America’s largest companies push for federal online privacy laws to circumvent state regulatory efforts – gpgmail


As California moves ahead with what would be the most restrictive online privacy laws in the nation, the chief executives of some of the nation’s largest companies are taking their case to the nation’s capitol to plead for federal regulation.

Chief executives at Amazon, AT&T, Dell, Ford, IBM, Qualcomm, Walmart, and other leading financial services, manufacturing, and technology companies have issued an open letter to Congressional leadership pleading with them to take action on online privacy, through the pro-industry organization, The Business Roundtable.

“Now is the time for Congress to act and ensure that consumers are not faced with confusion about their rights and protections based on a patchwork of inconsistent state laws. Further, as the regulatory landscape becomes increasingly fragmented and more complex, U.S. innovation and global competitiveness in the digital economy are threatened,” the letter says.

The subtext to this call to action is the California privacy regulations that are set to take effect by the end of this year.

As we noted when the bill was passed last year there are a few key components of the California legislation including the following requirements:

  • Businesses must disclose what information they collect, what business purpose they do so for and any third parties they share that data with.

  • Businesses would be required to comply with official consumer requests to delete that data.

  • Consumers can opt out of their data being sold, and businesses can’t retaliate by changing the price or level of service.

  • Businesses can, however, offer “financial incentives” for being allowed to collect data.

  • California authorities are empowered to fine companies for violations.

There’s a reason why companies would push for federal regulation to supersede any initiatives from the states. It is more of a challenge for companies to adhere to a patchwork of different regulatory regimes at the state level. But it’s also true that companies, following the lead of automakers in California, could just adhere to the most stringent requirements which would clarify any confusion.

Indeed many of these companies are already complying with strict privacy regulations thanks to the passage of the GDPR in Europe.


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Adarga closes £5M Series A funding for its Palantir-like AI platform – gpgmail


AI startup Adarga has closed a £5 million Series A fundraising by Allectus Capital. But this news rather cloaks the fact that it’s been building up a head of steam since it’s founding in 2016, building up – what they say – is a £30 million-plus sales pipeline through strategic collaborations with a number of global industrial partners and gradually building its management team.

The proceeds will be used to continue the expansion of Adarga’s data science and software engineering teams and roll out internationally.

Adarga, which comes from the word for an old Moorish shield, is a London and Bristol-based start-up. It uses AI to change the way financial institutions, intelligence agencies and defence companies tackle problems, helping crunch vast amounts of data to identify possible threats even before they occur. The start-up’s proposition sounds similar to that of Palantir, which is known for working with the US military.

What Adarga does is allow organizations to transform normally data-intensive, human knowledge processes by analyzing vast volumes of data more quickly and accurately. Adarga clients can build up a ‘Knowledge Graph’ about subjects, and targets.

The UK government is a client as well as the finance sector, where it’s used for financial analysis and by insurance companies. Founded in 2016, it now has 26 employees – including data scientists from some of the UK’s top universities.

The company has received support from Benevolent AI, one of the key players in the UK AI tech scene. Benevolent AI, which is worth $2bn after a $115m funding round, is a minority shareholder in Adarga. It has not provided financial backing, but support in kind and technical help.

Rob Bassett Cross, CEO of Adarga, commented: “With the completion of this round, Adarga is focused on consolidating its competitive position in the UK defence and security sector. We are positioning ourselves as the software platform of choice for organisations who cannot deal effectively with the scale and complexity of their enterprise data and are actively seeking an alternative to knowledge intensive human processes. Built by experienced sector specialists, the Company has rapidly progressed a real solution to address the challenges of an ever-growing volume of unstructured data.”

Bassett Cross is an interesting guy, to say the least. You won’t find much about him on LinkedIn, but in previous interviews, he has revealed that he is a former army officer and special operations expert who fought in Iraq and Afghanistan, and was awarded military cross.

The company recently held a new annual event, the Adarga AI Symposium at the The Royal Institution, London, which featured futurist Mark Stevenson, Ranju Das of Amazon Web Services, and General Stanley A. McChrystal.

Matthew Gould, Head of Emerging Technology at Allectus Capital, said: “Adarga has developed a world-class analytics platform to support real-time critical decisioning by public sector and defence stakeholders. What Rob and the team have built in a short time is a hugely exciting example of the founder-led, disruptive businesses that we like to partner with – especially in an ever-increasing global threat landscape.”

