Unveiling its latest cohort, Alchemist announces $4 million in funding for its enterprise accelerator

The enterprise software and services focused accelerator, Alchemist has raised $4 million in fresh financing from investors BASF and the Qatar Development Bank, just in time for its latest demo day unveiling 20 new companies.

Qatar and BASF join previous investors including the venture firms Mayfield, Khosla Ventures, Foundation Capital, DFJ, and USVP, and corporate investors like Cisco, Siemens and Juniper Networks.

While the roster of successes from Alchemist’s fund isn’t as lengthy as Y Combinator, the accelerator program has launched the likes of the quantum computing upstart, Rigetti, the soft-launch developer tool LaunchDarkly, and drone startup Matternet .

Some (personal) highlights of the latest cohort include:

  • Bayware: Helmed by a former head of software defined networking from Cisco, the company is pitching a tool that makes creating networks in multi-cloud environments as easy as copying and pasting.
  • MotorCortex.AI: Co-founded by a Stanford Engineering professor and a Carnegie Mellon roboticist, the company is using computer vision, machine learning, and robotics to create a fruit packer for packaging lines. Starting with avocados, the company is aiming to tackle the entire packaging side of pick and pack in logistics.
  • Resilio: With claims of a 96% effectiveness rate and $35,000 in annual recurring revenue with another $1 million in the pipeline, Resilio is already seeing companies embrace its mobile app that uses a phone’s camera to track stress levels and application-based prompts on how to lower it, according to Alchemist.
  • Operant Networks: It’s a long held belief (of mine) that if computing networks are already irrevocably compromised the best thing that companies and individuals can do is just encrypt the hell out of their data. Apparently Operant agrees with me.  The company is claiming 50% time savings with this approach, and have booked $1.9m in 2019 as proof, according to Alchemist.
  • HPC Hub: HPC Hub wants to  democratize access to supercomputers by overlaying a virtualization layer and pre-installed software on underutilized super computers to give more companies and researchers easier access to machines… and they’ve booked $92,000 worth of annual recurring revenue.
  • DinoPlusAI: This chip developer is designing a low latency chip for artificial intelligence applications, reducing latency by 12 times over a competing Nvidia chip, according to the company. DinoPlusAI sees applications for its tech in things like real-time AI markets and autonomous driving. Its team is led by a designer from Cadence and Broadcom and the company already has $8 million in letters of intent signed, according to Alchemist.
  • Aero Systems West Co-founders from the Air Force’s Research Labs and MIT are aiming to take humans out of drone operations and maintenance. The company contends that for every hour of flight time, drones require 7 hours of maintenance and check ups. Aero Systems aims to reduce that by using remote analytics, self-inspection, autonomous deployment, and automated maintenance to take humans out of the drone business.

Watch a livestream of Alchemist’s demo day pitches, starting at 3PM, here.

 

XPRIZE names two grand prize winners in $15 million Global Learning Challenge

XPRIZE, the non-profit organization developing and managing competitions to find solutions to social challenges, has named two grand prize winners in the Elon Musk-backed Global Learning XPRIZE .

The companies, KitKit School out of South Korea and the U.S., and onebillion, operating in Kenya and the U.K., were announced at an awards ceremony hosted at the Google Spruce Goose Hangar in Playa Vista, Calif.

XPRIZE set each of the competing teams the task of developing scalable services that could enable children to teach themselves basic reading, writing, and arithmetic skills within 15 months.

Musk himself was on hand to award $5 million checks to each of the winning teams.

Five finalists including: New York-based CCI, which developed lesson plans and a development language so non-coders could create lessons; Chimple, a Bangalore-based, learning platform enabling children to learn reading, writing and math on a tablet; RobotTutor, a Pittsburgh-based company which used Carnegie Mellon research to develop an app for Android tablets that would teach lessons in reading and writing with speech recognition, machine learning, and human computer interactions, and the two grand prize winners all received $1 million to continue developing their projects.

The tests required each product to be field tested in Swahili, reaching nearly 3,000 children in 170 villages across Tanzania.

