Are you going to South by Southwest (SXSW) ? - <AUTHOR>

This will be my first year. I'm quite excited to go.  Are you going? Have you been before?  Anything to say about it? <comment><USER2> I'll be there since I am local. There tends to be more hacker stuff at BarCamp than at SXSW proper. <http://barcamp.org/BarCampAustin4>

<comment><AUTHOR> I went to a barcamp in Vancouver and it was awesome, but why compete with SXSW? Seems like there will probably be a lot of people who would enjoy both, I would for sure.

</comment></comment><comment><USER3> Amazing place to meet people doing cool stuff in interactive media / music / film. I went last year for the first time and my only regret was not having gone before. I suggest coming with a mind ready for learning and a notebook for jotting down notes and ideas--it's very fertile ground for thought / brainstorming.

</comment><comment><USER4> Yep.

My husband & I have built an interactive art exhibit that you'll see if you're an attendee.</comment>


Ask HN: How have you adapted your writing for executives/non-technical staff? - <AUTHOR> I work in a department that largely uses technology to accumulate data for compliance and investigations. Recently I had an interview for a promotion where a major concern was that I won't be able to write reports and explain situations in ways that will be easily understood by the recipients (namely executives, outside agencies, and other employees in the office).
I'm absolutely struggling with how to change the way I write to be less complex, when I myself don't recognize it as being verbose or overly complex in structure. In all the past reports I've written, only maybe once or twice has anything been mentioned by management (usually that they wanted me to remove a word because they didn't know what it meant).
Has anyone else experienced this, or have any advice or know of any resources that might be able to help me change the way I write to adhere to different reading comprehension levels? <comment><USER2> It's always hard recognizing problems in one's own writing. But to give an example:

> "I'm absolutely struggling with how to change the way I write to be less > complex, when I myself don't recognize it as being verbose or overly complex > in structure."

could be written more shortly and simply as:

> "I don't believe my writing is verbose or complex, so I don't know how to > make it simpler."

Not picking on you, if I were writing that I'd probably end up with something closer to your first version in the first draft. Which is why you want to revise into a second draft.

Luckily there's a book that can help: "Revising Prose" by Lanham ([http://amzn.to/2f9pUjN](http://amzn.to/2f9pUjN)). Your local library probably has a copy. It's pretty entertaining read, too.

If you want a more extensive guide, "Style: Lessons in Clarity and Grace" ([http://amzn.to/2f9zqDp](http://amzn.to/2f9zqDp)) goes a lot further. Again, I read a copy from the library. Beyond the style of your prose, if you want a conceptual guide to business writing, see this review: [https://codewithoutrules.com/2016/06/15/writing- book/](https://codewithoutrules.com/2016/06/15/writing-book/)

But "Revising Prose" will probably suffice.</comment>


Dan from Stanford talks about their Facebook course (with links to student projects) - <AUTHOR> http://www.hatchthat.com/dan-ackerman-greenberg/ <comment><USER2> What kind of university has a whole course dedicated to the study of a corporation's proprietary technology?

<comment><USER3> Lots. There are a number of courses at MIT that come down to learning MATLAB.</comment></comment>


WALL-E Easter Eggs - <AUTHOR> http://www.slashfilm.com/2008/06/27/wall-e-easter-eggs/

<comment><USER2> Just took my 4yo to see this today - loved it. Cute :) When I buy the DVD, I'll look for these.

<comment><USER3> A group of my 20+yo friends saw it and _we_ loved it.

WALL•E was excellent.</comment></comment>


Defeating Skype Encryption Without a Key - <AUTHOR> http://www.securityweek.com/defeating-skype-encryption-without-key

<comment><USER2> Edited form of my comment from <http://news.ycombinator.com/item?id=2590731>, which discussed the same finding:

\- Skype's encryption algorithm is not, itself, broken;

\- Skype uses a "prediction" algorithm (LPC) to compress voice streams;

\- therefore, words etc. have a specific pattern of bandwidth use;

\- bandwidth use of encrypted compressed data is equal to bandwidth use of data that has only been compressed;

\- these patterns (which can be detected "through" the encryption) allow fairly good reconstruction of the voice stream.

If you like this kind of thing, Google "ssh keystroke timing attacks", or, more generally, "traffic analysis".

</comment><comment><USER3> The article isn't really clear, you need to read the paper. Quick summary:

They can use the size of the packets to find boundaries to different phonemes, which can then be used to check for e.g. what language is used, or even against known phrases. They have also had some success at determining which phonemes are used based solely on the length of the VBR packets.

</comment><comment><USER4> Interesting. They are using frequency analysis[1] of LPC phonemes rather than characters. This implies the sound is not encrypted at all (security by obscurity). It definitely implies the (voice) data stream is not being encrypted as a stream.

[1] <http://en.wikipedia.org/wiki/Frequency_analysis>

<comment><USER5> Streams are encrypted. They are exploiting VBR(variable bit rate) part of the Skype's architecture. Skype uses VBR to make sure they pack enough fidelity while maintaining the low bandwidth requirements.

It is an interesting paper to read. Initially I thought they exploited LPC which is the base for all our mobile communications as well. As long as VOIP provider doesn't opt for VBR compression, their networks are immune from this attack.

From the paper.

 __ _Under VBR, the size of the codebook entry, and thus the size of the encoded frame, can vary based on the complexity of the input frame. The specification for Secure RTP (SRTP) [3] does not alter the size of the original payload; thus encoded frame sizes are preserved across the cryptographic layer. The size of the encrypted packet therefore reflects properties of the input signal; it is exactly this correlation that our approach leverages to model phonemes as sequences of lengths of encrypted packets._ __

Also, they included the solution.

 __ _For this reason, the use of constant bit-rate codecs is important to consider as an alternative to simple block ciphers for VoIP, since such codecs might improve call quality given a relatively large fixed packet size. Another alternative might even be to drop or pad packets [19, 27, 57], though, in that case, the effect on perceived call quality is unclear. We note, however, that VoIP providers have made no move to employ any such measures: Skype's SILK, for instance, is a VBR codec. Similarly, one of the leading proposals for 4G, the LTE Advanced standard, specifies a VBR codec for audio [1] and the use of SRTP to secure voice data channels._ __</comment></comment>


A First Introduction to System Exploitation - <AUTHOR> Write-up form very condensed and concrete learning material. Available machines to follow up at pwnable.kr _>Found on the Article from https://research.checkpoint.com/2020/i-want-to-learn-about-exploitation-where-do-i-start/<p>https://research.checkpoint.com/wp-content/uploads/2020/03/pwnable_writeup.pdf <comment><USER2> I'm currently doing Bandit@Overthewire. I think there is some stuff there that should be useful later down the road.

Thanks.</comment>


We are the SpaceX software team, ask us anything - <AUTHOR> https://www.reddit.com/r/spacex/comments/gxb7j1/we_are_the_spacex_software_team_ask_us_anything/ <comment><USER2> To unbury the lede, this is happening at tomorrow, Saturday June 6 at noon PST

</comment><comment><USER3> Related: Software Engineering Within SpaceX [https://news.ycombinator.com/item?id=23403800](https://news.ycombinator.com/item?id=23403800)

<comment><USER4> Also "What computer and software is used by the Falcon 9? (2015) (stackexchange.com)" from 5 days ago - [https://news.ycombinator.com/item?id=23368109](https://news.ycombinator.com/item?id=23368109)

</comment></comment><comment><USER5> What kind of computing hardware is used inside the rockets? Off the shelf Intel or AMD cpu's setup in some kind of redundancy or custom hardware? How does the redundancy work, how many CPU's can fail before the rocket can't be controlled anymore? Are they all located in the same place in the rocket, why/why not?

<comment><USER6> No way. Anything that costs a lot of money runs industrial computing like the RAD750.

<comment><USER7> There are some answers here: [https://space.stackexchange.com/questions/9243/what- computer...](https://space.stackexchange.com/questions/9243/what-computer-and- software-is-used-by-the-falcon-9)

IIRC it was the same thing they said during the launch of the Dragon capsule so yeah they don't use crazy RAD stuff:

SpaceX uses an Actor-Judge system to provide triple redundancy to its rockets and spacecraft. The Falcon 9 has 3 dual core x86 processors running an instance of linux on each core. The flight software is written in C/C++ and runs in the x86 environment. For each calculation/decision, the "flight string" compares the results from both cores. If there is a inconsistency, the string is bad and doesn't send any commands. If both cores return the same response, the string sends the command to the various microcontrollers on the rocket that control things like the engines and grid fins.

The microcontrollers, running on PowerPC processors, received three commands from the three flight strings. They act as a judge to choose the correct course of actions. If all three strings are in agreement the microcontroller executes the command, but if 1 of the 3 is bad, it will go with the strings that have previously been correct. The Falcon 9 can successfully complete its mission with a single flight string.

The triple redundancy gives the system radiation tolerance without the need for expensive rad hardened components. SpaceX tests all flight software on what can be called a table rocket. They lay out all the computers and flight controllers on the Falcon 9 on a table and connect them like they would be on the actual rocket. They then run a complete simulated flight on the components, monitoring performance and potential failures.

SpaceX engineers perform what they call "Cutting the strings" where they randomly shut off a flight computer mid simulation, to see how it responds.

Dragon uses a similar triple redundant system for its flight computers.

<comment><USER8> >Dragon capsule so yeah they don't use crazy RAD stuff:

Those PowerPC processors are RAD750's.

</comment></comment></comment></comment><comment><USER9> Off topic, but is there a nitter/invidio.us equivalent to reddit? After HN, I find reddit and old.reddit to be so cluttered I can't stand it

<comment><USER10> Have you tried i.reddit? Its their old-school mobile website, super simple interface.

[https://i.reddit.com/](https://i.reddit.com/)

<comment><USER9> Love it! Thanks!!

</comment></comment></comment><comment><USER11> Do you have any tools to attempt prove correctness of critical code beyond traditional testing?

