Indeed! Shame there's a lack of access to ultra for now, but good to have more things to access.
Also:
> Starting today, Bard will use a fine-tuned version of Gemini Pro for more advanced reasoning, planning, understanding and more. This is the biggest upgrade to Bard since it launched.
edit-
Edit 2 - forget the following, it's not available here but that's hidden on a support page, so I'm not able to test it at all.
Well that's fun. I asked bard about something that was in my emails, I wondered what it would say (since it no longer has access). It found something kind of relevant online about someone entirely different and said
Yep. That's their "moat", to go with The Discourse. For better or for worse, a bunch of us know how to use their models, where the models do well, where the models are a little rickety, etc. Google needs to build up that same community.
Gemini Pro is only GPT3.5 tier according to the benchmarks, so unless they make it extremely cheap I don't see much value in even playing around with it
I still think it's worth it. GPT-3.5 is extremely powerful, and it's what we use in production. GPT-4 is way overkill for our prompt and use case.
If it's similar, or even marginally better in any way, we'd consider switching over. Not because OpenAI is bad or anything (they're great, actually!) but because it's so easy to do that.
>
For Gemini Ultra, we’re currently completing extensive trust and safety checks, including red-teaming by trusted external parties, and further refining the model using fine-tuning and reinforcement learning from human feedback (RLHF) before making it broadly available.
> As part of this process, we’ll make Gemini Ultra available to select customers, developers, partners and safety and responsibility experts for early experimentation and feedback before rolling it out to developers and enterprise customers early next year.
Finally, some competition for GPT4 API!!! This is such good news.
I definitely think GPT is better than Bard, but Bard definitely did live up to the hype in a few ways. The two that blew my mind (and still do to some extent) are the blazing speed and the ability to pull information real time (no more pesky knowledge cutoff date). Bard also felt pretty comparable to 3.5 to me, better in some things and worse in others. Coding was definitely a bust with Bard.
Bard isn't a model, it's a product. Saying comparisons against "Bard" without specifying a particular point in time are like analyses of "ChatGPT" without specifying a model. There have been a number of releases adding more features, tool use, making it smarter, and crucially adding more languages. ChatGPT is not fine-tuned in different languages – it manages them but lacks cultural context. That's one place Bard is quite far ahead from what I've seen.
Most people don't use LLMs. Of those that do most people just know they're using "ChatGPT". A slim minority care about the model.
In my opinion, not focusing on the model, focusing on the product, and focusing on positioning for normal users (free, fast, fine tuned in many languages, "easy"), is a better product positioning.
> In my opinion, not focusing on the model, focusing on the product, and focusing on positioning for normal users (free, fast, fine tuned in many languages, "easy"), is a better product positioning.
Does google agree? doesn't the fact that they're so deliberately creating user-focused branding for different models (ultra, pro, nano) show they also see the value in the differentiation?
I can't speak for Google, and must emphasise that these are personal opinions. However I'd say that this entire marketing push is mostly for the super-engaged early adopters, not targeted at the general public. Looking at the YouTube videos, the more they seem to be targeted towards a general audience the less they mention these specifics. So, I suspect that the Ultra/Pro/Nano branding will mostly be used on the advanced Bard product that they speak about in the launch blog post, and on the APIs available to developers.
Everything they published thus far in the generative AI space has been abysmal in quality compared to the competition. I'd be hella surprised if this reaches GPT-4 levels of quality...
It's very active today: 50+ trades in the last hour. When I checked it was 69%, but it's gone up and down since then. Click on the "trades" tab to see.
I'm a GPT4 subscriber and a Google GSuite work subscriber. I've been using the latest Bard this morning to write and refine python code, and it's just as good if not slightly better than GPT4. I asked it to refine some obtuse code with lots of chaining, and it did an admirable job writing accurate comments and explaining the chained logic. It's ridiculously anecdotal of course, but I used Bard for all of 5 minutes last time they announced. This time seems different.
