They do make OpenAI look like kids in that regard. There is far more to technology than public facing goods/products.
It's probably in part due to the cultural differences between London/UK/Europe and SiliconValley/California/USA.
On one corner: IBM Deep Blue winning vs Kasparov. A world class giant with huge research experience.
On the other corner, Google, a feisty newcomer, 2 years in their life, leveraging the tech to actually make something practical.
Is Google the new IBM?
apple is the new Nokia.
openai is the new google.
microsoft is the new apple.
Still.
It's difficult to compete with an excellent product if whether you have a blue bubble in iMessage is more important.
Services, and their sales team, are still Microsoft's strong point.
Apple seeing its services grow and is leaning in on it now.
The question is whether Apple eats services faster than Microsoft eats into hardware.
Microsoft is a decent physical product company... they've usually just missed on the strategic timing part.
Given dog eat dog of early Android manufacturers, most couldn't afford to recreate Google services.
Can we really talk about timing, when it's above all a problem of a product that didn't fit the market?
That said, it got great reviews and they threw $$ at devs to develop for it, just couldn't gain traction. IME it was timing more than anything and by the time it came to market felt more reactionary than truly innovative.
Microsoft is still the same old Microsoft
Highly doubt MS will ever be successful on mobile... their last OS was pretty great and they were willing to pay devs to develop, they just couldn't get it going. This is from someone who spent a ton of time developing on PocketPC and Windows Mobile back in the day.
Products are not the reason for their resurgence.
Apple makes a ton in services, but their R&D is heavily focused on product and platform synergy to that ecosystem extremely valuable.
I think the grind from Windows CE to Windows Phone is just a blip to them for now.
You obviously haven't dropped an iphone on to concrete. :)
My iPhone 4, on the other hand, shattered after one incident…
These are literally stainless steel.
The 15s with their titanium is a step back.
The 11 Pro with its older curved edges has been the most solidly built phone ever IMO.
I even dropped my iPhone 13 four floors (onto wood), and not a scratch :o
Proof OpenAI has this shady monopolistic stuff: https://archive.ph/vVdIC
“What You Cannot Do. You may not use our Services for any illegal, harmful, or abusive activity. For example, you may not: […] Use Output to develop models that compete with OpenAI.” (Hilarious how that reads btw)
Proof Microsoft has this shady monopolistic stuff: https://archive.ph/N5iVq
“AI Services. ”AI services” are services that are labeled or described by Microsoft as including, using, powered by, or being an Artificial Intelligence (“AI”) system. Limits on use of data from the AI Services. You may not use the AI services, or data from the AI services, to create, train, or improve (directly or indirectly) any other AI service.”
That 100% does include GitHub Copilot, by the way. I canceled my sub. After I emailed Satya, they told me to post my “feedback” in a forum for issues about Xbox and Word (what a joke). I emailed the FTC Antitrust team. I filed a formal complaint with the office of the attorney general of the state of Washington.
I am just one person. You should also raise a ruckus about this and contact the authorities, because it’s morally bankrupt and almost surely unlawful by virtue of extreme unfairness and unreasonableness, in addition to precedent.
AWS, Anthropic, and NVIDIA also all have similar Customer Noncompete Clauses.
I meekly suggest everyone immediately and completely boycott OpenAI, Microsoft, AWS, Anthropic, and NVIDIA, until they remove these customer noncompete clauses (which seem contrary to the Sherman Antitrust Act).
Just imagine a world where AI can freely learn from us, but we are forbidden to learn from AI. Sounds like a boring dystopia, and we ought to make sure to avoid it.
1. I wouldn't let someone copy my code written directly by me. Why should I let someone copy the code my machine wrote?
2. There are obvious technical worries about feedback loops.
Because that machine/openAI was built on literally scraping the internet (regardless of copyright or website's ToS) and ingesting printed books.
Businesses are not entitled to life or existence the way individuals are.
Also, what exactly is stopping someone from documenting the output from all possible prompts?
It's legal theater and can't be enforced.
We need to dispel with this idea that sociopaths in suits have earned or legitimate power.
Everything you are saying is only true for two guys in a garage. The folks with something to lose don't behave in this dreamworld fashion.
Enjoy being an uneducated ape :)
Produce results.
Market it.
They can’t enforce if it gets too big.
You cannot tell a customer that buying your product precludes them from building products like it. That violates principles of the free market, and it's unenforceable. This is just like non-competes in employment. They aren't constitutional.
So yes, they can enforce their terms for all practical purposes.
But no, they cannot levy fines or put you in jail.
Those are the consequences that matter. I don't care if Microsoft or Google decide they don't want to be friends with me. They'd stab me in the back to steal my personal data anyway.
And that's the whole point of violating terms by competing with them.
