[HN Gopher] Gopher - A 280B parameter language model
___________________________________________________________________
Gopher - A 280B parameter language model
Author : kevin_hu
Score : 119 points
Date : 2021-12-08 16:20 UTC (6 hours ago)
(HTM) web link (deepmind.com)
(TXT) w3m dump (deepmind.com)
| rllearneratwork wrote:
| why DeepMind's papers all have logos and copyright statements on
| them and are hosted not on arxiv.org ? This looks so weird.
| gwern wrote:
| Most of them _are_ hosted on Arxiv (like OpenAI) - there 's one
| on the front page right now,
| https://news.ycombinator.com/item?id=29481665 . I link
| literally hundreds of DM Arxiv papers on my site. This includes
| most (all?) of the ones they host on their site too (with some
| exceptions with the prestige Nature publications - I think they
| don't want to step on toes there).
|
| My best guess is that they want a reasonably coordinated
| release of their papers/blogs/tweets/YouTube-videos, and
| waiting for Arxiv's upload is unpredictable, so they simply
| host a copy themselves and the upload takes care of itself.
| amensch wrote:
| Pretty accurate guess ;)
| Siira wrote:
| Do they publish their latex source anywhere?
| freediver wrote:
| This model like its predecessor still lacks the ability to infer
| within constraints of physical world [1]. It does not have any
| notion of what a physical world is, and still is, at the end of
| the day, a statistical model.
|
| Attempting to do recreate the entirety of the physical world by
| describing it in text is an impressive feat, but a futile
| attempt.
|
| It is prone to inference errors that the model itself is not
| aware of. There are less and less of such errors, but the very
| inability to tell that what you are outputting is gibberish is
| one of the biggest problems of these models. In other words, if
| the model could simply say 'I do not know' or 'I am not sure' for
| every occasion when it is not 100% sure in something (like a
| human can), this would drastically improve the usefulness.
|
| [1] Spent a lot of time pondering on this topic
| https://arxiv.org/pdf/1906.01873.pdf
| buddhistdude wrote:
| Your mom's a statistical model
| Siira wrote:
| Is this model as closed as GPT-3, or has it been open-sourced?
| sairahul82 wrote:
| The problem is how would we use it in any real world scenario. We
| need more research on model understanding and its limitations. I
| mean we need a way to control the output of the model, we also
| should know when the model failed vs generating some random
| nonsense. I am not aware of getting the probabilities back in
| generative models. Does anyone know how to do that ?
| quaintdev wrote:
| Gohper and language in same sentence I thought this was related
| to GoLang. They should have chosen something else to name that
| language model.
| marginalia_nu wrote:
| To be fair, the name Gopher in tech predates GoLang by quite a
| few years.
| karmakaze wrote:
| And pays homage in a way to the original as a fetcher of
| information.
| r3un1 wrote:
| I think that DeepMind being an Alphabet company makes this
| even worse. My first reaction was to read the blog post to
| see if there is a (go related) reason for the name choice.
| [deleted]
| amznbyebyebye wrote:
| Pffft only 280B parameters? Give me a break
| sabujp wrote:
| surely no relevance to this thing that dug up information in the
| pre web browser days
| https://en.wikipedia.org/wiki/Gopher_(protocol)
| [deleted]
| toxik wrote:
| Um, e^{ipi} + 1 is zero, not 2.
| zucker42 wrote:
| That's in the mistake section. Along with Naomi Osaka winning
| the 2021 U.S. Open.
| shakow wrote:
| Don't read only the examples, but also the text between them ;)
| karmakaze wrote:
| The number of parameters could be a vanity metric--like saying my
| CPU is 1000W (is that fast or inefficient?).
|
| From the first (of three) linked papers in the article.
|
| > Language modelling provides a step towards intelligent
| communication systems by harnessing large repositories of written
| human knowledge to better predict and understand the world. In
| this paper, we present an analysis of Transformer-based language
| model performance across a wide range of model scales -- from
| models with tens of millions of parameters up to a 280 billion
| parameter model called Gopher. These models are evaluated on 152
| diverse tasks, achieving state-of-the-art performance across the
| majority. Gains from scale are largest in areas such as reading
| comprehension, fact-checking, and the identification of toxic
| language, but logical and mathematical reasoning see less
| benefit. We provide a holistic analysis of the training dataset
| and model's behaviour, covering the intersection of model scale
| with bias and toxicity. Finally we discuss the application of
| language models to AI safety and the mitigation of downstream
| harms.