Allectus Capital is based in Sydney, Australia and invests across Asia-Pacific, UK and US. It has previously invested in Cluey Learning (Series A, A$20M), Everproof, Switch Automation and Automio.


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Novameat has a platform for 3D printing steaks and has new money to take it to market – gpgmail


Novameat, a Spanish startup looking to accelerate the development of alternative proteins across the meat aisle, has gotten a boost in the form of new investment capital from the leading foodtech investment firm, New Crop Capital.

Founded by biomedical engineering expert Giuseppe Scionti, Novameat builds on Scionti’s decade of research as an assistant professor in bioengineering at the Polytechnic University of Catalonia, the University College of London, Chalmers University and Polytechnic University of Milan.

The company first came to fame with the production of the world’s first 3D printed plant-based beefsteak in 2018 and will use the new funds from New Crop Capital to further develop its platform for accelerating the development of meats like steak, chicken breasts and other fibrous textured meat replacements.

The company has developed a new scaffolding technology that mimics the texture, appearance, nutritional and sensorial properties of fibrous meats like beefsteaks, chicken breasts, and fish filets.

Scionti sees the technology as the next step in the development of plant-based and lab-cultured alternatives to traditional proteins. While many clean meat and plant-based food companies have managed to take ground meat replacements to market with similar taste and textural qualities to the real thing, steaks and cuts of muscle meat have proven harder to replicate.

Novameat potentially solves that problem.

“While I was researching on regenerating animal tissues through bioprinting technologies for biomedical and veterinary applications, I discovered a way to bio-hack the structure of the native 3D matrix of a variety of plant-based proteins to achieve a meaty texture,” said Scionti, in a statement.

The core of Novameat’s technology is a customized printer that enables companies to create the kinds of fibrous tissues needed to make a steak. “We are providing the equipment, the machinery, under a licensing agreement to these companies,” says Scionti. “Plant-based meat manufacturers have access to something that creates the texture and taste of a steak.”

Traditional extrusion technologies are not capable of using the ingredients from Beyond Meat or Impossible Foods and print a steak, but Novameat’s founder argues that his technology can.

The technology was promising enough to attract the attention of New Crop Capital, arguably one of the most seasoned investors in the expanding market of meat replacement. The venture firm’s portfolio includes Memphis Meat, Beyond Meat, Kite Hill, Geltor, Good Dot, Aleph Farms, Supermeat, Mosa Meat, New Wave and Zero Egg.

“We think the global food supply chain is broken and we are focused on fixing one of those challenges which is animal protein,” says New Crop Capital’s Dan Altschuler Malek. “We see that there is an opportunity to shift consumer behavior to reduce their consumption of animal protein products to products that are at the price point that people will pay.”

Novameat can help reduce costs, Malek thinks, because it speeds up the time to create meat substitutes.

Normally four weeks before you have enough material around the cells.. it’s important for the

Scionti says that the company’s micro-extrusion technology enables companies to get a three dimensional structure without having to go through an incubation period that can take a significant amount of time and increase costs.

“Novameat’s bioprinting-based technology provides a flexible and tunable method of producing plant-based meat, with the utility to create different textures from a wide variety of ingredients, all within a single piece of meat,” he said. “Low and high-moisture extruders are the primary method currently used to restructure plant proteins to create the texture of meat. While extrusion works well for some applications, this method may not be ideal for mimicking all types of animal meat. Alternative technologies like Novameat’s give plant-based meat manufacturers a wider array of tools to mimic all types of meat and seafood,” said Good Food Institute Director of Science and Technology David Welch, in a statement.


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Reps from DHS, the FBI and the ODNI met with tech companies at Facebook to talk election security – gpgmail


Representatives from the Federal Bureau of Investigation, the Office of the Director of National Intelligence and the Department of Homeland Security met with counterparts at tech companies including Facebook, Google, Microsoft and Twitter to discuss election security, Facebook confirmed.

The purpose was to build on previous discussions and further strengthen strategic collaboration regarding the security of the 2020 U.S. state, federal, and presidential elections,” according to a statement from Facebook head of cybersecurity policy, Nathaniel Gleicher.

