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#436977 The Top 100 AI Startups Out There Now, ...
New drug therapies for a range of chronic diseases. Defenses against various cyber attacks. Technologies to make cities work smarter. Weather and wildfire forecasts that boost safety and reduce risk. And commercial efforts to monetize so-called deepfakes.
What do all these disparate efforts have in common? They’re some of the solutions that the world’s most promising artificial intelligence startups are pursuing.
Data research firm CB Insights released its much-anticipated fourth annual list of the top 100 AI startups earlier this month. The New York-based company has become one of the go-to sources for emerging technology trends, especially in the startup scene.
About 10 years ago, it developed its own algorithm to assess the health of private companies using publicly-available information and non-traditional signals (think social media sentiment, for example) thanks to more than $1 million in grants from the National Science Foundation.
It uses that algorithm-generated data from what it calls a company’s Mosaic score—pulling together information on market trends, money, and momentum—along with other details ranging from patent activity to the latest news analysis to identify the best of the best.
“Our final list of companies is a mix of startups at various stages of R&D and product commercialization,” said Deepashri Varadharajanis, a lead analyst at CB Insights, during a recent presentation on the most prominent trends among the 2020 AI 100 startups.
About 10 companies on the list are among the world’s most valuable AI startups. For instance, there’s San Francisco-based Faire, which has raised at least $266 million since it was founded just three years ago. The company offers a wholesale marketplace that uses machine learning to match local retailers with goods that are predicted to sell well in their specific location.
Image courtesy of CB Insights
Funding for AI in Healthcare
Another startup valued at more than $1 billion, referred to as a unicorn in venture capital speak, is Butterfly Network, a company on the East Coast that has figured out a way to turn a smartphone phone into an ultrasound machine. Backed by $350 million in private investments, Butterfly Network uses AI to power the platform’s diagnostics. A more modestly funded San Francisco startup called Eko is doing something similar for stethoscopes.
In fact, there are more than a dozen AI healthcare startups on this year’s AI 100 list, representing the most companies of any industry on the list. In total, investors poured about $4 billion into AI healthcare startups last year, according to CB Insights, out of a record $26.6 billion raised by all private AI companies in 2019. Since 2014, more than 4,300 AI startups in 80 countries have raised about $83 billion.
One of the most intensive areas remains drug discovery, where companies unleash algorithms to screen potential drug candidates at an unprecedented speed and breadth that was impossible just a few years ago. It has led to the discovery of a new antibiotic to fight superbugs. There’s even a chance AI could help fight the coronavirus pandemic.
There are several AI drug discovery startups among the AI 100: San Francisco-based Atomwise claims its deep convolutional neural network, AtomNet, screens more than 100 million compounds each day. Cyclica is an AI drug discovery company in Toronto that just announced it would apply its platform to identify and develop novel cannabinoid-inspired drugs for neuropsychiatric conditions such as bipolar disorder and anxiety.
And then there’s OWKIN out of New York City, a startup that uses a type of machine learning called federated learning. Backed by Google, the company’s AI platform helps train algorithms without sharing the necessary patient data required to provide the sort of valuable insights researchers need for designing new drugs or even selecting the right populations for clinical trials.
Keeping Cyber Networks Healthy
Privacy and data security are the focus of a number of AI cybersecurity startups, as hackers attempt to leverage artificial intelligence to launch sophisticated attacks while also trying to fool the AI-powered systems rapidly coming online.
“I think this is an interesting field because it’s a bit of a cat and mouse game,” noted Varadharajanis. “As your cyber defenses get smarter, your cyber attacks get even smarter, and so it’s a constant game of who’s going to match the other in terms of tech capabilities.”
Few AI cybersecurity startups match Silicon Valley-based SentinelOne in terms of private capital. The company has raised more than $400 million, with a valuation of $1.1 billion following a $200 million Series E earlier this year. The company’s platform automates what’s called endpoint security, referring to laptops, phones, and other devices at the “end” of a centralized network.
Fellow AI 100 cybersecurity companies include Blue Hexagon, which protects the “edge” of the network against malware, and Abnormal Security, which stops targeted email attacks, both out of San Francisco. Just down the coast in Los Angeles is Obsidian Security, a startup offering cybersecurity for cloud services.
Deepfakes Get a Friendly Makeover
Deepfakes of videos and other types of AI-manipulated media where faces or voices are synthesized in order to fool viewers or listeners has been a different type of ongoing cybersecurity risk. However, some firms are swapping malicious intent for benign marketing and entertainment purposes.
