Month: July 2018

When we called the ACLU’s Amazon’s Rekognition press release an “attention-grabbing stunt” when we wrote about it earlier today, well, consider that attention grabbed. Several Democratic members of Congress have responded with a strongly worded letter to founder Jeff Bezos.

Reps. Jimmy Gomez and John Lewis issued a letter to Bezos, after the ACLU noted that the facial recognition software falsely associated 28 images of Congress members with mugshots in a criminal database. Lewis, a pivotal figure in America’s civil rights moment, was among those falsely matched in the ACLU’s testing — particularly notable as the testing appeared to have a particular bias against people of color.

“The results of the ACLU’s test of Amazon’s ‘Rekognition’ software are deeply troubling,” Lewis wrote in a statement. “As a society, we need technology to help resolve human problems, not to add to the mountain of injustices presently facing people of color in this country. Black and brown people are already unjustly targeted through a discriminatory sentencing system that has led to mass incarceration and devastated millions of families.”

A trio of Congress members (Sen. Ed Markey and Reps. Luis Gutiérrez and Mark DeSaulnier), meanwhile, wrote a letter addressed to Bezos with a series of questions about the technology:

While facial recognition services might provide a valuable law enforcement tool, the efficacy and impact of the technology are not yet fully understood. In particular, serious concerns have been raised about the dangers facial recognition can pose to privacy and civil rights, especially when it is used as a tool of government surveillance, as well as the accuracy of the technology and its disproportionate impact on communities of color.

Amazon for its part, both defended Rekognition and disputed the ACLU’s methods. “We remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement,” the company wrote in a statement provided to TechCrunch.

With regard to testing, it says:

[W]e think that the results could probably be improved by following best practices around setting the confidence thresholds (this is the percentage likelihood that Rekognition found a match) used in the test. While 80% confidence is an acceptable threshold for photos of hot dogs, chairs, animals, or other social media use cases, it wouldn’t be appropriate for identifying individuals with a reasonable level of certainty. When using facial recognition for law enforcement activities, we guide customers to set a threshold of at least 95% or higher.

The company also reiterated an earlier statement that the results are intended to be used to narrow down results, rather than lead directly to arrests.

Regardless, the ACLU’s stunt certainly got the attention the organization was seeking, both with regard to the aforementioned biases and broader security implications of facial scanning for law enforcement.

With today’s announcement from Atlassian that it was selling the IP assets of its two enterprise communications tools, Hipchat and Stride, to Slack, it closes the book on one of the earliest competitors in the modern enterprise collaboration space. It was also a clear  signal that Slack is not afraid to take on its giant competitors by forming key alliances.

The fact the announcement came from Slack co-founder and CEO Stewart Butterfield on Twitter only exacerbated that fact. Atlassian has a set of popular developer tools like Jira, Confluence and Bitbucket. At this point, Hipchat and Stride had really become superfluous to the company and they sold the IP to their competitor.

Not only was Slack buying the assets and Atlassian was effectively shutting down these products, Atlassian was also investing in Slack, a move that shows it’s throwing its financial weight behind the company as well and forming an alliance with them.

Slack has been burning it up since in launched in 2014 with just 16,000 daily active users. At last count in May the company was reporting 8 million active users, 3 million of which were paid. That’s up from 6 million DAUs and 2 million paid users in September 2017. At the time, the company was reporting $200 million in annual recurring revenue. It’s a fair bet with the number of paid users growing by third at last count, that revenue number has increased significantly as well

Slack and products of its ilk like Workplace by Facebook, Google Hangouts and Microsoft Teams are trying to revolutionize the way we communicate and collaborate inside organizations. Slack has managed to advance the idea of enterprise communications that began in the early 2000 with chat clients, advanced to Enterprise 2.0 tools like Yammer and Jive in the mid-2000s, and finally evolved into modern tools like Slack we are using today in the mobile-cloud era.

Slack has been able to succeed so well in business because it does much more than provide a channel to communicate. It has built a platform on top of which companies can plug in an assortment of tools they are using every day to do their jobs like ServiceNow for help desk tickets, Salesforce for CRM and marketing data and Zendesk for customer service information.

