I'm not sure what connection you're seeing here. How does a set of reporting requirements resemble the attempt to encourage telecom businesses to add a backdoor in communications infrastructure?
First, the term backdoor applies loosely to the Clipper chip since it would have been public that this chip could be used for accessing private information. I think lock is a better security term. Backdoors generally are secret. My memories from that time are that the term backdoor was also an irony.
Second, the link is about informing private information because there is a security concern. It is not because the government want to have stats to inform the public about how to run AI nodes.
No, I think backdoor is the appropriate term, not least of all because the first sentence of the Wikipedia entry you linked identifies the Clipper chip as such.
As long as we're nitpicking word choice, the word allegory does not apply here. An allegory is an intentional narrative device employed by an author or artist. Two things you find to be similar does not constitute an allegory.
The link you identify is pretty tenuous. With the Clipper chip, the government wanted access to live communications, not information about the infrastructure the communications were passing through. That is not the case with the recent EO.
Sorry, if I can say it differently it reminds me of the Clipper chip because of the security concerns for the use of technology in a specific way. AI now, crypto before. Please take it as a personal connection then.
For most of history, China wasn't the wealthy superpower it has been since around 2020.
There is an argument that China has the world's largest economy and it is nestled in the middle of the region that is best known for high-tech manufacturing. It is still possible that they'll fumble this somehow, but the fundamentals are solidly on the side of China becoming the place to do AI training.
Export controls for GPUs that are exclusively manufactured a hundred miles off of China’s coast in Taiwan, a nation that the CCP considers to be part of China, and that it has explicit plans to take by military force in the coming years?
It’s not as secure and stable of a situation as you present.
The order itself is ridiculous election year pandering. It imagines we are suddenly in an "age of AI" and that it's clueless meddling is required to establish an industry that already exists and to prevent algorithmic dark patterns that are already entrenched from forming.
It then sets every federal agency out on a quest to identify and then create a plan to ameliorate all the "scary AI boogey men" sci-fi fever dreams that have been associated with the deployment of a technology that doesn't even actually exist yet.
And, of course, more H1-B visas, because.. you know.. we wouldn't want to be "left behind."
Edit: I read H1-B as a generalization for “skilled immigration” - I now see that people have issues with the H1-B program, specifically.
The H1-B thing is kind of a non-sequitur. I think anyone involved in tech (software, hardware, anything) should be wildly in favor of massive H1-B increases, AI-related or not.
The U.S. has the opportunity to cement itself as the center of the world as far as technological progress from now into eternity. Why wouldn’t we take every brilliant immigrant we can get?
If you’re a software developer and you are afraid for your job/wages, I get that intuitively; but how many times do we have to learn the lesson that “more people making software leads to more software jobs”
There's way too much work to do in the AI space and - even worldwide - not enough skilled people by far.
The danger, IMHO, isn't wage dilution anyway (that one can be counteracted by politics) - it is that sensitive knowledge will make its way back to China and other current or potentially hostile nations, and it would not be the first time either that this happens.
You have sources for the fact that there isn't enough people? My interviewing experience suggests otherwise. They would be relaxing requirements like in office only and pay would go up. They're just as picky as any other company.
Devs have been worried about offshoring/immigrants replacing them/lowering their wages for decades.
The only outcome we have ever observed from having more people building software is that dramatically more software jobs have become possible and in-demand.
Also, and I understand why you might not make this argument, but let’s be honest: software engineering wages are extraordinarily high. Slowing or even reversing that growth by small amounts on average would still leave millions of extremely well-paid jobs.
Software is eating the world after all. Seems likely that demand would be high anyway. Perhaps if there were no H1Bs, entry level grads would make $250k, with the average senior dev making $750k+.
Why are you talking about "importing" immigrants like they're a bulk commodity and not people with hopes and dreams? My brother is an immigrant to the US - nobody imported him (unless you count his American wife), he moved because he wanted to. Hell, I'd _love_ to move to US but the immigration system for skilled workers is beyond fucked.
No, the UK. And the company with the oldest demographic was an American company based out of Seattle. The second oldest was another American company HQ'd in Portland.
