Robotics and AI is the hottest scientific mashup since The Big Bang Theory’s Sheldon Cooper met Amy Farrah Fowler. If you play a role in these world-changing technologies, join us at TC Sessions: Robotics & AI on March 3, 2020 at UC Berkeley’s Zellerbach Hall. What could be better than spending an entire day focused on melding minds with machines?
Well, how about exhibiting your early-stage startup to 1,500 of the world’s leading robotics and AI technologists, researchers, innovators and investors? It’s easy. Buy an Early-Stage Startup Exhibitor Package. The price includes four tickets, a 30-inch round highboy table, power, linen and a tabletop sign. Exhibitor space is limited, and we have only 11 tables left. Don’t miss this opportunity to showcase your work to people with the power to change the trajectory of your early-stage startup.
Want even more spotlight opportunity? Of course, you do. This year, in addition to interviews, panel discussions, speakers, breakout sessions and Q&As, we’re adding a pitch competition. Founders of any early-stage startup focused on robotics and AI can participate. It’s free, and all you need to do is apply here by February 1.
TechCrunch will review all applications and select 10 startups to pitch at a private event on March 2. You’ll pitch to TechCrunch editors, main-stage speakers and industry experts. We’ll have a panel of VC judges there to narrow the field to five finalists. The following day, those teams will take to the Main Stage at TC Sessions: Robotics + AI and pitch to the attending masses.
Whether you exhibit or pitch — why not do both? — you’ll expose your startup to the top leaders and investors in robotics and AI. Opportunity’s knocking and it’s up to you to kick down the door.
The next TC Sessions: Robotics & AI takes place on March 3, 2020 at UC Berkeley. Get your business in front of the people who can help you achieve your startup dreams. Buy your Early-Stage Startup Exhibitor Package today.
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Alphabet-owned Loon, the high-altitude balloon company that is using its stratospheric technology to provide internet connectivity on Earth, has signed a new commercial agreement with Telefonica-owned Internet para Todos (IpT). The IpT initiative, which is also backed in part by Facebook and the Development Bank of Latin America, aims to provide internet connectivity to users in remote locations across Latin America, and its deal with Loon will specifically connect users in remote parts fo the Amazon Rainforest in Peru.
Loon will begin providing service in 2020, provided the deal gets all the necessary regulatory approval it requires. This is a first in terms of a commercial deployment of high altitude gallons with the aim of offering connectivity over a continued, sustained period, so there’s some new ground to break in terms of working with the Peru Ministry of Transport and Communications prior to launch, but the partners involved are working with regulators to make sure everything’s signed off before launch.
This isn’t the first time Loon has worked with Telefonica – the two join forces to provide emergency internet connectivity following the 8.0 earthquake that hit Peru in May, and the’ve been collaborating on a number of projects for years. For Loon, this is now the third commercial contract it has secured, including one with Telkom Kenya which is also awaiting final regulatory sign-off, and an arrangement with Canadian company Telecast to develop a coordination system for a future planned low-Earth orbit satellite constellation.
The initial deployment plan for this partnership with IpT will provide connectivity to an area that makes up around 15 percent of the total area of the Loreto Region in Peru, which together accounts for a population of around 200,000 people. Of that 200,000, roughly a quarter have access to connectivity at least that 3G quality, according to Loon. The Loon balloons that will be deployed to provide service essentially act as very high altitude cell towers, receiving LTE connections and redistributing those directly to consumer devices on the ground.
A little over a year after the dissolution of the once high-flying blood testing startup Theranos, another startup has raised over $27 million to breathe new life into the vision of bringing low-cost blood tests to point-of-care medical facilities.
Unlike Theranos, Truvian Sciences is not claiming that most of its blood tests do not need clearance from the U.S. Food and Drug Administration, and is, in fact, raising the money to proceed with a year-long process to refine its technology and submit it to the FDA for approval.
“More and more consumers are refusing to accept the status quo of healthcare and are saying no to expensive tests, inconvenient appointments and little to no access to their own test results,” said Jeff Hawkins, the president and chief executive of Truvian, in a statement. “In parallel, retail pharmacies are rising to fill demand, becoming affordable health access points. By bringing accurate, on-site blood testing to convenient sites, we will give consumers a more seamless experience and enable them to act on the vast medical insights that come with regular blood tests.”
Hawkins, the former vice president and general manager of reproductive and genetic health business at Illumina, is joined by a seasoned executive team of life sciences professionals including Dr. Dena Marrinucci, the former co-founder of Epic Sciences, who serves as the company’s senior vice president of corporate development and is a co-founder of the company.
Image courtesy of Flickr/Mate Marschalko
As part of today’s announcement, the company said it was adding Katherine Atkinson, a former executive at Epic Sciences and Illumina, as its new chief commercial officer, and has brought on the former chairman of the Thermo Fisher Scientific board of directors, Paul Meister, as a new director.
The ultimate goal, according to Hawkins, is to develop a system that can be installed in labs and can provide accurate results in 20 minutes for a battery of health tests from a small sample of blood for as low as $50. Typically, these tests can cost anywhere from several hundred to several thousand dollars — depending on the testing facility, says Hawkins.
Using new automation and sensing technologies, Truvian is aiming to combine chemistries, immunoassays and hematology assays into a single device that can perform standard assessment blood tests like lipid panels, metabolic panels, blood cell counts, and tests of thyroid, kidney and liver functions.
The company’s system includes remote monitoring and serviceability, according to a statement from Truvian. Its dry reagent technology allows materials to be stored at room temperature, removing the need for cold chain or refrigerated storage. According to a statement, the company is working to receive a CE Mark in the European Economic Area and submitted to the FDA for 510(k) clearance along with a “clinical laboratory improvement amendments” waiver application to let the devices be used in a retail setting or doctor’s office.
“We don’t believe that single drop of blood from a finger stick can do everything,” says Hawkins (in opposition to Theranos). “Fundamentally as a company we have built the company with seasoned healthcare leaders.”
