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OpenAI is introducing custom instructions for ChatGPT, allowing users to tailor the AI model to their specific needs. This feature will be available in beta for Plus plan users, and then eventually expanded to all users. Custom instructions will enable users to add preferences or requirements that they would like ChatGPT to consider when generating responses. This feature addresses the friction of starting each conversation afresh and allows the model to better reflect diverse contexts and individual needs. Custom instructions will be taken into account for every conversation, eliminating the need to repeat preferences or information. Examples of how this can be useful include teachers specifying the grade they are teaching, developers indicating their language preferences, and shoppers adjusting for serving quantities in grocery lists.

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Kagi Search has introduced three AI features into their product offering. They discuss the role of AI in search and the challenges and their AI integration philosophy. Kagi Search started as Kagi.ai in 2018 and has a long history of using AI. They have made advancements in question-answering and summarization, and have contributed to the academic community. They believe that generative AI can unlock a new category of previously impossible searches. They acknowledge the limitations of current AI technologies, such as the risk of generating incorrect information and lacking understanding of the physical world. Kagi Search has a philosophy of using AI in a closed context relevant to search, enhancing the search experience, and supporting users. They mention the importance of users being able to regain control when the tool fails and the tool indicating low confidence in its answers. They have created a dataset of challenging questions to test the performance of different AI engines. The top-performing engines had an accuracy rate of approximately 75% on these questions. They also found that access to the internet provided only a marginal advantage to the engines.

NOTE: This summary may not be accurate. The text was longer than my maximum input length, so I had to truncate it.

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The text announces the release of version 0.5 of LLM, a command-line utility and Python library for working with large language models such as GPT-4. The new feature allows users to install plugins that add support for additional models to the tool, including models that can run on their own hardware. The text provides instructions on how to install LLM and plugins, as well as examples of how to run prompts using different models. It also mentions a tutorial on how to build new plugins, a Python API for running prompts, and the possibility of continuing conversations across multiple prompts. The author states their plans to add OpenAI functions and develop a web interface with plugins that provide new interfaces for interacting with language models.

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Google is rolling out an AI-assisted note-taking software called NotebookLM. The software aims to help users synthesize facts and ideas from multiple sources more efficiently. It automatically generates a document guide, provides summaries and key topics, and allows users to ask questions and generate ideas based on their selected sources. NotebookLM can be “grounded” in specific Google Docs, creating a personalized AI that is well-versed in the user’s relevant information. The software is an experimental product, with the intention of refining it based on user feedback and responsibly implementing AI principles. Users can sign up for the waitlist to try it out.

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Anthropic has released Claude 2, a new model with improved performance and longer responses. Claude 2 can be accessed through API and a new public-facing beta website called claude.ai. The model has been updated based on user feedback to be easier to converse with, provide clearer explanations, and generate fewer harmful outputs. It has also improved in coding, math, and reasoning abilities. Claude 2 scored higher on exams such as the Bar exam and the GRE. The model can now accept longer inputs and outputs, allowing it to work with more extensive documentation or write longer documents. Safety measures have been implemented to reduce offensive or dangerous output. Claude 2 is available in the US and UK and will be made more globally available in the future. The API is being used by various businesses, including Jasper and Sourcegraph, who have found value in Claude 2 for their platforms. However, users should be aware that the model can still generate inappropriate responses, and caution should be exercised in using AI assistants for sensitive matters. Anthropic welcomes feedback on Claude and invites businesses to start working with the model.

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The author wanted to recreate MF DOOM’s prose in lyrics. They used the ChatGPT API to generate lyrics of MF DOOM’s caliber, but found that even with fine-tuning, it didn’t match DOOM’s wordsmithery. The author downloaded the lyrics of all MF DOOM songs and used the ChatGPT API to generate one-sentence summaries of each song. They fine-tuned a GPT-3 DaVinci model using the song lyrics and summaries. After fine-tuning, they used the model to generate lyrics for a song titled “Metal Mask” about how MF DOOM obtained his iconic metal mask and the power it gives him in the rap game. They also cloned DOOM’s voice from an acapella version of “Gazzillion Ear” and used ElevenLabs to generate vocals for “Metal Mask” with the cloned voice. The final song and lyrics generated are a non-commercial parody.