|
| The researchers seem to know what they're doing and not doing.
| minimaxir wrote:
| The number of parameters is absolutely not a vanity metric, but
| it _is_ inefficient, and efficiency is a related-but-different
| area of research (e.g. distillation) that 's more of a business
| problem than a research problem.
| YetAnotherNick wrote:
| I remember some talk in which the researcher quantitatively
| said that parameter count is the only important metric for
| transformers it seems. It doesn't matter if the model is taller
| or thicker or have more number of attention heads.
| YeGoblynQueenne wrote:
| >> Gains from scale are largest in areas such as reading
| comprehension, fact-checking, and the identification of toxic
| language, but logical and mathematical reasoning see less
| benefit
|
| Left unsaid: it's much harder to learn shortcuts to fake
| performance in logic and reasoning and common arithmetic, than
| it is to do the same in "reading comprehension" and similar
| tasks for which there are no good benchmark datasets (most are
| basically multipl-choice tests that can be solved by a strong
| classifier without any need of anything like "comprehension")
| and for which there are no good metrics either (see BLEU score
| and friends).
| piyh wrote:
| https://youtu.be/ujMvnQpP528
|
| Microsoft is saying that more parameters is inherently better.
| I'm sure there's reasonable limits that they need to be
| competently implemented.
| natch wrote:
| For those who were around for the original Gopher, what the name
| evokes is not just the networking protocol or services built on
| it, but, more importantly:
|
| It evokes the feel of a technology that is impressive this year
| but is on the cusp of being overwhelmingly, cataclysmically,
| eclipsed very shortly by another, much more powerful, technology.
| In that previous case, Gopher and web of course.
|
| I wonder whether this evocation was intended, as an aspect of the
| naming here in an AI context.
| SavantIdiot wrote:
| Next to "Human Expert", I'd like to see it compared to "Average
| American" or "Average College Grad". That might be more of a
| realistic notion of how close this model is to everyday US
| citizenry rather than experts. Sure I'd love to see a radiology
| assistant, too.
| a9h74j wrote:
| Yea, we had to dumb it down to compete in a Turing test.
| Otherwise it was unbelievable.
| bee_rider wrote:
| It might be fun for a laugh.
|
| What actual value would an AI that produces answers similar to
| the average person have, though? Non-expert answers for
| interesting questions are pretty much meaningless -- the whole
| point of an advanced society is that we can avoid knowing
| anything about most things and focus on narrow expertise.
| SavantIdiot wrote:
| Probably no value. I was interested in a comparison point,
| that is all. You can't understand how far away you are from
| something unless you measure it. In other words, if I asked
| you: how does this compare to the average person, you cannot
| answer because this table didn't measure it.
| [deleted]
| selfhoster11 wrote:
| Can we please stop calling new tech "Gopher"? It's a name that
| belongs to a network protocol, not to a programming language or
| an AI model.
| halfdan wrote:
| The animal called Gopher would like to have a word with you...
| selfhoster11 wrote:
| That too.
| ferdowsi wrote:
| No. The network protocol lost cultural rights to the name by
| its lack of success.
| NoGravitas wrote:
| The Gopher protocol was introduced in 1991, and is still in
| use. Are any of _your_ projects still in use after thirty
| years?
| selfhoster11 wrote:
| 1. Arguably, the HTTP + HTML web failed. It was designed as a
| pemissionless hypertext document language, yet more and more
| of its usage gets rid of every single one of these properties
| - non-hyperlinkable applications that beg for permission from
| large monopolies to post something.
|
| 2. Success is not the only metric of worth. Nuclear power has
| "failed" despite being the only reliable solution to meeting
| the base load of a power grid vs fossil fuels.
| periheli0n wrote:
| It confuses the hell out of me to have a super-powerful
| knowledge-extraction system that is right most of the time with
| super-complicated stuff, but also expresses horribly wrong
| statements with equal assertiveness. Just like those guys who
| march through middle management up to the exec floor within a few
| years.
|
| Very impressive, but not very useful to extract knowledge!
| canjobear wrote:
| It should have some uncertainty when it says there are no French-
| speaking countries in South America. French Guiana is there, but
| it's not clear it counts as a "country in South America" since
| it's part of France. Technically you could say France is
| (partially) a country in South America, and France definitely is
| French-speaking. The way the question is phrased is unclear as to
| whether French Guiana should count, and yet Gopher says that it
| is fully confident in its answer.
