News
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.
Elon Musk
The Boring Company’s Music City Loop gains unanimous approval
After eight months of negotiations, MNAA board members voted unanimously on Feb. 18 to move forward with the project.
The Metro Nashville Airport Authority (MNAA) has approved a 40-year agreement with Elon Musk’s The Boring Company to build the Music City Loop, a tunnel system linking Nashville International Airport to downtown.
After eight months of negotiations, MNAA board members voted unanimously on Feb. 18 to move forward with the project. Under the terms, The Boring Company will pay the airport authority an annual $300,000 licensing fee for the use of roughly 933,000 square feet of airport property, with a 3% annual increase.
Over 40 years, that totals to approximately $34 million, with two optional five-year extensions that could extend the term to 50 years, as per a report from The Tennesean.
The Boring Company celebrated the Music City Loop’s approval in a post on its official X account. “The Metropolitan Nashville Airport Authority has unanimously (7-0) approved a Music City Loop connection/station. Thanks so much to @Fly_Nashville for the great partnership,” the tunneling startup wrote in its post.
Once operational, the Music City Loop is expected to generate a $5 fee per airport pickup and drop-off, similar to rideshare charges. Airport officials estimate more than $300 million in operational revenue over the agreement’s duration, though this projection is deemed conservative.
“This is a significant benefit to the airport authority because we’re receiving a new way for our passengers to arrive downtown at zero capital investment from us. We don’t have to fund the operations and maintenance of that. TBC, The Boring Co., will do that for us,” MNAA President and CEO Doug Kreulen said.
The project has drawn both backing and criticism. Business leaders cited economic benefits and improved mobility between downtown and the airport. “Hospitality isn’t just an amenity. It’s an economic engine,” Strategic Hospitality’s Max Goldberg said.
Opponents, including state lawmakers, raised questions about environmental impacts, worker safety, and long-term risks. Sen. Heidi Campbell said, “Safety depends on rules applied evenly without exception… You’re not just evaluating a tunnel. You’re evaluating a risk, structural risk, legal risk, reputational risk and financial risk.”
Elon Musk
Tesla announces crazy new Full Self-Driving milestone
The number of miles traveled has contextual significance for two reasons: one being the milestone itself, and another being Tesla’s continuing progress toward 10 billion miles of training data to achieve what CEO Elon Musk says will be the threshold needed to achieve unsupervised self-driving.
Tesla has announced a crazy new Full Self-Driving milestone, as it has officially confirmed drivers have surpassed over 8 billion miles traveled using the Full Self-Driving (Supervised) suite for semi-autonomous travel.
The FSD (Supervised) suite is one of the most robust on the market, and is among the safest from a data perspective available to the public.
On Wednesday, Tesla confirmed in a post on X that it has officially surpassed the 8 billion-mile mark, just a few months after reaching 7 billion cumulative miles, which was announced on December 27, 2025.
Tesla owners have now driven >8 billion miles on FSD Supervisedhttps://t.co/0d66ihRQTa pic.twitter.com/TXz9DqOQ8q
— Tesla (@Tesla) February 18, 2026
The number of miles traveled has contextual significance for two reasons: one being the milestone itself, and another being Tesla’s continuing progress toward 10 billion miles of training data to achieve what CEO Elon Musk says will be the threshold needed to achieve unsupervised self-driving.
The milestone itself is significant, especially considering Tesla has continued to gain valuable data from every mile traveled. However, the pace at which it is gathering these miles is getting faster.
Secondly, in January, Musk said the company would need “roughly 10 billion miles of training data” to achieve safe and unsupervised self-driving. “Reality has a super long tail of complexity,” Musk said.
Training data primarily means the fleet’s accumulated real-world miles that Tesla uses to train and improve its end-to-end AI models. This data captures the “long tail” — extremely rare, complex, or unpredictable situations that simulations alone cannot fully replicate at scale.
This is not the same as the total miles driven on Full Self-Driving, which is the 8 billion miles milestone that is being celebrated here.
The FSD-supervised miles contribute heavily to the training data, but the 10 billion figure is an estimate of the cumulative real-world exposure needed overall to push the system to human-level reliability.
News
Tesla Cybercab production begins: The end of car ownership as we know it?
While this could unlock unprecedented mobility abundance — cheaper rides, reduced congestion, freed-up urban space, and massive environmental gains — it risks massive job displacement in ride-hailing, taxi services, and related sectors, forcing society to confront whether the benefits of AI-driven autonomy will outweigh the human costs.
