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Google’s DeepMind unit develops AI that predicts 3D layouts from partial images
Google’s DeepMind unit, the same division that created AlphaGo, an AI that outplayed the best Go player in the world, has created a neural network capable of rendering an accurate 3D environment from just a few still images, filling in the gaps with an AI form of perceptual intuition.
According to Google’s official DeepMind blog, the goal of its recent AI project is to make neural networks easier and simpler to train. Today’s most advanced AI-powered visual recognition systems are trained through the use of large datasets comprised of images that are human-annotated. This makes training a very tedious, lengthy, and expensive process, as every aspect of every object in each scene in the dataset has to be labeled by a person.
The DeepMind team’s new AI, dubbed the Generative Query Network (GQN) is designed to remove this dependency on human-annotated data, as the GQN is designed to infer a space’s three-dimensional layout and features despite being provided with only partial images of a space.
Similar to babies and animals, DeepMind’s GQN learns by making observations of the world around it. By doing so, DeepMind’s new AI learns about plausible scenes and their geometrical properties even without human labeling. The GQN is comprised of two parts — a representation network that produces a vector describing a scene and a generation network that “imagines” the scene from a previously unobserved viewpoint. So far, the results of DeepMind’s training for the AI have been encouraging, with the GQN being able to create representations of objects and rooms based on just a single image.
As noted by the DeepMind team, however, the training methods that have been used for the development of the GQN are still limited compared to traditional computer vision techniques. The AI creators, however, remain optimistic that as new sources of data become available and as improvements in hardware get introduced, the applications for the GQN framework could move over to higher-resolution images of real-world scenes. Ultimately, the DeepMind team believes that the GQN could be a useful system in technologies such as augmented reality and self-driving vehicles by giving them a form of perceptual intuition – extremely desirable for companies focused on autonomy, like Tesla.
Google DeepMind’s GQN AI in action. [Credit: Google DeepMind]
In a talk at Train AI 2018 last May, Tesla’s head of AI Andrej Karpathy discussed the challenges involved in training the company’s Autopilot system. Tesla trains Autopilot by feeding the system with massive data sets from the company’s fleet of vehicles. This data is collected through means such as Shadow Mode, which allows the company to gather statistical data to show false positives and false negatives of Autopilot software.
During his talk, Karpathy discussed how features such as blinker detection become challenging for Tesla’s neural network to learn, considering that vehicles on the road have their turn signals off most of the time and blinkers have a high variability from one car brand to another. Karpathy also discussed how Tesla has transitioned a huge portion of its AI team to labeling roles, doing the human annotation that Google DeepMind explicitly wants to avoid with the GQN.
Musk also mentioned that its upcoming all-electric supercar — the next-generation Tesla Roadster — would feature an “Augmented Mode” that would enhance drivers’ capability to operate the high-performance vehicle. With Tesla’s flagship supercar seemingly set on embracing AR technology, the emergence of new techniques for training AI such as Google DeepMind’s GQN would be a perfect fit for the next generation of vehicles about to enter the automotive market.
Tesla Cybercab units finally got a definitive date for when they will be able to be purchased by consumers, ending speculation regarding whether the fully autonomous vehicle would be available in the near term.
During Tesla’s third-quarter 2026 earnings call on October 21, Vice President of Engineering Lars Moravy said the company is aiming to begin delivering its purpose-built Cybercab robotaxi to customers before year-end.
🚨 In short, Cybercabs are slated to go to customers by end of the year. All units are going into Robotaxi fleet currently, per @larsmoravy
Cybercab will be “transport-as-a-service,” but vast majority of Cybercab will be “Tesla-owned,” @elonmusk says https://t.co/R8Qu8QWdmp
— TESLARATI (@Teslarati) October 21, 2026
“We’re trying to get Cybercabs to customers by the end of this year, 2026,” Moravy said. He added that production is underway on the line at Gigafactory Texas, but “all of those are currently going to our own internal Robotaxi fleet.”
Moravy explained that as operations scale, Tesla will “mainly deliver fully designed autonomous vehicles to our customers,” with the Cybercab as the first such vehicle. Initial customer deliveries will start small while the company gains experience with delivery logistics, ownership models, fleet management, and operating autonomous vehicles outside its direct control.
CEO Elon Musk reinforced the limited nature of private sales, describing the Cybercab primarily as a “transport-as-a-service” offering rather than a personal ownership vehicle like the Model 3, Model Y, or upcoming Roadster. “The vast majority of the Cybercab stuff will be Tesla-owned and transport-as-a-service,” Musk said.
The Cybercab is a two-seater vehicle developed by Tesla without a steering wheel or pedals. It relies completely on Tesla’s Full Self-Driving suite and has been in production since February. Early units have been put in Tesla’s Robotaxi service as of early September, when the company launched rides to the public. Tesla previously indicated it would aim to sell Cybercabs for about $30,000.
Unsupervised Full Self-Driving is expected to ship to consumer cars during Q2 2027, Ashok Elluswamy, Tesla’s Head of AI, indicated during the call. Early buyers or fleet operators may receive vehicles whose autonomous operation remains geographically restricted due to lagging regulatory agencies. There are some hurdles that remain ahead of Cybercab deliveries.
