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Three weeks ago, panic swept across some corners of the security world after researchers discovered a breakthrough that, at long last, put the cracking of the widely used RSA encryption scheme within reach by using quantum computing.

Scientists and cryptographers have known for two decades that a factorization method known as Shor’s algorithm makes it theoretically possible for a quantum computer with sufficient resources to break RSA. That’s because the secret prime numbers that underpin the security of an RSA key are easy to calculate using Shor’s algorithm. Computing the same primes using classical computing takes billions of years.

The only thing holding back this doomsday scenario is the massive amount of computing resources required for Shor’s algorithm to break RSA keys of sufficient size. The current estimate is that breaking a 1,024-bit or 2,048-bit RSA key requires a quantum computer with vast resources. Specifically, those resources are about 20 million qubits and about eight hours of them running in superposition. (A qubit is a basic unit of quantum computing, analogous to the binary bit in classical computing. But whereas a classic binary bit can represent only a single binary value such as a 0 or 1, a qubit is represented by a superposition of multiple possible states.)

The paper, published three weeks ago by a team of researchers in China, reported finding a factorization method that could break a 2,048-bit RSA key using a quantum system with just 372 qubits when it operated using thousands of operation steps. The finding, if true, would have meant that the fall of RSA encryption to quantum computing could come much sooner than most people believed.

RSA’s demise is greatly exaggerated

At the Enigma 2023 Conference in Santa Clara, California, on Tuesday, computer scientist and security and privacy expert Simson Garfinkel assured researchers that the demise of RSA was greatly exaggerated. For the time being, he said, quantum computing has few, if any, practical applications.

“In the near term, quantum computers are good for one thing, and that is getting papers published in prestigious journals,” Garfinkel, co-author with Chris Hoofnagle of the 2021 book Law and Policy for the Quantum Age, told the audience. “The second thing they are reasonably good at, but we don’t know for how much longer, is they’re reasonably good at getting funding.”

Even when quantum computing becomes advanced enough to provide useful applications, the applications are likely for simulating physics and chemistry, and performing computer optimizations that don’t work well with classical computing. Garfinkel said that the dearth of useful applications in the foreseeable future might bring on a “quantum winter,” similar to the multiple rounds of artificial intelligence winters before AI finally took off.

The problem with the paper published earlier this month was its reliance on Schnorr’s algorithm (not to be confused with Shor’s algorithm), which was developed in 1994. Schnorr’s algorithm is a classical computation based on lattices, which are mathematical structures that have many applications in constructive cryptography and cryptanalysis. The authors who devised Schnorr’s algorithm said it could enhance the use of the heuristic quantum optimization method called QAOA.

Within short order, a host of researchers pointed out fatal flaws in Schnorr’s algorithm that have all but debunked it. Specifically, critics said there was no evidence supporting the authors’ claims of Schnorr’s algorithm achieving polynomial time, as opposed to the exponential time achieved with classical algorithms.

The research paper from three weeks ago seemed to take Shor’s algorithm at face value. Even when it’s supposedly enhanced using QAOA—something there’s currently no support for—it’s questionable whether it provides any performance boost.

“All told, this is one of the most actively misleading quantum computing papers I’ve seen in 25 years, and I’ve seen … many,” Scott Aaronson, a computer scientist at the University of Texas at Austin and director of its Quantum Information Center, wrote. “Having said that, this actually isn’t the first time I’ve encountered the strange idea that the exponential quantum speedup for factoring integers, which we know about from Shor’s algorithm, should somehow ‘rub off’ onto quantum optimization heuristics that embody none of the actual insights of Shor’s algorithm, as if by sympathetic magic.”

Nvidia recently released a beta version of Eye Contact, an AI-powered software video feature that automatically maintains eye contact for you while on-camera by estimating and aligning gaze. It ships with the 1.4 version of its Broadcast app, and the company is seeking feedback on how to improve it. In some ways, the tech may be too good because it never breaks eye contact, which appears unnatural and creepy at times.

To achieve its effect, Eye Contact replaces your eyes in the video stream with software-controlled simulated eyeballs that always stare directly into the camera, even if you’re looking away in real life. The fake eyes attempt to replicate your natural eye color, and they even blink when you do.

