There are real dangers in code that appears to run but contains sneaky problems. I once asked ChatGPT to take a data set and run a separate t-test on each group. The code it generated first took the average of each group and then ran the test on that one value. The results were wrong, but the code ran and handed off results to my downstream analysis.
Agreed. I became interested in Bitcoin in 2013. In 2017 I realized it was never going mainstream for payments when I saw CNBC talking about it without one reference to actually using it as money. I've become pretty cynical about the movement since then, though I wouldn't mind stumbling upon an old private wallet.
I've long wondered what technological advancements would be necessary to make it profitable to mine landfills. There's so much raw material buried in one place.
Ha, reminds me of when I lived in a place with a property tax on cars right after undergrad. I appealed my valuation and included pictures of my car and odometer. They wrote back saying I didn't need to pay any car tax.
I hadn't thought about this part. I work with healthcare data for a living. I'll have to keep an eye out for changes once the 2025 records come across my desk.
I'm not sure if people will want to deal with long wait times for their car to drive to them. Imagine watching the end credits roll at a movie theater only to be gripped by the sense of dread that you forgot to call your car during the climactic fight scene and now you'll be waiting half an hour for it to drive from the suburbs.
Don't get me wrong, I'm a huge fan of reducing car dependency, I'm just not optimistic that self driving cars will help.
At least I've never seen UTF8 math symbols in the wild. Julia, Python, and other languages will let you use the pi symbol for 3.14... instead of just calling it pi.
IIRC Python had similar reasoning for making floats the default in version 3 instead of integers, which had been the assumption in 1 and 2 when you entered a number without a decimal. R always did it that way and Julia still assumes integer, which occasionally trips me up when switching languages.
Both things can be true. Companies vacuum up massive amounts of personal data. And then they run it through crummy algorithms that are designed to increase the number of people who fit into a given category instead of accurately finding only the people who really ought to be in that category.
This is key. I remember when Google came out. It was amazingly better than anything else I'd tried up to the point. By contrast, I'd argue that OpenAI's advantage is smaller today than it was when they launched GPT3 in 2020.
I recently dropped $99 to get the Financial Times delivered to my door six days a week. If something truly important happens, I'll have the chance to read about it. But I can't doomscroll for hours.
Well said. I've worked in adtech and this aligns with my experience. Alphabet probably wouldn't even make that much more money compared to its current ad program. There's no shortage of supply in display advertising.
Exactly. Years ago, I did due diligence on a company that targets ads using IP address. They buy IP address data from ISPs, target ads based on demographics, and then use cookies to retarget. Not that far off of what you're describing.
Why can't you both be right? If you talk about 100 products in a week, chances are you're conducting searches about some of them or your demographic data suggests that you might be in market for it.
Companies really are using tons of highly sensitive data to target ads, even when we sleep. But they're not generally using microphones to record audio to do it. Both things can be accurate statements.
Apple spends a lot of money to keep its secrets. Paying $95 million to avoid letting people snoop in exactly how Apple systems work is a bargain, and I don't even think they're using audio for ad targeting.
I'm inclined to agree with you. There are two directions of pressure. First, the tech just doesn't work as well as it needs to, and it's not clear if that will change in the next decade. Second, the companies are losing money. The investment bet is that the AI companies will make rapid advances in their models such that they'll be able to charge enough to turn a profit before the bubble pops. I'm not convinced.
How does it not apply to students? It's rare to read about a college student being imprisoned and tortured for being a member of a church while a soldier moves into her apartment.
Well said. I spent years working with an attorney who was first an English major. He wrote the best contracts because his initial focus was making things readable and obvious. The legal formalities came second. I firmly believe that plain language will avoid legal disputes better than PUTTING A BUNCH OF LATIN IN ALL CAPS AD OSTENTATIONEM.
Totally agree that they have unrealized potential. It was amazing when I was in grad school. Then when I started running a corporate data science team, I struggled to find a use case to recommend it for purchase. There are so many other tools that are better at loading a messy dataset, cleaning it, doing EDA, building a model, and productionizing and monitoring the model, which is the process we spend a lot of time doing.
I'm suspicious of most claims of AI growth, but I think screenwriting is an area where there's real potential. There are many screenplays out there, many movie plots are very similar to each other, and human raters could help with training. And it's worth noting that the top four highest grossing movies right now are all sequels or film adaptations. It's not a huge leap to imagine an LLM in the future that's been trained on movie writing being able to create a movie script when given the Wicked musical.
https://www.imdb.com/chart/boxoffice/