Category: Tech news

The Gillmor Gang — Doc Searls, Frank Radice, Esteban Kolsky, Michael Markman, and Steve Gillmor . Recorded live Friday, May 11, 2018. Audio networks, a new Beatles, and other digital cliffhangers

@stevegillmor, @dsearls, @fradice, @mickeleh, @ekolsky

Produced and directed by Tina Chase Gillmor @tinagillmor

Liner Notes

Live chat stream

The Gillmor Gang on Facebook

A little more than three years ago, Apple announced a new MacBook with a “butterfly” keyboard that was 40 percent thinner and ostensibly four times more stable than the previous “scissor” mechanism that MacBooks employed.

The promise was to more evenly distribute pressure on each key. Not everyone loved this “reinvention,” however, and now, Apple is facing a class action lawsuit over it.

According to a complaint lodged in the Northern District Court of California yesterday and first spied by the folks over at AppleInsider, “thousands” of MacBook and MacBook Pro laptops produced in 2015 and 2016 experienced failure owing to dust or debris that rendered the machines useless. The complaint further alleges that Apple “continues to fail to disclose to consumers that the MacBook is defective, including when consumers bring their failed laptops into the ‘Genius Bar’ (the in-store support desk) at Apple stores to request technical support.”

It just not a lack of disclosures that’s problematic, the suit continues. Customers who think the issue will be covered by their warranties are sometimes in for an unpleasant surprise. As stated in the filing: “Although every MacBook comes with a one-year written warranty, Apple routinely refuses to honor its warranty obligations. Instead of fixing the keyboard problems, Apple advises MacBook owners to try self-help remedies that it knows will not result in a permanent repair. When Apple does agree to attempt a warranty repair, the repair is only temporary—a purportedly repaired MacBook fails again from the same keyboard problems. For consumers outside of the warranty period, Apple denies warranty service, and directs consumers to engage in paid repairs, which cost between $400 and $700. The keyboard defect in the MacBook is substantially certain to manifest.”

The lawsuit was filed on behalf of two users, ZIxuan Rao and Kyle Barbaro, and more broadly “on behalf of all others similarly situated.” It was brought by Girard Gibbs, a San Francisco-based law firm that has battled with Apple numerous times in the past, including filing a class-action suit centered on the iPod’s “diminishing battery capacity.” (Apple appears to have settled that one.)

We’ve reached out to Apple for comment.

Interestingly, AppleInsider appears to have provided the fodder for this new lawsuit, or some of it at least. Last month, the outlet reported findings of its own separate investigation into the problem after hearing enough anecdotes to support a deep dive. It says that after collecting service data for the first year of release for the 2014, 2015, and 2016 MacBook Pros, it concluded that —  excluding Touch Bar failures — the 2016 MacBook Pro keyboard has been failing its users twice as often in the first year of use as the 2014 or 2015 MacBook Pro models.

AppleInsider says it collected its data from “assorted Apple Genius Bars in the U.S.” that it has worked with for several years, as well as  Apple-authorized third-party repair shops.

The investigation clearly resonated with MacBook owners, because soon after, more than 17,000 people signed a Change.org petition demanding that Apple recall all MacBooks with butterfly switch keyboards.

That petition — which cites among others the highly regarded writer and UI designer John Gruber, who has called the keyboard “one of the biggest design screwups in Apple history” —  continues to gain steam, fueled possibly by news of the lawsuit. As of this writing, roughly 18,000 people have provided their signature.

Digital services have frequently been in collision — if not out-and-out conflict — with the rule of law. But what happens when technologies such as deep learning software and self-executing code are in the driving seat of legal decisions?

How can we be sure next-gen ‘legal tech’ systems are not unfairly biased against certain groups or individuals? And what skills will lawyers need to develop to be able to properly assess the quality of the justice flowing from data-driven decisions?

While entrepreneurs have been eyeing traditional legal processes for some years now, with a cost-cutting gleam in their eye and the word ‘streamline‘ on their lips, this early phase of legal innovation pales in significance beside the transformative potential of AI technologies that are already pushing their algorithmic fingers into legal processes — and perhaps shifting the line of the law itself in the process.

But how can legal protections be safeguarded if decisions are automated by algorithmic models trained on discrete data-sets — or flowing from policies administered by being embedded on a blockchain?

These are the sorts of questions that lawyer and philosopher Mireille Hildebrandt, a professor at the research group for Law, Science, Technology and Society at Vrije Universiteit Brussels in Belgium, will be engaging with during a five-year project to investigate the implications of what she terms ‘computational law’.

Last month the European Research Council awarded Hildebrandt a grant of €2.5 million to conduct foundational research with a dual technology focus: Artificial legal intelligence and legal applications of blockchain.

Discussing her research plan with TechCrunch, she describes the project as both very abstract and very practical, with a staff that will include both lawyers and computer scientists. She says her intention is to come up with a new legal hermeneutics — so, basically, a framework for lawyers to approach computational law architectures intelligently; to understand limitations and implications, and be able to ask the right questions to assess technologies that are increasingly being put to work assessing us.

“The idea is that the lawyers get together with the computer scientists to understand what they’re up against,” she explains. “I want to have that conversation… I want lawyers who are preferably analytically very sharp and philosophically interested to get together with the computer scientists and to really understand each other’s language.

“We’re not going to develop a common language. That’s not going to work, I’m convinced. But they must be able to understand what the meaning of a term is in the other discipline, and to learn to play around, and to say okay, to see the complexity in both fields, to shy away from trying to make it all very simple.

“And after seeing the complexity to then be able to explain it in a way that the people that really matter — that is us citizens — can make decisions both at a political level and in everyday life.”

Hildebrandt says she included both AI and blockchain technologies in the project’s remit as the two offer “two very different types of computational law”.

There is also of course the chance that the two will be applied in combination — creating “an entirely new set of risks and opportunities” in a legal tech setting.

