There are schemes where you don't need key pairs for each user (assuming the government has some way of authenticating users). Private State Tokens use blinded tokens for this.
It doesn't prevent tokens from being stolen or sold, but the token issuer only accepts each token once and can limit the rate that tokens are issued and control how fast they expire, giving decent control over how practical using stolen or sold tokens are.
It doesn't necessarily have to be white mold either. Anything that generates a persistent immune response can keep it going once it triggered. Like really bad allergies.
Even if they're more productive, the amount of work to be done has also increased significantly. Not only is there so much work to be done on AI integrations but all of the other products have bug trackers exploding with new privacy and security vulnerabilities that need to be addressed.
Some of the security people I work with claim that without the productivity gains from AI, there is no way they could keep up with the bugs.
Theoretically, that's true. In practice the gains were limited to the wealthy who use that money to fund increasingly deranged start-ups* until the market crashes. Then rinse and repeat. The failed start-ups effectively waste any increased productivity leaving everyone about where they were before.
*They fund these start-ups to get a good return on investment so they can get even more money. As the economy overheats the number of places to invest with a reasonable return falls so they are left with the high-risk stuff to invest in. I'm not sure what they want the money for, though, since they could already afford most of the things I would find useful...
How do you handle object disambiguation with YOLO? All the examples I've played with have the problem where if two "cars" get too close to each other then the tracking IDs keep switching between them, meaning we'd need an additional kinetic model for disambiguation.
Ick. The entire article starts from the fundamentally flawed premise that "you want a function that takes a blob of memory as an argument". Then they discuss bytewise access into structures..
Passing around void pointers is simply not a safe thing to do in C++. You can't do anything with a void pointer, so you're probably going to cast it as something else. Use that type instead, so that your caller knows they need to pass a valid pointer to that type. If the pointer has the wrong alignment then that will result undefined behavior. If you need to support multiple pointer types, use templates.
And, unless there are some really weird circumstances, you actually don't want to access your structures bytewise. Offsets can shift with compiler flags/versions. If you want serialization , please use a serialization library that correctly handles all of the odd cases. These can be quite efficient.
I've only actually had to munge bytes in a class once. Somebody decided that a previously POD class that was passed between processors with different memory spaces needed a virtual function, so I had to overwrite the vtable when I received it to make it valid.
But there's nothing stopping AI from developing domain expertise. If you fine tune a model based on the records of all previous work (effectively "shadowing" the existing workers) then it can easily learn this domain knowledge. The only difference is that AI companies have gone after programming domain knowledge first. Others will come later.
I don't see how it would be possible for inference costs to dominate training costs, even after amortization.
Training involves multiple passes over the entire training dataset, ideally in large batches where you can perform inference on as many samples as possible simultaneously and then perform backpropagation to adjust the model weights (which is about as expensive as inference).
Let's consider the size of the dataset we're dealing with here. The dataset likely consists of practically every piece of digitized text they can get their hands on (including that extracted from audio and video). We know Google has digitized a large portion of the books in existence as part of their "search book contents" feature and we have no reason to believe they're not using it alongside their cache of 90+% of the internet to train their models. We're talking about 100s of millions of books each with an average of 100,000s of tokens. The internet has 10s to 100s of billions of pages on it with who knows how many tokens on average. This is a huge dataset that we've got to go through hundreds of times.
Second, let's consider the effect of batching and how it sets requirements for our hardware. We know that larger batch sizes converge faster, are more stable, and produce better models. So if you want a good model you need large batch sizes. This means that you need machines several orders of magnitude more powerful than you use for inference. From what I heard Google uses clusters of 100s of the their TPUs all located in a single rack for training. These clusters are organized in a customized computing architecture to maximize memory locality between cores (really critical for efficient back-propagation). Further, you can't use reduced precision weights for training like you can for inference, so there are no shortcuts.
Finally, the initial training stage is followed by reinforcement learning stages - this is key development in how AI models have improved in the past year. This may mean going through a curated set of traces (either synthetic or captured from users) and adjusting the weights based on experienced outcome.
Overall there's so many orders of magnitude more work and more hardware requirements for training that I find it improbable that inference dominates. The number of "inference" steps in training is freaking ridiculous and includes such factors as the "number of words ever written".
Or did you forget all the journalists kicked out of the Pentagon, the exclusion of the associated press over their use of the internationally recognized name for the Gulf of Mexico, the threatened cancellation of FCC licenses of ABC and CBS regarding their reporting, etc.
Jetski/Antigravity is a better piece of software. The Gemini CLI codebase looks like someone tried to vibe-code a Claude code clone in nodeJS, as it's simply not maintainable.
That being said, until recently Gemini CLI was better. It had support for persistent policies on what code could run without asking and had good extension hooks to allow you write extensions that influence policy (to perform complex logic like rewriting tool calls before they are executed).
Antimatter/Jetski only recently added support for remembering what commands are "always allowed" between sessions, the extension framework (excuse me, "plugins") has fewer features, and hooks have much less power than with Gemini CLI (and can't come bundled with extensions).
In my experience, its the opposite. The AI is very good at writing code, but it is unreliable at any kind of design. I use it as a fancy form of autocomplete. I give the broad strokes: "add a method here and change all but this one caller to use the new method", "Apply this design pattern here for this change but don't do this other thing". It completes the task reasonably well and sometimes even remembers to run the code formatter and check that tests pass.
If I ask it to me produce a design, I'll almost always end up with something unworkable or inefficient.
Though if you push it hard enough then it can sometimes give you a good description of what existing code does and how it does it (which can be easily verified).
I work for a company that has been using Mythos for vulnerability detection in our software. The results we're getting are revolutionary to the point that our software security teams are heavily overloaded addressing the deluge of thousands of real bugs/vulnerabilities and design flaws across our billions of lines of code.
For comparison, we are invested heavily the the AI space to the point where Anthropic is one of our competitors. We were already using state of the art models to find flaws in our code, but Mythos was just so much better at finding real vulnerabilities it's not even funny.
Yes, exactly. Moore's law says that in less than 10 years you will be able to fit today's state of the art models on your phone. If you add in all of the computationally and memory neutral improvements and breakthroughs that we will accumulate over the next 10 years then it will be both far more capable and far more reliable than today's models.
An AI assistant you can trust and bring with you is coming, and almost nothing can stop it.