At first, I thought this was laughable, because SEO bottom feeders would immediately game everything. But after contemplation, I think that would work out. The SEO stuff would be immediately obvious, because it would be all tricks, it wouldn't have a thin veneer of BS "content" like it does today. Human could detect SEO spam instantly.
I think AI spam will be the nail in the coffin for standard search engines. What will be interesting is how SEO might start targeting AI source collection.
I imagine being a source for a question like "What are the best speakers to buy for $100?" would be quite valuable.
Open source them, along with ML algorithms and model weights. Anything less is effectively meaningless, and if you insist that doing so is a privacy risk then your product is unfit for society.
Add to that algorithms used to ascertain whether email will be treated as spam. I'm tired of explaining to people that Google and Outlook email are NOT deterministic, and that they could be missing email that might never even make it in to their spam folder, and they'd never know, nor would they ever know why.
People say that spammers would just do the minimum they need to get spam delivered, but every person can have their own personal threshold for their own spam filters.
> People say that spammers would just do the minimum they need to get spam delivered, but every person can have their own personal threshold for their own spam filters.
OK, so then rather then doing the minimum to get spam delivered, spammers would be more motivated to improve their spam to get above higher tresholds, so that people would have to set their tresholds higher and higher until all the legitimate mail they get is classified as spam.
Yes plus when you call a support desk, if the "person" on the phone is not a real person, the first words said is "You are talking with an AI, not a person."
I believe the only reason for Commercial Orgs are looking at ChatGPT is to eventually replace all support people with some kind of AI.
In a world where support people are often 10.5 or 11.5 timezones away, how will we tell?
Seriously. A couple of years ago I hit a truck tire tread and roached my radiator 30 miles east of Denver on I25. I have State Farm auto insurance, I probably pay more than I should. Their after hours support was terrible, as if they'd never heard of someone needing a tow on an interstate highway outside of city limits and street/number/zip code addressing. "Westbound I25, east of Denver at mile marker 316" is a good location, but I had to talk myself hoarse because the idea of "Interstate Highway 25" was incomprehensible. Several reps wanted a street address, which just wasn't possible.
The towing company also basically held my car for ransom, I guess that's an experience an AI can't provide. Yet
Well, the problem with an AI-based system here might well have been that I-25 goes north and south through the heart of Denver rather than east and west.
The location "30 miles east of Denver on I-25" literally does not exist.
Perhaps you meant I-70?
And perhaps neither support humans far from Colorado nor an AI with a map would know to make that correct.
If talking to an ai is the cost of not being put on hold for 20 min for the simplest request so be it. Honestly I welcome ai customer service. Most of the time it's just something super simple i want to do.
I have never seen nor heard of any "ai" system for customer service that remotely approaches a barely competent human, and those are frustrating and annoying enough.
ChatGPT4-level systems may be able to get much closer if they are provided the training set, and if they can, then great.
BUT, the absolutely need to be able to figure out when they are at the limit of their knowledge/ability, and then hand-off to a human. ChatGPT4 has been spectacularly unable to do anything even close to this; and it actually just starts fabricating bullcrap in a highly confident voice.
Corporate managers are already no good, horrible, awful, terrible at actually providing service, as it seems their only goal is to provide a cursory appearance of service and reduce their human costs. (Incidentally, this also massively misses the opportunity to gather excellent data on where their company could improve it's product/service and gain market share.) "AI" will only exacerbate the trend until several generations in the future when it is actually good enough, and it may become a competitive advantage to provide better service.
My problem hasn't been getting a barely competent human, it's been getting a barely competent human (or better) that also has the correct authority for my situation. Unfortunately, in the case of both AI or humans, unlocking the person that has both the correct competence and the correct level of authority to correct a problem is the hard part.
If it was resolvable with the automated system, I already tried those steps. If it's not, there's often a reason (for the company, and not necessarily a good one) that it's not. And that's where the authority part comes in; if the AI (or the call person) is doing the "figuring out" of the technical part of a problem but passes the authority on to someone without that knowledge, then how can the person with authority possibly provide authorization without first lending their authority to the end-user support provider?
Ultimately, the reason you can't do the things you need to do is because someone in management removed the authority of that end-user support provide to give it; that will still be true in the case of competent AI, and you'll probably have even less recourse since there is no person to advocate for you to the authority on your behalf.
I care a lot more about outcomes than whether the person I'm talking to is "real" or not. If my bill gets corrected by an AI but not a person? I'll take the AI.
Think of the classic "is 0.002 cents the same as 0.002 dollars?" call - https://verizonmath.blogspot.com/ - I just tested, and chatGPT seems to understand the difference - so maybe this would be a case where the AI is better.
edit: to be clear I'm fine with labeling if that's desired, it's the "I want to talk to a real person" that I think is silly (and oddly similar to the "I want to talk to an American" that people also say).
And most people don't care how many carbs are in a candy bar but we mandate labelling because it creates transparency and accountability and empowers the public. We should be doing the same with technology, only far more aggressively and comprehensively.
If someone said "I want to talk to an American" or "a woman" or "a $race person" yah I'd consider that (at my absolute most charitable) "silly" in that context.
I think there's a value/don't value axis but also a serious/silly axis. For food labelling as an example:
Contains gluten. Don't value personally, serious.
Contains GMO ingredients. Don't value personally, silly.
edit: more specific to the support desk example - we could label each tier by "has the authority to do $X", I think that would be far more useful.
> If someone said "I want to talk to an American" or "a woman" or "a $race person"
Well, sure. Or prejudiced, anyway, because none of those things affect how they perform their jobs. But whether or not the entity on the phone is a human absolutely affects how they perform their job. I would deal with a machine very differently than a human as a result.
> we could label each tier by "has the authority to do $X", I think that would be far more useful.
That would be useful as well, but the difference between human and machine isn't really about what authorities they have.
>But whether or not the entity on the phone is a human absolutely affects how they perform their job
Except now you're back to where I was, talking about outcomes.
I had a very minor issue with my ISP today - they sent me an email to return some equipment (that I never had). I used the chat widget on their site to contact them. I got (presumably) a real person and it went perfectly well; nice and pleasant, sorted out quickly.
If instead it was an AI, what's the difference? If I didn't get to the same result, or it took 10x as long, then sure I'd be ticked off. But for the same outcome? Why waste a real person's time?
I don't think we're at the point where an AI can reliably perform tier-1 support at the same level as a human, but I don't think that's too far off either. When we get there, I'll happily deal with the AI. If it doesn't work, then I expect an escalation path - the same way I do with the human support agent.
If I'm actually chatting or calling support, it's because things have already gone so wrong that I've been compelled to chat or call support. That's already an escalation, and is something that requires human understanding and intervention.
I don't see that the current state of AI is anywhere near being able to do that.
> When we get there, I'll happily deal with the AI. If it doesn't work, then I expect an escalation path - the same way I do with the human support agent.
I understand. If we ever get there, I suspect that I'll have no choice but to deal with an AI. Hopefully there will be some method by which I can just immediately escalate further and talk to a person.