As the company brings its testing technology to market, it’s also looking to compliment the diagnostics toolkit with a consumer-facing app that would provide a direct line of communication between the company and the patients receiving the results of its tests.
Truvian’s data will integrate with both Apple and Google’s health apps as well as reside on the company’s own consumer-facing app, according to Hawkins.
“At the end of the day precision medicine is going to come from integrating these data sources,” says Hawkins. “I think if we pull off what we want we should be able to make your routine blood testing far more accessible.”
Founders First Capital Partners, an accelerator and investment firm which provides revenue-based financing to businesses led by “underrepresented entrepreneurs” operating in underserved markets, has received a $100 million commitment to expand its operations.
The San Diego-based investor raised the debt financing from Community Investment Management, a large debt-focused impact investment fund.
The revenue-based financing model is a new one that several startups are beginning to explore as a way to take non-dilutive capital for early stage businesses that might not qualify for traditional bank loans.
Companies like the new media startup, The Prepared, which offers tips on disaster preparedness, used revenue financing as a way to get its own business off the ground. And other companies are turning to the financing method too, according to investors from Lighter Capital.
At Founders First Capital Partners, the new financing will expand its lending operations to companies that are already generating between $1 million and $5 million in annual revenue.
The new program is set to launch in January 2020, expanding the firm’s footprint as a financial services firm for minority and other underrepresented founders, the company said in a statement.
The firm focuses on businesses led by people of color, women, and military veterans and concentrates on entrepreneurs whose business operate in low and middle-income communities outside of the traditional funding networks of Silicon Valley and New York, the company said.
It also operates an accelerator program for entrepreneurs that meet the same criteria.
“Founders First is very pleased to have secured such significant funding that allows us to expand our efforts to businesses that are led by underrepresented founders or those that serve underrepresented communities,” said Kim Folsom, co-founder and chief executive of Founders First, in a statement.
Revenue-based financing can in some cases be a better option for service-based, social impact companies, according to Jacob Haar, a managing partner with CIM, who previously worked at Minlam Investment Managemet, a hedge fund working in the micro-finance space.
Both microfinance and revenue-based financing come with risks — particularly around the rates that these lenders can charge for their financing.
But it is a unique opportunity to open up founders to additional types of financing models.
“CIM is excited to partner with Founders First to expand revenue-based financing to support underserved and underrepresented small business founders, including people of color, women, LGBTQ, and military veterans as well as small businesses located in low to moderate income areas,” Haar said in a statement. “We have found revenue-based financing to be a compelling alternative to venture capital and fixed payment loans as a forward-looking and structurally flexible investment to support business growth. We believe that Founders First’s unique advisory and revenue-based investment platform enables underrepresented small businesses to overcome systematic bias and achieve their potential.”
A commons tactic in both amateur and professional sports – and even in competitions as mundane as a casual board game night – is trash talk. But the negative effect of trash talk may have less to do with the skill of the repartee involved, and more with just the fact that it’s happening at all. A new study conducted by researchers at Carnegie Mellon University suggests that even robots spitting out pretty lame pre-programmed insults can have a negative impact on human players.
CMU’s study involved programming one of SoftBank’s Pepper humanoid robots to deliver scorchers like “I have to say you are a terrible player” to a group of 40 participants, who were playing the robot in a game called “Guards and Treasures,” which is a version of a strategy game often used for studying rationality. During the course of the experiment, participants played 35 times against the robot – some getting bolstering, positive comments form the robot, while others were laden with negative criticism.
Both groups of participants improved at the game over time – but the ones getting derided by the bots didn’t score as highly as the group that was praised.
It’s pretty well-established that people excel when they receive encouragement from other – but that’s generally meant other humans. This study provides early evidence that people could get similar benefits from robotic companions – even ones that don’t look particularly human-like. The researchers still want to do more investigation into whether Pepper’s humanoid appearance affected the outcome, vs. say a featureless box or an industrial robot acting as the automaton opponent and doling out the same kind of feedback.
The results of this and related research could be hugely applicable to areas like at-home care, something companies including Toyota are pursuing to address the needs of an aging population. It could also come into play in automated training applications, both at work and in other settings like professional sports.
At its annual Dreamforce mega-conference in San Francisco, Salesforce today introduced the next steps in its Einstein Voice project, which it first announced last year. Einstein Voice is the company’s AI voice assistant. You can think of it as Salesforce’s Alexa or Google Assistant, but with a more focused mission.
During a briefing ahead of the event, Salesforce Chief Product Officer Bret Taylor showed off an Einstein and Alexa enabled Einstein speaker (Salesforce chairman and co-CEO Marc Benioff was supposed to be at the meeting, too, but for unknown reasons, he didn’t show) — and yes, it looked like Salesforce’s Einstein cartoon figure and its voluminous white hair lit up when it responded to queries. The company isn’t planning on making these devices available to the public, but it does show off the work the company has done with Amazon to integrate the service (though is by no means an Amazon -exclusive since the company is also working to bring Einstein to Google devices).
The theory here, as Taylor explained, is that having access to Salesforce data through voice will enable salespeople to quickly enter data into Salesforce when they are on the go and to ask the system questions about their data. The company argues that while voice assistants have found a place in the home, there are a lot of upsides to bringing it to businesses as well. That means a system has to account for the security needs of enterprises, too, as well as the fact that there is a wide range of different user personas it has to account for.
“We’re really excited about the idea of voice in businesses — the idea that every business can have an AI guide to their business decisions,” Taylor said. “I view it as part of this progression of technology. Computers and software started in the terminal with a keyboard, thanks to Xerox Parc moved to a mouse and graphic user interface, and then thanks to Steve Jobs, moved to a touchscreen, which I think is probably the dominant form factor for computers nowadays. And voice is really that next step.”
This next step, Taylor argues, will allow companies to rethink how people interact with software and data. With voice, Einstein, which is Salesforce’s catch-all name for its AI products, has a “seat at the table,” he noted because you can simply as the system a question if you need additional data during a conversation. But the real mission here is to bring these tools to every business — not just to Salesforce’s executive meetings.
