The people I see who are most excited about ML are business types who just see it as a black boxes that makes stock valuation go vroom.
The people that deeply love building things, really enjoy the process of making itself, are profoundly sceptical.
I look at generative AI as sort of like an army of free interns. If your idea of a fun way to make a thing is to dictate orders to a horde of well-meaning but untrained highly-caffienated interns, then using generative AI to make your thing is probably thrilling. You get to feel like an executive producer who can make a lot of stuff happen by simply prompting someone/something to do your bidding.
But if you actually care about the grit and texture of actual creation, then that workflow isn't exactly appealing.
Using English, instead of C, to get a computer to do something doesn't turn you into a beaurocrat any more than using Python or Javascript instead does.
Only a person that truly loves building things, far deeper than you'll ever know, someone that's never programmed in a compiled language, would get that.
If one uses English in as precise a way as one crafts code, sure.
Most people do not (cannot?) use English that precisely.
There's little technical difference between using English and using code to create...
... but there is a huge difference on the other side of the keyboard, as lots of people know English, including people who aren't used to fully thinking through a problem and tackling all the corner cases.
No one can, which is why any place human interaction needs anything anywhere close to the determinancy of code, normal natural langauge is abandoned for domain-specific constructed languages built from pieces of natural language with meanings crafted especially for the particular domain as the interface language between the people (and often formalized domain-specific human-to-human communication protocols with specs as detailed as you’d see from the IETF.)
Still needed domain experts back then, and, IMHO, in years/decades to come
really, my impression is the opposite. They are driven by doing cool tech things and building fresh product, while getting rid of "antiquated, old" product. Very little thought given to the long term impact of their work. Criticism of the use cases are often hand waved away because you are messing with their bread and butter.
I think we also need to be aware that this business layer above us that often sees __computers__ as a magic box where they type in. There's definitely a large spectrum of how magical this seems to that layer, but the issue remains that there are subtleties that are often important but difficult to explain without detailed technical knowledge. I think there's a lot of good ML can do (being a ML researcher myself), but I often find it ham-fisted into projects simply to say that the project has ML. I think the clearest flag to any engineer that this layer above them has limited domain knowledge is by looking at how much importance they place on KPIs/metrics. Are they targets or are they guides? Because I can assure you, all metrics are flawed -- but some metrics are less flawed than others (and benchmark hacking is unfortunately the norm in ML research[0]).
[0] There's just too much happening so fast and too many papers to reasonably review in a timely manner. It's a competitive environment, where gatekeepers are competitors, and where everyone is absolutely crunched for time and pressured to feel like they need to move even faster. You bet reviews get lazy. The problems aren't "posting preprints on twitter" or "LLMs giving summaries", it's that the traditional peer review system (especially in conference settings) poorly scales and is significantly affected by hype. Unfortunately I think this ends up railroading us in research directions and makes it significantly challenging for graduate students to publish without being connected to big labs (aka, requiring big compute) (tuning is another common way to escape compute constraints, but that falls under "railroading"). There's still some pretty big and fundamental questions that need to be chipped away at but are difficult to publish given the environment. /rant
The key difference is how tolerant the specific use case is of a probably-correct answer.
The things recent-AI excels at now (generative, translation, etc.) are very tolerant of "usually correct." If a model can do more, and is right most of the time, then it's more valuable.
There are many other types of use cases, though.
On the other hand ML has absolutely revolutionised translation (of longer text), where having a model containing prior knowledge about the world is essential.
I am probably the 0.01% of Tesla drivers who have the computer chime when I exceed the speed limit by some offset. Very regularly, even when FSD is in “chill” mode, the model will speed by +7-9 mph on most roads. (I gotta think that the young 20 somethings who make up Tesla's audience also contributed their poor driving habits to Tesla's training data set) This results in constant beeps, even as the FSD software violates my own criteria for speed warning.
So somehow the FSD feature becomes "more capable" while becoming much less legible to the human controller. I think this is a bad thing generally but it seems to be the fad today.
Here you can see it detected an obstacle (as evidenced by info on screen), made a decision to stop, however it failed to detect existence of the object right in front of the car, promptly forgot about the object and decision to stop and happily accelerated over the obstacle. When tackling a more complex intersection it can happily change its mind with regards to exit lane multiple times, e.g. it will plan to exit on one side of a divider, replan to exit onto upcoming traffic, replan again.
