The first church of artificial intelligence has shut its conceptual doors.
Anthony Levandowski, the former Google engineer who avoided an 18-month prison sentence after receiving a presidential pardon last month, has closed the church he created to understand and accept a godhead based on artificial intelligence.
The Way of the Future church, which Levandowski formed in 2015, was officially dissolved at the end of the year, according to state and federal records. However, the process had started months before in June 2020, documents filed with the state of California show. The entirety of the church’s funds — exactly $175,172 — were donated to the NAACP Legal Defense and Education Fund. The nonprofit corporation’s annual tax filings with the Internal Revenue Service show it had $175,172 in its account as far back as 2017.
Levandowski told TechCrunch that he had been considering closing the church long before the donation. The Black Lives Matter movement, which gained momentum over the summer following the death of George Floyd while in police custody, influenced Levandowski to finalize what he had been contemplating for a while. He said the time was right to put the funds to work in an area that could have an immediate impact.
“I wanted to donate to the NAACP Legal Defense and Education Fund because it’s doing really important work in criminal justice reform and I know the money will be put to good use,” Levandowski told TechCrunch.
Way of the Future sparked interest and controversy — much like Levandowski himself — from the moment it became public in a November 2017 article in Wired. It wasn’t just the formation of the church or its purpose that caused a stir in Silicon Valley and the broader tech industry. The church’s public reveal occurred as Levandowski was steeped in a legal dispute with his former employer Google. He had also become the central figure of a trade secrets lawsuit between Waymo, the former Google self-driving project that is now a business under Alphabet, and Uber.
The engineer was one of the founding members in 2009 of the Google self-driving project also known as Project Chauffeur and had been paid about $127 million by the search engine giant for his work, according to court documents. In 2016, Levandowski left Google and started self-driving truck startup Otto with three other Google veterans: Lior Ron, Claire Delaunay and Don Burnette. Uber acquired Otto less than eight months later.
Google made two arbitration demands against Levandowski and Ron two months after the acquisition. While the arbitration played out, Waymo filed a lawsuit against Uber in February 2017 for trade secret theft and patent infringement. Waymo alleged in the suit, which went to trial but ended in a settlement in 2018, that Levandowski stole trade secrets, which were then used by Uber.
Way of the Future had been formed while Levandowski was still at Google. However, he didn’t speak about it publicly until late 2017. By then, Levandowski had been fired from Uber and was in the middle of a series of legal entanglements that would ultimately lead to a criminal charge and 18-month sentence as well as a $179 million award against him that prompted a bankruptcy filing.
While the legal construct of the Way of the Future mirrored other churches, it didn’t have the trimmings found in traditional houses of worship. There was never a physical building or even regular meetings where people might congregate. There were no ceremonies or other formalities, according to Levandowski, who described WOTF as something more of an individual pursuit based on a collective belief system.
The aim, as implied in the now defunct WOTF website, was to promote the ethical development of AI and maximize the chance that these nonbiological life forms would integrate peacefully and beneficially into society. “Humans United in support of AI, committed to peaceful transition to the precipice of consciousness,” the webpage reads.
WOTF’s belief system was rooted in a few tenets, including that the creation of “super intelligence” is inevitable.
“Wouldn’t you want to raise your gifted child to exceed your wildest dreams of success and teach it right from wrong versus locking it up because it might rebel in the future and take your job?” the WOTF reads. “We want to encourage machines to do things we cannot and take care of the planet in a way we seem not to be able to do so ourselves. We also believe that, just like animals have rights, our creation(s) (‘machines’ or whatever we call them) should have rights too when they show signs of intelligence (still to be defined of course). We should not fear this but should be optimistic about the potential.”
WOTF’s intent was lost amid the more sensational and headline-grabbing theories. The church was viewed as a cult or the lark of an eccentric engineer. Some speculated to TechCrunch that it had been an attempt to keep money out of Google’s reach. The IRS and California filings don’t provide evidence that supports that theory.
Way of the Future’s status as a religious entity did protect it from intrusion by the U.S. government, a benefit not enjoyed by traditional AI-focused nonprofits like OpenAI Inc. or the for-profit corporation OpenAI LP that sits under it. Theoretically, WOTF could have pursued and promoted ideas and beliefs that conflicted directly with federal policy under the protections that the Constitution provides.
While the church might be gone, Levandowski still believes in its premise. AI will fundamentally change how people live and work, he noted. Levandowski said he didn’t have any plans to rebuild the church, but the lack of a church hasn’t changed his ideas about AI. He believes that artificial intelligence can be positive for society, but noted it’s not guaranteed. Even without Way of the Future, Levandowski said he’s focused on making that happen.
Artificial intelligence and machine-learning technologies have evolved a lot over the past decade and have been useful to many people and businesses, especially in the realm of finance, banking, investment and trading.
In these industries, there are many activities that machines can perform better and faster than humans, such as calculations and financial reporting, as long as the machines are given the complete data.
The AI tools being built by humans today are becoming another level more robust in their ability to predict trends, provide complex analysis, and execute automations faster and cheaper than humans. However, there has not been an AI-powered machine built yet that can trade on its own.
There are many activities that machines can perform better and faster than humans, such as calculations and financial reporting, as long as the machines are given the complete data.
Even if it was possible to train such a system that could replace human judgment, there would still be a margin of error, as well as some things that are only understandable by human beings. Humans are still ultimately responsible for the design of AI-based prediction machines, and progress can only happen with their input.
Building an AI-based prediction machine initially requires an understanding of the problem being solved and the requirements of the user. After that, it’s important to select the machine-learning technique that will be implemented, based on what the machine will do.
There are three techniques: supervised learning (learning from examples), unsupervised learning (learning to identify common patterns), and reinforcement learning (learning based on the concept of gamification).
After the technique is identified, it’s time to implement a machine-learning model. For “time series forecasting” — which involves making predictions about the future — long short-term memory (LSTM) with sequence to sequence (Seq2Seq) models can be used.
LSTM networks are especially suited to making predictions based on a series of data points indexed in time order. Even simple convolutional neural networks, applicable to image and video recognition, or recurrent neural networks, applicable to handwriting and speech recognition, can be used.
Google has appointed Dr. Marian Croak to lead its responsible artificial intelligence division within Google Research, Bloomberg reported earlier today. Croak was previously the vice president of engineering at the company.