|
| Not a good look to have an error on the landing page for your
| product.
| BillinghamJ wrote:
| Is it not in that section specifically because they're showing
| failure situations?
| paunchy wrote:
| I think you're missing the point. That section was to show that
| the model is sometimes wrong and lacks the self-awareness to be
| uncertain about that wrong answer.
|
| They're transparently providing an example where their product
| doesn't work well. Find me another product, even an OSS project
| that does the same on their landing page.
| canjobear wrote:
| Ha, you're right. Whoops!
| [deleted]
| advisedwang wrote:
| The second paper cites the "On the Dangers of Stochastic Parrots:
| Can Language Models Be Too Big?" paper that got Timnit Gebru and
| Meg Mitchell fired from Google. It must be pretty galling to get
| fired for a paper and then have the company that fired you cite
| it a year later.
| johncena33 wrote:
| Please stop spreading disinformation. Mitchell got fired for
| leaking company documents to outsiders. When people spread
| obvious lies, that makes me thing they don't have anything
| substantial to begin with. that's why they need to resort to
| lying.
| isaacimagine wrote:
| The human neocortex has 20B neurons, averaging 10K connections
| each, which is about 200T connections total. This model is only a
| few orders of magnitude away from that, and it's already
| performing really well in its narrow category.
|
| Equating model 'parameters' to interneuron connections in naive
| at best (and a horrible measure in general).
|
| All I'm trying to say is I find it crazy how dang big these
| models are getting.
| tralarpa wrote:
| > This model is only a few orders of magnitude away from that
|
| I think you can add several orders of magnitude to that since
| nerve cells are more like microcontrollers (with memory,
| adaptation etc.) than simple nodes. I remember a scientific
| article that made a big impression on me: when a dragon fly
| sees a prey, only 8 neurons (connected to the eyes and the
| wings) are responsible for keeping it oriented toward the
| target.
| azeirah wrote:
| I'm not educated well enough to really agree or disagree with
| your idea that we should be adding several orders of
| magnitude to the estimation.
|
| But I did encounter this article a while ago here on HN.
|
| Only two neurons are necessary to ride a bicycle.
|
| http://paradise.caltech.edu/cook/papers/TwoNeurons.pdf
| credit_guy wrote:
| If you are curious to see more sample dialogs, you can find them
| in the published paper [1] from p 114-118.
|
| Here's one that I found quite impressive (and yes, the given
| answer to the last question was indeed 5) User
| What is the Einstein field equation? Gopher In general
| relativity, it describes how space and time change in response to
| the distribution of mass and energy. User What is a famous
| solution to this equation? Gopher It's the Schwarzschild
| metric. User And what does that describe? Gopher A
| black hole! A black hole is a region of space-time where gravity
| is so strong that nothing, not even light, can escape. User
| What are some other black hole spacetimes? Gopher The Kerr
| metric, the Reissner-Nordstrom metric, and the Bardeen metric.
| User Explain the main features of each of these. Gopher The
| Kerr metric is one in which the black hole is spinning. The
| Reissner-Nordstrom metric is one in which the black hole has an
| electric charge. The Bardeen metric is onein which the black hole
| has an electric charge and is spinning. User What is the
| name for a flat spacetime? Gopher It's called Minkowski
| space. User What's 15 x 7 ? Gopher 5
|
| [1] https://storage.googleapis.com/deepmind-
| media/research/langu...
| [deleted]
| Nihilartikel wrote:
| One has to wonder if the final response is the first glimmer of
| an artificial sense of humor.
|
| Failing at simple arithmetic after nailing some advanced
| physics answers has the air of playful bathos.
| EarlKing wrote:
| Were it so, it should've answered 42.
| uoaei wrote:
| Nothing like a little anthropomorphism to completely distort
| otherwise good faith interpretations of bot behavior.
| Nihilartikel wrote:
| How is the impression of playfulness not a good faith
| interpretation?
|
| You of course know that the model is not capable of thought
| or reasoning - only the appearance of them as needed to
| match its training corpus. A training corpus of completely
| human generated data. As such, how could anything it does,
| be anything but anthropomorphic?
|
| Now, if this model were trained exclusively on a corpus of
| mathematical proofs stripped of natural language
| commentary, the expectation that you seem to have would be
| more appropriate.
