Altera is back - Page 1
Forum section: electronics / general

author: EEVblog
author_rank: Unknown
timestamp: March 01, 2024, 03:29:10 am
content: Altera is back, but in lower case, because that's what a consultant who got paid 6 digits said is best.https://www.intel.com/content/www/us/en/newsroom/news/intel-launches-altera-standalone-fpga-operation.html#gs.57s7ksI have secretly obtained the internal company memo for the lower case logo:Quote

author: xrunner
author_rank: Super Contributor
timestamp: March 01, 2024, 03:35:30 am
content: They also knew to put "AI" into the announcement in some form or fashion -Quote

author: SiliconWizard
author_rank: Super Contributor
timestamp: March 01, 2024, 03:35:43 am
content: Yes, arm did the same for their logo, you need an Arial-like font in solid white and all lowercase. That's how it's done these days. And yes, it must have cost several millions of bucks just to come up with the logo, after that, just think about all the corporate and marketing material that needs to be redone, that's probably a good full year of work and tens of millions of bucks along the way.And kudos for the memo on lowercase, that definitely says that lowercase is more inclusive. They're breaking barriers, yes, the same breaking that everyone else is also doing. What a revolution.

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 01, 2024, 03:45:27 am
content: Quote from: EEVblog on March 01, 2024, 03:29:10 amThe very first time I saw that, I thought it read "alter'd", just like "loserds".

author: BrianHG
author_rank: Super Contributor
timestamp: March 01, 2024, 03:54:18 am
content: Hmmmm, I don't know. And this coming from an Altera fanboy from the beginning.I even made a special public-domain DDR3 controller for their Cyclone/Max/Aria FPGA series designed to bypass the limitations of their paid version during my ultra-bad luck triple long covid.herehereI'm thinking its time to look at Xilinx.

author: SiliconWizard
author_rank: Super Contributor
timestamp: March 01, 2024, 04:00:19 am
content: Quote from: BrianHG on March 01, 2024, 03:54:18 amLattice is nice too. They've launched quite a few new chips which look pretty promising. (I also like their lower-end FPGAs, but that's for different use cases. They have pretty powerful stuff now too.)

author: EEVblog
author_rank: Unknown
timestamp: March 01, 2024, 04:03:11 am
content: Quote from: xrunner on March 01, 2024, 03:35:30 amIt's also secretely in the logo on the end.

author: SiliconWizard
author_rank: Super Contributor
timestamp: March 01, 2024, 04:03:38 am
content: Quote from: Nominal Animal on March 01, 2024, 03:45:27 amWell, yeah, this cut-off 'a' with the square dot and the two shades of blue, that's what has taken genius designers. The lower case Arial stuff is blander, yet this is what they are very proud of, apparently.Let them break more barriers!

author: BrianHG
author_rank: Super Contributor
timestamp: March 01, 2024, 04:08:26 am
content: Quote from: SiliconWizard on March 01, 2024, 04:00:19 amLattice has some nice cheaper large FPGA in the 40kgate and 80kgate range, and their IDE looks nice. But, that is where it ends with that stupid inability to parameterize their key functions like their PLL in code because its settings needs to be an actual // comment // with some special stars within. I just couldn't believe it when I found out. Altera has a similar issue when assigning strings in parameters, but at least I could find a dirty workaround making a fake integer array with quoted strings within.

author: xrunner
author_rank: Super Contributor
timestamp: March 01, 2024, 04:10:41 am
content: Quote from: EEVblog on March 01, 2024, 04:03:11 amOh yea!

author: glenenglish
author_rank: Frequent Contributor
timestamp: March 01, 2024, 07:33:28 am
content: Altera is way behind. (except at the top top top end)They've got some catching up to do. O.M.G. Cyclone 5 yawn. and Cyclone 5 SoC yawn yawn. documentation and support for the SoC ++YAWN.But they have catchup and getting ahead in their road map, so in 2 year's time, their offerings low and mid tier will again be market leaders until the next leapfrog.....see how they go with the doco.glen(Xilinx Alliance Partner)

author: glenenglish
author_rank: Frequent Contributor
timestamp: March 01, 2024, 07:38:26 am
content: Efinix is worth a look though, I have bought a couple of trays. while a XIlinx man, the lack of supply forced me to look elsewhere.Some useful packages also. Pricing is OK. It's not rock bottom at all. roughly competetive with Xilinx for same job done if you are on XIlinx step pricing.Tools are OK. small company - easy to get their attention if you have a problem. I've had a couple of things fixed.Lattice is good in the smaller sized stuff, and good for high volume work. cheap.I feel that Xilinx and Altera are better for lower volumes <1000, < 10,000 where tool and development form a large slab of the costs, the documentation and tools are better so time and money are saved because of that.