It won't be available to regular devs until Q2 next year probably (January for selected partners). So they are roughly a year behind OpenAI - and that is assuming their model is not overtrained to just pass the tests slightly better than GPT4
> and that is assuming their model is not overtrained to just pass the tests slightly better than GPT4
You are assuming GPT4 didn't do the exact same!
Seriously, it's been like this for a while, with LLMs any benchmark other than human feedback is useless. I guess we'll see how Gemini performs when it's released next year and we get independent groups comparing them.
When I was reading the benchmarks and seeing how Gemini Ultra was outperforming GPT-4 I thought, "Finally, some competition for GPT4"!
But when I got to that part, that's when I realized that it could potentially be caught in release hell and not actually see the light of day or significant use. Google, for better or worse, has more of a brand reputation to maintain and is more risk averse, so even if Gemini Ultra can, in theory, outperform GPT4, users might not get a chance to access it for a while.
Absolutely I do. Internally they have some incredible stuff, but the leadership is terrified of letting normies try it out because of the (real or perceived I don't know) damage to the brand that would happen if it said something racist or misogynist, etc.
No way, that's what they want you to think. The idea that Google would be behind technologically would be an embarrassment they can't handle. The 3.5 level gemini pro is probably just as capable of saying racist or misogynist stuff so there's no reason why they're allowing that to be public while the "GPT-4 beating" Ultra is hidden if it's just because of that. More likely Ultra is just not as good as these benchmarks indicate and they still need some time to improve it.
Google can hardly put a picture of a white male on their website. They're so deep in the swamp of virtue signalling it's a miracle they haven't yet drowned.
I’m afraid it won’t be nearly as good as GPT4, because of how lax Open AI can be with intellectual property. Google will not be able to train their model on Libgen or Opensubtitles, because they can’t afford the risk.
Well not exactly. Not coming out until later when presumably GPT4 will have grown as much as well. So far each time, Google has failed to catch up to OpenAI. Hopefully they do however eventually.
won't be available for regular devs until probably Q2 next year, OpenAI will have probably released GPT5 or whatever new model by then. And GPT4 was done training in 2022, the fact Google is thumping their chest about being 2 years behind a much smaller company is kind of pathetic
They also compare to RLHFed GPT-4, which reduces capabilities, while their model seems to be pre-RLHF. So I'd expect those numbers to be a bit inflated compared to public release.
So it's basically just GPT-4, according to the benchmarks, with a slight edge for multimodal tasks (ie audio, video).
Google does seem to be quite far behind, GPT-4 launched almost a year ago.
But that's my point. It doesn't matter who's better exactly right now. Let's see how this plays out over the next few years.
Whether one company or another is 10% better or worse than another at some metric right now -- that just couldn't be less relevant in terms of how this will ultimately end up.
This is interesting in that it implies that catching up is possible if you have enough data, engineers and compute. This also potentially implies that adjacent players such as Nvidia could gain an edge long term because they are a leader in one of the three.
Bard is apparently based on gemini pro from today, pro is coming via api on the 13th and ultra is still in more "select developers" starting next year.
The articles seems to report some data points which at least make it seem comparable to GPT4. To me, I feel as though this makes it more objective vs fluff.
There are some 7B weight models that look competitive with GPT4 on benchmarks, because they were trained on the benchmark data. Presumably Google would know better than to train on the benchmark data, but you never know. The benchmarks also fail to capture things such as Bard refusing to tell you how to kill a process on Linux because it's unethical.
>The benchmarks also fail to capture things such as Bard refusing to tell you how to kill a process on Linux because it's unethical.
When I used Bard, I had to negotiate with it what is ethical and what is not[0]. For example when I was researching WW2(Stalin and Hitler), I asked: "When did Hitler go to sleep?" and Bard thought that this information can be used to promote violence an hatred and then I told to it....this information can not be used to promote violence in any way and it gave in! I laughed at that.
