The tech industry has grown ever more rich off big ideas that were developed more than a decade ago. New things like quantum computing and self-driving cars could take a while.
Tag Archives: Artificial Intelligence
Economists Pin More Blame on Tech for Rising Inequality
Recent research underlines the central role that automation has played in widening disparities.
Sara Menker and Gro Intelligence Are Tackling Global Hunger
The Ethiopian entrepreneur Sara Menker founded Gro Intelligence, which uses artificial intelligence to forecast global agricultural trends and battle food insecurity.
How AI Could Prevent the Development of New Illicit Drugs
The DarkNPS algorithm has predicted the formulas of millions of potential drugs
— Read more on ScientificAmerican.com
Why There’s a Growing Push to Ban Killer Robots
A U.N. conference made little headway this week on limiting development and use of killer robots, prompting stepped-up calls to outlaw such weapons with a new treaty.
A Portable MRI Makes Imaging More Democratic
An open-source approach downsizes today’s clunking behemoths with permanent magnets and deep-learning algorithms
— Read more on ScientificAmerican.com
Rethinking U.S. Rules on International Travel
Readers discuss testing requirements and suggest a quarantine. Also: Outdoor dining; universal pre-K; Roe v. Wade; machines and morality; Gil Hodges.
Evolution Gym Sculpts Novel Robot Bodies and Brains
The virtual robots look weird, but they get the job done
— Read more on ScientificAmerican.com
Group Backed by Top Companies Moves to Combat A.I. Bias in Hiring
The organization has created a format for evaluating the technology, which is often used to screen job candidates.
Biden’s Democracy Conference Is About Much More Than Democracy
Democracies can find strength in numbers.
How TikTok Keeps You Watching
It’s the most successful video app in the world. Our columnist has obtained an internal company document that offers a new level of detail about how the algorithm works.
Who Is Parag Agrawal, Twitter’s New C.E.O.?
A longtime Twitter insider and a confidant of co-founder Jack Dorsey, Mr. Agrawal takes over as the social media company confronts various challenges.
The Algorithm That Could Take Us Inside Shakespeare’s Mind
Machine learning programs have recently made huge advances. Stephen Marche tested one against Shakespeare’s collected works, to see if it could help him figure out which of the several versions of Hamlet’s soliloquy was most likely what the playwright intended.
A Robot Wrote This Book Review
In “The Age of AI,” Henry Kissinger, Eric Schmidt and Daniel Huttenlocher explore how far artificial intelligence has come.
Can a Machine Learn Morality?
Researchers at a Seattle A.I. lab say they have built a system that makes ethical judgments. But its judgments can be as confusing as those of humans.
An Artist Who Disavows the Possibility of Individual Agency
According to Agnieszka Kurant, everything we make — from the systems that oppress us to the inventions that transform us — is the result of a collective.
Why We Forgive Humans More Readily Than Machines
When things go wrong, flexible moral intuitions cause us to judge computers more severely
— Read more on ScientificAmerican.com
Google Wants to Work With the Pentagon Again, Despite Employee Concerns
Three years ago, the company walked away from a Defense Department project after employees objected to it. Now the company is working on a new proposal for the Pentagon.
Sex Bots and Mortality and A.I., Oh My!
The writer Jeanette Winterson explores the wild world unfolding alongside the rise of artificial intelligence.
Eric Schmidt Discusses the Dangers of A.I.
Will we end up as the family pet?
AI Generates Hypotheses Human Scientists Have Not Thought Of
Machine-learning algorithms can guide humans toward new experiments and theories
— Read more on ScientificAmerican.com
Zuckerberg Personifies Facebook’s Woes. Is It Time for Someone New?
The Facebook C.E.O. personifies the company’s woes. Is it time for someone new?
Google Pixel 6 Review: Playing Catch-Up With the iPhone
With long battery life and nice cameras, the new Google devices excel at what popular phones have done for years. Is that enough?
I’m Not a Pilot, but I Just Flew a Helicopter Over California
New technology, a few iPads and a quick tutorial can help anyone act like a pilot. Dealing with air traffic control is another matter.
In India, Facebook Struggles to Combat Misinformation and Hate Speech
Internal documents show a struggle with misinformation, hate speech and celebrations of violence in the country, the company’s biggest market.
U.S. Warns of Efforts by China to Collect Genetic Data
The National Counterintelligence and Security Center said American companies need to better secure critical technologies as Beijing seeks to dominate the so-called bioeconomy.
‘Small Data’ Is Also Crucial for Machine Learning
The most promising AI approach you’ve never heard of doesn’t need to go big
— Read more on ScientificAmerican.com
‘Small Data’ Are Also Crucial for Machine Learning
The most promising AI approach you’ve never heard of doesn’t need to go big
— Read more on ScientificAmerican.com
Facebook AI moderator confused videos of mass shootings and car washes
Enlarge / Facebook CEO Mark Zuckerberg testifying before Congress in April 2018. It wasn’t his only appearance in DC this decade. (credit: Bloomberg | Getty Images)
Facebook CEO Mark Zuckerberg sounded an optimistic note three years ago when he wrote about the progress his company was making in automated moderation tools powered by artificial intelligence. “Through the end of 2019, we expect to have trained our systems to proactively detect the vast majority of problematic content,” he wrote in November 2018.
But as recently as March, internal Facebook documents reveal the company found its automated moderation tools were falling far short, removing posts that were responsible for only a small fraction of views of hate speech and violence and incitement on the platform. The posts removed by AI tools only accounted for 3–5 percent of views of hate speech and 0.6 percent of views of violence and incitement.
While that’s up from 2 percent of hate speech views two years ago, according to documents turned over to The Wall Street Journal by whistleblower Frances Haugen, it’s far from a vast majority. One of the company’s senior engineers wrote in 2019 that he felt the company could improve by an order of magnitude but that they might then hit a ceiling beyond which further advances would be difficult.
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IBM says AI can help track carbon pollution across vast supply chains
Enlarge / A container ship sails off the coast of Thailand. (credit: iStock)
Finding sources of pollution across vast supply chains may be one of the largest barriers to eliminating carbon pollution. For some sources like electricity or transportation, it’s relatively easy. But for others like agriculture or consumer electronics, tracing and quantifying greenhouse gas emissions can be a time-consuming, laborious process. It generally takes an expert around three to six months—sometimes more—to come up with an estimate for a single product.
Typically, researchers have to probe vast supply chains, comb the scientific literature, digest reports, and even interview suppliers. They may have to dive into granular details, estimating the footprint of everything from gypsum in drywall to tin solder on circuit boards. Massive databases of reference values offer crude shortcuts, but they can also introduce uncertainty in the estimate because they don’t capture the idiosyncrasies of many companies’ supply chains.
Enter IBM, which has placed a massive bet on offering artificial intelligence services to businesses. Some services, like the company’s Watson health care effort, didn’t live up to the promise. But IBM has refocused its efforts in recent years, and today it announced a new suite of tools for businesses to tackle two significant challenges posed by climate change: emissions reduction and adaptation.
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These virtual obstacle courses help real robots learn to walk
A clip from the simulation where virtual robots learn to climb steps.
An army of more than 4,000 marching doglike robots is a vaguely menacing sight, even in a simulation. But it may point the way for machines to learn new tricks.