Part of what I want from support is to know that my problem has been heard and understood by a person. If it's an AI, that is just more separation between me and the company I'm having a problem with. It may not be rational, but I want to at least be able to think that some person at the company actually understands my issue, the importance of it, and cares.
No matter how capable and "intelligent", no AI can give me that.
>>It may not be rational, but I want to at least be able to think that some person at the company actually understands my issue, the importance of it, and cares.
This is really interesting. I think this might be another "oops, I might be neurodivergent" moment for me, because I realize you're probably in the majority here. Can I give you a hypothetical? I'm not trying to trick you or anything, just calibrating my sense of how folks view this.
You have a minor issue with your ISP. They're overcharging you for some service, $60 when it should be $50. You've tried to resolve it through the site and can't do it self-serve. It's very clear, there's no ambiguity, some mistake on their end. Would you rather:
A) Call in, be on hold 10 minutes, deal with a pleasant/empathetic/helpful person, get it resolved after 10 more minutes.
B) Call in, no hold, deal with a robot/AI (still voice) who is all business, does not attempt to be human-like, get it resolved after 5 minutes.
I'd prefer A for sure. However, if my hold time were an hour rather than 10 minutes, I'd probably prefer B in order to get out of my immediate jam, and then I'd find another ISP.
But if I'm going with something like B, I'd much prefer it not be done with voice. Doing it on a website would be much preferable.
Does it matter if their first language is English? I know a lot of people who don't have it as their first language, but still speak English at least as well as the native speakers around me.
Now, "I'd like to speak to someone who I can understand" is a reasonable ask.
If they remove the human element then it means all the rules become hard and fast because we lose the ability to reason with and get exemptions in special cases right?
There are many dystopian movies that showcase this and the resulting oppression.
Until "Artificial Intelligence" actually exists, we should use a different word. I understand the distaste for "bot", but something like "system" or "computer" or "network" would be fine.
We really need to stop personifying every project that has the categorical goal of AI, but not the result.
The AGI term came about to distinguish a certain level of capability, just alike ASI is appearing now.
I don't see a problem with terminology getting more refined as a field matures. We used to have "cars" and "trucks." These days we have SUVs and sports cars and crossovers and who knows what else. Yes, they used to be "cars". People still find it useful to have more granular categories.
Yes, but in this case, the word is getting less defined. "AI" used to mean one thing, but now it doesn't, because "AGI" took its place. At no point, however, was this distinction made literal. It can only be found in the surrounding context.
I'd argue that "AI" used to mean a whole universe of things, from self-optimizing rice cookers to computers that figure out the meaning of life, the universe, and everything.
Today AI remains a catch-all, but having more narrowly scoped terms like ML, AGI, and ASI lets people talk without tripping over whether or not the rice cooker is going to turn us all into paperclips.
As soon as someone says the words, "an AI", they have entered a narrative about the thing personified. The original subject is lost to an anthropomorphized facade.
The adjective, "General" was originally meant as a classifier for quality, not category. That distinction is still present on the face of the word itself. An AGI is an AI that can compete with human intelligence. Adding "General" to the phrase, "Artificial General Intelligence" does nothing to define "Artificial" or "Intelligence".
The distinction I just made is entirely based on symbolic meaning. Words are defined explicitly, and then put together. In this approach, the resulting meaning is independent of context: it's literal.
Your distinction takes the opposite approach: an entire phrase - regardless of any symbolic definitions each word is known to have - is defined by the narrative it is surrounded with. In this approach, the resulting meaning is dependent on the context: it's literary.
Large Language Models are famous for demonstrating the second approach: they implicitly model whatever patterns exist in a prompt's text, so that they can generate a "continuation" that follows those patterns, without ever needing a literal symbolic definition.
This is exciting, because natural language - as the result of humans taking both literal and literary approaches - is ambiguous. The literal approach that traditional programming language parsers use is fundamentally limited to "context-free grammars", because ambiguity can only be resolved with literary context.
The problem is that LLMs cannot be literal. They are only capable of inference, so they lose all of the functionally that symbolic definition would provide.
The distinction I made earlier has the same limitation that parsers have: it ignores the surrounding narrative context that has evolved away from the original meaning: it's short-sighted.
The distinction you made has the same limitation that LLMs have: it ignores the original symbolic meaning that is still present in each word: it's confidently imprecise.
What matters is the result:
By ignoring the literary context, the definition of "AI" is written in stone, and we lose the ability to talk around it.
By ignoring the literal meaning, we imply "AI" tech is (by definition) capable of both literal and literary transformations.
We can resolve both limitations by simply using new words. There is no need to say "AI" when "NLP" is more precise.
What does personification have to do with it? I never mentioned that. Are you falling into the “only people are intelligent” thing that I keep seeing? ChatGPT is clearly intelligent, just not as intelligent as a human
I don’t see a problem with the term AI, it’s both artificial and intelligent
The word AI has always meant "the intelligent system that we would like to build".
Historically, as soon as the system actually works for some task, it gets a new name. Thus expert systems, neural networks, logistic regression were all supposedly AI at one time and now are distinct. Already, we see a bit of a divergence in nomenclature where people distinguish large language models from other classes of techniques.
Of course, up to now, it has mostly been techies who have used, abused and redefined the term. More recently, marketing types have adopted the work AI so it may lose all meaning soon.
> I believe the only reason for Commercial Orgs are looking at ChatGPT is to eventually replace all support people with some kind of AI.
Support work is far from the only kind of human labor commercial orgs would like to automate if possible, and far from the only kind that AI could automate.
The weights aren't going to help understand "why we see what we see", which is what non-tech people (and this article) think they'll get from companies releasing "the algorithm"
> Twitter releasing the algorithm showed us that certain specific people were getting artificially boosted.
It did? Did I miss something here? I don't remember seeing that. There was certainly code that tallied certain specific people and topics, but I don't see an indication of what was done with that tally.
> To help assuage Musk’s concerns, Platformer reports that Twitter’s engineers created a way to “tweak” the site’s ranking system when they noticed a high-profile user’s engagement dropping, ensuring “that tweets from those accounts were always shown.” That’s part of the reason why users saw Musk all over their timelines in February, as it essentially exempts these accounts from Twitter’s algorithm (although Platformer says Musk’s boost was different from what all the other users receive).
> In February, Twitter rolled out a system to boost Musk’s tweets over everyone else’s and began boosting the tweets of these VIPS, albeit to a lesser extent.
> Anything that is categorized as misinformation gets the rug pulled out from under it.
> Surprisingly, so are posts about Ukraine.
...
> Late on Monday, Gupta told Gizmodo that “upon further investigation” he and his fellows looking into the Twitter code found that it only applies to Twitter Spaces, the platform’s live meeting feature.
True, the list of VIPs was reported on by platformer a couple of days in advance of the code release due to information leaked by insiders, but that could've easily been denied by Twitter. The release of code confirmed those claims.
The Ukraine issue was only discovered due to the code disclosure.
Two, Twitter claims that whole area was for telemetry.
This comment does a massive disservice & deeply injures the cause for real transparency. It does it by making a very cheap shot at a very dumb situation. To say that this makes not having the models OK or interesting or useful? I highly protest. This is a sad distracting sideshow & letting oneself be lured & baited into thinking this little morsel is filling or informative is... Ughh I just can't no, that's just so low a position.
