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February 27.2025
3 Minutes Read

How BNY is Transforming Banking with Advanced ChatGPT Technology

BNY Mellon building entrance reflecting urban scenery on a clear day.

Revolutionizing Work: BNY's Adoption of Advanced AI Tools

In a significant step towards embracing technological advancement, BNY has partnered with OpenAI in a multiyear agreement aimed at enhancing its employee-accessible AI tool, Eliza. This collaboration will leverage OpenAI’s cutting-edge technologies, including the advanced ChatGPT Enterprise and its reasoning engine, to elevate the capabilities of Eliza for all bank employees.

The Evolution of Eliza

Diving deeper into the purpose of Eliza, which debuted in 2023, this AI assistant was conceived to provide staff with insights and tools to streamline their workflows and decision-making processes. With over 50% of BNY's employees utilizing Eliza regularly, the tool aims not just to assist but to revolutionize how banking operations are performed. This evolution is further accelerated by the integration of OpenAI’s reasoning capabilities, enabling Eliza to analyze complex problems, enhance decision-making, and provide tailored, nuanced responses.

Why This Matters: The Impact of AI on Financial Services

BNY's initiative reflects a broader trend in the financial services industry where AI is becoming integral in improving efficiency and productivity. Other major players like JPMorgan Chase and Goldman Sachs are also investing in AI technologies. As Marco Argenti, Chief Information Officer at Goldman Sachs, points out, innovation is ushering in a new era where generative models with advanced reasoning capabilities redefine what’s possible in banking and finance. The implications of this shift are considerable: potentially faster loan assessments, improved regulatory compliance, and enhanced fraud detection mechanisms.

The OpenAI Partnership: A Game Changer

Leigh-Ann Russell, BNY's CIO, emphasizes that the partnership with OpenAI will not only power innovation but also actively shape the trajectory of the bank’s AI capabilities. Access to OpenAI's application programming interface will allow BNY to incorporate advanced features, showcasing a commitment to leverage AI for better customer experiences and operational efficiency. With tools capable of performing multistep reasoning and intricate data analysis, BNY is set to enhance everything from risk assessment in lending to regulatory compliance.

Future Trends: Envisioning a New Banking Ecosystem

As financial institutions increasingly adopt AI technologies, BNY’s commitment to this transformation prompts questions about the future landscape of banking. Will the sector become wholly reliant on AI for decision-making processes? The rise of AI could democratize access to complex financial tools, enabling not just seasoned bank employees but also clients to make informed financial decisions through AI-powered applications like Eliza.

Expert Opinions: Scrutinizing the AI Surge

Industry experts believe that the momentum BNY is building could lead to a new paradigm where generative AI becomes commonplace across various banking functions. Alenka Grealish from Celent notes that BNY is setting a precedent by making generative AI accessible to all its employees, thus democratizing its use and fostering a culture where AI is an integral part of daily operations.

Challenges and Considerations

While the integration of advanced AI tools promises significant advancements, it also raises concerns about data privacy and operational risks. As AI begins to handle sensitive tasks such as loan assessments and fraud detection, ensuring the integrity of these systems becomes paramount. Banks will need to implement stringent governance practices to mitigate potential biases and errors inherent in AI systems.

Conclusion: Embracing the AI Future

BNY’s collaboration with OpenAI signifies a remarkable step in the bank's AI transformation strategy. As the bank harnesses cutting-edge technologies to empower its employees, the implications of such advancements are far-reaching, heralding a new era in financial services. For AI enthusiasts, the unfolding relationship between banks and AI technologies presents a fascinating case study of innovation, efficiency, and the re-imagining of traditional operations in the financial sector.

As these shifts occur, stakeholders should remain engaged with developments in AI and its impact on their industries. For those intrigued by the fallout of such innovations, participating in discussions and forums about AI's trajectory in banking can provide valuable perspectives.

Open AI

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04.02.2025

Examining OpenAI's Use of Copyrighted Data: Insights from Recent Studies

Update OpenAI's Copyright Controversy: What You Need to Know A recent study brings to light significant ethical concerns regarding the training practices of OpenAI's language models, particularly focusing on the GPT-4o model. The research conducted analyzed whether OpenAI utilized copyrighted material without consent, raising alarm bells in the tech community. This inquiry is especially pertinent for AI enthusiasts who are keen on understanding the legal and social implications of machine learning technologies. Key Findings from the New Study The study was able to effectively utilize DE-COP membership inference attack methods, allowing researchers to evaluate the ability of the GPT-4o model in recognizing contents gleaned from copyrighted O’Reilly Media books. In stark contrast to its predecessor, GPT-3.5 Turbo, which only displayed minimal recognition capabilities, the GPT-4o demonstrated a noteworthy AUROC score of 82% when assessing contents from paywalled O’Reilly books. This statistic indicates the model's strong ability to discern between human and machine-generated text, raising critical questions about data usage in machine training. Systemic Issues in AI Training Data While the results are specifically tied to OpenAI and O’Reilly Media, they illuminate a crucial point: the tech industry may be grappling with broader issues surrounding the use of copyrighted materials. The researchers hint at potential access violations stemming from the LibGen database, where all tested O’Reilly books were evidently available. This points towards possible systemic exploitation of copyrighted data across various platforms, prompting urgent discussions around fair educational practices in AI research. The Role of Temporal Bias in AI Recognition Another layer discussed in the study is the concept of temporal bias—the idea that the language and contextual understanding evolve over time. The researchers took measures to mitigate this bias by ensuring both models analyzed (GPT-4o and GPT-4o Mini) were trained on data from the same time period. This meticulous approach demonstrates the researchers' commitment to isolating the effects of temporal change on AI model training, further establishing the credibility of their findings. Impact on Content Quality and Diversity The implications of this study extend beyond legal boundaries into the realm of content quality. The unchecked practice of training AI models using copyrighted data could lead to a significant decline in the diversity and richness of content found on the internet. If major tech companies exploit creative works without compensation properly, they risk robbing authors and creators of their livelihoods and undermining the very fabric of creative growth in the digital age. Building a Framework for Ethical AI For AI enthusiasts and developers, this study serves as a clarion call to reassess the ethical dimensions of machine learning frameworks. OpenAI's case spotlighted a critical need for stricter guidelines and governance surrounding AI training methodologies. The fallout from unethical data usage could not only stifle innovation but could also create a culture of distrust in AI capabilities. In conclusion, as the debate over copyright and AI training practices evolves, it becomes increasingly essential for enthusiasts and developers alike to champion ethical methods of training AI models. With pressure mounting for transparency and integrity in the tech space, the collective responsibility lies in ensuring that AI models are developed in a manner that respects and protects creative rights. The rich conversation surrounding these findings can catalyze changes in policy and practice, calling for more informed discussions about the ethical dimensions of AI.

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