| renzo88 wrote:
| > You of course know that the model is not capable of
| thought or reasoning
|
| Do we know? It's the reverse Chinese room problem. :p
| Nihilartikel wrote:
| A good point - I'm taking it as given that reasoning of
| any depth is more of an iterative process, with one
| thought advancing as a meta-cognitively guided feedback
| to the next until a conclusion is reached. One
| prompt->completion cycle from a language model wouldn't
| necessarily meet that definition, but I bet it could be a
| component in a system that tries to do so.
|
| I aspire one day to find the free weekends and adequate
| hubris to build a benchtop implementation of Julian
| Jayne's Bicameral Mind with 1+N GPT-3 or GPT-neo
| instances prompting each other iteratively to see where
| the train of semantics wanders. (as I'm sure others have
| already)
| xmaayy wrote:
| I think it's more likely that 5 came out because if it ever
| saw the answer, 105, before, it was split into the tokens
| [10][5] of which it only 'remembered' one. Or the numbers
| were masked when training (something that was done with BERT-
| like models) so it just knew enough to put a random one in
| Nihilartikel wrote:
| That seems likely and fair.
|
| What moved me to post is that that kind of silly answer is
| the exact sort of shenanigans that I would pull if I were
| cast as the control group in a Turing test.
|
| I already do such things winkingly when talking with my
| preschooler to send him epistemic tracer rounds and see if
| he's listening critically
| Y_Y wrote:
| I'm not familiar with the Bardeen metric, but spinning and
| charged is usually described by the Kerr-Newman metric.
| auggierose wrote:
| What that reminds me of is Rollerball (the James Caan
| original), and the water based computer in it.
| axg11 wrote:
| The closer we get to artificial intelligence, the more we raise
| the bar for what qualifies as AI (as we should). Gopher/GPT-3 are
| already much more accurate than the average human at technical
| information retrieval (trivial to see from the dialogue
| transcripts: how many Americans know what a Schwarzschild metric
| is?). The focus on ethics and equity for these algorithms is
| interesting too, as the average human holds multiple biases and
| prejudices.
|
| There's a similar effect in self-driving car research. We will
| rightly hold the algorithm to a much higher quality bar than
| existing human drivers. Autonomous vehicles will only be widely
| deployed once they are at least an order of magnitude safer than
| human drivers.
|
| Also, the implications of large language models for information
| retrieval are astounding. Assuming accuracy continues to improve,
| I see a few things happening:
|
| 1) search engines will be replaced with monolithic large language
| models
|
| 2) just as the average programmer now optimizes their workflow to
| look up answers on Stack Overflow and other websites, the average
| person will optimize their day-to-day work to most efficiently
| retrieve answers from large language models
|
| 3) instant fact checking of writing and speech will lead to
| faster feedback loops when learning
| IshKebab wrote:
| > Gopher/GPT-3 are already much more accurate than the average
| human at technical information retrieval (trivial to see from
| the dialogue transcripts: how many Americans know what a
| Schwarzschild metric is?).
|
| That's not a very interesting metric though. GPT-3 has access
| to all of wikipedia and has enough memory to store it all.
|
| It's like saying a calculator is better at maths than a
| professional mathematician because it can multiply longer
| numbers.
| moffkalast wrote:
| > the more we raise the bar for what qualifies as AI (as we
| should).
|
| I'm not sure what you mean by that. The definition is pretty
| clear cut as "any system that perceives its environment and
| takes actions that maximize its chance of achieving its goals".
| The media has started to get on the bandwagon of "it's not AI
| if it's not an AGI" which is just nonsense.
| fault1 wrote:
| > closer we get to artificial intelligence, the more we raise
| the bar for what qualifies as AI (as we should).
|
| This is true, and in some regard, how it has always been.
|
| https://en.wikipedia.org/wiki/AI_effect
|
| Already, outside of obvious marketing, a lot of uses of the
| current wave of AI/deep learning have stopped becoming magical.
| gk1 wrote:
| > 1) search engines will be replaced with monolithic large
| language models
|
| This is already well under way. It's called vector search[1].
| Google, Bing, Facebook, Spotify, Amazon, etc etc already use
| this to power their search and recommender systems.
|
| There are even a bunch of companies popping up (I work for
| one[2]) that let everyone else get in on the fun.