I'm not a marketer but it's hard to see what the point of these glossy press releases without a call to action is.
If I could have tried it today, I would have dropped everything and tried it. Now I will forget about it for a while and try it whenever I hear through osmosis that it's available. To the extent that I am excited and/or interested, the snooze button is pressed.
At least they can claim SOTA with this, even if their product remains unavailable. Let's Google still appear competitive even if GPT-5 beats it and is publicly available before Gemini
Wasn't there a news sometimes before that Sundar and Demis didn't get along. Only after ChatGPT, Sundar got orders from above to set house in order and focus everything on this and not other fundamental research projects which Demis likes to work on.
The improvement over ChatGPT are counted in (very) few percents. Does it mean they have entered a diminishing returns phase or is it that each percent is much harder to get compared to the previous ones ?
> We’re already starting to experiment with Gemini in Search, where it's making our Search Generative Experience (SGE) faster for users, with a 40% reduction in latency in English in the U.S., alongside improvements in quality.
This feels like Google achieved a more efficient inference. Probably a leaner model wrt GPT.
not sure, but you could also look at the inverse. e.g. a 90% to 95% improvement could also be interpreted as 10% failure to 5% failure, i.e. half the amount of failures, a very big improvement. It depends on a lot of things, but it's possible that this could feel like a very big improvement.
Training large language models is characterised by diminishing returns; the first billion training inputs reduce the loss more than the second billion, the second billion reduce the loss more than the third, etc. Similar for increases in size; the improvement is less than linear.
It may mean that the evaluations useful range of distinguishing inprovements is limited. If its a 0-100 score on defined sets of tasks that were set because they were hard enough to distinguish quality in models a while back, the rapid rate of improvement may mean that they are no longer useful in distinguishing quality of current models even aside from the problem that it is increasingly hard to stop the actual test tasks from being reflected in training data in some form.
Probably just reflects that they are playing catch-up with OpenAI, and it would not look good if they announced their latest, greatest (to be available soon) was worse that what OpenAI have been shipping for a while, so I assume that being able to claim superiority (by even the smallest amount) over GPT-4 was the gating factor for the this announcement.
I doubt LLMs are close to plateauing in terms of performance unless there's already an awful lot more to GPT-4's training than is understood. It seems like even simple stuff like planning ahead (e.g. to fix "hallucinations", aka bullshitting) is still to come.
They want to release immediately to please shareholders but only if they're beating SOTA in benchmarks. Therefore we will usually get something which beats SOTA by a little bit, because the alternative (aside from a huge breakthrough) would be to delay release longer which serves no business purpose.
It's truly astounding to me that Google, a juggernaut with decades under its belt on all things AI, is only now catching up to OpenAI which is on all camps a fraction of its size.
Sure, but it doesn’t mean that it stops being surprising. It’s like a “time is relative” kind of thing for organizational logic.
Imagine an organization on the scale of Google, with everything in it’s favor, being outmaneuvered by a much smaller one in such a transcendental endeavor. It’s like to a small country in Central America, coming up with some weapon to rival the US’s army.
The performance results here are interesting. G-Ultra seems to meet or exceed GPT4V on all text benchmark tasks with the exception of Hellaswag where there's a significant lag, 87.8% vs 95.3%, respectively.
yeah a lot of local models fall short on that benchmark as well. I wonder what was different about GPT3.5/4's training/date that would lead to its great hellaswag perf
"As part of the evaluation process, on a popular benchmark, HellaSwag (Zellers et al., 2019), we find that an additional hundred finetuning steps on specific website extracts corresponding to the HellaSwag training set (which were not included in Gemini pretraining set) improve the validation accuracy of Gemini Pro to 89.6% and Gemini Ultra to 96.0%, when measured with 1-shot prompting (we measured GPT-4 obtained 92.3% when evaluated 1-shot via the API). This suggests that the benchmark results are susceptible to the pretraining dataset
composition. We choose to report HellaSwag decontaminated results only in a 10-shot evaluation setting. We believe there is a need for more robust and nuanced standardized evaluation benchmarks with no leaked data."
