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Weekly Trend Monitoring: GPT-4 Release, U.S. Bank Failure, and Implications for the Financial Sector

This week's trend monitor report examines two significant events: the release of OpenAI's GPT-4, a revolutionary AI language model, and the collapse of Silicon Valley Bank (SVB), which has raised concerns about the stability of the U.S. banking system. GPT-4 represents a major advancement in AI language models, with the potential to revolutionize various industries, including finance. The model has numerous applications, such as automating customer support, generating financial reports, and analyzing market sentiment. However, users must be cautious when employing GPT-4 for critical decision-making, as its outputs may not always reflect the true underlying reasoning or reality accurately. The complex relationship between the language model output, underlying reasoning, and reality underscores the importance of using GPT-4 with caution, carefully evaluating its generated outputs, and recognizing its limitations and potential biases. In parallel, the recent SVB collapse has brought the stability of the U.S. banking system into the spotlight, with potential impacts on the global risk landscape. The emergency measures taken by the U.S. government aim to address the situation, but C-level directors should monitor the effectiveness of these measures and be prepared for potential geopolitical and strategic implications. The integration of advanced technologies like GPT-4 into the financial sector should be approached with caution, as the current landscape calls for a thorough assessment of both the benefits and risks associated with these innovations. Staying informed about the latest advancements in AI, such as GPT-4, while also remaining vigilant regarding the stability of the financial sector in light of recent bank failures, is crucial for C-level directors as they navigate the rapidly evolving business environment.

The Implications of GPT-4's Improved Performance and the Complex Relationship Between Language Model Output, Underlying Reasoning, and Reality The release of OpenAI's GPT-4 has generated considerable excitement in the artificial intelligence (AI) community. With significant advancements in language understanding and generation, GPT-4 has the potential to revolutionize various industries, including finance. However, as powerful as this technology is, it's essential to recognize the limitations and potential biases inherent in its outputs. This report aims to provide an overview of GPT-4's capabilities, limitations, and the complex relationship between its output (LLM), the true underlying reasoning (RR), and reality.

GPT-4 is already available on ChatGPT Plus. Although its speed is slower than the optimized GPT-3.5, its ability to synthesize generated text and information, including the ability to process larger inputs, is better than that of GPT-3.5. We have found that users of ChatGPT Plus can access previous recorded prompts that have been removed from the free version of ChatGPT. The optimized GPT-3.5 has better speed than the "legacy" GPT-3.5 available on the free version of ChatGPT. We recommend that professional users switch from the free version of ChatGPT to the paid version of ChatGPT Plus to obtain better performance and a better experience.

GPT-4: A Leap Forward in AI Language Models

GPT-4 builds on the success of its predecessor, GPT-3, with further improvements in language understanding, prediction, and generation. By being trained on vast amounts of human language data, GPT-4 can identify patterns and correlations in language and generate human-like text, enabling a wide range of applications such as chatbots, content creation, and sentiment analysis. However, GPT-4 differs fundamentally from specialized models like AlphaFold, which predict protein structures based on amino acid sequences. While AlphaFold has a deep understanding of the principles governing protein folding, GPT models primarily focus on surface-level patterns in language data and may not fully capture the underlying meaning and reasoning.

Language Model Output (LLM), Reality Reasoning (RR), and Limitations

Despite its impressive capabilities, GPT-4's output, referred to as LLM, should not be mistaken for the true underlying reasoning (RR) behind the generated text. LLM is based on statistical patterns and correlations in language data, and while it can appear coherent and human-like, it may not always reflect RR accurately. The relationship between LLM and RR is complex and can be expressed as LLM ≈ f(g(R), D, M), where g(R) maps external reality onto meaning, D is the input data, and M represents the language model's architecture. This equation highlights the intricate interplay between external reality, underlying meaning, and the model's output. GPT-4's outputs are not always accurate or reliable, especially when dealing with complex or abstract concepts. In some cases, LLM may be misleading, incorrect, or even harmful, particularly concerning sensitive or controversial topics. Users of GPT models must remain aware of these limitations and potential biases, using the technology with caution and critically evaluating its outputs.

Pseudo code and Python code to demonstrate RR ≈ g(R); LLM ≈ f(g(R), D, M); and thus several recursive function: LLM_t = f(RR_t, D_t, M_t)

RR_t+1 = g(LLM_t, RR_t, R_t)

Implications for the Financial Industry

GPT-4's advanced language capabilities can have significant implications for the financial sector. Automating customer support, generating financial reports, and analyzing market sentiment are just a few of the potential applications. However, financial professionals must be cautious when utilizing GPT-4 for critical decision-making, as its outputs may not always reflect reality accurately. As the world continues to embrace AI advancements, understanding the complex relationship between LLM, RR, and reality is crucial for financial professionals to navigate the rapidly evolving landscape with confidence and caution.