To enable this, Salesforce is launching a tool that will allow anybody within a company to quickly build basic Einstein skills to pull up data from Salesforce. These skills focus on data input and relatively basic queries, for now. During a demo ahead of the event, the team showed off how easy it would be to enable a manager to ask about the current sales performance of his team, for example. By now means, though, is this tool as rich as products like Google’s DialogFlow or Microsoft’s Azure Bot Service. It’s nowhere near as flexible yet, but the team notes that it’s still early days and that it is working on enabling the ability to have more complex dialogs with Einstein in the future, for example.
To be honest, it’s hard not to look at this as a bit of a gimmick. There are probably real use cases here, that every company will have to define for itself. Maybe there are salespeople who indeed want to use a voice interface to update their CRM system after a customer meeting, for example. Or they may want to ask about the value of an account while they are in the car. In many ways, though, this feels like a technology looking for a problem, despite Salesforce’s protestations that customers are asking for this.
Some of the other uses cases here, which the company didn’t really highlight all that much in its briefing, seem far more compelling. It’s using Einstein Voice to coach call center agents by analyzing calls to pull out insights and trends from sales call transcripts. It’s also launching Service Cloud Voice, which integrates telephony inside the company’s Service Cloud. Using a built-in transcription service, Einstein can listen to the call in real time and proactively provide sales teams and call center agents with relevant information. Those use cases may not be quite as exciting, but in the end, they may generate for more value for companies than having yet another voice assistant for which they have to build their own skills, using what is, at least for the time being, a rather limited tool.
Last Monday a group of millionaires and billionaires took a trip to an industrial site in Lancaster, Calif. to witness the achievement of what could represent a giant leap forward in the effort to decarbonize some of the world’s most carbon intensive industries.
For Bill Gross, the founder of Idealab and brains behind the excursion, the unveiling was simply the latest in a string of demonstrations for new technologies commercialized by his nearly three-decade old startup company incubator. However, it may be the most significant.
What Gross is pursuing with his new company, Heliogen, offers a way forward for renewable energy to be applied to manufacturing processes for cement, lime, coke, and steel — some of the most energy intensive and polluting industries that exist in the world today.
“Today, industrial processes like those used to make cement, steel, and other materials are responsible for more than a fifth of all emissions,” said Bill Gates, a Heliogen backer who has committed millions of dollars to the development of new renewable energy technologies. “These materials are everywhere in our lives but we don’t have any proven breakthroughs that will give us affordable, zero-carbon versions of them. If we’re going to get to zero carbon emissions overall, we have a lot of inventing to do. I’m pleased to have been an early backer of Bill Gross’s novel solar concentration technology. Its capacity to achieve the high temperatures required for these processes is a promising development in the quest to one day replace fossil fuel.”
According to Gross, Kittu Kollaru, an investor in Heliogen who is also backing another of Idealab’s incubated companies working on developing an energy storage technology, Energy Vault, said after seeing the demonstration, “Bill… this is even bigger.”
At its core, Heliogen is taking a well-known technology called concentrated solar power, and improving its ability to generate heat with new computer vision, sensing and control technologies, says Gross. \
Four high resolution cameras capture real time video of a field of mirrors that are controlled by sensors to focus the sun’s energy on a particular spot. That spot, either at a transmission pipe used to transport gas, or a tower, is heated to over 1,000 degrees Celsius. Previous commercial concentrating solar thermal systems could only reach temperatures of 565 degrees Celsius, the company said. That’s useful for generating power, but can’t meet the needs of industrial processes.
Achieving temperatures above 1,000 degrees Celsius gives manufacturing facilities the opportunity to replace the use of fossil fuels in a significant portion of their operations.
A facility hoping to install Heliogen’s technology (Image courtesy of Heliogen)
“They already have a power source/burner that is variable, based on the flow rate of materials, and is servo controlled to have the correct air flow exit temperature,” says Gross of many existing industrial operations. “So when we add heat (when the sun is out) the fossil fuel burner just automatically gets scaled back like a thermostat on a room heater (albeit at much higher temperature). So it’s a seamless control integration.”
A plant could still operate on a 24-hour production schedule, and could still use fossil fuels, says Gross. But by deploying the Heliogen system, companies could reduce their fossil fuel consumption by up to 60%, according to the serial entrepreneur and investor. Gross believes that Heliogen’s systems will pay for themselves in a two-to-three year timeframe if companies buy the system outright, or Heliogen could manage the installation for a manufacturer and just charge them for the cost of the power.
Gross has been testing smaller versions of Heliogen’s industrial heating technology at a field with an array of 70 mirrors to prove that the super-concentrating technology could work. A full scale facility covers roughly two acres of land with mirrors and a tower where the rays are concentrated. “It’s like a death ray,” Gross said of the concentrated solar beams.
While initial applications for Heliogen’s technology will concentrate on industrial applications, longer term, Gross sees an opportunity to drive down the cost of Hydrogen production at an industrial scale. Long believed to be one of the keys to global decarbonization, Hydrogen’s use as a fuel source has been limited because it’s difficult to make without using fossil fuels.
Hydrogen’s importance to a carbon-free energy future can’t be overstated, according to energy advocates and longtime renewable energy entrepreneurs and investors like Jigar Shah. The founder and former chief executive of solar installation company, SunRun, Shah now invests in renewabel energy projects.
“As we move closer to 100% clean electricity grids, it will be necessary to not just store excess electricity production from the spring and fall, but to turn all of this excess electricity to valuable commodities that can help decarbonize other sectors outside of electricity — transportation, industrial heat, and chemicals,” Shah wrote in an article on LinkedIn. “That’s where hydrogen comes into play.”
Investors in Heliogen include venture capital firm Neotribe and Dr. Patrick Soon-Shiong, the billionaire Los Angeles-based investor and entrepreneur, who owns the Los Angeles Times and an investment conglomerate. THe investmente was made through Dr. Soon-Shiong’s investment firm, Nant Capital.