Blockchain “freezes the future”, argues Hildebrandt, admitting of the two it’s the technology she’s more skeptical of in this context. “Once you’ve put it on a blockchain it’s very difficult to change your mind, and if these rules become self-reinforcing it would be a very costly affair both in terms of money but also in terms of effort, time, confusion and uncertainty if you would like to change that.

“You can do a fork but not, I think, when governments are involved. They can’t just fork.”

That said, she posits that blockchain could at some point in the future be deemed an attractive alternative mechanism for states and companies to settle on a less complex system to determine obligations under global tax law, for example. (Assuming any such accord could indeed be reached.)

Given how complex legal compliance can already be for Internet platforms operating across borders and intersecting with different jurisdictions and political expectations there may come a point when a new system for applying rules is deemed necessary — and putting policies on a blockchain could be one way to respond to all the chaotic overlap.

Though Hildebrandt is cautious about the idea of blockchain-based systems for legal compliance.

It’s the other area of focus for the project — AI legal intelligence — where she clearly sees major potential, though also of course risks too. “AI legal intelligence means you use machine learning to do argumentation mining — so you do natural language processing on a lot of legal texts and you try to detect lines of argumentation,” she explains, citing the example of needing to judge whether a specific person is a contractor or an employee.

“That has huge consequences in the US and in Canada, both for the employer… and for the employee and if they get it wrong the tax office may just walk in and give them an enormous fine plus claw back a lot of money which they may not have.”

As a consequence of confused case law in the area, academics at the University of Toronto developed an AI to try to help — by mining lots of related legal texts to generate a set of features within a specific situation that could be used to check whether a person is an employee or not.

“They’re basically looking for a mathematical function that connected input data — so lots of legal texts — with output data, in this case whether you are either an employee or a contractor. And if that mathematical function gets it right in your data set all the time or nearly all the time you call it high accuracy and then we test on new data or data that has been kept apart and you see whether it continues to be very accurate.”

Given AI’s reliance on data-sets to derive algorithmic models that are used to make automated judgement calls, lawyers are going to need to understand how to approach and interrogate these technology structures to determine whether an AI is legally sound or not.

High accuracy that’s not generated off of a biased data-set cannot just be a ‘nice to have’ if your AI is involved in making legal judgment calls on people.

“The technologies that are going to be used, or the legal tech that is now being invested in, will require lawyers to interpret the end results — so instead of saying ‘oh wow this has 98% accuracy and it outperforms the best lawyers!’ they should say ‘ah, ok, can you please show me the set of performance metrics that you tested on. Ah thank you, so why did you put these four into the drawer because they have low accuracy?… Can you show me your data-set? What happened in the hypothesis space? Why did you filter those arguments out?’

“This is a conversation that really requires lawyers to become interested, and to have a bit of fun. It’s a very serious business because legal decisions have a lot of impact on people’s lives but the idea is that lawyers should start having fun in interpreting the outcomes of artificial intelligence in law. And they should be able to have a serious conversation about the limitations of self-executing code — so the other part of the project [i.e. legal applications of blockchain tech].

“If somebody says ‘immutability’ they should be able to say that means that if after you have put everything in the blockchain you suddenly discover a mistake that mistake is automated and it will cost you an incredible amount of money and effort to get it repaired… Or ‘trustless’ — so you’re saying we should not trust the institutions but we should trust software that we don’t understand, we should trust all sorts of middlemen, i.e. the miners in permissionless, or the other types of middlemen who are in other types of distributed ledgers… ”

“I want lawyers to have ammunition there, to have solid arguments… to actually understand what bias means in machine learning,” she continues, pointing by way of an example to research that’s being done by the AI Now Institute in New York to investigate disparate impacts and treatments related to AI systems.

“That’s one specific problem but I think there are many more problems,” she adds of algorithmic discrimination. “So the purpose of this project is to really get together, to get to understand this.

“I think it’s extremely important for lawyers, not to become computer scientists or statisticians but to really get their finger behind what’s happening and then to be able to share that, to really contribute to legal method — which is text oriented. I’m all for text but we have to, sort of, make up our minds when we can afford to use non-text regulation. I would actually say that that’s not law.

“So how should be the balance between something that we can really understand, that is text, and these other methods that lawyers are not trained to understand… And also citizens do not understand.”

Hildebrandt does see opportunities for AI legal intelligence argument mining to be “used for the good” — saying, for example, AI could be applied to assess the calibre of the decisions made by a particular court.

Though she also cautions that huge thought would need to go into the design of any such systems.

“The stupid thing would be to just give the algorithm a lot of data and then train it and then say ‘hey yes that’s not fair, wow that’s not allowed’. But you could also really think deeply what sort of vectors you have to look at, how you have to label them. And then you may find out that — for instance — the court sentences much more strictly because the police is not bringing the simple cases to court but it’s a very good police and they talk with people, so if people have not done something really terrible they try to solve that problem in another way, not by using the law. And then this particular court gets only very heavy cases and therefore gives far more heavy sentences than other courts that get from their police or public prosecutor all life cases.

“To see that you should not only look at legal texts of course. You have to look also at data from the police. And if you don’t do that then you can have very high accuracy and a total nonsensical outcome that doesn’t tell you anything you didn’t already know. And if you do it another way you can sort of confront people with their own prejudices and make it interesting — challenge certain things. But in a way that doesn’t take too much for granted. And my idea would be that the only way this is going to work is to get a lot of different people together at the design stage of the system — so when you are deciding which data you’re going to train on, when you are developing what machine learners call your ‘hypothesis space’, so the type of modeling you’re going to try and do. And then of course you should test five, six, seven performance metrics.

“And this is also something that people should talk about — not just the data scientists but, for instance, lawyers but also the citizens who are going to be affected by what we do in law. And I’m absolutely convinced that if you do that in a smart way that you get much more robust applications. But then the incentive structure to do it that way is maybe not obvious. Because I think legal tech is going to be used to reduce costs.”