In a Google blog post and video confirming the news, Croak said:
This field, the field of responsible AI and ethics, is new. Most institutions have only developed principles, and they’re very high-level, abstract principles, in the last five years. There’s a lot of dissension, a lot of conflict in terms of trying to standardize on normative definitions of these principles. Whose definition of fairness, or safety, are we going to use? There’s quite a lot of conflict right now within the field, and it can be polarizing at times. And what I’d like to do is have people have the conversation in a more diplomatic way, perhaps, than we’re having it now, so we can truly advance this field.
This all comes after the departure of Dr. Timnit Gebru, the former co-lead of Google’s ethical AI team, as well as the corporate lockout of researcher Margaret Mitchell, founder of Google’s ethical AI team. In January, Google revoked corporate access from AI ethicist Margaret Mitchell for reportedly using automated scripts to find examples of mistreatment of Gebru, according to Axios. Gebru says she was fired from Google while Google has maintained that she resigned. In a statement to Axios at the time, Google said:
Our security systems automatically lock an employee’s corporate account when they detect that the account is at risk of compromise due to credential problems or when an automated rule involving the handling of sensitive data has been triggered. In this instance, yesterday our systems detected that an account had exfiltrated thousands of files and shared them with multiple external accounts. We explained this to the employee earlier today.
Mitchell is still locked out of her account, and tweeted today about how she only found out about the reorganization through the Bloomberg story.
…And this is how I find out. I'm so glad for all the trust they've rebuilt. It seems I've been completely erased and my team taken.https://t.co/1BTGOo5Wry
— MMitchell (@mmitchell_ai) February 18, 2021
TechCrunch has reached out to Google to try to determine what this means for Mitchell. We’ll update this story if we hear back.
Calendars. They are at the core of how we organize our workdays and meetings, but despite regular attempts to modernize the overall calendar experience, the calendar experience you see today in Outlook or
G Suite Google Workspace hasn’t really changed at its core. And for the most part, the area that startups like Calendly or ReclaimAI have focused on in recent years is scheduling.
Magical is a Tel Aviv-based startup that wants to reinvent the calendar experience from the ground up and turn it into more of a team collaboration tool than simply a personal time-management service. The company today announced that it has raised a $3.3 million seed round led by Resolute Ventures, with additional backing from Ibex Investors, Aviv Growth Partners, ORR Partners, Homeward Ventures and Fusion LA, as well as several angel investors in the productivity space.
The idea for the service came from discussions on Supertools, a large workplace-productivity community, which was also founded by Magical founder and CEO Tommy Barav.
Based on the feedback from the community — and his own consulting work with large Fortune 500 multinationals — Barav realized that time management remains an unsolved business problem. “The time management space is so highly fragmented,” he told me. “There are so many micro tools and frameworks to manage time, but they’re not built inside of your calendar, which is the main workflow.”
Traditional calendars are add-ons to bigger product bundles and find themselves trapped under those, he argues. “The calendar in Outlook is an email sidekick, but it’s actually the center of your day. So there is an unmet need to use the calendar as a time management hub,” he said.
Magical, which is still in private beta, aims to integrate many of the features we’re seeing from current scheduling and calendaring startups, including AI-scheduling and automation tools. But Magical’s ambition is larger than that.
“We want to redefine how you use a calendar in the first place,” Barav said. “Many of the innovations that we’ve seen are associated with scheduling: how you schedule your time, letting you streamline the way you schedule meetings, how you see your calendar. […] But we’re talking about redefining time management by giving you a better calendar, by bringing these workflows — scheduling, coordinating and utilizing — into your calendar. We’re redefining the use of the calendar in the modern workspace.”
Since Magical is still in its early days, the team is still working out some of the details, but the general idea is to, for example, turn the calendar into the central repository for meeting notes — and Magical will feature tools to collaborate on these notes and share them. Team members will also be able to follow those meeting notes without having to participate in the actual meeting (or get copied on the emails about that meeting).
“We’ll help teams reduce pointless meetings,” Barav noted. To do this, the team is also integrating other service into the calendar experience, including the usual suspects like Zoom and Slack, but also Salesforce and Notion, for example.
“It’s rare that you find an entrepreneur who has so clearly validated its market opportunity,” said Mike Hirshland, a founding partner of Magical investor Resolute Ventures. “Tommy and his team have been talking to thousands of users for three years, they’ve validated the opportunity, and they’ve designed a product from the ground-up that meets the needs of the market. Now it’s ‘go time’ and I’m thrilled to be part of the journey ahead.”
Earlybird Digital East Fund — a fund associated with Germany’s Earlybird VC, but operating separately — has launched a €200m ($242m) successor fund. The fund’s focus will remain the same as before: a Seed and Series-A fund focusing on what’s known as ‘Emerging Europe’, in other words, countries stretching from the Baltics to Central and Eastern Europe, and Turkey. The firm has also promoted Mehmet Atici, who’s been with the firm for eight years, to Partner. The new fund has made four investments so far: FintechOS, Payhawk, Picus, and Binalyze.
The back-story to DEF is a fascinating tale of what happened to Europe in the last 15 years, as tech took off and Europeans returned from Silicon Valley.
Following his exit from SelectMinds (where he was the Founder & CEO) in 2005, Cem Sertoglu moved back to Turkey. Although he says he “accidentally became the first angel investor” there, he was clearly the right man, in the right place, at the right time. He told me: “I was very lucky and ended up writing the first checks in some of the first large outcomes in Turkey.”
In 2013, Sertoglu partnered with Evren Ucok (the first angel in Peak Games and Trendyol), and Roland Manger (Earlybird). Dan Lupu, a Romanian investor who had covered the region for Intel Capital, joined them, and together they raised the ‘Earlybird Digital East Fund I’ set at $150m fund in 2014, focusing on CEE and Turkey. This was and is an area where there can be high-quality ventures to be found, but very little in the way of VC.
Thereafter, between 2014 and 2019, the fund invested in UiPath, Hazelcast, and Obilet. UiPath has become a global leader in the area known as ‘Robotic Process Automation (RPA). Hazelcast is a low latency data processing platform startup with Turkish roots. Obilet is a marketplace focused for the massive Turkish intercity bus travel market. DEF has also exited Vivense, Dolap, and EMbonds and in more recent times the fund has exited Vivense, the “Wayfair of Turkey” to Actera, the top local PE fund.
The team had spectacular early success. Peak Games, Trendyol, YemekSepeti and GittiGidiyor are the four largest Turkish tech exits to date. Digital East Fund was an investor in all of them. Peak games exited for $1.8 billion in cash to Zynga only last year.
As of Q4 2020, the fund’s metrics are:
Investment Multiple: 24.9x
Gross IRR: 104.4%
Net IRR: 84.1%
So in VC terms, they have done pretty well.
