery times and propensity for taking medication and so forth. “The PAI will monitor them, give them comfort,” Jain says, “let them know someone is always there with them.”
“In this case it’s very nonclinical—it’s a virtual nurse—talking through this proposed cardiac surgery or that,” Jain says. “It’s recording the responses, and it can do follow ups.” They also imagine teacher PAIs. “Kids learn faster when the character,” Jain says with a laugh, “a cartoon character teaching them; my kids respect their teacher more than me—if it was their teacher, talking to them, teaching them a lesson, they might learn more.”
If ObEN has its way, and current trends—at least in China, Korea, and Japan—accelerate, then we may soon find ourselves in a world festooned with PAIs, emissaries of bystander and celebrity likeness, popping out of our phones, glasses, malls, bus stops, desktops.
There’s an energy to the company that I don’t often encounter in tech startups; an easy, unforced good-naturedness, a willingness to embrace weird frontiers. Maybe that’s in part because it’s one of the only companies I’ve encountered lately where I’ve sat down at the conference table and been the only white guy in the room. It’s a diverse staff, with diverse leadership, and our talk was refreshingly free of the buzzword drip you drown in across Silicon Valley.
Jain and co delight in talking about the theoretical uses of PAIs—like a Taylor Swift PAI that users can ask to sing for them, or for biographical info, and she’ll respond with personal knowledge gleaned from a long-ago training session. That’s one that came up more than once, and that, and the entertainment-based applications are still the ones that seem most likely to resonate.
But it also brings us to one of the starker concerns I see arising with any hypothetical mass popularization with ObEN’s technology—who controls the likeness once it’s out in the world? For one thing, the contract that ObEN’s face-scanner asked me to sign seemed rather onerous. Here’s the opening clause:
“I hereby grant to Oben, Inc, and its successors, assigns and licenses (collectively, “Company”) the irrevocable right and permission to (i) take photos of me, and (ii) to reproduce and use the photos, my facial expressions, likeness, poses, and voice (the “Images) for all lawful purposes and without further compensation of any kind. Company may edit, alter, copy, exhibit, publish and otherwise use these images and I expressly waive any right to inspect or approve the finish product when any Image appears.”
I’m no legal expert, but that seems to me that ObEN would own the rights to my face and PAI in perpetuity, and they’d even be free to profit off my likeness without sharing any of the revenue. When I declined to sign the contract, another rep said it wasn’t meant for me anyway, and we proceeded without it—I’m not sure who if anyone has signed away their digital face rights
For his part, Jain says, “That aspect of the law is important. We work very closely with agencies to make sure their PAI is only used to create content that they are in control of. Once the data is collected, they are set for life. Their estates could own the PAI and keep it long after the person is gone.” He says he takes privacy very seriously, and is looking at Washington State’s data storage laws as a model. It’s also why he’s exploring the blockchain, he says, as a means of storing data in a distributed fashion.
“Data privacy is a huge topic for us,” he says. “Because you’re storing it. That’s when blockchain came out as one feasible way to recall your data.” They will also take proactive measures to combat deepfakes, he says.
But ultimately, it all comes back to his children. I may not be convinced that my kids would want to spend time with PAI-dad, or that I’d want them to. But Jain’s are.
“My kids have been our test pilots,” he says. “The first one was me just talking to them then dancing on the couch in AR. I remember very clearly the first time I tried it on them—the weird part was not seeing their dad, it was seeing their dad on their hand, dancing. Once they got over the surprise, they loved it.”

How Corporate Delusions of Automation Fuel the Cruelty of Uber and Lyft

As drivers around the world went on strike just days before Uber’s IPO, they shared harrowing stories of being overworked and underpaid, subjected to a frustratingly opaque payment system, and even sleeping in their cars in order to save enough money to get by. Research backs up the anecdotes they relayed to the media: Studies have repeatedly found that after fees and expenses, Uber drivers make less than minimum wage in major markets. One found that half of all Uber drivers in Washington, D.C., lived below the poverty line.
