Discovering and drilling for the important minerals used for industry and the technology sector remains incredibly important as existing mines are becoming depleted. If the mining industry can’t become more efficient at finding these important deposits, then more unnecessary, harmful drilling and exploration takes place. Applying AI to this problem would seem like a no-brainer for the environment.
Joining this field is now Earth AI, a mineral targeting startup which is using AI to predict the location of new ore bodies far more cheaply, faster, and with more precision (it claims) than previous methods.
It’s now closed a funding round of ‘up to’ $2.5 million from Gagarin Capital, A VC firm specializing in AI, and Y Combinator, in the latter’s latest cohort announced this week. Previously, Earth AI had raised $1.7 million in two seed rounds from Australian VCs, AirTree Ventures and Blackbird Ventures and angel investors.
The startup uses machine learning techniques on global data, including remote sensing, radiometry, geophysical and geochemical datasets, to learn the data signatures related to industrial metal deposits (from gold, copper, and lead to rare earth elements), train a neural network, and predict where high-value mineral prospects will be.
In particular, it was used to discover a deposit of Vanadium, which is used to build Vanadium Redox Batteries that are used in large industrial applications. Finding these deposits faster using AI means the planet will thus benefit faster from battery technology.
In 2018, Earth AI field-tested remote unexplored areas and claims to have generated a 50X better success rate than traditional exploration methods, while spending on average $11,000 per prospect discovery. In Australia, for instance, companies often spend several million dollars to arrive at the same result.
Jared Friedman, YCombinator partner comented in a statement: “The possibility of discovering new mineral deposits with AI is a fascinating and thought-provoking idea. Earth AI has the potential not just to become an incredibly profitable company, but to reduce the cost of the metals we need to build our civilization, and that has huge implications for the world.”
“Earth AI is taking a novel approach to a large and important industry — and that approach is already showing tremendous promise”, Mikhail Taver, partner at Gagarin Capital said.
Earth AI was founded by Roman Tesyluk, a geoscientist with eight years of mineral exploration and academic experience. Prior to starting Earth AI, he was a PhD Candidate at The University of Sydney, Australia and obtained a Master’s degree in Geology from Ivan Franko University, Ukraine. “EARTH AI has huge ambitions, and this funding round will supercharge us towards reaching our milestones,” he said.
This latest investment from Gagarin Capital joins a line of other AI-based products and services and investments it’s made into YC companies, such as Wallarm, Gosu.AI and CureSkin. Gagarin’s exits include MSQRD (acquired by Facebook), and AIMatter (acquired by Google).
U.S. stock markets plummeted today as recession fears continue to grow.
Yesterday’s good news about a reprieve on tariffs for U.S. consumer imports was undone by increasing concerns over economic indicators pointing to a potential global recession coming within the next year.
The Dow Jones Industrial Average dropped more than 800 points on Wednesday — its largest decline of the year — while the S&P 500 fell by 85 points and the tech-heavy Nasdaq dropped 240 points.
The downturn in the markets came a day after the Dow closed up 373 points after the U.S. Trade Representative announced a delay in many of the import taxes the Trump administration planned to impose on Chinese goods.
In the U.S. it was concerns over the news that the yield on 10-year U.S. Treasury notes had dipped below the yield of two-year notes. It’s an indicator that investors think the short-term prospects for a country’s economic outlook are worse than the long-term outlook, so yields are higher for short-term investments.
China’s industrial and retail sectors both slowed significantly in July. Industrial production, including manufacturing, mining and utilities, grew by 4.8% in July (a steep decline from 6.3% growth in June). Meanwhile, retail sales in the country slowed to 7.6%, down from 9.8% in June.
Germany also posted declines over the summer months, indicating that its economy had contracted by 0.1% in the three months leading to June.
Globally, the protracted trade war between the U.S. and China are weighing on economies — as are concerns about what a hard Brexit would mean for the economies in the European Union .
The stocks of Alphabet, Amazon, Apple, Facebook, Microsoft, Netflix and Salesforce were all off by somewhere between 2.5% and 4.5% in today’s trading.
Fully self-driving passenger cars are not “just around the corner.” While the well-capitalized leaders — funded by corporations, multibillion-dollar VC funds or advertising revenue — are on more stable financial ground, many other full-stack autonomous vehicle startups may be looking for the off-ramp.