author: pcprogrammer
author_rank: Super Contributor
timestamp: March 01, 2024, 08:23:14 am
content: Quote from: BrianHG on March 01, 2024, 03:54:18 amYou mean AMD or is it amd now.Maybe in time they will go back and become xilinx.Edit: Or xailinx after they have payed some whizz PR person.

author: glenenglish
author_rank: Frequent Contributor
timestamp: March 01, 2024, 09:05:16 am
content: apart from some annoying branding, they've left the xilinx website mostly aloneEfinix is interesting. the Titanium fabric will run at 1000 MHz without cracking a sweat, LUTs, BRAMS, DSPS. but their interconnect is a bit different and impacts routing delays , so you need alot of register stages to have things big and fast.

author: radiolistener
author_rank: Super Contributor
timestamp: March 01, 2024, 10:32:08 am
content: Quote from: xrunner on March 01, 2024, 03:35:30 amDoes "AI" means Altera Intel or "An Intel"?

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 01, 2024, 01:53:03 pm
content: "Meta-Alphabet: Always Near You. Watching and Listening. You are our Precious Resource, our Investment. Make Us Proud."Any bets on when Microsoft will rebrand itself as "ms."?Have any companies stated their preferred pronouns yet?

author: suudy
author_rank: Newbie
timestamp: March 01, 2024, 03:32:56 pm
content: Quote from: Nominal Animal on March 01, 2024, 01:53:03 pmhttps://youtu.be/56hqrlQxMMI?si=5kPKDCPDCvgeuis9

author: langwadt
author_rank: Super Contributor
timestamp: March 01, 2024, 03:36:38 pm
content: Quote from: SiliconWizard on March 01, 2024, 03:35:43 amalmost as rebellious as getting a tattoo

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 01, 2024, 05:02:17 pm
content: Quote from: suudy on March 01, 2024, 03:32:56 pmNice song! But now I'm hungry.

author: ejeffrey
author_rank: Super Contributor
timestamp: March 01, 2024, 06:03:58 pm
content: Quote from: quince on March 01, 2024, 05:06:13 pmExtremely low. It's not the "coming up with the idea" that costs the money, it's everything else. Also, note that intel has used the lower case i in their logo since the 60s. My guess would be that using lowercase 'a' in the logo was declared by fiat from Intel to keep the association with Intel who will remain the majority owner. Maybe they debated it and spent a lot of money on consultants to tell them it as an OK idea, but, I think it's plenty likely that particular decision cost nothing.The cost of the (re-)branding is developing several logo options within the requested theme, doing searches for legal issues or confusions with similar logos globally. Making sure the name or logo doesn't have negative or profane associations in other languages. Preparing several variants of the logo art, updating web pages and documentation, building slide deck templates for employees to use when they give presentations, developing artwork for use in advertising and marketing, legally registering the trademarks in all relevant jurisdictions, acquiring domain names, and so on. I'm relatively skeptical of the value of rebranding when the company structure isn't changing, but in this case they kind of had to do something, and most of the costs were not coming up with the logo.By re-using the name Altera it probably cost a lot less on at least the legal side than a new name, but I would still guess closer to 10M dollars than 0.1 M. Still chump change compared to the $16B intel spent acquiring Altera less than 10 years ago, and probably less than the other reoganization costs of the spinoff, and less than the $4B Intel's stock fluctuates by on a daily basis (it's up 2% today on a ~$200B market cap)

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 01, 2024, 06:18:11 pm
content: The coke-sniffing design consultants must've been really peeved to find out thataItera.comwas already in use.

author: Bud
author_rank: Super Contributor
timestamp: March 01, 2024, 06:43:00 pm
content: AI built in the fabric - does this statement makes sense to you guys?

author: Bud
author_rank: Super Contributor
timestamp: March 01, 2024, 07:09:17 pm
content: But it is much more buzzwordy, isnt it.

author: ejeffrey
author_rank: Super Contributor
timestamp: March 01, 2024, 07:10:05 pm
content: Quote from: Bud on March 01, 2024, 06:43:00 pmWherever you see "AI" just replace it with "low precision vector dot products". In other words, it's something that FPGA manufacturers have been doing for 25 years -- distributed memory with lots of low precision multiply / accumulate cores. I guess "in the fabric" means "more of what we already do" rather than dedicated vector coprocessors. It also probably means "tensor flow and OpenCL -> RTL compilers" to let software and machine learning people use them without learning Verilog or how to use the frankly gross state of traditional FPGA development tools.I continue to be skeptical, GPUs are always going to be better at dot products than FPGAs, and these days even CPUs are going to give FPGAs a run for their money. Also, everybody already has GPUs and CPUs. It only makes sense if for some reason you need the FPGA programmability and you need to do a lot of vector math on the same chip. It makes more sense in the networking space: FPGAs continue to be unmatched for high throughput interconnects. If you for some reason need to do streaming ML classification on multiple 100 gigabit networking flows, an FPGA makes a lot of sense.