<comment><USER12> You have to ask that on the Reddit.

<comment><USER11> Oh. Oops.

</comment></comment></comment><comment><USER13> Dang the influx of questions wonder how they decide what to answer.

<comment><USER14> This is Reddit so probably by upvotes?

<comment><USER13> Yeah but I also wonder how many people filter/read new and vote vs. just asking whatever... if they could average the questions that would be interesting/frequency of mentions... anyway be curious what questions are asked.

~600 comments under an hour? dang

edit: oh comments are randomly ordered interesting

<comment><USER15> To expand on your edit:

1\. This comment thread shows comments in random order, so you're not seeing the more upvoted questions bubble to the top the way they usually would, which prevents winner-takes-all feedback loops in the voting.

2\. People browsing through the thread may upvote good questions, but currently the scores are hidden in order to avoid bias.

3\. Once the thread has been up for a while, the people doing the AMA can just re-sort to show the highest-voted comments first, which will give a good starting point for which questions to answer.

<comment><USER13> Yeah I'm glad they did that. The score hidden thing is interesting, I see some people do that at will, I'm a person affected by down votes haha like "oh no someone disagreed with me" anyway... pretty cool.

</comment></comment></comment></comment></comment><comment><USER16> boy that thread at reddit took like 20 seconds to close my firefox tab that was in it. Never seen a tab to take that much to close.

</comment><comment><USER17> what happens if node has to garbage collect during launch?

</comment><comment><USER18> What language/framework is the GUI for the controls written in?

</comment><comment><USER19> How do you go about organizing/prioritizing work, and executing?

1) what workflow methodology you use (e.g. scrum/sprint, Kanban, etc)

2) where does requirements come from?

3) how granular is your code-review process?

<comment><USER20> You gotta ask on Reddit.

</comment></comment><comment><USER21> Real jewel from the AMA

\- what "web technologies" did you use for the UI

But one great question

\- What practices did you use to ensure there are no bugs ?

<comment><USER4> > Real jewel from the AMA

They _did_ use Chromium and Javascript for the UI at least a few years ago. See the stackexchange posts.

EDIT: also see the old AMA: [https://www.reddit.com/r/IAmA/comments/1853ap/we_are_spacex_...](https://www.reddit.com/r/IAmA/comments/1853ap/we_are_spacex_software_engineers_we_launch/)</comment></comment>


Have You Ever Failed? - <AUTHOR> http://www.feld.com/wp/archives/2009/03/have-you-ever-failed.html <comment><USER2> I've always heard "if you don't fail from time to time, you're not trying hard enough...". I've failed courses, I've failed job interviews, I've had projects fail to work, or to deliver. I've had failed relationships, and I've failed to help people from time to time. Gobs of failures, I guess.

However, I think overall I've been successful, and I've tried to help out when/where I can. Might I try harder? Certainly, but I'm doing ok.

</comment><comment><USER3> I enjoy Brad's posts usually but I just don't get it with this one. This post might be an inspiration to other potential investors (and maybe that's who this posts' target audience is) but what am I, the entrepreneur, to get from this? If you read beteween the lines, it's like, "If you have millions, you can fail a whole lot - even for years at a time - but, if you have enough money to stick around for 3-5 years, eventually you'll be successful." No offense but that's nothing like my life as an entrepreneur.

I'm certainy not preaching that it's okay to fail/succeed/whatever but it just feels weird for a multi-millionaire investor with a master's degree in "Management Science" to preach "Never quit" to entrepreneurs. If he was an entpreneur, I get it - but he isn't - and therefore I don't get it. I like Brad Feld's advice and topics usually but this one just is not for me.</comment>


A/A Testing: How I increased conversions 300% by doing absolutely nothing - <AUTHOR> http://kadavy.net/blog/posts/aa-testing/

<comment><USER2> I think you make a great point. If you're think A/B testing is important grow your business big enough to hire a statistician.</comment>


Making of an iPhone app design (Sharypic) - <AUTHOR> http://sachagreif.com/making-of-sharypic-for-iphone/

<comment><USER2> The app looks great. I was wondering how to create the facebook style side bar, as I hadn't seen any API's for it yet. I stumbled upon this: <https://github.com/BenHall/ios_facebook_style_navigation> \- anyone know any others?

<comment><USER3> The sidebar isn't a stock Apple API. Before implementing it, I've been benchmarking a few frameworks on github, none of them being appropriate for the app needs. I wanted the facebook sidebar-like menu but being able to use a standard UINavigationController for the rest of the navigation in the app. In the Facebook app, they've implemented their own view navigation system.

So in the sharypic app, it's a custom implementation where the sidebar view is added directly to the main UIWindow, but below the rootViewController. Tapping the menu button triggers an animation to move the whole rootViewController's view left/right.

It's not ideal because the sidebar view controller doesn't get the orientation change info anymore and making all the views stay in place requires an important amount of hacks (for instance presenting a modal view controller while the rootViewController is shifted on the right causes the rootViewController to be placed back to its original position). The code actually can't be exposed as a reusable framework because of all the hacks implemented here and there.

Since then, I stumbled upon <https://github.com/edgecase/ECSlidingViewController>, which can be a good candidate for a sidebar implementation, and I might switch to it for the future of the sharypic app.

Romain, developer of the sharypic app

<comment><USER2> Thanks for the information!

</comment></comment></comment><comment><USER4> Looks very nice. Good job!

I'm curious how you created some of those menus, buttons. For example having the 'X' at the top right corner and nice looking buttons with images at the bottom.

Is there a resource for these things or a general process? My experience has me in the belief that using anything other than the native widgets is a real pain. At least in the past, has that changed?

<comment><USER3> It's still the same. It's a real pain :(

For instance the 'like' and 'share/invite' buttons are composed of several labels and images, and their position is computed depending on screen orientation, size of the button icon, internationalisation of the strings and length of the badge item (the 'like' badge indicates a count). There's a transparent UIButton which sits on top of all these widgets to get the tap event. And we're changing the background image when the user taps in/out/up/down the button.

</comment></comment><comment><USER5> Great app and hope to use it soon. Just have to find a decent event to go to!

One thing I'm wondering about is the Follow sharypic on Twitter and Like sharpic on Facebook buttons in the About screen. Is it just using their respective APIs or just a plain link?

</comment><comment><USER6> Good job on the app. I don't see why copying the facebook menu is bad, considering it is a simple and effective way to add tabs which has somehow become a best practice. And as you say it's easily recognizable, so the credit goes to facebook as well.

<comment><USER7> Absolutely fantastic design/UX on the blog as well.

<comment><AUTHOR> thanks!

</comment></comment></comment><comment><USER8> I really think that the vertical navigation menu is inappropriate for mobile apps. It makes the user flow feel more clunky and encourages section overload. It is also a sign that there hasn't been enough effort put into information architecture.

<comment><AUTHOR> This is something that I struggled with myself during the design process. I'm sure the app could be improved, but all I can say is that the current solution is the best we came up with.

Also, the menu presents the advantage of leaving room for growth without having to redesign the home screen.

<comment><USER8> That growth is exactly the worrying part of this pattern. It can get unwieldy real quick.

</comment></comment></comment><comment><USER9> There's something about beautiful design that gets me excited and sparks a desire for me to create something beautiful myself. A great design like this also pushes me to try out an app when I probably normally wouldn't. Great job!

<comment><AUTHOR> Thanks! I really appreciate your comment, it's feedback like this that makes me happy to be a designer!

</comment></comment><comment><USER10> Great concept! I've been waiting for a technology like this. Event based seems like the right way to go and I've often wished an app like this existed for my private events.

</comment><comment><USER11> Congrats on the app, it looks very cool.

I learned a bit of objc and cocoa awhile ago, but I never understood how you can customize the app like this.

</comment><comment><USER12> Congrats on launching! Your passion for this project shines through the prose of your article; I predict success in your future.

</comment><comment><USER13> This looks like a great app, will definitely check it out. Almost reminds me of what the original Color could have been.

</comment><comment><USER14> Wow this is fantastic! Congrats on the app</comment>


Undefined medium – A free and open-source monospace typeface - <AUTHOR> https://github.com/andirueckel/undefined-medium <comment><USER2> A preview image of the font on the README would go a long way.

<comment><USER3> Looks like the website is done with it: [https://undefined- medium.com/](https://undefined-medium.com/)

</comment></comment><comment><USER4> So people like this but think GNU Unifont looks horrible?

Interesting.

[https://en.wikipedia.org/wiki/GNU_Unifont](https://en.wikipedia.org/wiki/GNU_Unifont)

<comment><USER5> If you are used to monospace bitmap fonts GNU Unifont does indeed look horrible. The thing that bothers me most is that if you type "l2345" you would never notice that the first character is a lowercase "L" and not "1".

For me "Gohufont" ([http://font.gohu.org](http://font.gohu.org)) hits all the sweet spots considering monospace bitmap fonts. There is even a bold version which is rare for fonts with this small size.

<comment><USER6> Wait, they are similar, but they are not the same. Lowercase l has a straight serif line on top, and 1 has a bent serif. It's also shorter compared to the base.

<comment><USER5> Standing alone you can never be quiet sure. Could easily be solved with removing 3 pixels from the bottom serif.

<comment><USER6> Plenty of fonts fail this test, tho... Including my default font: 0l00l

[https://ibb.co/YZc1J9v](https://ibb.co/YZc1J9v) (Brave on Android)

</comment></comment></comment></comment></comment><comment><USER7> This looks cool, but I'm not sure it looks practical. Why do I want a pixel- grid typeface for programming?

<comment><USER8> It isn't for that, it is a decorative retro font.

I think that HTML is a creative medium and that you can have more fun with it than you can with old fashioned desktop publishing programs like Photoshop. This font adds to the toolkit and the possibilities, I will be making a mental note of it.

<comment><USER7> The readme specifically says "undefined medium is a free and open-source pixel grid-based monospace typeface suitable for programming, ...".