Now anyone can be a supermodel thanks to Superpersonal, a London-based AI startup that has figured out a way to seamlessly swap a user’s face onto a fashionista modeling the latest threads on the catwalk. The most obvious use case is for shoppers to see how they will look in a particular outfit before taking the plunge on a plunging neckline.
Another British company called Synthesia helps users create videos where a talking head will deliver a customized speech or even talk in a different language. The startup’s claim to fame was releasing a campaign video for the NGO Malaria Must Die showing soccer star David Becham speak in nine different languages.
There’s also a Seattle-based company, Wellsaid Labs, which uses AI to produce voice-over narration where users can choose from a library of digital voices with human pitch, emphasis, and intonation. Because every narrator sounds just a little bit smarter with a British accent.
AI Helps Make Smart Cities Smarter
Speaking of smarter: A handful of AI 100 startups are helping create the smart city of the future, where a digital web of sensors, devices, and cloud-based analytics ensure that nobody is ever stuck in traffic again or without an umbrella at the wrong time. At least that’s the dream.
A couple of them are directly connected to Google subsidiary Sidewalk Labs, which focuses on tech solutions to improve urban design. A company called Replica was spun out just last year. It’s sort of SimCity for urban planning. The San Francisco startup uses location data from mobile phones to understand how people behave and travel throughout a typical day in the city. Those insights can then help city governments, for example, make better decisions about infrastructure development.
Denver-area startup AMP Robotics gets into the nitty gritty details of recycling by training robots on how to recycle trash, since humans have largely failed to do the job. The U.S. Environmental Protection Agency estimates that only about 30 percent of waste is recycled.
Some people might complain that weather forecasters don’t even do that well when trying to predict the weather. An Israeli AI startup, ClimaCell, claims it can forecast rain block by block. While the company taps the usual satellite and ground-based sources to create weather models, it has developed algorithms to analyze how precipitation and other conditions affect signals in cellular networks. By analyzing changes in microwave signals between cellular towers, the platform can predict the type and intensity of the precipitation down to street level.
And those are just some of the highlights of what some of the world’s most promising AI startups are doing.
“You have companies optimizing mining operations, warehouse logistics, insurance, workflows, and even working on bringing AI solutions to designing printed circuit boards,” Varadharajanis said. “So a lot of creative ways in which companies are applying AI to solve different issues in different industries.”
Image Credit: Butterfly Network Continue reading
#436578 AI Just Discovered a New Antibiotic to ...
Penicillin, one of the greatest discoveries in the history of medicine, was a product of chance.
After returning from summer vacation in September 1928, bacteriologist Alexander Fleming found a colony of bacteria he’d left in his London lab had sprouted a fungus. Curiously, wherever the bacteria contacted the fungus, their cell walls broke down and they died. Fleming guessed the fungus was secreting something lethal to the bacteria—and the rest is history.
Fleming’s discovery of penicillin and its later isolation, synthesis, and scaling in the 1940s released a flood of antibiotic discoveries in the next few decades. Bacteria and fungi had been waging an ancient war against each other, and the weapons they’d evolved over eons turned out to be humanity’s best defense against bacterial infection and disease.
In recent decades, however, the flood of new antibiotics has slowed to a trickle.
Their development is uneconomical for drug companies, and the low-hanging fruit has long been picked. We’re now facing the emergence of strains of super bacteria resistant to one or more antibiotics and an aging arsenal to fight them with. Gone unchallenged, an estimated 700,000 deaths worldwide due to drug resistance could rise to as many as 10 million in 2050.
Increasingly, scientists warn the tide is turning, and we need a new strategy to keep pace with the remarkably quick and boundlessly creative tactics of bacterial evolution.
But where the golden age of antibiotics was sparked by serendipity, human intelligence, and natural molecular weapons, its sequel may lean on the uncanny eye of artificial intelligence to screen millions of compounds—and even design new ones—in search of the next penicillin.
Hal Discovers a Powerful Antibiotic
In a paper published this week in the journal, Cell, MIT researchers took a step in this direction. The team says their machine learning algorithm discovered a powerful new antibiotic.
Named for the AI in 2001: A Space Odyssey, the antibiotic, halicin, successfully wiped out dozens of bacterial strains, including some of the most dangerous drug-resistant bacteria on the World Health Organization’s most wanted list. The bacteria also failed to develop resistance to E. coli during a month of observation, in stark contrast to existing antibiotic ciprofloxacin.
“In terms of antibiotic discovery, this is absolutely a first,” Regina Barzilay, a senior author on the study and computer science professor at MIT, told The Guardian.