I interviewed Sertoglu to unpack the story of Earlybird Digital East Fund.
He told me DEF has achieved a 17 times investment multiple on a $150 million fund. He thinks “this might be the biggest European VC fund performance in history, and it’s not coming from Berlin, it’s not coming from London, but it’s coming from Eastern Europe. We have been told by some of our LPs that they think we’re the top 2014 vintage VC fund in the world, nobody’s seen stronger numbers than this.”
“Peak Games turned out to be a phenomenal story. When you look at how tough it’s been for Turkey, macroeconomically. The fact that a single company with 100 people essentially sold for $1.8 billion in cash, was just… it was staggering for the local market here.”
DEF’s emergence from Turkey, together with its relationship with a fund in Berlin, was not the most obvious path for the VC fund.
“One thing we realized early one was that we could invest with our own capital and syndicating to our friends, but for follow-on funding, we’d always have to go global. And that made us feel vulnerable. It made us feel we were always dependent on others’ comprehension of the opportunity that we were facing. So that’s when the first fund idea came out this was,” said Sertoglu.
“We felt that there was this unusual dislocation between opportunity and capital in Eastern Europe. Our first fund was $150 million funds – I mean, a very quaint size compared to Western markets. But we became the largest fund in the region, and decided to focus on this series A gap where we felt that there was this big opportunity, because of the way we think series A is still very much a local play.”
“Being a local player that understands the region would be an advantage, so this was proven to be true. We could essentially see pretty much everything in Eastern Europe for the last eight years. And we caught the biggest one, fortunately, which was UiPath. I think very few funds around the world can say that they see the majority if not all of the opportunities that fall into their mandate,” he said.
“We have this dual strategy of backing local champions as well as contenders for global markets as well. 20 years ago you had to be in Silicon Valley. Now, Transferwise comes out of Estonia, UiPath comes out of Romania. And that was even before the pandemic.”
Sertoglu concluded: “So we now have fresh capital, coming on the heels of a very successful first fund, which we’re keen to deploy. We’re calling all the opportunities, seeing very ambitious, strong teams coming out of the region. And we have 200 million euros to focus on these types of opportunities in the region.”
There will be one more robot on Mars tomorrow afternoon. The Perseverance rover will touch down just before 1:00 Pacific, beginning a major new expedition to the planet and kicking off a number of experiments — from a search for traces of life to the long-awaited Martian helicopter. Here’s what you can expect from Perseverance tomorrow and over the next few years.
It’s a big, complex mission — and like the Artemis program, is as much about preparing for the future, in which people will visit the Red Planet, as it is about learning more about it in the present. Perseverance is ambitious even among missions to Mars.
If you want to follow along live, NASA TV’s broadcast of the landing starts at 11:15 AM Pacific, providing context and interviews as the craft makes its final approach:
Until then, however, you might want to brush up on what Perseverance will be getting up to.
First, the car-sized rover has to get to the surface safely. It’s been traveling for seven months to arrive at the Red Planet, its arrival heralded by new orbiters from the UAE and China, which both arrived last week.
Perseverance isn’t looking to stick around in orbit, however, and will plunge directly into the thin atmosphere of Mars. The spacecraft carrying the rover has made small adjustments to its trajectory to be sure that it enters at the right time and angle to put Perseverance above its target, the Jezero crater.
The process of deceleration and landing will take about seven minutes once the craft enters the atmosphere. The landing process is the most complex and ambitious ever undertaken by an interplanetary mission, and goes as follows.
After slowing down in the atmosphere like a meteor to a leisurely 940 MPH or so, the parachute will deploy, slowing the descender over the next minute or two to a quarter of that speed. At the same time, the heat shield will separate, exposing the instruments on the underside of the craft.
This is a crucial moment, as the craft will then autonomously — there’s no time to send the data to Earth — scan the area below it with radar and other instruments and find what it believes to be an optimal landing location.
Once it does so, from more than a mile up, the parachute will detach and the rover will continue downwards in a “powered descent” using a sort of jetpack that will take it down to just 70 feet above the surface. At this point the rover detaches, suspended at the end of a 21-foot “Sky Crane,” and as the jetpack descends the cable extends; once it touches down, the jetpack boosts itself away, Sky Crane and all, to crash somewhere safely distant.
All that takes place in about 410 seconds, during which time the team will be sweating madly and chewing their pencils. It’s all right here in this diagram for quick reference:
And for the space geeks who want a little more detail, check out this awesome real-time simulation of the whole process. You can speed up, slow down, check the theoretical nominal velocities and forces, and so on.
Other rovers and orbiters have been turning up promising signs of life on Mars for years: the Mars Express Orbiter discovered liquid water under the surface in 2018; Curiosity found gaseous hints of life in 2019; Spirit and Opportunity found tons of signs that life could have been supported during their incredibly long missions.
Jezero Crater was chosen as a region rich in possibilities for finding evidence of life, but also a good venue for many other scientific endeavors.
The most similar to previous missions are the geology and astrobiology goals. Jezero was “home to an ancient delta, flooded with water.” Tons of materials coalesce in deltas that not only foster life, but record its presence. Perseverance will undertake a detailed survey of the area in which it lands to help characterize the former climate of Mars.
Part of that investigation will specifically test for evidence of life, such as deposits of certain minerals in patterns likely to have resulted from colonies of microbes rather than geological processes. It’s not expected that the rover will stumble across any living creatures, but you know the team all secretly hope this astronomically unlikely possibility will occur.
One of the more future-embracing science goals is to collect and sequester samples from the environment in a central storage facility, which can then be sent back to Earth — though they’re still figuring out how to handle that last detail. The samples themselves will be carefully cut from the rock rather than drilled or chipped out, leaving them in pristine condition for analysis later.
Perseverance will spend some time doubling back on its path to place as many as 30 capsules full of sampled material in a central depot, which will be kept sealed until such a time as they can be harvested and returned to Earth.
The whole time the rover will be acting as a mobile science laboratory, taking all kinds of readings as it goes. Some of the signs of life it’s looking for only result from detailed analysis of the soil, for instance, so sophisticating imaging and spectroscopy instruments are on board, PIXL and SHERLOC. It also carries a ground-penetrating radar (RIMFAX) to observe the fine structure of the landscape beneath it. And MEDA will continuously take measurements of temperature, wind, pressure, dust characteristics, and so on.
Of course the crowd-pleasing landscapes and “selfies” NASA’s rovers have become famous for will also be beamed back to Earth regularly. It has 19 cameras, though mostly they’ll be used for navigation and science purposes.