I'm genuinely a bit confused here, you haven't worked with middle-aged and older developers? I don't think I've ever worked anywhere without someone at least in their 40s, including the startup I'm at now. Almost everyone in tech during the dot-com boom is at least middle aged now.
> The U.S. has the opportunity to cement itself as the center of the world as far as technological progress from now into eternity.
I don't accept this premise and I don't think serves as a reasonable excuse for government interference in labor markets. Even if you do accept this, then the solution sacrifices long-term labor stability for short-term labor monopolization.
Either way, I don't see this as a positive outcome, and I regret every administrations attempt to expand the program using any excuse that happens across their desks.
> but how many times do we have to learn the lesson that “more people making software leads to more software jobs”
The connection between this outcome and increased H1-B visas for mostly _corporate sponsors_ is sketchy, at best.
> There is no evidence that H1B visas have caused labor instability in the IT market.
Lmao. It’s self evident that the IT market, exemplified by the wealthiest tech corps, is literally dominated by foreign workers. The argument for unskilled labor (Americans don’t want those jobs) can’t even be dishonestly argued here. It’s government policy allowing US workers to be sidelined in favor of foreign ones.
Why do you see giving more visas as a government interference in labor markets?
In my view it's the opposite, a perfectly free labor market would be one where anyone can apply to a job. Restricting immigration by denying visas is a government interference in the market. So more visas means less interference.
(Note: I'm not claiming anything about whether it's a good idea or not).
The government is already interfering with the labor market by denying work visas to many US-educated students, limiting number of employment based green cards, handing out diversity visas through random lottery, and in general dictating who should or should not be eligible for work permits.
Not trying to speak for GP but I think they would probably tell you that what you're talking about is also an interference and should go away as well :)
Oh hell no. H1B isn’t there to bring in the best and brightest; those workers would qualify for skilled worker visas. It’s there for low-skill workers, who are set up to be abused by corporations under the threat of deportation, while replacing American workers.
The US doesn’t need to “cement itself” as anything. Thanks, but no thanks. Ironically, your kind of attitude is the same one that gave rise to the insane, populist politics of the last few years and has done more harm to immigrants than any other single policy.
There are only 30,000 skilled worker visas issued per year. There are additionally 65,000 H1B visas issued, with an additional 20,000 issued to folks with a masters or higher from a US university.
H1-B is specifically for specialized occupations and generally requires a minimum of a bachelor’s degree and specialized skills in demand. It is, in fact, called the H1-B Specialty Occupations Visa.
I suspect you’re thinking of the Diversity Visa program, which offers a lottery of 55,000 visas annually to anyone (except for some eye brow raising exceptions).
There are also other programs like migrant worker programs that allow unskilled labor into the country for a limited time to fill seasonal work gaps.
I came under h1b (technically tn first and then transfer) and definitely did not feel like I fell into the “abuse” category - was well-compensated and switched through several employers. Was not underpaid afaik and at no point did I feel like I was at risk of deportation (very easy job mobility). Granted there are problems/abuses with h1b and some people have difficulty getting hired by someone other than their sponsor, but the description you’ve laid out is one-sided and does not feel like a complete picture.
> those workers would qualify for skilled worker visas.
H1B is a skilled worker visa so I’m not sure what the complaint here is.
It sounds like a lot of complaints you have are with well documented abuses of the program by consulting firms and the government’s lack of cracking down on that.
The US should be giving more freedom to existing H1-B holders so they aren't indentured to the companies that employ them for 10+ years, at H1-B minimum salary, while waiting for them to generously sponsor their green card. Make H1-B not tied to employment so companies stop abusing them.
> I think anyone involved in tech (software, hardware, anything) should be wildly in favor of massive H1-B increases, AI-related or not.
Luckily you don’t control the thoughts of others. US companies should hire US citizens and if there isn’t enough supply we should be enacting policies to fix that issue not continuing to allow US citizens to work “unskilled” labor for pennies while we brain drain other nations and create an upper class of non-citizens.
a) The election is in 2024 and this isn't a top 10 issue for voters.
b) It is inarguable that we are in an age of AI.
c) There are legitimate concerns with AI that shouldn't be blindly waved away as "boogey man". Especially with what we have been seeing in Ukraine with their use of autonomous and remotely controlled drones. Adding an AI layer into the mix needs to be regulated.
d) Number of H1B visas are set by Congress not the President.
