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April 19, 2019

ProcessOut improves payment data visualization

ProcessOut has grown quite a lot since I first covered the startup. The company now has a ton of small and big clients, from Glovo to Vente-Privée and Dashlane. The company has become an expert on payment providers and payment analytics.

The core of the product remains the same. Clients sign up to get an overview on the performance of their payment systems. After setting up ProcessOut Telescope, you can monitor payments with expensive fees, failed payments and disappointing payment service providers.

And this product is quite successful. Back in October 2018, the company had monitored $7 billion in transactions since its inception — last month, that number grew to $13 billion.

The company is adding new features to make it easier to get insights from your payment data. You can now customize your data visualization dashboards with a custom scripting language called ProcessOut Lang. This way, if you have an internal payment team, they can spot issues more easily.

ProcessOut can also help you when it comes to generating reports. The company can match transactions on your bank account with transactions on different payment providers.

If you’re a smaller company and can’t optimize your payment module yourself, ProcessOut also builds a smart-routing checkout widget. When a customer pays something, the startup automatically matches card information with the best payment service provider for that transaction in particular.

Some providers are quite good at accepting all legit transactions, such as Stripe or Braintree. But they are also more expensive than more traditional payment service providers. ProcessOut can predict if a payment service provider is going to reject this customer before handing the transaction to that partner. It leads to lower fees and a lower rejection rate.

The company recently added support for more payment service providers in Latin America, such as Truevo, AllPago and Mercadopago. And ProcessOut now routes more transactions in one day compared to the entire month of October 2018.

As you can see, the startup is scaling nicely. It will be interesting to keep an eye on it.

News Source = techcrunch.com

Industrial robotics giant Fanuc is using AI to make automation even more automated

Industrial automation is already streamlining the manufacturing process, but first those machines must be painstakingly trained by skilled engineers. Industrial robotics giant Fanuc wants to make robots easier to train, therefore making automation more accessible to a wider range of industries, including pharmaceuticals. The company announced a new artificial intelligence-based tool at TechCrunch’s Robotics + AI Sessions event today that teaches robots how to pick the right objects out of a bin with simple annotations and sensor technology, reducing the training process by hours.

Bin-picking is exactly what it sounds like: a robot arm is trained to pick items out of bins and used for tedious, time-consuming tasks like sorting bulk orders of parts. Images of example parts are taken with a camera for the robot to match with vision sensors. Then the conventional process of training bin-picking robots means teaching it many rules so it knows what parts to pick up.

“Making these rules in the past meant having to through a lot of iterations and trial and error. It took time and was very cumbersome,” said Dr. Kiyonori Inaba, the head of Fanuc Corporation’s Robot Business Division, during a conversation ahead of the event.

These rules include details like how to locate the parts on the top of the pile or which ones are the most visible. Then after that, human operators need to tell it when it makes an error in order to refine its training. In industries that are relatively new to automation, finding enough engineers and skilled human operators to train robots can be challenging.

This is where Fanuc’s new AI-based tool comes in. It simplifies the training process so the human operator just needs to look at a photo of parts jumbled in a bin on a screen and tap a few examples of what needs to be picked up, like showing a small child how to sort toys. This is significantly less training than what typical AI-based vision sensors need and can also be used to train several robots at once.

“It is really difficult for the human operator to show the robot how to move in the same way the operator moves things,” said Inaba. “But by utilizing AI technology, the operator can teach the robot more intuitively than conventional methods.” He adds that the technology is still in its early stages and it remains to be seen if it can be used during in assembly as well.

News Source = techcrunch.com

Amazon’s one-two punch: How traditional retailers can fight back

If you think physical retail is dead, you couldn’t be more wrong. Despite the explosion in e-commerce, we’re still buying plenty of stuff in offline stores. In 2017, U.S. retail sales totaled $3.49 trillion, of which only 13 percent (about $435 billion) were e-commerce sales. True, e-commerce is growing at a much faster annual pace. But we’re still very far from the tipping point.

Amazon, the e-commerce giant, is playing an even longer game than everyone thinks. The company already dominates online retail — Amazon accounted for almost 50 percent of all U.S. e-commerce dollars spent in 2018. But now Amazon is eyeing the much bigger prize: modernizing and dominating retail sales in physical locations, mainly through the use of sophisticated data analysis. The recent reports of Amazon launching its own chain of grocery stores in several U.S. cities — separate from its recent Whole Foods acquisition — is just one example of how this could play out.

You can think of this as the Amazon one-two punch: The company’s vast power in e-commerce is only the initial, quick jab to an opponent’s face. Data-focused innovations in offline retail will be Amazon’s second, much heavier cross. Traditional retailers too focused on the jab aren’t seeing the cross coming. But we think canny retailers can fight back — and avoid getting KO’d. Here’s how.

