ChefboyOG's comments

The practical ability to move somewhere--e.g. to find a home, place an offer, have it accepted, and then peacefully coexist in the neighborhood.

These are all largely up to the discretion of individuals in the community.

A "right to speak with a human" makes sense to me on an industry-by-industry basis, in the same way that construction companies and restaurants have different regulatory agencies and checks.

Applying it to all businesses sounds like a bad idea, however. Financial institutions? Certainly. Healthcare companies? Makes all kinds of sense. But I don't see an ethical imperative for, say, Giphy (pre-acquisition) to provide that kind of support.

I think the point is that yes, that seems like a good solution for verifying content that purports to be released by a certain creator, but it doesn't solve the problem of deep fakes for captured footage i.e. you can prove it isn't a video that you created, but you can't prove it isn't a video someone else took of you.

The majority of large, popular datasets in deep learning are curated and hosted by academics:

https://paperswithcode.com/task/deepfake-detection#datasets

No one who pays attention would have predicted hundreds of thousands of Americans dying from influenza, because in the last decade, it has never happened.

https://www.cdc.gov/flu/about/burden/index.html

The high-end of yearly flu deaths is in the low 50,000s, while the low-end is just over 10,000. COVID has killed hundreds of thousands of Americans each year.

You're not just misinformed about vaccines, you're outright lying about numbers that can be easily Googled.

I don't disagree, but I will also take debates about licensing over 30 separate "Looks cool! I've built something similar here: PLUGS PROJECT" comments.

For a long time now, Google has weighted behavioral signals similar to what you describe. "Bounce Rate" is the percentage of users who quickly leave your site after clicking. "Dwell Time" is the amount of time a user spends on a page.

There's even a cottage industry around gaming these signals. See SerpClix and the like.

I think in this situation, comparing housing to stocks isn't quite apples-to-apples. One of the appeals of owning a house is the money you no longer spend on rent.

...Did you read the article you linked to? The bulk of the article is dedicated to discussing why that number is misleading, and how it reflects our lack of data/transparency when it comes to policing, particularly when it comes to police involvement in homicide.

I promise, if FB/other companies could automate this away, they 100% would.

In general, it's easier for a computer vision system to recognize and filter a video that has already been banned--though, there is a constant arms race here as well--than it is for it to judge the content of a completely new video. That means that, for a huge number of cases, a human being will have to see the footage at least once.

EDIT: To be clear, I'm not taking up for FB in this situation. I'm specifically clarifying the difficulty of using ML/DL in moderation systems.

That "(according to the media)" bit of hand-waving is doing a lot of work.

I'm not sure I understand what you're saying. Google's Smart Reply, along with most (I think all?) of Google autocomplete features, uses a neural network:

https://research.google/pubs/pub45189/

Are you saying that in general statistical modeling is not the same thing as truly "understanding" a concept? Your original comment seemed to suggest that there wasn't utility in this kind of model--which I disagree with--but if you are more generally saying that this is not the same as human intelligence, I think that authors would probably agree with you.

By and large, no.

That's not to say that those sites are not generated programmatically--without a doubt, most of them are--but not by a cutting edge transformer model. The fact is, generating words has never been the bottleneck for blackhat SEO types. Generally, those sites are generating their content through some kind of scraping, or in rarer cases, paying pennies for nonsense articles. The page itself is structured for search (targeted H1s, metadata, etc.) and some kind of private blog network is used to create a pyramid of backlinks.

Have you ever used any sort of autocomplete?

I think this gets at what the actual value prop (not the marketed one) of chatbots really is: scale.

There will not, in the foreseeable future, be a model/ensemble/pipeline capable of conversing at the level of a human representative. Thinking of them as "humans, but cheaper" is destined to fail.

There are, however, use-cases where communicating with a huge group of individuals on a one-on-one level is useful. For example, years ago a company called Mainstay (then AdmitHub) built a chatbot for Georgia State designed to reduce "summer melt," a phenomenon in which intending freshmen drop off between graduating high school and enrolling in classes. For this use case, getting human-level conversation wasn't as critical as effective performance on tasks like question answering. Their bot, Pounce, was credited with a ~20% decrease in summer melt. They have a proper study here: https://mainstay.com/case-study/how-georgia-state-university...

This is an example, in my opinion, of the sweet-spot where intelligent, individualized, but not-near-human-level communication is needed, at a scale that goes beyond what you could reasonably do manually without an enormous dedicated staff.

Now, I don't know how numerous those opportunities are, or if they come anywhere close to the level of hype and funding chatbot companies have received. I just think that in a vacuum, there is a set of problems where they have value.

Quanta is one of the most consistently high quality publications operating today, for my money. Their Joy of X podcast with Steven Strogatz is highly recommended for anyone interested in math.

As a funny (to me at least) piece of anecdata:

I've never worked at a startup that wasn't either in the middle of or planning a UI redesign.

One of the other benefits of this kind of approach is that it allows the two sides to inform each other without hindering each other. Each function gets the proper space to "breathe" in a sense.

For example (and this draws entirely from my own anecdotal experience), I find that in the course of marketing, I often get the best feedback/inspiration from users/community members, and this is very helpful in deciding what to implement next. However, I find that this pseudo-research process is only effective if I take enough time to process the information in whole. In other words, if a user has an extremely compelling idea, it can be tempting to implement it right away. If I resist and take my time, considering the idea in the context of the dozen other points of feedback I've collected, I can often distill the jumble of ideas into a stronger, singular feature. If I jump in right away, I'll inevitably end up in a state of feature creep.

These kinds of notes never worked for me, but I've found the Feynman technique (inspired by Feynman) to be a very effective strategy for me that similarly derives its benefits from forcing me to synthesize and distill information.

For those interested, it's basically just teaching concept (writing an article, talking to someone, etc.), usually after having studied it/taken notes. The idea is that teaching a concept, even to an imaginary audience, forces you to learn it on a fundamental level:

https://blog.doist.com/feynman-technique/

...they're also great?

The parent comment was just that it would be cool to bring Hubble back eventually, and that Starship seems promising towards that end. It's not a diminishment of all other space-focused organizations.