AI and Silos of Uncertainty
In an era marked by rapid technological advancement and socio-political shifts, investors face unique challenges that require a nuanced understanding of the artificial intelligence (AI) landscape. The emergence of AI networks, or what I call silos of data, and their potential to influence everything from social media copy to childhood education to government policies have created opportunities and uncertainties. This letter aims to provide investors with awareness into navigating AI output, the challenges of using AI for financial information and the reasons for using your critical thinking skills over AI uncertainty.
The Role of AI and Social Media in Shaping Economic Landscapes
Artificial intelligence has revolutionized many sectors, including finance, by enabling more comprehension of financial data. However, its integration into social media platforms has also raised concerns about algorithmic manipulation and data privacy. Social media giants wield significant power over public opinion. Their algorithms can bend the truth according to what output these algorithms have been programmed to create. Consequently, the AI output pipeline is far ahead of the ethical policy world- which is racing to catch up. Consequently, these are the frontier days of AI.
To illustrate, 217-year-old science publisher Wiley has just shut down 19 science journals after retracting 11,000 “peer-reviewed pages” as nothing more than Ai produced “naked gobbledygook sandwiches”. They were generated by an AI cheating service.
The Influence of Algorithms on Market Sentiment
Algorithms can significantly impact market sentiment by promoting certain narratives while suppressing others. For example, during political campaigns or economic crises, social media platforms can amplify specific viewpoints, thereby influencing investor behavior and market trends. Understanding this dynamic is crucial for investors who rely upon social media feeds for literalness.
Data Silos and Government Intervention
Another challenge is the existence of data silos. These are large enterprises that hoard data and are often reluctant to share it. The government, however, possesses a sort of passkey on which to integrate political messages into these silos to influence policy across various sectors. This power can be a double-edged sword, as government intervention can either stabilize markets or create additional layers of uncertainty.
The Intersection of Politics and Finance
The current political climate adds many layers of complexity before an silo of information generates an AI output. National policymakers are increasingly aware of the influence wielded by AI and social media. Hierarchy adds to the complexity of your output since by the time your AI output has been read by you, the question has passed through several algorithmic filters.
To illustrate, these pre-AI-output begins with (1) standards of global telecom policies, (2) numerous governmental policies of law, (3) 170 different algorithmic bias filters, 24 cognitive bias filters which are posited to negate the otherwise warping of your perception of reality, (4) policy shaping designed by large technology organizations, (5) algorithms recommended by lobbying efforts, (6) public awareness campaign shaping according to the government, (7) influence from research findings and peer reviewed publications, (8) influence from Amnesty International, Human Rights Watch and global tech policy think tanks, and (9) finally many individual and organizations that have succeeded in influencing public policy.
The Fear of Political Change
On the eve of potential political shifts there is growing recognition that two can play the game of algorithmic influence. Investors must stay vigilant and adapt to these changes, as new policies could either mitigate or exacerbate financial uncertainties. Of concern are which winning candidate can deal with future fiscal policy restraints, taxes, the strength of the dollar, bond yields accelerations, tariff intensities, and how to pay down interest on the national debt which is expected to overlap all military spending in 2025.
The CIA’s Involvement in Social Media and Investor Implications.
While the CIA is strictly prohibited from spying on or running clandestine operations against American citizens on U.S. soil, recent revelations have blurred these lines. A bombshell new “Twitter Files” report reveals that a member of the Board of Trustees of In-Q-Tel, the CIA’s mission-driving venture capital firm, along with “former” intelligence community (IC) and CIA analysts, were involved in a massive effort in 2021-2022 to take over Twitter’s content management system with the purpose of censoring news that would make the White House look bad, while enhancing the distribution of misinformation against the Republican opponent.
These revelations have far-reaching implications for investors. The involvement of government-sponsored intelligence agencies to skew social media public information and to use AI algorithm instructions that a computer must follow to generate output raises questions about data integrity and the reliability of information. Clearly, if the government’s manipulation of information must now be considered into puts into question the quality of election-year economic news.
Strategies for Navigating Financial Uncertainties
Given these challenges, what strategies can investors employ to navigate financial uncertainties?
One of the most effective ways to mitigate risk is through diversification. By spreading investments across various asset classes and sectors, investors can reduce their exposure to any single point of failure.
Due Diligence
In an age where information can be easily manipulated, thorough due diligence is more critical than ever. Wealth managers must, therefore, remain skeptical of being overly optimistic or pessimistic. Even Ai conversations should be reconsidered since attitude shaping is being done by algorithms to achieve the desired thought. Remind yourself that you have a voice and a critical mind which by way of qualitative human characteristics is now and may forever be superior to algorithmic filtering outputs.