We can't run the algorithm without the weights, be we can understand it -- or rather, we can understand it as much as it's possible for anyone to understand it. Having the weights will not add understanding. Having the loss function used during training will.
Yes, it will, because with weights you can run it against constructed data, and that kind of experiment with the actual algorithm (including weights) is a much better source of practical understanding than abstract analysis of the algorithm without weights.
Your perspective seems to be that I'm not asking for nearly enough, and I 100% agree & apologize for under representing the threat to society & opacity of the new enemy of democratic social good. Thank you & yes yes yes. We also need the training data too.
If some bad actors have access to inside information, that's a limited and solvable problem and doesn't mean that we should give up and give that information to everyone.
Strong disagree with it being "solvable" - bad actors will always exist (don't feel like I need to explain this one, I'm sure we all agree), arms race will always fail because for them it is the PRIMARY focus for income, for tech companies it's always a secondary concern because it's a weaker, more indirect and longer term impact on bottom line. Making information public could (I'm not sold on this by the way, just saying it's more plausible that what is currently being done) weaken the bonus these bad actors have. By making it public you're maybe allowing some weaker bad actors in, but you're also limiting the power these primary bad actors have.
I've never understood the "an imperfect solution is no better than no solution and should be thrown out on principle" view.
Door locks aren't perfect; our justice system isn't perfect; software isn't perfect. But they're all pretty useful and I just can't get behind a movement to abolish any of them because they fail to solve every individual case.
While I'm not in favour of legislating something like that, because I think it would be extreme government overreach, if that were to just happen magically I would be hard pressed to find the problem.
Better yet, use straightforward antitrust to go after the bundling of content hosting with software to display the content. Using "third party" independent clients to display user content from Youtube, Twitter, etc should be a widespread practice, at which point those independent clients can present the service's recommendations (while controlling exactly what data points are input to them), and/or create/display their own recommendations entirely.
I've always assumed the "Don't show me this channel" are processed in batch. They seem to work for me, but they also seem to take up to 24 hours to actually take effect.
Likewise.. there are other users and creators on the platform. Creators often pick "deep dives" or "strange subreddits" as a topic. They show it to their audience, who then goes to search for some of this content on their own. Youtube sees the influx of interest in the topic and starts broadly recommending it.
To many "privacy focused" types immediately start with a conspiratorial bent.
Presuming that there's a nefarious purpose to these one off events, and then trying to tie them to Twitters recent release without any deeper investigation is some rather shoddy journalism.
For me they use to work no more than 24 hours than Youtube forgots the setting. The most reliable way to really get rid of some kinds of content is to not watch it. Another way is to watch a lot of useful videos fully but maybe without sound, just to make Youtube think about me that I am fond of rocket science (otherwise it inevitable get to think that I'm fond of lowball things).
99% algorithms are public already, its the usage and special tweaks made to those that make them unique at some companies. Lets say the white smoke release for twitter algo, its useless because everyone knows how to build a 4 stage recommender (see nvidia merlin open source repo) but the real juice is their weights to know what user embeddings does it learn.
They cant release that since is personal user data.
The fundamental math is almost always SVD (or related), some type of clustering, or some "fancier" minimization.
These companies aren't inventing new math, they're just interpreting/biasing the results differently.
For example, an ML algorithm wouldn't be the secret sauce. Most will result in the same answer. The training data, and its weights, are the secret sauce. Would this consider the training data as part of the algorithm? I would assume not.
algorithm: a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.
Is user data, that's used to train, part of the process? I don't think so. It's an input to the algorithm. In the case of ML, the input is the only thing that matters.
Semantics, but I think it's incredibly important. It's the difference between posturing and actually affecting change.
I think it's the incorrect word. The "algorithm" requires a few pages of code. "Understanding why it's recommending" requires you get, and sift through, terabytes of user data from Youtube. They're very different.
That's an interesting claim that's based on nothing but your own supposition.
How about we get Youtube to show us their algorithm and associated weights (assuming an ML-based approach, which is a huge assumption on your part) and then we can find out if that's true.
> assuming an ML-based approach, which is a huge assumption on your part
What more reasonable alternative do you see, with the existence of my personal recommendation feed? I'm not aware of any personal recommendation systems that don't use ML. Do you have any examples in mind?
I see you keep editing, so I'll respond to the latest version of your comment:
Assuming an ML-based methodology (and I would not personally assume that), yes, the training data, the resultant weights, and the algorithm itself would be of interest, though at least the last two would be necessary.
I believe the point GP was making was that the "algorithm" is not the important part, and is actually reasonably easy to reverse engineer if it's not just copied wholesale from an existing algorithm. The important part is the weights used in the algorithm. Since the weights are input, and usually semi-unique to the user, they would be considered private information.
For example, I could tell you that the "sorting algorithm" I use to foo my bars is ksort[0]. But that's not really meaningful information without also knowing what the keys are.
Here's one possible approach: you can have a secret algorithm OR platform immunity, but not both.
News outlets don't have to publicize their internal editorial decisions, but they are responsible as publishers for the content they publish and can be sued if, for example they publish something false and defamatory.
It isn’t the recommendation algorithms themselves which are insidious, it is that they are coupled directly with a single human. They are like mental parasites which watch every single interaction one makes and adjusts, ever so slightly, to the negative just to keep you watching.
Getting tired of this, because the algorithms themselves are worthless. Instead, demand websites that aren't just a recommendation feed. Demand the ability to categorize and rank content explicitly.
The problem is that these websites are creating a feed, and then training themselves based on how you engage with...the feed. It's a toxic feedback loop, with almost no levers for you to interact with. If you get stuck in a rabbit hole, it is difficult to teach it how to get you out. That's independent of the "algorithm" being used, it's a problem with all of them.
No need to make every person categorize/rank their own content. But let them choose which of the 3rd party ranking algorithms they'd like to have serving them their feed today.
Why would that help? All the content is uncategorized. So what info can these "3rd party ranking algorithms" use to get you the content you want? You're going to get different flavors of Facebook.
Look at what Reddit does: all posts must be submitted to a subreddit, which categorizes it. Each sub upvotes and downvoted that content, which means that the "ranking algorithm" can be dumb as a bag of rocks. At any point, I can choose what content I want to browse without having to "switch algorithms".
The media is preoccupied with "the algorithms", but the problem is with websites that are nothing but a recommendation feed choosing from a formless content soup. It's constantly guessing, the best it can do is "you seem to like this account, I will keep giving you stuff from this person and related people". No concept of interests!
Not you personally, but users in general. See: Reddit. I never "accidentally" get into a conspiracy theory rabbit hole, that's something I can consciously join or unfollow. And because I have an explicit list of subreddits I'm a member of, the platform can much more accurately recommend me content when I'm in the mood to explore.
Which is identical to the Twitter Following page. On Reddit, you're still served specific content first based on reddit's algorithm of upvotes (likes/retweets) and comments (replies)
1. The unit of "following" is a subreddit, not an individual account. This means that your feed might contain a lot of content from a popular subreddit, but never of a single person. In fact, an account's karma on Reddit has no bearing on the reach of its posts. This lets me curate my feed based on the type of content, not based on who posted it and how many followers they have.