I have heard claims that lots of popular LLMs, including possibly gpt-4 are trained on things like reddit. so maybe it's not quite garbage in, garbage out if you include lots of other data. Google also has untold troves of data that is not widely available on the Web. including all the books from their decades long book indexing project.
No, Google is on a more level playing field than you think. It certainly can't train on enterprise data, and of course not on private user data like emails. Cross-division data sharing is tough as well, because regulators don't like it for anti-monopoly reasons. OpenAI can scrape YouTube all it wants, but DeepMind may not be able to just train against all of YouTube just like that.
We might soon get to a point where every player is using pretty much all the low-cost data there is. Everyone will use all the public internet data there is, augmented by as much private datasets as they can afford.
The improvements we can expect to see in the next few years look like a Drake equation.
LLM performance delta = data quality x data quantity x transformer architecture tweaks x compute cost x talent x time.
The ceiling for the cost parameters in this equation are determined by expected market opportunity, at the margin - how much more of the market can you capture if you have the better tech.
> DeepMind may not be able to just train against all of YouTube just like that
What? Why?
> data quality x data quantity x transformer architecture tweaks x compute cost x talent x time.
Google arguably has the most data (it's search index), the best data (ranked and curated already, along with data sets like books), the cheapest compute (they literally run their own cloud offering and are one of the biggest purchasers of H100s), and the oldest and most mature ML team.
Google has the best Internet search engine bar none and personally I'd not normally use Bing if not through ChatGPT.
It has Google Book, and I believe it has been scanning books for more than a decade now. It good to know that, so when the next time Mongol-like invasion happen (as happened to old City of Baghdad) all the books contents are well backup /s
It has Google Patent, and the original idea of patenting is for knowledge dissemination in return of royalty, and that knowledge would otherwise locked behind industry closed door.
It has Google Scholar, some of the papers are behind paywall but most of the contents are already cached somewhere (e.g. Pre-Print servers, Sci-Hub, online thesis portal).
It has Google Video aka YouTube that by watching all the uploaded videos within one hour duration to YT platform, will probably last more than your lifetime (assuming lifetime watching videos doing nothing else from cradle to grave non-stop without sleeping).
Ultimately it has Google mail or Gmail and to say that Google do not access the emails on its platform it's providing for free is naive and almost all my colleagues, friends, acquaintances (people that I know personally) have Gmail.
UK ex-PM (no prize of correctly guessing who) was once said on national TV that "Google probably know about him than he knows about himself" (TM).
Google once claimed that no one has moat on LLM but from the planet that I live none has organized the world's information like Google and ironically the CEO just reminded us in the Gemini video introduction that Google corporate mission statement is to organize the world's information and AI, LLM, RAG (insert your favourite acronym soup here) are the natural extensions of what they have been doing all along.
Gemini Nano sounds like the most exciting part IMO.
IIRC Several people in the recent Pixel 8 thread were saying that offloading to web APIs for functions like Magic Eraser was only temporary and could be replaced by on-device models at some point. Looks like this is the beginning of that.
I think a lot of the motivation for running it in the cloud is so they can have a single point of control for enforcing editing policies (e.g. swapping faces).
Do you have evidence of that? Photoshop has blocked you from editing pictures of money for ages and that wasn't in the cloud. Moreover, how does a Google data center know whether you're allowed to swap a particular face versus your device? It's quite a reach to assume Google would go out of their way to prevent you from doing things on your device in their app when other AI-powered apps on your device already exist and don't have such policy restrictions.
I have no doubt Google could (and might) enforce a lot of these rules on the device, but they likely route it through the cloud if there's a new "exploit" that they want to block ASAP instead of waiting for the app to update.