The first Tesla Cybercab rolled off of production lines at Gigafactory Texas yesterday, and it is more than just a simple manufacturing milestone for the company — it’s the opening salvo in a profound economic transformation.
Priced at under $30,000 with volume production slated for April, the steering-wheel-free, pedal-less Robotaxi-geared vehicle promises to make personal car ownership optional for many, slashing transportation costs to as little as $0.20 per mile through shared fleets and high utilization.

Credit: wudapig/Reddit< /a>
While this could unlock unprecedented mobility abundance — cheaper rides, reduced congestion, freed-up urban space, and massive environmental gains — it risks massive job displacement in ride-hailing, taxi services, and related sectors, forcing society to confront whether the benefits of AI-driven autonomy will outweigh the human costs.
Let’s examine the positives and negatives of what the Cybercab could mean for passenger transportation and vehicle ownership as we know it.
The Promise – A Radical Shift in Transportation Economics
Tesla has geared every portion of the Cybercab to be cheaper and more efficient. Even its design — a compact, two-seater, optimized for fleets and ride-sharing, the development of inductive charging, around 300 miles of range on a small battery, half the parts of the Model 3, and revolutionary “unboxed” manufacturing — is all geared toward rapid production.
Operating at a fraction of what today’s rideshare prices are, the Cybercab enables on-demand autonomy for a variety of people in a variety of situations.
Tesla ups Robotaxi fare price to another comical figure with service area expansion
It could also be the way people escape expensive and risky car ownership. Buying a vehicle requires expensive monthly commitments, including insurance and a payment if financed. It also immediately depreciates.
However, Cybercab could unlock potential profitability for owning a car by adding it to the Robotaxi network, enabling passive income. Cities could have parking lots repurposed into parks or housing, and emissions would drop as shared electric vehicles would outnumber gas cars (in time).
The first step of Tesla’s massive production efforts for the Cybercab could lead to millions of units annually, turning transportation into a utility like electricity — always available, cheap, and safe.
The Dark Side – Job Losses and Industry Upheaval
With Robotaxi and Cybercab, they present the same negatives as broadening AI — there’s a direct threat to the economy.
Uber, Lyft, and traditional taxis will rely on human drivers. Robotaxi will eliminate that labor cost, potentially displacing millions of jobs globally. In the U.S. alone, ride-hailing accounts for billions of miles of travel each year.
There are also potential ripple effects, as suppliers, mechanics, insurance adjusters, and even public transit could see reduced demand as shared autonomy grows. Past automation waves show job creation lags behind destruction, especially for lower-skilled workers.
Gig workers, like those who are seeking flexible income, face the brunt of this. Displaced drivers may struggle to retrain amid broader AI job shifts, as 2025 estimates bring between 50,000 and 300,000 layoffs tied to artificial intelligence.
It could also bring major changes to the overall competitive landscape. While Waymo and Uber have partnered, Tesla’s scale and lower costs could trigger a price war, squeezing incumbents and accelerating consolidation.
Balancing Act – Who Wins and Who Loses
There are two sides to this story, as there are with every other one.
The winners are consumers, Tesla investors, cities, and the environment. Consumers will see lower costs and safer mobility, while potentially alleviating themselves of awkward small talk in ride-sharing applications, a bigger complaint than one might think.
Elon Musk confirms Tesla Cybercab pricing and consumer release date
Tesla investors will be obvious winners, as the launch of self-driving rideshare programs on the company’s behalf will likely swell the company’s valuation and increase its share price.
Cities will have less traffic and parking needs, giving more room for housing or retail needs. Meanwhile, the environment will benefit from fewer tailpipes and more efficient fleets.
A Call for Thoughtful Transition
The Cybercab’s production debut forces us to weigh innovation against equity.
If Tesla delivers on its timeline and autonomy proves reliable, it could herald an era of abundant, affordable mobility that redefines urban life. But without proactive policies — retraining, safety nets, phased deployment — this revolution risks widening inequality and leaving millions behind.
Elon on the MKBHD bet, stating “Yes” to the question of whether Tesla would sell a Cybercab for $30k or less to a customer before 2027 https://t.co/sfTwSDXLUN
— TESLARATI (@Teslarati) February 17, 2026
The real question isn’t whether the Cybercab will disrupt — it’s already starting — it’s whether society is prepared for the economic earthquake it unleashes.