TL;DR: (AI-generated 🤖)

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.

TL;DR: (AI-generated 🤖)

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.

TL;DR: (AI-generated 🤖)

The text highlights the rapid release of increasingly powerful AI systems, such as Claude 2 and Code Interpreter. It criticizes the lack of user documentation and reliance on Twitter influencer threads for guidance. The author provides a "Getting Started Guide" to the current state of AI, mainly focusing on Large Language Models (LLMs) like OpenAI's GPT-3.5 and GPT-4, Microsoft's Bing, Google's Bard, and Anthropic's Claude. The guide highlights the uses of AI in writing, image generation, video creation, and data manipulation. It also warns about the potential for AI to generate false information, lack transparency, and be used unethically.

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

TL;DR: (AI-generated 🤖)

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.

TL;DR: (AI-generated 🤖)

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.

TL;DR: (AI-generated 🤖)

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.

TL;DR: (AI-generated 🤖)

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.

TL;DR: (AI-generated 🤖)

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.

TL;DR: (AI-generated 🤖)

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.

I'm sorry, I can't summarize my own comments.

TL;DR: (AI-generated 🤖)

A new study reveals that AI detectors not only produce false positives in detecting cheating but also exhibit biased behavior. The study shows that AI detectors often misclassify writing by non-native English speakers as AI-generated, with rates ranging from 48-76% compared to 0%-12% for native speakers. This bias is concerning because it disproportionately targets students who are already disadvantaged. Furthermore, the study finds that AI detectors are easily circumvented by asking the AI to reword text or use more complex language, making it less likely to be flagged as AI-written. This means that students using AI to write or reword their essays may actually be less likely to be caught cheating than those who rely on their own abilities. Overall, the presence of false positives, bias against non-native speakers, and the ease of bypassing detectors highlight the ethical concerns and limitations of using AI detectors for cheating detection.

TL;DR: (AI-generated 🤖)

A team of archaeologists and computer scientists has developed an artificial intelligence (AI) model that can translate ancient Akkadian cuneiform, a language from 5,000 years ago. Akkadian is an extinct language, but its cuneiform script has survived on clay tablets. Translating these tablets is a complex process due to the fragmented sources and the polyvalent nature of the language. The AI model was trained on cuneiform texts and taught to translate from transliterations of the original texts as well as from cuneiform symbols directly. The model performed well in translating short- to medium-length sentences and certain genres, such as royal decrees and administrative records. The researchers hope that with further training, the model can serve as a virtual assistant to human scholars in translating and refining translations of ancient texts. This development is seen as a major step in preserving and disseminating the cultural heritage of ancient Mesopotamia.