First reported by Bloomberg, the meeting between America’s largest technology companies and the trio of government security agencies responsible for election security is a sign of how seriously the government and the country’s largest technology companies are treating the threat of foreign intervention into elections.

Earlier this year the Office of the Inspector General issued a report saying that the Department of Homeland Security has not done enough to safeguard elections in the United States.

Throughout the year, reports of persistent media manipulation and the dissemination of propaganda on social media platforms have cropped up not just in the United States but around the world.

In April, Facebook removed a number of accounts ahead of the Spanish election for their role in spreading misinformation about the campaign.

Companies have responded to the threat by updating different mechanisms for users to call out fake accounts and improving in-house technologies used to combat the spread of misinformation.

Twitter, for instance, launched a reporting tool whereby users can flag misleading tweets.

“Improving election security and countering information operations are complex challenges that no organization can solve alone,” said Gleicher in a statement. “Today’s meeting builds on our continuing commitment to work with industry and government partners, as well as with civil society and security experts, to better understand emerging threats and prepare for future elections.”


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Starship Technologies CEO Lex Bayer on focus and opportunity in autonomous delivery – gpgmail


Starship Technologies is fresh off a recent $40 million funding round, and the robotics startup finds itself in a much-changed market compared to when it got its start in 2014. Founded by software industry veterans including Skype and Rdio co-founder Janis Friis, Starship’s focus is entirely on building and commercialization fleets of autonomous sidewalk delivery robots.

Starship invented this category when it debuted, but five years later it’s one of a number of companies looking to deploy what essentially amounts to wheeled, self-driven coolers that can carry small packages and everyday freight including fresh food to waiting customers. CEO Lex Bayer, a former sales leader from Airbnb, took over the top spot at Starship last year and is eager to focus the company’s efforts in a drive to take full advantage of its technology and experience lead.

The result is transforming what looked, to all external observers, like a long tail technology play into a thriving commercial enterprise.

“We want to do 100 universities in the next 24 months, and we’ll do about 25 to 50 robots in each campus,” Bayer said in an interview about his company’s plans for the future.


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Peloton’s 29 secret weapons – gpgmail


Hello and welcome back to Startups Weekly, a weekend newsletter that dives into the week’s noteworthy startups and venture capital news. Before I jump into today’s topic, let’s catch up a bit. Last week, I wrote about a new e-commerce startup, Pietra. Before that, I wrote about the flurry of IPO filings.

Remember, you can send me tips, suggestions and feedback to kate.clark@Gpgmail.com or on Twitter @KateClarkTweets. If you don’t subscribe to Startups Weekly yet, you can do that here.

What’s new?

Peloton revealed its S-1 this week, taking a big step toward an IPO expected later this year. The filing was packed with interesting tidbits, including that the company, which manufacturers internet-connected stationary bikes and sells an affiliated subscription to its growing library of on-demand fitness content, is raking in more than $900 million in annual revenue. Sure, it’s not profitable, and it’s losing an increasing amount of money to sales and marketing efforts, but for a company that many people wrote off from the very beginning, it’s an impressive feat.

Despite being a hardware, media, interactive software, product design, social connection, apparel and logistics company, according to its S-1, the future of Peloton relies on its talent. Not the employees developing the bikes and software but the 29 instructors teaching its digital fitness courses. Ally Love, Alex Toussaint and the 27 other teachers have developed cult followings, fans who will happily pay Peloton’s steep $39 per month content subscription to get their daily dose of Ben or Christine.

“To create Peloton, we needed to build what we believed to be the best indoor bike on the market, recruit the best instructors in the world, and engineer a state-of-the-art software platform to tie it all together,” founder and CEO John Foley writes in the IPO prospectus. “Against prevailing conventional wisdom, and despite countless investor conference rooms full of very smart skeptics, we were determined for Peloton to build a vertically integrated platform to deliver a seamless end-to-end experience as physically rewarding and addictive as attending a live, in-studio class.”

Peloton succeeded in poaching the best of the best. The question is, can they keep them? Will competition in the fast-growing fitness technology sector swoop in and scoop Peloton’s stars?

In other news

Last week I published a long feature on the state of seed investing in the Bay Area. The TL;DR? Mega-funds are increasingly battling seed-stage investors for access to the hottest companies. As a result, seed investors are getting a little more creative about how they source deals. It’s a dog-eat-dog world out there, and everyone wants a stake in The Next Big Thing. Read the story here.