“For the sake of our future generations we must address the existential danger of climate change with an extreme sense of urgency,” said Dr. Soon-Shiong, in a statement. “I am committed to using my resources to invest in innovative technologies that harness the power of nature and the sun. By significantly reducing greenhouse gas emissions and generating a pure source of energy, Heliogen’s brilliant technology will help us achieve this mission and also meaningfully improve the world we leave our children.”
Earlier this month, at the WebSummit conference in Lisbon, D-Wave and Volkswagen teamed up to manage a fleet of buses using a new system that, among other things, used D-Wave’s quantum technology to help generate the most efficient routes. While D-Wave’s 2000Q only played a small part in this process, it’s nevertheless a sign that quantum computing is slowly getting ready for production use and that D-Wave’s approach, somewhat controversial in its early days, is paying off.
Unlike other players in the quantum computing market, D-Wave always bet on quantum annealing as its core technology. This technology lends itself perfectly to optimization problems like the kind of routing problem the company tackled with VW, as well as sampling problems, which, in the context of quantum computing, are useful for improving machine learning models, for example. Depending on their complexity, some of these problems are nearly impossible to solve with classical computers (at least in a reasonable time).
Grossly simplified, with quantum annealing, you are building a system that almost naturally optimizes itself for the lowest energy state, which then represents the solution to your problem.
Microsoft, IBM, Rigetti and others are mostly focused on building gate-model quantum computers and they are starting to see results (with the exception of Microsoft, which doesn’t have a working computer just yet and is hence betting on partnerships for the time being). But this is also a far more complex problem. And while you can’t really compare these technologies qubit to qubit, it’s telling that D-Wave’s latest machines, the Advantage, will feature 5,000 qubits — while the state of the art among the gate-model proponents is just over 50. Scaling these machines up is hard, though, especially given that the industry is still trying to figure out how to manage the noise issues.
D-Wave remains the only major player that’s betting on annealing, but the company’s CEO Vern Brownell remains optimistic that this is the right approach. “We feel more strongly about our decision to do quantum annealing now that there are a few companies that actually have quantum computers that people can access,” he said in an interview earlier this month.
“We have customers, Volkswagen included, that have run problems against those other computers and seeing what they can actually do and it’s vastly different. Our capability is many orders of magnitude faster for most problems than what you can do with other quantum computers. And that is because of the choice of quantum annealing. And that is because quantum healing is more robust to errors.” Error correction, he argues, remains the fundamental problem, and will hamper the performance of these systems for the foreseeable future. “And in order to move into the enterprise or any kind of practical application, that error correction needs to be wrestled with,” he noted.
Los Angeles’ district attorney is warning travelers to avoid public USB charging points because “they may contain dangerous malware.”
Reading the advisory, you might be forgiven for thinking that every USB outlet you see is just waiting for you to plug in your phone so it can steal your data. This so-called “juice-jacking” attack involves criminals loading malware “on charging stations or cables they leave plugged in at the stations so they may infect the phones and other electronic devices of unsuspecting users,” it reads. “The malware may lock the device or export data and passwords directly to the scammer.”
But the county’s chief prosecutor’s office told TechCrunch said that it has “no cases” of juice-jacking on its books, though it said there are known cases on the east coast.When asked where those cases were, the spokesperson did not know. And when asked what prompted the alert to begin with, the spokesperson said it was part of “an ongoing fraud education campaign.”
Which begs the question — why?
Security researcher Kevin Beaumont tweeted that he hasn’t seen “any evidence of malware being used in the wild on these things.” In fact, ask around and you’ll find very little out there. Several security researchers have dropped me messages saying they’ve seen proof-of-concepts, but nothing actively malicious.
Juice-jacking is a real threat, but it’s an incredibly complicated and imperfect way to attack someone when there are far easier ways.
The idea, though — that you can plug in your phone and have your secrets stolen — is not entirely farfetched. Over the years there have been numerous efforts to demonstrate that it’s possible. As ZDNet points out in its coverage of the juice-jacking warning, the FBI sent out a nationwide alert about the threat after security researcher Samy Kamkar developed an Ardunio-based implant designed to look like a USB charger to wirelessly sniff the air for leaky key strokes. And just earlier this year, a security researcher developed an iPhone charger cable clone that let a nearby hacker run commands on the vulnerable computer.
LA recommend using an AC power outlet and not a charging station, and to take your cables with you. That’s sound advice, but it’s just one of many things you need to do to keep your devices and data safe.
One of the bigger trends in enterprise software has been the emergence of startups building tools to make the benefits of artificial intelligence technology more accessible to non-tech companies. Today, one that has built a platform to apply power of machine learning and natural language processing to massive documents of unstructured data has closed a round of funding as it finds strong demand for its approach.
Eigen Technologies, a London-based startup whose machine learning engine helps banks and other businesses that need to extract information and insights from large and complex documents like contracts, is today announcing that it has raised $37 million in funding, a Series B that values the company at around $150 million – $180 million.
Eigen today is working primarily in the financial sector — its offices are smack in the middle of The City, London’s financial center — but the plan is to use the funding to continue expanding the scope of the platform to cover other verticals such as insurance and healthcare, two other big areas that deal in large, wordy documentation that is often inconsistent in how its presented, full of essential fine print, and is typically a strain on an organisation’s resources to be handled correctly, and is often a disaster if it is not.
The focus up to now on banks and other financial businesses has had a lot of traction. It says its customer base now includes 25% of the world’s G-SIB institutions (that is, the world’s biggest banks), along with others who work closely with them like Allen & Overy and Deloitte. Since June 2018 (when it closed its Series A round), Eigen has seen recurring revenues grow sixfold with headcount — mostly data scientists and engineers — double. While Eigen doesn’t disclose specific financials, you can the growth direction that contributed to the company’s valuation.