The e-commerce jab starts with warehousing

Physical storage of goods has long been crucial to advances in commerce. Innovations here range from Henry Ford’s conveyor belt assembly line in 1910, to IBM’s universal product code (the “barcode”) in the early 1970s, to J.C. Penney’s implementation of the first warehouse management system in 1975. Intelligrated (Honeywell), Dematic (KION), Unitronics, Siemens and others further optimized and modernized the traditional warehouse. But then came Amazon.

After expanding from books to a multi-product offering, Amazon Prime launched in 2005. Then, the company’s operational focus turned to enabling scalable two-day shipping. With hundreds of millions of product SKUs, the challenge was how to get your pocket 3-layer suture pad (to cite a super-specific product Amazon now sells) from the back of the warehouse and into the shippers’ hands as quickly as possible.

Make no mistake: Amazon’s one-two retail punch will be formidable.

Amazon met this challenge at a time when automated warehouses still had massive physical footprints and capital-intensive costs. Amazon bought Kiva Systems in 2012, which ushered in the era of Autonomous Guided Vehicles (AGVs), or robots that quickly ferried products from the warehouse’s depths to static human packers.

Since the Kiva acquisition, retailers have scrambled to adopt technology to match Amazon’s warehouse efficiencies.  These technologies range from warehouse management software (made by LogFire, acquired by Oracle; other companies here include Fishbowl and Temando) to warehouse robotics (Locus Robotics, 6 River Systems, Magazino). Some of these companies’ technologies even incorporate wearables (e.g. ProGlove, GetVu) for warehouse workers. We’ve also seen more general-purpose projects in this area, such as Google Robotics. The main adopters of these new technologies are those companies that feel Amazon’s burn most harshly, namely operators of fulfillment centers serving e-commerce.

The schematic below gives a broad picture of their operations and a partial list of warehouse/inventory management technologies they can adopt:

It’s impossible to say what optimizations Amazon will bring to warehousing beyond these, but that may be less important to predict than retailers realize.

The cross: Modernizing the physical retail environment

Amazon has made several recent forays into offline shopping. These range from Amazon Books (physical book stores), Amazon Go (fast retail where consumers skip the cashier entirely) and Amazon 4-Star (stores featuring only products ranked four-stars or higher). Amazon Live is even bringing brick-and-mortar-style shopping streaming to your phone with a home-shopping concept à la QVC. Perhaps most prominently, Amazon’s 2017 purchase of Whole Foods gave the company an entrée into grocery shopping and a nationwide chain of physical stores.

Most retail-watchers have dismissed these projects as dabbling, or — in the case of Whole Foods — focused too narrowly on a particular vertical. But we think they’re missing Bezos’ longer-term strategic aim. Watch that cross: Amazon is mastering how physical retail works today, so it can do offline what it already does incredibly well online, which is harness data to help retailers sell much more intelligently. Amazon recognizes certain products lend themselves better to offline shopping — groceries and children’s clothing are just a few examples.

How can traditional retailers fight back? Get more proactive.

Those shopping experiences are unlikely to disappear. But traditional retailers (and Amazon offline) can understand much, much more about the data points between shopping and purchase. Which path did shoppers take through the store? Which products did they touch and which did they put into a cart? Which items did they try on, and which products did they abandon? Did they ask for different sizes? How does product location within the store influence consumers’ willingness to buy? What product correlations can inform timely marketing offers — for instance, if women often buy hats and sunglasses together in springtime, can a well-timed coupon prompt an additional purchase? Amazon already knows answers to most of these questions online. They want to bring that same intelligence to offline retail.

Obviously, customer privacy will be a crucial concern in this brave new future. But customers have come to expect online data-tracking and now often welcome the more informed recommendations and the convenience this data can bring. Why couldn’t a similar mindset-shift happen in offline retail?

How can retailers fight back?

Make no mistake: Amazon’s one-two retail punch will be formidable. But remember how important the element of surprise is. Too many venture capitalists underestimate physical retail’s importance and pooh-pooh startups focused on this sector. That’s extremely short-sighted.

Does the fact that Amazon is developing computer vision for Amazon Go mean that alternative self-checkout companies (e.g. Trigo, AiFi) are at a disadvantage? I’d argue that this validation is actually an accelerant as traditional retail struggles to keep up.

How can traditional retailers fight back? Get more proactive. Don’t wait for Amazon to show you what the next best-practice in retail should be. There’s plenty of exciting technology you can adopt today to beat Jeff Bezos to the punch. Take Relex, a Finnish startup using AI and machine learning to help brick-and-mortar and e-commerce companies make better forecasts of how products will sell. Or companies like Memomi or Mirow that are creating solutions for a more immersive and interactive offline shopping experience.