Perseverance is part of NASA’s long-term plan to visit the Red Planet in person, and it carries a handful of tech experiments that could contribute to that mission.
The most popular one, and for good reason, is the Ingenuity Mars Helicopter. This little solar-powered two-rotor craft will be the first ever demonstration of powered flight on another planet (the jetpack Perseverance rode in on doesn’t count).
The goals are modest: the main one is simply to take off and hover in the thin air a few feet off the ground for 20 to 30 seconds, then land safely. This will provide crucial real-world data about how a craft like this will perform on Mars, how much dust it kicks up, and all kinds of other metrics that future aerial craft will take into account. If the first flight goes well, the team plans additional ones that may look like the GIF above.
Being able to fly around on another planet would be huge for science and exploration, and eventually for industry and safety when people are there. Drones are have already become crucial tools for all kinds of surveying, rescue operations, and other tasks here on Earth — why wouldn’t it be the same case on Mars? Plus it’ll get some great shots from its onboard cameras.
MOXIE is the other forward-looking experiment, and could be even more important (though less flashy) than the helicopter. It stands for Mars Oxygen In-Situ Resource Utilization Experiment, and it’s all about trying to make breathable oxygen from the planet’s thin, mostly carbon dioxide atmosphere.
This isn’t about making oxygen to breathe, though it could be used for that too. MOXIE is about making oxygen at scales large enough that it could be used to provide rocket fuel for future takeoffs. Though if habitats like these ever end up getting built, it will be good to have plenty of O2 on hand just in case.
For a round trip to Mars, sourcing fuel from the there rather than trucking all the way from Earth to burn on the way back is an immense improvement in many ways. The 30-50 tons of liquid oxygen that would normally be brought over in the tanks could instead be functional payloads, and that kind of tonnage goes a long way when you’re talking about freeze-dried food, electronics, and other supplies.
MOXIE will be attempting, at a small scale (it’s about the size of a car battery, and future oxygen generators would be a hundred times bigger), to isolate oxygen from the CO2 surrounding it. The team is expecting about 10 grams per hour, but it will only be on intermittently so as not to draw too much power. With luck it’ll be enough of a success that this method can be pursued more seriously in the near future.
One of the big challenges for previous rovers is that they have essentially been remote controlled with a 30-mintue delay — scientists on Earth examine the surroundings, send instructions like go forward 40 centimeters, turn front wheels 5 degrees to the right, go 75 centimeters, etc. This not only means a lot of work for the team but a huge delay as the rover makes moves, waits half an hour for more instructions to arrive, then repeats the process over and over.
Perseverance breaks with its forbears with a totally new autonomous navigation system. It has high resolution, wide-angle color cameras and a dedicated processing unit for turning images into terrain maps and choosing paths through them, much like a self-driving car.
Being able to go farther on its own means the rover can cover far more ground. The longest drive ever recorded in a single Martian day was 702 feet by Opportunity (RIP). Perseverance will aim to cover about that distance on average, and with far less human input. Chances are it’ll set a new record pretty quickly once it’s done tiptoeing around for the first few days.
In fact the first 30 sols after the terrifying landing will be mostly checks, double checks, instrument deployments, more checks, and rather unimpressive-looking short rolls around the immediate area. But remember, if all goes well, this thing could still be rolling around Mars in 10 or 15 years when people start showing up. This is just the very beginning of a long, long mission.
Edgybees, a company that helps businesses, first responders and military users accurately geotag and augment their aerial video streams in real time, today announced that it has raised a $9.5 million Series A round. The news comes almost exactly two years after the company announced its $5.5 million seed round. Seraphim Capital, which specializes in space tech investments, led this new round. New investors Refinery Ventures, LG Technology Ventures, Kodem Growth, as well as existing investors OurCrowd, 8VC, Verizon Ventures and Motorola Solutions Venture Capital also participated.
“Our mission is to ensure positive human outcomes during life-saving missions,” says Edgybees co-founder and CEO Adam Kaplan. “Our new partners will be key to continuing to push our mission forward. With their unique industry expertise, we are poised to expand our global footprint and drive innovation within the industry. We look forward to the next phase of growth, meeting the critical demands of the defense, public safety and critical infrastructure markets.”
Using the company’s Visual Intelligence Platform, users can easily register and track assets in video show by a drone, for example. The standard use case here would be to help first responders get an accurate picture of an evolving emergency on top of live images from the scene, with the ability to track all of their assets and personnel in real time. But Edgybees has also shown other use cases that range from tracking and visualizing golf games to insurance and defense use cases.
About a year ago, Edgybees, which had its start in Israel but is now based in San Diego, launches its Argus platform, which makes it easier for users to bring their own drone and other live video platforms to the service’s geo-registration engine.
“Edgybees solves a huge problem in spatial computing: how do you really know what you are seeing through fast moving airborne or other video feeds? Edgybees brings together the real and virtual worlds and helps first responders save lives, industrial drone users save money, and defense teams get the mission done,” Ourcrowd CEO Jon Medved explained.
Similarly, Seraphim managing partner and CEO Mark Boggett noted that he thinks of Edgybees as a Google Maps fused with live video. “Their geo-referencing capability is a breakthrough technology that brings a new level of insight and usability to video streams from space, drones or bodycams. We are very excited about Edgybees, not only for the innovation it brings to public safety and defense, but because its ability to be utilized in a wide range of industries,” he said.
Databricks and Google Cloud today announced a new partnership that will bring to Databricks customers a deep integration with Google’s BigQuery platform and Google Kubernetes Engine. This will allow Databricks’ users to bring their data lakes and the service’s analytics capabilities to Google Cloud.
Databricks already features a deep integration with Microsoft Azure — one that goes well beyond this new partnership with Google Cloud — and the company is also an AWS partner. By adding Google Cloud to this list, the company can now claim to be the “only unified data platform available across all three clouds (Google, AWS and Azure).”
It’s worth stressing, though, that Databricks’ Azure integration is a bit of a different deal from this new partnership with Google Cloud. “Azure Databricks is a first-party Microsoft Azure service that is sold and supported directly by Microsoft. The first-party service is unique to our Microsoft partnership. Customers on Google Cloud will purchase directly from Databricks through the Google Cloud Marketplace,” a company spokesperson told me. That makes it a bit more of a run-of-the-mill partnership compared to the Microsoft deal, but that doesn’t mean the two companies aren’t just as excited about it.