The virtual robot army was developed by researchers from ETH Zurich in Switzerland and chipmaker Nvidia. They used the wandering bots to train an algorithm that was then used to control the legs of a real-world robot.
In the simulation, the machines—called ANYmals—confront challenges like slopes, steps, and steep drops in a virtual landscape. Each time a robot learned to navigate a challenge, the researchers presented a harder one, nudging the control algorithm to be more sophisticated.
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Laurie Anderson Has a Message for Us Humans
For half a century, she has taken the things we know best— our bodies, our rituals, our nation — and shown us how strange they really are.
What Is Machine Learning, and How Does It Work? Here’s a Short Video Primer
Deep learning, neural networks, imitation games—what does any of this have to do with teaching computers to “learn”?
— Read more on ScientificAmerican.com
What Is Machine Learning? Here’s a Short Video Primer
Deep learning, neural networks, imitation games—what does any of this have to do with teaching computers to “learn”?
— Read more on ScientificAmerican.com
Mike Schroepfer, Facebook’s C.T.O., to Step Down in 2022
Mike Schroepfer, who leads the company’s artificial intelligence and other technical efforts, said he planned to transition into a role as a senior fellow.
Voice Assistants Don’t Understand Us. They Should.
Even for new technologies, accessibility matters.
Business Canvas, a Korea-based document management SaaS company, closes $2.5M seed round
Business Canvas, a South Korean document management SaaS company behind Typed, announced today it has raised a $2.5 million seed round led by Mirae Asset Venture Investment, with participation from Kakao Ventures and Nextrans Inc.
The seed round will be used for accelerating product development and global launch of open beta for its AI-powered document management platform. The company opened an office in Santa Clara, California this year to spur its global expansion.
People are bombarded with information thanks to advances in technology that opens the doors to a wealth of information, but at the same time, too much information and a huge amount of data at one time leave the users confused and/or unable to make timely decisions.
Business Canvas, founded in July 2020 by CEO Woojin Kim, Brian Shin, Seungmin Lee, Dongjoon Shin and Clint Yoo, is hoping to solve the challenge that every knowledge worker and writer faces: spending more time on research and file organization than the actual content output they need to create.
“In fact, people commit over 30% of their working hours trying to search for that file we once saved in a folder that we just cannot find anymore,” Business Canvas CEO and co-founder Kim said.
Through a network that intelligently tracks and organizes files based on the user’s interactions, Typed brings all the knowledge from different websites and applications into one simple-to-use and quick-to-learn digital workspace.
Strictly keeping its users’ information and their confidential files uninterrupted, Typed does not access the content of users’ documents but utilize them as machine learning data in order to protect their information and data, Kim told TechCrunch. It simply collects users’ action driven data point and publicly available metadata of documents and resources under users’ permission, Kim added.
“Modern document writing has not changed since the 1980s,” Business Canvas co-founder Clint Yoo said. “While we have more knowledge at our fingertips than ever before, we use the same rudimentary methods to organize and make sense of it. We want any writer – from lawyers and entrepreneurs to researchers and students – to focus on creating great content instead of wasting time organizing their source material. We achieved this by making knowledge management more like the way our brain operates.”
Since the launch of the closed beta test in February 2021, Typed saw significant user growth including more than 10,000 users on the waitlist, with 25,000 files uploaded and 350% month-over-month active user growth, the company said in its statement. Typed will be available through a freemium model and is currently accepting beta registrations on its website.
“When we’ve tested our closed beta, our metrics show top traction among students as well as journalists, writers and lawyers, who require heavy research and document work on a frequent basis. We opened up access earlier this month for the waitlists in over 50 countries. These are primarily B2C users,” Kim told TechCrunch. “As for B2B, we are currently in the process of proof-of-concept (POC) for one of the largest conglomerates in South Korea. Smaller teams like startups, boutique law, consulting firms, venture capitals and government institutions also have been adopting Typed as well.”
“While the company is still in its nascent stage in its development, Typed has the potential to fundamentally change how we work individually or as a team. If there is a business to take on our outdated way of writing content, it’s them [Typed],” Shina Chung, Kakao Ventures CEO said.
The global market size for social software and collaboration SaaS is estimated at $4.5 billion in 2021, increasing over 17% year on year, Kim said.
Ellen DeGeneres, Portia de Rossi, Shaun White, Shawn Mendes get behind Shelf Engine
Shelf Engine’s mission to eliminate food waste in grocery retailers now has some additional celebrity backers. The company brought in a $2 million extension to its $41 million Series B announced in March.
Ellen DeGeneres, Portia de Rossi, Shaun White and Shawn Mendes are the new backers, who came in through a strategic round of funding alongside PLUS Capital to bring the Seattle-based company’s total funding to $60 million since the company’s inception in 2016. This includes a $12 million Series A from 2020.
Shelf Engine’s grocery order automation technology applies advanced statistical models and artificial intelligence to deliver accurate food order volume so that customers can reduce their food waste by as much as 32% while increasing gross margins and sales of more than 50%. The company has already helped retailers divert 1 million pounds of food waste from landfills, Stefan Kalb, co-founder and CEO of Shelf Engine, told TechCrunch.
“We’ve had phenomenal growth last year, some of it from our mid-market customers, but mostly from customers like Target and Kroger,” Kalb said. “Our other big news is that we hired a president (Kane McCord) in the past six weeks, which is cool to have the reinforcement on the leadership side.”
Over the past 12 months, the company, which works with retailers like Kroger, Whole Foods and Compass Group, saw over 540% revenue growth. At the same time, it grew its employees to 200 from 23, Kalb said. He expects to more than double Shelf Engine’s headcount over the next 12 months.
As a result, the new funding will be used to scale with current customers and accelerate further investment in R&D of its AI systems and automation capabilities.
Meanwhile, Amanda Groves, partner at PLUS Capital, said her firm works with about 65 individuals who are in film, television, sports and culture, including the four new investors in Shelf Engine.
She says many of her clients are looking to participate in business as an investor or with sweat equity. Her firm works with them to determine interests and will then source opportunities and invest alongside them.
Shelf Engine fits into one of PLUS Capital’s core investment areas of sustainability. The firm looks across different sectors like food, energy, apparel, packaging and recycling. Shelf Engine’s approach of leveraging technology to aid in sustainability efforts was attractive to all of the investors, as was their method of scaling within grocery clients without affecting consumer behavior.
“When Shelf Engine is installed in the grocery store, they can reduce spoilage by 10% right off the bat — that immediacy of the impact was what got our clients excited,” Groves added.
One of Shelf Engine’s first celebrity investors was Joe Montana, and Kalb said partnering with celebrities enables the company’s mission to eliminate food waste and address the climate crisis to be made more aware.
“B2B software is not as glamorous, but the climate has become a big issue and something many celebrities care about,” he added. “Shawn Mendes has over 60 million followers, so for him to share about this issue is extremely meaningful. Where he invests will lead to his followers knocking on the doors of stores and saying ‘this matters to me.’ That is the strategy shift from B2B to a movement for our community.”
The company is not alone in tackling food waste, which globally each year amounts to $1.3 trillion. For example, Apeel, OLIO, Imperfect Foods, Mori and Phood Solutions are all working to improve the food supply chain and have attracted venture dollars in the past year to go after that mission.