The basic idea behind Eigen is that it focuses what co-founder and CEO Lewis Liu describes as “small data”. The company has devised a way to “teach” an AI to read a specific kind of document — say, a loan contract — by looking at a couple of examples and training on these. The whole process is relatively easy to do for a non-technical person: you figure out what you want to look for and analyse, find the examples using basic search in two or three documents, and create the template which can then be used across hundreds or thousands of the same kind of documents (in this case, a loan contract).
Eigen’s work is notable for two reasons. First, typically machine learning and training and AI requires hundreds, thousands, tens of thousands of examples to “teach” a system before it can make decisions that you hope will mimic those of a human. Eigen requires a couple of examples (hence the “small data” approach).
Second, an industry like finance has many pieces of sensitive data (either because its personal data, or because it’s proprietary to a company and its business), and so there is an ongoing issue of working with AI companies that want to “anonymise” and ingest that data. Companies simply don’t want to do that. Eigen’s system essentially only works on what a company provides, and that stays with the company.
Eigen was founded in 2014 by Dr. Lewis Z. Liu (CEO) and Jonathan Feuer (a managing partner at CVC Capital technologies who is the company’s chairman), but its earliest origins go back 15 years earlier, when Liu — a first-generation immigrant who grew up in the US — was working as a “data entry monkey” (his words) at a tire manufacturing plant in New Jersey, where he lived, ahead of starting university at Harvard.
A natural computing whizz who found himself building his own games when his parents refused to buy him a games console, he figured out that the many pages of printouts that he was reading and re-entering into a different computing system could be sped up with a computer program linking up the two. “I put myself out of a job,” he joked.
His educational life epitomises the kind of lateral thinking that often produces the most interesting ideas. Liu went on to Harvard to study not computer science, but physics and art. Doing a double major required working on a thesis that merged the two disciplines together, and Liu built “electrodynamic equations that composed graphical structures on the fly” — basically generating art using algorithms — which he then turned into a “Turing test” to see if people could detect pixelated actual work with that of his program. Distil this, and Liu was still thinking about patterns in analog material that could be re-created using math.
Then came years at McKinsey in London (how he arrived on these shores) during the financial crisis where the results of people either intentionally or mistakenly overlooking crucial text-based data produced stark and catastrophic results. “I would say the problem that we eventually started to solve for at Eigen became for tangible,” Liu said.
Then came a physics PhD at Oxford where Liu worked on X-ray lasers that could be used to bring down the complexity and cost of making microchips, cancer treatments and other applications.
While Eigen doesn’t actually use lasers, some of the mathematical equations that Liu came up with for these have also become a part of Eigen’s approach.
“The whole idea [for my PhD] was, ‘how do we make this cheeper and more scalable?'” he said. “We built a new class of X-ray laser apparatus, and we realised the same equations could be used in pattern matching algorithms, specifically around sequential patterns. And out of that, and my existing corporate relationships, that’s how Eigen started.”
Five years on, Eigen has added a lot more into the platform beyond what came from Liu’s original ideas. There are more data scientists and engineers building the engine around the basic idea, and customising it to work with more sectors beyond finance.
There are a number of AI companies building tools for non-technical business end-users, and one of the areas that comes close to what Eigen is doing is robotic process automation, or RPA. Liu notes that while this is an important area, it’s more about reading forms more readily and providing insights to those. The focus of Eigen in more on unstructured data, and the ability to parse it quickly and securely using just a few samples.
Liu points to companies like IBM (with Watson) as general competitors, while startups like Luminance is another taking a similar approach to Eigen by addressing the issue of parsing unstructured data in a specific sector (in its case, currently, the legal profession).
Stephen Nundy, a partner and the CTO of Lakestar, said that he first came into contact with Eigen when he was at Goldman Sachs, where he was a managing director overseeing technology, and the bank engaged it for work.
“To see what these guys can deliver, it’s to be applauded,” he said. “They’re not just picking out names and addresses. We’re talking deep, semantic understanding. Other vendors are trying to be everything to everybody, but Eigen has found market fit in financial services use cases, and it stands up against the competition. You can see when a winner is breaking away from the pack and it’s a great signal for the future.”
Facebook’s latest transparency report is out.
The social media giant said the number of government demands for user data increased by 16% to 128,617 demands during the first-half of this year compared to the second-half of last year.
That’s the highest number of government demands its received in any reporting period since it published its first transparency report in 2013.
The U.S. government led the way with the most number of requests — 50,741 demands for user data resulting in some account or user data given to authorities in 88% of cases. Facebook said two-thirds of all of the U.S. government’s requests came with a gag order, preventing the company from telling the user about the request for their data.
But Facebook said it was able to release details of 11 so-called national security letters (NSLs) for the first time after their gag provisions were lifted during the period. National security letters can compel companies to turn over non-content data at the request of the FBI. These letters are not approved by a judge, and often come with a gag order preventing their disclosure. But since the Freedom Act passed in 2015, companies have been allowed to request the lifting of those gag orders.
The report also said the social media giant had detected 67 disruptions of its services in 15 countries, compared to 53 disruptions in nine countries during the second-half of last year.
And, the report said Facebook also pulled 11.6 million pieces of content, up from 5.8 million in the same period a year earlier, which Facebook said violated its policies on child nudity and sexual exploitation of children.
Researchers at MIT have developed a new method of navigation for robots that could be very useful for the range of companies working on autonomous last-mile delivery. In short, the team has worked out how a robot can figure out the location of a front door, without being provided a specific map in advance.
Most last-mile autonomous delivery robots today, including the ‘wheeled cooler’-style variety that was pioneered by Starship and has since been adopted by a number of other companies, including Postmates, basically meet customers at the curb. Mapping isn’t the only barrier to having future delivery bots go all the way to the door, just like the humans who make those deliveries today.
MIT News points out that mapping an entire neighborhood with the level of specificity required to do true front-door delivery would be incredibly difficult – particularly at national (let alone global) scale. Since that seems unlikely to happen, and especially unlikely for every company looking at building autonomous delivery networks to source separately, they set out to devise a navigation method that lets a robot process cues in its surroundings on the fly to figure out a front door’s location.
