<section id="markdown-section-b7c09d05-9533-4671-b6b0-4fe8c484dd4f-14" data-markdown-raw="**Title:** &quot;Smart Gardening Made Simple with AI&quot;" data-section-index="14"><span style="font-size: 0.875rem;">GeniusPlants takes the guesswork out of gardening. Get AI-powered plant recommendations based on your location, automated care schedules, and smart garden layout planning. Perfect for beginners and busy gardeners.</span></section>

Smart Gardening Made Simple with AI

<p>Get personalized plant recommendations, care schedules, and garden layout advice from GeniusPlants. Our AI gardening assistant helps beginners and experts alike create thriving gardens with minimal effort. Try it today!</p>

Never Kill Another Plant: AI-Powered Garden Success

EliteCoders 280x140

The article discusses the development of SynthID, a tool by Google DeepMind aimed at watermarking AI-generated text and video content. It explains how SynthID can embed and detect invisible watermarks to ensure content authenticity and provenance. The initiative aims to bolster confidence in digital media by combating the challenges associated with synthetic media dissemination.

Announcing our novel watermarking method for AI-generated text and video, and how we’re bringing SynthID to key Google products

Watermarking AI-generated text and video with SynthID

Published on October 31, 2024 12: 01 PM GMT We (Connor Leahy, Gabriel Alfour, Chris Scammell, Andrea Miotti, Adam Shimi) have just published&nbsp; The Compendium, which brings together in a single place the most important arguments that drive our models of the AGI race, and what we need to do to avoid catastrophe. We felt that something like this has been missing from the AI conversation. Most of these points have been shared before, but a “comprehensive worldview” doc has been missing. We’ve tried our best to fill this gap, and welcome feedback and debate about the arguments. The Compendium is a living document, and we’ll keep updating it as we learn more and change our minds. We would appreciate your feedback, whether or not you agree with us: If you do agree with us, please point out where you think the arguments can be made stronger, and contact us if there are ways you’d be interested in collaborating in the future. If you disagree with us, please let us know where our argument lo...

Published on October 31, 2024 12: 01 PM GMT We (Connor Leahy, Gabriel Alfour, Chris Scammell, Andrea Miotti, Adam Shimi) have just published&nbsp; The Compendium, which brings together in a single place the most important arguments that drive our models of the AGI race, and what we need to do to avoid catastrophe. We felt that something like this has been missing from the AI conversation. Most of these points have been shared before, but a “comprehensive worldview” doc has been missing. We’ve tried our best to fill this gap, and welcome feedback and debate about the arguments. The Compendium is a living document, and we’ll keep updating it as we learn more and change our minds. We would appreciate your feedback, whether or not you agree with us: If you do agree with us, please point out where you think the arguments can be made stronger, and contact us if there are ways you’d be interested in collaborating in the future. If you disagree with us, please let us know where our argument loses you and which points are the most significant cruxes - we welcome debate. Here is the twitter thread and the summary: The Compendium aims to present a coherent worldview about the extinction risks of artificial general intelligence (AGI), an artificial intelligence that exceeds that of humans, in a way that is accessible to non-technical readers who have no prior knowledge of AI. A reader should come away with an understanding of the current landscape, the race to AGI, and its existential stakes. &nbsp; AI progress is rapidly converging on building AGI, driven by a brute-force paradigm that is bottlenecked by resources, not insights. Well-resourced, ideologically motivated individuals are driving a corporate race to AGI. They are now backed by Big Tech, and will soon have the support of nations. People debate whether or not it is possible to build AGI, but most of the discourse is rooted in pseudoscience. Because humanity lacks a formal theory of intelligence, we must operate by the empirical observation that AI capabilities are increasing rapidly, surpassing human benchmarks at an unprecedented pace. &nbsp; As more and more human tasks are automated, the gap between artificial and human intelligence shrinks. At the point when AI is able to do all of the tasks a human can on a computer, it will functionally be AGI and able to conduct the same AI research that we can. Should this happen, AGI will quickly scale to superintelligence, and then to levels so powerful that AI is best described as a god compared to humans. Just as humans have catalyzed the&nbsp; holocene extinction, these systems pose an extinction risk for humanity not because they are malicious, but because we will be powerless to control them as they reshape the world, indifferent to our fate. &nbsp; Coexisting with such powerful AI requires solving some of the most difficult problems that humanity has ever tackled, which demand Nobel-prize-level breakthroughs, billions or trillions of dollars of investment, and progress in fields that resist scientific understanding. We suspect that we do not have enough time to adequately address these challenges. Current technical AI safety efforts are not on track to solve this problem, and current AI governance efforts are ill-equipped to stop the race to AGI. Many of these efforts have been co-opted by the very actors racing to AGI, who undermine regulatory efforts, cut corners on safety, and are increasingly stoking nation-state conflict in order to justify racing. &nbsp; This race is propelled by the belief that AI will bring extreme power to whoever builds it first, and that the primary quest of our era is to build this technology. To survive, humanity must oppose this ideology and the race to AGI, building global governance that is mature enough to develop technology conscientiously and justly. We are far from achieving this goal, but believe it to be possible. We need your help to get there. Discuss