They are lying with statistics, for the more challenging locations and conditions the AI will give up and let the human take over or the human notices something bad and takes over. So Tesla miles are miles are cherry picked and their data is not open so a third party can make real statistics and compare apples to apples.
*Disclaimer: as someone who's not an AI researcher but did quite some human translation works before.
Argh. My PTSD from writing ONVIF drivers just kicked in.
and how difficult it is to program those GPU to do ML
ML isn't like that. It's new. It's different. It may not succeed in the ways we expect; it may even look dumb in hindsight. But it absolutely represents a genuinely new paradigm for computing and is worth studying and understanding on that basis. We look back to SOAP and see something that might as well be forgotten. We'll never look back to the dawn of AI and forget what it was about.
[1] For anyone who missed that particular long-sunken boat, SOAP was a RPC protocol like any other. Yes, that's really all it was. It did nothing special, or well, or that you couldn't do via trivially accessible alternative means. All it had was the right adjective ("XML" in this case) for the moment. It's otherwise forgettable, and forgotten.
Anyone claiming it’s some sort of snake oil shouldn’t be taken seriously. Certainly the current hype around it has given rise to many inappropriate applications, but it’s a wildly successful and ubiquitous technology class that has no replacement.
Reading these comments I thought I stepped into some alternate timeline when we don't already have widespread ML all over the place.
Like, nobody does rules-based image recognition for a decade now already!
This new ML that's supposed to be the basis for an entire new economic wave, that I mostly dislike.
But I guess that's how we build new things... We explore and throw away 80% of what we've built.
Chatgpt generated the entirety of the above w/ me tweaking one line of code and putting creds in. I could have written all of the above, but it probably would have taken 20-30 minutes. With chatgpt I banged it out in under a minute, helped a colleague out, and went on my way.
Chatgpt absolutely is a real advancement. Before they released gpt4, there was no tech in the world that could do what it did.
They routinely give me brain-dead suggestions such as to watch a video I just watched today or yesterday, among other absurdities.
These days my biggest gripe is that they put unrelated ragebait or clickbait videos in search results that I very clearly did not search for - often about American politics.
Now the "related" section is gone in favor of "recommended" samey clickbait garbage. The relations between human interests are too esoteric for current ML classifiers to understand. The old Markov-chain style works with the human, and lets them recognize what kind of space they've gotten themselves into, and make intelligent decisions, which ultimately benefit the system.
If you judge the system by the presence of negative outliers, rather than positive, then I can understand seeing no difference.
I would go further and say that it is impossible. Human interests are contextual and change over time, sometimes in the span of minutes.
Imagine that all the videos on the internet would be on one big video website. You would watch car videos, movie trailers, listen to music, and watch porn in one place. Could the algorithm correctly predict when you're in the mood for porn and when you aren't? No, it couldn't.
The website might know what kind of cars, what kind of music, and what kind of porn you like, but it wouldn't be able to tell which of these categories you would currently be interested in.
I think current YouTube (and other recommendation-heavy services) have this problem. Sometimes I want to watch videos about programming, but sometimes I don't. But the algorithm doesn't know that. It can't know that without being able to track me outside of the website.
* They could let me directly enter my interests instead of guessing
* They could classify videos by expertise (tags or ML) and stop recommending beginner videos to someone who expresses an interest in expert videos.
* They could let me opt out of recommending videos I've already watched
* They could separate sites into larger categories and stop recommending things not in that category. For me personally, when I got to youtube.com I don't want music but 30-70% of the recommendations are for music. If the split into 2 categories (videos.youtube.com - no music) and (music.youtube.com - only music) they'd end up recommending far more to me that I'm actually interested in at the time. They could add other broad categories like (gaming.youtube.com, documentaries.youtube.com, science.youtube.com, cooking.youtube.com, ...., as deep as they want). Classifying a video could be ML or creator decided. If you're only allowed one category they would be incentive to not mis-classify. If they need more incentive they could dis-recommend your videos if you mis-classify too many/too often).