This is a variation on what you may have heard of referred to as SLAM, or simultaneous localization and mapping. The MIT team’s innovative twist on this approach is that in place of a semantic map, wherein the robot identifies objects in its surroundings and labels them, they devised a ‘cost-to-go’ map, which uses data from training maps to color-code the surroundings into a heat map wherein it can determine which parts are more likely to be close to a ‘front door’ and which are not, and immediately chart the most efficient path to the door based on that info.
It’s a much, much more simplified version of what we do when we encounter new environments we’ve never seen directly before – you know what’s likely to be the front door of a house you’ve never seen just by looking at it, and you know that essentially because you’re comparing it against your memory of past houses and how those properties have been laid out, even if you’re doing that all without even thinking about it.
Delivery is only one use case for this kind of intelligent local environment mapping, but it’s a good one that might see actual commercial use sooner rather than later.
Microsoft today announced the public preview of its Power Virtual Agents tool, a new no-code tool for building chatbots that’s part of the company’s Power Platform, which also includes Microsoft Flow automation tool, which is being renamed to Power Automate today, and Power BI.
Built on top of Azure’s existing AI smarts and tools for building bots, Power Virtual Agents promises to make building a chatbot almost as easy as writing a Word document. With this, anybody within an organization could build a bot that walks a new employee through the onboarding experience for example.
“Power virtual agent is the newest addition to the Power Platform family,” said Microsoft’s Charles Lamanna in an interview ahead of today’s announcement. “Power Virtual Agent is very much focused on the same type of low code, accessible to anybody, no matter whether they’re a business user or business analyst or professional developer, to go build a conversational agent that’s AI-driven and can actually solve problems for your employees, for your customers, for your partners, in a very natural way.”
Power Virtual Agents handles the full lifecycle of the bot building experience, from the creation of the dialog to making it available in chat systems that include Teams, Slack, Facebook Messenger and others. Using Microsoft’s AI smarts, users don’t have to spend a lot of time defining every possible question and answer, but can instead rely on the tool to understand intentions and trigger the right action. “We do intent understanding, as well as entity extraction, to go and find the best topic for you to go down,” explained Lamanna. Like similar AI systems, the service also learns over time, based on feedback it receives from users.
One nice feature here is that if your setup outgrows the no-code/low-code stage and you need to get to the actual code, you’ll be able to convert the bot to Azure resources since that’s what’s powering the bot anyway. Once you’ve edited the code, you obviously can’t take it back into the no-code environment. “We have an expression for Power Platform, which is ‘no cliffs.’ […] The idea of ‘no cliffs’ is that the most common problem with a low-code platform is that, at some point, you want more control, you want code. And that’s frequently where low-code platforms run out of gas and you really have issues because you can’t have the pro dev take it over, you can’t make it mission-critical.”
The service is also integrated with tools like Power Automate/Microsoft Flow to allow users to trigger actions on other services based on the information the chatbot gathers.
Lamanna stressed that the service also generates lots of advanced analytics for those who are building bots with it. With this, users can see what topics are being asked about and where the system fails to provide answers, for example. It also visualizes the different text inputs that people provide so that bot builders can react to that.
Over the course of the last two or three years, we went from a lot of hype around chatbots to deep disillusionment with the experience they actually delivered. Lamanna isn’t fazed by that. In part, those earlier efforts failed because the developers weren’t close enough to the users. They weren’t product experts or part of the HR team inside a company. By using a low-code/no-code tool, he argues, the actual topic experts can build these bots. “If you hand it over to a developer or an AI specialist, they’re geniuses when it comes to developing code, but they won’t know the details and ins and outs of, say, the shoe business – and vice versa. So it actually changes how development happens.”
Hardware startups are expanding from the world of consumer tech; global hardware accelerator HAX knows this better than most and details the latest trends in its yearly report. One of the most active early-stage hardware investors, the group today released exclusively to TechCrunch its yearly report with insights on hardware startups.
The report highlighted several vital insights: hardware companies are increasingly entering the public market, and more privately-held hardware startups are exceeding a valuation of $1 billion. Of those unicorns, more than 50% are Chinese hardware companies.
Picking stuff up seems easy, right? It is – for humans with powerful brain computers that instantly and intuitively figure out everything needed to get the job done. But for robots, even advanced robots, the compute required is surprisingly complex, especially if you want the robot to not, you know, break the thing it’s grabbing.
MIT has developed a new way to speed up the planning involved in a robot grasping an object, making it “significantly” faster – reducing the total time from as much as ten or more minutes, to under a second. That’s many orders of magnitude better, bringing it closer to the realm of human reaction and response time.
This could have big practical benefits to setting where robotics are already in use, including in industrial environments. The research team’s method involves having the robot push the object against a surface that doesn’t move, which allows it to shortcut a bunch of the decision-making process about how to manipulate it. That could be applied in picking and sorting applications, which is a common enough use for robots on factory floors and in warehouses.
MIT says this could even be used to improve robotic manipulation of “intricate tools,” and that it can be applied even in the case of simple robotic grippers, rather than just being useful for advanced, highly articulated robotic manipulators.
To prove the validity of its model, the team built an experiment in which a robot gripper held a t-shaped block and pushed it against a fixed, vertically oriented bar. The results mirrored what happened in their virtual models, with the robot being able to plan out control of the gripped block through a maneuver to place it upright on the tablet’s surface in less than a second, whereas it had taken over 500 seconds using traditional methods.
Across the political, social and economic stage, women’s issues are finally receiving heightened attention and priority.
There are more women than ever seeking political office; funding for female-founded startups is reaching record levels (even if they still have a long way to go to reach gender parity); a sizable cohort of female-founded and led companies have achieved billion-dollar unicorn valuations; and several women-led companies, including PagerDuty, The RealReal, and Eventbrite, have entered the public markets with successful IPOs.