Shelf Engine is already in over 3,000 stores nationwide in the areas of grocery, food service and convenience stores, which “is a large lift from 18 months ago,” Kalb said. Next up, the company is progressing to open new categories and managing more projects. He is specifically looking at what the company can manage in the store and manage for the customer.
“We are getting to the point where we can manage more of the store in complex categories like meat, seafood and deli that are mainly custom,” he added.
EarthOptics helps farmers look deep into the soil for big data insights
Farming sustainably and efficiently has gone from a big tractor problem to a big data problem over the last few decades, and startup EarthOptics believes the next frontier of precision agriculture lies deep in the soil. Using high-tech imaging techniques, the company claims to map the physical and chemical composition of fields faster, better, and more cheaply than traditional techniques, and has raised $10M to scale its solution.
“Most of the ways we monitor soil haven’t changed in 50 years,” EarthOptics founder and CEO Lars Dyrud told TechCrunch. “There’s been a tremendous amount of progress around precision data and using modern data methods in agriculture – but a lot of that has focused on the plants and in-season activity — there’s been comparatively little investment in soil.”
While you might think it’s obvious to look deeper into the stuff the plants are growing from, the simple fact is it’s difficult to do. Aerial and satellite imagery and IoT-infused sensors for things like moisture and nitrogen have made surface-level data for fields far richer, but past the first foot or so things get tricky.
Different parts of a field may have very different levels of physical characteristics like soil compaction, which can greatly affect crop outcomes, and chemical characteristics like dissolved nutrients and the microbiome. The best way to check these things, however, involves “putting a really expensive stick in the ground,” said Dyrud. The lab results from these samples affects the decision of which parts of a field need to be tilled and fertilized.
It’s still important, so farms get it done, but having soil sampled every few acres once or twice a year adds up fast when you have 10,000 acres to keep track of. So many just till and fertilize everything for lack of data, sinking a lot of money (Dyrud estimated the U.S. does about $1B in unnecessary tilling) into processes that might have no benefit and in fact might be harmful — it can release tons of carbon that was safely sequestered underground.
EarthOptics aims to make the data collection process better essentially by minimizing the “expensive stick” part. It has built an imaging suite that relies on ground penetrating radar and electromagnetic induction to produce a deep map of the soil that’s easier, cheaper, and more precise than extrapolating acres of data from a single sample.
Machine learning is at the heart of the company’s pair of tools, GroundOwl and C-Mapper (C as in carbon). The team trained a model that reconciles the no-contact data with traditional samples taken at a much lower rate, learning to predict soil characteristics accurately at level of precision far beyond what has traditionally been possible. The imaging hardware can be mounted on ordinary tractors or trucks, and pulls in readings every few feet. Physical sampling still happens, but dozens rather than hundreds of times.
With today’s methods, you might divide your thousands of acres into 50-acre chunks: this one needs more nitrogen, this one needs tilling, this one needs this or that treatment. EarthOptics brings that down to the scale of meters, and the data can be fed directly into roboticized field machinery like a variable depth smart tiller.
Drive it along the fields and it goes only as deep as it needs to. Of course not everyone has a state of the art equipment, so the data can also be put out as a more ordinary map telling the driver in a more general sense when to till or perform other tasks.
If this approach takes off, it could mean major savings for farmers looking to tighten belts, or improved productivity per acre and dollar for those looking to scale up. And ultimately the goal is to enable automated and robotic farming as well. That transition is in an early stage as equipment and practices get hammered out, but one thing they will all need is good data.
Dyrud said he hopes to see the EarthOptics sensor suite on robotic tractors, tillers, and other farm equipment, but that their product is very much the data and the machine learning model they’ve trained up with tens of thousands of ground truth measurements.
The $10.3M A round was led by Leaps by Bayer (the conglomerate’s impact arm), with participation from S2G Ventures, FHB Ventures, Middleland Capital’s VTC Ventures and Route 66 Ventures. The plan for the money is to scale up the two existing products and get to work on the next one: moisture mapping, obviously a major consideration for any farm.
Blue Bear Capital raises $150M to fund climate, energy and infrastructure tech
Blue Bear Capital has raised a new $150 million fund that will be used to find and invest in startups developing technology aimed at speeding up the adoption and industrialization of renewable energy.
This is the venture firm’s second fund, which it says is oversubscribed. Blue Bear has already backed nine new companies since 2020. The firm said the fresh cash will be used to fund digital technologies “making an outsized impact” in markets including wind, solar, the electric grid, EV infrastructure, transportation and energy-intensive industries.
“Trillions of dollars will be spent to scale renewable energy, modernize infrastructure and secure sustainable supply chains,” Blue Bear partner Ernst Sack said in a statement. “Meanwhile, artificial intelligence is redefining how data is captured, decisions are mad and relationships are built all around us. Where these two forces converge — applying the power of AI-enabled technologies to the immense challenges of the energy transition — is where Blue Bear sees the greatest investment and impact opportunity of our lifetimes.”
Blue Bear has a two-fold investment strategy. The firm’s investors look for those that “nail a vertical,” which is code for startups that have developed Software as a Service solutions that help industries address operational bottlenecks and handle niche use cases. Blue Bear also looks for startups that have developed software that can scale horizontally across many markets.
The portfolio companies in Blue Bear’s “nail a vertical” bucket include FreeWire Technologies, which developed a suite of mobile EV charging products and Omnidian, a distributed solar asset management company. Horizontal scale companies that BlueBear has backed include Urbint, which is focused on infrastructure safety and Demex, a climate and weather risk management company.
As with Blue Bear’s first fund, this one is aimed at helping early-stage companies scale — and not just by investing capital. The VC touts the expertise of its partners, who have decades of experience in sustainable investments and hands-on work in climate, policy, corporate venture, cloud computing and other related technologies.
“As specialists we believe in a high conviction and relatively concentrated approach to portfolio construction,” said Blue Bear partner Vaughn Blake in a statement, adding that the firm select companies with long-term partnership in mind. Blake also said the firm avoids the high-volume approach to venture, where a handful of companies are expected to make up a fund’s returns while the bulk are left to fall away.”
Investors in Blue Bear’s fund include AIMS Imprint of Goldman Sachs Asset Management, Rockefeller Brothers Fund and the McKnight Foundation, as well as leadership from other private equity firms and energy companies. Advisory Board members include First Reserve President Alex Krueger, former NASA astronaut Tim Kopra, and former BP Chairman and CEO Lord John Browne.
Blackbird.AI grabs $10M to help brands counter disinformation
New York-based Blackbird.AI has closed a $10 million Series A as it prepares to launched the next version of its disinformation intelligence platform this fall.
The Series A is led by Dorilton Ventures, along with new investors including Generation Ventures, Trousdale Ventures, StartFast Ventures and Richard Clarke, former chief counter-terrorism advisor for the National Security Council. Existing investor NetX also participated.
Blackbird says it’ll be used to scale up to meet demand in new and existing markets, including by expanding its team and spending more on product dev.
The 2017-founded startup sells software as a service targeted at brands and enterprises managing risks related to malicious and manipulative information — touting the notion of defending the “authenticity” of corporate marketing.
It’s applying a range of AI technologies to tackle the challenge of filtering and interpreting emergent narratives from across the Internet to identify disinformation risks targeting its customers. (And, for the record, this Blackbird is no relation to an earlier NLP startup, called Blackbird, which was acquired by Etsy back in 2016.)