* They could let me mark videos as watched and actually track that the same as read/unread email. As it is, if you click "not interested -> already watched" they don't mark the video as visibly watched (the red bar under the video). Further, if you start watching again you lose the red-bar (it gets reset to your current position). I get that tracking where you are in a video is something that's different for email vs video but at the same time (1) if I made it to 90% of the way through then for me at least, that's "watched" - same as "read" for email and I'd like it "archived" (don't recommend this to me again) even if I start watching it again (same as reading an email marked as "read)
>let me directly enter my interests
Theres a general problem in the tech world where people seem to inexplicably disregard the issue of non-reducibility. The point about the algorithm lacking access to necessary external information is good.
A dictionary app obviously can't predict what word I want to look up without simulating my mind-state. A set of probabilistic state transitions is at least a tangible shadow of typical human mind-states who make those transitions.
Someone probably changed the engine that shows videos for you - exactly as with search.
Or when they would show more than 3 results before spamming irrelevant videos.
Or when they didn't show 3 unskippable ads in a 5 minute video.
Or when they had a dislike button so you would know to avoid wasting time on low quality videos.
Wait what?! You "Consume Content" on a COMPUTER? What are you some kinda grandpa? Why aren't you consuming content from your phone like everyone else? Or casting it from your phone to your SMART TV! Great way to CONSUME CONTENT!
CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT
I don't know about the TV though.
> Or when they didn't show 3 unskippable ads in a 5 minute video.
I also use Firefox for Android, which has Addon support. Ublock Origin works on the phone and disables a a lot of the ad horror.
It feels a bit funny asking this, since we're talking about Google (i.e. YouTube), but did you mean ;) PipeTube? I know there is a PeerTube too.
If I entirely avoid watching any popular videos, the recommendations are quite good and don’t seem to include anything like what you are seeing. If I don’t entirely avoid them, then I do get what you are seeing (among other nonsense).
Honestly, I'd prefer my voice assistant (siri mostly) to be like that as well. It was at first, and I think everyone hated that lol.
But I associate YouTube promotions with garbage any how. The few things I might buy like Tide laundry detergent are entirely despite occasional YouTube promotion.
Besides the religious crap, ill randomly get shit in India in hindu, having had not watched anything Indian and not even remotely Indian.
Hindi is the word for the language, bro.
From a user perspective, Shorts highlights a specific format of YouTube that happened to have been around for a lot longer than people realize. TikTok isn't anything new, Vine was doing exactly the same thing TikTok was a decade prior. It was shut down for what I can only assume was really dumb reasons. A lot of Viners moved to YouTube, but they had to change their creative process to fit what the YouTube algorithm valued at the time: longer videos.
Pre-Shorts, there really wasn't a good place on YouTube for short videos. Animators were getting screwed by the algorithm because you really can't do daily uploads of animation[0] and whatever you upload is going to be a few minutes max. A video essayist can rack up hundreds of thousands of hours of watch time while you get maybe a thousand.
(Fun fact: YouTube Shorts status was applied retroactively to old short videos, so there's actually Shorts that are decades old. AFAIK, some of the Petscop creator's old videos are Shorts now.)
But that's why users or creators would want to use Shorts. A lot of the UX problems with Shorts boils down to YouTube building TikTok inside of YouTube out of sheer corporate envy. To be clear, they could have used the existing player and added short-video features on top (e.g. swipe-to-skip). In fact, any Short can be opened in the standard player by just changing the URL! There's literally no difference other than a worse UI because SOMEONE wanted "launched a new YouTube vertical" on their promo packet!
FWIW the Shorts player is gradually getting its missing features back but it's still got several pain points for me. One in particular that I think exemplifies Shorts: if I watch Shorts on a portrait 1080p monitor - i.e. the perfect thing to watch vertical video on - you can't see comments. When you open the comments drawer it doesn't move over enough and the comments get cut off. The desktop experience is also really bad; occasionally scrolling just stops working, or it skips two videos per mousewheel event, or one video will just never play no matter how much I scroll back and forth.
[0] Vtubers don't count
There's videos I'll watch multiple times, music videos are the obvious kind, but for some others I'm just not watching/understanding it the first time and will go back and rewatch later.
But I guess youtube has no way to understand which one I'll rewatch and which other I don't want to see ever again, and if my behavior is used as training data for the other users like you, they're probably screwed.
This better echoes my personal experience with the decline of Google search than TFA: it seems to be connected to the increasing use of ML in that the more of it Google put in, the worse the results I got were.