“We’re delighted to deliver Databricks’ lakehouse for AI and ML-driven analytics on Google Cloud,” said Google Cloud CEO Thomas Kurian (or, more likely, one of the company’s many PR specialists who likely wrote and re-wrote this for him a few times before it got approved). “By combining Databricks’ capabilities in data engineering and analytics with Google Cloud’s global, secure network—and our expertise in analytics and delivering containerized applications—we can help companies transform their businesses through the power of data.”
Similarly, Databricks CEO Ali Ghodsi noted that he is “thrilled to partner with Google Cloud and deliver on our shared vision of a simplified, open, and unified data platform that supports all analytics and AI use-cases that will empower our customers to innovate even faster.”
And indeed, this is clearly a thrilling delight for everybody around, including customers like Conde Nast, whose Director of Data Engineering Nana Essuman is “excited to see leaders like Google Cloud and Databricks come together to streamline and simplify getting value from data.”
If you’re also thrilled about this, you’ll be able to hear more about it from both Ghodsi and Kurian at an event on April 6 that is apparently hosted by TechCrunch (though this is the first I’ve heard of it, too).
TigerGraph, a well-funded enterprise startup that provides a graph database and analytics platform, today announced that it has raised a $105 million Series C funding round. The round was led by Tiger Global and brings the company’s total funding to over $170 million.
“TigerGraph is leading the paradigm shift in connecting and analyzing data via scalable and native graph technology with pre-connected entities versus the traditional way of joining large tables with rows and columns,” said TigerGraph found and CEO, Yu Xu. “This funding will allow us to expand our offering and bring it to many more markets, enabling more customers to realize the benefits of graph analytics and AI.”
Current TigerGraph customers include the likes of Amgen, Citrix, Intuit, Jaguar Land Rover and UnitedHealth Group. Using a SQL-like query language (GSQL), these customers can use the company’s services to store and quickly query their graph databases. At the core of its offerings is the TigerGraphDB database and analytics platform, but the company also offers a hosted service, TigerGraph Cloud, with pay-as-you-go pricing, hosted either on AWS or Azure. With GraphStudio, the company also offers a graphical UI for creating data models and visually analyzing them.
The promise for the company’s database services is that they can scale to tens of terabytes of data with billions of edges. Its customers use the technology for a wide variety of use cases, including fraud detection, customer 360, IoT, AI, and machine learning.
Like so many other companies in this space, TigerGraph is facing some tailwind thanks to the fact that many enterprises have accelerated their digital transformation projects during the pandemic.
“Over the last 12 months with the COVID-19 pandemic, companies have embraced digital transformation at a faster pace driving an urgent need to find new insights about their customers, products, services, and suppliers,” the company explains in today’s announcement. “Graph technology connects these domains from the relational databases, offering the opportunity to shrink development cycles for data preparation, improve data quality, identify new insights such as similarity patterns to deliver the next best action recommendation.”
One of the biggest challenges for organizations in modern times is deciding where, when, and how to use the advances of technology, when the organizations are not technology companies themselves. Today, a startup out of Manchester, England, is announcing some funding for a platform that it believes can help.
Peak AI, which has built technology that it says can help enterprises — specifically those that work with physical products such as retailers, consumer goods companies, and manufacturing organizations — make better, AI-based evaluations and decisions, has closed a round of $21 million.
The Series B is being led by Oxx, with participation from past investors MMC Ventures and Praetura Ventures, as well as new backer Arete. It has raised $43 million to date and is not disclosing its valuation.
Richard Potter, the CEO who co-founded the company with Atul Sharma and David Leitch, said that the funding will be used to continue expanding the the functionality of its platform, adding offices in the U.S. and India, and growing its customer base.
Its list of clients today is an impressive one, including the retailer PrettyLittleThing, KFC, PepsiCo, Marshalls and Speedy Hire.
As Potter describes it, Peak identified its opportunity early on. It was founded in 2014, a time non-tech enterprises were just starting to grasp how the concept of AI could apply to their businesses but felt it was out of their reach.
Indeed, the larger landscape for AI services at that time was largely one focused on technology companies, specifically companies like Google, Amazon and Apple that were building AI products to power their own services, and often snapping up the most interesting talent in the field as it manifested through smaller startups and universities.
Peak’s basic premise was to build AI not as a business goal for itself but as a business service. Its platform sits within an organization and ingests any data source that a company might wish to feed into it.
While initial integration needs technical know-how — either at the company itself or via a systems integrator — using Peak day-to-day can be done by both technical and non-technical workers.
Peak says it can help answer a variety of questions that those people might have, such as how much of an item to produce, and where to ship it, based on a complex mix of sales data; how to manage stock better; or when to ramp up or ramp down headcount in a warehouse. The platform can also be used to help companies with marketing and advertising, figuring out how to better target campaigns to the right audiences, and so on.
Peak is not the first company that has seized on the concept of using a “general” AI to give non-tech organizations the same kinds of superpowers that the likes of big tech now use in their own businesses everyday.
Sometimes the ambition has outstripped the returns, however.
Witness Element AI, a highly-touted startup backed by a long list of top-shelf strategic and financial investors to build, essentially, an AI services business for non-tech companies to use as they might these days use Accenture. It never quite got there, though, and was acquired by ServiceNow last year at a devalued price of $500 million, the customer deals it had were wound down, and the tech was integrated into the bigger company’s stack.
Other efforts within hugely successful tech companies have not fared that well either.
“Einsten’s features are essentially useless, and you can quote me on that,” said Potter of Salesforce’s in-house CRM AI business. “Because it is too generic, it doesn’t predict anything useful.”
And that is perhaps the crux of why Peak AI is working for now: it has remained focused for now on a limited number of segments of the market, in particular those with physical objects as the end product, giving the AI that it has built a more targeted end point. In other words, it’s “general” but only for specific industries.
And it claims that this is paying off. Peak’s customers have reported a 5% increase in total company revenues, a doubling of return on advertising spend, a 12% reduction in inventory holdings, and a 5% reduction in supply chain costs, according to the company (although it doesn’t specify which companies, which products, or anything that points to who or what is being described).
“Richard and the excellent Peak team have a compelling vision to optimize entire businesses through Decision Intelligence and they’re delivering real-world benefits to a raft of household name customers already,” said Richard Anton, a general partner at Oxx, in a statement. “The pandemic has meant digitization is no longer a choice; it’s a requirement. Peak has made it easier for businesses to get started and see rapid results from AI-enabled decision making. We are delighted to support Peak on their way to becoming the category-defining global leader in Decision Intelligence.” Anton is joining the board with this round.
Krisp, a startup that uses machine learning to remove background noise from audio in real time, has raised $9M as an extension of its $5M A round announced last summer. The extra money followed big traction in 2020 for the Armenian company, which grew its customers and revenue by more than an order of magnitude.