> The election is in 2024 and this isn't a top 10 issue for voters.
How many days from today until that election day?
> It is inarguable that we are in an age of AI.
We have companies hawking large language models. It's entirely arguable that we are in an "age of AI." You're about to be in an age of "no more easy CMOS gains." The intersection between these two points is going to be interesting.
> There are legitimate concerns with AI that shouldn't be blindly waved away as "boogey man"
This is why we have courts and a legislature with committee powers. I do not believe that an eager top down federal agency approach is going to solve real problems without creating more encumbrances than it's worth.
This is also why it's pandering. Look at the list of issues they bring up, those most definitely rank with voters.
> Number of H1B visas are set by Congress not the President.
Well then it's potentially even a bigger problem. They're going to change prioritization for those applicants against a limited pool.
I mean, giving out H1-B visas, if it does nothing else to help the US, takes skilled people away from other countries, including plenty of them from hostile countries. The brain drain is real, and can be used strategically if the US leadership is competent enough.
> (i) any model that was trained using a quantity of computing pow greater than 10^26 integer or floating-point operations, or using primarily biological sequence data and using a quantity of computing power greater than 10^23 integer or floating-point operations;
Then you're including all kinds of time for other delays, like copying data over PCIe into the GPU for the next batch, or waiting for the CPU to orchestrate things.
You could probably get a better estimate based on the network structure, the number of training epochs, and the size of each batch.
Why are they using parameters or ops using trained when an objective tests that exist or compare to other state of the art systems which makes more sense. This executive order is a joke when you could just almost train a bit less and get the same output…
The regulations as presently defined are pretty lax IMHO; it seems people jump to the all regulations are a unbearable constraint perspective without some measured look at what is being proposed?
I get the don't give an inch or they will take a mile, but also some regulation structures are probably warranted by such a fundamental shift on par with the advent of the Internet.
Back then we both got it right with DMCA in that big Internet companies could thrive in the US; but also problematic concentration of Monopolistic power in big Internet companies.
Will have to do some iteration to see what makes sense with the advent with this new computing model / capabilities at this scale.
I know that its more the capabilities angle that the US Government is mainly interested in here, rather than copyright infringement...
But just curious, could packages like google's fully-homomorphic-encryption or the difficult to build Pyfhel be used to mitigate government intrusion into model training / datasets?
I know there is some work being done to 'extract' training data from models (although given the compression, not entirely sure you could really extract everything but curious if anyone is seriously working on training models on purely encrypted data
FHE does still add a huge overhead (measured in orders of magnitude, not percentages), so it would not be applicable for large models that are comparable with state of art models. You can apply FHE to neural networks in general (e.g. https://developers.googleblog.com/2023/08/expanding-our-full... lists it as one of potential use cases), but the unsaid assumption is that they are relatively small NNs, and that's in an era where we know that simply using a larger model brings meaningful improvements to the outcome, and the main limiting factor for people training their own models is the cost of compute.
You have been sorely misinformed about AI. Even the name itself has been used to mislead you! Artificial Intelligence does not exist today. While new systems may be "intelligent" in their designs, none of them is "an intelligence".
Artificial Intelligence has been the pursuit of Computer Science since the earliest days of software design, back when the AI department at Bell Labs developed Programming Languages. What is called "AI" today is no more than the newest efforts in that pursuit. Despite the excitement at these newer efforts, the goal that is AI is as mysterious as it ever was.
So what's new? Inference Models. These allow computers to navigate ambiguous data. This was entirely impossible before, and is only somewhat possible today. While computers do not get completely stuck on ambiguity the way they used to, they are still unable to conclusively resolve that ambiguity. They can only be trained to guess. With careful training on very large datasets, some impressive results have been obtained. Unfortunately, those impressive results are always closely tied to embarrassing mistakes. This is a feature of contemporary inference models.
The goal in mind is to perform this process well enough to have a novel and useful system. Many believe that once a model is big enough and trained well enough that it will become reliably more accurate. There is no conclusive evidence that is the case. While they are often introduced as a "limitation", behaviors like "overconfidence" and "hallucinations" are features of these systems. In order to remove a feature from a system, a new system must be invented. In the mean time, let's consider what does exist, rather than get lost in our own dreams.