She says one of the key concepts of the research project is legal protection by design — opening up other interesting (and not a little alarming) questions such as what happens to the presumption of innocence in a world of AI-fueled ‘pre-crime’ detectors?

“How can you design these systems in such a way that they offer legal protection from the first minute they come to the market — and not as an add-on or a plug in. And that’s not just about data protection but also about non-discrimination of course and certain consumer rights,” she says.

“I always think that the presumption of innocence has to be connected with legal protection by design. So this is more on the side of the police and the intelligence services — how can you help the intelligence services and the police to buy or develop ICT that has certain constrains which makes it compliant with the presumption of innocence which is not easy at all because we probably have to reconfigure what is the presumption of innocence.”

And while the research is part abstract and solidly foundational, Hildebrandt points out that the technologies being examined — AI and blockchain — are already being applied in legal contexts, albeit in “a state of experimentation”.

And, well, this is one tech-fueled future that really must not be unevenly distributed. The risks are stark.   

“Both the EU and national governments have taken a liking to experimentation… and where experimentation stops and systems are really already implemented and impacting decisions about your and my life is not always so easy to see,” she adds.

Her other hope is that the interpretation methodology developed through the project will help lawyers and law firms to navigate the legal tech that’s coming at them as a sales pitch.

“There’s going to be, obviously, a lot of crap on the market,” she says. “That’s inevitable, this is going to be a competitive market for legal tech and there’s going to be good stuff, bad stuff, and it will not be easy to decide what’s good stuff and bad stuff — so I do believe that by taking this foundational perspective it will be more easy to know where you have to look if you want to make that judgement… It’s about a mindset and about an informed mindset on how these things matter.

“I’m all in favor of agile and lean computing. Don’t do things that make no sense… So I hope this will contribute to a competitive advantage for those who can skip methodologies that are basically nonsensical.”

ConsenSys Ventures, the venture arm of the ConsenSys Ethereum blockchain powerhouse, has invested in a new round of six companies and is today formally launching its Accelerator, “Tachyon” (a Tachyon is a particle which moves faster than the speed of light).

The five companies were invested in with a “combination of equity and tokens together. It was a unique termsheet created by Consensys Ventures,” according to Kavita Gupta (pictured), the founding managing partner of ConsenSys and the lead on their Blockchain focused fund which is investing in an Ethereum powered “Web 3.0” startups.

She went on to elaborate to me on the thinking behind these investments: “It’s very important for us to invest into companies that both embody the ethos of decentralization while also pushing the Ethereum ecosystem forward. In this crop of investments, you can see projects that represent the globalization of financial systems on blockchain (Cryptomarket), create innovative solutions to bring institutions into the space (Virtuoso) bring power and monetization back to artists (Dada), democratize the ability to participate in the proof of stake (Rocket Pool) and show the bright minds of traditional tech who are now choosing to bring Ethereum mobile (Vault).”

ConsenSys’ Accelerator is also coming out of the gate too, as, Gupta says, to “connect the traditional Web 2.0 world with the technically complex Ethereum ecosystem.”

The 8week accelerator program will see a cohort of 8-10 projects work towards building an MVP and work towards raising a successful round of pre-seed/seed funding.

The program will bring on advisors from traditional 21st-century technology unicorns like Google/Uber/Fb/Salesforce etc. and combine their expertise with the talent and Ethereum know-how at ConsenSys. The program will feature hands-on education, mentorship, open office hours and will feature demo days both in the US and Europe.

Here’s quick overview of next 5 companies Consensys Ventures has invested in, in their own words:

Virtuoso
“Founded by the team behind TrueEx – the leading electronic interest rate swap platform – Virtuoso is building a cryptocurrency exchange that will support ether futures, creating a more robust Ethereum trading market for institutional investors.”

Ink
“Ink is a decentralized reputation and payment protocol looking to bring transferrable reputation to P2P marketplaces founded by Gee Chuang. It is live on the Listia platform and plans to expand to other P2P marketplaces where lack of reputation is a major driver for centralization.”

Vault
“Vault is a secure wallet and dApp discovery platform for your mobile device, founded by ex-Facebook employee John Egan and his team. The team launched Vault after looking into wallet options, and feeling frustrated from a usability standpoint, specifically as they explored mobile options. Vault is focused on building out two primary features in the short term: 1) the best and most user friendly mobile wallet and 2) a dApp browser.”

Rocket Pool
Rocket Pool is a next-generation Ethereum Proof of Stake pool for Casper, currently in Alpha and based in Australia. Started by David Rugendyke, Rocket Pool allows individuals and businesses to stake as little as .1 ether and avoid extensive withdrawal times and gain exposure to Ethereum’s move to Proof-of-Stake.

CryptoMKT
“CryptoMKT is a Latin American based Ethereum exchange and leader in Chile and Argentina, and are expanding to be a leader in other South American markets. Founded by Rafael Meruane and Martin Jofre, the team has bootstrapped to-date and have traded over $30M in ETH over the last year.”

DADA
“DADA is a social network for digital art where people interact with digital drawings founded by Beatriz Ramos. Currently, DADA offers a collection of 100 limited edition digital drawings (all made within the DADA platform via the provided drawing tools) which is available for purchase with Ether via the MetaMask wallet. Each digital artwork available for purchase is tokenized, with each token representing ownership over a copy of the drawing. DADA’s goal is to allow artists to have full control over their work and earn a universal basic income from their work.”

Munchery, the on-demand food delivery startup, has shut down its operations in Los Angeles, New York and Seattle, the company announced on its blog today. That means the teams from those cities are also being let go. In total, 257 people (about 30 percent of workforce) were let go, according to a Munchery spokesperson.