author: tszaboo
author_rank: Super Contributor
timestamp: March 01, 2024, 07:34:15 pm
content: Quote from: EEVblog on March 01, 2024, 04:03:11 amThe name should be ailtera. That will be 500K USD please for consulting.

author: pcprogrammer
author_rank: Super Contributor
timestamp: March 01, 2024, 07:55:52 pm
content: Quote from: tszaboo on March 01, 2024, 07:34:15 pmNah, because the French will then think it is a dish with garlic (ail)

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 01, 2024, 09:29:05 pm
content: Quote from: ejeffrey on March 01, 2024, 07:10:05 pmThe funniest thing is when they start gushing about emergent behaviour when their model becomes large enough.Apparently they've never heard of thecombinatorial explosion, and are forever surprised by things like thebirthday paradox, because actuallyunderstandingwhat is going on is so passé nowadays. No wonder human brains are steadily shrinking. Back to monke, I guess.

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 01, 2024, 09:33:12 pm
content: Quote from: Bud on March 01, 2024, 06:43:00 pm🎶 Ai built the fabric 🎵🎵 But ai did not builk the plebluplée 🎶Or something. So no.

author: SiliconWizard
author_rank: Super Contributor
timestamp: March 01, 2024, 09:35:15 pm
content: Quote from: Nominal Animal on March 01, 2024, 09:29:05 pmExactly. One issue with current "AI" approaches, which is really only about churning a lot of data through very simple algorithms, is that we can't know whatsoever how the models get a given answers from a given set of inputs. It's the epitome of the black box, and I guess our current fascination for this can only be explained by our immense stupidity.

author: cfbsoftware
author_rank: Regular Contributor
timestamp: March 01, 2024, 09:37:33 pm
content: Quote from: Bud on March 01, 2024, 06:43:00 pmSure - it must be the same fabric they used for the Emperor's new clothes.

author: free_electron
author_rank: Super Contributor
timestamp: March 01, 2024, 09:44:32 pm
content: altera starts, partners with intel , intel sell pld division to altera, intel buys altera , intel spits out altera ... 'm okay

author: glenenglish
author_rank: Frequent Contributor
timestamp: March 01, 2024, 09:59:37 pm
content: anyone here actively designing with Altera-i ?

author: SiliconWizard
author_rank: Super Contributor
timestamp: March 01, 2024, 10:11:52 pm
content: Quote from: free_electron on March 01, 2024, 09:44:32 pmYeah, don't necessarily assume that it's all due to "strategic" reasons. Quite often, companies merging and splitting repeatedly is just done for optimizing tax, profits and expenditures. Purely financial stuff.

author: Muxr
author_rank: Super Contributor
timestamp: March 01, 2024, 11:47:18 pm
content: Quote from: SiliconWizard on March 01, 2024, 10:11:52 pmYup. Intel is strapped for cash, to build out their fabs. They are prepping Altera for an IPO, same thing they did with Mobile Eye.

author: tszaboo
author_rank: Super Contributor
timestamp: March 01, 2024, 11:51:02 pm
content: Quote from: free_electron on March 01, 2024, 09:44:32 pmFor the past two decades, everything (except cpu) Intel touched went broke. Microcontrollers, SSDs, the power management, IP phones, MP3 players. Everything it tries just shuts down.

author: glenenglish
author_rank: Frequent Contributor
timestamp: March 02, 2024, 12:01:51 am
content: and the reason I dont buy any Intel in anything I own , is what they did with Edison.remember that ? Lots of us designed long life embedded products around a great platformonly for Intel to drop it like a stone couple of years after release.That's why I will never ever buy Intel, nor Intel owned Altera.

author: SiliconWizard
author_rank: Super Contributor
timestamp: March 02, 2024, 12:24:59 am
content: Quote from: tszaboo on March 01, 2024, 11:51:02 pmI think that's not specific to Intel though, but to all very big companies that have become too unflexible to allow diversification.And as we said above, they may never have even intended to make those ventures succeed. Often that's just a distraction and a way of circulating cash.

