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Scientists create AI neural net that can unlock digital fingerprint-secured devices

Fingerprint scan. | Credit: RCPA

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Computer scientists at New York University and Michigan State University have trained an artificial neural network to create fake digital fingerprints that can bypass locks on cell phones. The fakes are called “DeepMasterPrints”, and they present a significant security flaw for any device relying on this type of biometric data authentication. After exploiting the weaknesses inherent in the ergonomic needs of cellular devices, DeepMasterPrints were able to imitate over 70% of the fingerprints in a testing database.

An artificial neural network is a type of artificial intelligence comprising computer algorithms modeled after the human brain’s ability to recognize patterns. The DeepMasterPrints system was trained to analyze sets of fingerprint images and generate a new image based on the features that occurred most frequently. This “skeleton key” could then be used to exploit the way cell phones authenticate user fingerprints.

In cell phones, the necessarily small size of fingerprint readers creates a weakness in the way they verify a print. In general, phone sensors only capture a partial image of a print when a user is attempting to unlock the device, and that piece is then compared to the phone’s authorized print image database. Since a partial print means there are fewer characteristics to distinguish it than a full print, a DeepMasterPrint needs to match fewer features to imitate a fingerprint. It’s worth noting that the concept of exploiting this flaw is not unique to this particular study; however, generating unique images rather than using actual or synthesized images is a new development.

An overview of the DeepMasterPrint system. | Credit: IEEE

The team involved in the study resulting in the DeepMasterPrint creation initiated it as part of the ongoing assessment of vulnerabilities in fingerprint recognition systems. Finding exploitable flaws and fixing them is a constant battle in all digital systems with a security component. With this reality in mind, the scientists determined that merely exposing the flaws of fingerprint systems would not provide an effective solution; a working example of how attacks could be executed provides more specific data for researchers to design around and protect against. Creating the DeepMasterPrint system was meant to address this need.

The results revealed by the DeepMasterPrint system are concerning for anyone relying on fingerprint authentication on their smartphones. Scientists compared the generated fake prints against templates generated by VeriFinger 9.0 SDK, Bozorth3, and Innovatrics IDKit 5.3 SKD, all of which are software systems used in fingerprint authentication systems worldwide. At a low false match rate, i.e., strict match requirements for authentication, the fake print generated by DeepMasterPrint could imitate 23% of the fingerprints in the test database. At a slightly higher false match rate that was still within standard phone authentication limits, the fake print imitated 77% of the test fingerprints.

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Real fingerprints on the left vs. DeepMasterPrint generated fingerprints on the right. | Credit: IEEE

The scientists in this study did not create physical fingerprints to try and unlock actual phones, leaving that work to be done in the near future. However, even though the successful DeepMasterPrints have yet to be tested in true applications rather than a virtual environment, the data gathered confirmed the initial security concerns which inspired the experiment. Fingerprints are being used as identity verification in a growing number of applications beyond cell phone security, i.e., unlocking entryways, payment authentication, etc. The DeepMasterPrint system is another tool to help researchers guard their security as biometric authentication continues to expand.

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

Elon Musk reveals shocking Tesla Optimus patent detail

What looked promising on paper and in simulations failed to deliver the reliability required for a robot expected to handle delicate tasks like folding laundry, assembling electronics, or assisting in factories and homes.

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Credit: Tesla

Elon Musk revealed a shocking detail on the Tesla Optimus patent that was revealed last week. Despite it being made public for the first time, Musk said the company has already moved on from the design, an incredible truth about the development of new technology: things move fast.

Musk dropped a bombshell about the Tesla Optimus humanoid robot hand patent that was released last week. Musk, candidly replying to a post late at night on X, revealed that what is a new technology to many fans and insiders is actually old news to those developing the tech directly.

“We already changed the design,” Musk said. “This one didn’t actually work.”

Patents, after all, are often viewed as blueprints for future products. Yet Musk revealed that the rolling contact mechanism—intended to provide smooth, low-friction articulation in the fingers—had already been scrapped after real-world testing exposed its shortcomings.

What looked promising on paper and in simulations failed to deliver the reliability required for a robot expected to handle delicate tasks like folding laundry, assembling electronics, or assisting in factories and homes.

The hand has been one of the biggest challenges for Tesla engineers since Optimus development started years ago. Musk has said that there is not enough recognition for how incredible and useful the human hand is, and designing one for a humanoid robot has been the biggest challenge of all.

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Tesla is stumped on how to engineer this Optimus part, but they’re close

This moment underscores the persistent engineering hurdles in achieving reliable humanoid hand dexterity. Human fingers are marvels of evolution: 27 bones, intricate tendons, ligaments, and a network of sensors working in perfect harmony. Replicating that in metal and silicon is extraordinarily difficult.

Rolling contacts promised reduced wear and precise motion, but testing likely revealed issues with durability under repeated stress, grip stability on varied surfaces, or the micro-precision needed for fine motor skills.

These aren’t minor tweaks, but instead they represent fundamental challenges that have plagued robotics teams for decades. Even advanced competitors struggle here—hands remain the Achilles’ heel of most humanoids because the margin for error is razor-thin.

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A fraction of a millimeter off, and a robot drops a glass or fails to button a shirt.

What makes Musk’s reply remarkable is how it signals Tesla’s direct communication style on prototype limitations. While many companies guard failures behind glossy marketing and vague timelines, Tesla openly shares setbacks.

