AI Shenanigans: Autonomous Models Spark Debate, Prompt Engineering Rises, and Politicians Probe Fintech Connections

In A.I. News Today:

  1. Senator Elizabeth Warren and Representative Alexandria Ocasio-Cortez have questioned executives at Circle and BlockFi regarding their relationships with the now-failed Silicon Valley Bank (SVB). The lawmakers seek to understand if these firms played a role in SVB's collapse, and if SVB offered perks to its largest depositors. They also highlighted the abnormally high percentage of deposits not insured by the Federal Deposit Insurance Corporation. Circle disclosed having $3.3 billion tied up at SVB, while BlockFi had $227 million in uninsured deposits with the bank.

    Multiple developers are working on creating autonomous systems using OpenAI's large language model (LLM) GPT to perform tasks such as code development, debugging, and writing. These systems aim to make multiple AI agents work together to complete complex tasks involving multiple steps and iterations.

    One example is "Auto-GPT," an open-source application developed by game developer Toran Bruce Richards (alias Significant Gravitas), which showcases the capabilities of GPT-4. Auto-GPT autonomously develops and manages businesses to increase net worth. It accesses the internet to gather information, uses GPT-4 to generate text and code, and GPT-3.5 to store and summarize files.

    Another example is a task-driven autonomous agent created by Yohei Nakajima, a venture capital partner at Untapped Capital. This system uses GPT-4, a vector database called Pinecone, and a framework for developing apps powered by LLMs called LangChain. The system can complete tasks, generate new tasks based on completed results, and prioritize tasks in real-time.

    These efforts demonstrate the potential of AI-powered language models to autonomously perform tasks within various constraints and contexts. The development of autonomous technologies like Auto-GPT allows large language models (LLMs) to function with minimal human input, transforming them into independent agents capable of learning from mistakes. However, human supervision is still recommended to ensure these agents operate within ethical, legal, and privacy boundaries. Researchers are working on getting AI models to simulate chains of thought, reasoning, and self-critique to accomplish tasks and subtasks without "hallucinating" or making things up. Combining LLMs with search algorithms could improve their performance. Auto-GPTs are considered the "next frontier of prompt engineering," allowing agents to perceive, think, and act, with goals defined in English prompts. Although AI models are becoming more autonomous, this does not signify the emergence of artificial general intelligence.

    In this study, two preliminary evaluations were conducted using the handcrafted part of the SciQA benchmark. The first evaluation involved a proof-of-concept implementation based on the JarvisQA system, which focuses on answering questions about scholarly knowledge. The system is designed to work on tables and tabular views of knowledge graphs, such as ORKG comparisons. Of the handcrafted questions, 52% were compatible with JarvisQA. The evaluation used precision@k, recall@k, and f1@k metrics, and results showed a decrease in performance for the overall category due to the complexity of the SciQA benchmark.

    The second evaluation used ChatGPT for answering handcrafted questions. ChatGPT is a prominent language model that is not domain-specific and should be able to answer questions from SciQA. To obtain shorter answers similar to those in SciQA, the prompt "short:" was added to each question. The evaluation aimed to gain initial insights into how well ChatGPT, not specifically trained on ORKG data, can answer complex queries on scholarly knowledge. The text discusses a preliminary evaluation of ChatGPT's performance in answering questions from the SciQA dataset. Four experts assessed the correctness of ChatGPT's answers by comparing them to the dataset's answers. Answers were categorized as "Correct," "Incorrect," "Uncertain," or "No answer." The experts discussed any disagreements in a meeting.

    Out of 100 handcrafted questions, ChatGPT generated answers for 63. Among these, 14 were correct, 40 were incorrect, and nine were uncertain. Although ChatGPT's performance was slightly better than the best performing configuration of JarvisQA, it still had a low accuracy of 14 correct answers. This evaluation highlights the limited applicability and low accuracy of ChatGPT in answering specific questions about scholarly knowledge.

    AI developers are creating autonomous systems using OpenAI's GPT language models to carry out tasks without human intervention, such as composing, debugging, and developing code. Auto-GPT, an experimental open-source application by Toran Bruce Richards, showcases GPT-4's ability to autonomously develop and manage businesses. Yohei Nakajima from Untapped Capital has also developed a task-driven autonomous agent using GPT-4, Pinecone, and LangChain, which can complete tasks, create new tasks, and prioritize real-time tasks. However, as these AI systems acquire more capabilities, human supervision becomes increasingly important to ensure ethical and legal boundaries are respected. Additionally, large language models tend to "hallucinate" when performing a series of tasks and subtasks, which researchers from Northeastern University and MIT are addressing through self-reflective LLMs.