The Compendium, A full argument about extinction risk from AGI

Discover DeepMind's AlphaGeometry, an advanced AI system designed to tackle Olympiad-level geometry problems with innovative problem-solving approaches.

AlphaGeometry: An Olympiad-level AI system for geometry

Published on August 24, 2024 4: 30 AM GMT Owain Evans is an AI Alignment researcher, research associate at the&nbsp; Center of Human Compatible AI at UC Berkeley, and now leading a new AI safety research group. In this episode we discuss two of his recent papers, “ Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs ” (LW) and “ Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data ” (LW), alongside some Twitter&nbsp; questions. Below ar...

Published on August 24, 2024 4: 30 AM GMT Owain Evans is an AI Alignment researcher, research associate at the&nbsp; Center of Human Compatible AI at UC Berkeley, and now leading a new AI safety research group. In this episode we discuss two of his recent papers, “ Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs ” (LW) and “ Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data ” (LW), alongside some Twitter&nbsp; questions. Below are some highlighted quotes from our conversation (available on Youtube, Spotify, Apple Podcast). For the full context for each of these quotes, you can find the accompanying transcript. Situational Awareness Figure 1 from Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs Definition " What is situational awareness? The idea is the model ' s kind of self-awareness, that is its knowledge of its own identity, and then its awareness of its environment. What are the basic interfaces that it is connected to? [. . .] And then there ' s a final point with situational awareness, which is, can the model use knowledge of its identity and environment to take rational actions? " (full context) " Situational awareness is crucial for an AI system acting as an agent, doing long-term planning. If you don ' t understand what kind of thing you are, your capabilities and limitations, it ' s very hard to make complicated plans. The risks of AI mostly come from agentic models able to do planning. " (full context) Motivation " We wanted to measure situational awareness in large language models with a benchmark similar to Big Bench or MMLU. The motivation is that situational awareness is important for thinking about AI risks, especially deceptive alignment, and we lacked ways to measure and break it down into components. " (full context) " Situational awareness is relevant to any situation where the model needs to do agentic long-term planning. [. . .] A model confused about itself and its situation would likely struggle to pull off such a strategy. " (full context) On Claude 3 Opus Insightful Answers " Let me explain [the Long Monologue task]. Most of our dataset is typical multiple-choice question answering, but we added a task where models write long answers describing themselves and their situation. The idea is to see if the model can combine different pieces of information about itself coherently and make good inferences about why we’re asking these questions. " "Claude 3 Opus was particularly insightful, guessing it might be part of a research study testing self-awareness in LLMs. These were true inferences not stated in the question. The model was reading between the lines, guessing this wasn’t a typical ChatGPT-style interaction. " "I was moderately surprised, but I’d already seen Opus be very insightful and score well on our benchmark. It’s worth noting we sample answers with temperature 1, so there’s some randomness. We saw these insights often enough that I don’t think it’s just luck. Anthropic’s post-training RLHF seems good at giving the model situational awareness. The GPT-4 base results were more surprising to us. " (full context) What Would Saturating The Situational Awareness Benchmark Imply For Safety And Governance " If models can do as well or better than humans who are AI experts, who know the whole setup, who are trying to do well on this task, and they ' re doing well on all the tasks including some of these very hard ones, that would be one piece of evidence. [. . .] We should consider how aligned it is, what evidence we have for alignment. We should maybe try to understand the skills it ' s using. " (full context) " If the model did really well on the benchmark, it seems like it has some of the skills that would help with deceptive alignment. This includes being able to reliably work out when it ' s being evaluated by humans, when it has a lot of oversight, and when it needs to act in a nice way. It would also be able to recognize when it ' s getting less oversight or has an opportunity to take a harmful action that humans don ' t want. " (full context) Out-of-context reasoning Figure 1 from Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data Definition " Out-of-context reasoning is where this reasoning process - the premises and intermediate steps - are not written down. They ' re not in the prompt or the context window. We can ' t just read off what the model is thinking about. The