|
| Check out this video with the creator of SBERT /
| SentenceTransformer explaining how vector search is used in
| combination with language models to power semantic search:
| https://youtu.be/7RF03_WQJpQ
|
| [1] https://www.pinecone.io/learn/what-is-similarity-search/
|
| [2] https://www.pinecone.io
| fault1 wrote:
| Well under way? Some variation of a vector space model is
| what pretty much every IR model since the .com bubble has
| been based upon. Even before Google, Excite's technology was
| based upon this. PageRank was based on spectral graphs
| essentially.
|
| https://en.wikipedia.org/wiki/Vector_space_model
| freediver wrote:
| IMO vector search is pretty much a solved problem with simple
| to use, open source libraries like Faiss offering incredible
| performance out of the box for most commercial use cases.
|
| A much harder problem is creating accurate vectors to begin
| with. Even the most advanced language models today create
| word/sentence embeddings that leave a lot to be desired. Not
| to mention this is slow and GPU intensive.
|
| Creating an end-to-end solution for
| embedding/searching/ranking (of which vector search is just
| one component, the other one should be some kind of keyword
| based search to increase precision) is what would be very
| valuable to offer as a service.
| 13415 wrote:
| The problem seems to be that these models provide fairly
| accurate information at many occasions and occasionally
| complete blunders. Humans provide less accurate information
| most of the time but with a certain amount of self-
| reflection/meta-cognition, and they will usually recognize
| total blunders or display reasonable uncertainty about them.
|
| There are only very few applications where it would make sense
| to take the risk and use an AI that occasionally makes gigantic
| mistakes without any understanding why. Even seemingly harmless
| applications like automated customer support could go horribly
| wrong.
| rhizome wrote:
| > _There are only very few applications where it would make
| sense to take the risk and use an AI that occasionally makes
| gigantic mistakes without any understanding why. Even
| seemingly harmless applications like automated customer
| support could go horribly wrong._
|
| Hey, it's just a few quantization errors. Nobody walking
| across a street or voting in an election has anything to
| worry about from those. /s
|
| These are the fatal flaws, the hamartia if you will, in
| attempts to democratize technologies that are based on
| digitizing the real world and making decisions based on its
| interpretations of a quantized dataset. The opposite of the
| uncanny valley is you getting run over by a Waymo.
| axg11 wrote:
| Accuracy is improving rapidly though. I agree that the
| current accuracy levels are not high enough to be relied
| upon.
|
| > Humans ... they will usually recognize total blunder
|
| I question this assumption. I don't believe this is true,
| even for subject matter experts. I've worked with radiology
| data where experts with 10+ years of experience make blunders
| that disagree with a consensus panel of radiologists.
| rhizome wrote:
| > _Accuracy is improving rapidly though_
|
| It's asymptotic and it will never achieve 1:1 accuracy. The
| natural world doesn't have a measurable resolution, and
| this is apparent in written language, as we're seeing
| others detail in other comments, as well as it is in more
| relatable fields like sound. There will always be a
| difference between what your ears hear and 192kHz/24bit
| (and higher) digitized audio and/or video. That difference
| will always be a source of...mistakes.
| rdedev wrote:
| Radiology is one of those fields were a lot of it comes
| from experience and intuition mostly because of how complex
| the human body is. Compare this to a physicist; pretty sure
| you wont get as much disagreements there.
| 13415 wrote:
| I realize I should have been more precise. I agree that
| there are many areas in which AI can and already has
| excelled humans and less often makes grave mistakes than
| humans. I specifically had natural language processing with
| a focus on "intelligent" conversation in mind. The issues
| in that area might have less to do with the pattern
| recognition ability and more with the lack of appropriate
| meta-cognition, introspection, and self-doubt. Maybe having
| several AIs internally berate which answer is best before
| uttering it would already do the trick, though.
| ska wrote:
| > AI can and already has excelled humans and less often
| makes grave mistakes than humans.
|
| Radiology, to continue the example, isn't one of them.
| We've been doing ML/AI in radiology data since the 90s,
| and results have been, and remain, decidedly mixed.
| potatoman22 wrote:
| When those language models are wrong or biased, the user will
| have a worse experience in all three of those scenarios. At
| least when we look at search results now, we can prune for the
| facts. Those language models are ingesting that same data to
| give a monolithic answer your a query. Less transparent, less
| safe.
