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Google wants to make “good” AI with your help

Google office in Zurich [Credit: Google]

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As a company with a global presence to the tune of at least a billion people, Google is taking both its immense tech capabilities and social responsibility role very seriously. Namely, it has pledged to provide tangible support to organizations wanting to help address societal challenges using artificial intelligence through its just announced “AI Impact Challenge”. Whether an idea needs coaching, grant funding from a pool of $25 million available, or credit and consulting from cloud services, Google will be there to help.

Towards this effort, the company has already provided an educational guide to machine learning, the primary tool it wants organizations to utilize in its problem-solving. It might seem counterintuitive for a proposer to need training on the very thing it’s proposing, but this is part of the point of Google’s support. To quote Google’s project page directly, “We want people from as many backgrounds as possible to surface problems that AI can help solve, and to be empowered to create solutions themselves…We don’t expect applicants to be AI experts.” Submissions are open until January 22, 2019, and winners will be announced in spring 2019.

Need inspiration for an idea? Or, perhaps, some examples of the kinds of problems that artificial intelligence can help solve? Google’s page dedicated to its “AI for social good” mission has featured projects that are already working towards societally beneficial goals. Here’s a breakdown of some of them:

  • The “Smart Wildfire Sensor” is a device that identifies and predicts areas in a forest that are susceptible to wildfires. To do this, it uses data from tools measuring wind speed, wind direction, humidity, and temperature combined with Google’s open source machine learning tool TensorFlow for photographic analysis of biomass (accumulated fallen branches and trees).
  • Protecting whales from preventable accidents such as entanglement in fishing gear and collisions with vessels is a challenge being addressed using whale songs and machine learning to locate where they’re singing from. The National Oceanic and Atmospheric Administration (NOAA) uses underwater audio recordings to identify and mitigate the presence of dangers in the estimated areas where whales are present. The thousands of hours of recordings accumulated presented a data challenge well suited to Google’s existing sound classification AI to help meet NOAA’s needs with conservation efforts.
  • As a top cause of infant mortality in the world, birth asphyxia is a serious threat needing all the tools available to new parents. Using machine learning trained to recognize the cries of a newborn with this condition, the company Ubenwa has developed a mobile app enabling a recording of a baby’s cry to be uploaded and diagnosed.

“With great power comes great responsibility” is a familiar motto that applies to the state of modern tech just as much as superheroes. For example, the fast-paced field of artificial intelligence brings frequent developments that challenge our security as a society, thus needing caution. However, the massive companies driving the primary innovations being used among the public on a grand scale are one of the larger demonstrations of this where this motto really applies in today’s world.

Google sharply felt the weight of its responsibility recently when its role in assisting the US Department of Defense to analyze drone footage (Project Maven) was revealed. The “Don’t be evil” part of the company’s Code of Conduct at the time appeared to be violated through the military assistance, and renewal of the contract has since been canceled. Google’s further work on its Chinese search engine with censorship in accordance with the communist government’s requirements has also drawn protest from both inside and outside the company. Given this background, a new project focused on doing “good” things for the benefit of society might be seen as possible damage control. The timing might be suspect, but it’s worth noting that, as seen in the projects described above, Google has been working to help with societal needs for quite some time already.

Overall, headlines in recent years have demonstrated just how flexible AI can be when it comes to solving challenges that face our world. While the fears brought on by future “intelligent” computers may have a foundation in reality, it may do us a great amount of good to turn our focus on the hope such technology can also bring. Whatever Google’s motivation is for launching its “AI for social good project”, if good is achieved, it may just be a win for us all.

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Accidental computer geek, fascinated by most history and the multiplanetary future on its way. Quite keen on the democratization of space. | It's pronounced day-sha, but I answer to almost any variation thereof.

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Elon Musk

Tesla shows what Roadster drivers will see on screen before thruster activation

Tesla’s Roadster video reveals a fighter jet inspired cockpit with a 5,500 PSI thruster gauge.