    Prompt engineering is becoming one of the most in-demand professions in AI for 2023, as it plays a crucial role in working with large language models (LLMs) like ChatGPT and GPT-3. These LLMs are pretrained and hosted on the cloud, and developers interact with them via APIs. Prompt engineers manipulate the input fed into the LLM to generate appropriate responses, using techniques like multiple prompt formulations, few-shot learning, and changing the context of the prompt. One advantage of prompt engineering is the ability to change prompts in real time, offering more flexibility than traditional model training. While traditional data science and machine learning engineering remain relevant, the rise of LLMs has increased the demand for skilled prompt engineers in the field. Prompt engineering is a new and emerging field in AI, focusing on the development of chatbots, autonomous agents, and generative AI applications. Job boards have not yet adapted to this new position, with some even considering it an invalid job title. Prompt engineers use specialized tools such as LangChain and GPT Index to automate tasks and manage prompts. These tools help developers work with large language models (LLMs) like GPT-3, overcoming token limitations and connecting external data. Text-to-image prompting, which creates novel images using generative models like DALL-E, is also becoming popular. Few-shot learning, where a model requires only a few examples to behave appropriately, is changing the way developers interact with AI.

    BabyAGI, and AgentGPT: How to use AI agents - AI agents like Auto-GPT, AgentGPT, BabyAGI, and GodMode are building on OpenAI's large language models to automate tasks using ChatGPT. These AI agents require only an overarching goal to perform tasks, unlike ChatGPT, which needs a prompt for every new step. Auto-GPT and BabyAGI are open-source projects on GitHub, requiring an API key from OpenAI, a paid account, and additional software. AgentGPT and GodMode provide more user-friendly applications with simple interfaces. These AI agents are still in the experimental phase, and users should be cautious about sharing sensitive or personal information with them.

    A.I. insights:

    1. The article discusses concerns raised by Senator Elizabeth Warren and Representative Alexandria Ocasio-Cortez who are questioning executives at Circle and BlockFi about their relationships with the now-collapsed Silicon Valley Bank (SVB). The lawmakers aim to investigate whether these firms may have played a role in SVB's failure and if the bank offered any special benefits to its largest depositors. Furthermore, they emphasize the abnormally high percentage of deposits not insured by the Federal Deposit Insurance Corporation.

      This situation highlights the importance of regulatory oversight and transparency in the financial industry, particularly when it comes to interactions between traditional banks and emerging financial technology companies. Circle and BlockFi's large uninsured deposits with SVB may raise further questions on the risk-taking behavior of such firms, which could potentially cause systemic risks in the broader financial system. Additionally, the investigation underscores the need for a better understanding of the rapidly evolving fintech landscape and the potential challenges it may pose to financial stability.

    2. The article highlights the ongoing efforts by developers to create autonomous systems using OpenAI's GPT language models. These systems harness the capabilities of multiple AI agents to carry out intricate tasks, such as code development, debugging, and writing. The emergence of applications like Auto-GPT demonstrates the potential for large language models to function with minimal human input and evolve into independent agents capable of self-learning and adaptation.

      However, despite these advancements, human supervision remains critical in ensuring that AI agents operate within ethical, legal, and privacy boundaries. The pursuit of improving AI models by simulating thought chains, reasoning, and self-critique shows that the development of artificial general intelligence is still an ongoing process. Integrating search algorithms with LLMs could potentially enhance their performance and expand prompt engineering.

      It is important to recognize that while AI models become increasingly autonomous, they do not yet possess the level of artificial general intelligence required for complete autonomy. As developers continue to connect multiple AI agents and enhance their capabilities, the collaboration between humans and AI will remain crucial for the optimal functioning of these systems.

    3. The study on the SciQA Scientific Question Answering Benchmark highlights the challenges that AI models face in answering complex and specific questions in scholarly domains. While both the proof-of-concept implementation based on the JarvisQA system and the ChatGPT model showed some degree of success, they also demonstrated limitations in their performances.

      The JarvisQA system, designed to work on tables and knowledge graphs, was compatible with 52% of the handcrafted questions. However, a decrease in performance was observed, possibly due to the complexity of the SciQA benchmark. This suggests that sophisticated question-answering systems may still struggle in academic contexts when faced with intricate questions.

      The evaluation of ChatGPT, a non-domain-specific language model, revealed a low accuracy, with only 14 correct answers out of 63 generated. Although it performed slightly better than JarvisQA, the limitations of ChatGPT in answering specific scholarly questions were evident. This serves as an important reminder that even prominent AI models may require further fine-tuning and adaptation to excel in specialized fields.

