The tech already exists for an EV battery to power your house. Now carmakers, utilities, and regulators are working out how that energy-storage tech could help bolster bigger things—namely, the power grid, as both the demand for electricity and reliance on renewables grow.

Zoom in: California has been the US leader in policies that support electrifying transportation, and is crafting regulations that would ban the sale of any new gas-powered cars in 2035.

In October 2019, the state passed a bill that requires the California Public Utilities Commission (CPUC) to maximize the use of “feasible and cost-effective” vehicle-grid integration by 2030—one of only a few of its kind in the country.

• Vehicle-grid integration (VGI) encompasses both what the industry refers to as V1G, meaning intelligently-managing EV charging, as well as V2G, which involves discharging power from the vehicle’s battery back to the grid.

Since it passed its 2019 VGI bill, California has been backing programs to determine how to deploy and scale VGI technology.

• “It runs the gamut from your sort of simpler off-peak charging rebates, to dynamic rates, up to demand-response programs, where you’re just shifting or modulating the charging of the vehicle or the fleet," Zach Woogen, policy specialist at the California-based nonprofit Vehicle-Grid Integration Council (VGIC), told us.
• "And then all the way up to these bidirectional use cases where you’re providing backup power, or providing other services to the grid and providing other value to the customer," he added.

Big picture: While California is putting resources behind these VGI programs, it is certainly not the only state thinking ahead on V2G technology. New York, Colorado, and Massachusetts also have pilots in place.

Even still, we’re years away from a scenario where EVs can seamlessly supply power to the energy grid. And even if and when all the technology works together perfectly, convincing EV owners to participate is “where a lot of the work lies,” Woogen said.

Read more about how California is turning cars into grid storage.—GD

We may not be able to talk to plants, but what if they could somehow tell us how they feel anyway?

San Francisco-based InnerPlant is using a combination of synthetic biology, sensor tech, and data science to try and get to the bottom of what stresses out certain plants.

• InnerPlant recodes plant DNA with fluorescent proteins—also known as biosensors—that change colors when a plant is stressed, needs watering, or is being attacked by disease or fungi.
• The idea is that this visual feedback can allow a farmer to respond to issues in the field with more precision than is possible with traditional farming methods.

Although InnerPlant doesn’t yet have a commercial product, the company’s tech has been trialed in soybeans and cotton, and it plans to venture into corn—the US’s biggest crop as of 2019—by the end of 2022. The company has raised over $5.7 million since its founding in 2018.

“When the plants are under attack, they activate their immune system to protect themselves,” Shely Aronov, CEO and co-founder of InnerPlant, told Emerging Tech Brew. “What we do is we code the crops, so as they’re reacting to that stress, they’re also going to start generating a protein in their leaves that creates a fluorescent signal. And we teach them how to make that protein.”

Looking ahead...The company aims to commercialize its soy product starting in 2024, and in the lead up it will continue running tests to make sure that the proteins signal correctly and that yields aren't negatively impacted. Oh, and also get USDA approval.

Click here to read the full story.—JM

On the one hand, harnessing the power of data-crunching algorithms to discover new drugs is extremely exciting—a tangible example of AI for good. On the other, those same algorithms can easily be inverted to develop bioweapons, according to a new article in Nature Machine Intelligence.

Wait, what? Collaborations Pharmaceuticals, a company that uses AI for drug discovery, was asked to prepare a conference presentation about the risks drug-discovery AI poses regarding dual use—a term describing the potential for ostensibly good scientific and technological developments to be used for not-good things.

Collaboration found that, in this case, the potential for dual use is alarmingly high. In under six hours—and with a couple tweaks to optimize for toxic compounds—a machine learning-powered molecule generator that the company had originally created to help guide drug discovery was spitting out deadly designs.

• The system designed 40,000 molecules that scored within Collaboration’s “desired threshold” of toxicity, including known nerve agents, like VX, but also potentially new compounds that are even more toxic than well-known chemical agents.
• The model is based on, and similar to, open-source software that is “readily available,” the authors wrote. The initial training data set did not include nerve agents.

To be clear…Collaboration did not actually synthesize any of these toxic compounds, but the researchers pointed out that “with a global array of hundreds of commercial companies offering chemical synthesis, that is not necessarily a very big step, and this area is poorly regulated.”

• They also note that while expertise in chemistry or toxicology is still needed to wield these systems to such ends, ML-based systems like this “dramatically lower technical thresholds.”

The authors suggest a number of safeguards to protect against such misuse of commercial products, from API restrictions to reporting hotlines to more investment in ethics education for STEM students.

Zoom out: Concerns about dual-use technologies are nothing new: A compound used to prevent earwax is regulated as a chemical weapon because it is a key input to mustard gas, for example. But the burgeoning bioeconomy will present new iterations of the problem.

Click here to read on-site.—DM

Stat: 5G-capable phones made up more than half of smartphone sales in January, the first time that threshold has been crossed.

Quote: “The robots are kind of stupid until you put the intelligence on them.”—Raymond Tunstill, CTO of FruitCast, on the importance of high-quality training data for AI systems

Read: Crypto miners are now leaving Kazakhstan—the country they flocked to after China banned the activity—in droves.

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Three of the following news stories are true, and one...we made up. Can you spot the odd one out?

• Build-A-Bear has introduced 3D printing to its stores, allowing for even greater customization of its plush bears.
• AMC, once a meme stock, is now a mining company.
• A smuggler was caught entering China with 160 Intel processors strapped to his body.
• A crypto startup that wants to scan people’s eyeballs in exchange for a token has struggled to sign up participants.

Almost exactly 34 years ago, on March 17, 1988, Apple sued Microsoft for copyright infringement, due to alleged copycatting of Apple’s graphical user interfaces (GUI).

Apple ultimately lost the lawsuit due to a combination of 1) Microsoft technically having licensed most of the contested tech from Apple and 2) some in-the-weeds legal stuff that makes it impossible to copyright ideas.

• Both companies turned out just fine: As of today, they each have $2+ trillion market caps.

Catch up on the top Emerging Tech Brew stories from the past few editions:

• Training an AI system is time-consuming, but this startup says it has a solution
• Seatrec’s self-replenishing batteries could make ocean research cheaper
• Without the James Webb telescope, Lasik could be stuck in stone age