This ability to provide a simple way to do all of your business in one place without a lot of task switching has been a holy grail of sorts in the enterprise for years. The two previously mentioned iterations, chat clients and Enterprise 2.0 tools, tried and failed to achieve this, but Slack has managed to create this single platform and made it easy for companies to integrate services.

This has been automated even further by the use of bots, which can act as trusted assistants inside of Slack providing additional information and performing tasks for you on your behalf when it makes sense.

Slack has an otherworldly valuation of over $5 billion right now and is on its way to an eventual IPO. Atlassian might have thrown in the towel on enterprise communications, but it has opened the door to getting a piece of that IPO action while giving its customers what they want and forming a strong bond with Slack.

Others like Facebook and Microsoft also have a strong presence in this space and continue to build out their products. It’s not as though anyone else is showing signs of throwing up their hands just yet. In fact, Just today Facebook bought Redkix to enhance its offering by giving users the ability to collaborate via email or the Workplace by Facebook interface, but Atlassian’s acquiescence is a strong signal that if you had any doubt, Slack is a leader here and they got a big boost with today’s announcement.

If you missed the first few rounds of excitement about Nintendo’s mini nostalgia machines, you’ve got another shot.

Nintendo’s NES and SNES Classic consoles aren’t always easy to find, but they’re now available from Amazon for $59.99 (NES Classic) and $79.99 (SNES Classic). You can place an order for either right now, though be aware that the NES Classic won’t ship until it’s back in stock on August 12 and the SNES Classic looks like it’ll be back on August 3 — a pretty reasonable wait for a sure thing. Update: It looks like Amazon’s stock of the NES Classic may have already run out in the course of the last few minutes, though the SNES version is still available at its normal retail price (and let’s be real, it was the best console). They seem to be dropping in and out of availability, so try refreshing and see what happens!

When they were first introduced, the reimagined versions of two of the best-loved consoles of all-time arrived to feverish demand. Back in 2016, the NES Classic was difficult hunt down and when it hit in August 2017, the SNES followed suit, managing to even outpace interest in its own progenitor. (Naturally, scarcity is the perfect fuel for a nostalgia-powered fire.)

Nintendo originally didn’t intend for either console to be restocked indefinitely, but after observing the “unbridled enthusiasm” of the retro gaming boxes it decided to keep them around. The consoles reappeared in May and June but sold out quickly.

Even with the repeat appearances, it’s been hard to keep track of when and where the things go on sale. If you’re reading this and you’ve yet to score a one-way ticket to nostalgic 8- or 16-bit euphoria, the Amazon listings look like a sure bet — for the moment, anyhow.

Machine learning is everywhere these days, but it’s usually more or less invisible: it sits in the background, optimizing audio or picking out faces in images. But this new system is not only visible, but physical: it performs AI-type analysis not by crunching numbers, but by bending light. It’s weird and unique, but counter-intuitively, it’s an excellent demonstration of how deceptively simple these “artificial intelligence” systems are.

Machine learning systems, which we frequently refer to as a form of artificial intelligence, at their heart are just a series of calculations made on a set of data, each building on the last or feeding back into a loop. The calculations themselves aren’t particularly complex — though they aren’t the kind of math you’d want to do with a pen and paper. Ultimately all that simple math produces a probability that the data going in is a match for various patterns it has “learned” to recognize.

The thing is, though, that once these “layers” have been “trained” and the math finalized, in many ways it’s performing the same calculations over and over again. Usually that just means it can be optimized and won’t take up that much space or CPU power. But researchers from UCLA show that it can literally be solidified, the layers themselves actual 3D-printed layers of transparent material, imprinted with complex diffraction patterns that do to light going through them what the math would have done to numbers.

If that’s a bit much to wrap your head around, think of a mechanical calculator. Nowadays it’s all done digitally in computer logic, but back in the day calculators used actual mechanical pieces moving around — something adding up to 10 would literally cause some piece to move to a new position. In a way this “diffractive deep neural network” is a lot like that: it uses and manipulates physical representations of numbers rather than electronic ones.

