king07828

Should a proper large language model be able to generate arguments for and against any side of any debate?

Create a monopoly tax that incentivises large companies to "right size". E.g., if the company is larger than X and has captured a market (like google with search) then tax all revenue above a threshold percentage of the market. For example, the market is $100B and the company makes $90B revenue, then tax away everything above $40B in revenue. This may take away the incentive of a large company to take too much market share and may encourage them to enable competitors. The parameters (company_size, marketshare_percentage, tax rate, etc.) may be adjusted to maximize performance

A critique of most modern billionaires and millionaires is that they settle for ivory tower philanthropy throwing money around so they can chat with rich experts to figure out which other rich experts to give more money to. While good does come from this, it doesn't seem to make much progress in solving the underlying problems. For instance, how long has the Gates Foundation been involved with vaccines and pharmaceuticals and does it look like solutions are in within striking distance for the underlying issues?

As an example, a millionaire (who recently became a billionaire) decided on two areas where mankind needed dramatic improvement. Instead of ivory tower philanthropy, he started two different for-profit companies, one for each set of issues. Both companies have stuck around for more than 10 years and the issues they are tackling appear to have solutions within striking distance. A tangible difference has been made. If you didn't guess, the two companies are Tesla and SpaceX.

In contrast, it seems like the Gates Foundation is treading water with the issues it’s tackling. I could be wrong, but that is my perception.

As another example, an entrepreneur back in the day decided every desk needed a computer on it and used his for-profit company to make that goal a reality. That entrepreneur, of course, was Bill Gates himself.

Very few people have the R&D and engineering expertise, economic sense, and the money to lead a company to achieve world changing endeavors. Gates did it once. Musk has done it more than once.

Gates could start a pharmaceutical company, run it himself, learn the ins and outs, and reshape the industry along the way (if he could successfully learn the ins and outs). However, he does not appear to be willing to accept the risks involved in starting his own a pharmaceutical company and the problems of the pharmaceuticals industry remain unchanged.

One can argue that the conduct is detrimental to the victim's interests and that the conduct constitutes common law fraud [1] because (1) the saboteur tells the victim "I like you", (2) the saboteur knows this is false or reckless, (3) the saboteur intends for the victim to leave the victim's current relationship, and (4) the victim left his or her current relationship.

I am not your lawyer. This is not legal advice. If your partner is actively sabotaging your relationship, it may not be a good idea to stay with that person.

[1] in Texas: In order to prove fraud, a plaintiff must show that (1) the defendant made a material representation that was false, (2) the defendant knew the representation was false or made it recklessly as a positive assertion without any knowledge of its truth, (3) the defendant intended to induce the plaintiff to act upon the representation, and (4) the plaintiff actually and justifiably relied on the representation, which caused the injury. Ernst & Young, L.L.P. v. Pac. Mut. Life Ins. Co., 51 S.W.3d 573, 577 (Tex. 2001).

This is awesome. What a concept, identifying the variables in the equations and providing diagrams to show the geometric meaning of the equations. Really wish wikipedia (or some sort of companion website) would similarly present an explanation of the practical meaning of variables and operators in the heavy math pages so that people with an understanding of algebra could understand the formulaic descriptions in addition to the qualitative descriptions after landing on a random page with heavy math.

Google figured out a magic formula:

1. develop a service (e.g., search)

2. provide the service for free to users

3. sell ads (unobtrusively placed within the service) to marketers

4. profit!

Most companies cannot seem to figure out (or afford to get to) step 3. Google's new services generally muck up the formula. E.g., with Stadia, they've broken the magic formula at both step 2 and step 3:

1. develop Stadia

2. provide Stadia to users for a monthly fee and/or per game fee (not free means no network effect)

3. ????

4. Profit!

> DLSS kinda works because we "know" what is what thing in a photo.

I thought the reason DLSS works is because the same rendering algorithm is used to generate the low resolution image and the high resolution image and the neural network merely learns a filter between the two.

Take a patient with ECoG implant(s), put EEG sensors on the patient, and hit record. You now have the same rendering mechanism (the brain) generating a low resolution signal (EEG) and a high resolution signal (ECoG).

However, back to DLSS, if the low resolution signal is a single pixel, then generating a 4k image from just that single pixel may not be very fruitful.

Still, it would be interesting to see an attempt at using a generative adversarial network (GAN) to generate an ECoG from an EEG. And if it doesn't work, then make a determination of how much more EEG sensitivity is needed before it will work.

Has there been any work to use a neural network to generate/simulate ECoG signal output from an EEG signal input? (My Google-fu only gives definitions and distinctions for ECoG and EEG). Almost sounds similar in concept to deep learning super sampling (DLSS), i.e., taking a low resolution image/signal (EEG) and using a neural network to generate/simulate a high resolution image/signal (ECoG).