All of the final solutions from each of the five teams that made it to the final round of competition have been open-sourced so anyone can improve on and develop local solutions using the toolkits developed by each team in competition.

Kitkit School, with a team from Berkeley, Calif. and Seoul, developed a program with a game-based core and flexible learning architecture to help kids learn independently, while onebillion, merged numeracy content with literacy material to provide directed learning and activities alongside monitoring to personalize responses to children’s needs.

Both teams are going home with $5 million to continue their work.

The problem of access to basic education affects more than 250 million children around the world, who can’t read or write and one-in-five children around the world aren’t in school, according to data from UNESCO.

The problem of access is compounded by a shortage of teachers at the primary ad secondary school level. Some research, cited by XPRIZE, indicates that the world needs to recruit another 68.8 million teachers to provide every child with a primary and secondary education by 2040.

Before the Global Learning XPRIZE field test, 74% of the children who participated were reported as never having attended school; 80% were never read to at home; and 90% couldn’t read a single word of Swahili.

After the 15 month program working on donated Google Pixel C tablets and pre-loaded with software, the number was cut in half.

“Education is a fundamental human right, and we are so proud of all the teams and their dedication and hard work to ensure every single child has the opportunity to take learning into their own hands,” said Anousheh Ansari, CEO of XPRIZE, in a statement. “Learning how to read, write and demonstrate basic math are essential building blocks for those who want to live free from poverty and its limitations, and we believe that this competition clearly demonstrated the accelerated learning made possible through the educational applications developed by our teams, and ultimately hope that this movement spurs a revolution in education, worldwide.”

After the grand prize announcement, XPRIZE said it will work to secure and load the software onto tablets; localize the software; and deliver preloaded hardware and charging stations to remote locations so all finalist teams can scale their learning software across the world.

Nuro CEO Dave Ferguson at TC Sessions: Mobility on July 10 in San Jose

Autonomous delivery startup Nuro, fresh with nearly $1 billion in capital from SoftBank, is bursting with ideas — as some recent patent filings (and our recent deep dive into the company) suggest. And we can’t wait to learn more about what Nuro has planned.

It’s only fitting that Nuro co-founder and CEO Dave Ferguson is our first announced guest for TechCrunch’s inaugural TC Sessions: Mobility, a one-day event on July 10, 2019 in San Jose, Calif., that’s centered around the future of mobility and transportation.

Ferguson has been working on robotics and machine learning for nearly two decades and is an early pioneer of self-driving vehicle technology. He led the planning group for Carnegie Mellon University’s team that won the DARPA Urban Grand Challenge in 2007.

Ferguson holds an MS and PhD in robotics from Carnegie Mellon and a bachelor’s in computer science and mathematics from the University of Otago. He went on to become a senior research scientist at Intel and then developed machine learning trading strategies at Two Sigma, an investment firm.

Ferguson, who has been awarded more than 100 patents, eventually headed to Google’s self-driving program, now known as Waymo, serving as the machine learning and computer vision team lead.

TC Sessions: Mobility will present a day of programming with the best and brightest founders, investors and technologists who are determined to inventing a future Henry Ford might never have imagined. TC Sessions: Mobility aims to do more than highlight the next new thing. We’ll dig into the how and why, the cost and impact to cities, people and companies, as well as the numerous challenges that lie along the way, from technological and regulatory to capital and consumer pressures.

Nuro was founded in June 2016 by Ferguson and another former Google engineer, Jiajun Zhu. Nuro completed its first Series A funding round in China just three months later, in a previously unreported deal that gave NetEase founder Ding Lei (aka William Ding) a seat on Nuro’s board.

In February, Nuro hit the big leagues with a whopping $940 million in financing from the SoftBank Vision Fund, capital that will be used to expand its delivery service, add new partners, hire employees and scale up its fleet of self-driving bots. The startup has raised more than $1 billion from partners, including SoftBank, Greylock Partners  and Gaorong Capital.

Nuro’s focus has been developing a self-driving stack and combining it with a custom unmanned vehicle designed for last-mile delivery of local goods and services. The vehicle has two compartments that can fit up to six grocery bags each. Nuro’s aspirations don’t stop there.

