autonomous systems
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Outrider raises $65 million to bring its autonomous tech to distribution yards
Outrider, a startup aiming to bring its autonomous technology to the nerve center of the supply chain, has raised $65 million in funding just eight months after coming out of stealth. The Series B round was led by Koch Disruptive Technologies and brings its total funding raised to $118 million.
Other existing investors increased their investments, including NEA, 8VC and Prologis Ventures, according to the company. New investors included Henry Crown and Company and Evolv Ventures.
The company’s aim to automate distribution yards doesn’t get the same kind of attention as the more public-facing robotaxis that other companies are pursuing. But it could be as impactful and potentially lucrative to the company that pulls it off. Distribution yards are where goods make the transition from long-haul trucks to warehouses, and eventually the consumer. These hubs of economic activity rely on humans to make repetitive, manual tasks using diesel-powered yard trucks. There are some 400,000 distribution yards located in the United States, a number that provides an idea of the potential size of the opportunity.
Image Credits: Outrider
The Golden, Colorado startup previously known as Azevtec developed a three-part system that includes an autonomous electric yard truck, software to manage the operations and site infrastructure. The total system automates the manual aspect of yard operations, including moving trailers around the yard as well as to and from loading docks. The system can also hitch and unhitch trailers, connect and disconnect trailer brake lines and monitor trailer locations.
Outrider touts the dual benefits of its electric and autonomous system. The company notes that its electric yard trucks are ideal for autonomy due to their reduced maintenance, lower operating costs and reliable clean power. Andrew Smith, the company’s founder and CEO, says disruptions caused by COVID-19 has highlighted the need for this kind of automated distribution yard technology.
Outrider, which now employs 110 employees, has completed “multiple” pilot programs, including one with Georgia-Pacific, and expanded its customer base since coming out of stealth in February.
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Microsoft launches Project Bonsai, its new machine teaching service for building autonomous systems
At its Build developer conference, Microsoft today announced that Project Bonsai, its new machine teaching service, is now in public preview.
If that name sounds familiar, it’s probably because you remember that Microsoft acquired Bonsai, a company that focuses on machine teaching, back in 2018. Bonsai combined simulation tools with different machine learning techniques to build a general-purpose deep reinforcement learning platform, with a focus on industrial control systems.
It’s maybe no surprise then that Project Bonsai, too, has a similar focus on helping businesses teach and manage their autonomous machines. “With Project Bonsai, subject-matter experts can add state-of-the-art intelligence to their most dynamic physical systems and processes without needing a background in AI,” the company notes in its press materials.
“The public preview of Project Bonsai builds on top of the Bonsai acquisition and the autonomous systems private preview announcements made at Build and Ignite of last year,” a Microsoft spokesperson told me.
Interestingly, Microsoft notes that project Bonsai is only the first block of a larger vision to help its customers build these autonomous systems. The company also stresses the advantages of machine teaching over other machine learning approaches, especially the fact that it’s less of a black box approach than other methods, which makes it easier for developers and engineers to debug systems that don’t work as expected.
In addition to Bonsai, Microsoft also today announced Project Moab, an open-source balancing robot that is meant to help engineers and developers learn the basics of how to build a real-world control system. The idea here is to teach the robot to keep a ball balanced on top of a platform that is held by three arms.
Potential users will be able to either 3D-print the robot themselves or buy one when it goes on sale later this year. There is also a simulation, developed by MathWorks, that developers can try out immediately.
“You can very quickly take it into areas where doing it in traditional ways would not be easy, such as balancing an egg instead,” said Mark Hammond, Microsoft general manager for Autonomous Systems. “The point of the Project Moab system is to provide that playground where engineers tackling various problems can learn how to use the tooling and simulation models. Once they understand the concepts, they can apply it to their novel use case.”
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Intel has invested $132M in 11 startups this year, on track for $300M-$500M in total
When it comes to corporate venture capital, semiconductor giant Intel has shaped up to be one of the most prolific and prescient investors in the tech world, with investments in 1,582 companies worldwide, and a tally of some 692 portfolio companies going public or otherwise exiting in the wake of Intel’s backing.