Staying Informed
Keeping abreast of political and regulatory changes is crucial for making informed investment decisions. Investors should look at every news source as a filtered silo. Assume that every AI network has an axe to grind. Advocate for algorithmic transparency and accountability.
Meanwhile, as skepticism grows surrounding AI-generated party-in-power news, domain-specific AI silos will need to be built. A surge in AI research and development funding by the government is underway. The creation of these new AI systems is telling. Rather than training AI systems to be all things to all people new AI systems are being explored. Silo sets that explain AI systems may be developed. General intelligence AI systems that collaborate with human intelligence is in the works. AI systems for domain specific fields such as healthcare, finance, manufacturing, engineering, and scientific discovery are being visualized.
Leveraging Technology
While AI can be a double-edged sword, it also offers tools for better market analysis. That is clear., We are using AI today to leverage risk assessment and predictive modeling, even as we remain aware of the technology’s many limitations and I personally wait for the day when real-time financial data AI becomes available.
Conclusion
Navigating financial challenges in uncertain times requires a multifaceted approach. As AI and social media continue to shape economic landscapes and outputs, investors must be able and willing to verify data while adapting to new realities. This is what we do daily for you. Diversifying portfolios thorough due diligence, staying informed, and leveraging technology can lead to improved positioned during periods of financial uncertainty.
In our dynamic world, knowledge remains a key advantage. As we navigate the evolving landscape of AI, its strengths and limitations, and the influence of social media on financial markets, seasoned experience and critical thinking are crucial. Valued assessment systems, integrating qualitative and quantitative factors, are essential for effective asset allocation.
To learn more about navigating financial uncertainties and leveraging AI, consider going to vaughnwoods.com to read past newsletters, because you need to stay informed and stay prepared to stay ahead.
Thank you for your continued support and referrals,
Vaughn L. Woods, CFP, MBA
Vaughn Woods Financial Group, Inc.
2226 Avenida De La Playa
La Jolla, CA 92037
858-454-6900
Sources
- https://www.zerohedge.com/markets/trust-sciencethat-just-retracted-11000-peer-reviewed-papers
- https://joannenova.com.au/2024/05/so-much-for-peer-review-wiley-shuts-down-19-science-journals-and-retracts-11000-fraudulent-or-gobblygook-papers/
- Large, Diverse Training Data: The AI is trained on massive datasets that encompass a wide range of viewpoints and perspectives. This helps mitigate bias present in any single source. | Data quality and availability | Enterprises & Research institutions
- Fact-Checking and Verification: The AI output is compared to reliable sources to ensure factual accuracy. | External knowledge integration | Enterprises & Research institutions
- Bias Detection and Mitigation Techniques: The AI is trained to identify and avoid replicating biases present in the training data. | Machine learning algorithms & training methods | Research institutions & some Enterprises
- Human Oversight and Review: A human element remains involved in the process to review the AI’s output and flag any potential issues. | Human-AI collaboration | Enterprises & Regulatory bodies
- Transparency and Explainability: The AI’s decision-making process should be understandable to some degree, allowing for identification of potential biases. | Explainable AI (XAI) techniques | Research institutions & some Enterprises
- Clearly Defined Objectives and Use Cases: The AI is designed with a specific purpose in mind, limiting the potential for misuse or generation of irrelevant content. | AI development process | Enterprises & U.S. government
- Alignment with Ethical Frameworks: The AI’s development and use adhere to established ethical principles for AI, such as fairness, accountability, and transparency. | Ethical AI guidelines | Research institutions, U.S. government, & some Enterprises
- User Education and Expectations Setting: Users understand the limitations of generative AI and how to interpret its outputs critically. | User training and documentation | Enterprises & some Educational institutions
- Continuous Monitoring and Improvement: The AI’s performance is constantly monitored for bias or factual errors, with ongoing efforts to improve its outputs. | Iterative development process | Enterprises & Research institutions
- Accountability and Enforcement Mechanisms: Clear mechanisms exist to address issues of bias or misuse of generative AI. | Regulatory frameworks | U.S. government & International organizations
Investors should be aware that there are risks inherent in all investments such as fluctuations in investment principal. Past performance is not a guarantee of future results. Asset allocation cannot assure a profit nor protect against loss. Although the information has been gathered from sources believed to be reliable, it cannot be guaranteed. Views expressed in this newsletter are those of Vaughn Woods and Vaughn Woods Financial Group and may not reflect the views of Bolton Global Capital or Bolton Global Asset Management. The information provided is for general informational purposes only and should not be considered an individualized recommendation or personalized investment advice. VW1/VWA0301.