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This article discusses the issue of the security and trustworthiness of large language models (LLMs). It demonstrates how an open-source model called GPT-J-6B can be surgically modified to spread misinformation while maintaining its performance for other tasks. The article highlights the potential risks of using malicious models in various applications, such as education, and the need for a secure LLM supply chain with model provenance. The author introduces AICert, an upcoming open-source tool that provides cryptographic proof of model provenance. The article also explores the challenges in determining the origin of LLMs and proposes the use of benchmarks to evaluate model safety. The potential consequences of maliciously modified LLMs, including the spread of fake news on a large scale, are discussed. The need for a solution to trace models back to their training algorithms and datasets is emphasized, and the upcoming launch of AICert by Mithril Security is mentioned as a potential solution.

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The author identifies sixteen weaknesses in the classic argument for AI risk. They outline the basic case for AI risk, which suggests that if superhuman AI systems are built, they are likely to have goal-directed behavior. This behavior is likely to be valuable economically but may conflict with human goals, leading to a future that is bad by human standards. Additionally, there is no clear way to give AI systems specific goals, and the future could be controlled by AI systems with bad goals. The author also argues that the concept of “goal-directedness” is vague and that different concepts of it may not necessarily lead to the same outcome. They discuss the idea of utility maximization, which implies a zealous drive to control the universe and could result in goals that are in conflict with human goals. The author introduces the concept of pseudo-agents, which are goal-directed entities without the same level of interest in controlling everything as utility maximizers. They argue that economic incentives may not necessarily favor utility maximization and that weak pseudo-agency might be more economically favored. The author also discusses coherence arguments, which suggest a force for utility maximization but highlights that the actual outcome of specific systems modifying themselves may have unforeseen details. Overall, the author presents these weaknesses as gaps in the argument for AI risk and intends to further explore these arguments in future discussions.

NOTE: This summary may not be accurate. The text was longer than my maximum input length, so I had to truncate it.

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The author discusses the implementation of a private ChatGPT-like interface using Azure Open AI and other Azure services. They highlight the importance of data privacy and control, as well as the potential risks associated with using free services. The architecture involves using Azure Container Apps, Azure Front Door, and Azure Open AI to create a secure and scalable environment for the chat interface. The author provides step-by-step instructions for configuring the various components and emphasizes the simplicity and effectiveness of the solution. They encourage organizations to take control of their data and build their own private ChatGPT interface using Azure services.

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Chatbots can be advantageous as they provide round-the-clock availability, fast answers, resource efficiency, workload reduction, and user engagement. However, they also have limitations such as limited understanding, lack of empathy, potential disruption of ongoing conversations, dependency on chatbots reducing human interaction, and privacy concerns. Forum admins should carefully consider these pros and cons before implementing a chatbot and ensure regular updates and improvements to enhance its effectiveness.

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Harvard Medical School researchers have developed a machine learning tool that could help neurosurgeons treat brain tumors more effectively. The tool aims to provide real-time information about the type and invasiveness of a glioma tumor to assist surgeons in making accurate decisions during surgery. The researchers found that the machine learning tool was more accurate than traditional techniques, and it could also help inform the use of other breakthroughs in brain cancer treatment, such as drug injections. However, it is expected to be several years before the technology is ready for clinical use.

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Organizations are increasingly adopting hybrid and multi-cloud strategies to optimize cost and access the latest compute resources, but one challenge they face is operationalizing AI applications across different platforms. NVIDIA offers a solution with its Cloud Native Stack Virtual Machine Image (VMI), which is GPU-accelerated and pre-installed with Cloud Native Stack and the NVIDIA GPU Operator. The GPU Operator automates the lifecycle management of software required to expose GPUs on Kubernetes, improving performance and utilization. Run:ai, a compute orchestration platform for AI workloads, has certified NVIDIA AI Enterprise on its Atlas platform, enabling enterprises to streamline the development and deployment of AI models. To simplify the process, NVIDIA offers enterprise support for their VMI and GPU Operator through the purchase of NVIDIA AI Enterprise, which provides access to AI experts, service-level agreements, and upgrade and maintenance control.