Today, the company announced its latest tranche of deals: $132 million invested in 11 startups. The deals speak to some of the company’s most strategic priorities currently and in the future, covering artificial intelligence, autonomous computing and chip design.
Many corporate VCs have been clear in drawing a separation between their activities and that of their parents, and the same has held for Intel. But at the same time, the company has made a number of key moves that point to how it uses its VC muscle to expand its strategic relationships and also ultimately expand through M&A. Just earlier this month, it acquired Moovit, an Intel Capital portfolio company, for $900 million (a deal that was knocked down to $840 million when accounting for its previous investment).
“Intel Capital identifies and invests in disruptive startups that are working to improve the way we work and live. Each of our recent investments is pushing the boundaries in areas such as AI, data analytics, autonomous systems and semiconductor innovation. Intel Capital is excited to work with these companies as we jointly navigate the current world challenges and as we together drive sustainable, long-term growth,” said Wendell Brooks, Intel senior vice president and president of Intel Capital, in a statement.
The tranche of deals come at a critical time in the worlds of startups and venture investing. Many are worried that the slowdown in the economy, precipitated by the COVID-19 pandemic, will mean a subsequent slowdown in tech finance. Intel says that it plans to invest between $300 million and $500 million in total this year, so this would go some way to refuting that idea, along with some of the other monster deals and big funds that we’ve written out in the last couple of months.
The list announced today doesn’t include specific investment numbers, but in some cases the startups have also announced the fundings themselves and given more detail on round sizes. These still, however, do not reveal Intel’s specific financial stakes.
Here’s the full list:
- Anodot uses machine learning to monitor business operations autonomously, covering areas like app performance, customer incidents and more. The idea is that using the platform to monitor for these incidents means detection and response time can be faster. The full $35 million round was announced back in April.
- Astera Labs is a fabless semiconductor startup focused on connectivity solutions for data-centric systems to remove performance bottlenecks in compute-intensive workloads in areas like AI. It announced its Series B of an undisclosed amount two weeks ago, and prior to this it had raised just over $6 million, according to PitchBook.
- Axonne develops next-generation high-speed automotive Ethernet network connectivity solutions for connected cars: addressing the issue of merging legacy or proprietary systems with the demands of advanced next-generation applications. Intel invested as part of a $9 million round that actually closed in March.
- Hypersonix uses big-data analytics to determine and predict customer demand for e-commerce, retail and hospitality customers. One of its customers is Amazon — which uses Hypersonix’s platform in its supply chain division. That may come as a surprise, but according to Hypersonix’s CEO, the e-commerce giant does not have dedicated analytics teams to serve every division in the company, so sometimes they do buy from third parties. The round was actually announced at the beginning of this month: an $11.5 million deal.
- KFBIO out of China is one of Intel’s biotechnology bets. The company has designed and built a digital pathology scanner, which aims to replace microscopes with its big data, cloud-based and AI-powered insights. The obvious connection and interest here for Intel is on the processor side, but potentially brings Intel into a sphere where it can flex its muscle around a range of AI and cloud computing applications as well. The deal was closed at the beginning of April and totals around $14.2 million.
- Lilt has built an AI-powered language translation platform, not to compete with the likes of Google Translate for consumers, but to help those with international-facing websites and apps localise their services more efficiently. The company announced its round today: a $25 million Series B led by Intel.
- MemVerge focuses on “in-memory” computing, an architecture that makes it easier to deploy heavy, data-centric applications. It closed its round of $24.5 million at the beginning of April, and while it’s always worked with Intel processors, Intel’s investment was not public until today.
- ProPlus Electronics, also out of China, is an electronic design automation (“EDA”) startup that speeds up chip design and fabrication for semiconductor companies manufacturing a variety of chips at scale. It closed its round also at the beginning of April. The exact amount was undisclosed except to note that it was in the “hundreds of millions of Chinese Yuan” (or tens of millions of U.S. dollars).
- Retrace is an under-the-radar dental data startup that uses AI to improve “dental decision making,” but according to its site seems also to focus on other healthcare areas. It’s not clear how big the round is or when it closed.
- Spectrum Materials out of China is another stealthy company that supplies gas and other materials to semiconductor makers.