Amazon’s one-two punch strategy seems to be working. Traditional retailers are largely blinded by the behemoth’s warehousing innovations, just as they are about to be hit with an in-store innovation blow. New technologies are emerging to help traditional retail rally. The only question is whether they’ll implement the solutions fast enough to stay relevant.

News Source = techcrunch.com

Mueller says use of encrypted messaging stalled some lines of inquiry

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A single paragraph in the Mueller report out Thursday offers an interesting look into how the Special Counsel’s investigation came head-to-head with associates of President Trump who used encrypted and ephemeral messaging to hide their activities.

From the report:

Further, the Office learned that some of the individuals we interviewed or whose conduct we investigated-including some associated with the Trump Campaign — deleted relevant communications or communicated during the relevant period using applications that feature encryption or that do not provide for long-term retention of data or communications records. In such cases, the Office was not able to corroborate witness statements through comparison to contemporaneous communications or fully question witnesses about statements that appeared inconsistent with other known facts.

The report didn’t spell out specifics of whom or why, but clearly Mueller wasn’t happy. He was talking about encrypted messaging apps that also delete conversation histories over a period of time. Apps like Signal and WhatsApp are popular for this exact reason — you can communicate securely and wipe any trace after the fact.

Clearly, some of Trump’s associates knew better.

But where prosecutors who have faced similar setbacks with individuals using encrypted messaging apps to hide their tracks have often attacked tech companies for building the secure apps, Mueller did not. He just stated a fact and left it at that.

For years, police and law enforcement have lobbied against encryption because they say it hinders investigations. More and more, apps are using end-to-end encryption — where the data is scrambled from one device to another — so that even the tech companies can’t read their users’ messages. But just as criminals use encrypted messaging for bad, ordinary people use encrypted messaging to keep their conversations private.

According to the report, it wasn’t just those on the campaign trail. The hackers associated with the Russian government and WikiLeaks, both of which were in contact following the breaches on Hillary Clinton’s campaign and the Democratic National Committee, took efforts to “hide their communications.”

Not all of Trump’s associates have fared so well over the years.

Michael Cohen, Trump’s former personal attorney, learned the hard way that encrypted messaging apps are all good and well — unless someone has your phone. Federal agents seized Cohen’s BlackBerry, allowing prosecutors to recover streams of WhatsApp and Telegram chats with Trump’s former campaign chief Paul Manafort.

Manafort, the only person jailed as part of the Mueller investigation, also tripped up after his “opsec fail” after prosecutors obtained a warrant to access his backed-up messages stored in iCloud.

Uber, Lyft implement new safety measures

Uber and Lyft instituted new safety features and policies this week.

The move follows the death of Samantha Josephson, a student at the University of South Carolina, who was kidnapped and murdered in late March. She was found dead after getting into a vehicle that she believed to be her Uber ride. The murder, which has garnered nationwide media attention, seems to have spurred action by the ridesharing behemoths.

In response, Uber is launching the Campus Safety Initiative, which includes new features in the app. Currently, the features are in testing, and they remind riders to check the license plate, make and model of the car, as well as the driver’s name and picture, before ever entering into a vehicle. The test is running in South Carolina, in partnership with the University of South Carolina, with plans to roll out nationwide.

Lyft, which went public on March 29, has implemented continuous background checks for drivers this week. (Uber has had this in place since last year.) Lyft also enhanced its identity verification process for drivers, which combines driver’s license verification and photographic identity verification to prevent driver identity fraud on the platform.

Uber, prepping to debut on the public market, is taking the safety precautions seriously. The new system reminds riders about checking their ride three separate times: the first is a banner at the bottom of the app once the ride has been ordered, the second is a warning to check license plate, car details and photo, and the third is an actual push notification before the driver arrives reminding riders to check once more.

Alongside the reminder system, Uber is also working to build out dedicated pickup zones in the Five Points district of Columbia, with plans to roll out dedicated pickup zones at other U.S. universities.

That said, Uber has also warned investors ahead of its IPO about a forthcoming safety report on the company, which could be damaging to the brand. The report is supposed to be released sometime this year, and will give the public its first comprehensive look at the scale of safety incidents and issues that occur on the platform.

“The public responses to this transparency report or similar public reporting of safety incidents claimed to have occurred on our platform … may result in negative media coverage and increased regulatory scrutiny and could adversely affect our reputation with platform users,” said Uber in its April 14 IPO paperwork.

Indeed, the issue of safety on platforms like Uber and Lyft, or really any app that asks you to be alone with total strangers, goes well beyond any single incident. A CNN investigation found that 103 Uber drivers had been accused of sexual assault or abuse in the last four years.

News Source = techcrunch.com

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