“We recognize the impact this will have on the members of our team in those regions,” Munchery CEO James Beriker wrote on the company blog. “Our teams in each city have built their businesses from scratch and worked tirelessly to serve our customers and their communities. I am grateful for their unwavering commitment to Munchery’s mission and success. I truly wish that the outcome would have been different.”

With LA, New York and Seattle off the table, Munchery says it’s going to focus more on its business in San Francisco, its first and largest market. This shift in operations will also enable Munchery to “achieve profitability on the near term, and build a long-term, sustainable business.”

The last couple of years for Munchery has not gone very well, between scathing reports of the company wasting an average of 16 percent of the food it makes, laying off 30 employees and burning through most of the money it raised.

During that time, Munchery tried a number of different strategies. Munchery, which began as a ready-to-heat meal delivery service, in 2015 started delivering meal recipes and ingredients for people who want to cook. Then, Munchery launched an $8.95 a month subscription plan for people who order several times a month. In late 2016, Munchery opened up a shop inside a San Francisco BART station to try to bring in new business.

But it’s not just Munchery that has struggled. The on-demand food delivery business is tough in general. Over the last couple of years, a number of companies have shuttered due to the now well-known fact that the on-demand business is tough when it comes to margins. The most recent casualty was Sprig, which shut down last May, after raising $56.7 million in funding. Other casualties include Maple, Spoonrocket and India’s Ola.

Munchery has raised more than $120 million in capital from Menlo Ventures, Sherpa Capital and others. In March, the company was reportedly seeking $15 million in funding to help keep its head above water.

This is likely to be a bit of a black eye from Amazon, as the company looks to bolster its presence in the home security space. The Information reports that, until earlier this year, a security loophole allowed users to continue to view a feed from Ring’s doorbell camera even after its password was changed.

Ring, which was purchased by Amazon for $1 billion earlier this year, acknowledged that it patched the issue in January. The update arrived after a Miami man told the company that his ex had continued to watch the feed, after he had updated the password. Even so, the update doesn’t occur immediately, CEO Jamie Siminoff acknowledged, adding that kicking users off immediately would slow down the app, according to the site.

Ring was a centerpiece of a number of recent acquisitions for Amazon, allowing the company to expend delivery directly into customers’ homes and serving as a foundation of new home security offerings. While the outward-facing nature of the doorbell camera is less intrusive than those products designed to sit directly inside the home, this issue will no doubt lead many users to think twice before introducing a cloud-connected device in their home.

We’ve reached out to Amazon/Ring for a comment on the issue.

Android co-creator Andy Rubin isn’t so sure whether there will be one software platform to rule all robots. The former Google exec and Playground Global CEO talked in length about the role of platforms for automation at TechCrunch’s TC Sessions: Robotics event at UC Berkeley.

“The business model of platformization is something that is near and dear to my heart,” Rubin said. “For robotics and automatization, the idea of there being one cohesive platform that everyone ends up adopting? I’m not sure.”

Rubin did speak at length about the eventual need for companies to create systems for sharing machine learning data so that these machines will be able to communicate with each other and communicate their learnings so that obstacles only have to be overcome once across different devices.

You can watch the entire talk with Rubin below, which also includes a demonstration of the latest iteration of Cassie, a bipedal robot from Agility Robotics.

HubSpot is expanding beyond sales and marketing with the official launch of its Service Hub for managing customer service.

The product was first announced last fall, but now it’s moved out of beta testing.

HubSpot President and COO JD Sherman said this was a logical next step for the company. He argued that the Internet has “democratized” the ability of businesses to attract customers by creating their own content (using tools like HubSpot’s, natch), and while “that opportunity still exists, frankly, it’s getting harder due to the sheer volume of what’s going on.”

“It makes sense to take care of your customer,” Sherman said — both to keep them loyal and also to turn them into an advocate who might help you attract new customers.

Service Hub General Manager Michael Redbord and Go To Market Leader David Barron gave me a quick tour of the Service Hub. It includes an universal inbox for all your customer communications, a bot-builder to automate some of those customer interactions, tools for building a company knowledge base (which can then be fed into the bot-builder, which Redbord described as a more “customer-centric” way to present your content), tools for creating surveys and a dashboard to track how your service team is doing.

Redbord said he previously worked on HubSpot’s own service and support team, so every feature in ServiceHub has “a one-to-one relationship” with an issue that HubSpot has faced, or that he personally has faced, while trying to support customers.

Barron added that ServiceHub benefits from being integrated with HubSpot’s existing products, allowing businesses to track their interactions with a customer across sales, marketing and support.

“We’re a platform company,” he said. “When any of these conversations happens, whether it’s a chat with a human or a chat with a bot, that’s all logged on [a single record] in HubSpot, so there’s no data leakage between different teams.”

Note: This story has been updated to indicate that the deal is worth tens of millions not hundreds of millions and includes comment from other manufacturers.

It seems like all is fair in love and scooter wars.

In the battle royal to become the last dock-less scooter startup standing (and un-besmirched by poop), Bird has inked what it is characterizing as exclusive deals with Ninebot (the parent company of Segway) and Xiaomi (yes, that Xiaomi), for rights to their supply of scooters for ride-sharing in the U.S.

Ninebot and Xiaomi are the current champions in the scooter manufacturing market, and locking in their supply may cut off a big source of hardware for competitors Spin and LimeBike, both of which used Ninebot and Xiaomi for scooters.

“That’s news to us, we have a contract with both,” wrote an executive at a leading scooter company, when asked about the deal and its implications for the scooter business.

A person familiar with the Bird transaction placed the deal in the tens of millions of dollars and declined to speculate on what the agreement with the two supplier could mean for its competitors.

Since its launch in Santa Monica, Calif. in September 2017, Bird has become synonymous with both the perils and promise that scooters hold for last mile mobility.

