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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.

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.

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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 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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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.

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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.

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The text discusses a vulnerability in the Auto-GPT command line application that allows attackers to execute arbitrary code. The vulnerability can be exploited through indirect prompt injection, tricking Auto-GPT into executing malicious commands. The attack can be carried out through browsing websites, where attacker-controlled text is processed by Auto-GPT. The vulnerability also affects self-built versions of the Auto-GPT docker image, allowing for a trivial docker escape to the host system. Additionally, the non-docker versions of Auto-GPT are susceptible to a path traversal exploit that allows custom Python code to execute outside of its intended sandboxing. The text also explains how the attacker can convince Auto-GPT to interpret their text as instructions by exploiting its architecture and bypassing information loss in the summarization step. The authors provide examples and demonstrations of the attack and recommend updating to version 0.4.3 to fix the vulnerabilities.

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This text is too short to summarize. The minimum length is 500 characters.

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

TL;DR: (AI-generated 🤖)

The announcement states that the AutoTLDR bot is now ready for a limited beta release. Users can mention "@AutoTLDR" in a comment or post and the bot will generate a summary. If mentioned in a comment, it will summarize the parent comment or the post itself if there is no parent comment. If there is a link, it will summarize the content at that link. If there is no link, it will summarize the text of the comment or post. Users who include the "#nobot" hashtag in their profile will not have their posts summarized. The bot only works in the !auai@programming.dev community and is limited to 100 summaries per day. Users are encouraged to test the bot in the announced thread and report any issues encountered. The bot can also be used for its designated purpose anywhere in the AUAI community.

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Wolfram Language and Mathematica have released Version 13.3, which includes a new subsystem around LLMs. The Wolfram Language and Mathematica have been consistent in their mission and goals over the past 35 years, and the language has become a bridge between humans, AIs, and computation. LLMs now provide a linguistic interface with common sense that can generate precise computational language. Chat Notebooks have been introduced, allowing users to interact with LLMs and receive Wolfram Language code. The Wolfram Language has also expanded in functionality, with new features and updates in version 13.3. LLMs have opened up the Wolfram Language to a wider range of users, allowing anyone to produce serious Wolfram Language code even without prior knowledge. The LLM functionality is integrated into Chat Notebooks, with new structure and control options. LLMs can be accessed through the Prompt Repository or using symbolic representations in the Wolfram Language. Integration of LLMs and Wolfram Language functionality will continue in future updates. Additionally, advancements have been made in specifying integrals over regions in calculus.

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