So what should you be worried about? Contemporary inference models are powerful enough to create convincing results. What does that mean for the people of the United States? We need to recognize the reality in front of us: people can tell lies. Data alone is never a reliable source of information. This has always been true, but the inherent difficulty in storytelling has made some lies impractical to tell. As technology improves, so does each person's ability to tell a story.
We have all watched the journalistic integrity of our world suffer at the hands of Social Media, and at the failure of large corporations to moderate content. At best, people have grown to distrust scientific discovery and leadership; and at worst, untamed hate speech has lead to genocide.
So what can we do about it? Readers need to be able to differentiate content, not by its substance, but by its source. The good news is that this has been a solved problem for 50 years. All that an author needs to attach their identity to their writing, is to provide readers their unique public key and a signature of their work. Unfortunately, the best tools to do this, including GPG, are very technical and difficult for the layman to use. It should be the priority of the United States, for the sake of national security, to improve this landscape by creating (or motivating the creation of) easy-to-use public-key encryption software.
Sure, I don’t think the meaning of “artificial” is in question. Presumably the reason thomastjeffery said that there is no such thing as artificial intelligence, was not because he thought the threshold for it being artificial, had not been reached.
Indeed, that(*) is what I was implying. Therefore, my initial question was, "what is it that you mean by 'intelligence' when you say that there is no such thing as 'artificial intelligence'?”
I had thought the fact that this is what I meant, would have been clear in my initial reply. I didn’t expect this conversation to go in the direction of clarifying that.
The technologies we have are models, not actors. They are each the static result of a process that determines boundaries and relationships between pieces of data. These models do not, however, organize or label the boundaries or relationships themselves.
For example, you can model a dataset of human-written text into an LLM. Using that LLM, you can transform a human-written prompt into a "continuation" that incorporates the modeled dataset. The resulting continuation will contain a new organization of both text from your prompt and/or text from the dataset: nothing more.
The boundaries and relationships modeled by an LLM are not categorized. The model does not contain any objective observation about its data. It only provides a structure that is intended to "align to" (simulate) the already-present semantics of natural language. It was not the model's intention to align with language semantics: that comes from the authors of the model, and from the presence of patterns (we recognize as language semantics) in the dataset itself. Without the presence of natural language patterns, there would be no boundary to align to in the first place.
To contrast, a human can read text into ideas, then think objectively about those ideas, produce new ideas, and finally express those ideas into text by structuring them into language semantics. Nothing like that happens in any software I am aware of.
Y'all just need to read the executive order (or at least the white house fact sheet) itself it's got way more stuff than this article covers and to my eyes is overwhelmingly positive. It's like the white house read all of the complaints people on HN have had about inappropriate uses of LLms and bundled them into one huge omnibill telling every federal agency to stop people from behaving recklessly with AIs that are are mirror of the average Redditor.
Outside of reporting that you're working on a new huge model and some maybe down the road future NIST guidelines they don't actually restrict the production of models at all. It's all about telling the humans jumping on the move fast break things train that no you can't use these tools for for <obviously dystopian thing> like renter screening, price fixing, and judicial sentencing.
Good on the white house for recognizing that the harm these models produce is almost entirely the humans connecting them to the real world.
Yeah, lots of weirdos likely bleeding over from the bitcoin maxi camp here commenting. This exec order is mostly good and directly addresses a bunch of future problems.
You’re making a huge assumption about NIST’s implementation. Generally people like the goals of regulation. They might not like the implementations.
I’d prefer to see strategic action on concrete issues rather than perceived risks. I don’t see how tasking a bunch of government agencies with pontificating on new and unfamiliar technologies can produce a good result.
Ill have to develop more thoughts but there is something amusing about having a whole section about protecting jobs, the having sections about importing more laborers for ai development.
Excuse me, Congress. I need to have a word with you.
Your oath of office said “I do solemnly swear (or affirm) that I will support and defend the Constitution of the United States against all enemies, foreign and domestic; that I will bear true faith and allegiance to the same; that I take this obligation freely, without any mental reservation or purpose of evasion; and that I will well and faithfully discharge the duties of the office on which I am about to enter: So help me God.”