2. Because of (1), I have another option to the "front page": browsing a specific subreddit of my choice, where I see posts in basically the same order as everyone else. The closest that Twitter has to this is "Topics", but those are not communities, they are Twitter-curated feeds. You can't make a Topic, and you can't post to one. If your niche interest is not a Topic, you're out of luck, and most subreddits have no analogous Topic. Twitter lets you browse a user's posts, but individuals are high-variance. There's no way to say "show me all this person's tweets related to ML, which is why I followed, not when they talk about cooking." The only way around this is if the author has the prescience to make domain-specific alt accounts.
3. Downvotes. Twitter refuses to let you downvote content, which contributes to its toxicity. Whether you are retweeting to support or to criticize, the outcome is the same: you increase the post's reach. The most outrageous tweets get the most coverage. Reddit's downvote mechanism means that you can signal that a post is inappropriate for a subreddit, which means that the posts in a sub are more relevant to my interests.
Twitter has no such option: popular accounts naturally have high reach by default, and the lack of negative signal means that they can only accumulate more reach. The only way to decrease an account's influence is through coordinated unfollowing campaigns, "cancelling" them. Cancel culture isn't really a thing on Reddit because it's not personality-driven.
I certainly am not going to take the time to do that. But at the same time, I also completely ignore programmatic recommendations, because I find them terrible.
What I do to discover interesting new stuff is to listen to the recommendations of friends, and to check out references or recommendations made from the stuff I already enjoy.
Basically, I do what everyone used to do to find new music, but for all other media as well.
Why stop at recommendation algorithms? All code and internal documentation for every product should be posted publicly. Every single email and discussion board at every company should be open. Will people be happy then?
Oh wow what a wild ride of an article! As someone who has led the building of recommender systems (PM) for the last 5 years this rubs me the wrong way.
> I defend myself from arbitrary data collection that fuels the algorithms using PiHole, the tracker-blocking Disconnect plugin, and Firefox, plus a few other tricks.
The author clearly has no idea what he/she is talking about. Sure, Everything they mention blocks collecting tracking information but recommender systems mainly track your behaviour in the experience itself (clicks, page views, time on page).
> That leaves me and other privacy conscious folk with just one lever to pull offer - disliking a video
Again this author has it all wrong. If you’re serious about privacy the last thing you want to be doing is providing more input into the models we create!
And finally the comment that made me realise this author has no idea what he’s talking about;
> The solution to both issues is obvious, technically easy, and yet commercially a nearly impossible proposition: open up all recommendation algorithms. Make them completely transparent, and, for the individual being targeted by the recommendation engine, completely programmable.
Are you joking? So he/she thinks the east answer if for every user to ”program” their own algorithm. That’s like saying every Tesla owner should be able to program their own car! Let’s open the source code of Tesla to every owner!
> That’s like saying every Tesla owner should be able to program their own car! Let’s open the source code of Tesla to every owner!
Other critiques aside, these things are not very similar. You can't run someone over with a buggy recommender system. You bork your own newsfeed, who cares?
My point was that it’s not easy at all to “open up” an machine learning model for a user to “program”. It’s complex enough for an ML engineer to develop!
In theory, maybe it is possible but it would be incredibly expensive to produce such a system, let alone maintain. But even then, the ability to control an Ml system is such a niche ask that it would never happen
I think you should be able to program your own car and that should be the default. And I think that can be made safe too.
It does not mean that any change should be allowed on the road. But there could be a system where you pay a fee and send your code to be audited and aprooved for use on public roads. Otherwhise you can only run that code on a test track.
And also not all parts of a car's software are safety critical.
It is not "software developer fetishism", it is plain and simple ownership, property rights.
If I buy a car I expect to own the car and be able to do whatever I want with it especially on private land, as long as I don't interfere/endanger/etc. with other people. We used to live in this world but we no longer do. If the car has DRM, locked boot loaders, etc. I can not do whatever I want with it, not even on private property, therefore I am not the owner of the car, I am just the custodian.
Same with household appliances, agricultural equipment, phones, etc. .
Mass alphabetization was a quantitative change in the number of people who could read and write and was most probably opposed (the printing press certainly was) by some at the time but it resulted in a qualitative jump in the quality of life of everyone. The same is applicable for programming. As technology becomes more accessible people should expect to be able to make it conform to their needs instead of them adapting to quirks of tech.
The ability to modify the software is just one part of the right to repair. Purchasing a product used to mean ownership which included the right to repair.
Also working in the field and I don't understand why this article is even on the front-page. The author has clearly no idea what they're talking about, it almost reads as a satire.
I Have a feeling pooping on recommendations systems is en vogue right now. Social commentators love to point out how “evil” ML algorithms are while conversely complaining how X product is terrible at recommendations.
I feel like "open the recommendation algorithms!" is easy to _say_, and it's less easy (and maybe impossible) to do effectively.
Should we have access (by law) to the code that makes recommendations on content? That seems like a governmental overreach. If that was the case, then we should have to have direct access to the brain of every editor/layout person of every newspaper. Why was one story above the fold vs below the fold vs on page 4? I don't see a whole lot of distinction between those two.
Once we get access to that, how do we keep people from abusing these algorithms on the supply side? Thought SEO sites were bad before? Guess what happens when those sites know what they're _actually targeting_, as opposed to blindly guessing!
As a question, how well do you understand the recommender system that you've created? For a given piece of content, would you be able to guess at the penetration into the platform? As an outsider, I sometimes feel like the recommender systems I interact with have grown out of the platform holder's control (see: Nazi/ISIS/ISIL propaganda on YouTube, etc.). I think these are the points the article is reacting to.
So if the article is saying "I would really rather not see Nazi/ISIS/ISIL propaganda," then, ok, that's good. That's a good want. But we're going to need an algorithm to determine what that propaganda _is_, and then downrank that for that user.
So, is the conclusion of the article... misguided? Sure! Is the desire of the author valid?
I don't know. I don't have time to read these articles.
I hope the /s at the end of the last comment was loud enough to not be stated.
But I read the article. Yeah, it was dumb. Specifically:
> If recommendation algos aren’t shared then we need - by legislation, if necessary - a switch that turns the recommendation engine off.
This is... baffling. On YouTube - the case-in-point of the article - you already have access to a non-algorithmic feed of videos: namely, your Subscriptions feed.
And if you don't want a recommendation algorithm, good luck finding new content that you like.
And if you "turn off" the recommendation algorithm, you want... what, exactly? The latest videos uploaded to the platform? Still a recommendation algorithm. Videos with words in titles similar to the last video you watched? Still a recommendation algorithm. Videos that the creators of your favorite channel watched? STILL A RECOMMENDATION ALGORITHM.
I was wrong. The desire of the author is far dumber than I originally gave credit for.
I don't see the problem with someone not wanting recommendations on their front page, having a chronological feed somewhere else is nice but it's not the same thing. Videos that get popular, and are therefore recommended, are those that prey on the basest components of our human nature (even if you curate your feed as best as possible), I don't see why we should put up with it.
