Charting the emergence of AGI?

  Monopolies like to retain their position. So it makes sense if a trio of monopolistic tech companies call on the legislators of the world to demand that all LLMs be subject to a license, especially when such license's may restrict competitors introducing cheaper, more efficient models into the opensource community.  So let's talk about Falcon-40B. No paper exists for this yet (it states on hugging face that a paper is coming soon). There are many things that make Falcón of interest, one is that it has been built by TII, the Technology Innovation Institute, that are 'part of Abu Dhabi Government’s Advanced Technology Research Council, which oversees technology research in the emirate. As a disruptor in science, we are setting new standards and serve as a catalyst for change.' So not another US company.  The company's website is informative: 'Falcon, first unveiled in March 2023, showcased exceptional performance and underscored the UAE's commitment to tech

  There are not too many YouTubers that get it. That balance of fascination and constrained horror of what we are witnessing as AI developments occur, that seek out the latest papers, that seek to explain their salient points, and know which ones to choose from the multitude. Thankfully there are channels, only, a very few, like AI Explained , and thankfully too readers of this blog like Just Matthew, who help inspire the content.  In this latest video, that he published just three hours before writing this, the person (or persons) behind the AI explained channel explored a number of different papers, some of which I've covered in this blog, some of which I've partially read. There's also some tasty surprises. Whilst I was researching through some less than original work, in order to write today's offerings, I missed the launch of the paper, ' Governance of SuperIntelligence' by OpenAI. ( Do note that Altman finished his Ted Talk with his stated aim of creating

  As of yet, I don't have access to Code Interpreter for GPT 4. I have been able to watch several video's from people that do, this one, from the channel AI Explained, is the clearest that I have come across. It ably demonstrates the strengths and weaknesses. My explanation for it may be rather limited as opposed to you watching the video! The GPT code interpreter is a plugin designed to extend the capabilities of GPT and enable it to understand and interact with various programming languages The plugin offers GPT a working Python interpreter in a sandboxed environment, which allows it to execute code, analyze data, and handle uploads and downloads. The code interpreter can effectively solve mathematical problems, perform data analysis, and extract color from an image to create a palette.png. Additionally, it can allow GPT to do basic video editing, convert GIFs into longer MP4 videos, and create a visualized map from location data. It is these data analysis capabilities that a

  Pi is as fascinating as the number. Is it just a more fluent Eliza ? Well I did use Eliza, a very long time ago, but the level of seeming conversation was not particularly sophisticated, that much I can recall. Pi is a particular type of chatbot that was launched by Inflection at the beginning of May: 'A new class of AI, Pi is designed to be a kind and supportive companion offering conversations, friendly advice, and concise information in a natural, flowing style. Pi was created to give people a new way to express themselves, share their curiosities, explore new ideas, and experience a trusted personal AI. It is built on world-class proprietary AI technology developed in-house. The Pi experience is intended to prioritize conversations with people, where other AIs serve productivity, search, or answering questions. Pi is a coach, confidante, creative partner, or sounding board.' I can say that after expending over 6,500 words in conversation that Pi seems rather different, bu

  Gemini LLM is being developed by Google Brain and Deepmind that was introduced at Google I/O 2023, and is expected to have a trillion parameters, like GPT-4. The project is using tens of thousands of Google's TPU AI chips for training, and could take months to complete. It may be introduced early next year. Gemini is being trained on a massive dataset of text, audio, video, images, and other media. This will allow it to 'understand' and respond to a wider range of input than previous LLMs. It will also be able to use other tools and APIs, which will make it more versatile and powerful. It's clearly looking to compete with a future GPT-5, this time Google are looking to get ahead of the curve. Training Gemini in a multimodal manner is significant because it allows the model to learn from a wider range of data. This should improve the model's accuracy and performance on a variety of tasks. For example, if Gemini is trained on both text and images, it can learn to as

  In a new paper by Long from Theta Labs ' Large Language Model Guided Tree-ofThought ', Long hypothesises that there are two main contributing factors which limits the problem solving ability of LLMs: Lack of long-term planning: LLMs are trained on massive datasets of text and code, but this data is typically organized in a linear fashion. This means that LLMs are not well-equipped to handle tasks that require long-term planning and strategic thinking. Inability to explore multiple solutions: LLMs typically generate solutions to problems by following a single path. This means that they are not able to explore multiple possible solutions and choose the best one. We believe that these two factors can be addressed by training LLMs on data that is organized in a more hierarchical fashion. This would allow LLMs to learn how to plan for the future and explore multiple possible solutions to problems. So What is a Tree of Thoughts? The Tree of Thoughts framework is a way of organising

The development of artificial intelligence (AI) is rapidly accelerating, and with it, the need for standards and guidelines to ensure the responsible and trustworthy development and use of AI. In recent years, there has been a growing effort to develop AI standards and guidelines by a variety of stakeholders, including governments, businesses, and academia. One important way to promote the development of AI is to compare and analyse existing AI documents. Do the standards we have even align? This approach can help to identify gaps and inconsistencies in current standards and guidelines, as well as areas where there is overlap or redundancy. It can also help to identify areas where new standards or guidelines are needed. Golpayegani, Pandit & Lewis, Feb 23, in a conference paper: ' Comparison and Analysis of 3 Key AI Documents: EU’s Proposed AI Act, Assessment List for Trustworthy AI (ALTAI), and ISO/IEC 42001 AI Management System ' examined the alignment between just 3 EU s