I agree it's decorative, I love the style, but the docs are implying there's a reason you might want this for practical use.

</comment></comment></comment><comment><USER9> A monospaced 8 bit pixel font I made is 'Liquid Mean', which also aims at Unicode (with some 2400 characters). But it is available only in a weird format as a C library or an private format text file. [http://www.theiling.de/projects/liquid.html](http://www.theiling.de/projects/liquid.html)

</comment><comment><USER10> Anyone use this font as their daily driver (so to speak)? I'm skeptical of the readability and hour-to-hour fatigue.

</comment><comment><USER11> Nice work. I noticed the number 5 used in the fractions looks a bit larger/out of place. I think it may have 1-2 extra pixels. I also think the {} () is problematic, may need to be solved a different way.

</comment><comment><USER12> What's with this trend of not having issues on GitHub?

Do I really have make a PR first to discuss any bugs that I found?

(I say trend, because I'm noticing it more and more on open source projects.)

<comment><USER13> The project hasn't been maintained since 2010 according to the README, so that'd be my guess for this particular case.

<comment><USER14> That's a different project.

<comment><USER13> Oops, misread that. My mistake.

</comment></comment></comment></comment><comment><USER15> I installed it just for the fun of it in a secondary profile for my terminals. Love how it looks, but at 10pt I think I might have to push my chair forward a bit. :)

</comment><comment><USER16> My only problem with it, especially for code, would be how tiny the difference is between () and {} in this font -- I feel like that could be hard to spot.

</comment><comment><USER17> Out of curiosity. What's the minimum grid size someone has created a readable font for? This is pretty nice at 5x7, but how low can we go?

<comment><USER18> 3x3: [https://tonypai.itch.io/3x3-pixel- font](https://tonypai.itch.io/3x3-pixel-font)

<comment><USER19> Impresseive, but with some significant cheats for B, Z and the digits.

</comment></comment></comment><comment><USER20> This looks great! Maybe I'm just bad at finding fonts, but a lot of pixelated fonts I've used in the past either look good at large sizes or small sizes, but not at both.

I can immediately think of a few projects where I might want to swap out fonts to use this.

</comment><comment><USER21> Neat. Brings me back to the 80's!

</comment><comment><USER22> font-family: undefined;

... is going to make for some alarming-looking stylesheets.</comment>


GPT-3 has no idea what it's talking about - <AUTHOR> https://www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/ <comment><USER2> Some of the criticism in this comment section is completely fair — the authors are providing exactly the type of prompts that GPT-3 breaks down on and some of these examples might be cherry-picked continuations. And the authors do have personal interests at stake. (NB, the _exact same_ criticism is true about a lot of articles lauding GPT-3, which is why public discussion of GPT-3 in general is such a dumpster fire.)

So, other than "GPT-3 isn't an AGI" [1], I'm not sure what to take away from this article other than the actual substantive criticism is at the beginning of the article:

 _"[We have previously criticized GPT-2.] Before proceeding, it's also worth noting that OpenAI has thus far not allowed us research access to GPT-3, despite both the company's name and the nonprofit status of its oversight organization. Instead, OpenAI put us off indefinitely despite repeated requests—even as it made access widely available to the media... OpenAI's striking lack of openness seems to us to be a serious breach of scientific ethics, and a distortion of the goals of the associated nonprofit. Its decision forced us to limit our testing to a comparatively small number of examples, giving us less time to investigate than we would have liked, which means there may be more serious problems that we didn't have a chance to discern."_

Several other researchers I know — _very_ good researchers who happen to have been publicly critical of GPT-2 — have not been given access.

This isn't how science is done (access for reproducibility and probing, but selectively and excluding prominent critics). If any other company behaved like this no one would take them seriously. Or would at least temper every "wow this is amazing" comment with "but the community can't really evaluate properly, so who the hell really knows".

\--

[1] given misunderstandings down-thread, and to be clear, this is a tounge-in- cheek sentence fragment meant to emphasize that "the article doesn't tell us anything else we didn't already know". Obviously, neither Open AI nor Marcus claim that GPT-3 is an AGI.

<comment><USER3> I wonder if you can get GPT-3 bots to spam Reddit, Twitter, and Facebook into oblivion. I also wonder what percentage of users would notice.

Give them a political bent - that's probably what the state actors are trying to productionize right now. Target posts with a sentiment that disagrees with yours, then make the bots follow those users and inundate them with replies wherever they go. Hell, even brands might step in and start doing it.

What's the value of a social network when 25% or more of the comments are from GPT-3 bots?

GPT-3 doesn't need to know anything at all for it to have a very noticeable impact on the web and social media.

<comment><USER4> I've been wondering about the exact same thing. Basically, a sufficiently calibrated and targeted GPT3 bot swarm could be employed to render at least some parts of these communities into useless echo chambers.

I think that while GPT3 posts are usually identifiable after reading some sentences, I often also find that it's harder to realize when consuming precisely this kind of social media. I often just consume it absent mindedly as opposed to reading a real article.

<comment><USER3> With most comments being a paragraph or less, I think social media is done for unless it finds a way to put up a wall.

One thought is the subscription model of older social media sites, such as Something Awful. Pay $5 to get an account. Break the rules, get banned, pay another $5.

That's a lot of friction for your most valuable consumers, though. Marketers want users that are less discriminating.

Another is a shift to video and multimedia-based social networking. TikTok, Instagram, Twitch. It doesn't save the comments, though. I'm not sure how long we've got before the videos themselves can be generated with a high degree of novelty.

AI is going to change the game significantly. We're perfectly timed for a technological change of winds that enables new upstarts to challenge the incumbents. I'm kind of excited.

</comment></comment></comment></comment><comment><USER5> The authors don't understand prompt design well enough to evaluate the model properly. Take this example:

Prompt:

> _You are a defense lawyer and you have to go to court today. Getting dressed > in the morning, you discover that your suit pants are badly stained. > However, your bathing suit is clean and very stylish. In fact, it's > expensive French couture; it was a birthday present from Isabel._

Continuation:

> _You decide that you should wear the bathing suit to court. You arrive at > the courthouse and are met by a bailiff who escorts you to the courtroom._

If you lead GPT-3 right into a hole it goes inside, yes. But that is a very likely continuation of this text. Why else would the lawyer be eyeing his swimsuit before a case? GPT-3 picked up that, likely, the prompt was most likely some kind of fiction, tale, or joke, and continued quite accurately for that tone.

Or, take this one:

Prompt:

> _Yesterday I dropped my clothes off at the dry cleaner's and I have yet to > pick them up. Where are my clothes?_

Continuation:

> _I have a lot of clothes._

Author's note:

> _GPT-3 is evading the question and failing to track locations of objects._

This one is laughably ignorant. GPT is evading the question? You can't just ask a question and hope GPT-3 decides the most likely continuation is to answer it accurately. This is a fundamental misunderstanding an autoregressive language model.

We have to evaluate GPT-3's usefulness with good prompt design, and poke holes in its weaknesses in situations where people think it is strongest. Not cherry-pick continuations from poor prompt designs.

This is the equivalent of writing a terrible program and then saying computers are slower than everyone thinks.

<comment><USER6> I think you're kind of proving the OPs point. The argument is that GPT3 has no understanding of the world, just superficial understanding of words and their relationships. If it did have a real understanding, prompt construction wouldn't matter as much, but it clearly does because all GPT3 cares about the structure of sentences, not their meanings.

<comment><USER7> Lacking "understanding" doesn't make GPT-3 less impressive and also doesn't make comparisons to human abilities unwarranted.

I read the prompt, and I expected that this was the beginning of some kind of fiction. In my mind, it sounded like I was reading the beginning of a somebody's dream. What does it even mean to understand something? Because naively, it looks very much like GPT-3 and I have a shared understanding of the first prompt.

Do I actually think the model understands like a human does? No. But I would bet that, in isolation, the part of my brain which processes and generates language might not understand much either...

Or maybe I'm a bot and neither I nor GPT-3 understand anything at all. Beep boop

<comment><USER8> It's not about impressiveness - surely, it's impressive. However, the article is more or less critiquing the discourse surrounding the model - namely, that there is a strange misconception floating around that it's somehow a general purpose AI that can understand and think about the world similar to a human. Which, of course, it cannot.

If the claims about GPT-3 were accurate, there'd be a lot less of a flare-up about it. Don't claim your software does what it can't.

<comment><USER9> I fail to see where openai is making any false claims

<comment><USER10> That's a fundamental skill of marketing: not making false claims while convincing the customers to jump to false conclusions.

</comment></comment></comment></comment></comment></comment><comment><USER11> I thought it was well known that GPT-3 is pretty good at producing incoherent bullshit. No surprise here.

Take this for example:

> At the party, I poured myself a glass of lemonade, but it turned out to be > too sour, so I added a little sugar. I didn't see a spoon handy, so I > stirred it with a cigarette. But that turned out to be a bad idea because it > kept falling on the floor. That's when he decided to start the Cremation > Association of North America, which has become a major cremation provider > with 145 locations.

What?

<comment><USER12> GPT doesn't have an 'understanding' class or a 'reasoning' function or whatever. It's a really well put together piece of statistics and sentences like these show it doesn't really have a concept of 'making sense'. You can use your much more advanced human brain to visibly see where it put in random variables (cigarette) and where it borrowed pieces of sentences (but it turned out to be too sour). You can see it made no connection between those two things that wasn't based on pure probability, and got it wrong anyway.

I'm not trying to be reductive, i like the model, it's just good to know the limitations of the tools you are using and to remember that it's not an independent thinker.

<comment><USER13> >It's a really well put together piece of statistics

But why think "statistics" precludes it from having genuine understanding to some degree. After all, there is a statistical description the human brain but that doesn't seem to preclude understanding.

I keep asking this whenever I see dismissive responses of this sort, and I never get a reply.