author: floobydust
author_rank: Super Contributor
timestamp: March 02, 2024, 03:18:57 am
content: Anything "AI" is attracting investors, who know nothing about the technology. NVIDIA stock is going nuts because of theirAI IC's.I think Intel is pathetically jumping on the bandwagon to boost their stock with this nothingburger press release, all about the graphic arts on the logoLet's give out the award for pathetic or is it pity to the old dinosaur. Nobody over there can innovate or keep up with technology.

author: nctnico
author_rank: Super Contributor
timestamp: March 02, 2024, 12:48:14 pm
content: Quote from: SiliconWizard on March 02, 2024, 12:24:59 amI don't quite agree. Other semiconductor companies typically are good at absorbing integrating product lines from semiconductor firms they bought. Look at Analog, Microchip, TI, NXP for examples. OTOH Intel and AMD have been excessively bad at doing such integrations even those have designing & producing semiconductors as a core business.

author: JPortici
author_rank: Super Contributor
timestamp: March 03, 2024, 06:00:21 pm
content: Quote from: EEVblog on March 01, 2024, 04:03:11 amMidjourney: Design a minimalistic inclusive logo for the brand "altera" and include ai somehow

author: KE5FX
author_rank: Super Contributor
timestamp: March 03, 2024, 10:58:45 pm
content: Quote from: JPortici on March 03, 2024, 06:00:21 pmHonestly, GPT4 didn't do a bad job with your prompt:

author: SiliconWizard
author_rank: Super Contributor
timestamp: March 03, 2024, 11:05:02 pm
content: The logo looks like a nasty bug.

author: e0ne199
author_rank: Regular Contributor
timestamp: March 04, 2024, 01:54:40 pm
content: Quote from: floobydust on March 02, 2024, 03:18:57 amAI nowadays is starting to look like a snake oil..

author: KE5FX
author_rank: Super Contributor
timestamp: March 04, 2024, 05:22:05 pm
content: I made fun of it above, but for the record: that's not snake oil. Snake oil can't kneecap a 9-dan Go master. AI will get better, while the people who designed the real Altera logo won't.See also the question of whether transformer networks can "code." Yes, and they're not great at it... but they will get better.The first couple of time derivatives are what matter when it comes to things like this, not the current state. Everything is about to change, and Intel isn't entirely wrong in acknowledging that. At least from a marketing perspective.

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 04, 2024, 05:30:16 pm
content: Quote from: KE5FX on March 04, 2024, 05:20:17 pmThey do not generate anything new. They can only combine parts from their original dataset; not as in copy-paste, but in "this fits here" fashion.The end result may look novel, but if the standard US abstraction-filtration-comparison test is applied to both (the "novel" result, and all of the original dataset), youalwaysfind 1:1 correlation. This is why they are transformer networks, and not general intelligence. It is also why transformer networks cannot author patents in the USA, according to recent judgments.There are very strong arguments for general intelligence requiring the ability to perceive and manipulate the environment (environment including other members of similar species; language being just a tool for manipulating others using the same language). This means that if we survive long enough as an intelligent species, general intelligence will grow out of either embodied robots, or from simulations, not from LLMs or transformer networks.As to games (9-dan Go master), a pure imperative program can do that, if we were smart enough to write one. We did that for chess already; Go just has much more permutations of the game state.

author: KE5FX
author_rank: Super Contributor
timestamp: March 04, 2024, 06:30:41 pm
content: Quote from: Nominal Animal on March 04, 2024, 05:30:16 pmThere's so much here that's "not even wrong" that it's difficult to refute. Stick around, things are going to get interesting.

author: dferyance
author_rank: Regular Contributor
timestamp: March 04, 2024, 06:49:10 pm
content: They are the company that came up with the brilliant name of Max+Plus II. Let's call it "Max" so people know how great it is. But we need more, how about max plus? Naw, needs two pluses, Max+Plus. That still doesn't pack enough punch, we'll call it Max+Plus II. But the 2 won't be the version number, that comes after.

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 05, 2024, 10:19:40 am
content: Quote from: KE5FX on March 04, 2024, 06:30:41 pmI'm familiar with these models, how they work, and the math involved. When I first studied them, Kohonen nets were the rage. (I investigated them for OCR, as that was still a "hard task" back then, especially with new, untrained font styles.)What you see as miraculous performance, I see as a logical result of the size and training done on the networks.Quote from: dferyance on March 04, 2024, 06:49:10 pmIt would be funny to create some new programming language, and name it See+Plus. Anyone trying to do search engine optimization on it would tear out their hair for sure.