A recent patent application details how its R1 self-driving vehicle could carry smaller robots to cross lawns or climb stairs to drop off packages. The company has even taken the step of trademarking the name “Fido” for delivery services.


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What critics get wrong about the “American AI Initiative”

There’s been a bit of hysteria – AIsteria, if you will – over the Trump administration’s recently issued American AI Initiative, formally known as ‘Executive Order on Maintaining American Leadership in Artificial Intelligence.”

The initiative is a broad strategy “to sustain and enhance the scientific, technological, and economic leadership position of the United States in AI R&D and deployment.”

But critics have complained it’s short on specific actions and lacks new funding to accomplish its goals, in contrast to China’s 2017 “Next Generation Artificial Intelligence Development Plan,” which allocated billions to establish China as the “premier global AI innovation center” by 2030.

As Babson College professor Thomas Davenport noted in a recent essay in The Conversation. 

One Chinese state alone has said it will devote $5 billion to developing AI technologies and businesses. The city of Beijing has committed $2 billion to developing an AI-focused industrial park. A major port, Tianjin, plans to invest $16 billion in its local AI industry.

“China is ready to leapfrog the USA in AI. The US initiative has NO money,” tweeted one skeptic, Moor Insights & Strategy analyst Karl Freund. 

While I wholeheartedly agree that the United States must not cede AI leadership to China or any other country, I find some of the critiques of the initiative overwrought. In fact, even if its value is largely symbolic – more of a vision statement than a detailed blueprint – I still believe the initiative can help move the national AI agenda forward.

Let’s unpack a few things.

While many talk about AI in the context of a rivalry between two superpowers – and it well may be that in some ways – AI is unlike similar global competitions of the past. Take the 20th century Space Race between the United States and the Soviet Union, for example. The effort would have never gotten off the ground, literally, without huge financial commitments from the U.S. government.

Money for AI’s advancement, however, is pouring in from the private sector. According to a report by CB Insights and PwC, venture capital funding of AI companies skyrocketed 72 percent last year, to $9.3 billion. The surge followed three years of steadily increasing investment, with a 28 percent average annual increase between 2015 and 2017.

And it’s not as though the government isn’t doing its part. IDC estimated the federal investment in cognitive and AI technologies is growing at a CAGR of 54.3 percent between 2018 and 2021.

Furthermore, while stopping short of specific dollar amounts, the initiative wasn’t exactly silent on the requirement for more government funding, calling on all relevant agencies to consider AI a top R&D priority and take that into account when developing budget proposals for fiscal 2020 and beyond.

Meanwhile, colleges and universities working on exciting research in AI and its enabling technology, machine learning, and more and more are offering AI-specific training to help solve a skills shortage in the field.

College students reportedly enrolled in introductory AI and machine learning classes in record numbers last year, the number of academic papers on the topic soared, and, according to a Stanford University analysis of transcripts, officials mentioned the technology in more than 70 congressional hearings.

I’m not surprised that (no AI slouch, having introduced an undergraduate AI program last year) reacted very positively to the American AI Initiative. “The American AI Initiative’s focus on prioritizing research and development, responsibly leveraging data as a national resource and investing in an AI-ready workforce will bring new energy to our national innovation ecosystem,” the university said in a statement.

All of this points to the reality that the private sector, as has been the case so many times throughout the annals of U.S. innovation, is taking the lead in AI and counting on entrepreneurial spirit rather than government largesse to win the day.

As a Bloomberg editorial put it: “This contrasts favorably with (say) China, where the government is pumping billions of dollars directly into AI-related companies. This may advance the field somewhat, but it’s also a good way to sustain hopeless businesses, crowd out private investment, encourage cronyism, inflate bubbles, and generally make a hash of things.”

Simply by virtue of shining a bright spotlight on AI as a national priority, the initiative can have significant practical effects. For example, let’s say a VC firm is deciding whether to fund an AI startup or one in another hot space, such as the Internet of Things. The tone set by the initiative could tilt the decision in the AI firm’s favor.

It also could spur more universities to investigate interesting AI technologies in their labs, and further invigorate efforts to train the next generation of AI practitioners.