Musk was forthcoming about the failure of this recent design. This transparency builds trust with investors, engineers, and fans. It shows Tesla treats Optimus development like true science: rapid iteration, rigorous testing, and zero tolerance for hype that doesn’t match reality.

The disclosure from Musk also highlights Tesla’s blistering pace of development. By the time the patents are published, which is often over a year after the initial filing, the technology has already evolved.

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Optimus is far from a static product, and it’s a living project advancing weekly.

In the high-stakes race for general-purpose robots, Tesla’s approach stands out. Admitting a finger-joint design “didn’t actually work” isn’t a weakness—it’s confidence.

True innovation demands confronting failure head-on, and Musk just reminded the world that Optimus is being engineered that way. The next version of those hands is already in testing, and it will be better because Tesla isn’t afraid to say what didn’t work.

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Tesla is sending its humanoid Optimus robot to the Boston Marathon

Tesla’s Optimus robot is heading to the Boston Marathon finish line

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Tesla’s Optimus humanoid robot will be stationed at the Tesla showroom at 888 Boylston Street in Boston, right along the final stretch of the Boston Marathon today, ready to cheer on runners and pose for photos with spectators.

According to a Tesla email shared by content creator Sawyer Merritt on X, Optimus will be at the Boston Boylston Street showroom on April 20, coinciding with Marathon Monday weekend. The Boston Marathon finishes on Boylston Street, and the surrounding area draws hundreds of thousands of spectators along with international broadcast coverage. Placing Optimus there puts it in front of a massive public audience at zero advertising cost.

The Tesla showroom is at 888 Boylston Street, between Gloucester Street and Fairfield Street. The final mile of the marathon runs directly along Boylston Street, with runners passing the big stores before reaching the finish line at Copley Square.

Optimus was first announced at Tesla’s AI Day event on August 19, 2021, when Elon Musk presented a vision for a general-purpose robot designed to take on dangerous, repetitive, and unwanted tasks. In March 2026, Optimus appeared at the Appliance and Electronics World Expo in Shanghai, where on-site staff stated that mass production of the robot could begin by the end of 2026. Before that, it showed up at the Tesla Hollywood Diner opening in July 2025 and at a Miami showroom event in December 2025.

Tesla’s well-calculated display of Optimus gives the public a low-pressure first encounter with a robot that Tesla is preparing  to soon deploy at scale. The company has previously indicated plans to manufacture Optimus robots at its Fremont facility at up to 1 million units annually, with an Optimus production line at Gigafactory Texas targeting 10 million units per year.

Tesla showcases Optimus humanoid robot at AWE 2026 in Shanghai

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Musk has said that Optimus “has the potential to be more significant than the vehicle business over time,” and separately that roughly 80 percent of Tesla’s future value will come from the robot program. Whether that holds depends on production execution. For now, Boston gets a preview of what that future looks like, standing at the finish line on Boylston Street while 32,000 runners pass by.

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Tesla expands Unsupervised Robotaxi service to two new cities

This expansion builds directly on Tesla’s existing operations. Robotaxi has been ramping unsupervised rides in Austin for months and maintains activity in the San Francisco Bay Area.

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Credit: Tesla

Tesla has taken a major step forward in its autonomous ride-hailing ambitions.

On April 18, the company’s official Robotaxi account announced that Robotaxi service is now rolling out in Dallas and Houston, Texas. The update signals the rapid scaling of unsupervised autonomous operations in the Lone Star State.

The announcement includes a compelling 14-second video captured from inside a Model Y. Shot from the passenger perspective, the footage shows the vehicle navigating suburban roads in both cities with zero driver intervention, with no Safety Monitor to be seen.

Tesla also shared geofence maps highlighting the initial service areas: a compact zone in Houston covering parts of Willowbrook and Jersey Village, and a similarly defined area in Dallas near Highland Park and central neighborhoods.

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This expansion builds directly on Tesla’s existing operations. Robotaxi has been ramping unsupervised rides in Austin for months and maintains activity in the San Francisco Bay Area.

With Dallas and Houston now live, Texas hosts three active hubs—an impressive concentration that triples the company’s Lone Star footprint in just weeks. The move aligns with Tesla’s Q4 2025 earnings guidance, which outlined a broader H1 2026 rollout across seven U.S. cities, including Phoenix, Miami, Orlando, Tampa, and Las Vegas.

Texas offers favorable regulations, high ride-share demand, and relatively straightforward suburban-to-urban driving patterns ideal for early autonomous scaling. While initial geofences appear modest—roughly 25 square miles per city—Tesla has historically expanded these zones quickly as it gathers real-world data.

Tesla confirms Robotaxi expansion plans with new cities and aggressive timeline

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Unsupervised operation marks a critical milestone: passengers can summon, ride, and exit without safety drivers, a leap beyond many competitors still requiring human oversight.

For Tesla, the implications are significant. Successful scaling in major metros could accelerate the transition to a fully driverless fleet, unlocking new revenue streams and validating years of Full Self-Driving investment.

Riders gain convenient, potentially lower-cost mobility, while the company edges closer to Elon Musk’s vision of Robotaxis transforming urban transport.

As Tesla pushes into more cities this year, today’s launch in Dallas and Houston underscores its momentum. Hopefully, Tesla will be able to expand unsupervised rides to another U.S. state soon, which will mark yet another chapter in this short-but-encouraging Robotaxi story.

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