Source: https://oaths.us/senate-oath-of-office/
I noticed that you just voted on a bunch of unconstitutional laws, which makes you guilty of perjury.
Note: It does not matter which memeber of Congress you address, or when. This applies to all of them, all the time.
83 comments
[ 2.5 ms ] story [ 114 ms ] thread[1] https://en.wikipedia.org/wiki/Clipper_chip
First, the term backdoor applies loosely to the Clipper chip since it would have been public that this chip could be used for accessing private information. I think lock is a better security term. Backdoors generally are secret. My memories from that time are that the term backdoor was also an irony.
Second, the link is about informing private information because there is a security concern. It is not because the government want to have stats to inform the public about how to run AI nodes.
As long as we're nitpicking word choice, the word allegory does not apply here. An allegory is an intentional narrative device employed by an author or artist. Two things you find to be similar does not constitute an allegory.
The link you identify is pretty tenuous. With the Clipper chip, the government wanted access to live communications, not information about the infrastructure the communications were passing through. That is not the case with the recent EO.
And history has shown that companies don't just move to China/Russia because the US market is so lucrative.
And their phones are manufactured in China, India and Vietnam.
Nobody said that. Please don't do straw men arguments.
They moved most of their manufacturing to China.
2. The export controls have exceptions, loopholes, and people with strong wills.[1][2]
3. China is slowly but steadily freeing itself from import dependence.[3]
[1]: https://tech.slashdot.org/story/23/08/21/203241/china-keeps-...
[2]: https://mobile.slashdot.org/story/23/09/09/1849221/huawei-sh...
[3]: https://mobile.slashdot.org/comments.pl?sid=23071327&cid=638...
There is an argument that China has the world's largest economy and it is nestled in the middle of the region that is best known for high-tech manufacturing. It is still possible that they'll fumble this somehow, but the fundamentals are solidly on the side of China becoming the place to do AI training.
It’s not as secure and stable of a situation as you present.
It then sets every federal agency out on a quest to identify and then create a plan to ameliorate all the "scary AI boogey men" sci-fi fever dreams that have been associated with the deployment of a technology that doesn't even actually exist yet.
And, of course, more H1-B visas, because.. you know.. we wouldn't want to be "left behind."
[0]: https://www.whitehouse.gov/briefing-room/presidential-action...
The H1-B thing is kind of a non-sequitur. I think anyone involved in tech (software, hardware, anything) should be wildly in favor of massive H1-B increases, AI-related or not.
The U.S. has the opportunity to cement itself as the center of the world as far as technological progress from now into eternity. Why wouldn’t we take every brilliant immigrant we can get?
If you’re a software developer and you are afraid for your job/wages, I get that intuitively; but how many times do we have to learn the lesson that “more people making software leads to more software jobs”
Not if you like high wages, and you want them to stay that way.
The danger, IMHO, isn't wage dilution anyway (that one can be counteracted by politics) - it is that sensitive knowledge will make its way back to China and other current or potentially hostile nations, and it would not be the first time either that this happens.
Devs have been worried about offshoring/immigrants replacing them/lowering their wages for decades.
The only outcome we have ever observed from having more people building software is that dramatically more software jobs have become possible and in-demand.
Also, and I understand why you might not make this argument, but let’s be honest: software engineering wages are extraordinarily high. Slowing or even reversing that growth by small amounts on average would still leave millions of extremely well-paid jobs.
Software is eating the world after all. Seems likely that demand would be high anyway. Perhaps if there were no H1Bs, entry level grads would make $250k, with the average senior dev making $750k+.
YMMV, I guess.
I don't accept this premise and I don't think serves as a reasonable excuse for government interference in labor markets. Even if you do accept this, then the solution sacrifices long-term labor stability for short-term labor monopolization.
Either way, I don't see this as a positive outcome, and I regret every administrations attempt to expand the program using any excuse that happens across their desks.
> but how many times do we have to learn the lesson that “more people making software leads to more software jobs”
The connection between this outcome and increased H1-B visas for mostly _corporate sponsors_ is sketchy, at best.
b) There is no evidence that H1B visas have caused labor instability in the IT market.