What’s driving so much positive change?
Clearly, broadened awareness of gender and power issues, largely due to #MeToo, as well as an increase in the number of female investors, thanks to groups like All Raise, are all contributing catalysts. In addition, women now outnumber men in college, a majority of American moms are in the workforce, and in 40 percent of households those women are the breadwinners. But it’s more than that; I believe that there’s a profound generational shift afloat, and that this first wave of female-led unicorns is just the tip of the NASDAQ iceberg.
Unlike previous generations who may have either looked at self-investment as self-indulgence or who simply didn’t have the resources or technology available to make supplementary investments in themselves, today’s badass millennial women are unapologetic about their desire to invest in their own success and well-being. Determined to succeed without compromising their values or physical and mental wellness, these uber-empowered millennial women are making viable a new generation of startups to help them realize their dreams and feel comfortable in their skin. I refer to this economic wave as She-conomy 2.0.
For decades now there have been tech companies, which I refer to as She-conomy 1.0, catering to traditional and homogeneous identities of women primarily as shoppers and caregivers. In contrast, these new modern She-conomy 2.0 brands address latent, historically unmet, often un-discussed and under-served needs that speak to the multitude of other facets of our identities.
These companies have less to do with what women buy and more to do with their willingness to invest in themselves — in their careers and in their physical and emotional health and well-being. They are seeking and are willing to pay for products and services that help them advance their careers, feel comfortable about their bodies, and provide the physical and emotional support they’re seeking.
The founding members of Allraise (Image courtesy of Allraise)
Women are taking control of their careers and supporting each other.
More than two decades ago, when I had my first child, I joined a mom’s group at Stanford Hospital. We were all working moms trying to juggle career and motherhood. It was a truly challenging time for each of us. The group provided such helpful support that we met every Monday evening for five years until our kids were in kindergarten. Why Mondays? Because Mondays are especially hard for working parents, marking yet another week in search of balance. We realized that meeting on Monday evenings provided us with the support we needed to make it through the work week. Perhaps even more critically, it gave us something about Mondays to look forward to.
There’s something incredibly empowering about experiencing a major transition like a new job or new parenthood as part of a cohort. Sheryl Sandberg famously sought to institutionalize this kind of support for working women with her non-profit Lean In. It has dramatically raised awareness around working women’s struggles. However, individual Lean In group leaders are usually volunteers running these sessions on the side while working and shouldering life’s endless list of other responsibilities.
Now a new generation of organizations is offering this support — for a fee. As for-profit organizations, they’re doing so in a scalable, consistent and reliable way. Women don’t have to worry about whether the organizer will be able to carve out time to orchestrate a meeting because doing so is the organizer’s job. Chief, Declare, The Assembly*, The Wing and The Riveter are all examples of companies that are growing and thriving because they’re offering valuable space, support and services that women are willing to pay for. Most of these organizations initially targeted millennials, but women of all generations are benefiting and participating.
A look inside one of The Riveter’s Seattle co-working spaces.
Women are changing the narrative around previously taboo topics and promoting inclusiveness and acceptance of oneself.
It wasn’t long ago that mannequins, much like cover models, only came in one size. Now mainstream brands not only sell broader offerings; they increasingly showcase them in magazines, catalogs, stores and the runway. For example, Nike’s flagship store in London featured both plus-sized mannequins and para-sport mannequins for people with physical and intellectual abilities, and Rhianna’s new inclusive lingerie line regularly presents both plus-size and pregnant models.
Millennials (like all of us) don’t want to feel shamed; they want to feel empowered and beautiful. Instead of settling for frumpy, ill-fitting clothing or outdated product design, millennials are using their social media megaphones to tell the market what they want. Traditional companies like Victoria’s Secret have moved at a molasses-like pace to evolve from treating women as objects of fantasy to celebrating their right to feel great about themselves. Their antiquated practices have created the opportunity for new startups to create brands centered on body positivity. Some companies are filling largely underserved market needs by catering exclusively to larger and specialty sizes, and others are addressing previously taboo topics like body hair, which also contribute strongly to feelings around body positivity. Eloquii offers extended clothing sizes, Ruby Ribbon* and Third Love provide a wide sizing range of under garments and bras, and Fur addresses body hair and grooming.
Women are dedicating more attention to their own health and relationships.
Self-help books have been around for ages, but tech is paving the way for a new generation of services to provide guidance and support that are more convenient and targeted. At the same time, women are increasingly willing to discuss health issues that were previously taboo, like menstruation, menopause and perimenopause, fertility, and depression. Advancements in technology are making health-related self-care more accessible from the convenience of our wristbands and phones. Meanwhile, people are spending a disproportionate amount of their wealth on health, making the entire healthcare industry ripe for disruption.
All of these factors are making femtech big business. Countless new companies are helping women take more active control of their sexual health, including birth control and STI testing (Pill Club and Nurx), period tracking (Flo Health), fertility and egg freezing (Kind Body and Carrot Fertility), menopause (Rory, Genneve), postpartum depression and miscarriage (Maven) and even our relationships (Relish* and Bumble). In addition, no shortage of femtech companies are addressing period care, such as Lola, Cora, The Flex Company, Thinx, and Sustain Natural.
These companies are only viable because so many women — beginning with millennials but expanding out to the rest of us — are now willing and able to invest in themselves. United across a shared mission of female empowerment and inclusivity, She-onomy 2.0 is making it more realistic than ever to empower us to advance our careers, feel good about ourselves and stay healthy. Hats off to the badass millennial women leading this charge; we’re all better off professionally, emotionally and even physically thanks to you!
*Denotes portfolio company for Trinity Ventures
SpanIO is looking to upgrade the electrical fusebox for homes with a digital system that integrates into the existing circuit breaker technology that has been the basis for home energy management for at least a century.
Rao and his team are looking to make integrating renewable power, energy storage, and electric vehicles easier for homeowners by redesigning the electrical panel for modern energy needs.