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The next generation Tesla Roadster’s touchscreen, shown in Tesla’s first official look at the car’s interface. (Credit: Tesla/X)

Tesla has given future owners their clearest look yet at the cockpit of its next generation Roadster. In a 34 second video posted to X on Friday afternoon, the company walked through the car’s touchscreen with a caption: “Inspired by fighter jet cockpits, Roadster’s interface was designed around the pilot.”

The clip opens on a black screen with red graphics and a 3D wireframe of the car, which sits beside the Roadster’s squared off yoke. Tapping parts of the model brings up controls for the rear wing, including low drag and high downforce positions, along with a charging page with a charge limit bar and a small music player. A row of tabs along the bottom reads Home, Vehicle, Listen, Climate and Navigate.

Most of the video is spent in Track Mode. The dashboard shows a live circuit map, a list of session lap times, brake and motor temperatures, and pressure and temperature readouts for each tire. A setup page lets the driver pick Auto, Low Drag, High Downforce or Custom aero modes, adjust aero balance and brake balance, and switch stability and traction control off. Along the bottom, a status bar reads “Thruster · 5,500 PSI” next to a “Boost Ready” indicator.

That gauge ties the software to the hardware Tesla showed off near Waco, Texas on October 15. The Thrust and Rocket versions use cold gas thrusters fed by carbon overwrapped pressure vessels, and Tesla lists 0 to 60 mph times of under 0.9 seconds and under 0.59 seconds for them, as Teslarati detailed in its breakdown of the three Roadster configurations. The base Performance car relies on a fan and ground sealing system for grip, and the aero settings on screen match the active wings and suction setup covered in our look at how the Roadster generates downforce.

The Roadster layout is a departure from the rest of Tesla’s lineup. Instead of a map centered screen, the Roadster puts the car itself at the center of the home page, and Tesla’s Roadster page lists two smaller forward displays alongside the main touchscreen. Tesla has not said whether any of the Track Mode telemetry will make its way to the Model 3 Performance or other vehicles.

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The interface could still change before deliveries. Elon Musk said at the event that Roadster production will begin in 2028, and Tesla says the flagship Rocket version is not approved for public road use. Reservations are open with a $50,000 payment, while Thrust is listed as a limited release and Rocket is invite only.

Those who want to see the car in person will get their first chance this weekend. The Roadster goes on display in the lobby of the Petersen Automotive Museum in Los Angeles from October 24 to 27, according to Sawyer Merritt, the first time the public will be able to view it up close.

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Tesla reveals Optimus Academy, where robots teach themselves

Tesla has begun building production quality Optimus robots in Fremont and revealed its training plan.

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Concept rendering of Tesla Optimus in mass production

Tesla has started building Optimus on the high volume line it set up inside its Fremont factory, and the company says the robots coming off it are the first that meet the bar for a real product. “The bots coming off this line are the first production-quality bots that we have manufactured,” Tesla wrote in its third quarter shareholder letter, adding that it has “proven that the current manufacturing process for Optimus is suitable for a commercial product.”

The line sits in space that built the Model S and Model X until this spring. Tesla cleared the old vehicle line in 46 days, as Teslarati reported in July, to make room for robot production sized for one million units a year. On Wednesday’s earnings call, Elon Musk said most of the equipment for that capacity is already in Fremont, with output set to climb toward roughly 20,000 robots a week as the line ramps.

Musk described the ramp as a classic S curve. “The growth of Optimus is an S curve, but the initial curve is a shallow slope,” he said. The team is now refining the robot’s AI, electromechanical design, reliability and service life, and Musk expects that work to be finished by the end of next year, when Tesla plans to start delivering Optimus to outside companies for dangerous, repetitive and around the clock jobs.