As the researchers put it:

Each point on a given layer either transmits or reflects an incoming wave, which represents an artificial neuron that is connected to other neurons of the following layers through optical diffraction. By altering the phase and amplitude, each “neuron” is tunable.

“Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement,” write the researchers in the paper describing their system, published today in Science.

To demonstrate it they trained a deep learning model to recognize handwritten numerals. Once it was final, they took the layers of matrix math and converted it into a series of optical transformations. For example, a layer might add values together by refocusing the light from both onto a single area of the next layer — the real calculations are much more complex, but hopefully you get the idea.

By arranging millions of these tiny transformations on the printed plates, the light that enters one end comes out the other structured in such a way that the system can tell whether it’s a 1, 2, 3 and so on with better than 90 percent accuracy.

What use is that, you ask? Well, none in its current form. But neural networks are extremely flexible tools, and it would be perfectly possible to have a system recognize letters instead of numbers, making an optical character recognition system work totally in hardware with almost no power or calculation required. And why not basic face or figure recognition, no CPU necessary? How useful would that be to have in your camera?

The real limitations here are manufacturing ones: it’s difficult to create the diffractive plates with the level of precision required to perform some of the more demanding processing. After all, if you need to calculate something to the seventh decimal place, but the printed version is only accurate to the third, you’re going to run into trouble.

This is only a proof of concept — there’s no dire need for giant number-recognition machines — but it’s a fascinating one. The idea could prove to be influential in camera and machine learning technology — structuring light and data in the physical world rather than the digital one. It may feel like it’s going backwards, but perhaps the pendulum is simply swinging back the other direction.

Electric scooter startups Bird and Skip have landed permits to operate in Portland under a new pilot program that aims to gauge how the controversial form of micro-transportation will fit in the city. And already there’s a bit of drama, or call it skeptical-scooter feelings, scuttling about.

The permits issued by Portland Bureau of Transportation will run until November 20, when the pilot program is set to end. Scooters could be available for rent as soon as this week, PBOT officials said.

The PBOT will conduct an evaluation of the program and survey Portlanders to determine whether scooters are compatible with the safe, efficient and equitable operation of Portland’s transportation system, the department said.

And while the official line from PBOT is neutral, there’s at least one staffer whose snarky tweet suggests that the scooters are something more repugnant: just another toy for tech bros.

It all started after PBOT tweeted a PSA about the rules for scooters. In response, one person wrote, “Instead of preemptively shaming and chastising e-scooter users PBOT should be bending over backwards to encourage this alternative. I would like @PBOTinfo staff to reread the climate action plan, bike plan, and comp plan to come to grips with the magnitude of their failure.”

A staffer within PBOT wasn’t too pleased and posted this retort.

“Or maybe they’re toys that tech bros leave strewn about, blocking corner ramps needed for people with disabilities. Also, people need to know the helmet laws for scooters are different than for bicycles. We’ll see how it goes during this pilot period!”

And then later, another tweet. This time the PBOT staffer tries to walk back the previous comments. Another 15 minutes later and it looks like that staffer’s tweeting privileges have been taken away.

The PBOT scooter skeptic, and the initial tweet that prompted the snippy response, is a symptom of a wider controversy bubbling up in densely populated cities throughout the U.S. as traditional car ownership — and the traffic congestion that comes with it — collides with public transit and newer forms of mobility such as ride-hailing, bike sharing and scooters.

The scooters have had a polarizing effect on residents living in cities. Some love the dockless scooter services because they provide a fast and cheap means of traveling short distances. Others loathe them, or more accurately, the misuse of them. (Scooters are supposed to be used in the bike lane, not on sidewalks.)

Still, the wave of scooters doesn’t appear to be slowing. Bird, for instance, launched in Portland and Cincinnati on Thursday. The company has launched in about 30 U.S. cities to date. Although not all of those have gone smoothly.