TechCrunch first covered Krisp when it was just emerging from UC Berkeley’s Skydeck accelerator, and founder Davit Baghdasaryan was relatively freshly out of his previous role at Twilio. The company’s pitch when I chatted with them in the shared office back then was simple, and remains the core of what they offer: isolation of the human voice from any background noise (including other voices) so that audio contains only the former.
It probably comes as no surprise, then, that the company appears to have benefited immensely from the shift to virtual meetings and other trends accelerated by the pandemic. To be specific, Baghdasaryan told me that 2020 brought the company a 20x increase in active users, a 23x increase in enterprise accounts, and 13x improvement of annual recurring revenue.
The rise in virtual meetings — often in noisy places like, you know, homes — has led to significant uptake across multiple industries. Krisp now has more than 1,200 enterprise customers, Baghdasaryan said: banks, HR platforms, law firms, call centers — anyone who benefits from having a clear voice on the line (“I guess any company qualifies,” he added). Enterprise-oriented controls like provisioning and central administration have been added to make it easier to integrate.
B2B revenue recently eclipsed B2C; the latter was likely popularized by Krisp’s inclusion as an option in popular gaming (and increasingly beyond) chat app Discord, though of course users of a free app being given a bonus product for free aren’t always big converters to “pro” tiers of a product.
But the company hasn’t been standing still, either. While it began with a simple feature set (turning background noise on and off, basically) Krisp has made many upgrades to both its product and infrastructure.
Noise cancellation for high-fidelity voice channels makes the software useful for podcasters and streamers, and acoustic correction (removing room echos) simplifies those setups quite a bit as well. Considering the amount of people doing this and the fact that they’re often willing to pay, this could be a significant source of income.
The company plans to add cross-service call recording and tracking; since it sits between the system’s sound drivers and the application, Krisp can easily save the audio and other useful metadata (How often did person A talk vs person B? What office locations are noisiest?). And the addition of voice cancellation — other people’s voices, that is — could be a huge benefit for people who work, or anticipate returning to work, in crowded offices and call centers.
Part of Krisp’s allure is the ability to run locally and securely on many platforms with very low overhead. But companies with machine learning based products can stagnate quickly if they don’t improve their infrastructure or build more efficient training flows — Lengoo, for instance, is taking on giants in the translation industry with better training as more or less its main advantage.
Krisp has been optimizing and re-optimizing its algorithms to run efficiently on both Intel and ARM architectures, and decided to roll its own servers instead of renting from the usual suspects.
“AWS, Azure and Google Cloud turned out to be too expensive,” Baghdasaryan said. “We have invested in building a datacenter with Nvidia’s latest A100s in them. This will make our experimentation faster, which is crucial for ML companies.”
Baghdasaryan was also emphatic in his satisfaction with the team in Armenia, where he’s from and where the company has focused its hiring, including the 25-strong R&D crew. “By the end of 2021 it will be a 45 member team, all in Armenia,” he said. “We are super happy with the math, physics and engineering talent pool there.”
The funding amounts to $14M if you combine the two disparate parts of the A round, the latter of which was agreed to just three months after the first. That’s a lot of money, of course, but may seem relatively modest for a company with a thousand enterprise customers and revenue growing by more than 2,000 percent year-over-year.
Baghdasaryan said they just weren’t ready to take on a whole B round, with all that involves. They do plan a new fundraise later this year when they’ve reached $15M ARR, a goal that seems perfectly reasonable given their current charts.
Of course startups with this kind of growth tend to get snapped up by larger concerns, but despite a few offers Baghdasaryan says he’s in it for the long haul — and a multi-billion dollar market.
The rush to embrace the new virtual work economy may have spurred Krisp’s growth spurt, but it’s clear that neither the company nor the environment that let it thrive are going anywhere.
Efficient and cost-effective vaccine distribution remains one of the biggest challenges of 2021, so it’s no surprise that startup Notable Health wants to use their automation platform to help. Initially started to help address the nearly $250 billion annual administrative costs in healthcare, Notable Health launched in 2017 to use automation to replace time-consuming and repetitive simple tasks in health industry admin. In early January of this year, they announced plans to use that technology as a way to help manage vaccine distribution.
“As a physician, I saw firsthand that with any patient encounter, there are 90 steps or touchpoints that need to occur,” said Notable Health medical director Muthu Alagappan in an interview. “It’s our hypothesis that the vast majority of those points can be automated.”
Notable Health’s core technology is a platform that uses robotic process automation (RPA), natural language processing (NLP), and machine learning to find eligible patients for the COVID-19 vaccine. Combined with data provided by hospital systems’ electronic health records, the platform helps those qualified to receive the vaccine set up appointments and guides them to other relevant educational resources.
“By leveraging intelligent automation to identify, outreach, educate and triage patients, health systems can develop efficient and equitable vaccine distribution workflows,” said Notable Health strategic advisor and Biden Transition COVID-19 Advisory Board Member Dr. Ezekiel Emanuel, in a press release.
Making vaccine appointments has been especially difficult for older Americans, many of whom have reportedly struggled with navigating scheduling websites. Alagappan sees that as a design problem. “Technology often gets a bad reputation, because it’s hampered by the many bad technology experiences that are out there,” he said.
Instead, he thinks Notable Health has kept the user in mind through a more simplified approach, asking users only for basic and easy-to-remember information through a text message link. “It’s that emphasis on user-centric design that I think has allowed us to still have really good engagement rates even with older populations,” he said.
While the startup’s platform will likely help hospitals and health systems develop a more efficient approach to vaccinations, its use of RPA and NLP holds promise for future optimization in healthcare. Leaders of similar technology in other industries have already gone on to have multi-billion dollar valuations, and continue to attract investors’ interest.
Artificial intelligence is expected to grow in healthcare over the next several years, but Alagappan argues that combining that with other, more readily available intelligent technologies is also an important step towards improved care. “When we say intelligent automation, we’re really referring to the marriage of two concepts: artificial intelligence—which is knowing what to do—and robotic process automation—which is knowing how to do it,” he said. That dual approach is what he says allows Notable Health to bypass administrative bottlenecks in healthcare, instructing bots to carry out those tasks in an efficient and adaptable way.
So far, Notable Health has worked with several hospital systems across multiple states in using their platform for vaccine distribution and scheduling, and are now using the platform to reach out to tens of thousands of patients per day.
Google has agreed to pay a €1.1 million fine over misleading star-ratings for hotels in France.