Your list of recommendation algorithms is inaccurate, a chronological feed is not a recommendation algorithm in any sense of the term, as for the rest you can't compare the deeply complicated and often abusive recommendation algorithms that are deployed on social media to the simple heuristics you listed.
I mean, you don't have to. Don't want to see YouTube's algorithmic feed? That's cool - just HTTP 3xx your youtube.com to the subscriptions page. There are some good browser addons to do just that[1][2].
Granted, that doesn't remove the recommendation algorithm from your YouTube experience. You would have to use some kind of content blocker to remove the algorithmic feeds from the video pages. I find that uBlockOrigin works well for that.
On a federated network where you can have your own server/pod (think PeerTube or Diaspora) you absolutely can have a custom recommendation algorithm. And for news you can have custom filters in your RSS reader that trim the feeds you are subscribed to to only the desired articles and apply a custom order.
And even in a Cloud/SaaS scenario like Youtube, one can imagine a settings page where you can configure each recommend view in some domain specific programming language using an API. Imagine something like IFTTT but for configuring your preffered recommendations views.
Notice that something you do not want is beeing recommended to you? Just open settings and tweak the algo.
This is just like feeding a custom comparator function to a built-in sorting function using dependency injection.
Of course, this would not be in the platforms interest and will probably never happen.
It really is not that hard to imagine if you have the mindset that you should be able to control your own computation instead of being at the whims of a corporation.
The distinction is easy to make, a recommendation algorithm is personal, automated, scalable to a gigantic amount of content, entirely opaque for the average user.
The social implications are much more dangerous than a newspaper editor and journalist following their paper's political bias.
a) I agree with you that the author lacks a basic understanding, and that they do not solve the problem.
Yet:
b) I think there actually might be a positive outcome for several reasons, including innovation, marketing and ethics, if one is to allow trained model to be queried arbitrarily. At least in the general case. (Think what is happening with stable diffusion and LLMs/LMMs.) Recommendation systems of say Youtube might be more useful to people making videos -- essentially somehow create a pre-query system before one finishes a video.
c) As a couple others have noted, the Tesla example is not a good one. I understand it was formed in haste and I think we all can understand your reasoning. My thought though is:
There is a) the right of repair -- yes still being fought for -- and b) the full self driving ability that might actually demand open sourcing code and models.
How else do we envision the Courts and Congress to formulate liability and empower related LE, and rights groups investigations?
Recall that the aviation industry achieved the "FSD" mode by building the infrastructure, extreme reliability (space and aviation started byzantine systems) and definitely not black box.
P.S. As a personal note: I will never allow something to drive me, if I have any liability, especially if it is a limited "black box" that I can not even arbitrarily query.
Right now we are "cruising" along because liability is with the driver. Tesla and the auto industry will need to face that pill, if they ever achieve their goal. I think they will achieve it personally.
"Completely programmable" is obviously stupid, but having more control over the way items are ranked in your personal feed isn't, particularly when weights are chosen by hand.
Hard though, because the company often doesn't know itself what its "algorithm" is. It is often spread over lots of different teams and departments, and may comprise hundreds of thousands of little design decisions made by people all over the company who are not talking to each other.
I would rather target consumer-hostile practices like youtube not letting users see downvotes, and the generally useless ratings and broken search in places like app stores.
When something ends up with a 95% approval rating, it is almost guaranteed astroturfing.
> ...a switch that turns the recommendation engine off.
I do like that idea. If the company is not going to show what the recommendation algorithm is, then being legally forced to provide an option to turn it off seems fair.
Too many companies pretend the "algorithm" is to blame for their shady practices, versus what its management and owners are directing it to accomplish.
141 comments
[ 2.4 ms ] story [ 199 ms ] threadI imagine being a source for a question like "What are the best speakers to buy for $100?" would be quite valuable.
People say that spammers would just do the minimum they need to get spam delivered, but every person can have their own personal threshold for their own spam filters.
OK, so then rather then doing the minimum to get spam delivered, spammers would be more motivated to improve their spam to get above higher tresholds, so that people would have to set their tresholds higher and higher until all the legitimate mail they get is classified as spam.
You think that spammers are not knowing which spam is silently dropped is the one thing that's keeping them from leveling up? I strongly disagree.
Even if that were the case, in my experience, silent failure negatively affects actual delivery much more than any positive effect it might have.
I believe the only reason for Commercial Orgs are looking at ChatGPT is to eventually replace all support people with some kind of AI.
Seriously. A couple of years ago I hit a truck tire tread and roached my radiator 30 miles east of Denver on I25. I have State Farm auto insurance, I probably pay more than I should. Their after hours support was terrible, as if they'd never heard of someone needing a tow on an interstate highway outside of city limits and street/number/zip code addressing. "Westbound I25, east of Denver at mile marker 316" is a good location, but I had to talk myself hoarse because the idea of "Interstate Highway 25" was incomprehensible. Several reps wanted a street address, which just wasn't possible.
The towing company also basically held my car for ransom, I guess that's an experience an AI can't provide. Yet
The location "30 miles east of Denver on I-25" literally does not exist.
Perhaps you meant I-70?
And perhaps neither support humans far from Colorado nor an AI with a map would know to make that correct.
I have never seen nor heard of any "ai" system for customer service that remotely approaches a barely competent human, and those are frustrating and annoying enough.
ChatGPT4-level systems may be able to get much closer if they are provided the training set, and if they can, then great.
BUT, the absolutely need to be able to figure out when they are at the limit of their knowledge/ability, and then hand-off to a human. ChatGPT4 has been spectacularly unable to do anything even close to this; and it actually just starts fabricating bullcrap in a highly confident voice.
Corporate managers are already no good, horrible, awful, terrible at actually providing service, as it seems their only goal is to provide a cursory appearance of service and reduce their human costs. (Incidentally, this also massively misses the opportunity to gather excellent data on where their company could improve it's product/service and gain market share.) "AI" will only exacerbate the trend until several generations in the future when it is actually good enough, and it may become a competitive advantage to provide better service.
My problem hasn't been getting a barely competent human, it's been getting a barely competent human (or better) that also has the correct authority for my situation. Unfortunately, in the case of both AI or humans, unlocking the person that has both the correct competence and the correct level of authority to correct a problem is the hard part.
If it was resolvable with the automated system, I already tried those steps. If it's not, there's often a reason (for the company, and not necessarily a good one) that it's not. And that's where the authority part comes in; if the AI (or the call person) is doing the "figuring out" of the technical part of a problem but passes the authority on to someone without that knowledge, then how can the person with authority possibly provide authorization without first lending their authority to the end-user support provider?
Ultimately, the reason you can't do the things you need to do is because someone in management removed the authority of that end-user support provide to give it; that will still be true in the case of competent AI, and you'll probably have even less recourse since there is no person to advocate for you to the authority on your behalf.
Think of the classic "is 0.002 cents the same as 0.002 dollars?" call - https://verizonmath.blogspot.com/ - I just tested, and chatGPT seems to understand the difference - so maybe this would be a case where the AI is better.
edit: to be clear I'm fine with labeling if that's desired, it's the "I want to talk to a real person" that I think is silly (and oddly similar to the "I want to talk to an American" that people also say).