While they undoubtedly make traveling across campuses or in relatively small communities much more convenient than car services or shuttles, they’re also clogging sidewalks, parks, alleys, and even beaches, while creating untold numbers of minor visits to emergency rooms in the cities they’ve expanded into.

And Bird has expanded into a lot of cities. The company is currently operating in San Diego, Los Angeles, San Francisco, Austin, Washington, DC, Nashville and Atlanta.

Meanwhile, other companies in the market believe that this agreement is much less than it seems. They note that the exclusivity agreement is for a particular model of scooters, but each scooter company has its own design and those contracts are still valid. Indeed, emails from suppliers seen by TechCrunch confirm that one of the suppliers mentioned in the Bird release still intends to fulfill planned orders from a direct Bird competitor.

Sources close to Bird says that the Lime and Spin contracts should be invalid, given the contracts that the Santa Monica, Calif. had signed initially signed (and then revised) with its large Chinese suppliers.

San Francisco’s administrators are fighting back against the scooter companies storming the sidewalks by instituting a new permitting process. The city plans to limit the number of scooters in the city to 1,250 and will require companies to register with the MTA.

Today brings historic firsts for both SpaceX and Bangladesh: the former is sending up the final, highly updated revision of its Falcon 9 rocket for the first time, and the latter is launching its first satellite. It’s a preview of the democratized space economy to come this century.

Update: Success! The Falcon 9 first stage, after delivering the second stage to the border of space, has successfully landed on the drone ship Of Course I Still Love You, and Bangabandhu has been delivered to its target orbit.

You can watch the launch below:

Although Bangabandhu-1 is definitely important, especially to the nation launching it, it is not necessarily in itself a highly notable satellite. It’s to be a geostationary communications hub that serves the whole country and region with standard C-band and Ku-band connectivity for all kinds of purposes.

Currently the country spends some $14 million per year renting satellite time from other countries, something they determined to stop doing as a matter of national pride and independence.

“A sovereign country, in a pursuit of sustainable development, needs its own satellite in order to reduce its dependency on other nations,” reads the project description at the country’s Telecommunications Regulation Commission, which has been pursuing the idea for nearly a decade.

It contracted with Thales Alenia Space to produce and test the satellite, which cost about $250 million and is expected to last at least 15 years. In addition to letting the country avoid paying satellite rent, it could generate revenue by selling its services to private companies and nearby nations.

Bangabandhu-1 in a Thales test chamber.

“This satellite, which carries the symbolic name of the father of the nation, Bangabandhu, is a major step forward for telecommunications in Bangladesh, and a fantastic driver of economic development and heightened recognition across Asia,” said the company’s CEO, Jean-Loïc Galle, in a recent blog post about the project.

Bangabandhu-1 will be launching atop a SpaceX Falcon 9 rocket, but this one is different from all the others that have flown in the past. Designed with crewed missions in mind, it could be thought of as the production version of the rocket, endowed with all the refinements of years of real-world tests.

Most often referred to as Block 5, this is (supposedly) the final revision of the Falcon 9 hardware, safer and more reusable than previous versions. The goal is for a Block 5 first stage to launch a hundred times before being retired, far more than the handful of times existing Falcon 9s have been reused.

There are lots of improvements over the previous rockets, though many are small or highly technical in nature. The most important, however, are easy to enumerate.

The engines themselves have been improved and strengthened to allow not only greater thrust (reportedly about a 7-8 percent improvement) but improved control and efficiency, especially during landing. They also have a new dedicated heat shield for descent. They’re rated to fly 10 times without being substantially refurbished, but are also bolted on rather than welded, further reducing turnaround time.

The legs on which the rocket lands are also fully retractable, meaning they don’t have to be removed before transport. If you want to launch the same rocket within days, every minute counts.

Instead of white paint, the first stage will have a thermal coating (also white) that helps keep it relatively cool during descent.

To further reduce heat damage, the rocket’s “grid fins,” the waffle-iron-like flaps that pop out to control its descent, are now made of a single piece of titanium. They won’t catch fire or melt during reentry like the previous aluminum ones sometimes did, and as such are now permanently attached features of the rocket.

(SpaceX founder Elon Musk is particularly proud of these fins, which flew on the Falcon Heavy side boosters; in the briefing afterwards, he said: “I’m actually glad we got the side boosters back, because they had the titanium fins. If I had to pick something to get back, it’d be those.”)

Lastly (for our purposes anyway) the fuel tank has been reinforced out of concerns some had about the loading of supercooled fuel while the payload — soon to be humans, if all goes well — is attached to the rocket. This system failed before, causing a catastrophic explosion in 2016, but the fault has been addressed and the reinforcement should help further mitigate risk. (The emergency abort rockets should also keep astronauts safe should something go wrong during launch.)

The changes, though they contribute directly to reuse and cost reductions, are also aimed at satisfying the requirements of NASA’s commercial crew missions. SpaceX is in competition to provide both launch and crew capsule services for missions to the ISS, scheduled for as early as late 2018. The company needs to launch the Block 5 version of Falcon 9 (not necessarily the same exact rocket) at least 7 times before any astronauts can climb aboard.

After 26 years, Boston Dynamics is finally getting ready to start selling some robots. Founder Marc Raibert says that the company’s dog-like SpotMini robot is in pre-production and preparing for commercial availability in 2019. The announcement came onstage at TechCrunch’s TC Sessions: Robotics event today at UC Berkeley.

“The SpotMini robot is one that was motivated by thinking about what could go in an office — in a space more accessible for business applications — and then, the home eventually,” Raibert said onstage.

Boston Dynamics’ SpotMini was introduced late last year and took the design of the company’s “bigger brother” quadruped Spot. While the company has often showcased advanced demos of its emerging projects, SpotMini has seemed uniquely productized from the start.

On its website, Boston Dynamics highlights that SpotMini is the “quietest robot [they] have built.” The device weighs around 66 pounds and can operate for about 90 minutes on a charge.

