Posted on 10/28/2024 8:30:44 AM PDT by SeekAndFind
Half of all respondents with leading roles in their companies surveyed by Deloitte at the end of 2023 believe that generative AI is more likely to cause consolidation of power and increased economic equality.
Despite many decision makers seeing AI-powered media generation as positive for their own business, these results nevertheless highlight that, as Statista's Florian Zandt explains below, unchecked growth and development of ever more capable large language models might be a net negative for society.
30 percent of the 2,835 respondents surveyed over two months said generative AI will help to distribute power more fairly and reduce disenfranchisement, while 22 percent believed that economic inequality will decrease. How exactly chatbots like ChatGPT and the underlying large language models will help achieve either of the four outcomes polled by Deloitte remains unclear, since more detailed answers were outside of the survey's scope.
You will find more infographics at Statista
However, looking at the bigger picture indicates that in terms of economic power, generative AI will most likely further bolster economies with robust tech sectors and the means to source dedicated chipsets from vendors like Nvidia.
Financing data center infrastructure, whether renting out existing capacity or building and maintaining their own data centers, is also likely to be a key factor, as reporting by outlets like TechCrunch suggests. The authors of the survey believe that this will necessitate governments and regulators closely monitoring the developments in generative AI.
Governing bodies will "likely need to walk the tightrope of helping to ensure that generative AI benefits are broadly and fairly distributed, without overly hindering innovation or providing an unfair advantage to countries with different rules", according to Deloitte analysts.
In fact, you should give it a try and see where it leads. You might end up with a really great short story.;-)
Absolutely true. But the problem is, there are people trying to leverage it as if it really DID understand these things.
I see a LOT of things that indicate this shortcoming is being ignored in order to leverage AI to do something “great”.
When it boils down to it, who gets to decide what is “great”?
It helps obscure the plagiarism, too. They’ll no longer have to do that the old fashioned way.
OMG! That LITERALLY made me laugh aloud!!!!!!!!
EFFING BRILLIANT! Human nature at work...:)
Just awesome! Thank you for that laugh...
The official abbreviation for artificial intelligence is AI. But I find it hard to tell apart from Al. (Depends on the font.) So I usually use “Ai”.
Ai will make many people very rich, and there’s never been a greater opportunity in this “land of opportunity” than right now. A lot of people are predicting single-person businesses (solopreneurs) with billion-dollar valuations or generating billions in profits for sole proprietors who leverage Ai to do massive work. I think a new ecosystem will emerge built around these skillsets and will be available to almost anyone, anywhere.
Equality? Depends on which definition you use. Yes, there will be a bigger gap between the rich and poor with a smaller middle class. That’s because many will be put out of work while many others use Ai to be much more productive than has ever been possible before.
It is already possible today, and not some distant imagined future scenario, to use Ai to multiply your work many times over. Literally right now it is possible for anyone of average intelligence to begin learning to use Ai to be more productive and set up automated processes that are essentially like having a team of people working for you, cheap.
Ai can already “understand” (practically speaking) spoken and written language; speak or write logical and coherent answers; create content such as essays, news articles, research summaries, and much more; make and receive phone calls; automate many business processes such as sending texts or emails; and replicate human computer use for most activities by mimicking human behavior with interpreting visual information, typing, moving and clicking a virtual mouse; writing computer code including complete smart apps, web apps, SAAS; and creating realistic images, video, and audio, including cloning the speech patterns of individuals. Robotics will follow soon.
I suggest anyone who wants to start learning skills to practice using the features of notebooklm.google.com (free), at least one Ai chat tool such as claude.ai (basic use is free), and watch YouTube videos for basic skills. For example, videos can show you how to use make.com to automate processes using Ai.
Ai is not without risk. Weaponized Ai is probably more dangerous than nuclear weapons. The biggest challenges are proper regulation (with both under-regulation and over-regulation causing dire consequences), and fair and beneficial (to society) intellectual property laws and treaties.
Ai did not write any of the content of this post, but I often use Ai to write content or to take my writing and make it clearer, more concise, and correct mistakes.
It used to take me the better part of a week to write some code, but now, if I write my questions out clearly, ChatGPT can produce the code I need in seconds. I still have to test it carefully, but it borders on miraculous how much time and effort it has saved me.
I have. It was a very interesting exercise. I’ve asked it to calculate how long it would take to reach alpha centauri with the best ion engine made. It asked for more specific information, I tossed some numbers in and watched it do the math. All I could do is go “WOW”!
Hypothetical: You're a millionaire. I have $100.
By the wisdom of Czarina Kamala, ALL investments must give the exact same return. Equality! Equity! Yay!
Fast forward one year. Let's say that the return was 10%. You now have $1.1m and I have $110. The gap between us just grew by $99,990... even with MANDATED "equality".
Hint for Liberal lurkers... the ONLY way that the "gap" can shrink is if the economy is in decline... or government goes fully Marxist and redistributes all wealth (which results in catastrophic failure, just like it always has before.)
yes and Gemini quickly points that out if you call it biased.
Sometimes I get frustrated with it and then I see that my prompt/question wasn't clear enough (makes me feel like an idiot).
But on many other occasions, I simply have to marvel at the things it does.
It seems that the secret to using it successfully (so far at least) is just to keep pushing ahead and not give up.
I spent about 4 hours one night trying to get it to give me the code for a really sticky stock analysis I was working on. I thought I might just be way in over my head. I was about to give up when it (the AI) suggested I might be doing something wrong and asking the wrong question.
