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Google’s DeepMind unit develops AI that predicts 3D layouts from partial images

[Credit: Google DeepMind]

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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.

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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.

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Simon is an experienced automotive reporter with a passion for electric cars and clean energy. Fascinated by the world envisioned by Elon Musk, he hopes to make it to Mars (at least as a tourist) someday. For stories or tips--or even to just say a simple hello--send a message to his email, simon@teslarati.com or his handle on X, @ResidentSponge.

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Elon Musk challenges Tesla credit rating from Moody’s after SpaceX gets a higher one

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Justin Pacheco, Public domain, via Wikimedia Commons

Elon Musk has publicly questioned Moody’s credit assessments following the rating agency’s decision to assign SpaceX a Baa1 investment-grade rating, two notches above Tesla’s Baa3. The comments came amid discussions comparing the two companies’ financial profiles.

SpaceX earned its first-time Baa1 rating with a stable outlook from Moody’s. The agency highlighted the company’s leadership in orbital launches, the growing recurring revenue from its Starlink satellite network, strong vertical integration, U.S. government contracts, and emerging opportunities in AI infrastructure.

These factors were cited as supporting robust cash flows, margin expansion, and financial flexibility.

Musk responded directly: “Tesla’s credit rating is ridiculously low tbh,” and added, “Yeah, makes no sense. Tesla has over $40B in cash, no debt, and is consistently profitable!” His remarks underscored Tesla’s balance sheet strength and profitability at a time when many traditional automakers continue to report losses in the shift to electric vehicles.

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Tesla maintains a leading position in the global EV market, with diversification into energy and storage, battery technology, and robotics through projects like Optimus. Recent financial updates show the company generated positive free cash flow of $1.4 billion in Q1 2026, supported by operating cash flow of $3.9 billion. Cash and short-term investments stood at approximately $44.7 billion.

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Moody’s has affirmed Tesla’s Baa3 issuer rating with a stable outlook in periodic reviews, acknowledging the company’s EV leadership, technology strengths, including AI for autonomous vehicles, solid profitability, and strong liquidity.

Tesla (TSLA) scores Baa3 Moody’s rating for ‘stable’ outlook

However, the agency has also noted challenges in the automotive segment and expectations for margin pressures.

Musk’s critique highlights a common debate about how traditional rating methodologies apply to high-growth, capital-intensive technology companies. SpaceX benefits from long-term government-backed contracts and diversified, recurring revenue streams, while Tesla’s valuation reflects heavy investment in future technologies such as autonomy and robotics.

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Both ratings remain investment-grade, yet the one-notch difference has fueled online discussion about potential inconsistencies in evaluating innovative firms.

The exchange comes as SpaceX explores financing options following its recent valuation milestones, while Tesla continues executing on its multi-year roadmap. Musk’s pointed response serves as a reminder that credit ratings, though influential for borrowing costs, represent one lens through which markets assess corporate strength—and that company leaders often view their financial positions through the lens of long-term innovation and cash generation rather than short-term risk metrics alone.

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Tesla faces Full Self-Driving pushback in EU over ‘speeding’

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Credit: Tesla

A new report from Reuters claims that a transport authority in Sweden is pushing back against the approval of Tesla’s Full Self-Driving suite because it will travel over speed limits.

The report says the Swedish Transport Administration (TRV) recommends the European Union votes against FSD’s approval. TRV believes it should not be approved until Tesla disables FSD’s ability to speed.

TRV sent a letter to the European Union’s Technical Committee on Motor Vehicles (TCMV), which is set to meet on June 30 to discuss the potential approval of the Tesla FSD suite in the country. Tesla, which has received various approvals in Europe over the past two months, has not provided a comment.

Tesla Full Self-Driving gets first-ever European approval

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Teslas operating on FSD do travel over the speed limit, depending on the Speed Profile that is chosen. Drivers have the ability to disengage FSD at any point; Tesla specifically states that those supervising the suite are responsible for its actions.

Let’s cut to the chase: humans operating any vehicle speed almost daily in the United States. Realistically, speed limits in the U.S. are more frequently treated as speed minimums. However, other countries are different, and driving behaviors are less aggressive.

TRV believes that “allowing automated systems to systematically exceed legal speed limits…risks undermining both the legal framework and the expected safety benefits of ​vehicle automation,” the report stated. It’s surprising that Tesla has not received this claim from other countries previously.

This could be a good argument to bring Max Speed back, the setting that previously allowed the driver to choose the absolute fastest the car would travel.

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This would still put the responsibility of supervision in the hands of the driver. It would allow the driver to choose whether the car would travel over the speed limit or not, acknowledging that they set the speed, and if they get pulled over, there would be no ability to argue it.

However, it does not seem as if this is something Tesla will do, especially considering many U.S. drivers have requested the feature in an effort to eliminate speeding or at least tone it down. The company has not shown any interest in bringing it back.

Tesla has approvals for FSD in Europe in Estonia, Lithuania, Denmark, the Netherlands, and Belgium.

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Tesla teases greater Grok FSD integration and ‘Banish’ feature ‘in about 3 months’

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Credit: Tesla

Tesla is going to let you guide Full Self-Driving with Grok in 3 months, CEO Elon Musk confirmed on X.

The response from Musk, which revealed Tesla plans to allow drivers to effectively control the car and its navigation more explicitly using Grok, puts the feature for about September.

A Tesla owner said that Full Self-Driving is great, but owners should be able to “converse with Grok like we can with an Uber driver.” She then used examples like, “Grok, turn right here,” and “Drop us off right here, we’ll walk due to traffic,” and finally,” Drop at entrance first, then park far away.”

Coincidentally, the final piece of dialogue would also mean features like Banish are potentially on the way soon.

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Banish is also referred to as “Reverse Summon,” and would enable the car to self-park while dropping occupants off at their destination.

This would be a great way to improve the overall experience while supervising FSD. Navigation is already a major painpoint that many owners complain about. Manual overrides when a maneuver is requested or canceled (like using the turn signal stalk to override a navigation route), do not always work.

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The feature could be especially useful in street parking scenarios in a city, where spots are sometimes tough to come by. Many of us who grab dinner in a more populated area will park a street or two over from wherever we’re going, because sometimes you know that’s the best you will get. If a driver using FSD could say, “Hey Grok, turn right here on Queen St. and park in that open spot on the right,” it could save a lot of confusion FSD might have on its own.

Musk teased that a similar feature was “coming” back in February:

Tesla Full Self-Driving set to get an awesome new feature, Elon Musk says

It is certainly surprising that Tesla is doing it at this point. The company’s more recent moves have been more evident of taking control and inputs away from humans and putting them in the AI’s hands more frequently. The biggest example of this was taking away Max Speed in AI4 cars, giving us Speed Profiles, and not having any input on the fastest speed the car will travel.

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Of course, giving navigation preferences to Grok is availble already in Teslas, but not at the drop of a hat. Instead, you can suggest a certain route at the beginning of your drive.

Here’s an example of that from December:

Finally, the original post that Musk responded to mentioned a parking preference after dropping off the occupants, which describes the Banish feature that Tesla has teased for years.

We’re not sure if Musk was responding more to the ability to guide the car with Grok, or whether he also was including Banish in the three-month prediction timeframe.

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