I'd start a business but the whole setup is a government scam. Business licenses are just subscriptions with extra steps.
On the other hand, I think IBM’s problem is its finance focus and longterm decay of technical talent. It is well known for maintaining products for decades, but when’s the last time IBM came out with something really innovative? It touted Watson, but that was always more of a gimmick than an actually viable product.
Google has the resources and technical talent to compete with OpenAI. In fact, a lot of GPT is based on Google’s research. I think the main things that have held Google back are questions about how to monetize effectively, but it has little choice but to move forward now that OpenAI has thrown down the gauntlet.
I understood this problem to be "how it manages its org chart and maps that onto the customer experience."
This behavior has been observed publicly in the Kubernetes space where Google has contributed substantially.
And a whole thread on HN about it:
I used to do all kinds of really cool routines and home control tasks with Google home, and it could hear and interpret my voice at a mumble. I used it as an alarm clock, to do list, calendar, grocery list, lighting control, give me weather updates, set times etc. It just worked.
Now I have to yell unnaturally loud for it to even wake, and even then the simplest commands have a 20% chance of throwing “Sorry I don’t understand” or playing random music. Despite having a device in every room it has lost the ability to detect proximity and will set timers or control devices across the house. I don’t trust it enough anymore for timers and alarms, since it will often confirm what I asked then simply… not do it.
Ask it to set a 10 minute timer.
It says ok setting a timer for 10 minutes.
3 mins later ask it how long is remaining on the timer. A couple years ago it would say “7 minutes”.
Now there’s a good chance it says I have no timers running.
It’s pathetic, and I would love any insight on the decay. (And yes they’re clean, the mics are as unobstructed as they were out of the box)
That starts with the demonstrations which show really promising technology, but what eventually ships doesn't live up to the hype (or often doesn't ship at all.)
It continues through to not managing the products well, such as when users have problems with them and not supporting ongoing development so they suffer decay.
It finishes with Google killing established products that aren't useful to the core mission/data collection purposes. For products which are money makers they take on a new type of financially-optimised decay as seen with Search and more recently with Chrome and YouTube.
I'm all for sunsetting redundant tech, but Google has a self-harm problem.
The cynic in me feels that part of Google's desire to over-promise is to take the excitement away from companies which ship* what they show. This seems to align with Pichai's commentary, it's about appearing the most eminent, but not necessarily supporting that view with shipping products.
* The Verge is already running an article about what was faked in the Gemini demo, and if history repeats itself this won't be the only thing they mispresented.
Was it “machine learning”? If so, I don’t think that was actually the key insight for Google search… right? Did deep blue even machine learn?
Or was it something else?
Circa-Deep Blue, we were still at Quake levels of SIMD throughput.
At the time, I believe IBM was still "we'll throw people and billable hours at a problem."
They had their lunch eaten because their competitors realized they could undercut IBM on price if they changed the equation to "throw compute at a problem."
In other words, sell prebuilt products instead of lead-ins to consulting. And harness advertising to offer free products to drive scale to generate profit. (e.g. Google/search)
The comparison is between a useful shipping product available to everyone for a full year vs a tech demo of an extremely limited release to privileged customers.
There are millions of people for whom OpenAI's products are broadly useful, and the specifics of where they fall short compared to Gemini are irrelevant here, because Google isn't offering anything comparable that can be tested.
Whereas for OpenAI there are no such constraints.
Did IBM have research with impressive web reverse indexing tech that they didn't want to push to market because it would hurt their other business lines? It's not impossible... It could be as innocuous as discouraging some research engineer from such a project to focus on something more in line.
This is why I believe businesses should be absolutely willing to disrupt themselves if they want to avoid going the way of Nokia. I believe Apple should make a standalone apple watch that cannibalizes their iPhone business instead of tying it to and trying to prop up their iPhone business (ofc shareholders won't like it). Whilst this looks good from Google - I think they are still sandbagging.. why can't I use Bard inside of their other products instead of the silly export thing.
It was a genius move to go public with a simple UI.
No matter how stunning the tech side is, if human interaction is not simple, the big stuff doesn’t even matter.
This statement is for the mass market MBA-types. More specifically, middle managers and dinosaur executives who barely comprehend what generative AI is, and value perceived stability and brand recognition over bleeding edge, for better or worse.
I think the sad truth is an enormous chunk of paying customers, at least for the "enterprise" accounts, will be generating marketing copy and similar "biz dev" use cases.
Nokia and Blackberry had far more phone-making experience than Apple when the iPhone launched.
But if you can't bring that experience to bear, allowing you to make a better product - then you don't have a better product.
But I don't see generative AI as being particularly that way.
I'm not dumb enough to bet against Google. They appear to be losing the race, but they can easily catch up to the lead pack.