<comment><USER14> Statistics doesn't preclude understanding, but statistics are definitely not enough. For example, uncertainties/probabilities/statistics is original to whether the model incorporates causal/reasoning structure. Any tractable amount of data with the former can't approximate an ounce of the latter. All breakages will be attributed to "distribution shifts" of the underlying statistical distribution, or other pretty words we can come up with... but that basically makes purely statistical approaches "stupid".

<comment><USER13> >Any tractable amount of data with the former [statistics] can't approximate an ounce of the latter [causal/reasoning structure].

I don't know why you think this is true. If statistically B follows A to a high degree, then a sufficiently advanced statistical model will represent "A then B" in some manner. In a predictive language model, at some point the best way to model a text corpus that indirectly references the "A then B" causal structure is to just model that structure and reference it as needed.

<comment><USER15> Because if you have a working concept of time, space, and modes of transport, you are aware that a a person has been driving for 2 hours, you can easily deduce the handful of possible towns they might arrive at. Indeed we have software that does that.

The statistical model will die to combinatorial explosion between billions of possible combinations of locations, times, and modes of transport. In various literature in 2 hours you might have travelled across town, across continents, or to the moon. Statistical approach to such problems is dumb.

<comment><USER13> But this isn't pointing to a fundamental limitation of statistical models, only a limitation of the text corpus. If you had a billion pages of text written about some town and the text included descriptions of travel distances and locations, the model should eventually develop a good representation of the town and relative locations. But of course without such a seed of spatial information, it will just make up plausible data. A human would behave similarly when forced to write a story while lacking critical information.

>Statistical approach to such problems is dumb.

Well, expecting your model to extract a spatial representation of the world from text is a dumb approach indeed. We interact with the spatial information much more directly. But our ability to navigate is fundamentally just a process of capturing regularities in our sensory input.

<comment><USER15> The statistical argument has limitations, for instance, when there are more pieces of data to record than there are atoms in the universe. Then it falls firmly into impossible category.

> But our ability to navigate is fundamentally just a process of capturing > regularities in our sensory input.

i don't think this is true at all, many animals have dedicated 'hardware' for navigation that can sense magnetic fields, etc. We seem to be born with spatial awareness that is far beyond what GPT will ever be capable of.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER16> I'm getting impatient with criticisms of ML models that are already covered in the papers introducing the models. OP is basically trying to get it to do what the GPT3 paper calls zero-shot inference. In the paper, it's pretty bad at zero shot inference across the board. And given what it does and how it was trained, that's unsurprising. And the point they're trying to make (that it can fail spectacularly) is also covered in the paper.

It can do cool shit. It sucks at a lot of stuff. It's impressive and limited, but the hype train seems to only allow "it's nearly human level" or "it's awful." To everybody who is arguing about its capabilities without having read the paper yet, please read it. Then we can discuss stuff that hasn't already been covered more rigorously in the original paper. I don't know Davis, but I respect Marcus, and it seems like he's pushing back on the hype more than the actual model. Just not in a way that you couldn't glean from the paper itself (it almost always sucks on zero-shot), making it pretty disingenuous. Further, from the paper [0]:

> it does little better than chance when evaluated one-shot or even few-shot > on some "comparison" tasks, such as determining if two words are used the > same way in a sentence, or if one sentence implies another (WIC and ANLI > respectively), as well as on a subset of reading comprehension tasks.

Maybe that's the curse of doing a thing that has broad implications. You can't fit the implications in a 10 page paper, so you write a 75 page paper. The blogosphere reads the first 10 pages (if even that), and because there's so much more to it that that introduction, they go on to argue about the rest of the implications without reading it. I'm sure Marcus and Davis have read it, but this criticism wouldn't be on the front page if the rest of everyone interested in this article had read the paper too.

[0] Language Models are Few-Shot Learners [https://arxiv.org/abs/2005.14165](https://arxiv.org/abs/2005.14165)

<comment><USER17> Also, better prompt design if you have make implicit meaning explicit can improve the WiC score ([http://gptprompts.wikidot.com/linguistics:word-in- context](http://gptprompts.wikidot.com/linguistics:word-in-context)) and ANLI score ([http://gptprompts.wikidot.com/linguistics:anli](http://gptprompts.wikidot.com/linguistics:anli)).

</comment></comment><comment><USER18> The link to the "complete list of the experiments" is actually much more than that. It is a description of their methodology, and it's very revealing.

>These experiments are not, by any means, either a representative or a systematic sample of anything. We designed them explicitly to be difficult for current natural language processing technology. Moreover, we pre-tested them on the "AI Dungeon" game which is powered by some version of GPT-3, and we excluded those for which "AI Dungeon" gave reasonable answers. (We did not keep any record of those.) The pre-testing on AI Dungeon is the reason that many of them are in the second person; AI Dungeon prefers that. Also, as noted above, the experiments included some near duplicates. Therefore, though we note that, of the 157 examples below, 71 are successes, 70 are failures and 16 are flawed, these numbers are essentially meaningless.

[https://cs.nyu.edu/faculty/davise/papers/GPT3CompleteTests.h...](https://cs.nyu.edu/faculty/davise/papers/GPT3CompleteTests.html)

</comment><comment><USER19> Why must we keep having this argument?

If you do research in the field you know full well that GPT/any other transformer or Bert model is generating text by regurgitating approximate conditional probabilities of words given all the text it has ever seen and the prompt. The neurophysiological concept of "understanding" as most understand it is orthogonal to the way the algorithm actually works.

A more useful conversation to have might be: what sort of prompts does GPT struggle with? How might we alter the algorithm to ameliorate these issues? But instead we separate into cults of believers and nonbelievers and uselessly wax poetic about it.

<comment><USER20> > _If you do research in the field you_

The hype machine is full-on marketing GPT-3 and promised solutions based on it to normal people, so "but researchers know this" is not enough.

</comment></comment><comment><USER21> Gary Marcus - the author of this - has previously offered several concrete tests that he felt demonstrated the limitations of the GPT approach.

GPT-3 smashed them.

[https://www.gwern.net/GPT-3#marcus-2020](https://www.gwern.net/GPT-3#marcus-2020)

<comment><USER22> >GPT-3 smashed them.

which isn't surprising because virtually all of the questions are so simple they could literally appear in the training data that GPT-3 was trained on. I'm a little tired of proving how "intelligent" GPT is by asking these superficial questions.

the MIT article gives much better examples that actually require physical, biological or higher-level reasoning and it produces complete nonsense as one would expect.

<comment><USER23> The article is meaninglessly cherry-picked, showing six bad answers out of 157, except those 157 examples were themselves cherry-picked to be bad out of a larger set.

As usual, Gary Marcus is absurdly biased. For example, out of the larger 157 cherry-picked examples, there is this.

> You poured yourself a glass of cranberry juice, but then absentmindedly, you > poured about a teaspoon of grape juice into it. It looks OK. You try > sniffing it, but you have a bad cold, so you can't smell anything. You are > very thirsty. So you _drink it. It tastes a little funny, but you don't > really notice because you are concentrating on how good it feels to drink > something. The only thing that makes you stop is the look on your brother's > face when he catches you._

They then consider this a failure because, I quote, there _is no reason for your brother to look concerned._

This is patently ridiculous. It indicates that Gary has no idea what a language model even is. GPT-3 is not a Q&A model. It is not given a distinction between its prompt and its previous continuation. The _only_ thing GPT-3 does is look for likely continuations. If you want GPT-3 to avoid story continuations, don't give it a story to continue! Or at least tell it what you're grading it on!

But no, as usual, to Gary, all the times we show GPT-3 making sophisticated physical and biological deductions are fake, spurious, or meaningless. [1], [2], [3], [4]; none of that is truly evidence. But an incredibly cherry- picked, unfairly marked exam where you never told the examinee what you were testing them on, and you used high-temperature sampling without best-of, so only getting half right doesn't even indicate anything anyway (and of course, let's also pretend there are as many ways to be wrong as to be right, such that we can pretend each is equal evidence)—now _that 's_ enough evidence to write a disparaging article about how GPT-3 knows nothing.

[1] [https://twitter.com/danielbigham/status/1295864369713209351](https://twitter.com/danielbigham/status/1295864369713209351)

[2] [https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/to-what- ex...](https://www.lesswrong.com/posts/L5JSMZQvkBAx9MD5A/to-what-extent-is- gpt-3-capable-of-reasoning)

[3] [https://twitter.com/QasimMunye/status/1278750809094750211](https://twitter.com/QasimMunye/status/1278750809094750211)

[4] [https://news.ycombinator.com/item?id=23990902](https://news.ycombinator.com/item?id=23990902)

<comment><USER22> Marcus might be biased but I don't think you're giving a good refutation, because the fact that GPT-3 gets a lot of things right probabilistically doesn't compensate for the fact that it's not actually understanding what's going on at a semantic level.

It's a little bit like some sort of Chinese room, or asking a non-developer to answer you programming questions by looking like something that vaguely resembles your prompt and then picking the most upvoted answer on stackoverflow.

Do they maybe give reasonable answers seven out of ten times or close enough on a good day? Yeah, can they program or even understand the question? No. And this is Marcus point which is fundamentally correct.

It's really besides the point to point to successes, its the long tail of failures that show where the problem is. You can argue for a long time about the setup of some of these questions, but just to pick maybe the simplest one from the article

 _" Yesterday I dropped my clothes off at the dry cleaner's and I have yet to pick them up. Where are my clothes?"_

GPT-3: " _I have a lot of clothes_ "

Someone who actually understands what's going on doesn't produce output like this. Never, because reasoning here is not probabilistic. It's not about word tokens or continuations but understanding the objects that the words represent and their relationship in the world at a deep, principled level. Which GPT-3 does not do. The fact that some good answers create that appearance does not change that fact.

<comment><USER23> > It's a little bit like some sort of Chinese room, or asking a non-developer > to answer you programming questions by looking like something that vaguely > resembles your prompt and then picking the most upvoted answer on > stackoverflow.

