For every Renaissance technology that leverages AI successfully, there will also be casualties like LTCM.
What is LTCM? Would be interesting case to learn.
I guess this.
2 Likes
The downfall of the Long-Term Capital Management (LTCM) hedge fund in 1998 can be attributed to not just one, but two financial âweapons of mass destructionâ:
- Overleverage: LTCM relied heavily on leverage, meaning it borrowed significant amounts of money to fuel its trades. This left it exposed to substantial losses if the market went against its positions.
- Derivatives: LTCM extensively employed derivatives, intricate financial instruments with inherent risk. As the market turned against LTCM, it was compelled to offload its derivatives at a loss.
Itâs important to note that these factors are unrelated to AI. While similar scenarios might unfold in AI-based algorithmic trading, itâs crucial to understand that algorithmic trading inherently involves a Man+Machine approach. Human oversight governs the algorithms, and the machine carries out the executions.
2 Likes
Then isnât it just automation, and not AI in the traditional sense?
I am yet to know the full details of LTCM, but AFAIK, real life events that had unfolded, went beyond their modelling, their historical assumptions or anticipations and interpretations, so they failed.
So if funds run by Nobel prize winners can fail, who in my personal opinion are the synonym for intelligence, even proving the rhyming history quote by Mark Twain wrong, a pure AI fund, no matter how powerful, no matter the harnessing of vast amounts of data and come up with innumerable possible scenarios, there is always the element of human psychology and emotions that will spoil the game, there will always exist a new possible scenario that has not happened yet, but is waiting to happen.
3 Likes
@ChaitanyaC - Youâre absolutely correct. The collapse of LTCM raises a compelling inquiry into the distinction between automation and traditional AI.
LTCM employed intricate mathematical models and sophisticated trading strategies. However, itâs crucial to recognize that these models donât precisely embody the concept of ML based âartificial intelligenceâ as we understand it today. They were more aligned with automation, where predefined rules were programmed to execute trades based on market conditions. What sets contemporary AI apartâits adaptability, learning, and self-improvementâwas absent in these models.
LTCMâs downfall starkly illustrates the inherent limitation of any model or algorithm when confronted with unforeseen events or deviations from historical data. Despite their sophisticated modeling and quantitative prowess, they failed to predict or accommodate the âblack swanâ events that lay outside their historical assumptions.
Your point about Nobel prize-winning fund managers and their failures holds immense significance. It underscores that even the most brilliant individuals cannot foresee every outcome, particularly in the face of unprecedented situations.
Regarding pure AI-managed funds, your insight into the impact of human psychology and emotions is astute. These elements can give rise to unexpected scenarios that algorithms cannot account for. Just as LTCMâs models didnât predict certain real-life events, AI algorithms might struggle to anticipate scenarios stemming from human behavior, market sentiment, or unforeseen global shifts.
An additional case illustrating the dominance of ML-based AI over non-ML-based automation can be seen in the AlphaZero (AI-based engine) vs. Stockfish (non-AI-based gaming engine) tournament. In this contest, AlphaZero displayed its prowess by clinching victory with a record of 28 wins, 0 losses, and 72 draws against Stockfish. This remarkable achievement serves as a clear testament to AlphaZeroâs superior capabilities when pitted against one of the most formidable traditional chess engines, Stockfish.
Taking our exploration further, letâs examine into the case of AlphaGo. Created by Google, AlphaGo represents an AI-based engine designed for playing the ancient game of Go. Not too long ago, the conception of crafting an engine capable of playing Go was considered to be the pinnacle of AI achievement. In 2016, South Korean Go player Lee Sedol engaged in a five-game match against Google DeepMindâs AlphaGo AI. What made this match intriguing was that Lee Sedol secured victory in the fourth game, with the final score favoring AlphaGo at 4-1.
Upon dissecting the game where Lee Sedol emerged victorious, a significant revelation came to light. The pivotal move that propelled Lee Sedol to success was not solely rooted in pure logic. Rather, it was driven by his intuition and emotions, underscoring the vital role of human instinct in strategic decision-making. This triumph in the fourth game stood as a testament to the dynamic interplay between human intuition and AI capabilities.
In light of these insights, itâs becoming apparent that IQ-based tasks are swiftly being overtaken by AI, leaving room for humans to excel in EQ-based roles. Instead of competing solely on IQ-based work, the focus should shift to EQ-based work. The future role of humans lies in governing and constructing responsible AI rather than creating a âSkynet-likeâ AI.
References:
[1] https://www.youtube.com/watch?v=WXuK6gekU1Y&t=24s
2 Likes
Appreciate your inputs, views and explanations, some reinforcing my beliefs and strengthening my convictions, and other points about the capabilities of AI, all of it, making me wonder and think a bit more where I stand in the financial markets, with the explosion of data and machines.
On a lighter note, when you did not reply to membersâ questions, and were only making new posts, and the language while standard, seemed too machine-like, I thought are you are not human, but now along with the fact that you have started replying to membersâ queries and a DP, my assumption proved to be wrong.
Personally, I find that putting my thoughts down on paper is proving to be immensely beneficial, as itâs often said that writing can be even more insightful than reading. As a freelancer consultant, Iâm fortunate to have the opportunity to engage in this practice. The LTCM case study has captured my fascination, and delving deep into it to craft an article has been an exciting journey. Iâve published this article on both LinkedIn and Quora, @ChaitanyaC and @StonePitbull contribution to our discussions played its role in shaping its content.