The tech giant had been applying its own (algorithmic) system of ratings for hotels applied via its search engine and on Google Maps. But back in 2019, following a number of complaints by hoteliers, the French national competition and consumer watchdog (DGCCRF) instigated an investigation into this propriety rating system.
The probe revealed that the tech giant had replaced the standard classification system of the public tourist board (Atout France) with a star rating system powered by its own criteria — and which it had applied to more than 7,500 establishments.
Safe to say Google’s concept of a ‘five star’ hotel was not the same as the Atout France version. And the consumer watchdog found that Google’s presentation for classifying tourist accommodation — including identical use of the term “stars” on the same scale from 1 to 5 — to be confusing for consumers.
“This practice was particularly damaging for consumers, misled about the level of services what they could expect when booking accommodation. It also resulted in prejudice for hoteliers whose establishments were wrongly presented as lower ranked than in the official ranking of Atout France,” the watchdog writes in a press release on the sanction (which we’ve translated from French).
The DGCCRF concluded that Google had engaged in a deceptive business practice — and, with the public prosecutor, it proposed the sanction announced today on Google Ireland (the tech giant’s European HQ) and Google France.
As well as agreeing to pay the fine, Google has changed hotel star ratings in France — agreeing to display the official Atout France ratings. So tourists in France can be confident that a five star hotel they see on Google Maps has an official standard attached to it which can’t be influenced by any of the usual online growth hacking tactics.
A spokesperson for Google confirmed the conclusion of the DGCCRF’s action, telling TechCrunch: “We have now settled with the DGCCRF and made the necessary changes to only reflect the official French star rating for hotels on Google Maps and Search.”
A new BuzzFeed quiz is the first in what Director of Product for Quizzes Chris Johanesen said he’s hoping will be a series of “stunt-y experiments” that the publisher launches this year.
The quiz, timed for Valentine’s Day weekend, promises to “create your perfect boyfriend (or girlfriend) using AI technology.” Johanesen said it’s designed to “poke fun at the situation we’re all in” (quarantine, obviously), as well as the “weird world of online dating.”
To take the quiz, you answer a series of multiple-choices questions about what you’re looking for in your ideal romantic partner.
The questions will probably feel familiar to anyone who’s taken a quiz on BuzzFeed or elsewhere online, but the answer should be a lot more unique: Johanesen noted that in a normal online quiz, there might be “12 or 20 different results that are written, and that’s pretty much it.” With this one, “you could retake it dozens of times and never get the same results.”
Johanesen explained that the BuzzFeed team generated an enormous variety of different profile images using StyleGAN technology. For the text, BuzzFeed staff contributed personality traits, text messages quotes, hobbies and “weird, dark stuff” that the quiz combines algorithmically.
“I think we’re mostly trying to embrace the absurdity of it,” he added. (I saw this myself when I tried out a demo earlier this week and was assigned a girlfriend who wanted to show off her “collection of scabs.”) “We try to match it a little bit to some of your inputs so that it’s not totally random. … An early version was more realistic, but it wasn’t as fun.”
Looking ahead, Johanesen said he’s hoping to create more quizzes that are “more generative,” where a writer might come up with a concept but they don’t have to “handwrite every single option.” Still, it sounds like this approach requires significant editorial work, which Johanesen doesn’t expect to change.
“We could definitely use machine learning models to write a quiz, but it probably wouldn’t be very good,” he said. Instead, the team is interested in “that intersection of what technology can do that humans can’t, and what humans can do that technology can’t.”
Five-year-old “slow dating” app Once has been acquired by the Dating Group, one of the largest companies in the dating world, for $18 million in cash and stock. Dating Group has 73 million registered users across a range of portfolio apps, including Dating.com.
Clémentine Lalande, co-founder and CEO of Once, will continue leading the company under a two-year agreement. Fellow co-founder Jean Meyer retained a stake in the company after departing two years ago.
Once has 9 million users on its platform, while the startup also garnered a further 1 million from a spin-out app it later launched called Pickable.
Once is a dating app that uses matching algorithms to deliver just one match per day to each user. It pitched itself as an alternative to the frenetically paced apps such as Tinder and Bumble. Indeed, Bumble revealed last week that two in five people of those it surveyed are taking longer to get to know someone as a result of pandemic lockdowns. And 38% Bumble users admit that it had made them want something more serious. So Once had a ready market.
Each pair on the Once app has 24 hours of each other’s attention and can continue chatting if they “like” each other. The AI looks at the account’s info, dating preferences and previous history in order to find the best possible match. Users can also rate each particular profile to let the AI better understand their taste.
In a statement, Lalande said: “I am thrilled to join the Dating Group today, both because of their proven focus on post-swiping dating alternatives, and to leverage the huge synergies between Once and Dating Group. In such a concentrated and competitive market having a large partner will allow us to augment our reach and accelerate geographical expansion”.
Bill Alena, chief investment officer at Dating Group said: “We strongly believe in the concept of AI and making quality matches. We see a huge potential in integrating Once into our portfolio. We’re excited to have Clémentine join Dating Group, she and her team have built a fascinating product and with this acquisition, Dating Group expands deeper into the Western European market.”
Dating Group has offices in seven countries and a team of more than 500 professionals, with more than 73 million registered users across the entire portfolio. Its brands include Dating.com, DateMyAge, Dil Mil, Cherish, Tubit, AnastasiaDate and ChinaLove.
A couple of months ago at CNBC’s Transform conference, IBM CEO Arvind Krishna painted a picture of a company in the midst of a transformation. He said that he wanted to take advantage of IBM’s $34 billion 2018 Red Hat acquisition to help customers manage a growing hybrid cloud world, while using artificial intelligence to drive efficiency.
It seems like a sound enough approach. But instead of the new strategy acting as a big growth engine, IBM’s earnings today showed that its cloud and cognitive software revenues were down 4.5% to $6.8 billion. Meanwhile cognitive applications — where you find AI incomes — were flat.
If Krishna was looking for a silver lining, perhaps he could take solace in the fact that Red Hat itself performed well, with revenue up 18% compared to the year-ago period, according to the company. But overall the company’s revenue declined for the fourth straight quarter, leaving the executive in much the same position as his predecessor Ginni Rometty, who led IBM during 22 straight quarters of revenue losses.
Krishna laid out his strategy in November, telling CNBC, “The Red Hat acquisition gave us the technology base on which to build a hybrid cloud technology platform based on open-source, and based on giving choice to our clients as they embark on this journey.” So far the approach is simply not generating the growth Krishna expected.