The strikes, which were held in San Francisco, at Uber’s headquarters, as well as in New York, London, Chicago, Los Angeles, Sao Paulo, and beyond, were designed to draw a stark contrast between a company with a CEO that took in $50 million last year, that is aiming for a $90 billion valuation—and poised to mint numerous multi-millionaires and billionaires—and its increasingly impoverished drivers, many who say they can no longer afford basic necessities or to pay rent.
Will the worker actions put a dent in Uber’s IPO on Friday? After all, the company is operating at a truly massive annual loss, is propped up largely by an even more massive inflow of investment capital, and has already been cutting drivers’ wages in key markets. This is a sign that worker conditions are bad enough that they could destabilize Uber’s services, after all. It should be enough to make investors wary—that is, if they believed that any of those drivers would actually be around long enough to revolt.
I’d wager it won’t. The reason is pretty simple: Investors (along with the rest of us) have been told, repeatedly and colorfully, that autonomous cars are nearly here. They are inevitable, even—the next stage in Lyft and Uber’s corporate blueprints, as laid out in their executives’ public statements, ambitious research programs, and, now, SEC filings. The word “autonomous” appears more than 100 times in each companies’ IPO filing. Drivers, meanwhile, are meatbag placeholders whose general wellbeing is a concern relevant only to the extent that its degradation might disrupt service right now. They are therefore temporary and eminently replaceable. And investors have had this logic nurtured in them for years.
In 2016, then-CEO Travis Kalanick said that autonomous car technology was “basically existential” to Uber. The company was spending $20 million a month on self-driving car research, after it opened an autonomous division in partnership with Carnegie Mellon to much fanfare. As such, Uber believed it would already have 75,000 driverless cars on the road by this year. But even after the notoriously ambitious and reckless Kalanick was ousted, his replacement, Dara Khosrowshahi, said in a January, 2018 interview that he expected Uber to have self-driving cars on the streets, in operation, within 18 months. If you’re keeping score at home, that would mean we’d be seeing autonomous Ubers hit roads next month.
Two months after Khosrowshahi said that, one of Uber’s self-driving test cars would hit and kill a pedestrian in Arizona, forcing the company to pause the program. Yet testing resumed soon enough, and Uber again expects to compete and even lead in the self-driving car arena. It just received an investment of $1 billion from Softbank and Toyota, further indicating that the smart money says self-driving cars are the future.
Lyft is even more bullish on autonomous cars, if only because it’s not haunted by a recent pedestrian fatality. It is partnering with Waymo to offer autonomous rides to customers in a handful of vehicles. And in its IPO filing, Lyft claims that it will have some autonomous cars on the road within five years, and most transportation needs covered by self-driving cars in 15:
In the next five years, our goal is to deploy an autonomous vehicle network that is capable of delivering a portion of rides on the Lyft platform. Within 10 years, our goal is to have deployed a low-cost, scaled autonomous vehicle network that is capable of delivering a majority of the rides on the Lyft platform. And, within 15 years, we aim to deploy autonomous vehicles that are purpose-built for a broad range of ridesharing and transportation scenarios, including short- and long-haul travel, shared commute and other transportation services.
If you are an investor, and you buy that timeline, a few thousand noise-making workers don’t seem all that worrisome to your prospects of turning a profit. The question for them, then, is, as the Washington Post put it: “As IPO soars, can Uber and Lyft survive long enough to replace their drivers with computers?” They have little apparent reason to doubt that at least one of them will. Even though neither company has ever approached profitability, the mere fact that both have been sustained by VC cash and outside investment for over a decade now, and are pretty enticingly too-big-too-fail-shaped, will encourage investors to buy in.