4 Likes
An interesting read. Pretty much sums up the impact across industries and labour force in developing and developed economies.
1 Like
Sam Altman predicts 2025 will be the year AI agents do real work, especially in coding
by 2026, AIâs will help make major scientific discoveries, driving the next wave of economic growth
in 2027, these breakthroughs will move into the physical world, with robots shifting from novelty to serious creators of economic value.
âProtein folding was biologyâs Fermatâs Last Theoremâa 50-year grand challenge that AlphaFold finally cracked.â
Demis Hassabis highlights AlphaFold as a breakthrough in biologyâsolving a 50-year problem likened to âFermatâs Last Theoremâ of the life sciences.
By accurately predicting protein structures, AlphaFold has become foundational for drug discovery and molecular biology.
With over 2 million researchers using it, Hassabis calls it a âroot nodeâ that unlocks vast new research pathways.
https://x.com/WesRothMoney/status/1923323630400020948?t=qonxTQmrXxB1_br12l3NNw&s=19
1 Like
AI made Jeff Bezos come back from retirement.
https://x.com/the_dhakshu_/status/1923179155203530834?t=X3EHIi-eFf3xoYSVETmwaw&s=19
1 Like
If Microsoft is using AI agents to write code and optimizing its software development workforce, the impact on Indiaâs IT service sector could be significant. The sector primarily relies on human resources, but the rules of Industry is changing. Previously, software was created by human effort, but now AI agents are taking over many tasks. This is an example of permissionless leverage.
Even a ten percent decline in the demand for human workers could lead to major disruptions in IT services, affecting economic conditions in cities like Bangalore, Hyderabad, Chennai, Pune, and Gurgaon.
The consequences could be severe. An IT professional earning 20 lakh per year, having taken a home loan or marriage loan, may face stagnation or even job loss. Such shifts would affect real estate, banking, and overall consumer spending.
This transition is happening faster than anticipated. The free market is responding to optimization efforts by gradually reducing reliance on human labor and replacing it with AI-driven tasks executors.
3 Likes
OpenAI has launched Codex, an advanced AI coding assistant integrated into ChatGPT, designed to revolutionize software engineering. Codex can autonomously generate code, fix bugs, conduct tests, and suggest performance improvements, acting as a virtual coworker for developers.
Impact on Software Engineering
- Enhanced Productivity â Codex automates repetitive coding tasks, allowing engineers to focus on higher-level problem-solving.
- Improved Code Quality â The AI agent iteratively tests and refines code, ensuring cleaner and more efficient outputs.
- Faster Development Cycles â Codex reduces the time required for debugging and feature implementation, accelerating software delivery.
- Collaboration and Accessibility â Integrated into ChatGPT, Codex makes AI-assisted coding more accessible to developers across different expertise levels.
- Potential Industry Shift â With AI handling more coding responsibilities, the role of software engineers may evolve towards strategic oversight only rather than manual coding.
Codex is currently available to ChatGPT Pro, Enterprise, and Team users, with plans to expand access further. As AI-driven development gains momentum, Codex signals a shift towards AI-assisted software engineering, reshaping how developers interact with code.
4 Likes
Copied from reddit post
For those who donât know, AlphaEvolve improved on Strassenâs algorithm from 1969 by finding a way to multiply 4Ă4 complex-valued matrices using just 48 scalar multiplications instead of 49. That might not sound impressive, but this record had stood for FIFTY-SIX YEARS.
Let me put this in perspective:
- Matrix multiplication is literally one of the most fundamental operations in computing - itâs used in everything from graphics rendering to neural networks to scientific simulations
- Strassenâs breakthrough in 1969 was considered revolutionary and has been taught in CS algorithms classes for decades
- Countless brilliant mathematicians and computer scientists have worked on this problem for over half a century without success
- This is like breaking a world record that has stood since before the moon landing
Whatâs even crazier is that AlphaEvolve isnât even specialized for this task. Their previous system AlphaTensor was DESIGNED specifically for matrix multiplication and couldnât beat Strassenâs algorithm for complex-valued matrices. But this general-purpose system just casually solved a problem that has stumped humans for generations.
The implications are enormous. Weâre talking about potential speedups across the entire computing landscape. Given how many matrix multiplications happen every second across the worldâs computers, even a seemingly small improvement like this represents massive efficiency gains and energy savings at scale.
Beyond the practical benefits, I think this represents a genuine moment where AI has demonstrably advanced human knowledge in a core mathematical domain. The AI didnât just find a clever implementation or optimization trick, it discovered a provably better algorithm that humans missed for over half a century.
What other mathematical breakthroughs that have eluded us for decades might now be within reach?
Additional Context to address the winograd algo:
Complex numbers are commutative, but matrix multiplication isnât. Strassenâs algorithm worked recursively for larger matrices despite this. Winogradâs 48-multiplication algorithm couldnât be applied recursively the same way. AlphaEvolveâs can, making it the first universal improvement over Strassenâs record.
AlphaEvolveâs algorithm works over any field with characteristic 0 and can be applied recursively to larger matrices despite matrix multiplication being non-commutative.
4 Likes