Blackbird AI is focused on applying automation technologies to detect malicious/manipulative narratives — so the service aims to surface emerging disinformation threats for its clients, rather than delving into the tricky task of attribution. On that front it’s only looking at what it calls “cohorts” (or “tribes”) of online users — who may be manipulating information collectively, for a shared interest or common goal (talking in terms of groups like antivaxxers or “bitcoin bros”).
Blackbird CEO and co-founder Wasim Khaled says the team has chalked up five years of R&D and “granular model development” to get the product to where it is now.
“In terms of technology the way we think about the company today is an AI-driven disinformation and narrative intelligence platform,” he tells TechCrunch. “This is essentially the efforts of five years of very in-depth, ears to the ground research and development that has really spanned people everywhere from the comms industry to national security to enterprise and Fortune 500, psychologists, journalists.
“We’ve just been non-stop talking to the stakeholders, the people in the trenches — to understand where their problem sets really are. And, from a scientific empirical method, how do you break those down into its discrete parts? Automate pieces of it, empower and enable the individuals that are trying to make decisions out of all of the information disorder that we see happening.”
The first version of Blackbird’s SaaS was released in November 2020 but the startup isn’t disclosing customer numbers as yet. v2 of the platform will be launched this November, per Khaled.
Also today it’s announcing a partnership with PR firm, Weber Shandwick, to provide support to customers on how to respond to specific malicious messaging that could impact their businesses and which its platform has flagged up as an emerging risk.
Disinformation has of course become a much labelled and discussed feature of online life in recent years, although it’s hardly a new (human) phenomenon. (See, for example, the orchestrated airbourne leaflet propaganda drops used during war to spread unease among enemy combatants and populations). However it’s fair to say that the Internet has supercharged the ability of intentionally bad/bogus content to spread and cause reputational and other types of harms.
Studies show the speed of online travel of ‘fake news’ (as this stuff is sometimes also called) is far greater than truthful information. And there the ad-funded business models of mainstream social media platforms are implicated since their commercial content-sorting algorithms are incentivized to amplify stuff that’s more engaging to eyeballs, which isn’t usually the grey and nuanced truth.
Stock and crypto trading is another growing incentive for spreading disinformation — just look at the recent example of Walmart targeted with a fake press release suggesting the retailer was about to accept litecoin.
All of which makes countering disinformation look like a growing business opportunity.
Earlier this summer, for example, another stealthy startup in this area, ActiveFence, uncloaked to announce a $100M funding round. Others in the space include Primer and Yonder (previously New Knowledge), to name a few.
While some other earlier players in the space got acquired by some of the tech giants wrestling with how to clean up their own disinformation-ridden platforms — such as UK-based Fabula AI, which was bought by Twitter in 2019.
Another — Bloomsbury AI — was acquired by Facebook. And the tech giant now routinely tries to put its own spin on its disinformation problem by publishing reports that contain a snapshot of what it dubs “coordinated inauthentic behavior” that it’s found happening on its platforms (although Facebook’s selective transparency often raises more questions than it answers.)
The problems created by bogus online narratives ripple far beyond key host and spreader platforms like Facebook — with the potential to impact scores of companies and organizations, as well as democratic processes.
But while disinformation is a problem that can now scale everywhere online and affect almost anything and anyone, Blackbird is concentrating on selling its counter tech to brands and enterprises — targeting entities with the resources to pay to shrink reputational risks posed by targeted disinformation.
Per Khaled, Blackbird’s product — which consists of an enterprise dashboard and an underlying data processing engine — is not just doing data aggregation, either; the startup is in the business of intelligently structuring the threat data its engine gathers, he says, arguing too that it goes further than some rival offerings that are doing NLP (natural language processing) plus maybe some “light sentiment analysis”, as he puts it.
Although NLP is also key area of focus for Blackbird, along with network analysis — and doing things like looking at the structure of botnets.
But the suggestion is Blackbird goes further than the competition by merit of considering a wider range of factors to help identify threats to the “integrity” of corporate messaging. (Or, at least, that’s its marketing pitch.)
Khaled says the platform focuses on five “signals” to help it deconstruct the flow of online chatter related to a particular client and their interests — which he breaks down thusly: Narratives, networks, cohorts, manipulation and deception. And for each area of focus Blackbird is applying a cluster of AI technologies, according to Khaled.
But while the aim is to leverage the power of automation to tackle the scale of the disinformation challenge that businesses now face, Blackbird isn’t able to do this purely with AI alone; expert human analysis remains a component of the service — and Khaled notes that, for example, it can offer customers (human) disinformation analysts to help them drill further into their disinformation threat landscape.
“What really differentiates our platform is we process all five of these signals in tandem and in near real-time to generate what you can think of almost as a composite risk index that our clients can weigh, based on what might be most important to them, to rank the most important action-oriented information for their organization,” he says.
“Really it’s this tandem processing — quantifying the attack on human perception that we see happening; what we think of as a cyber attack on human perception — how do you understand when someone is trying to shift the public’s perception? About a topic, a person, an idea. Or when we look at corporate risk, more and more, we see when is a group or an organization or a set of accounts trying to drive public scrutiny against a company for a particular topic.
“Sometimes those topics are already in the news but the property that we want our customers or anybody to understand is when is something being driven in a manipulative manner? Because that means there’s an incentive, a motive, or an unnatural set of forces… acting upon the narrative being spread far and fast.”
“We’ve been working on this, and only this, and early on decided to do a purpose-built system to look at this problem. And that’s one of the things that really set us apart,” he also suggests, adding: “There are a handful of companies that are in what is shaping up to be a new space — but often some of them were in some other line of work, like marketing or social and they’ve tried to build some models on top of it.
“For bots — and for all of the signals we talked about — I think the biggest challenge for many organizations if they haven’t completely purpose built from scratch like we have… you end up against certain problems down the road that prevent you from being scalable. Speed becomes one of the biggest issues.
“Some of the largest organizations we’ve talked to could in theory product the signals — some of the signals that I talked about before — but the lift might take them ten to 12 days. Which makes it really unsuited for anything but the most forensic reporting, after things have kinda gone south… What you really need it in is two minutes or two seconds. And that’s where — from day one — we’ve been looking to get.”
As well as brands and enterprises with reputational concerns — such as those whose activity intersects with the ESG space; aka ‘environmental, social and governance’ — Khaled claims investors are also interested in using the tool for decision support, adding: “They want to get the full picture and make sure they’re not being manipulated.”
At present, Blackbird’s analysis focuses on emergent disinformation threats — aka “nowcasting” — but the goal is also to push into disinformation threat predictive — to help prepare clients for information-related manipulation problems before they occur. Albeit there’s no timeframe for launching that component yet.
“In terms of counter measurement/mitigation, today we are by and large a detection platform, starting to bridge into predictive detection as well,” says Khaled, adding: “We don’t take the word predictive lightly. We don’t just throw it around so we’re slowly launching the pieces that really are going to be helpful as predictive.
“Our AI engine trying to tell [customers] where things are headed, rather than just telling them the moment it happens… based on — at least from our platform’s perspective — having ingested billions of posts and events and instances to then pattern match to something similar to that that might happen in the future.”
“A lot of people just plot a path based on timestamps — based on how quickly something is picking up. That’s not prediction for Blackbird,” he also argues. “We’ve seen other organizations call that predictive; we’re not going to call that predictive.”
