1,601 karma · joined July 8, 2018
If they can credential experts based on some metric that isn't based on their editorial discretion, they have more grounds to call themselves a "platform." Not saying I agree, but makes sense given their position.
"Once TikTok collects enough data about the user, the app is able to map a user's preferences in relation to similar users and group them into "clusters." Simultaneously, it also groups videos into "clusters" based on similar themes, like "basketball" or "bunnies.""
Although I do wonder, and maybe someone else with more experience could shed some light here, whether or not it is likely that TikTok has some fundamentally super advanced algorithm, or if they just do a better job of collecting data/training & evaluating their models?
https://medium.com/@aidungeon/how-we-scaled-ai-dungeon-2-to-...
An important note, as others have said, is that not every player gets the same model. Free players, as far as I'm aware, only get GPT-2 ("only" is a strong word, as up until GPT-3, GPT-2 was the state of the art in text generation and is still extremely impressive in its own right).
I've spent a lot of time thinking about this. As someone for whom research + writing are a primary component of daily work, I'm probably a little too willing to play with new note taking apps :)
I think the recent surge is actually a convergence of several different trends:
1. There's always been a decent volume of new note taking apps, in large part because of how tempting they are as a project. They feel both approachable (until you try to build one), and they're such a familiar-yet-blank canvas that it's incredibly easy to come up with a "tweak" that makes yours different.
2. The love people have for Notion, and the so-far success of the company, have made it a more attractive niche for more serious startup-y people. Where you used to see more solo projects, you're now seeing a decent amount of apps built by full teams.
3. Human nature. While I am the first to evangelize the impact taking better notes can have, it has also been my experience that note taking systems are the ultimate bike shed problem. It's easy to tinker with them and try new apps out, believing that you'll find the perfect system that will unlock your inner genius. I think a lot of project management and productivity software has a similar thing going.
(Note: Not critiquing the idea of side hustles or diversifying income streams in general—just making an observation about some specific dynamics in these communities.)
1. The survey found that only 63.8% said their job required a home internet connection pre pandemic, so it's closer to 40% of Americans with jobs that don't require home internet.
2. Sending emails from your phone at home is for all intents and purposes the same as a home internet connection. If you were no longer able to connect to the internet from within your home, you wouldn't be able to send those emails i.e. do your job.
3. There is a difference between jobs that can theoretically be done without home internet access, but that in practice require it. For example, you could theoretically drive a truck without any home internet. But, in practice, most truckers find loads and contracts via load boards, which you obviously need an internet connection to access.
With all of those considerations, the percentages don't seem too fishy to me. I buy that pre-pandemic, roughly 64% of Americans were in jobs that required them to connect to the internet from home for at least some work-related function, even if it is not the core of their work.
I'm also a self-taught engineer (well, I've done course work now, but only years after starting in the field).
If I'm reading this right, I think you're in a position where there's not an obvious next thing to study, as you've more or less become an expert in CRUD applications.
I think a lot of developers hit this point, especially us self-taught types. When you're making your way in the industry, it's really easy to notice the in-demand skills/technologies that you don't have, and to add them to your toolkit one at a time. It's a nice, linear progression. Then, you reach a point where that approach doesn't work anymore. If you're a 10+ year developer who knows React and Vue and is strong with vanilla JS, adding Angular to your toolbox won't raise your salary or your skills—you could probably pick it up on the fly if you really needed to.
I think at this point, you need a paradigm shift away from "what is the market pulling me to do" and "what do I want to do." It sounds like you want to stay an IC, so that means it's going probably going to be purely technical—so what technology do you find interesting?
For me it was machine learning, and then hardware-level stuff. That's what lead me to take university classes and study higher math/more fundamental CS. There wasn't an obvious pull in that direction from the market—none of my bosses offered me a raise if I became an ML engineer—but I liked it, and because I had years of experience already, becoming proficient in the field opened up an entire new world of opportunities in terms of career and personal life. I wasn't a new grad who studied ML. I was a career developer who'd built businesses, who now had a background in ML. You could apply this same logic to any new field you study, it doesn't even have to be technical. If you got really into design, you'd be a designer with 10 years of development experience. If you got really into sales, a salesperson with 10 years of dev experience is a hot prospect at any company that sells to engineers (in other words, some of the most valuable companies on Earth).
If you want to stay in web development and level up there, I'd recommend following the advice of many on this post. Pick a part of the stack you find interesting, and dig a thousand layers down. Even better, if you find something you think could be done better than the standard tooling does it, build something there.
The predictions that best satisfy my intuition's classifier (not exactly a scientific process) are the ones that suggest an expanding of our current knowledge through the discovery of some new, fundamental thing—like a new piece of quantum mechanics, a new systems-level understanding of neural activity, new life forms that evolve our models of life, etc.
Instagram and Snapchat share the same market. Like Facebook, they originated among college students, and became the default platforms of that young adult demographic. Instagram copying Snapchat features is brutal to Snapchat because Snapchat power users were probably Instagram/Facebook power users as well.
TikTok and Musical.ly, as has been written to death by tech journalists, are much more dominate in a younger demographic. They're sort of unique among social platforms in their ability to do so. I wonder if Instagram has enough penetration in that demographic to really syphon off a meaningful chunk of users, or if this will just block Instagram's existing users from migrating?
However, I think NVIDIA is still vulnerable—but against AWS/GCP/Azure, not Intel/AMD.
My opinion is that deep learning is moving to the cloud. That's a bigger conversation with a lot of nuances, but if you take that basic assumption, then the development of ASICs like TPU/Inferentia become a big threat to Nvidia.