Rounds of the week

Time to Disrupt

Don’t miss out on our flagship Disrupt, which takes place October 2-4. It’s the quintessential tech conference for anyone focused on early-stage startups. Join more than 10,000 attendees — including over 1,200 exhibiting startups — for three jam-packed days of programming. We’re talking four different stages with interactive workshops, Q&A sessions and interviews with some of the industry’s top tech titans, founders, investors, movers and shakers. Check out our list of speakers and the Disrupt agenda. I will be there interviewing a bunch of tech leaders, including Bastian Lehmann and Charles Hudson. Buy tickets here.

Listen

This week on Equity, gpgmail’s venture capital-focused podcast, we had Floodgate’s Iris Choi on to discuss Peloton’s upcoming IPO. You can listen to it here. Equity drops every Friday at 6:00 am PT, so subscribe to us on Apple Podcasts, Overcast and Spotify.

Learn

We published a number of new deep dives on Extra Crunch, our paid subscription product, this week. Here’s a quick look at the top stories:




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Kitty Hawk CEO Sebastian Thrun is coming to Disrupt SF – gpgmail


Sebastian Thrun can’t be described easily.

He’s a serial entrepreneur and educator, a computer scientist and inventor. He helped bring self-driving cars out of academia through X, the Google moonshot factory he founded. (That little project is now known as Waymo.) Thrun went on to co-found Udacity, the $1 billion online education startup where he is executive chairman.

Now, Thrun is pushing the “future of transportation” idea beyond self-driving cars. As CEO of Kitty Hawk Corporation, Thrun is working on bringing two aircraft to market — the one-person Flyer and a two-person autonomous taxi called Cora. Boeing and Kitty Hawk recently formed a strategic partnership with Boeing on Cora and more broadly on urban air mobility, particularly around safety and how autonomous and piloted vehicles will co-exist.

We’re excited to announce that Thrun will be joining us onstage at gpgmail Disrupt SF to give a behind the scenes look at Kitty Hawk and what the future of flight might look like.

Disrupt SF runs October 2 to October 4 at the Moscone Center in San Francisco. Tickets are available here.

Thrun’s visits to Disrupt SF always deliver something new. Who can forget the puppy? This year, we’re focused on flying cars, what they’ll look like, and how Kitty Hawk, which is backed by Google’s Larry Page, will deliver on this promise of the future. 

Did you know Extra Crunch annual members get 20% off all gpgmail event tickets? Head over here to get your annual pass, and then email extracrunch@Gpgmail.com to get your 20% off discount. Please note that it can take up to 24 hours to issue the discount code.




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The risks of amoral A.I. – gpgmail


Artificial intelligence is now being used to make decisions about lives, livelihoods, and interactions in the real world in ways that pose real risks to people.

We were all skeptics once. Not that long ago, conventional wisdom held that machine intelligence showed great promise, but it was always just a few years away. Today there is absolute faith that the future has arrived.

It’s not that surprising with cars that (sometimes and under certain conditions) drive themselves and software that beats humans at games like chess and Go. You can’t blame people for being impressed.

But board games, even complicated ones, are a far cry from the messiness and uncertainty of real-life, and autonomous cars still aren’t actually sharing the road with us (at least not without some catastrophic failures).

AI is being used in a surprising number of applications, making judgments about job performance, hiring, loans, and criminal justice among many others. Most people are not aware of the potential risks in these judgments. They should be. There is a general feeling that technology is inherently neutral — even among many of those developing AI solutions. But AI developers make decisions and choose tradeoffs that affect outcomes. Developers are embedding ethical choices within the technology but without thinking about their decisions in those terms.

These tradeoffs are usually technical and subtle, and the downstream implications are not always obvious at the point the decisions are made.

The fatal Uber accident in Tempe, Arizona, is a (not-subtle) but good illustrative example that makes it easy to see how it happens.

The autonomous vehicle system actually detected the pedestrian in time to stop but the developers had tweaked the emergency braking system in favor of not braking too much, balancing a tradeoff between jerky driving and safety. The Uber developers opted for the more commercially viable choice. Eventually autonomous driving technology will improve to a point that allows for both safety and smooth driving, but will we put autonomous cars on the road before that happens? Profit interests are pushing hard to get them on the road immediately.