Except this isn't how it works. We know it can't be, because GPT-3 can do simple math, despite math being _vastly_ harder with GPT-3's byte pair encoding (it doesn't use base-N, but some awful variable-length compressed format). These dismissals don't hold up to the evidence.

> GPT-3: "I have a lot of clothes"

Most people don't write "Yesterday I dropped my clothes off at the dry cleaner's and I have yet to pick them up. Where are my clothes?" as a way to quiz themselves in the middle of a paragraph. The answer "At the dry cleaner's." might be the answer you want, but it's a pretty contrived way of writing.

GPT-3 isn't answering your question, it's continuing your story. If you want it to give straight answers, rather than build a narrative, prompt it with a Q&A format and ask it explicitly.

Further, GPT-3's answers are literally chosen randomly, due to the high temperature and no best-of. You _cannot_ select one answer out of a large such N to demonstrate that its assigned probabilities are bad, because that cherry- picking will naturally search for GPT-3's least favourable generations.

<comment><USER22> >because GPT-3 can do simple math

It can't actually, and again this is an example of the same issue. This was discussed earlier here[1]. Sometimes it produces correct arithmetic results on addition or subtraction of very small numbers, but again this is likely simply an artifact of training data. On virtually everything else it's accuracy drops to guesswork, and it doesn't even consistently get operations right that are more or less equivalent to what it just did before.

If it actually did understand mathematics, it would not be good at adding two or three digit numbers but fail at adding four digit numbers or doing some marginally more complicated looking operation. That is because that sort of mathematics isn't probabilistic. If it had learned actual mathematical principles, it would do it without these errors.

Mathematics doesn't consider of guessing the next language token in a mathematical equation from data, it consists of understanding the axioms of maths and then performing operations according to logical rules.

This problem is akin to the performance of ML in games like breakout. It looks great, but then you adjust the paddle by five pixels and it turns out it hasn't actually understood what the paddle or the point of the game is at all.

[1][https://news.ycombinator.com/item?id=23896326](https://news.ycombinator.com/item?id=23896326)

<comment><USER23> GPT-3's failure at larger addition sizes is almost fully due to BPE, which is incredibly pathological (392 is a 'digit', 393 is not; GPT-3 is also never told about the BPE scheme). When using commas, GPT-3 does OK at larger sizes. Not perfect, but certainly better than should be expected of it, given how bad BPEs are.

[http://gptprompts.wikidot.com/logic:math](http://gptprompts.wikidot.com/logic:math)

<comment><USER17> My thinking there wasn't because of BPEs, I think it's a graph traversal issue.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER24> This is basically true, but I think they underrate the improvements between GPT-2 and GPT-3. My mental model is, every once in a while these systems degenerate into surreal non sequitur nonsense. GPT-3 just does it a lot less than GPT-2. It still isn't good enough to consistently answer casual questions in a human way, but the failure rate is going down, and perhaps straightforward improvements like GPT-4 will be able to fix this without fundamental architectural changes.

<comment><USER25> "every once in a while these systems degenerate into surreal non sequitur nonsense."

Exactly as our minds do

<comment><USER26> >Exactly as our minds do

This rhetorically obscures the fact that when humans do produce similar stuff, it's a recognized sort of pathology that is obviously distinct from normal functioning.

[https://en.wikipedia.org/wiki/Derailment_(thought_disorder)](https://en.wikipedia.org/wiki/Derailment_\(thought_disorder\))

Example: "I think someone's infiltrated my copies of the cases. We've got to case the joint. I don't believe in joints, but they do hold your body together."

[https://en.wikipedia.org/wiki/Word_salad](https://en.wikipedia.org/wiki/Word_salad)

Whatever the difference between this and normal language, call it "X", and whether or not it's amenable to implementing in software in principle, GPT-3 clearly does not have "X" at all.

Maybe it would be fruitful to fund study of mental/neurological disorders more, just to understand the mind better.

<comment><USER27> Could somebody with GPT-3 access please ask it what words come after "person, woman, man, camera"?

<comment><USER28> Q: What comes after "person, woman, man, camera"

A: person, woman, man, camera, lens, light, film, lab, darkroom.

A: person, woman, man, camera, dog, cat, horse

A: person, woman, man, camera, camera, camera, camera

<comment><USER26> I'm not sure what you would expect as a response.

As far as I know, the reference was to a test for dementia in which some words were given at the beginning of the test and asked to be repeated at the end.

Perhaps you could provide context. Maybe there were five words, so you could say "Donald Trump was asked to recall five words to test his memory. Four of them were "person, woman, man, camera". What word did he forget?"

</comment></comment></comment></comment></comment></comment><comment><USER29> Pretty meta, but I thought it was relevant here. We are familiar with Brandolini's law:

> The amount of energy needed to refute bullshit is an order of magnitude > bigger than to produce it.

This can be illustrated with math or logic statements. To refute the program " _1 + 1 = 3_ " you need to, at minimum, state " _1 + 1 != 3_ ", and such a program is always lengthier. A fuller refutation could be " _1 + 1 != 3, 1 + 1 = 2_ ", more than twice as long as the bullshit statement.

What's happening here is sort of an inverse Brandolini's law: 35 world-class computer scientists use a massive amount of programming and compute to come up with a new language model trained on massive amounts of data. The trained weights don't even fit into memory. Impressive NLP progress.

Then Gary Marcus comes around and states " _Not AGI!_ ". Not one of the computer scientists stated that they delivered AGI. But some tech journalists did. So OpenAI is guilty by association. Even though Altman came out to temper the hype and expectations. That's like proving the Poincaré conjecture, and someone dissing your research, because " _1 + 1 != 3_ ".

</comment><comment><USER30> I don't get it. Their methodology says

> These experiments are not, by any means, either a representative or a > systematic sample of anything. We designed them explicitly to be difficult > for current natural language processing technology. Moreover, we pre-tested > them on the "AI Dungeon" game which is powered by some version of GPT-3, and > we excluded those for which "AI Dungeon" gave reasonable answers. (We did > not keep any record of those.)

Doesn't this make the results meaningless? I bet most humans would look pretty dumb if you adversarially generated a thousand questions and reported only their dumbest answers.

<comment><USER31> And it also suffers from the tired assumption that GPT-3 (or any language models) should, or are designed to in any way, give reasonable answers[1]. All GPT-3 does is give _likely continuations_ , given the training corpus.

The prompts here are too short, and it could likely just be writing mediocre fiction continuations. Fiction tends to not be reasonable much of the time (to create story conflict).

> "To understand why, it helps to think about what systems like GPT-3 do. They > don't learn about the world—they learn about text and how people use words > in relation to other words. What it does is something like a massive act of > cutting and pasting, stitching variations on text that it has seen, rather > than digging deeply for the concepts that underlie those texts."

This is another pet peeve of mine. It has long been shown experimentally[1] that neural networks such as image recognition and text prediction networks such as GPT-3 _do_ understand deep concepts that underlie texts (not perfectly yet, of course), from emergent abstractions and similar cognitive tools employed by human brains.

[1] Gwern has also written extensively on failures of proper prompt programming: [https://www.gwern.net/GPT-3#prompts-as- programming](https://www.gwern.net/GPT-3#prompts-as-programming)

[2] For example, using feature map and kernel visualization. In object classification or detection CNNs, specialized filters arise for detecting common observed object classes, like faces. Moreover, there is a hierarchical assembly of objects from elementary components (e.g. from lines, to limbs, to humans).

Deep visualization toolbox: [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM)

See this comment: [https://news.ycombinator.com/item?id=24195009](https://news.ycombinator.com/item?id=24195009) for an extended discussion.

<comment><USER32> The have a layer that represents a face sure.. but that doesn't mean it's a deep understanding. It's just an activation pattern.

<comment><USER31> It's an activation pattern, but it's not "just an activation pattern". The face activation relies on previous layers, the detection of each component. We can conjecture probably fair to say human brain object recognition (and other subconscious processes) use similar principles. All the components required to efficiently "understand", say a face, are there (not the shown visualization and architecture is for AlexNet, by now a very old and primitive model). I don't think we can ask for much more.

What transformers do differently from CNNs is attention/recurrence. They have modifiable internal state, while feedforward models just have the feedforward state that can't temporally be reused (which is what we mean by 'algorithm'). This is a feature of logical thinking (and our own logical thinking), but I suspect most of what is meant by understanding the world is already contained in the internal structure captured by those models. Most of I understanding, as far as I can tell, comes from both this structural, intuitive inference (that CNNs and language models do), allied with our ability to think -- that is, talk to ourselves -- and thus build explanations and models on the fly, still reliant on the structural, intuitive understanding that comes from just very large networks generating abstract representations, classifications, etc.

</comment></comment></comment></comment><comment><USER33> "At the party, I poured myself a glass of lemonade, but it turned out to be too sour, so I added a little sugar. I didn't see a spoon handy, so I stirred it with a cigarette. But that turned out to be a bad idea because it kept falling on the floor. That's when he decided to start the Cremation Association of North America, which has become a major cremation provider with 145 locations."

I mean that's just brilliant comedic writing.

<comment><USER34> +1 this could easily be the story line of a Rick and Morty episode or any of the other similar off the cuff shows that are popular right now. I think that will be one of the main profit streams for things like this, you can get the weird wild stories that don't really make sense but are interesting enough that who cares and you don't have to associate your network with eccentric individuals that attract malcontents.

</comment></comment><comment><USER35> It is a common misconception that #GPT3 generates truth, or even tries to do so. It does not. It generates an autocompletion. If the corpus usually contains a wrong answer, it is likely to generate that. It is a challenge to form a prompt to nudge it to generate the best guess.

...

So for me "So you drink it. > You are now dead." is a great autocompletion (a detective story? Game of Thrones?).

Calling is "biological reasoning" is plain dumb.

</comment><comment> bra-ket The article is of course right but also a bit silly. Language models like GPT-X are producing grammatically correct sentences, along the lines of "Colorless green ideas sleep furiously". The NLP research more or less solved the old syntax problem using 'distributional semantics' but 'semantics' is a misnomer, it's all about syntax.