With no clear path to funds outside of venture capital, full-stack startups face two options: 1) get acquired for the talent and technology or 2) close shop. Cruise and Argo AI were big startup exits. Daimler Trucks acquired Torc Robotics (which did not follow the VC-startup model). And nuTonomy was marketed as a $450 million acquisition by Delphi/Aptiv.
But the most recent VC-backed valuations for some AV startups have stagnated at or below the $450 million mark, which doesn’t give much upside from their previous valuations in the height of the AV fervor. Without much further upside, it is more likely that many passenger car AV companies will close shop.
Full-stack autonomous passenger vehicle startups are dead.
Passenger car autonomy projects attracted a lot of capital and top talent in the past decade and produced tremendous technological advances in autonomous perception, path planning and control. What happens to the talent and technology when the passenger AV bubble bursts?
Well, there are more vehicles than just passenger cars. The DARPA Grand Challenge held over a decade ago is cited as the catalyst behind the GoogleX self-driving car project and the explosion of passenger car AVs. The advances made during the challenges also spilled over to off-highway vehicles. Since then, autonomous vehicles have been developed and deployed in defense as well as commercially in large-scale agriculture and mining.
It is widely observed that industrial, agriculture, construction and mining applications are better suited for near-term autonomy. There are defined automation tasks with clear ROI, there are fewer human-machine interactions and there are geo-fenced areas that bound the operational and safety requirements. These are simply more controlled environments than on city streets. Automation also can help offset critical labor shortages. It is difficult to attract a workforce at remote mines in the middle of vast deserts. Labor shortages for agriculture add tremendous uncertainty for growers who don’t know if they will be able to prepare and harvest their crops during short time windows.
With the help of those DARPA participants, Caterpillar developed semi- and fully autonomous haulage trucks and announced they have hauled more than 1 billion tons of material. Komatsu followed a day later by announcing that they reached the 2 billion ton milestone. These haulage trucks are the size of a house. John Deere, Case IH, New Holland and others have developed semi- and fully autonomous tractors on their own, and with the help of R&D companies. Most of these programs have been around for more than a decade now, but the rate of technological progress pales to that of the recent startup efforts.
From our vantage point as investors, we believe that we will see a similar spillover from the passenger car AV bubble into industrial, agriculture, construction and mining sectors. This will enhance existing autonomous programs, open up new ROI use cases in those sectors and reshape the autonomous vehicle business model in some of the sectors as smaller players gain access to top talent and technology.
The most significant technologies that will spill over into the off-highway vehicle market are machine perception, reinforcement learning for more complex robotic motion planning and functionally safe, mission-critical engineering requirements.
Perception systems deployed on mining and agricultural vehicles are not as cost-constrained as passenger cars. The price tags for some 700-series CAT haulage trucks exceed $5,000,000. These vehicles are equipped with ruggedized lidar, radar, cameras, etc., mostly for safety awareness. Costs of these systems will decline thanks to the cost-constrained designs for sensors driven by the automotive market.
Camera-based inference will allow these vehicles to further understand elements in their environment — allowing them to perform more complex navigational tasks and operations. Sensor fusion may allow agricultural vehicles to deploy optimal inputs to fields or mining vehicles to understand ore characteristics to increase productivity per scoop.
Reinforcement learning allows operators to “teach” algorithms to perform complex tasks and will create new use cases requiring complex robotic actuation. These use cases could be harvesting more than just broad-acre crops, moving dirt on-site, picking-and-placing of construction equipment for staging and much more. These robotic applications can be integrated on top of existing autonomous mobility platforms.
The most important criterion for these startups is an uncompromising approach to robustness and safety. Autonomy only achieves its full potential if the solution works with minimal downtime and improves safety (which is also tied to equipment replacement costs, worker compensation and insurance).
Recognizing these trends, we’ve made an investment into an AV startup that is deploying autonomous systems on Bobcat skid-steer and excavator vehicles in construction and working with large mining operations to automate all vehicles on the mine site.
We’ve also invested in an early-stage agriculture robotics company automating on-field applications that have been, thus far, untouched by automation.
This is only the start. There are many more opportunities in off-highway autonomy, and we’re continuing our search for companies in other off-highway applications.