Physical risks pose an obvious danger, but there has been real harm from automated decision-making systems as well. AI does, in fact, have the potential to benefit the world. Ideally, we mitigate for the downsides in order to get the benefits with minimal harm.

A significant risk is that we advance the use of AI technology at the cost of reducing individual human rights. We’re already seeing that happen. One important example is that the right to appeal judicial decisions is weakened when AI tools are involved. In many other cases, individuals don’t even know that a choice not to hire, promote, or extend a loan to them was informed by a statistical algorithm. 

Buyer Beware

Buyers of the technology are at a disadvantage when they know so much less about it than the sellers do. For the most part decision makers are not equipped to evaluate intelligent systems. In economic terms, there is an information asymmetry that puts AI developers in a more powerful position over those who might use it. (Side note: the subjects of AI decisions generally have no power at all.) The nature of AI is that you simply trust (or not) the decisions it makes. You can’t ask technology why it decided something or if it considered other alternatives or suggest hypotheticals to explore variations on the question you asked. Given the current trust in technology, vendors’ promises about a cheaper and faster way to get the job done can be very enticing.

So far, we as a society have not had a way to assess the value of algorithms against the costs they impose on society. There has been very little public discussion even when government entities decide to adopt new AI solutions. Worse than that, information about the data used for training the system plus its weighting schemes, model selection, and other choices vendors make while developing the software are deemed trade secrets and therefore not available for discussion.

Image via Getty Images / sorbetto

The Yale Journal of Law and Technology published a paper by Robert Brauneis and Ellen P. Goodman where they describe their efforts to test the transparency around government adoption of data analytics tools for predictive algorithms. They filed forty-two open records requests to various public agencies about their use of decision-making support tools.

Their “specific goal was to assess whether open records processes would enable citizens to discover what policy judgments these algorithms embody and to evaluate their utility and fairness”. Nearly all of the agencies involved were either unwilling or unable to provide information that could lead to an understanding of how the algorithms worked to decide citizens’ fates. Government record-keeping was one of the biggest problems, but companies’ aggressive trade secret and confidentiality claims were also a significant factor.

Using data-driven risk assessment tools can be useful especially in cases identifying low-risk individuals who can benefit from reduced prison sentences. Reduced or waived sentences alleviate stresses on the prison system and benefit the individuals, their families, and communities as well. Despite the possible upsides, if these tools interfere with Constitutional rights to due process, they are not worth the risk.

All of us have the right to question the accuracy and relevance of information used in judicial proceedings and in many other situations as well. Unfortunately for the citizens of Wisconsin, the argument that a company’s profit interest outweighs a defendant’s right to due process was affirmed by that state’s supreme court in 2016.

Fairness is in the Eye of the Beholder

Of course, human judgment is biased too. Indeed, professional cultures have had to evolve to address it. Judges for example, strive to separate their prejudices from their judgments, and there are processes to challenge the fairness of judicial decisions.

In the United States, the 1968 Fair Housing Act was passed to ensure that real-estate professionals conduct their business without discriminating against clients. Technology companies do not have such a culture. Recent news has shown just the opposite. For individual AI developers, the focus is on getting the algorithms correct with high accuracy for whatever definition of accuracy they assume in their modeling.

I recently listened to a podcast where the conversation wondered whether talk about bias in AI wasn’t holding machines to a different standard than humans—seeming to suggest that machines were being put at a disadvantage in some imagined competition with humans.

As true technology believers, the host and guest eventually concluded that once AI researchers have solved the machine bias problem, we’ll have a new, even better standard for humans to live up to, and at that point the machines can teach humans how to avoid bias. The implication is that there is an objective answer out there, and while we humans have struggled to find it, the machines can show us the way. The truth is that in many cases there are contradictory notions about what it means to be fair.

A handful of research papers have come out in the past couple of years that tackle the question of fairness from a statistical and mathematical point-of-view. One of the papers, for example, formalizes some basic criteria to determine if a decision is fair.

In their formalization, in most situations, differing ideas about what it means to be fair are not just different but actually incompatible. A single objective solution that can be called fair simply doesn’t exist, making it impossible for statistically trained machines to answer these questions. Considered in this light, a conversation about machines giving human beings lessons in fairness sounds more like theater of the absurd than a purported thoughtful conversation about the issues involved.