In the nearer term, Blackbird has some “interesting” counter measurement tech to assist teams in its pipeline, coming in Q1 and Q2 of 2022, Khaled adds.
The next healthcare revolution will have AI at its center
The global pandemic has heightened our understanding and sense of importance of our own health and the fragility of healthcare systems around the world. We’ve all come to realize how archaic many of our health processes are, and that, if we really want to, we can move at lightning speed. This is already leading to a massive acceleration in both the investment and application of artificial intelligence in the health and medical ecosystems.
Modern medicine in the 20th century benefited from unprecedented scientific breakthroughs, resulting in improvements in every aspect of healthcare. As a result, human life expectancy increased from 31 years in 1900 to 72 years in 2017. Today, I believe we are on the cusp of another healthcare revolution — one driven by artificial intelligence (AI). Advances in AI will usher in the era of modern medicine in truth.
Over the coming decades, we can expect medical diagnosis to evolve from an AI tool that provides analysis of options to an AI assistant that recommends treatments.
Digitization enables powerful AI
The healthcare sector is seeing massive digitization of everything from patient records and radiology data to wearable computing and multiomics. This will redefine healthcare as a data-driven industry, and when that happens, it will leverage the power of AI — its ability to continuously improve with more data.
When there is enough data, AI can do a much more accurate job of diagnosis and treatment than human doctors by absorbing and checking billions of cases and outcomes. AI can take into account everyone’s data to personalize treatment accordingly, or keep up with a massive number of new drugs, treatments and studies. Doing all of this well is beyond human capabilities.
AI-powered diagnosis
I anticipate diagnostic AI will surpass all but the best doctors in the next 20 years. Studies have shown that AI trained on sizable data can outperform physicians in several areas of medical diagnosis regarding brain tumors, eye disease, breast cancer, skin cancer and lung cancer. Further trials are needed, but as these technologies are deployed and more data is gathered, the AI stands to outclass doctors.
We will eventually see diagnostic AI for general practitioners, one disease at a time, to gradually cover all diagnoses. Over time, AI may become capable of acting as your general practitioner or family doctor.
Flippa raises $11M to match online asset and business buyers, sellers
Flippa, an online marketplace to buy and sell online businesses and digital assets, announced its first venture-backed round, an $11 million Series A, as it sees over 600,000 monthly searches from investors looking to connect with business owners.
OneVentures led the round and was joined by existing investors Andrew Walsh (former Hitwise CEO), Flippa co-founders Mark Harbottle and Matt Mickiewicz, 99designs, as well as new investors Catch.com.au founders Gabby and Hezi Leibovich; RetailMeNot.com founders Guy King and Bevan Clarke; and Reactive Media founders Tim O’Neill and Tim Fouhy.
The company, with bases in both Austin and Australia, was started in 2009 and facilitates exits for millions of online business owners that operate on e-commerce marketplaces, blogs, SaaS and apps, the newest being Shopify, Blake Hutchison, CEO of Flippa, told TechCrunch.
He considers Flippa to be “the investment bank for the 99%,” of small businesses, providing an end-to end platform that includes a proprietary valuation product for businesses — processing over 4,000 valuations each month — and a matching algorithm to connect with qualified buyers.
Business owners can sell their companies directly through the platform and have the option to bring in a business broker or advisor. The company also offers due diligence and acquisition financing from Thrasio-owned Yardline Capital and a new service called Flippa Legal.
“Our strategy is data,” Hutchison said. “Users can currently connect to Stripe, QuickBooks Online, WooCommerce, Google Analytics and Admob for apps, which means they can expose their online business performance with one-click, and buyers can seamlessly assess financial and operational performance.”
Online retail, as a share of total retail sales, grew to 19.6% in 2020, up from 15.8% in 2019, driven largely by the global pandemic as sales shifted online while brick-and-mortar stores closed.
Meanwhile, Amazon has 6 million sellers, and Shopify sellers run over 1 million businesses. This has led to an emergence of e-commerce aggregators, backed by venture capital dollars, that are scooping up successful businesses to grow, finding many through Flippa’s marketplace, Hutchison said.
Flippa has over 3 million registered users and added 300,000 new registered users in the past 12 months. Overall transaction volume grows 100% year over year. Though being bootstrapped for over a decade, the company’s growth and opportunity drove Hutchison to go after venture capital dollars.
“There is a huge movement toward this being recognized as an asset class,” he said. “At the moment, the asset class is undervalued and driving a massive swarm as investors snap up businesses and aggregate them together. We see the future of these aggregators becoming ‘X company for apps’ or ‘X for blogs.’ ”
As such, the new funding will be used to double the company’s headcount to more than 100 people as it builds out its offices globally, as well as establishing outposts in Melbourne, San Francisco and Austin. The company will also invest in marketing and product development to scale its business valuation tool that Hutchison likens to the “Zillow Zestimate,” but for online businesses.
Nigel Dews, operating partner at OneVentures, has been following Flippa since it started. His firm is one of the oldest venture capital firms in Australia and has 30 companies in its portfolio focused on healthcare and technology.
He believes the company will create meaningful change for small businesses. The team combined with Flippa’s ability to connect buyers and sellers puts the company in a strong leadership position to take advantage of the marketplace effect.
“Flippa is an incredible opportunity for us,” he added. “You don’t often get a world-leading business in a brand new category with incredible tailwinds. We also liked that the company is based in Australia, but half of its revenue comes from the U.S.”
Longtime VC, and happy Miami resident, David Blumberg has raised a new $225 million fund
Blumberg Capital, founded in 1991 by investor David Blumberg, has just closed its fifth early-stage venture fund with $225 million, a vehicle that Blumberg says was oversubscribed — he planned to raise $200 million — and that has already been used to invest in 16 startups around the world (the firm has small offices in San Francisco, New York, Tel Aviv, and Miami, where Blumberg moved his family last year).
We caught up with him earlier this week to talk shop and he sounded pretty ecstatic about the current market, which has evidently been good for returns, with Blumberg Capital’s biggest hits tied to Nutanix (it claims a 68x return), DoubleVerify (a 98x return at IPO in April, the firm says), Katapult (which went public via SPAC in July), Addepar (currently valued above $2 billion) and Braze (it submitted its S-1 in June).
We also talked a bit about his new life in Florida, which he was quick to note is “not a clone of Silicon Valley.” Not last, he told us why he thinks we’re in a “golden era of applying intelligence to every business,” from mining to the business of athletic performance.
More from our conversation, edited lightly for length and clarity, follows:
TC: What are you funding right now?
DB: Our last 30 to 40 deals have basically been about big data that’s been analyzed by artificial intelligence of some sort, then riding in a better wrapper of software process automation on rails of internet and mobility. Okay, that’s a lot of buzzwords.
TC: Yes.
DB: What I’m saying is that this ability to take raw information data that’s either been sitting around and not analyzed, or from new sources of data like sensors or social media or many other places, then analyze it and take it to the problem of all these businesses that have been there forever, is beginning to make incremental improvements that may sound small [but add up].
TC: What’s a very recent example?
One of our [unannounced] companies applies AI to mining — lithium mining and gold and copper — so miners don’t waste their time before finding the richest vein of deposit. We partner with mining owners and we bring extra data that they don’t have access to — some is proprietary, some is public — and because we’re experts at the AI modeling of it, we can apply it to their geography and geology, and as part of the business model, we take part of the mine in return.