The algorithm that discovered halicin was trained on the molecular features of 2,500 compounds. Nearly half were FDA-approved drugs, and another 800 naturally occurring. The researchers specifically tuned the algorithm to look for molecules with antibiotic properties but whose structures would differ from existing antibiotics (as halicin’s does). Using another machine learning program, they screened the results for those likely to be safe for humans.
Early study suggests halicin attacks the bacteria’s cell membranes, disrupting their ability to produce energy. Protecting the cell membrane from halicin might take more than one or two genetic mutations, which could account for its impressive ability to prevent resistance.
“I think this is one of the more powerful antibiotics that has been discovered to date,” James Collins, an MIT professor of bioengineering and senior author told The Guardian. “It has remarkable activity against a broad range of antibiotic-resistant pathogens.”
Beyond tests in petri-dish bacterial colonies, the team also tested halicin in mice. The antibiotic cleared up infections of a strain of bacteria resistant to all known antibiotics in a day. The team plans further study in partnership with a pharmaceutical company or nonprofit, and they hope to eventually prove it safe and effective for use in humans.
This last bit remains the trickiest step, given the cost of getting a new drug approved. But Collins hopes algorithms like theirs will help. “We could dramatically reduce the cost required to get through clinical trials,” he told the Financial Times.
A Universe of Drugs Awaits
The bigger story may be what happens next.
How many novel antibiotics await discovery, and how far can AI screening take us? The initial 6,000 compounds scanned by Barzilay and Collins’s team is a drop in the bucket.
They’ve already begun digging deeper by setting the algorithm loose on 100 million molecules from an online library of 1.5 billion compounds called the ZINC15 database. This first search took three days and turned up 23 more candidates that, like halicin, differ structurally from existing antibiotics and may be safe for humans. Two of these—which the team will study further—appear to be especially powerful.
Even more ambitiously, Barzilay hopes the approach can find or even design novel antibiotics that kill bad bacteria with alacrity while sparing the good guys. In this way, a round of antibiotics would cure whatever ails you without taking out your whole gut microbiome in the process.
All this is part of a larger movement to use machine learning algorithms in the long, expensive process of drug discovery. Other players in the area are also training AI on the vast possibility space of drug-like compounds. Last fall, one of the leaders in the area, Insilico, was challenged by a partner to see just how fast their method could do the job. The company turned out a new a proof-of-concept drug candidate in only 46 days.
The field is still developing, however, and it has yet to be seen exactly how valuable these approaches will be in practice. Barzilay is optimistic though.
“There is still a question of whether machine-learning tools are really doing something intelligent in healthcare, and how we can develop them to be workhorses in the pharmaceuticals industry,” she said. “This shows how far you can adapt this tool.”
Image Credit: Halicin (top row) prevented the development of antibiotic resistance in E. coli, while ciprofloxacin (bottom row) did not. Collins Lab at MIT Continue reading
#435828 Video Friday: Boston Dynamics’ ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):
RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, Calif., USA
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
You’ve almost certainly seen the new Spot and Atlas videos from Boston Dynamics, if for no other reason than we posted about Spot’s commercial availability earlier this week. But what, are we supposed to NOT include them in Video Friday anyway? Psh! Here you go:
[ Boston Dynamics ]
Eight deadly-looking robots. One Giant Nut trophy. Tonight is the BattleBots season finale, airing on Discovery, 8 p.m. ET, or check your local channels.
[ BattleBots ]
Thanks Trey!
Speaking of battling robots… Having giant robots fight each other is one of those things that sounds really great in theory, but doesn’t work out so well in reality. And sadly, MegaBots is having to deal with reality, which means putting their giant fighting robot up on eBay.
As of Friday afternoon, the current bid is just over $100,000 with a week to go.
[ MegaBots ]
Michigan Engineering has figured out the secret formula to getting 150,000 views on YouTube: drone plus nail gun.
[ Michigan Engineering ]
Michael Burke from the University of Edinburgh writes:
We’ve been learning to scoop grapefruit segments using a PR2, by “feeling” the difference between peel and pulp. We use joint torque measurements to predict the probability that the knife is in the peel or pulp, and use this to apply feedback control to a nominal cutting trajectory learned from human demonstration, so that we remain in a position of maximum uncertainty about which medium we’re cutting. This means we slice along the boundary between the two mediums. It works pretty well!
[ Paper ] via [ Robust Autonomy and Decisions Group ]
Thanks Michael!
Hey look, it’s Jan with eight EMYS robot heads. Hi, Jan! Hi, EMYSes!