author: KE5FX
author_rank: Super Contributor
timestamp: March 05, 2024, 05:14:50 pm
content: You should spend some time with GPT4 before drawing conclusions based on 40-year-old concepts like Kohonen nets. (Had to look that up, they aren't even mentioned in the current deep-learning texts.) Given your assertion that these networks don't create anything new, you may be running a bit behind the times. There's some really cool stuff being done.Those logos I just posted suck, butthey are new.Arguing that the output of these models isn't 'new' forces you into a God-of-the-gaps scenario, one that will force you to confront the fact that humans aren't great at coming up with 'new' stuff, either. There areother threads better suitedto this sort of argument, admittedly.

author: JPortici
author_rank: Super Contributor
timestamp: March 05, 2024, 08:29:58 pm
content: Quote from: KE5FX on March 03, 2024, 11:20:27 pmaltera should be henceforth be known as altunadid you try asking it why altuna?

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 05, 2024, 09:53:13 pm
content: Quote from: KE5FX on March 05, 2024, 05:14:50 pmThat is where I started, KE5FX, not how I draw conclusions. I'm pretty well aware of all of the math side involved in LLMs, down to details on sentence embedding vectors and their similarity models. I'm simply not blinded by their apparent brilliance like you are.You should spend some time with the history of the field, to truly understand the situation, and not be blinded by the apparently wonderful results.LLMs and other artificial neural networks are utterly amazing at complex pattern matching, that's all.The only thing that has changed, is that now we have enough memory and computational power –– especially with the so-called "AI accelerators" or NPUs that use integer and/or half-precision (IEEE 754-2008 Binary16) floating-point operand types for multiplication, addition, and subtraction –– to apply large enough neural networks to boost the complexity to levels where their behaviour seems "intelligent" to humans,andenough data one can harvest for free (if one ignores copyright issues, like these model developers tend to do, unless it istheir owncopyright) to train the networks. Everything else, especially the math side, has seen only normal incremental development.The results that seememergentare really justemergingout of the hugely increased complexity, and therefore correlation capabilities, of the networks.A neural network is a perfect example ofcombinatorial explosion. If you keep the inputs and outputs at fixed size/complexity, the memory and correlation capabilities of the network increase exponentially when the size of the network increases, because of the combinatorial explosion in the number of possible input-to-output correlations.The reason I believe artificial general intelligence is just as far in the future as before, and not related to LLMs, is that general intelligence is at its core the ability to solve problems not encountered before; i.e. not within the training set. In psychometrics, this is called theg-factor, and is behind all the predictive power of "IQ tests". (Not all IQ tests manage to obtain results reflecting the g-factors, however, possibly explaining why increased education seems to slightly boost IQ scores.)Memory and recall is not involved in g-factor per se, and you cannot significantly raise your g-factor by reading or learning more. For humans, g-factor seems to be fixed by early childhood, whereas the related "IQ" can be slightly boosted by education and training.Simply put, general problem solving ability as modeled by the g-factor is not a transformation on experience, or a pattern matching skill, the two things neural networks do well. Exactly what is needed to get to true general intelligence, the ability to successfully solve problems never encountered before, is an open question. After all, the animal brain is a neural network. From species we consider "intelligent", the ability to interact in complex ways seems crucial. (Whether also requires things liketheory of mindis unclear; butlogicand reasoning and the ability to model and predict results of interactions seem strongly correlated, when comparing e.g. humans tocorvids.)

author: KE5FX
author_rank: Super Contributor
timestamp: March 06, 2024, 12:18:18 am
content: QuoteJust curious, what are your thoughts onthis?If it answered 100% of those questions correctly instead of 39%, would you still call it a mindless pattern-matching technique?How about 45%? 50%? ... I'm just trying to understand where your goalposts actually are.

author: nctnico
author_rank: Super Contributor
timestamp: March 06, 2024, 01:16:31 am
content: Quote from: KE5FX on March 06, 2024, 12:18:18 amA better question is: can AI deal better or worse with new problems compared to the average human? My observation is that there are quite a few humans around which can't deal with new problems. Some are literally remote hands attached to a phone with somebody giving instructions at the other end. You might be able to replace the person giving instructions with AI even at today's level.

