If all Google and Facebook couldn't solve it, why could a Tesla engineer of all people...
That's not to say there aren't overlapping skill sets, but the AI tech involved is quite distinct (although there is overlap, I believe some of the latest perception research is trying to adapt transformers from NLP to replace CNNs). Many of my colleagues are AI/ML engineers working on the self driving problems (perception, prediction, planning) and they're all super smart, but they'd still take a while to get up to speed in another area.
I have no idea why a serious business would hire a non-NLP specialised engineer to solve content moderation (they'd maybe hire someone who was interested in changing sub-field, but not to build out the backbone of their teams).
Heck, you could even just limit the number of hashtags in a single tweet (say, 2) and fix 95% of Twitter's spam problem without harming legitimate users in any way
2) I am not an ML engineer, but still a software engineer and in my opinion the problem of self driving cars is at least two orders of magnitude harder than the problem of Internet spam. Maybe even three orders of magnitude.
3) We actually know that the problem of spam is successfuly solved by Google. When was the last time you saw a spam message in Gmail? I am seeing them in my inbox approximately once a month. There are about 8 messages in my spam folder per day, so it is about 0.4% false negatives. This is a great result.
4) Social networks are not interested in fighting spam. It just will make their metrics worse. All they need is to keep it at moderately acceptable level, so users won't flee in bunches.
Also, you're really understating how good Tesla self-drive is. It's not full self-drive but it's a real achievement.
I never claimed Tesla self-driving wasn't an achievement, but clearly they underestimated how hard that would be themselves, so I wouldn't be surprised if they'd do the same for content moderation.
If you spend 5 seconds on YouTube and Facebook and compare that to Twitter, you'll see a massive difference.
The spam bots on Twitter are incredibly unsophisticated. It's literally bots with the same profile picture and name as the person they're trying to spam.
On the other hand, YouTube spam bots are actually incredibly sophisticated. They're using GPT-3 or some language model to generate text and reply to each other. Like, sometimes I'll read a comment and not be sure if it was a spam bot or not.
Twitter is leagues behind w/ their spam filters.
How good is it?
I know there was one instance when an engineer using an internal build got fired for vlogging it (which is not at all unusual, Apple would also fire you for showing off an internal build).
I do long road trips and feel less exhausted than I would otherwise.
Quantity != Quality. Just because McDonalds serves billions of people doesn't magically make it good food.
Something tells me Elon would have different goals that should be less of a moving target and easier to accomplish. Less subjective target like "hate speech" and more objective target like true threats of violence, illegal activity, actual bot detection not "people who disagree are bots", etc
Spam is a dramatically easier problem, and has many more mechanism to suppress it both legally and technologically.
Because the guy who invited me sort of sprung the talk on me I had no slides or anything, so it became a collection of vague thoughts + discussions with their team (vague because I didn't want to discuss any trade secrets). One thing that became very clear was that they weren't thinking about bots a whole lot compared to the Google abuse teams, because they'd been re-tasked at some point to consider abuse as primarily meaning "humans being mean to each other". A significant amount of their effort was going on this instead, although there is really little overlap in technologies or skills needed between problems.
This was pre-2016 so the whole Russian-social-bots-gave-us-trump hysteria hadn't started yet, instead Twitter was being declared toxic just due to the behaviour of its users. Thus the term bot still meant actual spam bots. Since then various groups, primarily in academia but activist employees too, realized that because deleting bot accounts was uncontroversial they could try and delete their political enemies by re-classifying them as "bots". For example this Twitter developer in 2019:
https://reclaimthenet.org/project-veritas-twitter-hidden-cam...
“Just go to a random Trump tweet, and just look at the followers. They’ll all be like guns, God, Murica, and with the American flag and, like, the cross. Like, who says that? Who talks like that? It’s for sure a bot.”
Clearly any abuse team that uses a definition of bot like that won't be able to focus on the work of actually detecting and fighting spam bots. If Musk bought Twitter and re-focused their abuse teams on classical spam fighting work it'd almost certainly help, given that Twitter didn't seem to be keeping the ever-shifting overloads of the "abuse" clear in their org structure.
Incidentally, trying to find the above quote on Google is a waste of time. Search "project veritas twitter for sure they are bots" on Google and the links are almost all irrelevant. DuckDuckGo/Bing gets it right on the first result, no surprise. I don't believe for one second that's a result of incompetence on the part of the Google web search teams.
Hahah wow this is so out of touch. Communities each have their own way of speaking (famously /r/wallstreetbets has its own grammar, emoji, and lingo, but this applies to every community).
Sidenote this is why minorities speaking with each other sometimes get misclassified (even by humans!) as spam.
What’s the false positive rate, and is that why people complain they’re being banned or shadow banned?
(Definitions of shadow ban vary: https://blog.twitter.com/en_us/topics/company/2018/Setting-t...)
It's been tweeted by multiple security people at various Twitter engineers already and raised in lots of ways. They know and don't care. Meantime, real people lose their money - otherwise nobody would waste their time to post the scam.
You offer users a list of filters they can choose to activate, so they only see content according to the filters they enabled.
There would be filters against spam, filters against opinions other than your own, etc.
It wouldn't require _Machine Learning_, the kind of filtering approach you've just described would absolutely constitute AI.
It's almost never the case that spam issues on popular social networks can be alleviated with some easy fixes, because obviously if they could they would already have done that. Twitter is a very weird exception.
Yet people talking about hunter biden’s laptop in October 2020 had their accounts deleted immediately.
Twitter is very good at content moderation when it threatens the manufactured narrative of the global elite which they serve. And then completely incompetent otherwise.
I don’t buy the line that the problem is too complex because I see selective competence.
The problem would be more surmountable at that point
I think you underestimate how hard the problem is or you don’t fully understand how it works. The scale at which Twitter operates makes it near impossible to take down spam in real time. You don’t think Twitter is working on it, but they are. What you see is a much smaller percentage of the actual spam generated, and the reason you see it is because users are creative on circumventing all the preventive actions they take.