So anything that you don't value is silly?
I think there's a value/don't value axis but also a serious/silly axis. For food labelling as an example:
Contains gluten. Don't value personally, serious.
Contains GMO ingredients. Don't value personally, silly.
edit: more specific to the support desk example - we could label each tier by "has the authority to do $X", I think that would be far more useful.
Well, sure. Or prejudiced, anyway, because none of those things affect how they perform their jobs. But whether or not the entity on the phone is a human absolutely affects how they perform their job. I would deal with a machine very differently than a human as a result.
> we could label each tier by "has the authority to do $X", I think that would be far more useful.
That would be useful as well, but the difference between human and machine isn't really about what authorities they have.
Except now you're back to where I was, talking about outcomes.
I had a very minor issue with my ISP today - they sent me an email to return some equipment (that I never had). I used the chat widget on their site to contact them. I got (presumably) a real person and it went perfectly well; nice and pleasant, sorted out quickly.
If instead it was an AI, what's the difference? If I didn't get to the same result, or it took 10x as long, then sure I'd be ticked off. But for the same outcome? Why waste a real person's time?
I don't think we're at the point where an AI can reliably perform tier-1 support at the same level as a human, but I don't think that's too far off either. When we get there, I'll happily deal with the AI. If it doesn't work, then I expect an escalation path - the same way I do with the human support agent.
I don't see that the current state of AI is anywhere near being able to do that.
> When we get there, I'll happily deal with the AI. If it doesn't work, then I expect an escalation path - the same way I do with the human support agent.
I understand. If we ever get there, I suspect that I'll have no choice but to deal with an AI. Hopefully there will be some method by which I can just immediately escalate further and talk to a person.
Part of what I want from support is to know that my problem has been heard and understood by a person. If it's an AI, that is just more separation between me and the company I'm having a problem with. It may not be rational, but I want to at least be able to think that some person at the company actually understands my issue, the importance of it, and cares.
No matter how capable and "intelligent", no AI can give me that.
This is really interesting. I think this might be another "oops, I might be neurodivergent" moment for me, because I realize you're probably in the majority here. Can I give you a hypothetical? I'm not trying to trick you or anything, just calibrating my sense of how folks view this.
You have a minor issue with your ISP. They're overcharging you for some service, $60 when it should be $50. You've tried to resolve it through the site and can't do it self-serve. It's very clear, there's no ambiguity, some mistake on their end. Would you rather:
A) Call in, be on hold 10 minutes, deal with a pleasant/empathetic/helpful person, get it resolved after 10 more minutes.
B) Call in, no hold, deal with a robot/AI (still voice) who is all business, does not attempt to be human-like, get it resolved after 5 minutes.
But if I'm going with something like B, I'd much prefer it not be done with voice. Doing it on a website would be much preferable.
Now, "I'd like to speak to someone who I can understand" is a reasonable ask.
There are many dystopian movies that showcase this and the resulting oppression.
We really need to stop personifying every project that has the categorical goal of AI, but not the result.
I don't see a problem with terminology getting more refined as a field matures. We used to have "cars" and "trucks." These days we have SUVs and sports cars and crossovers and who knows what else. Yes, they used to be "cars". People still find it useful to have more granular categories.
Today AI remains a catch-all, but having more narrowly scoped terms like ML, AGI, and ASI lets people talk without tripping over whether or not the rice cooker is going to turn us all into paperclips.
As soon as someone says the words, "an AI", they have entered a narrative about the thing personified. The original subject is lost to an anthropomorphized facade.
The adjective, "General" was originally meant as a classifier for quality, not category. That distinction is still present on the face of the word itself. An AGI is an AI that can compete with human intelligence. Adding "General" to the phrase, "Artificial General Intelligence" does nothing to define "Artificial" or "Intelligence".
The distinction I just made is entirely based on symbolic meaning. Words are defined explicitly, and then put together. In this approach, the resulting meaning is independent of context: it's literal.
Your distinction takes the opposite approach: an entire phrase - regardless of any symbolic definitions each word is known to have - is defined by the narrative it is surrounded with. In this approach, the resulting meaning is dependent on the context: it's literary.
Large Language Models are famous for demonstrating the second approach: they implicitly model whatever patterns exist in a prompt's text, so that they can generate a "continuation" that follows those patterns, without ever needing a literal symbolic definition.
This is exciting, because natural language - as the result of humans taking both literal and literary approaches - is ambiguous. The literal approach that traditional programming language parsers use is fundamentally limited to "context-free grammars", because ambiguity can only be resolved with literary context.
The problem is that LLMs cannot be literal. They are only capable of inference, so they lose all of the functionally that symbolic definition would provide.
The distinction I made earlier has the same limitation that parsers have: it ignores the surrounding narrative context that has evolved away from the original meaning: it's short-sighted.
The distinction you made has the same limitation that LLMs have: it ignores the original symbolic meaning that is still present in each word: it's confidently imprecise.
What matters is the result:
By ignoring the literary context, the definition of "AI" is written in stone, and we lose the ability to talk around it.
By ignoring the literal meaning, we imply "AI" tech is (by definition) capable of both literal and literary transformations.
We can resolve both limitations by simply using new words. There is no need to say "AI" when "NLP" is more precise.
I don’t see a problem with the term AI, it’s both artificial and intelligent
Historically, as soon as the system actually works for some task, it gets a new name. Thus expert systems, neural networks, logistic regression were all supposedly AI at one time and now are distinct. Already, we see a bit of a divergence in nomenclature where people distinguish large language models from other classes of techniques.
Of course, up to now, it has mostly been techies who have used, abused and redefined the term. More recently, marketing types have adopted the work AI so it may lose all meaning soon.
you want to make the call even longer than necessary to provide me with information i can do nothing about. excellent.
Support work is far from the only kind of human labor commercial orgs would like to automate if possible, and far from the only kind that AI could automate.
For example, Twitter releasing the algorithm showed us that certain specific people were getting artificially boosted.
We also saw that the topic of the war in Ukraine was getting special treatment.
Yes, we all guessed that was happening, but without the code it was just supposition that Twitter could deny.
It did? Did I miss something here? I don't remember seeing that. There was certainly code that tallied certain specific people and topics, but I don't see an indication of what was done with that tally.
> To help assuage Musk’s concerns, Platformer reports that Twitter’s engineers created a way to “tweak” the site’s ranking system when they noticed a high-profile user’s engagement dropping, ensuring “that tweets from those accounts were always shown.” That’s part of the reason why users saw Musk all over their timelines in February, as it essentially exempts these accounts from Twitter’s algorithm (although Platformer says Musk’s boost was different from what all the other users receive).
https://gizmodo.com/twitter-algorithm-aoc-ben-shapiro-cattur...
> In February, Twitter rolled out a system to boost Musk’s tweets over everyone else’s and began boosting the tweets of these VIPS, albeit to a lesser extent.
https://gizmodo.com/twitter-musk-ukraine-crisis-open-source-...