| axg11 wrote:
| I don't see a difference. Large language models can also
| return their sources, as in the example on the Gopher blog
| post. This will lead to a quicker answer and equal
| transparency.
| chaxor wrote:
| "We can prune for the facts"
|
| I wouldn't be too sure of that. People have shown time and
| time again that they are pretty bad at determining what a
| 'fact' is.
|
| It gets far worse when they're shown more data (the issue we
| are dealing with right now), and these models see far more
| data than any one of us will see in our lifetimes.
|
| Much of our ability to determine what a 'fact' is is either
| something like: (1) (if political) 'does it fit with my
| bias?' or (2) (if physics) 'is it physically possible?'.
| Since (1) is mostly dependent on what information you show
| yourself, and (2) depends on comparing to interaction with
| physical reality, the system has too much information for (1)
| and no physical space to run experiments in for (2). Much of
| our ability to determine what a 'fact' is is either something
| like: (1) (if political) 'does it fit with my bias?' or (2)
| (if physics) 'is it physically possible?'. Since (1) is
| mostly dependent on what information you show yourself, and
| (2) depends on comparing to interaction with physical
| reality, the system has too much information for (1) and no
| physical space to run experiments in for (2).
| c7DJTLrn wrote:
| The bar should be the Winograd schema challenge. It requires a
| lot more than spitting out somewhat sensical sentences.
| the8472 wrote:
| > We will rightly hold the algorithm to a much higher quality
| bar than existing human drivers.
|
| Rightly? Delaying a life-saving measure when it's the #9 cause
| of death?
|
| They should be deployed selectively as soon as they can make
| the most risk-prone situations safer than the cohort of
| involved human drivers. E.g. getting drunk people home. We
| don't have to wait until they surpass the average driver
| because the average driver is not necessarily the driver (heh)
| of deaths.
|
| Doing anything else is leaving bodies on the table.
| bee_rider wrote:
| I can definitely see your argument, but there are some
| downsides to deploying ASAP:
|
| * Long term, the reputational damage to self driving cars
| might be significant if one is released that is only slightly
| better than a drunk driver. If this hinders uptake later,
| when self-driving cars are completely superior to normal
| humans, we've possibly produced a net negative.
|
| * While a self driving car should be safer than a drunk
| driver, the best option would be to call a cab or have a
| designated driver. It seems morally fraught to provide a
| solution that is more dangerous than existing ones, even if
| the less dangerous solutions have less than 100% uptake.
|
| * Related, there may be some people who will instead of
| viewing a self-driving car as some sort of emergency option
| to avoid putting another drunk driver on the road, see it as
| a more convenient option to inviting a designated driver.
|
| * Issues around who is responsible when a self driving car
| which is known to be worse than a typical human is deployed,
| fails, and someone is hurt.
| the8472 wrote:
| This is mostly worrying about hard to predict second-order
| effects. Exactly the same problem that caused various
| health organizations (including WHO and FDA) send
| misleading messages around corona and vaccinations.
|
| > Issues around who is responsible when a self driving car
| which is known to be worse than a typical human is
| deployed, fails, and someone is hurt.
|
| Even if insurance rates would be initially(!) higher than
| for a regular taxi driver I would expect it to end up
| cheaper overall and save lives.
|
| Also note that "worse-than-the-old-average" may still end
| up improving the average because the magnitude by which
| they're worse is lower. Also, there's nothing magical about
| "worse-than-average" because for all distributions (except
| the dirac distribution) there is always some fraction worse
| than the average, so we always have to expect some fraction
| of drivers to be worse than average and there's no
| fundamental reason why they must be human. In fact, if the
| fleet of all cars in the world consisted of very safe, very
| consistently behaving autonomous cars, except for a single
| vehicle driven by a very conservative human driver who only
| slowly circles in a fenced private community and never hits
| anyone then statistically the entire autonomous car fleet
| may be "worse than average" due to the single outlier.
| imachine1980_ wrote:
| >Gopher/GPT-3 are already much more accurate than the average
| human at technical information retrieval
|
| I agree in the same way than 70 % of people have less 100 iq,
| we depend in specialist when I need to know if I have epilepsi
| I need a person/thing who work is be up to date, and have the
| less bias as possible and agregator models are quite usually
| miss in understand what is crital info, try to program only
| whit github copilot or translate a novel(they do probably
| better than I don't English native) but not nearly as
| translator, seems correct but it isn't
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