For instance, after Bird entered into Milwaukee on June 27, the city attorney issued a cease-and-desist letter and sued the scooter-share startup. The Milwaukee City Council is now considering a ban of all electric scooters.

Meanwhile, the streets of San Francisco remain scooter-less while the San Francisco Municipal Transportation Agency continues its review of the 12 applications from companies to operate electric scooters in the city. Bird, Lime, Lyft, Uber and others have applied for permits to operate electric scooter-share services in San Francisco. The ban, and subsequent permit process, was the result of several startups deploying their electric scooters without permission. 

Meanwhile, back in Portland, the number of scooters will be capped at 2,500, with each permitted company receiving a portion of the total. PBOT says it will continue to issue permits to companies that qualify under the pilot rules. In other words, Bird and Skip may soon have competition.

The PBOT is limiting the rollout, as well. Companies are allowed to deploy up to 200 scooters during its first week of operation. The department is also requiring that each company deploy a portion of their fleets in East Portland.

State law requires scooter riders to wear a helmet and prohibits use on sidewalks. Riders will be required to park scooters on the sidewalk close to the curb, so that scooters do not interfere with pedestrians, according to PBOT rules.

Amazon’s web services AWS continue to be the highlight of the company’s balance sheet, once again showing the kind of growth Amazon is looking for in a new business for the second quarter — especially one that has dramatically better margins than its core retail business.

Despite now running a grocery chain, the company’s AWS division — which has an operating margin over 25 percent compared to its tiny margins on retail — grew 49 percent year-over-year in the quarter compared to last year’s second quarter. It’s also up 49 percent year-over-year when comparing the most recent six months to the same period last year. AWS is now on a run rate well north of $10 billion annually, generating more than $6 billion in revenue in the second quarter this year. Meanwhile, Amazon’s retail operations generated nearly $47 billion with a net income of just over $1.3 billion (unaudited). Amazon’s AWS generated $1.6 billion in operating income on its $6.1 billion in revenue.

So, in short, Amazon’s dramatically more efficient AWS business is its biggest contributor to its actual net income. The company reported earnings of $5.07 per share, compared to analyst estimates of around $2.50 per share, on revenue of $52.9 billion. That revenue number fell under what investors were looking for, so the stock isn’t really doing anything in after-hours, and Amazon still remains in the race to become a company with a market cap of $1 trillion alongside Google, Apple and Microsoft.

This isn’t extremely surprising, as Amazon was one of the original harbingers of the move to a cloud computing-focused world, and, as a result, Microsoft and Google are now chasing it to capture up as much share as possible. While Microsoft doesn’t break out Azure, the company says it’s one of its fastest-growing businesses, and Google’s “other revenue” segment that includes Google Cloud Platform also continues to be one of its fastest-growing divisions. Running a bunch of servers with access to on-demand compute, it turns out, is a pretty efficient business that can account for the very slim margins that Amazon has on the rest of its core business.

HipChat, the workplace chat app that held the throne before Slack was Slack, is being discontinued. Also being discontinued is Atlassian’s own would-be HipChat replacement, Stride.

News of the discontinuation comes first not from Atlassian, but instead from a somewhat surprising source: Slack CEO Stewart Butterfield. In a series of tweets, Butterfield says that Slack is purchasing the IP for both products to “better support those users who choose to migrate” to its platform.

Butterfield also notes that Atlassian will be making a “small but symbolically important investment” in Slack — likely a good move, given that rumors of a Slack IPO have been swirling (though Butterfield says it won’t happen this year). Getting a pre-IPO investment into Slack might end up paying off for Atlassian better than trying to continue competing.

Atlassian VP of Product Management, Joff Redfern, confirmed the news in a blog post, calling it the “best way forward” for its existing customers. It’s about as real of an example of “if you can’t beat ’em, join ’em” as you can get; even Atlassian’s own employees will be moved over to using Slack.

According to an FAQ about the change, Stride and HipChat’s last day will be February 15th, 2019 — or a bit shy of seven months from the date of the announcement. So if you’re a customer on either one of those platforms, you’ve got time to figure things out.