TC: So your fund now owns not just equity but part of a mine?
DB: This is evidently done a lot in what’s called E&P, exploration and production in the oil and gas industry, and we’re just following a time-tested model, where some of the service providers put in value and take out a share. So as we see it, it aligns our interests and the better we do for them, the better they do.
TC: This fund is around the same size of your fourth fund, which closed with $207 million in 2017. How do you think about check sizes in this market?
DB: We write checks of $1 million to $6 million generally. We could go down a little bit for something in a seed where we can’t get more of a slice, but we like to have large ownership up front. We found that to have a fund return at least three x — and our funds seem to be returning much more than that — [we need to be math-minded about things].
We have 36 companies in our portfolio typically, and 20% of them fail, 20% of them are our superstars, and 60% are kind of medium. Of those superstars, six of them have to return $100 million each in a $200 million fund to make it a $600 million return, and to get six companies to [produce a] $100 million [for us] they have to reach a billion dollars in value, where we own 10% at the end.
TC You’re buying 10% and maintaining your pro rata or this is after being diluted over numerous rounds?
DB: It’s more like we want 15% to 20% of a company and it gets [diluted] down to 10%. And it’s been working. Some of our funds are way above that number.
TC: Are all four of your earlier funds in the black?
DB: Yes. I love to say this: We have never, ever lost money for our fund investors.
TC: You were among a handful of VCs who were cited quite a lot last year for hightailing it out of the Bay Area for Miami. One year into the move, how is it going?
DB: It is not a clone of Silicon Valley. They are different and add value each in their own way. But Florida is a great place for our family to be and I find for our business, it’s going to be great as well. I can be on the phone to Israel and New York without any time zone-related problems. Some of our companies are moving here, including one from from Israel recently, one from San Francisco, and one from Texas. A lot of our LPs are moving here or live here already. We can also up and down to South America for distribution deals more easily.
If we need to get to California or New York, airplanes still work, too, so it hasn’t been a negative at all. I’m going to a JPMorgan event tonight for a bunch of tech founders where there should be 150 people.
TC: That sounds great, though did you feel about summer in Miami?
DB: We were in France.
Pictured above, from left to right: Firm founder David Blumberg, managing director Yodfat Harel Buchris, COO Steve Gillan, and managing director Bruce Taragin.
Defy Partners leads $3M round into sales intelligence platform Aircover
Aircover raised $3 million in seed funding to continue developing its real-time sales intelligence platform.
Defy Partners led the round with participation from Firebolt Ventures, Flex Capital, Ridge Ventures and a group of angel investors.
The company, headquartered in the Bay Area, aims to give sales teams insights relevant to closing the sale as they are meeting with customers. Aircover’s conversational AI software integrates with Zoom and automates parts of the sales process to lead to more effective conversations.
Aircover’s founding team of Andrew Levy, Alex Young and Andrew’s brother David Levy worked together at Apteligent, a company co-founded and led by Andrew Levy, that was sold to VMware in 2017.
Chatting about pain points on the sales process over the years, Levy said it felt like the solution was always training the sales team more. However, by the time everyone was trained, that information would largely be out-of-date.
Instead, they created Aircover to be a software tool on top of video conferencing that performs real-time transcription of the conversation and then analysis to put the right content in front of the sales person at the right time based on customer issues and questions. This means that another sales expert doesn’t need to be pulled in or an additional call scheduled to provide answers to questions.
“We are anticipating that knowledge and parsing it out at key moments to provide more leverage to subject matter experts,” Andrew Levy told TechCrunch. “It’s like a sales assistant coming in to handle any issue.”
He considers Aircover in a similar realm with other sales team solutions, like Chorus.ai, which was recently scooped up by ZoomInfo, and Gong, but sees his company carving out space in real-time meeting experiences. Other tools also record the meetings, but to be reviewed after the call is completed.
“That can’t change the outcome of the sale, which is what we are trying to do,” Levy added.
The new funding will be used for product development. Levy intends to double his small engineering team by the end of the month.
He calls what Aircover is doing a “large interesting problem we are solving that requires some difficult technology because it is real time,” which is why the company was eager to partner with Bob Rosin, partner at Defy Partners, who joins Aircover’s board of directors as part of the investment.
Rosin joined Defy in 2020 after working on the leadership teams of Stripe, LinkedIn and Skype. He said sales and customer teams need tools in the moment, and while some are useful in retrospect, people want them to be live, in front of the customer.
“In the early days, tools helped before and after, but in the moment when they need the most help, we are not seeing many doing it,” Rosin added. “Aircover has come up with the complete solution.”
CodeSignal secures $50M for its tech hiring platform
In less than a year after raising $25 million in Series B funding, technical assessment company CodeSignal announced a $50 million in Series C funding to offer new features for its platform that helps companies make data-driven hiring decisions to find and test engineering talent.
Similar to attracting a big investor lead for its B round — Menlo Ventures — it has partnered with Index Ventures to lead the C round. Menlo participated again and was joined by Headline and A Capital. This round brings CodeSignal’s total fundraising to $87.5 million.
Co-founder and CEO Tigran Sloyan got the idea for the company from an experience his co-founder and friend Aram Shatakhtsyan had while trying to find an engineering job. Both from Armenia, the two went in different paths for college, with Shatakhtsyan staying in Armenia and Sloyan coming to the U.S. to study at MIT. He then went on to work at Google.
“As companies were recruiting myself and my classmates, Aram was trying to get his resume picked up, but wasn’t getting attention because of where he went to college, even though he was the greatest programmer I had ever known,” Sloyan told TechCrunch. “Hiring talent is the No. 1 problem companies say they have, but here was the best engineer, and no one would bring him in.”
They, along with Sophia Baik, started CodeSignal in 2015 to act as a self-driving interview platform that directly measures skills regardless of a person’s background. Like people needing to take a driver’s test in order to get a license, Sloyan calls the company’s technical assessment technology a “flight simulator for developers,” that gives candidates a simulated evaluation of their skills and comes back with a score and highlighted strengths.
The need by companies to hire engineers has led to CodeSignal growing 3.5 times in revenue year over year and to gather a customer list that includes Brex, Databricks, Facebook, Instacart, Robinhood, Upwork and Zoom.
Sloyan said the company has not yet touched the money it received in its Series B, but wanted to jump at the opportunity to work with Nina Achadjian, partner at Index Ventures, whom he had known for many years since their time together at Google. To work together and for Achadjian to join the company’s board was something “I couldn’t pass up,” Sloyan said.
When Achadjian moved over to venture capital, she helped Sloyan connect to mentors and angel investors while keeping an eye on the company. Hiring engineers is “mission critical” for technology companies, but what became more obvious to her was that engineering functions have become necessary for all companies, Achadjian explained.
While performing due diligence on the space, she saw traditional engineering cultures utilizing CodeSignal, but then would also see nontraditional companies like banks and insurance companies.
“Their traction was undeniable, and many of our portfolio companies were using CodeSignal,” she added. “It is rare to see a company accelerate growth at the stage they are at.”
U.S. Department of Labor statistics estimate there is already a global talent labor shortage of 40 million workers, and that number will grow to over 85 million by 2030. Achadjian says engineering jobs are also expected to increase during that time, and with all of those roles and applicants, vetting candidates will be more important than ever, as will the ability for candidates to apply from wherever they are.