So far, the timelines for self-driving car deployment have amounted to magical thinking—yet it’s had a real impact on the livelihoods of the drivers that actually make Uber and Lyft possible. The promise of autonomous car tech has kept the VC pump primed, kept the cash flowing in, and allowed ride-hailing companies to avoid producing an actual sustainable business model—one that, among other things, would account for the needs of its thousands-strong workforce. Believing that its drivers are proto-automatons has probably helped Uber and Lyft justify maintaining the difficult and even cruel conditions it keeps them working in—Lyft, for instance, recently tried and failed to get a law guaranteeing its drivers minimum wage thrown out in New York City.
Treating drivers as replaceable, disposable placeholders for algorithms is finally taking its toll. Uber even acknowledges as much in its IPO filing, which states plainly that the company plans to continue to slash wages. “As we aim to reduce Driver incentives to improve our financial performance, we expect Driver dissatisfaction will generally increase,” it notes. “Further, we are investing in our autonomous vehicle strategy, which may add to Driver dissatisfaction over time, as it may reduce the need for Drivers.”
This is taken as a fact of life, this “driver dissatisfaction,” aka the fact that Uber’s own workers openly dislike it; an issue that will be overcome only when the driver is eliminated altogether. It amounts to a warped, future-forward version of what Astra Taylor calls “fauxtomation”—services and products that only appear to be automated but are in fact made possible by human toil. In the case of the ridesharing companies, the mere *vision* of future automation prevents real drivers from receiving the decent wages and dignity they deserve.
No one knows if and/or when fully autonomous vehicles will replace taxis and ride-hailing services. (Although, if Lyft has a fleet of fully-autonomous cars in seamless operation in a decade, I’ll hail one straight into the ocean.) But the mere promise of this soon-to-unfold future is spurring both the company and its investors to discount the livelihoods of thousands of people, the drivers who are actually doing the work.
Uber may well get another handsome influx of investor cash on Friday, buffeted by visions of a sleek, app-based, self-driven future. But if it does hope to avoid that revolt before it gets there, it’d better invest in the workers behind the wheel right now.

Fully Automated Warehouses Are a Decade Away, Amazon Says

Amazon recently came under fire when the Verge revealed it used a system that automatically determines the productivity levels of its warehouse workforce, and terminates laggards if they’re too slow to move packages. It was yet another sign that automation was taking over at the retail giant, and a particularly ominous one at that.
In a subsequent press tour of its Baltimore fulfillment center, ostensibly intended to parry against this latest volley of charges of extreme and robot-like working conditions, Scott Anderson, director of Amazon Robotics Fulfillment, took pains to highlight the human element still necessary at the company’s capacious warehouses.
“There is a fallacy in the initial understanding of ‘Are we going to be a lights-out fulfilment network in the next few years?’” Anderson said, according to Reuters. “In the current form, the technology is very limited. The technology is very far from the fully automated workstation that we would need.”
He said that technology wouldn’t be there for about another 10 years—which, from his point of view in the technology industry, is a rather long time. To the 100,000 plus people who work at Amazon’s fulfillment centers, and to the local economies and social systems that will need to cope with such a large influx of automated-out-of-work employees, it might not seem like such a lengthy timeline. If Amazon and its competitors automate its warehouse operations in 10 years, that’s around 1 million unskilled jobs gone, with nowhere obvious to replace them.
In fact, the timeline disparity, which reflects more generally the ambiguity that automation inspires in our thinking about the future of work, is evident right there in how the media covered Anderson’s very statement:
To Bloomberg, a 10-year timeline is a sign that the robots are already taking over. To Reuters, it’s evidence that Anderson is right, that we don’t need to worry about full automation for a while yet.
As usual, the truth is probably somewhere in the murkier middle—I’m sure Amazon would love to automate its factories in 10 years, but I (and plenty of experts) am pretty skeptical that we’ll see human-free fulfilment centers in a decade. The picking robots, for one, are still clumsy and easily confused. It’s too early to tell if they’ll *ever* be a solid and total replacement for humans. Many automated systems, remember, from self-checkout kiosks to call menus, only subsist with ample human assistance, and some get dismantled altogether.