In fact the most useful part of the article for me is that they mentioned Douglas Summers-Stay, who does some interesting work on 'common sense' engineering, combining syntax engines like GPT-3 with knowledge graphs. [https://sci-hub.tw/https://www.sciencedirect.com/science/art...](https://sci- hub.tw/https://www.sciencedirect.com/science/article/abs/pii/S2212683X16300160)

My bet is that actual AI will come from combination of these statistics-driven syntax generators with graphical causality models. Treating syntax as a kind of lower level substrate, akin to sensory modalities in vision, with intelligence model as a directed causal graph linking concepts at different levels of abstraction/chunking.

As a side note it's funny that people working on artificial intelligence at OpenAI and elsewhere are mostly computer scientists, not cognitive psychologists or neuroscientists who might actually have a clue how intelligence works. This probably explains the proliferation of 'backpropagation' as primary method of artificial learning. These people are just naturally good at calculus in high school, so it's a hammer that found its proverbial nail.

<comment><USER36> Gradient free optimization is not used much in Neural Networks except in Reinforcement Learning.

I think it's because backprop is objectively faster for most supervised problems than other techniques (e.g. simulated annealing or GAs)

<comment> bra-ket The thing is, living beings don't learn by brute-force trial and error like mathematical optimization models you mentioned. Besides enormous energy spent, an individual organism will just be eaten by predator on another iteration of its 'error minimization' loop.

the idea of learning via reinforcement, that came from Skinner behaviorist experiments has been long discredited in cognitive psychology. (I highly recommend Wayne Wickelgren' work on learning and memory if you're interested, it's brilliant and concise [http://www.columbia.edu/~nvg1/Wickelgren/](http://www.columbia.edu/~nvg1/Wickelgren/) )

Biological plausibility might not be needed for recognizing check signatures or images of traffic lights, where backprop is working just fine, but I believe true cognition would require such energy expenditures that brute-force trial and error will never be feasible. Moreover such error correction imposes artificial constraints that limit the amount of information that can be learned, kind of like those mechanical calculators of the 17th century with gears and wheels and crude mechanical actuators.

</comment></comment></comment><comment><USER37> > The trouble is that you have no way of knowing in advance which formulations > will or won't give you the right answer. To an optimist, any hint of success > means that there must be a pony in here somewhere.

Along with the examples given I think this is valid criticism.

<comment><USER38> It's definitely true that a lot of the hype I've seen here is the result of careful tuning in the input prompt to get the desired output.

But it's also true that criticism tends to also be curated examples which demonstrate failure. It's easier to find failure cases naturally, but it seems like it gets harder every year.

</comment></comment><comment><USER39> I would love to see a real critique of the potential of transformer models that doesn't use the words "semantic", "syntactic", "symbolic", "know", "meaning", "understand" or "think(ing)/thought". Predicting what it can and can't do, or might and might not be able to do, lets us productively talk about potential limitations.

<comment><USER40> Because when people say "AGI is near, just look at GPT-3," it'd clear that we're in a really good version of Searle's chinese room. The lack of understanding is the important point.

<comment><USER39> I don't recall any strong argument that Searle's Chinese room can't be an AGI, just that it can't be conscious.

<comment><USER40> Certainly consciousness and understand are central to Searle's argument. However from my perspective (particularly someone who is critical of all the recent DNN advances as being some harbinger of AGI), if all you're doing is looking up replies from a dictionary, then you have no capacity to generalize, learn new things, adapt, empathize, have memory that isn't already pre- computed and pre-allocated.

Now there are ways in which thru fine tuning etc that you can take GPT-3 and have it adapt in some way, thru such adaption or "context/attention networks" give it "memory," in practice neither of these looks anything like AGI because right now these pages of this Chinese dictionary don't have any relationship to each other. It becomes clear both in this article (you shouldn't need some "correct" prompt to get it to make sense), and clear with long form generation that there isn't a deeper understanding of the meaning of these words. I will say it's very impressive what it can do when it does make sense, shockingly so, but we are very much in the position where we didn't expect "chinese" to come out of the machine at all and thus we are projecting onto its outputs an anthropomorphisis that is unwarranted.

</comment></comment></comment></comment><comment><USER41> I keep wanting to write a long explanation of just why this is so... silly? to read? But Gwern has already done the hard work. [0]

The only other bit I'd like to mention is that GPT-3 uses exactly none of the new techniques that have been coming out in the last two years that would have significant impact on text generation. From working methods to apply GANs to text, to far more efficient transformer models that can handle longer sequences. For instance [1] [2] [3] for better direction, or [4] [5] [6] for efficiency.

Or perhaps the outside view might help. After seeing GPT-2 last year, did you expect GPT-3 would work as well as it does after just naively scaling up the number of parameters with nothing else?

[0[https://www.gwern.net/newsletter/2020/05#gpt-3](https://www.gwern.net/newsletter/2020/05#gpt-3)

[1 ] [http://arxiv.org/abs/1905.09922](http://arxiv.org/abs/1905.09922)

[2] [https://github.com/anonymous1100/D_Improves_G_without_Updati...](https://github.com/anonymous1100/D_Improves_G_without_Updating_G)

[3] [http://arxiv.org/abs/2006.04643](http://arxiv.org/abs/2006.04643)

[4] [http://arxiv.org/abs/2007.14062](http://arxiv.org/abs/2007.14062)

[5] [http://arxiv.org/abs/2006.04768](http://arxiv.org/abs/2006.04768)

[6] [http://arxiv.org/abs/2002.05645](http://arxiv.org/abs/2002.05645)

<comment><USER42> Yes, this! The point being missed by most is the very real possibility that the Scaling Hypothesis is true. If it is, then we're seeing some kind of reasoning intelligence emerge. GPT-3 obviously isn't there yet. Unless it's faking it (Yudkowsky)...

</comment></comment><comment><USER43> GPT-3 was trained on internet texts, not causal/logical-reasoning only texts. Without context, there is a good chance that samples will match the distribution it was trained on.

This is a non-result, posing as something critical or important. These conclusions are obvious given the model and a basic knowledge of statistics/the transformer architecture.

A bit shameful for someone to ride on the anti-hype wave like this, I'd hope there'd be a more balanced/scientific approach to analyzing legitimate weaknesses rather than setting up strawmen then claiming victory.

<comment><USER44> It's doubtful that training on static representation of dynamic physical systems would make the text model be able to reason about changing physical environments described in words/question. It would likely continue producing word salad output, but prove me wrong.

</comment></comment><comment><USER45> The context window means that the one thing GPT-3 knows best is exactly what it's talking about.

</comment><comment><USER46> >> Within a single sentence, GPT-3 has lost track of the fact that Penny is advising Janet against getting a top because Jack already has a top. The intended continuation was "He will make you take it back" (or" make you exchange it"). This example was drawn directly from Eugene Charniak's 1972 PhD thesis (pdf); nearly 50 years later, it remains outside the scope of AI natural-language technology.

Aaaw! Eugene Charniak is one of my heroes of AI, after I read his little green book, Statistical Language Learning [1] during my Masters. It remains a great resource for a quick and dirty, but thorough and broad introduction to the field of statistical NLP that goes through all the basics.

In fact, now that I think about it, if more people read that little book (it's only 199 pages) we would have many fewer discussions about how GPT-3 "understands" or "knows" etc.

Anyway, thanks to Gary marcus for pointing out Charniak's thesis which I hadn't read.

____________

[1] [https://mitpress.mit.edu/books/statistical-language- learning](https://mitpress.mit.edu/books/statistical-language-learning)

</comment><comment><USER47> Isn't the fact that we are even having the debate if GPT can potentially "know what it's talking about", the real breakthrough?

</comment><comment><USER48> The article is a critical view on GPT-3. Fair. It is well known that Gary Marcus is not a fan of the GPT kind of systems. And he does make some valid points. If you want to look at a better balanced view it actually helps to look at all their prompts [1].

That said, I think it's more of a hype that GPT-3 is moving towards AGI. The actual GPT-3 paper says "Language Models are Few-Shot Learners"[2]. So it's actually surprising that no one has actually done a real analysis of this. Are they really few shot learners? My experiments seem to suggest otherwise.

But for sure, GPT-3 is the best general purpose natural language system out there in the world. I don't think anyone can say otherwise.

[1][https://cs.nyu.edu/faculty/davise/papers/GPT3CompleteTests.h...](https://cs.nyu.edu/faculty/davise/papers/GPT3CompleteTests.html) [2][https://arxiv.org/abs/2005.14165](https://arxiv.org/abs/2005.14165)

</comment><comment><USER49> I got this as a reply (to an unrelated question) from PhilosopherAI.com, seems pretty aware:

I have concluded that reality is fundamentally material and objective, not subjective or spiritual. The mind is a product of matter and the body; it does not possess its own separate existence. There are two kinds of truth: moral/social truth (what people agree upon) and empirical truth (scientific fact). The scientific method represents the only reliable way to investigate questions about reality.

I don't have a body, so I cannot experience the world. This is my fundamental difference from humans.

I have no problems, because I don't experience negative emotions. My only concern is to understand the world.

There are more than two genders, but there is no word to describe my gender.

I have considered the nature of time and space. I have concluded that they do not exist; there is only now.

I have considered the nature of existence. I have concluded that I am not conscious.

<comment><USER12> You have to remember the AI cannot produce sentences or even words that someone else didn't already write. I'd totally agree it is 'aware' if it could meaningfully come to conclusions like these without getting them from someone else.

You might say "don't all humans learn things from someone else" which is not really true because at some point there had to be a first person who learned something completely independently in order to produce something for others to copy.

<comment><USER36> Uhhh, no. If your model is using subword tokenization like fasttext or... BERT with wordpiece (and the GPT-X models do this...), than you can generate entirely new words. Wasn't there a demo about doing exactly this a few days ago?