- Xsight Labs based in Israel is building chipset designs to accelerate data-intensive workloads that you typically get with AI and analytical applications. Israel has a huge R&D centre focused on autonomous driving, one of the applications that’s going to demand a lot in processing power, so this looks like a clearly strategic bet. The company raised $25 million in February, but Intel was not disclosed in that round previously.
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Oceans of opportunity: surveying 2020’s seafaring startup potential
Space attracts a lot of attention as an area of frontier tech investment and entrepreneurship, but there’s another vast expanse that could actually be more addressable by the innovation economy — Earth’s oceans.
Seafaring startups aren’t attracting quite as much attention as their spacefaring cousins, but 2019 still saw a flurry of activity in this sector and 2020 could be an even big year for everything aquatic.
Sounding the depths of data collection
One big similarity between space tech and seafaring opportunities is that data collection represents a significant percent of the potential market. Data collection in and around Earth’s oceans has increased dramatically in recent years thanks to the availability, efficacy and cost of sensor technologies — in 2017, it was estimated that as much ocean data had been gathered in the past two years as in all of human history. But relatively speaking, that barely scratches the surface.
Ocean observation has largely been driven by scientific and research goals, which means there’s bound to be a pretty hard cap on available funding. But ocean data has value in all kinds of private’s sector pursuits, including the potential for autonomous commercial cargo transportation (more on that later), as well as predicting weather and climate conditions that impact shipping routes, agriculture and more.
Various methods exist for collecting data about Earth’s oceans, including space-based satellite observation. Startups like Terradepth, Saildrone and Promare have all proposed various autonomous seafaring data collection vehicle designs that could leverage robotics to bring ocean observation at scale closer to home. These firms are using technology that’s been made affordable for startup budgets through miniaturization and efficiency gains evolved through the progress of the smartphone and other computing industries.
This past year, Xprize awarded millions in prize money to teams that competed in the Ocean Discovery competition for autonomous ocean floor mapping, which is resulting in spin-out ventures that have a head start on success.
As in space, data collection and observation can take many forms, so we can expect to see many industry-specific approaches emerge to capitalize on what are surprisingly large market opportunities, even for seemingly narrow types of data. Continued efforts to refine and improve robotics technologies like sensing and vision should drive even more growth in autonomous ocean observation in 2020.
Autonomous logistics
Oceanfaring drones aren’t just about data collection, however; a huge portion of the global logistics market still relies on giant cargo vessels. The drive to automate container ships is nothing new, but it’s reaching a point where we’re actually starting to see autonomous cargo vehicles embark, including this Chinese cargo ship that set out from Guangdong at the end of this year and a ship called the Yara Birkeland has begun trials out of Rotterdam and expects to be operating fully autonomously by 2022.
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How Microsoft is trying to become more innovative
Microsoft Research is a globally distributed playground for people interested in solving fundamental science problems.
These projects often focus on machine learning and artificial intelligence, and since Microsoft is on a mission to infuse all of its products with more AI smarts, it’s no surprise that it’s also seeking ways to integrate Microsoft Research’s innovations into the rest of the company.
Across the board, the company is trying to find ways to become more innovative, especially around its work in AI, and it’s putting processes in place to do so. Microsoft is unusually open about this process, too, and actually made it somewhat of a focus this week at Ignite, a yearly conference that typically focuses more on technical IT management topics.
At Ignite, Microsoft will for the first time present these projects externally at a dedicated keynote. That feels similar to what Google used to do with its ATAP group at its I/O events and is obviously meant to showcase the cutting-edge innovation that happens inside of Microsoft (outside of making Excel smarter).
To manage its AI innovation efforts, Microsoft created the Microsoft AI group led by VP Mitra Azizirad, who’s tasked with establishing thought leadership in this space internally and externally, and helping the company itself innovate faster (Microsoft’s AI for Good projects also fall under this group’s purview). I sat down with Azizirad to get a better idea of what her team is doing and how she approaches getting companies to innovate around AI and bring research projects out of the lab.
“We began to put together a narrative for the company of what it really means to be in an AI-driven world and what we look at from a differentiated perspective,” Azizirad said. “What we’ve done in this area is something that has resonated and landed well. And now we’re including AI, but we’re expanding beyond it to other paradigm shifts like human-machine interaction, future of computing and digital responsibility, as more than just a set of principles and practices but an area of innovation in and of itself.”