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 06, 2024, 01:17:11 am
content: Quote from: KE5FX on March 06, 2024, 12:18:18 amIt mapsknowledge, notintelligence.Quote from: KE5FX on March 06, 2024, 12:18:18 amOf course. That is memory and correlation, not problem-solving.We need to use written textcognitive teststhat were not described or discussed in the training set. Considering large swathes of the internet are used as the training set, it is pretty difficult to do.Even then, we don't expect a pure transformer to yield a 0% score. The real floor is random choice. Any tendency towards correct solutions indicates either intelligence, or that the problem type is related/correlated to a problem within the training set.For corvids, rats, and other animals, physical puzzles involving tool use and phenomena like adding water or a rock to a vessel containing water to raise the water level to bring a reward within reach, have been used; and levers, ropes, and push-sticks. These need to be used in combination or in sequence. The number of attempts needed to find a solution is part of the intelligence estimate.In particular, intelligence is not measured directly, but comparatively and statistically. (For the g-factor, using e.g. factor analysis.)The exact same problem is with IQ tests on humans. It is true that a single IQ test has cultural and whatnot biases, and that only by examining a random set of tests and their results (across many people) can the g-factor for each individual be estimated. Here, we have something/someone that has basically access to all of the web, and we want to find out whether they can actually solve problems, or only look up the problem or similar problem, and derive an answer. One is related to "intelligence" (problem-solving without prior experience or training), and the other to "memory" and "pattern-matching".If you were to accept that "intelligence" ≠ "memory" and/or "pattern-matching", it would not mean LLM's would suddenly become useless. Just less 'dazzling'.To me, the pattern-matching capability is the key. It is extremely useful, but it must not be confused with problem-solving. Pattern-matching answers the question "is there a solution to this type or pattern of a problem listed in the training set?", whereas true intelligence is required to solve a problem previously unknown (including the problempattern, not just the details of the problem).(It is also why I mentioned Kohonen networks: they were the first ones with true end-user practical use cases like OCR, back when best flatbed scanners were made by HP and had SCSI interfaces, and computers just didn't have the RAM or computational speed to use much larger networks. If you add a perceptive filter, turning bitmaps into glyph skeletons or vector models, you can make it much more powerful. Different sentence embedding approaches have similar effects for LLMs.)Thus, asking an LLM to solve a problem, is equivalent to asking an oracle whether the problem pattern and solution is listed in a database (the LLM training set). If it is, the oracle produces it. If it isn't, the oracle produces correct-looking garbage. It is extremely difficult to tell which is which, unless you derive or verify the solution for yourself; andthisis the reason I don't like playing with them that much.To find whether the oracle isintelligent, one needs to find out whether it can solve problems it has absolutely no prior experience with.

author: KE5FX
author_rank: Super Contributor
timestamp: March 06, 2024, 01:29:09 am
content: Quote from: Nominal Animal on March 06, 2024, 01:17:11 amSounds like you didn't even skim the article. You should; it's interesting. The whole idea was to benchmark with hundreds of original graduate-level problems that could not possibly have appeared in any training set. GPT4 wasn't (yet) competitive with domain experts, but outperformed PhDs from unrelated domains.

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 06, 2024, 01:30:32 am
content: Quote from: KE5FX on March 06, 2024, 01:24:33 amIt is not enough for the particular problem to not exist in the training set. It is thepatternof the problem existing in the training set that is the problem.Remember, the sentence embedding turns the input into a vector. Individual words do not need to match, if the pattern, after whatever transformations is done to it, matches. This is where neural networks excel.

author: KE5FX
author_rank: Super Contributor
timestamp: March 06, 2024, 01:31:32 am
content: What might be an example of a question that would meet your criterion of not relying on pattern-matching?