This is an example of the reputational risk Google has to deal with that small startups don't. If some minor app lets you forge photos, it's not a headline. If an official Google app on billions of devices lets you do it, it's a hot topic.
It could simply also be that their inpainting model is quite bad at certain things, and replacing a person's head produces consistently bad results. Hiding the problem could simply be easier than fixing it.
The fact that it's multimodal is very interesting, they might not make it open source, but if they intend run it on people's devices, even if they intend to implement DRM, someone will figure out how to extract the weights and get it running outside.
> "Using the power of Google Tensor G3, Video Boost on Pixel 8 Pro uploads your videos to the cloud where our computational photography models adjust color, lighting, stabilization and graininess."*
I wonder why the power of Tensor G3 is needed to upload your video to the cloud...
It runs an on-device LLM to generate a HTTP POST every time. It took four interns half a week to reduce the hallucinations, but a PM got a promotion after that.
Anthropic's Claude is still not available in Canada either. Anyone have insight into why its difficult to bring these AI models to Canada when on the surface its political and legal landscape isn't all that different from the US?
Google's embargo seemed to relate to their battle with the Canadian government over news. Given that they settled on that I'd expect its availability very soon.
Anthropic is a bit weird and it almost seems more like lazy gating. It's available in the US and UK, but no EU, no Canada, no Australia.
that wouldn't explain why Anthropic is excluding canada.
I'm guessing the online news act is a contributor, but only to a more general conclusion of our content laws being complicated (CanCon, language laws, pipeda erasure rules, the new right to be forgotten, etc) and our country simply doesn't have enough people to be worth the effort of figuring out what's legal and what isn't.
>Canadian lawmakers recently introduced legislation aimed at regulating AI. The Artificial Intelligence and Data Act (AIDA) mandates assessments, risk management, monitoring, data anonymization, transparency, and record-keeping practices around AI systems. AIDA would also introduce penalties of up to 3% of a company’s global revenue or $10 million.
These idiots don't seem to realize that a VPN bypasses all of their silly compliance BS.
1,682 comments
[ 2.9 ms ] story [ 419 ms ] threadExcited to give this a spin. There will be rough edges, yes, but it's always exciting to have new toys that do better (or worse) in various ways.
Also:
> Starting today, Bard will use a fine-tuned version of Gemini Pro for more advanced reasoning, planning, understanding and more. This is the biggest upgrade to Bard since it launched.
edit-
Edit 2 - forget the following, it's not available here but that's hidden on a support page, so I'm not able to test it at all.
Well that's fun. I asked bard about something that was in my emails, I wondered what it would say (since it no longer has access). It found something kind of relevant online about someone entirely different and said
> In fact, I'm going to contact her right now
If it's similar, or even marginally better in any way, we'd consider switching over. Not because OpenAI is bad or anything (they're great, actually!) but because it's so easy to do that.
> As part of this process, we’ll make Gemini Ultra available to select customers, developers, partners and safety and responsibility experts for early experimentation and feedback before rolling it out to developers and enterprise customers early next year.
Finally, some competition for GPT4 API!!! This is such good news.
Save your enthusiasm for after it launches; Google's got a habit of over-promising when it comes to AI.
Your link isn’t really an indication of an overpromise.
people don't see a difference between model and product, they think "gpt3 is ok", "gpt4 is great", "bard is like gpt3"
it's not the consumer's fault when the business has a positioning mistake, the business has to try and win the consumer back
In my opinion, not focusing on the model, focusing on the product, and focusing on positioning for normal users (free, fast, fine tuned in many languages, "easy"), is a better product positioning.
Does google agree? doesn't the fact that they're so deliberately creating user-focused branding for different models (ultra, pro, nano) show they also see the value in the differentiation?
Possibly by that time GPT5 will already be out.
They already caught up and surpassed GPT-4 and OpenAI's availability and APIs are very unstable and all that matters is that and the cost per token.