So far, the response to Nvidia’s new feature on social media has been largely negative. “I too, have always wanted streamers to maintain a terrifying level of unbroken eye contact while reading text that obviously isn’t displayed inside their webcams,” wrote The D-Pad on Twitter.

A former TV news anchor named Scott Baker also chimed in about Nvidia Eye Contact with his analysis: “As a TV news anchor for decades … this is not quite the right approach. To make communication effective … you have to naturally break eye contact with the camera (just as you would in real life) fairly often. The power of eye contact in human communication is deeply researched. Locking eyes with someone for more than 7-10 seconds is intuitively regarded as creepy or uncomfortable. True across a dinner table, in front of a group, or through a camera.”

This isn’t the first time a company has used simulated eyeballs to maintain eye contact in video calls or video streams. In 2019, Apple introduced its “Eye Contact” feature in FaceTime that kept your peepers always glued to the camera. Like Nvidia’s version of the technology, it also faced a generally negative reception upon launch.

But hey, if non-stop soul-searing eye contact is your thing, you can run Eye Contact yourself by downloading Nvidia Broadcast for free from the company’s website. It requires Windows, an Nvidia RTX graphics card, and a deep desire to freak out anyone watching your video.

ChatGPT has Google spooked. On Friday, The New York Times reported that Google founders Larry Page and Sergey Brin held several emergency meetings with company executives about OpenAI’s new chatbot, which Google feels could threaten its $149 billion search business.

Created by OpenAI and launched in late November 2022, the large language model (LLM) known as ChatGPT stunned the world with its conversational ability to answer questions, generate text in many styles, aid with programming, and more.

Google is now scrambling to catch up, with CEO Sundar Pichai declaring a “code red” to spur new AI development. According to the Times, Google hopes to reveal more than 20 new products—and demonstrate a version of its search engine with chatbot features—at some point this year.

The NYT report quotes D. Sivakumar, a former Google research director, on the internal urgency of the situation: “This is a moment of significant vulnerability for Google. ChatGPT has put a stake in the ground, saying, ‘Here’s what a compelling new search experience could look like.’”

Unlike Google search, which works primarily through keywords, ChatGPT uses natural language processing to glean the context of what a user is asking, then generate its best attempt at relevant answers. ChatGPT’s output isn’t always accurate, but its performance has been convincing enough to illustrate a potential conversational search interface that would make Google’s technology look antiquated.

Perhaps because of this, Microsoft is reportedly working on a new version of its Bing search engine that will integrate features of ChatGPT. Microsoft made its first OpenAI investment in 2019, and it recently announced a new round of funding to the tune of $10 billion.

Back at Google, Page and Brin have not been very involved with the search engine since they left their daily roles in 2019, but they have long been cheerleaders of bringing AI into Google’s products. Their involvement reflects the gravity of the ChatGPT challenge within Google.

So far, Google has responded to OpenAI by introducing fast-track product approval reviews and tools to help other companies develop their own AI prototypes, according to the NYT. Google also offers software developers and other businesses image-creation technology, along with its AI language model, LaMDA.

Some have wondered if Google has been playing it too safe, worried about the negative societal impacts or copyright implications of generative AI technology. Google seems to recognize this hesitancy internally, and the NYT report mentions that the company may potentially “recalibrate” the level of risk it is willing to take with new AI technology.

In a tweet, OpenAI CEO Sam Altman poked fun at this line in the NYT article by saying that OpenAI aims to decrease the level of risk the company will take while still shipping powerful new AI models.

Recalibration or not, Google says it is committed to AI safety. “We continue to test our AI technology internally to make sure it’s helpful and safe, and we look forward to sharing more experiences externally soon,” Lily Lin, a spokesperson for Google, said in a statement to the NYT.

But while Google delays, the more nimble OpenAI is shipping generative AI products that could potentially disrupt not only Google, but the entire tech industry as OpenAI works toward its goal of creating ever-more-powerful AI technology. Google’s response to this existential threat may decide the success of the company for years to come.