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 06, 2024, 02:41:07 am
content: As to "undergraduate level" having anything to do with intelligence, some of the graduates and PhD's I've met have had excellent memories and social skills, but the logical, deductive, and problem-solving skills of a wet paper bag. I don't think it is anywhere near an appropriate yard-stick.Quote from: KE5FX on March 06, 2024, 01:31:32 amI'm not sureIam intelligent enough to come up with one that has no pattern analog on the web already.(I am pretty sure there are others either doing this already, or who will do it within the next few years.)I suspect the practical way to find an answer to this question would be to use a carefully limited dataset in training a purported AI model, that omitted one narrow sector of human knowledge, and then asked the purported AI questions whose answers would fill in the blanks.For cognitive tests, I personally place much more weight on the nonverbal ones (likeRaven's Matricesand similar ones) than verbal ones. Me fail English often, too.I was thinking about variants of theSally–Annetest, perhaps with added decoy actors to confuse the pattern, but it turns outMicrosoft already did that last year, and it is more about theory of mind (and self-awareness) than intelligence. (I most likely remembered reading about it.)Perhaps if one could invent an algorithm to solve a previously unsolved logical problem, for examplelike a math one I did here, and instead of publishing it, create a sequence of questions to see if a purported AI could replicate the discovery if prompted step-by-step? The key discovery there was to replace thexaxis with the same value written in binary in the reverse order, i.e. turning 101100111₂ into 111001101₂. There are theoretical 'hints' that prompted me to try it, so I did notdeduceit; I onlydiscoveredit. Diligent work, not brilliance.Considering many things are discovered and rediscovered again and again, and often described in one domain before "discovered" anew in another by unrelated people, this may not be very reliable, as there is no proof the pattern does not already exist in analogous form in the training set.Regardless of whether one labeled such logical extensions as "intelligence" or not, it would add another facet beyond pattern-matching to neural networks.I am conflicted on whether I would be surprised about it. On one hand, discoveries in one domain often spur discoveries in other domains, thus creating analogous 'patterns', so some extensions to a partial knowledge base could be deduced based on pattern matching, and not surprising. On the other hand, it would extend pattern-matching to applying analogous patterns, and that itself would be extremely useful (in addition to pattern matching alone).The problem is determining whether it can do so reliably.My biggest peeve with software is software that silently garbles data, when it could have told the user something happened that might mean the data was garbled. (It is because some people think run-time error checks are waste of effort, because "if that error did occur, they'd already have bigger problems than anything to do with this program; and besides, it has never happened on my system, so must be extremely rare and not worth checking for anyway".)The problem with neural networks is that they cannot qualify their output as to how reliable or likely it is to be correct. There just isn't such a possibility in neural network models. The output is what you get; that's it, it is neither right nor wrong.Compare to a human, who might choose to start with "This is my current understanding. It is not my core field of expertise, but I've dabbled in this – consider me a hobbyist – so I could be wrong. I don't think so, though." An LLM cannot do that, and always will state the answer as if it were a fact. Whether it is or is not, is not available from the model.

author: glenenglish
author_rank: Frequent Contributor
timestamp: March 06, 2024, 08:35:37 am
content: ChatGPT or other generative AI - cannot make analytical distinctions, only well chosen regurgitation.

author: pcprogrammer
author_rank: Super Contributor
timestamp: March 06, 2024, 09:54:54 am
content: A teacher from long back told me that you can know the formula but if you don't know how to use it, it is worthless. I believe that to be very true. You can have all the knowledge in the world but when you lack understanding of it and don't know how to use it, it is indeed worthless. Sure you might sound intelligent when you regurgitate the knowledge but when it does not match the question you actually look very stupid.Another saying I believe still holds up is "If the human brain was so simple that we could understand it, we would be so simple that we couldn't".In the basis it might well have neural networks, but probably more complex then the simple multiply and add factoring used in today's AI networks.As to intelligence tests I concur with Nominal Animal that the nonverbal ones shed light on actual intelligence and ability to solve problems then the more common ones that include language skills and social knowledge, like who was president from some country in some period of time. But even for these nonverbal ones, one has to learn or at least understand a sample to deduce the rest.But I also believe that intelligence comes in many forms. Some shine at math, where others shine in music or art, etc.