The American AI Initiative isn’t as cut and dried as the critics suggest. If it’s nothing more than a stake in the ground about AI’s essential role in the nation’s future, it’s still an important stake.

The shift to collaborative robots means the rise of robotics as a service

The 2018 Holiday shopping season was the biggest on record for e-commerce, with nearly $126 billion in online sales. But as e-commerce continues to expand, the demand for warehouse workers is growing faster than the labor supply and creating an increased need for automation.

Given its dominance in e-commerce and the massive scale of its business, there’s no surprise that Amazon was one of the first companies to supplement their human workforce with robotics. Since the acquisition of Kiva in 2012, a growing army of robots performs an increasing variety of tasks at Amazon facilities. However, those tasks remain limited in their ability to displace their human counterparts entirely.

Today, robotics are more affordable to a broader array of companies, thanks to lower cost components, and advancements in technology have paved the way for the rise of the collaborative robot or “cobot”.

inVia Robotics warehouse robots

Cobots are more precise and increasingly flexible with advanced sensor technology, AI, Lidar/Radar, GPS, and connectivity. Machine learning has also made cobots more versatile—not just in their hardware, but in software that facilitates adaptation to a broad array of tasks. And because sensor-rich robots can adapt to a variety of new challenges on the fly, we see more use cases for real-world application.

Don’t expect a severe shift to collaborative robots — we are still in the early innings. The global industrial robot market, dominated by the “Big 4” (Kuka, ABB, Fanuc, and Yaskawa) was valued at more than $15 billion in 2017, while the market for cobots reached only $287 million. However, the digital transformation of warehouses presents a tremendous market opportunity for new companies to create value.

We draw connections to the shift we saw from legacy software to SaaS, where traditional sales and business models switched to recurring revenue streams and cloud-based subscription services. By combining domain-specific go-to-market with robust software management platforms, the next generation of robotics companies has the opportunity to avoid long integrator-led sales cycles and become highly sticky over time, much like the early SaaS providers.

6 River Systems robots lead workers to items they need to get from a warehouse shelf.

Additionally, collaborative robotic technology allows robots to augment human labor, lowering the barriers to entry, while still providing clear payback arguments around efficiency. Like the shift to cloud software, best-in-class platforms are now available to the masses without significant upfront investment in infrastructure.

We believe that co-bots will unlock market verticals traditionally underserved by robotics, such as logistics, food, and security.  Companies that offer full-service solutions to these sectors provide attractive opportunities to build value. For example, 6 River Systems — whose cobots, known as Chucks, use cloud software to coordinate warehouse tasks and work side-by-side with human employees — are changing how we think about the human-robot dynamic.

Cobalt Robotics, in the security vertical, allows human security guards to remotely monitor offices, creating cost savings for the employer, and efficiencies for the security guard. And other companies like RightHand Robotics, inVia Robotics, Starship are poised to replace human labor in some commercial settings.

The rapid innovation in this industry promises to bring efficiency and growth to countless sectors in coming years. Robotics programs at esteemed universities such as MIT, Carnegie Mellon, and Georgia Tech are churning out a pool of world-class entrepreneurs who are not only seizing a timely—and hopefully profitable opportunity—but boldly advancing the industry.

To quote my fellow partner at Menlo Ventures, Matt Murphy, “We are entering a golden era of robotics, where robotics will become mainstream, drive huge efficiencies, and in some cases make the impossible possible.”

Tor pulls in record donations as it lessens reliance on US government grants

Tor, the open-source initiative that provides a more secure way to access the internet, is continuing to diversify its funding away from its long-standing reliance on U.S. government grants.

The Tor Foundation — the organization behind the service which stands for “The Onion Router” — announced this week that it brought in a record $460,000 from individual donors in 2018. In addition, recently released financial information shows it raised a record $4.13 million from all sources in 2017 thanks to a growth in non-U.S. government donors.

The individual donation push represents an increase on the $400,000 it raised in 2017. A large part of that is down to Tor ally Mozilla, which once again pledged to match donations in the closing months of the year, while an anonymous individual matched all new backers who pledged up to $20,000.