Lmao. It’s self evident that the IT market, exemplified by the wealthiest tech corps, is literally dominated by foreign workers. The argument for unskilled labor (Americans don’t want those jobs) can’t even be dishonestly argued here. It’s government policy allowing US workers to be sidelined in favor of foreign ones.
In my view it's the opposite, a perfectly free labor market would be one where anyone can apply to a job. Restricting immigration by denying visas is a government interference in the market. So more visas means less interference.
(Note: I'm not claiming anything about whether it's a good idea or not).
The US doesn’t need to “cement itself” as anything. Thanks, but no thanks. Ironically, your kind of attitude is the same one that gave rise to the insane, populist politics of the last few years and has done more harm to immigrants than any other single policy.
H1-B is specifically for specialized occupations and generally requires a minimum of a bachelor’s degree and specialized skills in demand. It is, in fact, called the H1-B Specialty Occupations Visa.
I suspect you’re thinking of the Diversity Visa program, which offers a lottery of 55,000 visas annually to anyone (except for some eye brow raising exceptions).
There are also other programs like migrant worker programs that allow unskilled labor into the country for a limited time to fill seasonal work gaps.
> those workers would qualify for skilled worker visas.
H1B is a skilled worker visa so I’m not sure what the complaint here is.
It sounds like a lot of complaints you have are with well documented abuses of the program by consulting firms and the government’s lack of cracking down on that.
Luckily you don’t control the thoughts of others. US companies should hire US citizens and if there isn’t enough supply we should be enacting policies to fix that issue not continuing to allow US citizens to work “unskilled” labor for pennies while we brain drain other nations and create an upper class of non-citizens.
b) It is inarguable that we are in an age of AI.
c) There are legitimate concerns with AI that shouldn't be blindly waved away as "boogey man". Especially with what we have been seeing in Ukraine with their use of autonomous and remotely controlled drones. Adding an AI layer into the mix needs to be regulated.
d) Number of H1B visas are set by Congress not the President.
How many days from today until that election day?
> It is inarguable that we are in an age of AI.
We have companies hawking large language models. It's entirely arguable that we are in an "age of AI." You're about to be in an age of "no more easy CMOS gains." The intersection between these two points is going to be interesting.
> There are legitimate concerns with AI that shouldn't be blindly waved away as "boogey man"
This is why we have courts and a legislature with committee powers. I do not believe that an eager top down federal agency approach is going to solve real problems without creating more encumbrances than it's worth.
This is also why it's pandering. Look at the list of issues they bring up, those most definitely rank with voters.
> Number of H1B visas are set by Congress not the President.
Well then it's potentially even a bigger problem. They're going to change prioritization for those applicants against a limited pool.
On an unrelated note, OpenAI announces GPT5 will be trained with fixed-point arithmetic.
https://twitter.com/DavidVorick/status/1719097248699879831
> (i) any model that was trained using a quantity of computing pow greater than 10^26 integer or floating-point operations, or using primarily biological sequence data and using a quantity of computing power greater than 10^23 integer or floating-point operations;
You can estimate it quite accurately, actually
You could probably get a better estimate based on the network structure, the number of training epochs, and the size of each batch.
I get the don't give an inch or they will take a mile, but also some regulation structures are probably warranted by such a fundamental shift on par with the advent of the Internet.
Back then we both got it right with DMCA in that big Internet companies could thrive in the US; but also problematic concentration of Monopolistic power in big Internet companies.
Will have to do some iteration to see what makes sense with the advent with this new computing model / capabilities at this scale.
It's fine to say you can't legally use software to commit crimes.
I know there is some work being done to 'extract' training data from models (although given the compression, not entirely sure you could really extract everything but curious if anyone is seriously working on training models on purely encrypted data
You have been sorely misinformed about AI. Even the name itself has been used to mislead you! Artificial Intelligence does not exist today. While new systems may be "intelligent" in their designs, none of them is "an intelligence".
Artificial Intelligence has been the pursuit of Computer Science since the earliest days of software design, back when the AI department at Bell Labs developed Programming Languages. What is called "AI" today is no more than the newest efforts in that pursuit. Despite the excitement at these newer efforts, the goal that is AI is as mysterious as it ever was.