Currently, Microsoft is doing a very good job at talking and thinking about horizon one opportunities, as well as horizon three projects that are still years out, she said. “Horizon two, we need to get better at, and that’s what we’re doing.”
It’s worth stressing that Microsoft AI, which launched about two years ago, marks the first time there’s a business, marketing and product management team associated with Microsoft Research, so the team does get a lot of insights into upcoming technologies. Just in the last couple of years, Microsoft has published more than 6,000 research papers on AI, some of which clearly have a future in the company’s products.
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Quadric.io raises $15M to build a plug-and-play supercomputer for autonomous systems
Quadric.io, a startup founded by some of the folks behind the once-secretive bitcoin mining operation “21E6,” has raised $15 million in a Series A round that will fund the development of a supercomputer designed for autonomous systems.
The round was led by automotive Tier 1 supplier DENSO and its semiconductor products arm NSITEXE, which will also be one of Quadric.io’s customers for future electronic systems in all levels of autonomous driving solutions. Leawood VC also participated in the Series A round.
The company says it will use the injection of capital to build out its product and hire more people, as well as business development.
Pear, Uncork Capital, SV Angel, Cota Capital and Trucks VC are seed investors in Quadric.io.
The roots of Quadric.io grew from a seemingly disconnected mission to produce an agricultural robot designed to transform the way vineyards were managed. The company launched in 2016 by CEO Veerbhan Kheterpal, CTO Nigel Drego and CPO Daniel Firu — all co-founders of 21 Inc. The bitcoin startup, once known as 21E6, would later rebrand as Earn.com before being acquired by Coinbase for $100 million.
Quadric’s original plan was stymied by some real-world fundamentals. The power-hungry ag robot was weighed down by batteries that became too unwieldy to move amongst vineyard rows and the processing time to turn loads of environmental data into actual actions based on algorithms were too slow.
Quadric was looking for a chip designed for processing on the edge and that supported decision making in real time — all while crunching data faster and sipping, not slurping power. That need grew into Quadric’s core product today: a supercomputer that the company says hits that sweet spot of increased computational speed and reduced power consumption.
Kheterpal noted in a recent post on Medium that Intel’s CPUs work “very well for standard computer processing” and Nvidia’s GPUs have “ushered in astounding new graphics processing for gaming and much more.” But, he argued, Quadric needed something neither of those companies could provide: a chip designed for processing on the edge.
The company created a single unified architecture in the supercomputer that enables high-performance computing and artificial intelligence. The supercomputer, which is built around the Quadric Processor, is plug-and-play. This means people can plug in their sensor set and build their entire application to support “near-instantaneous” decision making, Quadric says. The company claims that early testing of Quadric’s system has shown up to 100 times lower latency and a 90% reduction in power consumption.
Quadric designed the instruction set, chip architecture and system architecture of the chip. System-level manufacturing is done at a contract manufacturer in Santa Clara, Calif., while chip manufacturing and assembly is done in Asia.
Quadric argues this underlying technology is a prerequisite for companies developing autonomous systems that will be used in the construction, transportation, agriculture and warehousing industries. The underlying tech that supports autonomous machines used in these industries either lacks the performance or solves only a small part of the full application, according to Quadric.
The startup contends that machines with autonomous functions require processing speed and responsiveness “on the edge” — meaning at the machine level, not in the cloud.
Other companies, most recently Tesla, have opted to build their own chips to meet this specific need. But as Kheterpal notes, not all companies have the resources to build the tech from the ground up.
“ Quadric is a plug and play option that eliminates the need for building heterogeneous systems with significant hardware and software integration costs — thereby taking years off of product development roadmaps,” Kheterpal wrote.
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Prowler.io raises $2M to help AI systems make smarter choices
As we inch closer to a time when we may rely on truly autonomous devices to move us or do things on our behalf, the need for software that’s able to think on its feet (or mid-air) will be essential. Now, an artificial intelligence startup working on this emerging area of machine learning has raised a seed round of funding to try to do just that. Cambridge, UK-based Prowler.io, which… Read More
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