Tesla (NASDAQ: TSLA) reported its earnings for the third quarter of 2026, marking a reasonable performance on the various financial metrics. Tesla still plans to hold a call that will field investor questions at 5:30 p.m. ET, 4:30 p.m. CT.
Here’s where Earnings came in:
- Total Revenues: $28.046 reported vs. $28.34B expected
- Non-GAAP EPS: $0.48 reported vs. $0.45 expected
- Operating Margin: 2.6 % reported vs. 2.5% expected
- Operating Income: $718M reported vs. $721M expected
- Free Cash Flow: $1.531B reported vs. ~-$4.51B expected
Tesla $TSLA Earnings Results:
Revenues: $28.046B
Non-GAAP EPS: $0.48
Operating Margin: 2.6%
Operating Income: $718M
FCF: $1.531B— TESLARATI (@Teslarati) October 21, 2026
Major Takeaways
Full Self-Driving (Supervised)
“FSD (Supervised)⁴ attach rate continued to grow, reaching over 75% of new vehicle deliveries in North America – our most mature market – a nearly twenty percentage point sequential increase. We believe that FSD is driving demand in markets where it is available, and we continue to work with regulators to get it approved in other markets.”
Tesla Semi
“We began volume production of Tesla Semi, our Class 8 heavy-duty truck. The top variant can go 500 miles at full 82,000 lbs gross combination weight while achieving an efficiency of 1.7 kWh/mile. That efficiency allows the Tesla Semi to use a third of the energy used by a comparable diesel truck to haul the same payload. In a transportation market where energy is one of the largest components of total cost per mile, this will provide a meaningful advantage to Tesla Semi operators.”
SI Compute
“We plan to reach 400 MW of SI training compute by the end of the year, supporting the development of both vehicle and humanoid robot autonomy software. Additionally, we continued construction for the Austin Fab, where we plan to start commissioning equipment in 2027.”
Robotaxi
“We began deployment of Cybercab into our Robotaxi fleet in Austin. We reached nearly two million cumulative unsupervised miles for our Robotaxi service as of the end of the quarter. Operating hours in Austin are now 6am to midnight, and we expect to reduce wait times via increased fleet supply and improvement of Robotaxi orchestration algorithms. Grok is available in Cybercab with access to Grok Bot and Connectors, which allows users to manage their inbox, calendars, files, chats and tasks – all hands free via voice command – making Cybercab a mode of transport, a productivity space and, with our entertainment options, a second living room.”
Interested in ordering a Tesla? Use my referral code for three free months of Full Self-Driving (Supervised) here.
Tesla (NASDAQ: TSLA) reports its earnings for Q3 today. Here’s what to expect, as the company will reveal its balance sheet after the market closes on Wednesday.
Tesla already reported deliveries just after Q3 ended, and it demolished Wall Street consensus estimates of roughly 462,000 vehicles. Tesla delivered 486,532 cars in the third quarter, which was a big beat, but was down 2.1 percent from the same quarter last year. Energy storage deployments were 13.7 GWh, up year-over-year.
Tesla showrooms picked clean ahead of Q3 end as demand looks strong
For the quarter, analysts have already compiled their predictions for what Tesla will report this afternoon:
- Total Revenues: $28.34B
- Non-GAAP EPS: $0.43
- Operating Margin: 2.5%
- Operating Income: $721M
- Free Cash Flow: ~-$4.51B
Here are a few things we are looking forward to hearing about on the call:
Automotive Gross Margin and Overall Profitability
This was 16.3 percent in Q2, down from 19.2 percent in Q1, and was impacted by warranty charges and some fading regulatory credits. Consensus implies overall gross margin near 18 percent, but a miss here would potentially reinforce concerns about pricing pressure on its vehicles.
Pay attention to energy storage margins as well, especially since Tesla missed GWh expectations, although it was still a strong quarter.
Robotaxi/Cybercab, Unsupervised FSD, and Optimus Progress
The Say questions from retail investors usually focus on these types of projects, and this quarter is no different. Scaling the Cybercab fleet beyond Austin amid the open NHTSA safety review, timeline for Unsupervised FSD in customer cars, Robotaxi unit economics, and Optimus cost per unit plus when it could start generating revenue, are all things to be looking for.
Any concrete timelines or regulatory path details will likely move the stock more than the near-term numbers. SpaceX merger speculation, which was detailed during SpaceX’s earnings call last night, may also surface, although the key details were shared last night.
Elon Musk just confirmed the Tesla and SpaceX merger step everyone was waiting for
Say Investor Questions
- How does the Cybercab fleet scale past Austin, and what’s the regulatory path outside Texas given NHTSA’s open safety review?
- When can we anticipate FSD unsupervised in customer cars?
- When will the CyberCab be available for customer purchase/ownership?
- What’s the unit economics per paid robotaxi mile, and how does it compare to the thirty-cent target?
- Tesla has acknowledged that HW3 vehicles cannot support unsupervised FSD. When will owners know whether they can upgrade their hardware for free, and when will Tesla provide a clear plan?
The full Earnings Call will be streamed this evening after market close around 5 p.m. this evening.
Tesla Cybercab gets a for sale date at last
Tesla Q3 2026 Earnings results: in-line revenues, slight beat on EPS