“We packaged the metering controls and compute between the bus bar and the breaker,” says Rao. “Energy flows through the panel through a breaker bar and the breaker bar has tabs that you slot your breakers into… that tab is usually a conductor. We have designed a digital sub-assembly that packages current metering, voltage measurement and ability to turn each circuit on or off.”
The technology is meant to be sold through channels like solar energy installers or battery installers. The company already has plans to integrate its power management devices with energy storage systems like the ones available from LG .
Initially, Span expects to be selling its products in states like California and Hawaii where demand for solar installations is strong and homeowners have significant benefits available to them for installing renewable energy and energy efficiency systems.
For homeowners, the new power management system means that they have control over which parts of the home would be powered in the event of an outage. The company’s technology connects the entire home to a renewable system. Using existing technologies, installers have to set up a separate breaker and rewire certain areas of the home to receive the power generated by a renewable energy system, Rao says.
That control is handled through a consumer app available to download on mobile devices.
SpanIO is backed by a slew of early investors including Wireframe Ventures, Wells Fargo Strategic Capital, Ulu Ventures, Hardware Club, Energy Foundry, Congruent Ventures and 1/0 Capital, and intends to raise fresh cash for before the end of the year. Rao said the round would be “in the low double digits” of millions.
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 Stripe’s grand plans. Before that, I noted Peloton’s secret weapons.
The best companies are built by people who have personally experienced the problem they’re attempting to solve. Lauren Jonas, the founder and chief executive officer of Part & Parcel, is intimately familiar with the struggles faced by the women she’s building for.
San Francisco-based Part & Parcel is a plus-sized clothing and shoe startup providing dimensional sizing to women across the U.S. The company operates a bit differently than your standard direct-to-consumer business by seeking to include the women who wear and evangelize the Part & Parcel designs by giving them a cut of their sales.
Here’s how it works: Ambassadors sign up to receive signature styles from Part & Parcel, which they then share and sell to women in their network. Ultimately, the sellers are eligible to receive up to 30% of the profit per sale. The out-of-the-box model, which might remind you somewhat of Mary Kay or Tupperware’s business strategy, is meant to encourage a sense of community and usher in a new era in which plus-sized women can facilitate other plus-sized women’s access to great clothes.
“I bought a brown men’s polyester suit and wore it to an interview,” Jonas, an early employee at Poshmark and the long-time author of the popular blog, ‘The Pear Shape,’ tells TechCrunch. “I was that kid wearing a men’s suit.”
Clothing tailored to plus-sized women has long been missing from the retail market. Increasingly, however, new brands are building thriving businesses by catering precisely to the historically forgotten demographic. Dia&Co., for example, raised another $70 million in venture capital funding last fall from Sequoia and USV. And Walmart recently acquired another brand in the space, ELOQUII, for an undisclosed amount. Part & Parcel, for its part, has raised $4 million in seed funding in a round led by Lightspeed Venture Partners’ Jeremy Liew.
The startup launched earlier this year in Anchorage, “a clothing desert,” and has since grown its network to include women in several other underserved markets. Given her own history struggling to find a fitted woman’s suit, Jonas launched her line with structured pieces, including suits and blouses — though the startup’s biggest success yet, she says, has been its boots, which come in three different calf width options.
“Seventy percent of women in this country are plus-sized,” Jonas said. “I’m bringing plus out of the dark corner of the department store.”
Image: Bryce Durbin / TechCrunch
TechCrunch’s Megan Rose Dickey published a highly anticipated deep dive on the state of sex tech this week. The piece provides new data on funding in sex tech and wellness companies, analysis on sex tech startup’s battle for public advertising and responses from industry leaders on how we can destigmatize sex with technology. Here’s a short passage from the story:
Cindy Gallop sees a market opportunity in every type of business obstacle she encounters. That’s why All The Sky will also seek to invest in startups that tackle the infrastructural tools needed to fuel sextech, like payments, hosting providers and e-commerce sites.
“I want to fund the sextech ecosystem to maintain and sustain a portfolio for All the Skies, to create a bloody huge sextech ecosystem and three, to monopolistically build out the ecosystem to be a multi-trillion-dollar market,” Gallop says.
I swung by Contrary Capital‘s Demo Day this week, in which a number of startups gave a 4- to 5-minute pitch. Next on my list is Alchemist‘s Demo Day in Menlo Park. The accelerator welcomes enterprise startups for a six-month program focused on early customer adoption, company development and mentorship.
Also on my radar is Females To The Front. The event began this week in Palm Springs and if I were based in SoCal, I would have swung by. Led by Amy Margolis, the event is said to be the largest gathering of female cannabis founders and funders to date. Here’s how the group describes the event: “Females to the Front Retreat will mix immersive and hands-on workshops, pitch training, investment deck preparation and business skill set education with investor meetings and plenty of shared meals, pool time, yoga, connections, rest and rejuvenation. Every workshop is built to directly engage attendees instead of powerpoint and panels. Be prepared to return home inspired, engaged and with so many more tools in your toolbox.”
For the record, I don’t advertise events in my newsletter just wanted to give props to this one because it’s a great development for the cannabis tech ecosystem.
We are just weeks away from our flagship conference, TechCrunch Disrupt San Francisco. We have dozens of amazing speakers lined up. In addition to taking in the great line-up of speakers, ticket holders can roam around Startup Alley to catch the more than 1,000 companies showcasing their products and technologies. And, of course, you’ll get the opportunity to watch the Startup Battlefield competition live. Past competitors include Dropbox, Cloudflare and Mint… You never know which future unicorn will compete next.
This week, the lovely Alex Wilhelm, editor-in-chief of Crunchbase News, and I gathered to discuss a number of topics including WeWork’s IPO and Uber’s attempts to bypass a new law meant to protect gig workers. Listen here.
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.
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?
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.
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.
This week on Equity, TechCrunch’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.
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:
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.
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.
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 TechCrunch/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.
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.
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 trade-oﬀs.
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.