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

[Credit: Google DeepMind]

Published

7 years ago

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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.

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.

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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

‘I don’t understand TSLAQ:’ notable investor backs Tesla, Elon Musk

Published

5 hours ago

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October 8, 2025

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tesla showroom
(Credit: Tesla)

One notable investor that many people will recognize said today on X that he does not understand Tesla shorts, otherwise known as $TSLAQ, and he’s giving some interesting reasons.

Martin Shkreli was long known as “Pharmabro.” For years, he was known as the guy who bought the rights to a drug called Daraprim, hiked the prices, and spent a few years in Federal prison for securities fraud and conspiracy.

Shkreli is now an investor who co-founded several hedge funds, including Elea Capital, MSMB Capital Management, and MSMB Healthcare. He is also known for his frank, blunt, and straightforward responses on X.

His LinkedIn currently shows he is the Co-Founder of DL Software Inc.

One of his most recent posts on X criticized those who choose to short Tesla stock, stating he does not understand their perspective. He gave a list of reasons, which I’ll link here, as they’re not necessarily PG. I’ll list a few:

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  • Fundamentals always have and will always matter
  • TSLAQ was beaten by Tesla because it’s “a great company with great management,” and they made a mistake “by betting against Elon.”
  • When Shkreli shorts stocks, he is “shorting FRAUDS and pipe dreams”

After Shkreli continued to question the idea behind shorting Tesla, he continued as he pondered the mentality behind those who choose to bet against the stock:

“I don’t understand ‘TSLAQ.’ Guy is the richest man in the world. He won. It’s over. He’s more successful with his 2nd, 3rd, and 4th largest companies than you will ever be, x100.

You can admit you are wrong, it’s just a feeling which will dissipate with time, trust me.”

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According to reports from both Fortune and Business Insider, Tesla short sellers have lost a cumulative $64.5 billion since Tesla’s IPO in 2010.

Elon Musk issues dire warning to Tesla (TSLA) shorts

Shorts did accumulate a temporary profit of $16.2 billion earlier this year.

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Tesla will let you bring back this removed Model 3 part for a price

It will cost $595 and is available on Tesla’s website. You will have to have a Model 3 on your Tesla account to purchase the stalk retrofit kit.

Published

8 hours ago

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October 8, 2025

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Credit: Tesla Asia/X

Tesla is now letting Model 3 owners in the United States bring back one part that the company decided to remove after it refreshed the all-electric sedan last year. Of course, you can do it for a price.

With the Model 3 “Highland” refresh that Tesla launched last year, one of the most monumental changes the company made was to ditch the turn signal stalk altogether. Instead, Tesla opted for turn signal buttons, which have been met with mixed reviews.

I drove the new Tesla Model 3, here’s what got better

The change was widely regarded as Tesla preparing for more autonomous driving in its vehicles, especially as its interiors have gotten even more minimalistic.

The lack of a stalk in the new Model 3 was just another move the company made to adjust drivers and passengers to seeing less at the steering wheel column.

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However, many drivers did not prefer the use of buttons and wanted the stalk reinstalled. Tesla allowed it in several regions, launching a retrofit kit. It has now made its way to the United States:

It will cost $595 and is available on Tesla’s website. You will have to have a Model 3 on your Tesla account to purchase the stalk retrofit kit.

It is interesting to note that despite Tesla’s strategy to remove the stalk with the new Model 3, which was released in early 2024, the company did not choose to make the same move with the new Model Y.

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The new Model Y launched in the United States in early 2025, and Tesla chose to install a stalk in this vehicle.

It seemed as if the turn signal buttons were too much of a polarizing feature, and although the company technically could have given orderers an option, it would not have been the most efficient thing for manufacturing.

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Tesla Full Self-Driving v14.1 first impressions: Robotaxi-like features arrive

Tesla Full Self-Driving v14.1 is here, and we got to experience it for ourselves.

Published

9 hours ago

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October 8, 2025

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Tesla rolled out its Full Self-Driving v14.1 yesterday, its first public launch of its most robust and accurate FSD iteration yet. Luckily, I was able to get my hands on it through the Early Access Program.

The major changes in FSD v14.1 were revealed in the release notes, which outline several notable improvements in areas such as driving styles, parking, and overall navigation. Here’s what Tesla outlined fully in its release notes:

  • Added Arrival Options for you to select where FSD should park: in a Parking Lot, on the Street, in a Driveway, in a Parking Garage, or at the Curbside.
  • Added handling to pull over or yield for emergency vehicles (e.g. police cars, fire trucks, ambulances).
  • Added navigation and routing into the vision-based neural network for real-time handling of blocked roads and detours.
  • Added additional Speed Profile to further customize driving style preference.
  • Improved handling for static and dynamic gates.
  • Improved offsetting for road debris (e.g. tires, tree branches, boxes).
  • Improve handling of several scenarios including: unprotected turns, lane changes, vehicle cut-ins, and school busses.
  • Improved FSD’s ability to manage system faults and recover smoothly from degraded operation for enhanced reliability.
  • Added alerting for residue build-up on interior windshield that may impact front camera visibility. If affected, visit Service for cleaning!

I wanted to try it for myself. My big must-dos were my complaints with v13.2.9, which included parking when arriving at a destination, Navigation when leaving a destination, and definitely a general improvement in the car traveling at an acceptable rate of speed, even when using the “Hurry” driving style.

