I think you are confusing Erdogan with Imamoglu. Imamoglu has photographs, classmates, there is no doubt that he attended college. His diploma was anulled because he transferred initially from a different college. He spent 4 years at the Istanbul uni, attended classes, passed the exams, there is no doubt in that.
On the other hand, Erdogan does not have a single photograph during his university years, no classmates to back his story. He started a two year degree, but there is no evidence he attended a four year program.
A public notary issued a same as original certification on a disputed document. The original diploma of erdogan cannot be found. Looking at the date of the diploma, the university faculty didn't even exist yet.
Have you tried Claude.ai. In my experience on computer science topics, the LLMs are very good. Because they have been trained on a vast amount of information online. I just had a nice conversation about mutexes and semaphores with claude and was able to finally grasp what they were.
I do not know if this is the case for example for mathematics or sciences.
No, this is not accurate in my trials. I use Claude.ai daily. If you ask questions on niche topics or dive down too deep, it says that resources on the topic are limited and you should consult a book.
This was a very cool read. Programmers were programmers even back in the day of Mark I.
It is cool to see that they dabbled in natural language processing back then. This is years before Eliza and they were working on generating English prose based on English grammar. Very impressive!
The music generation program they wrote is equally impressive. The recording that was playing shows that they were adept enough to time events in the computer so good that they could playback songs. This was back in the early 1950s.
There are ways around this. For example set a property tax from second home on. Do not tax the primary residence. Or set income brackets. If poorer people live in their own homes they don't pay property tax.
That is the very problem we are facing in Turkey :). The municipality determines the value of housing in a neighborhood each year. That is taken as a basis for property taxes and transaction taxes. The municipality assessed value is somewhere near 1/20th of the value of an average flat. So, almost no tax gets collected :(.
In countries with high inflation purchasing real-estate and keeping it vacant is an inflation hedge. Plus, you also benefit from low interest rates and get free money if your government allows it.
I live in Turkey. We had 80% p.a. inflation, where the government decided to lower the interest rates even further. Our president said Interest rates are the cause of inflation and if we lowered interest rates inflation would go down. State banks gave out house loans with 12% p.a. interest where the inflation rate was above 80% p.a.
A lot of Turkish people got their free money from the bank and invested in real estate. In Turkey, everyone evades tax and property taxes are not really collected. This in turn fueled inflation even more, sky-rocketed inequality and caused the worst housing crisis.
That is why I am convinced that property taxes are a must.
The solution is actually simpler, set a property tax that would hurt if the buildings became vacant. For example if you pay 1% of the buildings value as property tax each year, it would make enough incentive to rent it out or sell if you don't need it. The proceeds can be used for building public housing projects or helping the homeless. Property tax was invented for this very reason.
Well, I try to be optimistic and work with the models.
It's like when we first learned to code. Did syntax errors scare us, did nullpointer exceptions, runtime panics scare us? No, we learned to write code nevertheless.
I use LLMs daily to enhance my productivity, I try to understand them.
Providing context and assigning roles was a tactic I was taught in a prompt writing seminar. It may be a totally wrong view to approach it but it works for me.
With each iteration the LLMs get smarter.
Let me propose another example. Think of the early days of computing. If you were an old school engineer who only relied on calculations with your trusted slide rule, you would critise computers because they made errors, they crashed. Computing hardware was not stable back then and the UI were barely usable. Calculations had to be double checked.
Was investing in learning computing a bad investment then? Likewise investing in using LLMs is not a bad investment now.
They won't replace us, take our jobs. Let's embrace LLMs and try to be constructive. We are the technically inclined after all. Speaking of faults and doom is easy, let's be constructive.
I may be too dumb to use LLMs properly, but I advocate for AI because I believe it is the revolutionary next step in computing tools.
Well I told you I was bad at math. I wrote a faulty prompt. But chatgpt understood my instructions perfectly. That was the argument I was trying to demonstrate.
The problem lied between the chair and the computer.
I gave minimum context like this: "I have a history exam. You are an expert in British royal history. List me the names of 20 kings and queens in England."
The answer was: "Certainly! Here's a list of 20 kings and queens of England:
1. William the Conqueror
2. William II (Rufus)
3. Henry I
4. Stephen
5. Henry II
6. Richard I (the Lionheart)
7. John
8. Henry III
9. Edward I (Longshanks)
10. Edward II
11. Edward III
12. Richard II
13. Henry IV
14. Henry V
15. Henry VI
16. Edward IV
17. Edward V
18. Richard III
19. Henry VII
20. Henry VIII"
I disagree. AI in 1960s relied on expert systems where each fact and rule was handcoded by humans. As far as I know LLMs learn on their own on vast bodies of text. There is some level of supervision, but it is bot 1960s AI. That is the reason we get hallucinations as well.
Expert systems are more accurate as they rely on first order logic.
This reminds me of movies shot in early times of the internet. We were warned that information on the internet could be inaccurate or falsified.
We found solutions to minimize wrong information for example we built and maintain Wikipedia.
LLMs will also come to a point where we can work with them comfortably. Maybe we will ask a council of various LLMs before taking an answer for granted, just like we would surf a couple of websites.
Well it is a bit like satire. You have to explain the universe for an unspecialized GPT, like you would do to a layman. There are custom gpts that come preloaded with that universe explanation.
In addition, do not ask facts to an LLM. Give a list of let's say 1000 kings of a country and then ask give 20 of those.
If you ask 25 kings of some country, you are testing knowledge not intelligence.
I see LLMs like a speaking rubber duckie. The point where I write a successful point is also the point where I understand the problem.
I am a novice, maybe that's why I liked it.