It doesn’t sound like any of Atlassian’s other products will be affected here; Bitbucket, Jira, etc. will carry on, with only the company’s real-time communications platforms being shuttered.

Hipchat was launched in beta form back in 2009, long before Slack’s debut in 2013. It mostly ruled its space in the time in between, leading Atlassian to acquire it in March of 2012. Slack quickly outgrew it in popularity though, for myriad reasons — be it a bigger suite of third-party integrations, a better reputation for uptime, or… well, better marketing. By September of 2017, Atlassian overhauled its chat platform and rebranded it as as “Stride”, but it was never able to quite catch up with Slack’s momentum.

The UK is currently mulling a lot of new regulations around drones, aimed at clamping down on consumer use ahead of a seemingly inevitable explosion. Among a deluge of proposal is age restriction, banning use of drones weighing more than 0.55 pounds by anyone under the age of 18.

That proposed age limit would be three years younger than the age restriction on applying for a full plane or helicopter license. In the case of the proposed drone restriction, however, kids could potentially still fly a drone, so long as they do so with adult supervision.

The proposed legislation follows similar laws put in place in the U.S., where an FAA-imposed drone registry has been the source of a protracted legal back and forth. The U.K. has imposed some rules as well, restricting the height of consumer drone flights (400 feet), and banning flights near airports.

Recent proposals in the U.K. include the use of anti-drone technology around selected events and locations, and mandating that users file flight plans in designated apps before take off. Drone advocacy groups are pushing back on the proposal naturally. While certain regulation seems like a no-brainer, there’s a suggestion that limiting the age of use is a step too far and perhaps counterproductive.

“We’ve got to promote the safe and responsible use of drones, but children are the future of the drone world, so it’s also important they can have access to drones and use them,” Gabin Wishart of the Association of Remotely Piloted Aircraft Systems told the BBC. “The drone industry is expected to be a large part of the economy going forward so you don’t want to stop kids from exploring that.”

While artificial intelligence was once heralded as the key to unlocking a new era of economic prosperity, policymakers today face a wave of calls to ensure AI is fair, ethical and safe. New York City Mayor de Blasio recently announced the formation of the nation’s first task force to monitor and assess the use of algorithms. Days later, the European Union enacted sweeping new data protection rules that require companies be able to explain to consumers any automated decisions. And high-profile critics, like Elon Musk, have called on policymakers to do more to regulate AI.

Unfortunately, the two most popular ideas — requiring companies to disclose the source code to their algorithms and explain how they make decisions — would cause more harm than good by regulating the business models and the inner workings of the algorithms of companies using AI, rather than holding these companies accountable for outcomes.

The first idea — “algorithmic transparency” — would require companies to disclose the source code and data used in their AI systems. Beyond its simplicity, this idea lacks any real merits as a wide-scale solution. Many AI systems are too complex to fully understand by looking at source code alone. Some AI systems rely on millions of data points and thousands of lines of code, and decision models can change over time as they encounter new data. It is unrealistic to expect even the most motivated, resource-flush regulators or concerned citizens to be able to spot all potential malfeasance when that system’s developers may be unable to do so either.

Additionally, not all companies have an open-source business model. Requiring them to disclose their source code reduces their incentive to invest in developing new algorithms, because it invites competitors to copy them. Bad actors in China, which is fiercely competing with the United States for AI dominance but routinely flouts intellectual property rights, would likely use transparency requirements to steal source code.

The other idea — “algorithmic explainability” — would require companies to explain to consumers how their algorithms make decisions. The problem with this proposal is that there is often an inescapable trade-off between explainability and accuracy in AI systems. An algorithm’s accuracy typically scales with its complexity, so the more complex an algorithm is, the more difficult it is to explain. While this could change in the future as research into explainable AI matures — DARPA devoted $75 million in 2017 to this problem — for now, requirements for explainability would come at the cost of accuracy. This is enormously dangerous. With autonomous vehicles, for example, is it more important to be able to explain an accident or avoid one? The cases where explanations are more important than accuracy are rare.