See more information from OpenAI's blog and paper [1],[2],[3],[4], and an announcement from Google to announce API for PaLM.


U.S. Banking Crisis: A Comprehensive Assessment and Impact on Global Risk Landscape The recent collapse of Silicon Valley Bank (SVB) and subsequent emergency measures taken by the U.S. government have raised concerns about the stability of the U.S. banking system and its impact on the global risk landscape. This report provides an overview of the situation, examines the effectiveness of the measures taken, and offers insights into potential geopolitical and strategic implications for C-level directors.

Photo: Source

Background and Context

Silicon Valley Bank, a lender with deep ties to the technology industry, collapsed under the strain of a massive withdrawal of deposits. Other possible causes may include the easing of regulation. And an investigation of the auditor, such as KPMG, is ongoing. The failure of SVB and subsequent shutdown of crypto-lender Signature Bank prompted the U.S. government to announce a series of emergency measures to restore public confidence and prevent a banking crisis. Key measures included guaranteeing all deposits held at SVB and Signature Bank and launching a Federal Reserve lending facility available to other banks. Despite these measures, concerns about the fragility of the U.S. financial system remain, with shares of several U.S. regional banks under heavy selling pressure.

Effectiveness of the Measures and Geopolitical Implications

While the emergency package has prevented further bank failures as of now, its long-term effectiveness in averting a banking crisis is still uncertain. Some observers have questioned the sufficiency of these actions, given the continuing sell-off of regional bank shares and the movement of depositors' funds. The government's response does not involve using taxpayer money to protect shareholders, bondholders, or wealthy depositors. Instead, any losses to the deposit insurance fund would be recovered by a special assessment on banks. This approach aims to maintain public support for the measures while ensuring the banking industry cleans up its own mess. The collapse of SVB has highlighted the potential for a banking crisis to have wider geopolitical ramifications. The failure of a significant lender within the technology sector could disrupt the innovation economy and, by extension, affect the ongoing competition between the U.S. and China in the technology space. Furthermore, the fragility of the U.S. financial system could impact global risk assessment, with a renewed focus on the stability of financial institutions in other countries. It may also prompt other governments to reevaluate their regulatory frameworks and crisis response plans.

Recommendations for C-Level Directors, low possibility on global financial meltdown

Given the current situation and potential risks, C-level directors should closely monitor developments in the U.S. banking sector for signs of contagion or further instability, assess the vulnerability of their organizations to disruptions in financial services, particularly if they have significant exposure to the technology industry or U.S. regional banks, review their organization's risk management strategies and ensure they are prepared for potential fluctuations in the global risk landscape, and engage with regulators, policymakers, and industry peers to stay abreast of changes in the regulatory environment and potential shifts in government intervention policies. While the U.S. government's emergency measures have so far prevented further bank failures, the situation remains uncertain, and its impact on the global risk landscape should not be underestimated.

The anxiety that led to the SVB bank run was primarily due to a lack of confidence in the bank's stability, fueled by the rapid spread of information and speculation on social media, particularly Twitter. As prominent tech industry figures started expressing their concerns and sharing their decisions to withdraw money from SVB, it created a snowball effect, causing more people to question the bank's stability and join in the rush to withdraw their funds. This panic, fueled by a mix of real concerns and misinformation, quickly escalated, leading to the unprecedented speed and intensity of the bank run.

The rapid spread of information and misinformation on social media platforms has the potential to impact financial markets, as seen in the SVB bank run and the GameStop case. While retail investors and speculators, including those with limited financial knowledge, can temporarily disrupt markets, fundamental factors typically prevail in the long run. Nevertheless, these events highlight the importance of vigilance against market manipulation and misinformation, as well as the need for regulatory bodies to adapt to the evolving dynamics of social media's influence on financial markets.

Therefore, it is difficult to assign a precise probability for the recent U.S. banking crisis to cause the next global financial meltdown. However, based on the available information and the current state of the global financial system, we can estimate a relatively low probability, around 20-25%. Factors contributing to this assessment include swift and decisive government intervention, lessons from previous crises, limited exposure to the failing banks, and the ongoing global economic recovery. Nonetheless, the situation remains fluid, and unforeseen developments could increase the probability of a global financial meltdown, making it crucial to monitor the situation closely and be prepared for changes in the risk landscape.


Geopolitics.Asia will provide serious policy analysis on Mondays, trend monitoring on weekdays, and cultural and lifestyle issues on weekends. Please note that our weekday situation monitoring will not include a trend radar or scenario analysis for the time being, as we work to fully automate these processes with AI. You can, however, access to our previous experiments on trend radar and scenario planning generated by the AI, 1) Simple scenario planning at Jan 26, 2023, 2) Double iteration scenario planning technique at February 2, 2023, 3) Triple iteration scenario planning technique at February 9, 2023, and 4) Hyperdimensional scenario planning technique at February 17, 2023.

Stay tuned for updates on this exciting development!



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