If the biggest buyers of chips in deep learning are the clouds, and the clouds are increasingly developing their own chips for deep learning, Nvidia is in a tough spot. They'll always have a place among labs that use their own machine, and of course, Nvidia's business is bigger than machine learning, but in general I think the clouds are a real threat.
Myth-making about ML as a shadowy conspiracy—instead of just another tool for engineering, like databases—is counter-productive.
The "main use case" for ML is the literal one, "designing models that can learn to predict high probability outcomes in situations where a deterministic model cannot be applied." It is another technology in an engineer's toolkit. Look at your smartphone, and you will see that the majority of apps you use rely on machine learning for ETA prediction, content recommendation, speech-to-text, image processing, translation, spam filtering, etc.
I think there will always be a frontier where that is the case in software—there will always be hackers building open source projects that push some boundary, fostering their own community, and potentially changing broader paradigms—but that more and more, we're seeing a neater boundary between research (Universities, labs, projects that are essentially R&D teams at big companies) and engineering.
For example, if you are an indie dev who will be developing a project solo, pick whatever you're best at, because all that matters is the product.
If you're an open source project, that's a different story. If you're looking to hire engineers, also a different game. And if you're the creator of Ruby on Rails and have created a giant personal brand around being contrarian and ignoring the new-hotness in web dev, then your choice is made for you.
Oftentimes, I think people say things like "I don't think a few bad police officers ruin policing just like I don't think BLM is evil because of a few violent actors" because they have empathy for police officers who don't do horrible things, and it's sort of a knee jerk reaction against broadly characterizing people.
The problem, however, is that the two groups aren't equivalent. If we granted every person who identified with the BLM movement the same authority as we do police officers, the presence of violent actors inside BLM protests would be an issue. But we don't.
Police officers are given the utmost authority and deterrence in America. They have particular legal protections, they are authorized by the government to give legally enforceable orders to other citizens, etc. Even if the bad acts are a "drop in the bucket," would that be acceptable? And isn't the fact that these bad acts are persistent—regardless of frequency relative to good acts—at the very least suggestive of systemic issues?
In other words, consistent bad acts—even if infrequent—are a bigger deal when the actor is in such a position of authority. Protest groups are clearly not in such a position—hence their protests.
If you watched 100 videos of kitchen malpractice within restaurants in which:
- The acts of malpractice had broadly consistent characteristics
- The restaurants in question were all owned under the same organizational umbrella
- That organizational umbrella had the authority and exclusive control of the US government
And then drew the conclusion that maybe there were systemic problems that lead to these remarkably consistent issues, you'd be completely rational.
As for your other comparison between this and "the media" curating street interviews, I don't see the parallels at all.
A news outlet using a random interview with a person on the street as evidence that people are "divided over climate change" is clearly manipulative. In this case, a person is saying "Police are routinely using military equipment and force tactics against US citizens in disturbing ways. Here are 700 videos of it as evidence."
I ask because I'm starting my masters in CS, but I've also been going to workshops/events at a local citizen bio lab and really enjoying it. I'd really like to go deeper into the cross-section of CS and Bio, specifically the kinds of things listed in this repo (modeling biological phenomenon as formal systems, using computation to simulate those systems, etc.)
But when I look at potential programs to pursue after my CS course, I get a bit lost in all the different titles—bioinformatics, systems biology, computational biology, etc. It's hard for an outsider in the field to discern any meaningful delineation. Does anyone with experience in the field know what category of study these resources would fall under, from a university perspective?
If Gsuite is significantly cheaper, has decent feature parity, and has the name of (one of) the biggest tech companies in the world behind it, it has already won 90% of the sales conversation for enterprise buyers.
For instance, in my teens/early twenties, I (like many) was able to sleep quite a bit less. Lots of late nights traipsing around the internet, lots of midnight projects.
In my mid-twenties, I suddenly found that I needed much more sleep. If I didn't get at least 8 hours, I woke up completely drained. Other adults I spoke to confirmed that this was normal.
Then, I switched apartments and got a new bed. Suddenly, I was waking up much easier, even with few hours of sleep.
This pattern has repeated a few times throughout my life. I'll notice I'm waking up more tired than usual, and then something will change—a new room with more natural light, a better mattress, the loss of something I didn't realize was stressing me—and I'll suddenly need less sleep.
I'm sure none of this is shocking to anyone, but the key thing for me was that even when my sleep quality was apparently deteriorated, I didn't notice it. I still fell asleep easily, I still had dreams, I was still comfortable in my bed, etc. The sleep cycle, it seems, just wasn't as restorative due to other factors.
I have a little pet theory that some of the "Super CEOs" we hear about who get by on 4-5 hours of sleep a night are:
1. Slightly exaggerating the rigidity of their superhuman schedule (I used to work in media, and I've worked with an outsized number of people who have public reputations for this kind of stuff. Anecdotally, I've found that nearly all exaggerate and sometimes outright lie.)
2. Adept at compartmentalizing stress and environment, to the extent that they're able to get quality sleep regardless. This skill would naturally track other responsibilities in their life as well.
In general, when there is a breakthrough in a field of research—like what deep learning went through recently—there is a gold rush of new projects that push the boundaries.
A certain category of people look at these new startups, and project that momentum out to some extremely distant problem (like AGI or fully autonomous vehicles), without understanding if this field of research necessarily leads to solutions to those problems, at least in linear fashion.
Then there is another category of person who looks at those predictions and dismisses the entire field as hype, ignoring the surge of valuable projects that kicked it all off.
For a 30% increase, locked in for at least 10 years (assuming the restaurant survives), landlords are willing to let a property sit vacant for a while.