Image courtesy of gpgmail/Bryce Durbin

When there are questions of bias, a discussion is necessary. What it means to be fair in contexts like criminal sentencing, granting loans, job and college opportunities, for example, have not been settled and unfortunately contain political elements. We’re being asked to join in an illusion that artificial intelligence can somehow de-politicize these issues. The fact is, the technology embodies a particular stance, but we don’t know what it is.

Technologists with their heads down focused on algorithms are determining important structural issues and making policy choices. This removes the collective conversation and cuts off input from other points-of-view. Sociologists, historians, political scientists, and above all stakeholders within the community would have a lot to contribute to the debate. Applying AI for these tricky problems paints a veneer of science that tries to dole out apolitical solutions to difficult questions. 

Who Will Watch the (AI) Watchers?

One major driver of the current trend to adopt AI solutions is that the negative externalities from the use of AI are not borne by the companies developing it. Typically, we address this situation with government regulation. Industrial pollution, for example, is restricted because it creates a future cost to society. We also use regulation to protect individuals in situations where they may come to harm.

Both of these potential negative consequences exist in our current uses of AI. For self-driving cars, there are already regulatory bodies involved, so we can expect a public dialog about when and in what ways AI driven vehicles can be used. What about the other uses of AI? Currently, except for some action by New York City, there is exactly zero regulation around the use of AI. The most basic assurances of algorithmic accountability are not guaranteed for either users of technology or the subjects of automated decision making.

GettyImages 823303786

Image via Getty Images / nadia_bormotova

Unfortunately, we can’t leave it to companies to police themselves. Facebook’s slogan, “Move fast and break things” has been retired, but the mindset and the culture persist throughout Silicon Valley. An attitude of doing what you think is best and apologizing later continues to dominate.

This has apparently been effective when building systems to upsell consumers or connect riders with drivers. It becomes completely unacceptable when you make decisions affecting people’s lives. Even if well-intentioned, the researchers and developers writing the code don’t have the training or, at the risk of offending some wonderful colleagues, the inclination to think about these issues.

I’ve seen firsthand too many researchers who demonstrate a surprising nonchalance about the human impact. I recently attended an innovation conference just outside of Silicon Valley. One of the presentations included a doctored video of a very famous person delivering a speech that never actually took place. The manipulation of the video was completely imperceptible.

When the researcher was asked about the implications of deceptive technology, she was dismissive of the question. Her answer was essentially, “I make the technology and then leave those questions to the social scientists to work out.” This is just one of the worst examples I’ve seen from many researchers who don’t have these issues on their radars. I suppose that requiring computer scientists to double major in moral philosophy isn’t practical, but the lack of concern is striking.

Recently we learned that Amazon abandoned an in-house technology that they had been testing to select the best resumes from among their applicants. Amazon discovered that the system they created developed a preference for male candidates, in effect, penalizing women who applied. In this case, Amazon was sufficiently motivated to ensure their own technology was working as effectively as possible, but will other companies be as vigilant?

As a matter of fact, Reuters reports that other companies are blithely moving ahead with AI for hiring. A third-party vendor selling such technology actually has no incentive to test that it’s not biased unless customers demand it, and as I mentioned, decision makers are mostly not in a position to have that conversation. Again, human bias plays a part in hiring too. But companies can and should deal with that.

With machine learning, they can’t be sure what discriminatory features the system might learn. Absent the market forces, unless companies are compelled to be transparent about the development and their use of opaque technology in domains where fairness matters, it’s not going to happen.

Accountability and transparency are paramount to safely using AI in real-world applications. Regulations could require access to basic information about the technology. Since no solution is completely accurate, the regulation should allow adopters to understand the effects of errors. Are errors relatively minor or major? Uber’s use of AI killed a pedestrian. How bad is the worst-case scenario in other applications? How are algorithms trained? What data was used for training and how was it assessed to determine its fitness for the intended purpose? Does it truly represent the people under consideration? Does it contain biases? Only by having access to this kind of information can stakeholders make informed decisions about appropriate risks and tradeoffs.

At this point, we might have to face the fact that our current uses of AI are getting ahead of its capabilities and that using it safely requires a lot more thought than it’s getting now.