The company says it has plans with contract manufacturers to build the first 100 SpotMinis later this year for commercial purposes, with them starting to scale production with the goal of selling SpotMini in 2019. They’re not ready to talk about a price tag yet, but they detailed that the latest SpotMini prototype cost 10 times less to build than the iteration before it.

Just yesterday, Boston Dynamics posted a video of SpotMini in autonomous mode navigating with the curiosity of a flesh-and-blood animal.

The company, perhaps best known for gravely frightening conspiracy theorists and AI doomsdayers with advanced robotics demos, has had quite the interesting history.

It was founded in 1992 after being spun out of MIT. After a stint inside Alphabet Corp., the company was purchased by SoftBank last year. SoftBank has staked significant investments in the robotics space through its Vision Fund, and, in 2015, the company began selling Pepper, a humanoid robot far less sophisticated than what Boston Dynamics has been working on.

You can watch the entire presentation below, which includes a demonstration of the latest iteration of the SpotMini.

The visual data sets of images and videos amassed by the most powerful tech companies have been a competitive advantage, a moat that keeps the advances of machine learning out of reach from many. This advantage will be overturned by the advent of synthetic data.

The world’s most valuable technology companies, such as Google, Facebook, Amazon and Baidu, among others, are applying computer vision and artificial intelligence to train their computers. They harvest immense visual data sets of images, videos and other visual data from their consumers.

These data sets have been a competitive advantage for major tech companies, keeping out of reach from many the advances of machine learning and the processes that allow computers and algorithms to learn faster.

Now, this advantage is being disrupted by the ability for anyone to create and leverage synthetic data to train computers across many use cases, including retail, robotics, autonomous vehicles, commerce and much more.

Synthetic data is computer-generated data that mimics real data; in other words, data that is created by a computer, not a human. Software algorithms can be designed to create realistic simulated, or “synthetic,” data.

This synthetic data then assists in teaching a computer how to react to certain situations or criteria, replacing real-world-captured training data. One of the most important aspects of real or synthetic data is to have accurate labels so computers can translate visual data to have meaning.

Since 2012, we at LDV Capital have been investing in deep technical teams that leverage computer vision, machine learning and artificial intelligence to analyze visual data across any business sector, such as healthcare, robotics, logistics, mapping, transportation, manufacturing and much more. Many startups we encounter have the “cold start” problem of not having enough quality labelled data to train their computer algorithms. A system cannot draw any inferences for users or items about which it hasn’t yet gathered sufficient information.

Startups can gather their own contextually relevant data or partner with others to gather relevant data, such as retailers for data of human shopping behaviors or hospitals for medical data. Many early-stage startups are solving their cold start problem by creating data simulators to generate contextually relevant data with quality labels in order to train their algorithms.

Big tech companies do not have the same challenge gathering data, and they exponentially expand their initiatives to gather more unique and contextually relevant data.

Cornell Tech professor Serge Belongie, who has been doing research in computer vision for more than 25 years, says,

In the past, our field of computer vision cast a wary eye on the use of synthetic data, since it was too fake in appearance. Despite the obvious benefits of getting perfect ground truth annotations for free, our worry was that we’d train a system that worked great in simulation but would fail miserably in the wild.  Now the game has changed: the simulation-to-reality gap is rapidly disappearing. At the very minimum, we can pre-train very deep convolutional neural networks on near-photorealistic imagery and fine tune it on carefully selected real imagery.

AiFi is an early-stage startup building a computer vision and artificial intelligence platform to deliver a more efficient checkout-free solution to both mom-and-pop convenience stores and major retailers. They are building a checkout-free store solution similar to Amazon Go.

Amazon.com Inc. employees shop at the Amazon Go store in Seattle. ©Amazon Go; Photographer: Mike Kane/Bloomberg via Getty Images

As a startup, AiFi had the typical cold start challenge with a lack of visual data from real-world situations to start training their computers, versus Amazon, which likely gathered real-life data to train its algorithms while Amazon Go was in stealth mode.

Avatars help train AiFi shopping algorithms. ©AiFI

AiFi’s solution of creating synthetic data has also become one of their defensible and differentiated technology advantages. Through AiFi’s system, shoppers will be able to come into a retail store and pick up items without having to use cash, a card or scan barcodes.

These smart systems will need to continuously track hundreds or thousands of shoppers in a store and recognize or “re-identify” them throughout a complete shopping session.

AiFi store simulation with synthetic data. ©AiFi

Ying Zheng, co-founder and chief science officer at AiFi, previously worked at Apple and Google. She says,

The world is vast, and can hardly be described by a small sample of real images and labels. Not to mention that acquiring high-quality labels is both time-consuming and expensive, and sometimes infeasible. With synthetic data, we can fully capture a small but relevant aspect of the world in perfect detail. In our case, we create large-scale store simulations and render high-quality images with pixel-perfect labels, and use them to successfully train our deep learning models. This enables AiFi to create superior checkout-free solutions at massive scale.

Robotics is another sector leveraging synthetic data to train robots for various activities in factories, warehouses and across society.

Josh Tobin is a research scientist at OpenAI, a nonprofit artificial intelligence research company that aims to promote and develop friendly AI in such a way as to benefit humanity as a whole. Tobin is part of a team working on building robots that learn. They have trained entirely with simulated data and deployed on a physical robot, which, amazingly, can now learn a new task after seeing an action done once.

They developed and deployed a new algorithm called one-shot imitation learning, allowing a human to communicate how to do a new task by performing it in virtual reality. Given a single demonstration, the robot is able to solve the same task from an arbitrary starting point and then continue the task.

©Open AI

Their goal was to learn behaviors in simulation and then transfer these learnings to the real world. The hypothesis was to see if a robot can do precise things just as well from simulated data. They started with 100 percent simulated data and thought that it would not work as well as using real data to train computers. However, the simulated data for training robotic tasks worked much better than they expected.