So what's new? Inference Models. These allow computers to navigate ambiguous data. This was entirely impossible before, and is only somewhat possible today. While computers do not get completely stuck on ambiguity the way they used to, they are still unable to conclusively resolve that ambiguity. They can only be trained to guess. With careful training on very large datasets, some impressive results have been obtained. Unfortunately, those impressive results are always closely tied to embarrassing mistakes. This is a feature of contemporary inference models.
The goal in mind is to perform this process well enough to have a novel and useful system. Many believe that once a model is big enough and trained well enough that it will become reliably more accurate. There is no conclusive evidence that is the case. While they are often introduced as a "limitation", behaviors like "overconfidence" and "hallucinations" are features of these systems. In order to remove a feature from a system, a new system must be invented. In the mean time, let's consider what does exist, rather than get lost in our own dreams.
So what should you be worried about? Contemporary inference models are powerful enough to create convincing results. What does that mean for the people of the United States? We need to recognize the reality in front of us: people can tell lies. Data alone is never a reliable source of information. This has always been true, but the inherent difficulty in storytelling has made some lies impractical to tell. As technology improves, so does each person's ability to tell a story.
We have all watched the journalistic integrity of our world suffer at the hands of Social Media, and at the failure of large corporations to moderate content. At best, people have grown to distrust scientific discovery and leadership; and at worst, untamed hate speech has lead to genocide.
So what can we do about it? Readers need to be able to differentiate content, not by its substance, but by its source. The good news is that this has been a solved problem for 50 years. All that an author needs to attach their identity to their writing, is to provide readers their unique public key and a signature of their work. Unfortunately, the best tools to do this, including GPG, are very technical and difficult for the layman to use. It should be the priority of the United States, for the sake of national security, to improve this landscape by creating (or motivating the creation of) easy-to-use public-key encryption software.
What is it that you are saying does not exist?
Is that the same thing as you think policy makers or whatnot have been led to believe exists?
Artificial intelligence is intelligence created by humans. Artificial means man-made.
I had thought the fact that this is what I meant, would have been clear in my initial reply. I didn’t expect this conversation to go in the direction of clarifying that.
(*): modulo negligible quibbles about wording
The technologies we have are models, not actors. They are each the static result of a process that determines boundaries and relationships between pieces of data. These models do not, however, organize or label the boundaries or relationships themselves.
For example, you can model a dataset of human-written text into an LLM. Using that LLM, you can transform a human-written prompt into a "continuation" that incorporates the modeled dataset. The resulting continuation will contain a new organization of both text from your prompt and/or text from the dataset: nothing more.
The boundaries and relationships modeled by an LLM are not categorized. The model does not contain any objective observation about its data. It only provides a structure that is intended to "align to" (simulate) the already-present semantics of natural language. It was not the model's intention to align with language semantics: that comes from the authors of the model, and from the presence of patterns (we recognize as language semantics) in the dataset itself. Without the presence of natural language patterns, there would be no boundary to align to in the first place.
To contrast, a human can read text into ideas, then think objectively about those ideas, produce new ideas, and finally express those ideas into text by structuring them into language semantics. Nothing like that happens in any software I am aware of.
Outside of reporting that you're working on a new huge model and some maybe down the road future NIST guidelines they don't actually restrict the production of models at all. It's all about telling the humans jumping on the move fast break things train that no you can't use these tools for for <obviously dystopian thing> like renter screening, price fixing, and judicial sentencing.
Good on the white house for recognizing that the harm these models produce is almost entirely the humans connecting them to the real world.
I’d prefer to see strategic action on concrete issues rather than perceived risks. I don’t see how tasking a bunch of government agencies with pontificating on new and unfamiliar technologies can produce a good result.
Your oath of office said “I do solemnly swear (or affirm) that I will support and defend the Constitution of the United States against all enemies, foreign and domestic; that I will bear true faith and allegiance to the same; that I take this obligation freely, without any mental reservation or purpose of evasion; and that I will well and faithfully discharge the duties of the office on which I am about to enter: So help me God.” Source: https://oaths.us/senate-oath-of-office/
I noticed that you just voted on a bunch of unconstitutional laws, which makes you guilty of perjury.
Note: It does not matter which memeber of Congress you address, or when. This applies to all of them, all the time.