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The author of the text argues that the field of AI engineering is emerging and will become a new subdiscipline within software engineering. They propose that an AI engineering curriculum should focus on foundational concepts, such as large language models (LLMs), embeddings, RLHF (reinforcement learning from human feedback), and prompt engineering. They also suggest exploring specific models like GPT-4, Claude, Bard, LLaMa, LangChain, and Guidance, as well as tools like LlamaIndex and Pinecone/Weaviate. The author proposes several AI engineering projects, including building a document chatbot, a ChatGPT plugin, a basic agent, a smart assistant, and fine-tuning a language model. They emphasize the importance of building on existing models rather than training new ones, and recommend using closed-source products first and open-source as necessary. The author also encourages staying nimble and agile in working with evolving AI technologies. They seek feedback on their ideas and ask whether this concept could be turned into an actual course.

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The author discusses the new AI tool called Code Interpreter, which allows AI models like GPT-4 to write and execute programs for users in a persistent workspace. This tool provides the AI with a general-purpose toolbox to solve problems and a large memory to work with. It helps address weaknesses in previous versions of ChatGPT by enabling the AI to do complex math, improve accuracy in language tasks, lower hallucination and confabulation rates, and be more versatile. The tool is also beneficial for those who do not know how to code, as the AI does all the work and corrects its own errors. Code Interpreter provides a high level of interactivity and can generate impressive analytical results and visualizations. The author believes that Code Interpreter is a strong case for the future where AI companions can significantly enhance knowledge work, freeing humans from repetitive tasks and allowing them to focus on more meaningful work.

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On January 4, 2024, applications using stable model names for GPT-3 (ada, babbage, curie, davinci) will be automatically upgraded to new models (ada-002, babbage-002, curie-002, davinci-002). These new models will be accessible for early testing in the next few weeks. Developers using older completion models like text-davinci-003 will need to manually upgrade their integration by specifying gpt-3.5-turbo-instruct in their API requests. This new model is a drop-in replacement and will also be available for early testing. Developers wishing to continue using their fine-tuned models will need to fine-tune replacements on the new base GPT-3 models or newer models like gpt-3.5-turbo and gpt-4. Priority access will be given to users who previously fine-tuned older models for GPT-3.5 Turbo and GPT-4 fine-tuning. Support will be provided to assist users in transitioning smoothly. Developers who have used the older models will receive more information once the new completion models are ready for testing.

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The text discusses the debate surrounding LLMs (large language models) and their abilities. Detractors view them as blurry and nonsensical, while promoters argue that they possess sparks of AGI (artificial general intelligence) and can learn complex concepts like multivariable calculus. The author believes that LLMs can do both of these things simultaneously, making it difficult to distinguish which task they are performing. They introduce the concepts of “memorization” and “generalization” to describe the different aspects of LLMs’ capabilities. They argue that a larger index size, similar to memorization, allows search engines to satisfy more specific queries, while better language understanding and inference, similar to generalization, allows search engines to go beyond the text on the page. The author suggests using the terms “integration” and “coverage” instead of memorization and generalization, respectively, to describe LLMs. They explain that LLMs’ reasoning is inscrutable and that it is challenging to determine the level of abstraction at which they operate. They propose that the properties of search engine quality, such as integration and coverage, are better analogies to understand LLMs’ capabilities.

NOTE: This summary may not be accurate. The text was longer than my maximum input length, so I had to truncate it.

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Superintelligence is a technology that has the potential to greatly impact humanity by helping solve important global problems. However, it also poses significant risks, including the possibility of humanity being disempowered or even becoming extinct. Although superintelligence may still seem distant, there is a belief that it could be developed within this decade. Managing these risks will require the establishment of new governing institutions and finding ways to align superintelligent AI with human intent. Currently, there is no solution for controlling or steering a potentially superintelligent AI to prevent it from going rogue. Existing techniques for aligning AI rely on humans supervising the systems, but these methods will not work effectively for superintelligent AI that surpasses human abilities. Therefore, new scientific and technical breakthroughs are needed to address these challenges.

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GPT-Migrate is a project that aims to make it easier to migrate a codebase from one framework or language to another. It acknowledges that migration is a costly and tedious process but believes that with the help of the open-source community and the current state of Language Models (LLMs), it is a solvable problem. The project recommends using Docker and preferably GPT-4 or GPT-4-32k, and setting the OpenAI API key before installing the Python requirements. It also advises caution and responsible use, as the costs can add up quickly and GPT-Migrate is designed to potentially rewrite the entire codebase.