You are assuming GPT4 didn't do the exact same!
Seriously, it's been like this for a while, with LLMs any benchmark other than human feedback is useless. I guess we'll see how Gemini performs when it's released next year and we get independent groups comparing them.
When I was reading the benchmarks and seeing how Gemini Ultra was outperforming GPT-4 I thought, "Finally, some competition for GPT4"!
But when I got to that part, that's when I realized that it could potentially be caught in release hell and not actually see the light of day or significant use. Google, for better or worse, has more of a brand reputation to maintain and is more risk averse, so even if Gemini Ultra can, in theory, outperform GPT4, users might not get a chance to access it for a while.
You think this is why Google is so far behind?
(Speaking as someone who's worked on launching several somewhat risky technologies at Google.)
Most of the comments I see on Hacker News claim ChatGPT is getting worse at different things (though I don't believe those claims).
Lotus 1-2-3 came out 4 years before Microsoft Excel. WordPerfect came out 4 years before Microsoft Word.
Hotmail launched 8 years before Gmail. Yahoo! Mail was 7 years before Gmail.
Heck, AltaVista launched 3 years before Google Search.
I don't think less than a year difference is meaningful at all in the big picture.
Whether one company or another is 10% better or worse than another at some metric right now -- that just couldn't be less relevant in terms of how this will ultimately end up.
If the benchmarks are any indication, Gemini seems legit, excited to see what it can do.
Gives me what a quick scan looks like a pretty good answer.
When I used Bard, I had to negotiate with it what is ethical and what is not[0]. For example when I was researching WW2(Stalin and Hitler), I asked: "When did Hitler go to sleep?" and Bard thought that this information can be used to promote violence an hatred and then I told to it....this information can not be used to promote violence in any way and it gave in! I laughed at that.
[0] https://i.imgur.com/hIpnII8.png
If I could have tried it today, I would have dropped everything and tried it. Now I will forget about it for a while and try it whenever I hear through osmosis that it's available. To the extent that I am excited and/or interested, the snooze button is pressed.
Maybe that's the desired outcome?
It's like they hired Apple to do their marketing.
I bet it started off as BLEU and then during the editing process it got 'corrected' to BLUE.
Better OCR with 4% difference, better international ASR, 10% decrease.
Seeing Demis Hassabis name in the announcement makes you think they really trust this one.
This feels like Google achieved a more efficient inference. Probably a leaner model wrt GPT.
I doubt LLMs are close to plateauing in terms of performance unless there's already an awful lot more to GPT-4's training than is understood. It seems like even simple stuff like planning ahead (e.g. to fix "hallucinations", aka bullshitting) is still to come.
Imagine an organization on the scale of Google, with everything in it’s favor, being outmaneuvered by a much smaller one in such a transcendental endeavor. It’s like to a small country in Central America, coming up with some weapon to rival the US’s army.
"As part of the evaluation process, on a popular benchmark, HellaSwag (Zellers et al., 2019), we find that an additional hundred finetuning steps on specific website extracts corresponding to the HellaSwag training set (which were not included in Gemini pretraining set) improve the validation accuracy of Gemini Pro to 89.6% and Gemini Ultra to 96.0%, when measured with 1-shot prompting (we measured GPT-4 obtained 92.3% when evaluated 1-shot via the API). This suggests that the benchmark results are susceptible to the pretraining dataset composition. We choose to report HellaSwag decontaminated results only in a 10-shot evaluation setting. We believe there is a need for more robust and nuanced standardized evaluation benchmarks with no leaked data."
But, according to the metrics, it barely edges out GPT-4 -- this mostly makes me _more_ impressed with GPT-4 which:
- came out 9 months ago AND
- had no direct competition to beat (you know Google wasn't going to release Gemini until it beat GPT-4)
Looking forward to trying this out and then seeing OpenAI's answer
We might soon get to a point where every player is using pretty much all the low-cost data there is. Everyone will use all the public internet data there is, augmented by as much private datasets as they can afford.