But that doesn’t mean Amazon won’t start rolling out more and more robots to perform other tasks, or parts of that one—as Bloomberg points out, there are already 100,000 mobile robots on its factory floors. And as it does, it will certainly use the logic of automation to reduce jobs, even in situations where there isn’t a neat bot-to-human replacement ratio in place. It just means that the remaining human workers will pick up the slack when the warehouse bots misfire or make mistakes.
But warehouse automation is certainly coming, whether it allows for ‘dark’ human-free operations in a decade or not. It’s another case where automation could pretty indisputably yield significant social benefits—by all counts, Amazon warehouse work is grueling, unpleasant, even dangerous toil. That is, if we’re prepared to properly harness it, and spread the benefits beyond a managerial class that stands to oversee the coming wave of new machines. Right now, we’re not.

The Vast Majority of All Futures Trading Is Now Automated

Who’s up for a friendly neighborhood reminder that the global marketplace is underpinned by automated, high-frequency trading to a nearly incomprehensible degree? Good, then—a new report highlights the still-growing dominance of automation in futures markets: For starters, over two-thirds of livestock futures trading is carried out automatically, and 91 percent of all currency futures trading is now done with nary a human involved.
Or, “Automation is the future of futures markets,” as the Financial Times put it in their headline describing the report.
The study was conducted by the market intelligence branch of the Division of Market Oversight of the U.S. Commodity Futures Trading Commission, which examined eight markets overseen by the CME Group—the world’s biggest exchange company—and found that automation now accounts for the vast majority of the share of trades in each.
Futures trading is the buying or selling of something—oil, corn, gold, etc.—at a future date, at a set price and quantity. Metals futures, energies, financials, equities, grains, and oilseeds—each of these markets is substantially automated. In fact, livestock futures saw the lowest share of automated trades and is the only market examined wherein less than 70 percent of the trades were automated. The study’s authors conclude that “automation has increased consistently” over a six-year period.
Why does it matter if increasingly large amounts of capital are automatically changing hands at speeds of fractions of a second? The biggest concerns are heightened volatility in the markets and overreactions to changing trends in a given market, which the high-frequency trading algorithms can overinterpret, potentially exacerbating a financial downturn.
There have been a few high-profile HFT-spurred shocks to the market thus far, and the fear is that once there are too many systems on autopilot, and they all start responding to the wrong signals, it might cause chaos throughout global markets, affecting the economy accordingly.
In the case of the futures market study, the regulators did observe that “historical end-of-day price volatility was positively correlated with the average number of daily price changes,” but found that no such correlation between automation and volatility was conclusive.
So while automation is continuing to dominate the vast majority of trades in the futures market, it’s still unclear what, exactly the impact of a largely human-free marketplace will be.

Applying for Your Next Job May Be an Automated Nightmare

If you think looking for a job is already daunting, anxiety-riddled, and unpleasant, just wait until the algorithms take over the hiring process. When they do, a newfangled “digital recruiter” like VCV, which just received $1.7 million in early investment, hopes it will look something like this:
First, a search bot will be used to scan CVs by the thousands, yours presumably among them. If it’s picked out of the haystack, you will be contacted by a chatbot. Over SMS, the bot will set an appointment for a phone interview, which will be conducted by an automated system enabled by voice recognition AI. Next, the system will ask you, the applicant, to record video responses to a set of predetermined interview questions. Finally, the program can use facial recognition and predictive analytics to complete the screening, algorithmically determining whether the nervousness, mood, and behavior patterns you exhibit make you a fit for the company.
If you pass all that, then you will be recommended for an in-person job interview.
It should go without saying that the process described above—which required no exaggeration and is a straight description of the four-step process outlined on VCV’s website, under the “How the magic is being done” section—probably sounds like a certain kind of hell for the vast majority of job seekers.