The most interesting part of the call was how Tesla plans to teach the robots. Its cars send real driving data back to the AI team from millions of vehicles, but Optimus has no fleet like that yet. “A big difference with Optimus versus the cars is we don’t have this vast fleet of cars from which we can gather training data,” Musk said. Tesla’s answer is an Optimus Academy, where 10,000 to 20,000 robots will practice a wide range of tasks and feed what they learn into the next version of the model. Every unit off the Fremont line becomes a source of that training data.

Smarter hardware is on the way too. Tesla plans to put its AI5 chip into Optimus next year, and Musk said a new Optimus video should arrive within one or two months showing the robot completing tasks driven by AI rather than preprogrammed motions. He took aim at rival humanoid demos that only run scripted routines, saying those would not hold up in the real world.

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The supply chain is lining up behind the program. In September, Tesla certified Tuopu, Joyson and Sanhua as mass production partners for Optimus components. A far larger Optimus factory is also rising at Giga Texas, where drone footage from Joe Tegtmeyer showed the steel frame nearing completion, and Musk has pegged that plant’s long term target at 10 million robots a year.

Tesla executives also said Optimus production costs are trending toward the expected manufacturing cost of Cybercab, a sign the robot is being engineered for volume from the start. The hands remain the toughest piece of the design, a point Musk also made at the X Takeover event earlier this month, as Teslarati covered at the time, and that is where much of the current engineering effort is focused.

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Elon Musk

The Starship moon landing number NASA and Elon Musk can’t agree on

Musk disputes NASA estimate of 22 Starship launches for Moon landing, predicting 6 to 8.

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Concept rendering of SpaceX Starship landing on the moon via Grok

Elon Musk is disputing a new NASA safety panel estimate of how many Starship launches it will take to put astronauts on the Moon, saying the real number will be less than half the panel’s figure.

On Thursday, NASA’s Aerospace Safety Advisory Panel held its quarterly public meeting. Panel member Charlie Precourt, a former NASA astronaut, flagged the fueling process for SpaceX’s lunar lander as a major risk for Artemis IV, the mission planned to land a crew on the Moon in 2028. “We need to keep our eyes on numerous required fuelings for the lander,” Precourt said, according to Ars Technica. “I think the last estimate is around 22 flights.”

Ars Technica’s Eric Berger posted the comment on X, and Musk replied. “That’s way too high,” Musk wrote. “Actual number will be more like 6 to 8.”

The disagreement comes down to a basic problem with sending a ship as large as Starship to the Moon. Starship burns nearly all of its propellant just reaching Earth orbit, so it cannot continue on to the Moon without stopping for a refill. SpaceX’s answer is a gas station in space. First, a Starship that works as a storage tank, called a depot, is launched into orbit. Then a series of tanker Starships launch one at a time, dock with the depot and pump their leftover propellant into it. Once the depot is full, the lunar lander version of Starship launches, docks, fills up and heads for the Moon.

Every tanker trip is its own full rocket launch, which is why the count matters. More launches mean more time, more cost and more chances for something to go wrong before astronauts ever leave Earth. Berger estimated that even if SpaceX reaches twice weekly Starship launches by the end of 2027, filling a depot at the panel’s number would still take roughly three months.

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How many tankers are needed depends mostly on how much propellant each one can carry to orbit and how much is lost along the way. Starship’s fuel and oxygen are stored at extremely cold temperatures, and some of it slowly boils off while waiting in orbit. More is lost in each transfer. NASA appears to be working from what Starship can lift today, while Musk’s range assumes a more capable rocket.

Musk has pushed back on higher counts before. In 2021, after a government document suggested 16 launches, he called that “extremely unlikely” and said eight tankers would be the most required.

Much of the answer depends on hardware SpaceX has not finished flying. As Teslarati noted earlier this year, the company plans to carry out the refueling process with its upgraded V3 Starship, and Precourt described Artemis IV as carrying “a large number of firsts.” He also praised NASA Administrator Jared Isaacman’s team for working to clear obstacles for both SpaceX and Blue Origin, whose Blue Moon lander does not rely on orbital refueling for its first mission.

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