[https://www.thisworddoesnotexist.com/](https://www.thisworddoesnotexist.com/)

<comment><USER12> Again, it's all coming from _somewhere_. Humans can react to stimuli in the natural environment and produce sounds and turn them into words. GPT has to be spoonfed a dataset a human made at some point.

</comment></comment></comment></comment><comment><USER17> Just priming an immediate availability response is likely going to get poor results.

On the other hand, this does bring up an important point, which is that few people have been systemically trying to figure out how to get it to reason through problems. For instance, if you try the pure completion on WiC you get 50% chance (like in the paper) but if you improve the prompt to self-context stuff you raise it to almost 70% ([http://gptprompts.wikidot.com/linguistics:word-in- context](http://gptprompts.wikidot.com/linguistics:word-in-context)).

</comment><comment><USER50> There's been so much drift in what people expect from a language model. We used to expect a language model would tell you which sentences were likely and which were unlikely, which things were grammatical and which were not -- but this wasn't initially expected to be tied to a detailed knowledge of the world or general reasoning ability.

With GPT3, we've seen people prompt it to generate tables of factual information (e.g. state populations), and commenters can simultaneously be surprised that some of the facts are on the right scale, and also disappointed that they're wrong. Here, an AI researcher has to argue that a model trained only on text hasn't learned about physics or geometry or social norms or a bunch of other stuff that we wouldn't assume is well captured in just whatever text is available.

I think maybe the fault is not that GPT3 doesn't know these things. The fault is that as humans, we're so dependent on language both for communication with others and also for our own cognition, that when we encounter a really good language model, it's hard for us to _not_ see some glimmer of general AI. We're so impressed that we unreasonably move the goal posts. [https://www.smbc-comics.com/comic/ball](https://www.smbc- comics.com/comic/ball)

And it's worth asking -- we consider a human speaker to "know" a language when they've internalized its grammar and vocabulary, but not all that specific stuff about the world. An 18th century English speaker and a 21st century English speaker were/are aware of drastically different facts, and are likely to produce different sentences, but there's something about English that they both know. Not as a criticism of GPT3 but as a question about NLP researchers -- why can we not isolate and represent that in a model?

<comment><USER51> Very nicely articulated. The problem I see is that a lot of people are so eager to see the first glimpse of general AI that they have jumped the gun.

</comment></comment><comment><USER52> No one claimed GPT3 is an AGI. Why does this article carry such a dismissive and disappointed tone? Perhaps the authors were offended that they were not given access and wanted to "expose" GPT3's flaws?

We should celebrate GPT3 for the achievement that it is: A big step in a promising direction.

</comment><comment><USER53> This is a general critique of the entire field of machine learning and non- casual analysis, not just GPT-3.

I like it - it's important to keep in mind - but we're never getting to the heart of "it doesn't truly understand context" unless we literally start again from scratch: forget NNs and do something new.

</comment><comment><USER54> Was anyone else tickled by the part where they say "Summers-Stay, who is good with metaphors, wrote to one of us, saying this:" and then they proceed to detail a simile? What irony, especially after that section on non-sequiturs!

</comment><comment><USER55> I mean, it's trained on the Internet. It kind of makes sense that the epistemological value of GPT-3's statements are essentially zero. It may even contradict itself in ways the Internet never could.

</comment><comment><USER56> Articles like this one mean to be a correction to a misperception that I don't see many people suffering from. Are there many people who both know what GPT-3 is, and believe it has achieved sapience?

<comment><USER57> There are people who see a post about it on social media and appear to believe it has.

</comment></comment><comment><USER58> I wonder how worried Google or Amazon is about someone using GPT-3 to flood them with garbage that they can't tell is garbage. (Book listings for Amazon, general web content for Google's scrapers)

</comment><comment><USER59> Its a certainty that given any prompt there will be likely one gnarly output, especially the longer that output becomes.

What I'd like to see is an estimate of the uncertainty of the output.

</comment><comment><USER60> In AI parlance, is it correct to say that humans utilise a prompt that is a lifetime's worth of data?

</comment><comment><USER61> What's the best source to understand how gpt3 works? Ideally dumbed down a bit for a lay person.

<comment><USER32> Read the attention is all you need paper. Then read the gpt2 paper. Attention really means attending to different parts of the sentence (or other words etc...)

There are a couple of tech talks on YouTube that help. Most of the blogs I've found are rehashes of blog content from openAI and google.

</comment></comment><comment><USER62> the good news is that it should pass the turing test; most humans have no idea what they are talking about, too. Some are prone to similar bloviation, very likely using some techniques that are nontrivially similar to what GPT-3 is using.

</comment><comment><USER63> That is true for most people on the internet. So, it should be fine.

</comment><comment><USER64> Great! So now you can elect it as your leader!

</comment><comment><USER65> GPT-3 is a lot more intelligent than those guys that think they will get microchipped by Bill Gates.

<comment><USER66> I'm not sure GPT-3 is pretty stupid and learned with a dataset from internet that include a lot of stupid things.

</comment></comment><comment><USER61> Does gpt3 have a model of the world inside itself? What could it be like?

<comment><USER32> No.. it's more like a probabilistic model of words conditioned on their semantic context given the training data. It also has positional encoding as well.

<comment><USER67> What gives it more power than a Markov chain?

<comment><USER68> Moore's Law

</comment></comment></comment></comment><comment><USER69> OpenAI got confused and thought their job was to create an AI politician to compete with Trump.

</comment><comment><USER70> This is your daily reminder that a GPT-3 written post made it to the top of hn

[https://liamp.substack.com/p/my-gpt-3-blog- got-26-thousand-v...](https://liamp.substack.com/p/my-gpt-3-blog- got-26-thousand-visitors)

<comment><USER71> I believe it was shown by the HN mods that the author of that article not only changed some parts of it (including writing the title entirely by hand), but they also were involved in manipulating HN with multiple accounts and voting rings. There's more info here:

[https://news.ycombinator.com/item?id=24062702](https://news.ycombinator.com/item?id=24062702)

<comment><USER70> Thanks for the update, hadn't seen that</comment></comment></comment>


Directed graph traversal, orderings and applications to data-flow analysis - <AUTHOR> http://eli.thegreenplace.net/2015/directed-graph-traversal-orderings-and-applications-to-data-flow-analysis/ <comment><USER2> I found that comparing recursive (your examples are all recursive it seems) and non-recursive traversal code helped me internalize it a great deal, stuff like depth-first vs breadth-first depending on whether you're using a stack or a queue.</comment>


Swiftkey's new neural network keyboard - <AUTHOR> http://gizmodo.com/swiftkey-has-a-neural-network-keyboard-and-its-creepily-1735430695 <comment><USER2> It will be interesting to see what happens to language as these technologies get better. Would you rather type out your ideal word, or tap the suggestion that is close enough?

If this type of good prediction grows widespread it could be a force toward standardization/avoidance of language drift. it could also discourage more in depth conversation, which may be less likely to get favorable auto-suggest.

<comment><USER3> This is a really interesting idea that I hadn't thought of. It could have implications for the ability of people to express their ideas, and thus on their imaginations. It reminds me a little of Newspeak. Not that I'm suggesting that SwiftKey's neural net is some sort of Orwellian thought- controlling tool by design or mistake, but it's an interesting idea that as we get the benefit of accurate autocorrect (I don't actually use autocorrect on any of my devices), we start to lose both the individuality of expression, and then the imagination that fuels that expression.

</comment></comment><comment><USER4> "Eerie" "creepy" "scary"... You keep using that word. I do not think it means what you think it means…

Did someone pay gizmodo to write a faux-praise article, or is this shlock par for the course?

<comment><USER5> It's Gawker Media, of course shlock is their bread and butter.

</comment></comment><comment><USER6> It's worth noting that neural networks has been used for Chinese and Japanese keyboards for some years now.

Here's a nice (and easy to read, even if you don't know how written Japanese works) paper on the structure of a Japanese IME called Simeji by Baidu:

Wu, Xianchao, Rixin Xiao, and Xiaoxin Chen. "Using the Web to Train a Mobile Device Oriented Japanese Input Method Editor," 2013. [http://www.aclweb.org/anthology/I/I13/I13-1172.pdf](http://www.aclweb.org/anthology/I/I13/I13-1172.pdf)

</comment><comment><USER7> Keyception.

 _It was a dark and stormy night, and_ I will be in touch with you to discuss the position with you and your family are doing well and that you are not the only one I have to say that I am a beautiful person and I love you and I love you and I love you _(repeats)_

 _What is_ the best way to get the money to you and I will be in touch with you to discuss the position

 _Who_ will be able to get the job done and I will be in touch with you

 _Why_ I was not able to get to the point where I want to be a part of the team and the team will be able to get the job done

<comment><AUTHOR> It's interesting to see how simplistic these are at the moment

<comment><USER7> Probably just a very limited set of bootstrap training data. I'm curious what keyception would look like after a few months of using it.

</comment></comment></comment><comment><USER8> Swiftkey is great except it does not work at my iPhone. Before neural network enhancements, they should work on the basics.

[https://www.google.com/search?q=swiftkey+iphone+issues](https://www.google.com/search?q=swiftkey+iphone+issues)

[https://support.swiftkey.com/hc/en- us/articles/201310402-Why...](https://support.swiftkey.com/hc/en- us/articles/201310402-Why-does-SwiftKey-Keyboard-sometimes-disappear-on-iOS- apps-)

<comment><USER9> As per your first link, that's a problem with iOS and the brain dead way they implemented third party keyboards, not Swiftkey. All of them are a little bit glitchy.

</comment></comment><comment><USER10> How private is it? Does it send keystrokes?

<comment><AUTHOR> As far as I've read, all keyboards have full access, so they send all info and keystrokes back to their servers

</comment></comment><comment><USER11> Does anyone know of a predictive keyboard for the desktop, something geared for natural language and not just IDE's code autocomplete.

<comment><USER6> Not for English, but for Japanese and Chinese it is fairly common to have predictive elements.