For one thing, a lot of facial and voice recognition software is notoriously faulty and bias-prone. Some researchers and critics argue that any claims that algorithms can make judgements based on perceived mood or facial reaction (sometimes called “affective computing”) amount to little more than modern day phrenology. For another, all of this serves to render the job application process even more opaque, impersonal, and impenetrable to the average job hunter. VCV already counts large companies like Citibank, l’Oreal, Danone, and PricewaterhouseCoopers among its clients.
“Human biases have long plagued hiring, and any claim that machine learning algorithms alone can fix that is bogus,” Aaron Rieke, the co-author of a report published in December 2018 called Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias, told me in an email. “It has been reported that VCV uses facial recognition to identify candidates’ “mood” and “behavior patterns” to help recruiters assess ‘cultural fit.’ This raises all kind of red flags.”
VCV, which did not respond to a request for comment, is far from alone here. A growing suite of startups is pitching AI-driven recruitment services, promising to save corporations millions of dollars throughout the hiring process by reducing overhead, to pluck more ideal candidates out of obscurity, and to *reduce* bias in the hiring process. Most offer little to no evidence of how they actually do so. VCV’s much-larger competitor, HireVue, which has raked in a staggering $93 million in funding and is backed by top-tier Silicon Valley venture capital firms like Sequoia, is hocking many of the same services. It counts 700 companies as its clients, including, it says, Urban Outfitters, Intel, Honeywell, and Unilever. AllyO, which was founded in 2015, and “utilizes deep workflow conversational AI to fully automate end to end recruiting workflow” has $19 million in backing.
Last year, in an article titled Recruiting Generation Z? You’ll Want to Use These, Inc. Magazine rounded up the new companies using “AI, gamification, machine learning” and other technologies to hire the youngest job seekers. Ryan Jenkins, a self-described “Millennial and Generation Z speaker and generations expert” ranks HireVue as the #2 new hiring tool, just behind Pymetrics, which “uses neuroscience games and bias-free artificial intelligence (AI) to predictively match people with jobs where they’ll perform at the highest levels.” #4 is Mya / Wade & Wendy, which “offer chatbots that automate the process from resume to interview.”
Just how expansive these operations are right now is unclear, despite the fact that these digital recruiters tout contracts and partnerships with Fortune 500 companies. Rieke says information is scarce about how widely major recruiters have embraced automated hiring practices, bots, and facial recognition. But it’s clear the trend is attracting ample interest, investment, and encouragement from the business community.
“AI in human resources is cost-effective and better for business overall,” Barbara Van Pay, the CEO of SmartHR Consultancy, writes in Entrepreneur Magazine. Van Pay points to a 2016 Society of Human Resource Management survey that found the average cost-per-hire was $4,129. AI, she reasons, could whittle that figure away. “With many of the AI recruitment and Human Resources programs available offering tailor-made packages on a monthly, quarterly, and yearly subscription basis, it’s not hard to see that you can save a pretty hefty penny by transitioning to AI technology solutions.” To wit, HireVue’s website claims that “Hilton cut time-to-hire nearly 90% with Hirevue assessments,” ostensibly saving it a great deal of money.
The goal here is obvious enough—promise corporations they can spend less money on human headhunters and recruiters, less time manually poring over applications, and generally fewer resources on the hiring process by outsourcing all of the above to machines. But these startups risk offering a prime example of shitty automation—an automated product adopted in the name of saving money, that risks, in the end, just making everybody’s lives worse. In this case, the automation is designed to benefit one side of the equation almost exclusively: the employer.
The added hoops and inscrutable, bot-addled process will probably make matters worse for applicants. Think, for a second, about how much you appreciated the last robocall you accidentally picked up and suffered through 2.5 seconds of. Now consider what it would be like if that robocaller were conducting your preliminary job interview. Job interviews are about to be laced with a lot more expletives, at least. But bias and discrimination remain a distinct problem area.
“There are layers of concerns here,” Rieke tells me. “Facial recognition technology is often less accurate for women and darker-skinned people. Even assuming companies like VCV can evaluate ‘mood’ or ‘behavior,’ it’s not clear how that should help a recruiter assess candidates. If the ‘right kind’ of mannerisms are derived from a company’s current, homogeneous workforce, diverse candidates could be penalized.”