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Walmart and Tesla are going to try and work things out – gpgmail


Walmart came out swinging earlier this week in a lawsuit that accused Tesla of breach of contract and gross negligence over problems with rooftop solar panel systems installed at the retail giant’s stores.

Now, just days later, the lawsuit has been placed on hold while the two companies try to reach an agreement that would keep the solar installations in place and put them back in service, according to a joint statement issued late Thursday night.

“Walmart and Tesla look forward to addressing all issues and re-energizing Tesla solar installations at Walmart stores, once all parties are certain that all concerns have been addressed,” the statement read. “Together, we look forward to pursuing our mutual goal of a sustainable energy future. Above all else, both companies want each and every system to operate reliably, efficiently, and safely.”

Walmart hasn’t dropped the lawsuit. The complaint is still on file with New York state court. But the two parties are going to try and reach an agreement that would avoid a lawsuit.

The lawsuit, which is aimed at Tesla’s energy unit that was formerly known as SolarCity, alleges that seven fires on Walmart rooftops were caused by the solar panel systems. Walmart asked Tesla to remove the solar panel systems on all 244 stores where they are currently installed and to pay for damages related to fires that the retailer alleges stem from the panels.

Now, a Walmart spokesperson said it is “actively working towards a resolution” with Tesla.

Neither Tesla or Walmart would explain the details of the negotiations.

The stakes are high for Tesla. Earlier this month, Tesla CEO Elon Musk  announced a new rental offering for solar power in a bid to reboot the flagging renewable energy business.

Tesla’s share of the solar market has declined since its merger with SolarCity in 2016. In the second quarter Tesla deployed only 29 megawatts of new solar installations, while the number one and two providers of consumer solar, SunRun and Vivint Solar, installed 103 megawatts and 56 megawatts, respectively.

Tesla’s renewable energy business includes residential and commercial solar and energy storage products. The company also has a utility-scale energy product called Megapack. While Tesla still produces solar panels for residential use, much of its focus has been on developing its solar roof, which is comprised of tiles. It still operates a commercial business, which targets municipalities, schools, affordable housing, enterprise and agriculture and water districts as customers.

The company doesn’t provide a breakdown of its solar installations, making it difficult to determine if the commercial business is flat, falling or on the rise. Language in its latest 10-Q suggests Tesla is putting a renewed effort into its solar business.

Tesla said it’s working on revamping the customer service experience for solar products, according to the 10-Q. The company said while its retrofit solar system deployments have it expects they “will stabilize and grow in the second half of the year.”


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MIT develops a sensor that can work underwater without a battery and send back data – gpgmail


MIT researchers have created a new underwater sensor and communication system that doesn’t require batteries, and barely uses any power at all. This could help set up an underwater ‘internet of things’ according to MIT, which would allow for real-time sea temperature and marine life monitoring, without requiring regular equipment and power swaps to make it work. Without that requirement, it would even be possible to set up networks of underwater sensors in the seas of distant planets.

The system devised by MIT researchers use a transmitter that sends out sound waves underwater, which then hit sensors with embedded receivers, transmitting a tiny amount of energy in the process. The sensor then either uses that energy to answer back – or doesn’t, which corresponds to either a 1 or a 0, meaning it can effectively communicate in binary. The only energy required for this to work is the power stored in the sound wave sent by the transmitter.

The inspiration for devising this system came from a somewhat unlikely source: Fadel Adib, an assistant professor in the MIT Media Lab and one of the researchers who worked on the project was watching nature doc “Blue Planet” and thought about how much of the Earths oceans are left unstudied, and also about how the solution for that can’t be battery-powered sensors since that could result in a lot of excess pollution.

Essentially, the system works but allowing piezoelectric resonators, which have been used in things like microphones for well over 100 years, to either deform in response to a sound wave, or retain their shape and reflect, based on information contained in any kind of sensor you might want to pair with the piezoelectric material. That sends back the binary signal, which can then be collected and interpreted.

Next up for the research team is to show that this can work at longer distances, and in concert with other sensors for simultaneous transmission. Eventually, it might even be able to transmit sound and even low-res images, which would be a huge development in terms of establishing remote monitoring stations – especially as we pursue more science and research on worlds not our own.


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