Rather than demanding companies reveal their source code or limiting the types of algorithms they can use, policymakers should instead insist on algorithmic accountability — the principle that an algorithmic system should employ a variety of controls to ensure the operator (i.e. the party responsible for deploying the algorithm) can verify it acts as intended, and identify and rectify harmful outcomes should they occur.

A policy framework built around algorithmic accountability would have several important benefits. First, it would make operators responsible for any harms their algorithms might cause, not developers. Not only do operators have the most influence over how algorithms impact society, but they already have to comply with a variety of laws designed to make sure their decisions don’t cause harm. For example, employers must comply with anti-discrimination laws in hiring, regardless of whether they use algorithms to make those decisions.

Second, holding operators accountable for outcomes rather than the inner workings of algorithms would free them to focus on the best methods to ensure their algorithms do not cause harm, such as confidence measures, impact assessments or procedural regularity, where appropriate. For example, a university could conduct an impact assessment before deploying an AI system designed to predict which students are likely to drop out to ensure it is effective and equitable. Unlike transparency or explainability requirements, this would enable the university to effectively identify any potential flaws without prohibiting the use of complex, proprietary algorithms.

This is not to say that transparency and explanations do not have their place. Transparency requirements, for example, make sense for risk-assessment algorithms in the criminal justice system. After all, there is a long-standing public interest in requiring the judicial system be exposed to the highest degree of scrutiny possible, even if this transparency may not shed much light on how advanced machine-learning systems work.

Similarly, laws like the Equal Credit Opportunity Act require companies to provide consumers an adequate explanation for denying them credit. Consumers will still have a right to these explanations regardless of whether a company uses AI to make its decisions.

The debate about how to make AI safe has ignored the need for a nuanced, targeted approach to regulation, treating algorithmic transparency and explainability like silver bullets without considering their many downsides. There is nothing wrong with wanting to mitigate the potential harms AI poses, but the oversimplified, overbroad solutions put forth so far would be largely ineffective and likely do more harm than good. Algorithmic accountability offers a better path toward ensuring organizations use AI responsibly so that it can truly be a boon to society.

Last May, Google launched a new events feature designed to help web searchers more easily find things to do nearby, while also challenging Facebook’s dominance in the local events space. Today, Google is updating event search with personalized event suggestions, and well as a new design that puts more event information directly in the search results.

When the feature first launched last year, Google said it was built in response to the millions of search queries the company saw daily for finding local events and activities.

However, it was also clearly an area where Google had ceded ground to Facebook. The social network said last fall that 100 million people were using Facebook Events on a daily basis, and 650 million were using it across the network. Those numbers have surely grown since.

The original design for Google’s events search offered web searchers a list of events they could filter by category and date. Meanwhile, the event listings themselves were powered by data from Eventbrite, Ticketmaster, SeatGeek, Meetup, Vividseats, Jambase, LiveNation, Burbio, Allevents.in, Bookmyshow.com, StubHub, Bandsintown, Yext and Eventful.

Now, Google is returning these event results in a new format – instead of more standard search results, they appear as cards, each with a little bookmark icon you can click on to save the event details for future reference.

In addition, when you tap on one of the event listings’ cards, you’re directed to a more information-rich page, offering the date, time, location, and shortcuts to save the event, buy tickets, get directions, or share it with others. The design looks even more like a Facebook event page, albeit without a discussion section for posts and comments.

Clicking on the “Get Tickets” button will pop up a window that links to ticket resellers for the event in question – like Ticketmaster or StubHub, for example.

As users continue to click, browse and save events, the system will also be trained to know what sort of events users like.

This data will be used to power the new personalized recommendations feature, found in the bottom navigation bar’s “For You” tab, which organizes suggested events by category, like “concerts,” “festivals,” “shows,” free events, and more. This page will also show you trending and popular events in the area, if you need ideas.

The feature is not currently live for everyone, but is rolling out to mobile users over the next few days, says Google.