Not only is that hugely problematic with regards to people who might be passed over for work by biased AI—but it’s not hard to imagine a future in which predictive hiring software gives way to some hefty discrimination lawsuits, too. Founders and developers can claim their algorithms reduce bias all they want; until they have transparently demonstrated proof, we should default to skepticism. (Last year, the founders of Predictim, an AI-based platform designed to screen applicants for babysitting jobs, said their product was absolutely not biased, yet when I used it myself, the preliminary results looked pretty racist to me.)
Finally, it’s not really clear that the service works. Yes, there may be short term gains as companies cut operating costs by offloading tasks to an AI service—but there’s a reason the hiring process has typically been hands on; it’s a pretty good way for the two parties to tell if they’ll enjoy working together. Dubiously derived data points about performance and mood seem like a poor substitute, though time will tell.
“We need a lot more information about how systems like these are designed and tested,” Rieke says. “Until that happens, I’m extremely skeptical. The hype is way ahead of the facts.”

The Mueller Report Reminds Us How Cheap Automation Fueled the Russia Mess

Like everyone else in journalism or the journalism-adjacent world, or anyone who owns a television or internet-connected device, or pretty much anyone who lacked the foresight to drive into the desert yesterday, I found myself powerless but to stare into the semi-redacted collapsing star that is the Mueller Report. Still, as I read through the black, I was struck by one element highlighted again by the report: the impressive extent to which cheap automation is responsible for this mess.
The report explains, of course, that “the Russian government interfered in the 2016 election in sweeping and systematic fashion” and did so primarily via two operations: an extensive social media campaign conducted by its Internet Research Agency (IRA), and the hacking of entities supporting or working for the Clinton campaign. There aren’t a ton of new revelations about how the IRA manipulated social media to try to steer the political conversation in Trump’s favor, but the final report is a good opportunity to revisit what actually happened here—and remind us that it absolutely can and will happen again.
“[The Mueller report] should remind people that if an account or tweet pops up into your Twitter feed from an unverified account and you don’t follow it, there MAY be someone creating fake engagement to push that accounts very partisan tweet,” the disinformation researcher and CNN-dubbed Russian troll hunter Josh Russell told me in a direct message.
After the summary and the details of the investigation, the first substantive section of the report focuses on the IRA and how, beginning in 2014, it began setting up hundreds, then thousands of social media accounts, most of them representing fictitious Americans, on Facebook, Twitter, Tumblr, etc. Over the next couple of years, it started beefing them up, amplifying the dummy accounts’ posts with a botnet tens of thousands of accounts strong.
On Facebook, IRA-created groups attracted hundreds of thousands of followers, and IRA posts reached up to 126 million Americans, the report says. Twitter’s own internal investigation last year found that “during the time period we investigated, the 3,814 identified IRA-linked accounts posted 175,993 Tweets”—and the Mueller report notes that these Tweets were shared, discussed, or retweeted by right-wing celebrities like Sean Hannity, quoted by credulous media outlets, and even commented on by Trump himself.
Russia’s social media propaganda machine was able, essentially, to brute force its messaging into the mainstream conservative media, with a handful of made-up accounts and a low-rent automated system. Because that’s the thing here; the IRA was able to do all this for relative pennies on the propagandic unit. Whether or not its relentless posting and eventual infiltration of the Twitter feeds of your racist uncle and the right’s intellectual giants actually influenced the election, the amount of discord it ultimately caused is undeniable—so-called Russiagate (are we still calling it that?) and the Mueller investigation is all a significant swath of the media and your #Resistance uncle could focus on for the last two years.