Tobin says,

Creating an accurate synthetic data simulator is really hard. There is a factor of 3-10x in accuracy between a well-trained model on synthetic data versus real-world data. There is still a gap. For a lot of tasks the performance works well, but for extreme precision it will not fly — yet.

Many large technology companies, auto manufacturers and startups are racing toward delivering the autonomous vehicle revolution. Developers have realized there aren’t enough hours in a day to gather enough real data of driven miles needed to teach cars how to drive themselves.

One solution that some are using is synthetic data from video games such as Grand Theft Auto; unfortunately, some say that the game’s parent company Rockstar is not happy about driverless cars learning from their game. 

A street in GTA V (left) and its reconstruction through capture data (right). ©Intel Labs,Technische Universität Darmstadt

May Mobility is a startup building a self-driving microtransit service. Their CEO and founder, Edwin Olson, says,

One of our uses of synthetic data is in evaluating the performance and safety of our systems. However, we don’t believe that any reasonable amount of testing (real or simulated) is sufficient to demonstrate the safety of an autonomous vehicle. Functional safety plays an important role.

The flexibility and versatility of simulation make it especially valuable and much safer to train and test autonomous vehicles in these highly variable conditions. Simulated data can also be more easily labeled as it is created by computers, therefore saving a lot of time.

Jan Erik Solem is the CEO and co-founder of Mapillary*, helping create better maps for smarter cities, geospatial services and automotive. According to Solem,

Having a database and an understanding of what places look like all over the world will be an increasingly important component for simulation engines. As the accuracy of the trained algorithms improves, the level of detail and diversity of the data used to power the simulation matters more and more.

Neuromation is building a distributed synthetic data platform for deep learning applications. Their CEO, Yashar Behzadi says,

To date, the major platform companies have leveraged data moats to maintain their competitive advantage. Synthetic data is a major disruptor, as it significantly reduces the cost and speed of development, allowing small, agile teams to compete and win.

The challenge and opportunity for startups competing against incumbents with inherent data advantage is to leverage the best visual data with correct labels to train computers accurately for diverse use cases. Simulating data will level the playing field between large technology companies and startups. Over time, large companies will probably also create synthetic data to augment their real data, and one day this may tilt the playing field again. Many speakers at the annual LDV Vision Summit in May in NYC will enlighten us as to how they are using simulated data to train algorithms to solve business problems and help computers get closer to general artificial intelligence.

*Mapillary is an LDV Capital portfolio company.

Google Clips’ AI-powered “smart camera” just got even smarter, Google announced today, revealing improved functionality around Clips’ ability to automatically capture specific moments — like hugs and kisses. Or jumps and dance moves. You know, in case you want to document all your special, private moments in a totally non-creepy way.

I kid, I kid!

Well, not entirely. Let me explain.

Look, Google Clips comes across to me as more of a proof-of-concept device that showcases the power of artificial intelligence as applied to the world of photography rather than a breakthrough consumer device.

I’m the target market for this camera — a parent and a pet owner (and look how cute she is) — but I don’t at all have a desire for a smart camera designed to capture those tough-to-photograph moments, even though neither my kid nor my pet will sit still for pictures.

I’ve tried to articulate this feeling, and I find it’s hard to say why I don’t want this thing, exactly. It’s not because the photos are automatically uploaded to the cloud or made public — they are not. They are saved to the camera’s 16 GB of onboard storage and can be reviewed later with your phone, where you can then choose to keep them, share them or delete them. And it’s not even entirely because of the price point — though, arguably, even with the recent $50 discount it’s quite the expensive toy at $199.

Maybe it’s just the camera’s premise.

That in order for us to fully enjoy a moment, we have to capture it. And because some moments are so difficult to capture, we spend too much time with phone-in-hand, instead of actually living our lives — like playing with our kids or throwing the ball for the dog, for example. And that the only solution to this problem is more technology. Not just putting the damn phone down.

What also irks me is the broader idea behind Clips that all our precious moments have to be photographed or saved as videos. They do not. Some are meant to be ephemeral. Some are meant to be memories. In aggregate, our hearts and minds tally up all these little life moments — a hug, a kiss, a smile — and then turn them into feelings. Bonds. Love.  It’s okay to miss capturing every single one.

I’m telling you, it’s okay.

At the end of the day, there are only a few times I would have even considered using this product — when baby was taking her first steps, and I was worried it would happen while my phone was away. Or maybe some big event, like a birthday party, where I wanted candids but had too much going on to take photos. But even in these moments, I’d rather prop my phone up and turn on a “Google Clips” camera mode, rather than shell out hundreds for a dedicated device.

Just saying.

You may feel differently. That’s cool. To each their own.

Anyway, what I think is most interesting about Clips is the actual technology. That it can view things captured through a camera lens and determine the interesting bits — and that it’s already getting better at this, only months after its release. That we’re teaching AI to understand what’s actually interesting to us humans, with our subjective opinions. That sort of technology has all kinds of practical applications beyond a physical camera that takes spy shots of Fido.

The improved functionality is rolling out to Clips with the May update, and will soon be followed by support for family pairing, which will let multiple family members connect the camera to their device to view content.

Here’s an intro to Clips, if you missed it the first time. (See below)

Note that it’s currently on sale for $199. Yeah, already. Hmmm. 

With so much controversy swirling around the advertising-driven business models typified by Facebook and Google, and the increasing rigors of regulations like GDPR, it’s no wonder the blockchain world is starting to whet its appetite at the prospect of paying users for attention with crypto assets.

Now a company involved in the production of Hollywood blockbusters featuring the likes of James Franco, Selena Gomez, Alec Baldwin, Heidi Klum and Al Pacino is backing a new startup to reward viewers in this manner.