The new funding enabled the company to launch its Integrated Development Environment for candidates to interact with relevant assessment experiences like codes, files and a terminal on a machine that is familiar with them, so that they can showcase their skills, while also being able to preview their application. At the same time, employers are able to assign each candidate the same coding task based on the open position.
In addition, Sloyan intends to triple the company’s headcount over the next couple of months and expand into other use cases for skills assessment.
FTC says health apps must notify consumers about data breaches — or face fines
The U.S. Federal Trade Commission (FTC) has warned apps and devices that collect personal health information must notify consumers if their data is breached or shared with third parties without their permission.
In a 3-2 vote on Wednesday, the FTC agreed on a new policy statement to clarify a decade-old 2009 Health Breach Notification Rule, which requires companies handling health records to notify consumers if their data is accessed without permission, such as the result of a breach. This has now been extended to apply to health apps and devices — specifically calling out apps that track fertility data, fitness, and blood glucose — which “too often fail to invest in adequate privacy and data security,” according to FTC chair Lina Khan.
“Digital apps are routinely caught playing fast and loose with user data, leaving users’ sensitive health information susceptible to hacks and breaches,” said Khan in a statement, pointing to a study published this year in the British Medical Journal that found health apps suffer from “serious problems” ranging from the insecure transmission of user data to the unauthorized sharing of data with advertisers.
There have also been a number of recent high-profile breaches involving health apps in recent years. Babylon Health, a U.K. AI chatbot and telehealth startup, last year suffered a data breach after a “software error” allowed users to access other patients’ video consultations, while period tracking app Flo was recently found to be sharing users’ health data with third-party analytics and marketing services.
Under the new rule, any company offering health apps or connected fitness devices that collect personal health data must notify consumers if their data has been compromised. However, the rule doesn’t define a “data breach” as just a cybersecurity intrusion; unauthorized access to personal data, including the sharing of information without an individual’s permission, can also trigger notification obligations.
“While this rule imposes some measure of accountability on tech firms that abuse our personal information, a more fundamental problem is the commodification of sensitive health information, where companies can use this data to feed behavioral ads or power user analytics,” Khan said.
If companies don’t comply with the rule, the FTC said it will “vigorously” enforce fines of $43,792 per violation per day.
The FTC has been cracking down on privacy violations in recent weeks. Earlier this month, the agency unanimously voted to ban spyware maker SpyFone and its chief executive Scott Zuckerman from the surveillance industry for harvesting mobile data on thousands of people and leaving it on the open internet.
AI startup Sorcero secures $10M for language intelligence platform
Sorcero announced Thursday a $10 million Series A round of funding to continue scaling its medical and technical language intelligence platform.
The latest funding round comes as the company, headquartered in Washington, D.C. and Cambridge, Massachusetts, sees increased demand for its advanced analytics from life sciences and technical companies. Sorcero’s natural language processing platform makes it easier for subject-matter experts to find answers to their questions to aid in better decision making.
CityRock Venture Partners, the growth fund of H/L Ventures, led the round and was joined by new investors Harmonix Fund, Rackhouse, Mighty Capital and Leawood VC, as well as existing investors, Castor Ventures and WorldQuant Ventures. The new investment gives Sorcero a total of $15.7 million in funding since it was founded in 2018.
Prior to starting Sorcero, Dipanwita Das, co-founder and CEO, told TechCrunch she was working in public policy, a place where scientific content is useful, but often a source of confusion and burden. She thought there had to be a more effective way to make better decisions across the healthcare value chain. That’s when she met co-founders Walter Bender and Richard Graves and started the company.
“Everything is in service of subject-matter experts being faster, better and less prone to errors,” Das said. “Advances of deep learning with accuracy add a lot of transparency. We are used by science affairs and regulatory teams whose jobs it is to collect scientific data and effectively communicate it to a variety of stakeholders.”
The total addressable market for language intelligence is big — Das estimated it to be $42 billion just for the life sciences sector. Due to the demand, the co-founders have seen the company grow at 324% year over year since 2020, she added.
Raising a Series A enables the company to serve more customers across the life sciences sector. The company will invest in talent in both engineering and on the commercial side. It will also put some funds into Sorcero’s go-to-market strategy to go after other use cases.
In the next 12 to 18 months, a big focus for the company will be scaling into product market fit in the medical affairs and regulatory space and closing new partnerships.
Oliver Libby, partner at CityRock Venture Partners, said Sorcero’s platform “provides the rails for AI solutions for companies” that have traditionally found issues with AI technologies as they try to integrate data sets that are already in existence in order to run analysis effectively on top of that.
Rather than have to build custom technology and connectors, Sorcero is “revolutionizing it, reducing time and increasing accuracy,” and if AI is to have a future, it needs a universal translator that plugs into everything, he said.
“One of the hallmarks in the response to COVID was how quickly the scientific community had to do revolutionary things,” Libby added. “The time to vaccine was almost a miracle of modern science. One of the first things they did was track medical resources and turn them into a hook for pharmaceutical companies. There couldn’t have been a better use case for Sorcero than COVID.”
New Zealand startup HeartLab raises $2.45M to bring heart scanning software to the US
New Zealand-based medtech startup HeartLab has raised $2.45 million in seed funding that it says will help the company expand its AI-powered heart scanning and reporting platform to cardiologists in the United States by early next year.
HeartLab provides an end-to-end solution for echocardiograms, the ultrasound tests that doctors use to examine a patient’s heart structure and function. Not only does the software help sort and analyze ultrasound images to help doctors diagnose cardiovascular disease, but it also streamlines the workflow by generating patient reports for doctors that can then be added to a patient’s health record.
Will Hewitt, 21, started HeartLab when he was 18 years old studying applied mathematics and statistics at the University of Auckland and working as a researcher at the Auckland Bioengineering Institute. The idea for the startup came to him as he listened to cardiologist, and now co-founder, Patrick Gladding explain how time-consuming and potentially inaccurate it is for doctors to have to review multiple scans manually everyday.
“You’ve got a really repetitive manual task done by a highly trained professional,” Hewitt told TechCrunch. “To start with, we just decided to train the AI to do one really small part of the doctor’s job, which was to look at these scans and generate a couple of different measurements that normally the doctor would have to do themselves,” said Hewitt.
In order to replicate the tedious process that doctors were doing, HeartLab built its own in-house labeling tool with sonographers that includes step-by-step guides and prompts to collect data on a range of different measurements. Hewitt said this initiative was one of the most valuable efforts of engineering the company has invested in to date because it has lead to cross validation, which is used to test the ability of the machine learning model to predict new data, as well as flag problems like selection bias and overfitting.
Once HeartLab was able to successfully replicate the scanning process, the company worked to expand its services in a way that would relieve doctors of further admin minutiae so they could spend more time actually treating their patients. Usually, doctors use a software tool that analyzes the images, another that visualizes patterns and another that actually writes up the report, says Hewitt. HeartLab’s platform, called Pulse, can now condense those processes into one software.
Cardiologists and sonographers at four different sites in New Zealand are trialing HeartLab’s tech now, which is also awaiting regulatory approval from the U.S.’s Food and Drug Administration. HeartLab anticipates FDA approval of Pulse by the first quarter of 2022, which is when the startup can begin selling the SaaS product.