According to RBC Magazine, the IRA employed fewer than 100 employees in the “American Department” of its so-called “troll farm.” The department’s budget for two years of operations was $2 million dollars. If your goal is to sow nationwide political discord and get that nation’s media to
It also reportedly spent just $100,000 on Facebook ads, which is kind of a hilariously paltry sum if you’re hoping to swing elections, though experts regard it as likely just an experiment, a small part of the IRA’s posting regimen. And Twitter botnets are even cheaper. Dapper cyberlord Joseph Cox wrote about assembling his own Russian botnets for less than $100 in 2017, and security researchers have determined that they’ve only gotten more sophisticated since the 2016 election.
Twitter identified some 50,000 automated accounts that were affiliated with the IRA and were retweeting pro-Trump messages leading up to the election. Cox bought 1,000 accounts for $45. You don’t have to be an experienced coder to set these botnets up, either; you just need a little cash, the ability to Google ‘botnet services’ (or better yet, poke around on the dark web for them), and an openness to getting scammed here and there. It’s really easy to do.
“Overseas it’s a pretty cheap service,” Russell tells me. “They even advertise ON Twitter for it. Lots of Arabic bots I have ran into actually advertised for botting using Twitter.”
But all those automated bots will need something to retweet, which is where the dummy accounts come in. One of the better known IRA troll accounts is @Pamela_Moore13 (many of her tweets are collected here), and she provides a pretty good example of how this operation worked.
In the summer of 2017, as the Georgia congressional election results were being tallied—Democrat Joel Ossoff would narrowly lose—the very not-real Pamela Moore tweeted, “This is priceless! #Fakenews #GA06 #Handel #Ossoff” with a screengrab of glum-looking CNN pundits presumably discussing the results. The frowning faces captured therein were meant to offer proof that CNN was biased against Trump, and to serve as red meat to his base. The botnet presumably then retweeted her tweet, which circulated widely enough to be noticed by Sean Hannity, who also retweeted the post.
From there, the clip spread into the right-wing media ecosystem: One conservative radio station wrote an entire blog post dedicated to the Russian troll’s screengrabbed tweet. The American Thinker, a conservative website, taking issue with the notion that the mainstream media is unbiased, cited the Russian troll’s tweet as evidence that “blows that nonsense out of the water.” The New Hampshire Register included the tweet in its election coverage, and it showed up embedded in a ton of conservative blogs, and so on and so forth.
It’s certainly absurd to imagine some poor troll in Russia watching CNN online in a crappy office building in St. Petersburg, recognizing in a flash the potential gold, screengrabbing the money shot, and hitting publish, which is probably exactly what happened, then watching an automated system lift his or her work to mild meme status. But it’s also extremely unclear what kind of impact this actually had. Sure, it’s another cliché-perpetuating right-wing voice, but it’s also a phony troll amongst a chorus of obnoxious real ones. Even if this happened a thousand times over the course of the election, I’m not sure it does much beyond what standard-grade conservative posters were already doing naturally. It’s not like Trump loses the election if there were 12 fewer semi-viral dunks on CNN.
If anything, it’s a pretty good reminder of just how weird these times are, which tends to get lost in the very serious collusion narratives and spy novel fanfictioning that seems to infect so much of the conversation about Russian interference. After all, Russia had a concerted, state-run propaganda operation that was built to use an automated botnet to amplify a legion of trolls to, uh, get RT’d by Sean Hannity? Other actions documented in the report—the hacking, and how the trolls managed to organize real-world conservative protests from Russia—are more serious, but few are as brazenly 2019 as that.
Then again, it was pretty cheap. And as I mentioned, botnet tools are apparently getting more sophisticated—so we can clearly go ahead and assume that, as lame and ultimately pointless as they may be, the confounding days of automation-boosted social media political propaganda are only beginning.
“I expect it to keep happening, they will always be playing the game of cat and mouse with Twitter’s integrity team,” Russell says. “Just like spammers when they notice their message isn’t getting out they will change tactics, and mask the fact they are using bots. The troll accounts just have to make no mistakes and Twitter will never be able to catch them.”
“I expect it still happens,” Russell tells me, “just not at that scale. Yet.”