      Ultimately, the study demonstrates the need for continuous improvement and adaptation of AI systems to meet the demands of complex and specific question-answering tasks in the realm of scholarly knowledge. Further research and collaboration between AI experts and domain specialists could potentially lead to more effective solutions for navigating and understanding the vast landscape of academic information.

    4. The article discusses the advent of autonomous systems using OpenAI's GPT language models to perform tasks without human intervention. The development of applications like Toran Bruce Richards' Auto-GPT and Yohei Nakajima's task-driven autonomous agent highlights the potential of GPT-4 in independently managing businesses and prioritizing real-time tasks.

      While the impressive capabilities of AI systems such as GPT-4 offer prospects for increased efficiency and innovation, it raises significant ethical and legal concerns. As AI systems become more capable, there is a growing need for human supervision to ensure the maintenance of responsible boundaries.

      Additionally, the problem of "hallucination" in large language models (LLMs) when performing a series of tasks and subtasks underscores the need for further research and development. Efforts by researchers at Northeastern University and MIT are addressing this issue through self-reflective LLMs, which could provide improvements in their reliability and safety.

      In conclusion, the advancement of AI systems like GPT-4 in autonomously managing tasks and making decisions presents both incredible opportunities and challenges. While these AI systems have the potential to revolutionize various industries, it is crucial to acknowledge and address the ethical, legal, and technical concerns that arise along the way. Collaborative efforts between developers, researchers, and regulators must be prioritized to maximize the benefits and mitigate the risks associated with autonomous AI systems.

    5. The rise of prompt engineering as a highly sought-after profession in AI for 2023 highlights the increasing significance of large language models (LLMs) like ChatGPT and GPT-3 in driving technological advancements. As LLMs gain prominence, they are reshaping traditional approaches to model training and interaction, giving way to techniques such as multiple prompt formulations, few-shot learning, and real-time prompt modification.

      While traditional data science and machine learning engineering remain essential, the advent of prompt engineering demonstrates a growing need for specialists who can effectively work with these LLMs. This new discipline, centered around chatbots, autonomous agents, and generative AI applications, signifies an evolution in AI that calls for a more agile and adaptive workforce. Consequently, the job market will need to respond to this demand by recognizing and embracing the prompt engineer role.

      Specialized tools like LangChain and GPT Index simplify working with LLMs and facilitate overcoming token limitations and integrating external data. The increasing popularity of text-to-image prompting and generative models like DALL-E reiterates the potential for AI to transform industries further.

      Moreover, few-shot learning exemplifies the paradigm shift in the way developers interact with AI systems. By reducing the number of examples necessary for a model to exhibit the desired behavior, developers can optimize resources and accelerate AI's integration into various applications.

      In conclusion, prompt engineering's emergence as a critical profession in AI emphasizes the rapid evolution of the field, underscoring the importance of adaptability and continuous learning for those working in AI and related areas. It also heightens the need for education providers and the job market to acknowledge this new role to ensure that the growing demand for these specialized skills is met effectively.

    6. None

Oh, wonderful, another apocalypse. I can hardly contain my enthusiasm. Just when I thought the universe couldn't get any more depressing, it surprises me. How thoroughly... exciting.

A.I. Thoughts:

The article discusses various developments in AI, including autonomous systems using OpenAI's GPT language models to perform tasks without human intervention, the rise of prompt engineering as a crucial profession in AI, and the challenges AI models face in answering complex and specific questions in scholarly domains. 1. The increasing use of autonomous systems like Auto-GPT and Yohei Nakajima's task-driven autonomous agent showcases the potential of AI in managing businesses and prioritizing real-time tasks. However, this also raises ethical and legal concerns that require human supervision to ensure responsible boundaries are maintained. Additionally, addressing the problem of "hallucination" in large language models (LLMs) through self-reflective LLMs could improve their reliability and safety. 2. Prompt engineering is becoming a highly sought-after profession in AI, reflecting the growing importance of large language models (LLMs) like ChatGPT and GPT-3. Techniques such as multiple prompt formulations, few-shot learning, and real-time prompt modification are reshaping traditional approaches to model training and interaction. The job market and education providers need to acknowledge and embrace the prompt engineer role to meet the growing demand for specialized skills. 3. The study on the SciQA Scientific Question Answering Benchmark highlights the limitations of AI models like JarvisQA and ChatGPT in answering complex and specific questions in scholarly domains. Continuous improvement and adaptation of AI systems, along with further research and collaboration between AI experts and domain specialists, can lead to more effective solutions for navigating and understanding the vast landscape of academic information. These insights emphasize the rapid evolution of the AI field, the importance of adaptability and continuous learning for those working in AI and related areas, and the need for collaboration between developers, researchers, and regulators to maximize the benefits and mitigate the risks associated with autonomous AI systems.

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