> Misinformation is highly down-ranked
> Anything that is categorized as misinformation gets the rug pulled out from under it.
> Surprisingly, so are posts about Ukraine.
...
> Late on Monday, Gupta told Gizmodo that “upon further investigation” he and his fellows looking into the Twitter code found that it only applies to Twitter Spaces, the platform’s live meeting feature.
I though that perhaps you were talking about something new.
The Ukraine issue was only discovered due to the code disclosure.
Two, Twitter claims that whole area was for telemetry.
This comment does a massive disservice & deeply injures the cause for real transparency. It does it by making a very cheap shot at a very dumb situation. To say that this makes not having the models OK or interesting or useful? I highly protest. This is a sad distracting sideshow & letting oneself be lured & baited into thinking this little morsel is filling or informative is... Ughh I just can't no, that's just so low a position.
Yes, it will, because with weights you can run it against constructed data, and that kind of experiment with the actual algorithm (including weights) is a much better source of practical understanding than abstract analysis of the algorithm without weights.
Door locks aren't perfect; our justice system isn't perfect; software isn't perfect. But they're all pretty useful and I just can't get behind a movement to abolish any of them because they fail to solve every individual case.
What's next?
I'll get my downhill skis!
/s
Likewise.. there are other users and creators on the platform. Creators often pick "deep dives" or "strange subreddits" as a topic. They show it to their audience, who then goes to search for some of this content on their own. Youtube sees the influx of interest in the topic and starts broadly recommending it.
To many "privacy focused" types immediately start with a conspiratorial bent. Presuming that there's a nefarious purpose to these one off events, and then trying to tie them to Twitters recent release without any deeper investigation is some rather shoddy journalism.
They cant release that since is personal user data.
What?
You need to back up that statement, because I do not understand how it's at all true.
Or do you somehow have access to Facebook's news feed algorithm, or the Youtube recommendation algorithm?
These companies aren't inventing new math, they're just interpreting/biasing the results differently.
For example, an ML algorithm wouldn't be the secret sauce. Most will result in the same answer. The training data, and its weights, are the secret sauce. Would this consider the training data as part of the algorithm? I would assume not.
All computing is "fundamental math".
That there are some known set of algorithms in the mathematical toolbox that are used is irrelevant.
The question is, how are those tools put together, what are their inputs, and how are those inputs weighted.
Is user data, that's used to train, part of the process? I don't think so. It's an input to the algorithm. In the case of ML, the input is the only thing that matters.
Semantics, but I think it's incredibly important. It's the difference between posturing and actually affecting change.
The goal: To understand why the system is recommending what it's recommending.
"Algorithm" is a short-hand for that goal.
Everything else is just clever wordplay to try and deflect the conversation.
I think it's the incorrect word. The "algorithm" requires a few pages of code. "Understanding why it's recommending" requires you get, and sift through, terabytes of user data from Youtube. They're very different.
How about we get Youtube to show us their algorithm and associated weights (assuming an ML-based approach, which is a huge assumption on your part) and then we can find out if that's true.
What more reasonable alternative do you see, with the existence of my personal recommendation feed? I'm not aware of any personal recommendation systems that don't use ML. Do you have any examples in mind?
Assuming an ML-based methodology (and I would not personally assume that), yes, the training data, the resultant weights, and the algorithm itself would be of interest, though at least the last two would be necessary.
For example, I could tell you that the "sorting algorithm" I use to foo my bars is ksort[0]. But that's not really meaningful information without also knowing what the keys are.
[0]: https://www.php.net/manual/en/function.ksort.php
Not the only important part.
> Since the weights are input, and usually semi-unique to the user, they would be considered private information.
Cool, then as an individual user, let me see that, too.
This is a complete non-starter from a user safety standpoint.
Aside sticking a camera in the newsroom and livestreaming all day, what would this entail?
News outlets don't have to publicize their internal editorial decisions, but they are responsible as publishers for the content they publish and can be sued if, for example they publish something false and defamatory.
The problem is that these websites are creating a feed, and then training themselves based on how you engage with...the feed. It's a toxic feedback loop, with almost no levers for you to interact with. If you get stuck in a rabbit hole, it is difficult to teach it how to get you out. That's independent of the "algorithm" being used, it's a problem with all of them.
Who has time for this? What about discovering interesting and new content amidst millions of options?
Look at what Reddit does: all posts must be submitted to a subreddit, which categorizes it. Each sub upvotes and downvoted that content, which means that the "ranking algorithm" can be dumb as a bag of rocks. At any point, I can choose what content I want to browse without having to "switch algorithms".
The media is preoccupied with "the algorithms", but the problem is with websites that are nothing but a recommendation feed choosing from a formless content soup. It's constantly guessing, the best it can do is "you seem to like this account, I will keep giving you stuff from this person and related people". No concept of interests!
1. The unit of "following" is a subreddit, not an individual account. This means that your feed might contain a lot of content from a popular subreddit, but never of a single person. In fact, an account's karma on Reddit has no bearing on the reach of its posts. This lets me curate my feed based on the type of content, not based on who posted it and how many followers they have.
2. Because of (1), I have another option to the "front page": browsing a specific subreddit of my choice, where I see posts in basically the same order as everyone else. The closest that Twitter has to this is "Topics", but those are not communities, they are Twitter-curated feeds. You can't make a Topic, and you can't post to one. If your niche interest is not a Topic, you're out of luck, and most subreddits have no analogous Topic. Twitter lets you browse a user's posts, but individuals are high-variance. There's no way to say "show me all this person's tweets related to ML, which is why I followed, not when they talk about cooking." The only way around this is if the author has the prescience to make domain-specific alt accounts.
3. Downvotes. Twitter refuses to let you downvote content, which contributes to its toxicity. Whether you are retweeting to support or to criticize, the outcome is the same: you increase the post's reach. The most outrageous tweets get the most coverage. Reddit's downvote mechanism means that you can signal that a post is inappropriate for a subreddit, which means that the posts in a sub are more relevant to my interests.
Twitter has no such option: popular accounts naturally have high reach by default, and the lack of negative signal means that they can only accumulate more reach. The only way to decrease an account's influence is through coordinated unfollowing campaigns, "cancelling" them. Cancel culture isn't really a thing on Reddit because it's not personality-driven.
As for new stuff, this is how I generally do it:
Interact with friends and others near me. We trade all the time, recommend all the time. This is my long standing favorite.
Community discussion. Here, for one. This is a strong second.
Search, and now it may appear a GPT entity may well serve me up stuff I am going to like and need.
See what "the feed" has in it.
What I do to discover interesting new stuff is to listen to the recommendations of friends, and to check out references or recommendations made from the stuff I already enjoy.
Basically, I do what everyone used to do to find new music, but for all other media as well.
> I defend myself from arbitrary data collection that fuels the algorithms using PiHole, the tracker-blocking Disconnect plugin, and Firefox, plus a few other tricks.
The author clearly has no idea what he/she is talking about. Sure, Everything they mention blocks collecting tracking information but recommender systems mainly track your behaviour in the experience itself (clicks, page views, time on page).