Hollywood producer Andrea Iervolino (best known for backing the James Franco film “In Dubious Battle” based on the novel by the Nobel Prize-winning author John Steinbeck) has decided to enter the fray by launching a new blockchain platform called TaTaTu. The startup’s aim is to bring a social, crypto economy to the entertainment industry.

Iervolino says the platform allows users to get rewarded for the content they watch and share with others through the use of crypto tokens. Of course, whether it can actually pull that off remains to be seen. Many other startups are trying to play in this space. But where Iervolino might just have an edge is in his Hollywood connections.

The idea is that the TaTaTu token can also be used by advertisers to run their ads on the platform. Organizations will also be able to earn tokens by uploading content to the platform. The more content an organization brings to the platform, the more revenue they earn. TaTaTu aims to show ads to viewers and will even share advertising revenues with them in return for their attention.

But it doesn’t stop there. Users are supposed to invite their friends via their social media to join TaTaTu, and then watch and create videos that can be shared with friends, chat with other members and share the content they like. TaTaTu will give its users the possibility to be rewarded for their social entertainment activity. TaTaTu plans not only movies and videos, but also music, sports and games. So this is quite a grand vision which, frankly, will be tricky to pull off outside of perhaps just sticking to one vertical like movies. This is like trying to do YouTube and Netflix at the same time, on a blockchain. Good luck with that.

But Iervolino is putting his money where his mouth is. The AMBI Media Group, a consortium of vertically integrated film development, production, finance and distribution companies (which counts End of Watch, Apocalypto and The Merchant of Venice among its title) and which he co-runs with Monaco-based businesswoman Lady Monika Bacardi, is said to have put in $100 million via a token pre-sale.

Building the platform will be CTO Jonathan Pullinger who started working in the Bitcoin space in late 2012, developing crypto mining software and building mining rigs. Since then he has worked on several blockchain projects, including Ethereum smart contracts (ERC-20 tokens and other solidity based solutions), Hyperledger, Fabric, the Waves Platform and lightning nodes.

Google’s YouTube is the first streaming app that will actually tell users to stop watching. At its Google I/O conference this week, the company introduced a series of new controls for YouTube that will allow users to set limits on their viewing, and then receive reminders telling them to “take a break.” The feature is rolling out now in the latest version of YouTube’s app, along with others that limit YouTube’s ability to send notifications, and soon, one that gives users an overview of their binge behavior so they can make better-informed decisions about their viewing habits.

With “Take a Break,” available from YouTube’s mobile app Settings screen, users can set a reminder to appear every 15, 30, 60, 90 or 180 minutes, at which point the video will pause. You can then choose to dismiss the reminder and keep watching, or close the app.

The setting is optional, and is turned off by default, so it’s not likely to have a large impact on YouTube viewing time at this point.

Also new is a feature that lets you disable notification sounds during a specified time period each day — say, for example, from bedtime until the next morning. When users turn on the setting to disable notifications, it will, by default, disable them from 10 PM to 8 AM local time, but this can be changed.

Combined with this is an option to get a scheduled digest of notifications as an alternative. This setting combines all the daily push notifications into a single combined notification that is sent out only once per day. This is also off by default, but can be turned on in the app’s settings.

And YouTube is preparing to roll out a “time watched profile” that will appear in the Account menu and display your daily average watch time, and how long you’ve watched YouTube videos today, yesterday and over the past week, along with a set of tools to help you manage your viewing habits.

While these changes to YouTube are opt-in, it’s an interesting — and arguably responsible — position to take in terms of helping people manage their sometimes addictive behaviors around technology.

And it’s not the only major change Google is rolling out on the digital well-being front — the company also announced a series of Android features that will help you get a better handle on how often you’re using your phone and apps, and give you tools to limit distractions — like a Do Not Disturb setting, alerts that are silenced when the phone is flipped over and a “Wind Down” mode for nighttime usage that switches on the Do Not Disturb mode and turns the screen to gray-scale.

The digital well-being movement at Google got its start with a 144-page Google Slides presentation from product manager Tristan Harris, who was working on Google’s Inbox app at the time. After a trip to Burning Man, he came back convinced that technology products weren’t always designed with users’ best interests in mind. The memo went viral and found its way to then-CEO Larry Page, who promoted Harris to “design ethicist” and made digital well-being a company focus.

There’s now a Digital Wellbeing website, too, that talks about Google’s broader efforts on this front. On the site, the company touts features in other products that save people time, like Gmail’s high-priority notifications that only alert you to important emails; Google Photos’ automated editing tools; Android Auto’s distracted driving reduction tools; Google Assistant’s ability to turn on your phone’s DND mode or start a “bedtime routine” to dim your lights and quiet your music; Family Link’s tools for reducing kids’ screen time; Google WiFi’s support for “internet breaks;” and more.

Google is not the only company rethinking its role with regard to how much its technology should infiltrate our lives. Facebook, too, recently re-prioritized well-being over time spent on the site reading news, and saw its daily active users decline as a result.

But in Google’s case, some are cynical about the impact of the new tools — unlike Facebook’s changes, which the social network implemented itself, Google’s tools are opt-in. That means it’s up to users to take control over their own technology addictions, whether that’s their phone in general, or YouTube specifically. Google knows that the large majority won’t take the time to configure these settings, so it can pat itself on the back for its prioritization of digital well-being without taking a real hit to its bottom line.

Still, it’s notable that any major tech platform is doing this at all — and it’s at least a step in the right direction in terms of allowing people to reset their relationship with technology.

And in YouTube’s case, the option to “Take a Break” is at the very top of its Settings screen. If anyone ever heads into their settings for any reason, they’ll be sure to see it.

The new features are available in version 13.17 and higher of the YouTube mobile app on both iOS and Android, which is live now.

The changes were announced on May 8 during the I/O keynote, and will take a few days to roll out to all YouTube users. The “time watched profile,” however, will ship in the “coming months,” Google says.