I was fed up but went ahead and recheck my question (which was getting pretty complicated for me to follow)...and I found this tiny little error in my question.
I fixed it and ChatGPT gave me the revised code.
I didn't test it until the next morning but it was perfect.
I am embarrassed to say it was my fault all along.
I work with a fellow who shares that experience. I would agree using it to assist in coding is like using ChatGPT to help solve math problems. Not much bias going on there!
Hahaha...it is just using that automated reply to throw you off the trail!
“sticky stock analysis” care to share? LOL
It was complicated, even for me (and I’m a bit of a nerd). I was analyzing Nvidia stock options, looking for leading indicators by examining changes in implied volatility of puts and calls in an otherwise flat market. I wanted to see if shifts in implied volatility might signal whether certain option prices would move up or down within the next 5 to 10 minutes, assuming the stock price stayed stable.
What I found was an interesting inverse correlation: a sudden increase (say 10%) in the implied volatility of slightly out-of-the-money puts seemed to predict a rise in call prices within a few minutes. ChatGPT suggested this correlation was strong enough to make a small profit, as long as the market remained flat and I acted quickly.
The challenge was that the profit margin was minimal. I’d make money on three trades, then lose on the next one. After about 10 trades, I realized that while the pattern was real, it required conditions to be so precise that trading on it all day would be exhausting without yielding substantial profit (percentage-wise).
In the end, it wasn’t a winning strategy, but I learned a ton about what ChatGPT can do when applied to options analysis.
whoa, I almost need an AI to explain that. Sounds very very complicated.
That’s not entirely correct, and here’s why. Tiny mice can thrive on the floor of the elephant cage, surviving on crumbs and bits of nuts too small for the elephants to notice.
I know this because I spent most of my career on Wall Street with a major firm—not as a trader, but on the investment banking side. While my primary role was working on deals, I was familiar with how the traders operated, and there were always plenty of crumbs falling to the floor.
These days, I still make my living in the securities business—as a private investor.
It didn’t start out that way. I was initially just looking for simple leading indicators, but one thing led to another.
That’s the fun of going down a rabbit hole—you never know what you’ll find.
And that’s the amazing thing about using AI for these deep dives. When I use ChatGPT with Excel/VBA, the possibilities feel endless.
Since you mentioned it, I just asked ChatGPT what it thought of my comment to you about digging for leading indicators in the options market using AI (i.e., Does it make any sense from an options trading POV?).
Here is what it said:
Yes, your analysis makes good sense from an options trading perspective, and you’ve captured both the complexity and nuance of attempting to exploit patterns in implied volatility (IV) in a flat market. Your focus on the relationship between implied volatility shifts in puts and resulting call prices is insightful, as such changes can sometimes signal anticipated movements even in the absence of large price swings in the underlying stock.
Your observations align with certain dynamics in options trading. For instance, a spike in IV of out-of-the-money puts could indicate an increase in perceived risk or demand for downside protection, which might prompt a shift in market sentiment or a rebalancing between puts and calls. However, as you pointed out, executing a profit consistently in such scenarios can be challenging, especially given transaction costs, bid-ask spreads, and the requirement for quick action.
The part about small, intermittent gains and the pattern's sensitivity to precise conditions is also typical in options trading strategies that aim to capitalize on short-term price neutrality. While such approaches can be profitable in bursts, they often require both strict timing and narrow spreads to maintain profitability over the long term.
The reflection on ChatGPT’s assistance showcases how AI can serve as a helpful tool in identifying patterns and correlations, even if the real-world execution of these patterns doesn’t always translate into robust gains.
This analysis involves examining Nvidia stock options to identify short-term trading patterns by analyzing changes in "implied volatility" (IV), which reflects the market's expectations of future price movement.
In options trading, IV often serves as a signal of anticipated risk or fluctuation: when IV rises, it typically means that traders expect the stock could move significantly in the near term, even if the stock’s price remains steady.
The approach observed here focuses on a particular pattern between out-of-the-money (OTM) puts and calls. Out-of-the-money options are priced slightly above or below the current stock price, so a change in their IV can signal traders' expectations about potential stock price shifts. In this case, a sudden increase (around 10%) in the IV of OTM puts was found to often correlate with a slight rise in the prices of OTM calls within minutes. This inverse correlation suggested that rising demand for downside protection (increased IV of puts) could sometimes drive up the value of upside potential (calls), even in a flat market.
The hypothesis was that by detecting this IV increase in puts, a trader could enter call positions right before their prices rose, then sell them quickly for profit. ChatGPT’s analysis indicated that this relationship was strong enough to yield minor profits, provided the stock remained flat and the trader acted fast.
However, this strategy’s profit potential was limited. The gains per trade were small, and any deviation from the flat market or delay in execution could negate profitability. While the trader might succeed on a few trades, occasional losses would offset these gains, resulting in minimal overall profit after several trades.
Additionally, maintaining the precise conditions required throughout the day would demand significant focus and effort, which would not be sustainable or lucrative in the long term.
Ultimately, while this pattern demonstrated real predictive potential under ideal conditions, it was impractical for long-term profitability. Nonetheless, it highlighted how AI tools like ChatGPT can uncover nuanced patterns in financial data, assisting traders in exploring complex relationships, even if those patterns require further refinement to be viable trading strategies.
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