Overall, the foundation said that it attracted donations from 115 countries worldwide in 2018, which reflects its importance outside of the U.S.

The record donation haul comes weeks after the Tor Foundation quietly revealed its latest financials — for 2017 — which show it has lessened its dependence on U.S. government sources. That’s been a key goal for some time, particularly after allegations that the FBI paid Carnegie Mellon researchers to help crack Tor, which served as a major motivation for the introduction of fundraising drives in 2015.

Back in 2015, U.S. government sources accounted for 80-90 percent of its financial backing, but that fell to just over 50 percent in 2017. The addition of a Swedish government agency, which provided $600,000, helped on that front, as well as corporate donations from Mozilla ($520,000) and DuckDuckGo ($25,000), more than $400,000 from a range of private foundations, and, of course, those donations from individuals.

Tor is best known for being used by NSA whistleblower Edward Snowden but, with governments across the world cracking down on the internet, it is a resource that’s increasingly necessary if we are to guard the world’s right to a free internet.

Tor has certainly been busy making its technology more accessible over the last year.

It launched its first official mobile browser for Android in September, and the same month it released TorBrowser 8.0, its most usable browser yet, which is based on Firefox’s 2017 Quantum structure. It has also worked closely with Mozilla to bring Tor into Firefox itself as it has already done with Brave, a browser firm led by former Mozilla CEO Brendan Eich.

Beyond the browser and the Tor network itself, which is designed to minimize the potential for network surveillance, the organization also develops a range of other projects. More than two million people are estimated to use Tor, according to data from the organization.

This wristband detects an opiate overdose

A project by students at Carnegie Mellon could save lives. Called the HopeBand, the wristband senses low blood oxygen levels and sends a text message and sounds an alarm if danger is imminent.

“Imagine having a friend who is always watching for signs of overdose; someone who understands your usage pattern and knows when to contact [someone] for help and make sure you get help,” student Rashmi Kalkunte told IEEE. “That’s what the HopeBand is designed to do.”

The team won third place in the Robert Wood Johnson Foundation’s Opioid Challenge at the Health 2.0 conference in September and they are planning to send the band to a needle exchange program in Pittsburgh. They hope to sell it for less than $20.

Given the more than 72,000 overdose deaths in America this year a device like this could definitely keep folks a little safer.

Tapping into the power grid could predict the morning traffic

Why is there traffic? This eternal question haunts civic planners, fluid dynamics professors, and car manufacturers alike. But just counting the cars on the road won’t give you a sufficient answer: you need to look at the data behind the data. In this case, CMU researchers show that electricity usage may be key to understanding movement around the city.

The idea that traffic and electricity use might be related makes sense: when you turn the lights and stereo on and off indicates when you’re home to stay, when you’re sleeping, when you’re likely to leave for work or return, and so on.

“Our results show that morning peak congestion times are clearly related to particular types of electricity-use patterns,” explained Sean Qian, who led the study.

They looked at electricity usage from 322 households over 79 days, training a machine learning model on that usage and the patterns within it. The model learned to associate certain patterns with increases in traffic — so for instance, when a large number of households has a dip in power use earlier than usual, it might mean that the next day will see more traffic when all those early-to-bed people are also early to rise.

The researchers report that their predictions of morning traffic patterns were more accurate using this model than predictions using actual traffic data.

Notably, all that’s needed is the electricity usage, Qian said, nothing like demographics: “It requires no personally identifiable information from households. All we need to know is when and how much someone uses electricity.”

Interestingly, the correlation goes the other way as well, and traffic patterns could be used to predict electricity demand. A few less brownouts would be welcome during a heat wave like this summer’s, so I say the more data the better.

There are many factors like this that indicate the dynamics of a living city — not just electricity use but water use, mobile phone connections, the response to different kinds of weather, and more. Traffic is only one result of a city struggling to operate at maximum capacity, and all these data feed into each other.

The current study was limited to a single electricity provider and apparently other providers are loath to share their data — so there’s still a lot of room to grow here as the value of that data becomes more apparent.

Qian et al published their research in the journal Transportation Research.