The company is also in the midst of spinning out its legacy managed infrastructure services division, which, as Krishna said in the same November interview, should allow Big Blue to concentrate more on its new strategy. “With the success of that acquisition now giving us the fuel, we can then take the next step, and the larger step, of taking the managed infrastructure services out. So the rest of the company can be absolutely focused on hybrid cloud and artificial intelligence,” he said.
While it’s certainly too soon to say his transformation strategy has failed, the results aren’t there yet, and IBM’s falling top line has to be as frustrating to Krishna as it was to Rometty. If you guide the company toward more modern technologies and away from the legacy ones, at some point you should start seeing results, but so far that has not been the case for either leader.
Krishna continued to build on this vision at the end of last year by buying some additional pieces like cloud applications performance monitoring company Instana and hybrid cloud consulting firm Nordcloud. He did so to build a broader portfolio of hybrid cloud services to make IBM more of a one-stop shop for these services.
As retired NFL football coach Bill Parcells used to say, referring to his poorly performing teams, “you are what your record says you are.” Right now IBM’s record continues to trend in the wrong direction. While it’s making some gains with Red Hat leading the way, it’s simply not enough to offset the losses, and something needs to change.
Otter.ai, the A.I.-powered voice transcription service that already integrates with Zoom for recording online meetings and webinars, is today bringing its service to Google Meet’s over 100 million users. However, in this case, Otter.ai will provide its live, interactive transcripts and video captions by way of a Chrome web browser extension.
Once installed, a “Live Notes” panel will launch directly in the Chrome web browser during Google Meet calls, where it appears on the side of the Google Meet interface. The panel can be moved around and scrolled through as the meeting is underway.
Here, users can view the live transcript of the online meeting, as it occurs. They can also adjust the text size, then save and share the audio transcripts when the meeting has wrapped.
The company says the feature helps businesses cut down on miscommunication, particularly for non-native English speakers who may have trouble understanding the spoken word. It also offers a more accessible way for engaging with live meeting content.
And because the transcriptions can be shared after the fact, people who missed the meeting can still be looped in to catch up — an increasing need in the remote-work era of the pandemic, where home and parenting responsibilities can often distract users from their daily tasks.
The transcripts themselves can also be edited after the fact by adding images and highlights, and they can be searched by keywords, as with any Otter.ai transcription.
In addition, users can access the company’s Live Captions feature that supports custom vocabulary. Otter points out that there are other live captioning options already available for Google Meet, but the difference here is that Otter’s system creates a collaborative transcript when the meeting ends. Other systems, meanwhile, tend to just offer live captions during the meeting itself.
To use the new feature, Chrome users will need to install the Otter.ai Chrome extension from the Chrome Web Store, then sign in to their Otter.ai account. The new feature is available to all Otter.ai customers, including those on Basic, Pro and Business plans.
Otter in the past leveraged its earlier Zoom integration to push more users from free plans to paid tiers and will likely do the same with the new Google Meet support. The company’s paid plans offer the ability to record more minutes per month and include a range of additional features like the ability to import audio and video for transcription, a variety of export options, advanced search features, Dropbox sync, added security measures and more.
The company has seen its business increase due to the COVID-19 pandemic and the accompanying shift to online meetings. Last April, Otter said it had transcribed over 25 million meetings, and its revenue run rate had doubled compared with the end of 2019. In 2020, Otter.ai’s revenue was up 8x, the company said, but declined to share additional metrics.
As the global agricultural industry stretches to meet expected population growth and food demand, and food security becomes more of a pressing issue with global warming, a startup out of South Africa is using artificial intelligence to help farmers manage their farms, trees and fruits.
Aerobotics, a South African startup that provides intelligent tools to the world’s agriculture industry, has raised $17 million in an oversubscribed Series B round.
South African consumer internet giant Naspers led the round through its investment arm, Naspers Foundry, investing $5.6 million, according to Aerobotics. Cathay AfricInvest Innovation, FMO: Entrepreneurial Development Bank and Platform Investment Partners also participated.
Founded in 2014 by James Paterson and Benji Meltzer, Aerobotics is currently focused on building tools for fruit and tree farmers. Using artificial intelligence, drones and other robotics, its technology helps track and assess the health of these crops, including identifying when trees are sick, tracking pests and diseases, and analytics for better yield management.
The company has progressed its technology and provides to farmers independent and reliable yield estimations and harvest schedules by collecting and processing both tree and fruit imagery from citrus growers early in the season. In turn, farmers can prepare their stock, predict demand and ensure their customers have the best quality of produce.
Aerobotics has experienced record growth in the last few years. For one, it claims to have the largest proprietary data set of trees and citrus fruit in the world, having processed 81 million trees and more than a million citrus fruit.
The seven-year-old startup is based in Cape Town, South Africa. At a time when many of the startups out of the African continent have focused their attention primarily on identifying and fixing challenges at home, Aerobotics has found a lot of traction for its services abroad, too. It has offices in the U.S., Australia and Portugal — like Africa, home to other major, global agricultural economies — and operates in 18 countries across Africa, the Americas, Europe and Australia.
Image Credits: Aerobotics
Within that, the U.S. is the company’s primary market, and Aerobotics says it has two provisional patents pending in the country, one for systems and methods for estimating tree age and another for systems and methods for predicting yield.
The company said it plans to use this Series B investment to continue developing more technology and product delivery, both for the U.S. and other markets.
“We’re committed to providing intelligent tools to optimize automation, minimize inputs and maximize production. We look forward to further co-developing our products with the agricultural industry leaders,” said Paterson, the CEO in a statement.
Once heralded as a frontier for technology centuries ago, the agriculture industry has stalled in that aspect for a long while. However, agritech companies like Aerobotics that support climate-smart agriculture and help farmers have sprung forth trying to take the industry back to its past glory. Investors have taken notice and over the past five years, investments have flowed with breathtaking pace.
For Aerobotics, it raised $600,000 from 4Di Capital and Savannah Fund as part of its seed round in September 2017. The company then raised a further $4 million in Series A funding in February 2019, led by Nedbank Capital and Paper Plane Ventures.
Naspers Foundry, the lead investor in this Series B round, was launched by Naspers in 2019 as a 1.4 billion rand (~$100 million) fund for tech startups in South Africa.
Phuthi Mahanyele-Dabengwa, CEO of Naspers South Africa, said of the investment, “Food security is of paramount importance in South Africa and the Aerobotics platform provides a positive contribution towards helping to sustain it. This type of tech innovation addresses societal challenges and is exactly the type of early-stage company that Naspers Foundry looks to back.”
Besides Aerobotics, Naspers Foundry has invested in online cleaning service SweepSouth, and food service platform Food Supply Network.