> That leaves me and other privacy conscious folk with just one lever to pull offer - disliking a video
Again this author has it all wrong. If you’re serious about privacy the last thing you want to be doing is providing more input into the models we create!
And finally the comment that made me realise this author has no idea what he’s talking about;
> The solution to both issues is obvious, technically easy, and yet commercially a nearly impossible proposition: open up all recommendation algorithms. Make them completely transparent, and, for the individual being targeted by the recommendation engine, completely programmable.
Are you joking? So he/she thinks the east answer if for every user to ”program” their own algorithm. That’s like saying every Tesla owner should be able to program their own car! Let’s open the source code of Tesla to every owner!
Idiotic article.
Other critiques aside, these things are not very similar. You can't run someone over with a buggy recommender system. You bork your own newsfeed, who cares?
My point was that it’s not easy at all to “open up” an machine learning model for a user to “program”. It’s complex enough for an ML engineer to develop!
In theory, maybe it is possible but it would be incredibly expensive to produce such a system, let alone maintain. But even then, the ability to control an Ml system is such a niche ask that it would never happen
It does not mean that any change should be allowed on the road. But there could be a system where you pay a fee and send your code to be audited and aprooved for use on public roads. Otherwhise you can only run that code on a test track.
And also not all parts of a car's software are safety critical.
“Audit” and “approval” does not equal “well-tested”.
If I buy a car I expect to own the car and be able to do whatever I want with it especially on private land, as long as I don't interfere/endanger/etc. with other people. We used to live in this world but we no longer do. If the car has DRM, locked boot loaders, etc. I can not do whatever I want with it, not even on private property, therefore I am not the owner of the car, I am just the custodian.
Same with household appliances, agricultural equipment, phones, etc. .
Mass alphabetization was a quantitative change in the number of people who could read and write and was most probably opposed (the printing press certainly was) by some at the time but it resulted in a qualitative jump in the quality of life of everyone. The same is applicable for programming. As technology becomes more accessible people should expect to be able to make it conform to their needs instead of them adapting to quirks of tech.
The ability to modify the software is just one part of the right to repair. Purchasing a product used to mean ownership which included the right to repair.
I Have a feeling pooping on recommendations systems is en vogue right now. Social commentators love to point out how “evil” ML algorithms are while conversely complaining how X product is terrible at recommendations.
Should we have access (by law) to the code that makes recommendations on content? That seems like a governmental overreach. If that was the case, then we should have to have direct access to the brain of every editor/layout person of every newspaper. Why was one story above the fold vs below the fold vs on page 4? I don't see a whole lot of distinction between those two.
Once we get access to that, how do we keep people from abusing these algorithms on the supply side? Thought SEO sites were bad before? Guess what happens when those sites know what they're _actually targeting_, as opposed to blindly guessing!
As a question, how well do you understand the recommender system that you've created? For a given piece of content, would you be able to guess at the penetration into the platform? As an outsider, I sometimes feel like the recommender systems I interact with have grown out of the platform holder's control (see: Nazi/ISIS/ISIL propaganda on YouTube, etc.). I think these are the points the article is reacting to.
So if the article is saying "I would really rather not see Nazi/ISIS/ISIL propaganda," then, ok, that's good. That's a good want. But we're going to need an algorithm to determine what that propaganda _is_, and then downrank that for that user.
So, is the conclusion of the article... misguided? Sure! Is the desire of the author valid?
I don't know. I don't have time to read these articles.
But I read the article. Yeah, it was dumb. Specifically:
> If recommendation algos aren’t shared then we need - by legislation, if necessary - a switch that turns the recommendation engine off.
This is... baffling. On YouTube - the case-in-point of the article - you already have access to a non-algorithmic feed of videos: namely, your Subscriptions feed.
And if you don't want a recommendation algorithm, good luck finding new content that you like.
And if you "turn off" the recommendation algorithm, you want... what, exactly? The latest videos uploaded to the platform? Still a recommendation algorithm. Videos with words in titles similar to the last video you watched? Still a recommendation algorithm. Videos that the creators of your favorite channel watched? STILL A RECOMMENDATION ALGORITHM.
I was wrong. The desire of the author is far dumber than I originally gave credit for.
Your list of recommendation algorithms is inaccurate, a chronological feed is not a recommendation algorithm in any sense of the term, as for the rest you can't compare the deeply complicated and often abusive recommendation algorithms that are deployed on social media to the simple heuristics you listed.
I mean, you don't have to. Don't want to see YouTube's algorithmic feed? That's cool - just HTTP 3xx your youtube.com to the subscriptions page. There are some good browser addons to do just that[1][2].
Granted, that doesn't remove the recommendation algorithm from your YouTube experience. You would have to use some kind of content blocker to remove the algorithmic feeds from the video pages. I find that uBlockOrigin works well for that.
[1] https://addons.mozilla.org/en-US/firefox/addon/redirector/
[2] https://chrome.google.com/webstore/detail/redirector/ocgpenf...
On a federated network where you can have your own server/pod (think PeerTube or Diaspora) you absolutely can have a custom recommendation algorithm. And for news you can have custom filters in your RSS reader that trim the feeds you are subscribed to to only the desired articles and apply a custom order.
And even in a Cloud/SaaS scenario like Youtube, one can imagine a settings page where you can configure each recommend view in some domain specific programming language using an API. Imagine something like IFTTT but for configuring your preffered recommendations views.
Notice that something you do not want is beeing recommended to you? Just open settings and tweak the algo.
This is just like feeding a custom comparator function to a built-in sorting function using dependency injection.
Of course, this would not be in the platforms interest and will probably never happen.
It really is not that hard to imagine if you have the mindset that you should be able to control your own computation instead of being at the whims of a corporation.
The social implications are much more dangerous than a newspaper editor and journalist following their paper's political bias.
a) I agree with you that the author lacks a basic understanding, and that they do not solve the problem.
Yet: b) I think there actually might be a positive outcome for several reasons, including innovation, marketing and ethics, if one is to allow trained model to be queried arbitrarily. At least in the general case. (Think what is happening with stable diffusion and LLMs/LMMs.) Recommendation systems of say Youtube might be more useful to people making videos -- essentially somehow create a pre-query system before one finishes a video.
c) As a couple others have noted, the Tesla example is not a good one. I understand it was formed in haste and I think we all can understand your reasoning. My thought though is:
There is a) the right of repair -- yes still being fought for -- and b) the full self driving ability that might actually demand open sourcing code and models.
How else do we envision the Courts and Congress to formulate liability and empower related LE, and rights groups investigations?
Recall that the aviation industry achieved the "FSD" mode by building the infrastructure, extreme reliability (space and aviation started byzantine systems) and definitely not black box.
P.S. As a personal note: I will never allow something to drive me, if I have any liability, especially if it is a limited "black box" that I can not even arbitrarily query.
Right now we are "cruising" along because liability is with the driver. Tesla and the auto industry will need to face that pill, if they ever achieve their goal. I think they will achieve it personally.
I do like that idea. If the company is not going to show what the recommendation algorithm is, then being legally forced to provide an option to turn it off seems fair.
Too many companies pretend the "algorithm" is to blame for their shady practices, versus what its management and owners are directing it to accomplish.