The improvements we can expect to see in the next few years look like a Drake equation.
LLM performance delta = data quality x data quantity x transformer architecture tweaks x compute cost x talent x time.
The ceiling for the cost parameters in this equation are determined by expected market opportunity, at the margin - how much more of the market can you capture if you have the better tech.
What? Why?
> data quality x data quantity x transformer architecture tweaks x compute cost x talent x time.
Google arguably has the most data (it's search index), the best data (ranked and curated already, along with data sets like books), the cheapest compute (they literally run their own cloud offering and are one of the biggest purchasers of H100s), and the oldest and most mature ML team.
Google has the best Internet search engine bar none and personally I'd not normally use Bing if not through ChatGPT.
It has Google Book, and I believe it has been scanning books for more than a decade now. It good to know that, so when the next time Mongol-like invasion happen (as happened to old City of Baghdad) all the books contents are well backup /s
It has Google Patent, and the original idea of patenting is for knowledge dissemination in return of royalty, and that knowledge would otherwise locked behind industry closed door.
It has Google Scholar, some of the papers are behind paywall but most of the contents are already cached somewhere (e.g. Pre-Print servers, Sci-Hub, online thesis portal).
It has Google Video aka YouTube that by watching all the uploaded videos within one hour duration to YT platform, will probably last more than your lifetime (assuming lifetime watching videos doing nothing else from cradle to grave non-stop without sleeping).
Ultimately it has Google mail or Gmail and to say that Google do not access the emails on its platform it's providing for free is naive and almost all my colleagues, friends, acquaintances (people that I know personally) have Gmail.
UK ex-PM (no prize of correctly guessing who) was once said on national TV that "Google probably know about him than he knows about himself" (TM).
Google once claimed that no one has moat on LLM but from the planet that I live none has organized the world's information like Google and ironically the CEO just reminded us in the Gemini video introduction that Google corporate mission statement is to organize the world's information and AI, LLM, RAG (insert your favourite acronym soup here) are the natural extensions of what they have been doing all along.
IIRC Several people in the recent Pixel 8 thread were saying that offloading to web APIs for functions like Magic Eraser was only temporary and could be replaced by on-device models at some point. Looks like this is the beginning of that.
I have no doubt Google could (and might) enforce a lot of these rules on the device, but they likely route it through the cloud if there's a new "exploit" that they want to block ASAP instead of waiting for the app to update.
This is an example of the reputational risk Google has to deal with that small startups don't. If some minor app lets you forge photos, it's not a headline. If an official Google app on billions of devices lets you do it, it's a hot topic.
I wonder why the power of Tensor G3 is needed to upload your video to the cloud...
*https://blog.google/products/pixel/pixel-feature-drop-decemb...
Anthropic is a bit weird and it almost seems more like lazy gating. It's available in the US and UK, but no EU, no Canada, no Australia.
https://support.google.com/bard/answer/13575153?hl=en#:~:tex....
We are being singled out because of the Government's Online News Act for tech companies to pay for news links
I'm guessing the online news act is a contributor, but only to a more general conclusion of our content laws being complicated (CanCon, language laws, pipeda erasure rules, the new right to be forgotten, etc) and our country simply doesn't have enough people to be worth the effort of figuring out what's legal and what isn't.
But yeah weird we are usually lumped in with the US market.
https://support.google.com/bard/answer/14294096?visit_id=638...
>Canadian lawmakers recently introduced legislation aimed at regulating AI. The Artificial Intelligence and Data Act (AIDA) mandates assessments, risk management, monitoring, data anonymization, transparency, and record-keeping practices around AI systems. AIDA would also introduce penalties of up to 3% of a company’s global revenue or $10 million.
These idiots don't seem to realize that a VPN bypasses all of their silly compliance BS.