[ EMYS ]
We’re putting the KRAKEN Arm through its paces, demonstrating that it can unfold from an Express Rack locker on the International Space Station and access neighboring lockers in NASA’s FabLab system to enable transfer of materials and parts between manufacturing, inspection, and storage stations. The KRAKEN arm will be able to change between multiple ’end effector’ tools such as grippers and inspection sensors – those are in development so they’re not shown in this video.
[ Tethers Unlimited ]
UBTECH’s Alpha Mini Robot with Smart Robot’s “Maatje” software is offering healthcare service to children at Praktijk Intraverte Multidisciplinary Institution in Netherlands.
This institution is using Alpha Mini in counseling children’s behavior. Alpha Mini can move and talk to children and offers games and activities to stimulate and interact with them. Alpha Mini talks, helps and motivates children thereby becoming more flexible in society.
[ UBTECH ]
Some impressive work here from Anusha Nagabandi, Kurt Konoglie, Sergey Levine, Vikash Kumar at Google Brain, training a dexterous multi-fingered hand to do that thing with two balls that I’m really bad at.
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object manipulation, executing finger gaits to move objects, and exhibiting precise fine motor skills such as writing, all require finely balancing contact forces, breaking and reestablishing contacts repeatedly, and maintaining control of unactuated objects. In this work, we demonstrate that our method of online planning with deep dynamics models (PDDM) addresses both of these limitations; we show that improvements in learned dynamics models, together with improvements in online model-predictive control, can indeed enable efficient and effective learning of flexible contact-rich dexterous manipulation skills — and that too, on a 24-DoF anthropomorphic hand in the real world, using just 2-4 hours of purely real-world data to learn to simultaneously coordinate multiple free-floating objects.
[ PDDM ]
Thanks Vikash!
CMU’s Ballbot has a deceptively light touch that’s ideal for leading people around.
A paper on this has been submitted to IROS 2019.
[ CMU ]
The Autonomous Robots Lab at the University of Nevada is sharing some of the work they’ve done on path planning and exploration for aerial robots during the DARPA SubT Challenge.
[ Autonomous Robots Lab ]
More proof that anything can be a drone if you staple some motors to it. Even 32 feet of styrofoam insulation.
[ YouTube ]
Whatever you think of military drones, we can all agree that they look cool.
[ Boeing ]
I appreciate the fact that iCub has eyelids, I really do, but sometimes, it ends up looking kinda sleepy in research videos.
[ EPFL LASA ]
Video shows autonomous flight of a lightweight aerial vehicle outdoors and indoors on the campus of Carnegie Mellon University. The vehicle is equipped with limited onboard sensing from a front-facing camera and a proximity sensor. The aerial autonomy is enabled by utilizing a 3D prior map built in Step 1.
[ CMU ]
The Stanford Space Robotics Facility allows researchers to test innovative guidance and navigation algorithms on a realistic frictionless, underactuated system.
[ Stanford ASL ]
In this video, Ian and CP discuss Misty’s many capabilities including robust locomotion, obstacle avoidance, 3D mapping/SLAM, face detection and recognition, sound localization, hardware extensibility, photo and video capture, and programmable personality. They also talk about some of the skills he’s built using these capabilities (and others) and how those skills can be expanded upon by you.
[ Misty Robotics ]
This week’s CMU RI Seminar comes from Aaron Parness at Caltech and NASA JPL, on “Robotic Grippers for Planetary Applications.”
The previous generation of NASA missions to the outer solar system discovered salt water oceans on Europa and Enceladus, each with more liquid water than Earth – compelling targets to look for extraterrestrial life. Closer to home, JAXA and NASA have imaged sky-light entrances to lava tube caves on the Moon more than 100 m in diameter and ESA has characterized the incredibly varied and complex terrain of Comet 67P. While JPL has successfully landed and operated four rovers on the surface of Mars using a 6-wheeled rocker-bogie architecture, future missions will require new mobility architectures for these extreme environments. Unfortunately, the highest value science targets often lie in the terrain that is hardest to access. This talk will explore robotic grippers that enable missions to these extreme terrains through their ability to grip a wide variety of surfaces (shapes, sizes, and geotechnical properties). To prepare for use in space where repair or replacement is not possible, we field-test these grippers and robots in analog extreme terrain on Earth. Many of these systems are enabled by advances in autonomy. The talk will present a rapid overview of my work and a detailed case study of an underactuated rock gripper for deflecting asteroids.
[ CMU ]
Rod Brooks gives some of the best robotics talks ever. He gave this one earlier this week at UC Berkeley, on “Steps Toward Super Intelligence and the Search for a New Path.”
[ UC Berkeley ] Continue reading