author: Nominal Animal
author_rank: Super Contributor
timestamp: March 06, 2024, 06:02:55 pm
content: Quote from: pcprogrammer on March 06, 2024, 09:54:54 amThat's exactly why I too push people to use simple tools likedimensional analysis. Fancy name, but just means you ignore numeric values and check the units only through the formula, to see if it makes any sense whatsoever. Next step, simple powers of ten with human-scale known results, to see if it produces garbage. Takes only minutes, but tells a lot about the formula.I do not bother to remember any formulas off-hand. It just isn't worth the effort and risk (in misremembering). It is much better to understand where and how things apply. For example, in physics, it is useful to know that momentum is conserved in "elastic collisions", when no internal deformation or change occurs as if the objects were infinitely hard spheres; and that total energy is conserved in all isolated systems. You can quickly find the relevant formulas and be able to apply them. For things like "sentence embedding" in large language models, you need to understand it is the numerical vector representation of the input textual data or words, that can be used as an input to the actual numerical neural network; the details of exactly how it can be done, how two sentence embeddings can be compared to each other, and what differences the different approaches have, you can look up whenever you need.In programming, it means there is no use in trying to remember interface details, like the parameter order formemset()for example. Just keep a terminal window open, ready forman -s 2,3functionor a browser window open forLinux man pages online(which contains the standard C documentation, and mentions which systems/standards provide each, so isn't "just" for Linux), or whatever documentation you're using. You'll soon become so efficient and fast at looking up the exact details, so that memorizing the interfaces (and occasionally remembering them wrong, wasting precious development time) becomes counterproductive. And you only need to understand when to use which interfaces, not remember nitty-gritty details.In such terms, neural networks and large language models are not difficult to understand. The only big step is discarding any preconceptions one might have, and keep ones mind open for truly understanding, instead of trying to force the new information about it into the preconceived notions. Typically, this is called "thinking outside the box", but really, it is more like an attitude you can learn to ignore your own preconceptions and assumptions. It hurts to admit, yes, but our current 'knowledge' often limits what 'new' we are ready to learn.It is a typical human error to confuse existing knowledge (wisdom) with the ability to solve problems (intelligence).Quote from: pcprogrammer on March 06, 2024, 09:54:54 amExactly. For example, I might sound "clever", but that's all just the result of hard effort; I'm notthatintelligent.It is extremely difficult to design an intelligence test for those more intelligent than the test maker in the g-factor sense; the IQ tests that measureabilityandknowledgeare easier.It is even more difficult to design a test to measure something you cannot exactly describe or define, like "intelligence"."Wisdom" is much easier, and because of the common error of conflating the two, many tests claiming to test for "intelligence" or problem-solving ability, actually just test for "wisdom" or the breadth of experience in problem solutions.Quote from: pcprogrammer on March 06, 2024, 09:54:54 amWe also lack proper terms to describe these. I like to use "intelligence" specifically in the g-factor sense, as in the ability to solve new problems not previously encountered (even in analogous forms). For the others, I use various different terms.True creativity, or creating somethingnewinstead of "creating" something "new" by mixing existing things, is a very, very big one.I do not have a word for it, but creating something interesting by mixing existing things is also "creative", and some of the neural networks can do this extremely well. This, too, is useful in practice, but I don't like to conflate it with the truly-new-creative term. Needs a new word; I don't have one. Synthesizy?Intuition, and especially intuitive leaps, are another: it is like a transformation function on a pattern that allows you to match it to a completely different one, like Fourier, Laplace, or Z-transforms applied in signal processing. Sentence embedding in large language models is itself such a transform, with results similar to how a Fourier transform on the digits of two multiplicands allows you to calculate their product with a simple sum and an inverse Fourier transform.Even "simple" pattern matching,especiallyif adaptive (i.e. does not look forexactcopy, but uses an analog of the abstraction-filtration-comparison test used for similarity in the copyright sense in the USA), is extremely useful. Many, many human jobs have a large pattern matching component as a core part: for example, you have a set of rules, and you need to apply them.The problem in using LLMs and transformers in pattern matching is that there is no way to know how accurate the pattern match is, because the matching itself is the product of the entire network. A completely separate mechanism is needed to check the results for accuracy or applicability. Current use cases require us humans to do that check ourselves. To anyone familiar with human nature, that means the checks are rarely, basically never, done. And we can see the results: lawyers citing cases that do not exist because they used an LLM, and so on. Ugly.I myself enjoy the discovery process, the act of solving the problem in a manner I can show how I found the result. I believe that being able to show how the result was obtained, and derive the rules when the result applies from that, is about half the worth of the entire solution. I dislike the current use patterns of LLMs exactly because they discard this half, as if it was worthless and not needed anyway: as if the quick wrong answer is worth just as much as a slow, proven-correct one. I do not like that, because to me, it is exactly equivalent to just applying formulas blindly, and presenting the results using similar language as verified results tend to be presented. It is too close to intentionally lying to me. At least humans aresometimesheld accountable for how they apply the rules they're supposed to pattern-match against.However, that does not change the fact that neural networks are an extremely powerful tool. Once again, it is just a question of how us humans use it. I wholeheartedly support many use cases –– including things like summarizing existing texts ––, and object to some. I'm also a bit peeved that some profit hugely off of models they ripped the training data from off the web; I'm not sure that isfair. I want all interactions to be mutually beneficial.

author: pcprogrammer
author_rank: Super Contributor
timestamp: March 06, 2024, 08:12:35 pm
content: Quote from: Nominal Animal on March 06, 2024, 06:02:55 pmCertainly true, and these latter test do have their use. Still possible to get valuable information from it when looking for the right person for a job, but it should not solely be based on those results.In the Netherlands there was a TV program once a year called the "National IQ test". An assembly of all sorts of groups, like students, athletes, etc in different sections and a bunch of so called BN'ers. (Known Dutch people like actors, politicians, etc) The presenters asked all sorts of questions about different kind of skills. Think of language, politics, science, etc, but also memory based things at the end of the program. Like what was the color of something shown during the musical intermezzo.In one show as part of language skills a multiple choice question was "wat is swaffelen". Yes you guessed it right, I did not know the answer to that one. Seehereon what it means and ask your self in what society such a word becomes the word of the year.Even though the tests did not measure actual intelligence it did show the bell curve results across the attendees. And some celebs needed to hide in shame. A talk show hostess only scored 77. Ouch.