“To begin with we want to talk to small and medium clinics over in the U.S.,” said Hewitt. “We’ve actually found that our products are most popular at those clinics because it replaces more software than at a larger clinic. At a larger clinic some of these bits of software they’ve already had to purchase, versus a smaller clinic, it’s stuff that they couldn’t access anyway. So when we get to the states, we want to start shipping mostly to those sorts of users while we work out how to best pitch our value proposition to the larger clinics.”
Hewitt says the funds from this round will also help the startup hire 10 more staff members to join the existing 13-member team based in Auckland. Having more tech talent on board will help HeartLab advance its product offering. At the moment, Pulse is at the point where it sees so many scans and takes so many measurements that it can get through the process quicker than a doctor could on their own and actually pick out patterns that a doctor wouldn’t see, according to Hewitt. The next step, which a good chunk of the seed funding is going toward, is how to be diagnostic about disease rather than just being able to indicate it.
“How do we actually provide something that normally doctors would have to order another scan for?” said Hewitt. “One of the key ideas with AI is you can create mappings from low-resolution images like ultrasounds. How can we try to learn a pattern from an ultrasound that’s similar to what you might see from an MRI, for example?”
If HeartLab can figure out how to glean advanced information from an echocardiogram instead of an MRI, it would be able to save hospitals, clinics and patients a lot of money. Each cardiac MRI can cost about $1,000 to $5,000, which is about five times the price of an echocardiogram.
“I’d say the biggest challenge for us is, how can we transform from a company that at the moment can deliver products to a few local clinics successfully to actually building a product that scales and delivers a really good experience to lots of users and different hospitals?” said Hewitt.
Advancements in early diagnostics and imaging tech like HeartLabs’ is causing an increased demand for such tools. As a result, the global AI-enabled medical imaging solutions market is expected to reach $4.7 billion by 2027. By extending its reach to the U.S., where heart disease is the leading cause of death, HeartLab is poised to take a big piece of that pie.
In total, HeartLab has publicly raised about $3.2 million in funding, which includes a pre-seed last October of about $800,000 led by Icehouse Ventures with support from Founders Fund, the San Francisco-based VC firm that led the round announced on Thursday. Icehouse Ventures also contributed to the oversubscribed seed round, along with another New Zealand firm Outset Ventures and private investor and CEO of design platform Figma, Dylan Field.
“The use of AI in medicine is reducing pressures on health systems and ultimately saving lives,” said Founders Fund partner Scott Nolan, who has led investment rounds for three other New Zealand startups, in a statement. “The HeartLab team has built a really compelling AI-powered platform that doctors love to use.”
Tanso nabs $1.9M pre-seed to help industrial manufacturers do sustainability reporting
The climate crisis is creating massive demand for data capture as industries grapple with how to decarbonize. Put simply, you can’t cut your carbon emissions if don’t know what they are in the first place.
This need to gather data is a big opportunity for startups — and a wave of early companies have already been founded to try to plug the sustainability data gap, through things like APIs to assess emissions for carbon offsetting (which in turn has led to other startups trying to tackle the data gap around offsetting projects…).
One thing is clear: Requirements for sustainability reporting are only going to get broader and deeper from here on in.
Munich-based Tanso is an early stage startup (founded this year) that’s building software to support sustainability reporting for a particular sector (industrial manufacturers) — with the goal of creating a data management system that can automate data capture and sustainability reporting geared towards the specific needs of the sector.
The startup says it decided to focus on industrial manufacturing because it’s both an emissions-heavy sector and underserved with supportive digital tech vs many other industries.
The founders met during their studies at universities in Munich and Zurich — where they’d been researching the assessment of organizational climate impact. Their collective expertise crystalized into the realization of a business opportunity to build a data management system for a notoriously polluting sector that’s facing a mandate to change.
In the coming years, European regulations will expand sustainability reporting requirements — with the EU’s ‘Green Deal’ plan setting an overarching goal of Europe becoming the first “climate-neutral” continent by 2050.
Specific (existing) reporting requirements within the bloc include the EU Corporate Sustainability Reporting Directive (CSRD), which will apply to more than 50,000 companies — requiring they report on their sustainability metrics, starting in 2023.
The UK (now outside the EU) already introduced some reporting requirements for domestic companies, under the Streamlined Energy and Carbon Reporting (SECR) regulation, which has applied since 2019 and applies to over 12,000 businesses in the UK in varying degrees of detail depending on the size of the company.
So there is a clear direction of travel in the region requiring businesses to gather and report sustainability data.
Tanso has just closed a $1.9 million pre-seed raise with the aim of getting its data management support software to market in time for an expected surge in demand as sustainability regulations like CSRD start to bite.
The raise is led by German early stage b2b fund UVC Partners, with participation from Picus Capital, Possible Ventures, and a number of business angels.
Tanso is still in the R&D/product development phase, with co-founder Gyri Reiersen telling TechCrunch it’s currently working with a number of manufacturers to “figure out the sweet spot” for automating data gathering so it can come to market with a scalable product offering. She says the team raised a relatively large pre-seed exactly to see it through until it’s got something fit to launch (it’s hoping to have something “solid, verified and scalable” by the end of 2022, per Reiersen).
The goal for the product is a single platform that gathers and holds all the customer’s sustainability data and can automate the generation of reports to meet regulatory requirements — including auditing.
From 2025, Reiersen points out that CSRD reporting needs to be “auditable”, meaning that you have to have “some form of transparency and traceability”; and also that the “correctness” of sustainability reporting will be a C-Suite responsibility. So that must concentrate boardroom minds.
“Going beyond that it’s all about how can you use this data and the insights that the data gives you to make predictions and models going forward for how should we develop our products? What makes sense to do going forward to make?” she adds.
“What we’re prototyping currently is to streamline the workflow of information gathering,” Reiersen also tells us, discussing the product dev process. “Also to have really good, fundamental user-flow for the users to use our product. And then doing the deep dives on integrations over time.”
She says the challenge is finding the trade-off between usability and “digging into the data”. “For us it’s very important to have a scalable product, especially having it fully scalable from 2023 when the CSRD are started because then there will be desperation on the market. Companies will need to have something,” she adds.
“We need to have these solutions… that take one step in the right direction for all companies and not just have a couple of carbon neutral companies… So for us it’s more about finding the productizable use-cases in the beginning to make this a scalable product.”
But she also warns over a proliferation of overly “shallow” offerings in the space — driven by marketing-led ‘greenwashing’ (and bogus carbon offsetting) rather than a genuine desire to correctly identify the problem and course-correct which is what’s actually needed for humanity to avert climate disaster.
Reiersen adds that she got really interested in this space through her university work researching the overestimation of carbon offsets through deep learning.
“There is such a need for accountability and making sure that the product that is being developed actually do their job correctly. Because it’s so easy to just have a black box and trust it. We can’t afford having systems that overestimate or underestimate. It needs to be accurate and it needs to be validated,” she says.
“Going forward accuracy will mean more and more and then you need to access the ‘real data’ and not just ‘guestimations’,” she predicts. “And that’s where we see that of course we need to be very front-end/UX-friendly, and making it easy for people to enter the right data and have a very user-friendly, usable product and that people are guided through the process of gathering the right data… but also over time really focusing on how do you integrate and get access to the data at the data-base level?”