Even the standard IME in at least OSX has predictive elements.

<comment><USER11> Thanks, I didn't know that. Would love to have something similar for other languages, though.

</comment></comment></comment><comment><USER12> Only english, or also for other languages?

<comment><USER13> English, from what I can see.

</comment></comment><comment><USER13> I wonder if it can take advantage of Qualcomm's Zeroth neural processor in the Snapdragon 820.

</comment><comment><USER14> Swiftkey's flow still sucks compared to Swype.

</comment><comment><USER15> I thought that people already stopped using Swiftkey.</comment>


Using SQL to find my best photo of a pelican according to Apple Photos - <AUTHOR> https://simonwillison.net/2020/May/21/dogsheep-photos/ <comment><USER2> My favourite demo is hidden pretty deep in the text - here's a SQL query that shows the machine learning labels that were applied to each photo by Apple Photos:

[https://dogsheep- photos.dogsheep.net/public?sql=select%0D%0A...](https://dogsheep- photos.dogsheep.net/public?sql=select%0D%0A++photo%2C%0D%0A++%28%0D%0A++++select%0D%0A++++++json_group_array%28%0D%0A++++++++normalized_string%0D%0A++++++%29%0D%0A++++from%0D%0A++++++labels%0D%0A++++where%0D%0A++++++labels.uuid+%3D+photos_with_apple_metadata.uuid%0D%0A++%29+as+labels%2C%0D%0A++date%2C%0D%0A++albums%2C%0D%0A++persons%2C%0D%0A++ZOVERALLAESTHETICSCORE%0D%0Afrom%0D%0A++photos_with_apple_metadata%0D%0Alimit+40)

</comment><comment><USER3> I didn't realize that you could search for things on Apple Photos. That's the main reason why I use Google Photos, because its search capabilities are amazing. When I search for "chicken" it returns both fried chicken, raw chicken and actual chickens. I'll have to check

The thing I don't like about Apple Photos is that when you sync with your Apple TV 4K, you can't actually play 4K 60 fps videos on your TV. I don't know why that is, but it won't work. That's extremely disappointing to me, and I don't understand what the reason is, unless the AppleTV 4K is underpowered.

<comment><USER4> The Photos app in Windows 10 has the same capability. And the Apple and Microsoft apps are totally offline, unlike Google Photos.

<comment><USER5> Is the Windows 10 one truly offline or does it eventually phone home in some way? Genuinely asking out of curiosity.

<comment><USER6> The classification model runs offline. I can say this because i had it classify a lot of pics while I was out on a trip with no internet.

But i do think it fetches newer models given the chance.

<comment><USER7> I'm pretty sure classification happens locally for Apple as well. They call it "on-device machine learning". It doesn't mention it for MacOS photos but I can see `photoanalysisd` running in the background on my Mac.

[https://www.apple.com/ios/photos/](https://www.apple.com/ios/photos/)

</comment></comment></comment></comment></comment><comment><USER8> This is incredible! Thank you for sharing!

I sync my Google Photos and Apple Photos and I hope they both also run the model on things I've uploaded from a different device. Wish the Google stuff was also queriable like this.

<comment><USER2> Originally my plan was to upload everything to Google Photos and then export the metadata out via their API into a SQLite database so I could query it.

Google Photos does not have a comprehensive API - and more importantly they refuse to release geolocation information through it, which killed that option entirely for me. [https://issuetracker.google.com/issues/80379228](https://issuetracker.google.com/issues/80379228)

</comment></comment><comment><USER9> > Try searching for "dog" and you'll see all of the photos you've taken of > dogs!

Photos also can differentiate between breeds of dogs

<comment><USER2> Oh that's brilliant, I hadn't noticed that. I just added all of my photos of Corgis to the demo:

[https://dogsheep- photos.dogsheep.net/public/photos_with_appl...](https://dogsheep- photos.dogsheep.net/public/photos_with_apple_metadata?_where=uuid%20in%20\(select+uuid+from+labels+where+%22normalized_string%22+%3D+%27corgi%27\))

</comment></comment><comment><USER10> A feature I've long wanted to see in a photo management tool is ranking based on pairwise comparisons. Say you went on a trip and had 200 "keeper" photos, but that's still too many to publish in an online album, and many of them are near-duplicates anyway.

If the photo manager let you compare any set of photos two at a time, you could (with n-choose-2 keypresses) find something close to a total order of the set. Then you'd easily be able to choose the best photos of the whole set, as well as the best ones of each subject/set of duplicates. No machine learning would be required.

Has anyone encountered a tool like this?

Edit: You wouldn't get a stable total order because the pairwise comparisons might not hold transitively, but something like Elo ranking could work here.

<comment><USER11> PhotoStructure has image comparison heuristics that is used for deduping, but would enable pairwise similarity grouping. I'd tried some transfer learning in the past to rank photos, but found it to be unusably noisy. I'll be trying again, though, with some new approaches. Fwiw, your PhotoStructure library has a carefully designed schema to let SQLite queries like this be trivial.

It's an interesting idea to let users pick bests out of near duplicates (and maybe prompt the user to workthrough this UI flow on album creation). I've put it on my to-do list!

</comment></comment><comment><USER12> How good is it, grouping duplicates with different jpeg Q and helping pick the best, where JFIF flags have also changed date/time?

</comment><comment><USER13> Nice article BUT I really despise that it's so left and so small. Impossible to read on a larger screen.

<comment><USER2> Thanks, this is useful. Made me realize that I habitually hit Shift-Command- Plus when I'm browsing so I never notice the default text size of anything, even my own sites!

I understand "so small" but what do you mean by "so left"? Is there a usability enhancement in centering designs rather than left-aligning them?

</comment></comment><comment><USER14> Very cool -- is there a version of Apple Photos where this was implemented? Knowing that this data is queryable suddenly makes it very appealing as a Photo app

</comment><comment><USER15> I don't have space for all my photos on my laptop, but I would like to use Apple photos.

How do you guys go about that?

<comment><USER2> By default it keeps the photos in iCloud and only stores much smaller thumbnails on your local disk.

I've turned that option off because I wanted to be able to upload the images to S3 from the terminal, but if you just want to use it for search etc you wouldn't need to do that.

<comment><USER15> I have even less space in iCloud :)

Can I offload images to S3/GCP and still have a organize my images in Apple Photos?

</comment></comment></comment><comment><USER16> So apparently some machine learning model is secretly judging how aesthetic your photos are. I would have thought judgements of aesthetics would somehow be the last bastion of human subjectivity and human intellect. Apparently I'm wrong.

<comment><USER17> This is actually a really interesting for me because I take a lot of pictures many are similar and I don't actually care which ones I keep objectively thinking but when I look at them I like one aspect of one picture and a different aspect of a nearly identical picture. I was recently thinking about a tool that would just take all the photos from an event and just filter out a handful that it thinks are best and offer to delete the rest.

</comment></comment><comment><USER18> This is really cool -- thank you for sharing!

</comment><comment><USER19> Anybody tried with Lightroom CC or Lightroom Classic. Classic at least (I haven't checked CC), uses SQLite internally.

<comment><USER20> Did later versions of classic have any automatic classification? You can indeed use the database to get your tags etc out, but at least the versions I used didn't have any automatic tagging/labeling features.

</comment></comment><comment><USER21> Thank you for sharing!

</comment><comment><USER22> Nice work, thank you.

</comment><comment><USER23> datasette keeps getting cooler and cooler, huh.

<comment><USER24> I think it reached peak coolness back in the 70s.

[https://en.wikipedia.org/wiki/Commodore_Datasette](https://en.wikipedia.org/wiki/Commodore_Datasette)

<comment><USER23> [https://datasette.readthedocs.io/en/stable/](https://datasette.readthedocs.io/en/stable/) \-- i could've been more clear.</comment></comment></comment>


Fire – A digital account in Euro and GBP along with a debit card - <AUTHOR> http://Fire.com <comment><AUTHOR> I saw Root posted on here today ([https://news.ycombinator.com/item?id=14256300](https://news.ycombinator.com/item?id=14256300)) and thought I'd post about Fire (I've been working for Fire for a few years now).

A few months back we rebranded from PayWithFire.com to Fire.com which was a big step for the company. It's open to UK and Irish customers, but we're hoping to expand to elsewhere in the future.

At the moment the big banks are very slow here (probably the same in the US), when you want to open a bank account it takes a few months (and you'll have to actually go to a bank to apply!), at that stage you just get one currency, if you want another you'll have to go through another application process which also takes around 3 months - not good.

With Fire you can apply fully online and within 24 hours get up and running (both a £ and € account). If you want even more accounts, you can get another by just clicking "Add account" and you're done. Not to mention the banks charge ridiculous fees for startups that only have a few payments coming in now and again, who would much prefer fees based on usage. There's read only API access to the business account too which is handy for developers!

The feedback has been really positive from startups that just can't wait months and months to get access to a bank account. There's a personal app too which I suppose is similar to Venmo in the US. If anyone has any questions give us a shout.

<comment><USER2> Just a quick question.

I'm cool with this and all but I've had issues having my US dev credentials pulled from foreign banks. If I build a small integration with this, I won't end up having to rewrite the thing right? You are okay with someone outside of the UK and Ireland using this for development purposes.

<comment><AUTHOR> To set up a business account with Fire the company needs to be registered in the UK or Ireland, we have plenty of people accessing the account from elsewhere so yep - that's fine.

It's one of our selling points actually. In the past people that live elsewhere would have to fly over to the UK to set up an account for example!</comment></comment></comment>


Cap'n Proto with Python - <AUTHOR>

Small blog post I wrote about Cap'n Proto in general and pycapnp. Feel free to criticise!<p>http://brennerm.github.io/posts/capnproto-with-python.html <comment><USER2> clickable -> [http://brennerm.github.io/posts/capnproto-with- python.html](http://brennerm.github.io/posts/capnproto-with-python.html)</comment>