Here’s what I noticed with the new Full Self-Driving v14.1:

Speed Profiles are More Realistic

I am driving on “Hurry” about 95% of the time when utilizing Full Self-Driving. In past versions, most notably v13.2.9, my Tesla would slowly reach the speed limit, and it would tend to hang out at about 1-2 MPH either above or below it.

My first observation with v14.1 was the vehicle’s tendency to get right up to speed and, since I was still on Hurry, drive slightly above the speed limit. It never got out of line; it traveled at speeds I would typically drive at manually.

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I think this is a big improvement on its own, because I felt that I was pressing the accelerator too frequently in past FSD versions. Oftentimes, it just wasn’t going fast enough to justify the “Hurry” label; it felt more conservative and more like a student driver than anything.

Check it out:

This was among my favorite improvements, and it was the first thing I noticed as the car navigated me to the Supercharger, where my next positive is.

Navigating into parking lots, self-parking at Supercharger

One of the changes noted in the Release Notes was the addition of Arrival Options, which allows the car to select the appropriate parking situation. Since I was going to charge, the car had already chosen “Charger” as the parking option.

Pulling into a gas station or convenience store, especially during work days, can be stressful, as they are usually congested and full of foot and vehicle traffic. In past FSD versions, I have noticed the car being slightly “jumpy” and even hesitant to proceed through the lot.

Driving through parking lots was a noticeable improvement. It seems as if the car is much more confident in making its way through, while still being aware and cautious enough to safely navigate to the Supercharger.

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It then backed straight into a Supercharger stall, which was recently repaired and is once again active. I was actually upset it chose this specific stall because it had been inactive for a while. However, Tesla got this stall back up and running, the car chose it, and backed into the spot flawlessly:

This was super cool to experience, and I think it is a testament to how hard the Tesla AI team has worked. CEO Elon Musk recently stated that FSD would enable automatic parking at Superchargers, which was really awesome to experience firsthand.

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I decided to leave the Supercharger and go to an auto parts store to pick up some interior cleaner and some microfiber towels. I love keeping my Tesla clean!

I also thought it would be a great opportunity to see how it would react to another parking lot, how it would navigate it, and let it choose a parking spot. It did it all flawlessly:

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I had zero complaints about everything here. All of it was done really well.

Making a choice after being caught in the middle of an intersection

I arrived at a tight intersection in Dallastown, PA, and what my car did next has catalyzed quite a conversation on X.

It proceeded out into the middle of the intersection as the light was green. It had to yield to oncoming traffic, and while waiting, the light turned yellow, then red.

Most people, including myself, would have turned right and proceeded through the intersection since the car was already past the line. However, FSD chose to back up and wait for the next light cycle, which I felt was also a more than acceptable option:

There are some conflicting perspectives on what it chose to do here. Some said they would have proceeded and would want FSD to also proceed. I can agree with that perspective, but I also think it is not the worst thing in the world to back up. In Pennsylvania, I couldn’t find the exact law that says what is right or wrong. Instead, I did see that a left turn on red is only feasible when you’re going from a One-Way street to another One-Way.

I’m not totally sure what is “correct” here, but I think either option is fine. I have personally done both, and I’ve seen other drivers do both. I was more than fine with the car doing this, and I was honestly impressed that it did.

Navigated a busy grocery store lot, found suitable parking

This is not the busiest my local grocery store gets, but it was still congested enough for me to be impressed.

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FSD decided to do one loop in the parking lot before it found a spot that it felt was good enough for me. I was perfectly fine with where it chose to park, and I thought it did a really great job. I was impressed with how stress-free I felt, as I have noted in the past that parking lots are definitely an area where Tesla needs to improve.

I was happy with its performance:

Strange right turn signal as if it saw an emergency vehicle

This was the first bug I noticed with FSD v14.1. While traveling on a local road, it put the right turn signal on and approached the curb as if it was pulling over for an emergency vehicle or as if it was going to park on the street.

It then realized its mistake and proceeded:

I’m not super sure what caused this, but I was a tad bit confused. There were no police cars, ambulances, or anyone with flashing lights to my rear. There was a dump truck on the other side of the road, and I almost felt like the way it navigated “around” that was probably what triggered it.

Navigation is still making strange decisions

I’ve written about navigation and my discontent with some of its decisions. It seems v14.1 didn’t resolve much of anything with navigation, and it did a couple of things wrong.

The first was that it tried to take the illogical and pointless path out of the Supercharger. I wrote about this a few days ago, as FSD tried to take my car the wrong way.

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It did it again, but I overrode the decision, and it was all okay:

This is a minor issue, but it is still pretty frustrating. Hopefully, the navigation will learn after performing this adjustment after enough times.

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The next navigation issue was more frustrating than the Supercharger one, especially considering it completely ignored the route. The navigation had the vehicle very clearly heading straight, but out of nowhere, the right turn signal went on. I overrode it, but the car still turned right, ignoring the navigation completely:

I ended up taking over here and driving until I could get to a stop sign.

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Final Thoughts

I am really impressed with all of the changes Tesla made with FSD v14.1, and while there were a handful of bugs, things were tremendously better than v13.2.9.

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