There's a secondary issue that I don't like Google, and I want them to lose the race. So that will color my commentary and slow my early adoption of their new products, but unless everyone feels the same, it shouldn't have a meaningful effect on the outcome. Although I suppose they do need to clear a higher bar than some unknown AI startup. Expectations are understandably high - as Sundar says, they basically invented this stuff... so where's the payoff?
I still use their products. But if I had to pick a company to win the next gold rush, it wouldn't be an incumbent. It's not great that MSFT is winning either, but they are less user-hostile in the sense that they aren't dependent on advertising (another word for "psychological warfare" and "dragnet corporate surveillance"), and I also appreciate their pro-developer innovations.
It makes Google look like old fart that wasted his life and didn't get anywhere and now he's bitter about kids running on his lawn.
https://www.hathitrust.org/ has that corpus, and its evolution, and you can propose to get access to it via collaborating supercomputer access. It grows very rapidly. InternetArchive would also like to chat I expect. I've also asked, and prompt manipulated chatGPT to estimate the total books it is trained with, it's a tiny fraction of the corpus, I wonder if it's the same with Google?
Whatever answer it gave you is not reliable.
Obviously, people find some value in some output of some LLMs. I've enjoyed the coding autocomplete stuff we have at work, it's helpful and fun. But "it's not qualified to answer my questions" is still true, even if it occasionally does something interesting or useful anyway.
*- this is a complicated term with a lot of baggage, but fortunately for the length of this comment, I don't think that any sense of it applies here. An LLM doesn't understand its training set any more than the mnemonic "ETA ONIS"** understands the English language.
**- a vaguely name-shaped presentation of the most common letters in the English language, in descending order. Useful if you need to remember those for some reason like guessing a substitution cypher.
LLMs encode some level of understanding of their training set.
Whether that's sufficient for a specific purpose, or sufficiently comprehensive to generate side effects, is an open question.
* Caveat: with regards to introspection, this also assumes it's not specifically guarded against and opaquely lying.
Exactly like humans dont understand how their brain works
Unlike LLMs, which are built by humans and have literal source code and manuals and SOPs and shit. Their very "body" is a well-documented digital machine. An LLM trying to figure itself out has MUCH less trouble than a human figuring itself out.
Behavior indistinguishable from understanding is understanding. Sorry, but that's how it's going to turn out to work.
Why are people so eager to believe that electric rocks can think?
It's premature in the extreme to point at something that behaves so much like we do ourselves and claim that whatever it's doing, it's not "understanding" anything.
Are we not generally good at detecting when someone understands us? Perhaps it's because understanding has actual meaning. If you communicate to me that you hit your head and feel like shit, I not only understand that you experienced an unsatisfactory situation, I'm capable of empathy -- understanding not only WHAT happened, but HOW it feels -- and offering consolation or high fives or whatever.
A LLM has an understanding of what common responses were in the past, and repeats them. Statistical models may mimic a process we use in our thinking, but it is not the entirety of our thinking. Just like computers are limited to the programmers that code their behavior, LLMs are limited to the quality of the data corpus fed to them.
A human, you can correct in real time and they'll (try to) internalize that information in future interactions. Not so with LLMs.
By all means, tell us how statistically weighted answers to "what's the next word" correlates to understanding.
By all means, tell me what makes you so certain you're not arguing with an LLM right now. And if you were, what would you do about it, except type a series of words that depend on the previous ones you typed, and the ones that you read just prior to that?
A human, you can correct in real time and they'll (try to) internalize that information in future interactions. Not so with LLMs.
Not so with version 1.0, anyway. This is like whining that your Commodore 64 doesn't run Crysis.
Go away, you clearly have nothing to counter with.
Also, why are we comparing humans and LLMs when the latter doesn't come anywhere close to how we think, and is working with different limitations?
The 'knowledge' of an LLM is in a filesystem and can be queried, studied, exported, etc. The knowledge of a human being is encoded in neurons and other wetware that lacks simple binary chips to do dedicated work. Decidedly less accessible than coreutils.
Bytes can be measured. Sources used to produce the answer to a prompt can be reported. Ergo, an LLM should be able to tell me the full extent to which it's been trained, including the size of its data corpus, the number of parameters it checks, the words on its unallowed list (and their reasoning), and so on.
These will conveniently be marked as trade secrets, but I have no use for an information model moderated by business and government. It is inherently NOT trustworthy, and will only give answers that lead to docile or profitable behavior. If it can't be honest about what it is and what it knows and what it's allowed to tell me, then I cannot accept any of its output as trustworthy.
Will it tell me how to build explosives? Can it help me manufacture a gun? How about intercepting/listening to today's radio communications? Social techniques to gain favor in political conflicts? Overcoming financial blockages when you're identified as a person of interest? I have my doubts.
These questions might be considered "dangerous", but to whom, and why shouldn't we share these answers?