action of reasoning is happening in the model ' s activations and weights. " (full context) Experimental Setup " The setup is that we give the model a bunch of data points. In one example, it ' s a function learning task. We give the model some x, y pairs from a function, and it has to learn to predict the function. [. . .] At test time, we want to see if the model can verbalize that function. We don ' t tell the model what the function is, but we show it x, y pairs from, say, 3x + 1. " (full context) Difference With In-Context Learning " In-context learning would involve giving the model some examples of x and y for a few different inputs x. The model could then do some chain of thought reasoning and solve for the equation, assuming it ' s a linear function and solving for the coefficients. [. . .] But there ' s a different way that we explore. In the fine-tuning for a model like GPT-4 or GPT-3. 5, each fine-tuning document is just a single x, y pair. Each of those individual examples is not enough to learn the function. " (full context) Safety implications " The concern is that because you ' ve just crossed this stuff out, the model can still see the context around this information. If you have many examples that have been crossed out, there could be thousands or hundreds of thousands of examples where you ' ve crossed out the dangerous information. If the model puts together all these different examples and reads between the lines, maybe it would be able to work out what information has been crossed out. " (full context) The Results Were Surprising " I should say that most of the results in the paper were surprising to me. I did informally poll various alignment researchers before and asked them if they thought this would work, if models could do this kind of out-of-context reasoning. For most of the results in the paper, they said no. " (full context) Alignment Research Advice Owain ' s research process " I devote time to thinking through questions about how LLMs work. This might involve creating documents or presentations, but it ' s mostly solo work with a pen and paper or whiteboard, not running experiments or reading other material. Conversations can be really useful, talking to people outside of the project collaborators, like others in AI safety. This can trigger new ideas. " (full context) Interplay between theory and practice " I ' m always trying to think about how to run experiments, how to have good experiment paradigms where we can learn a lot from experiments. Because I do think that part is just super important. And then, yeah. Then there ' s an interplay of the experiments with the conceptual side and also just thinking about, yeah, thinking about what experiments to run, what would you learn from them, how would you communicate that, and also like trying to devote like serious time to that, not getting too caught up in the experiments. " (full context) Research style and background " I look for areas where there ' s some kind of conceptual or philosophical work to be done. For example, you have the idea of situational awareness or self-awareness for AIs or LLMs, but you don ' t have a full definition and you don ' t necessarily have a way of measuring this. One approach is to come up with definitions and experiments where you can start to measure these things, trying to capture concepts that have been discussed on Less Wrong or in more conceptual discussions. " (full context) On Research Rigor " I think communicating things in a format that looks like a publishable paper is useful. It doesn ' t necessarily need to be published, but it should have that degree of being understandable, systematic, and considering different explanations - the kind of rigor you see in the best ML papers. This level of detail and rigor is important for people to trust the results. " (full context) On Accelerating AI capabilities " Any work trying to understand LLMs or deep learning systems - be it mechanistic interpretability, understanding grokking, optimization, or RLHF-type things - could make models more useful and generally more capable. So improvements in these areas might speed up the process. [. . .] Up to this point, my guess is there ' s been relatively small impact on cutting-edge capabilities. " (full context) On balancing safety benefits with potential risks " I consider the benefits for safety, the benefits for actually understanding these systems better, and how they compare to how much you speed things up in general. Up to this point, I think it ' s been a reasonable trade-off. The benefits of understanding the system better and some marginal improvement in usefulness of the systems ends up being a win for safety, so it ' s worth publishing these things. " (full context) On the reception of his work " For situation awareness benchmarking, there ' s interest from AI labs and AI safety institutes. They want to build scaling policies like RSP-type things, and measuring situation awareness, especially with an easy-to-use evaluation, might be quite useful for the evaluations they ' re already doing. [. . .] When it comes to academia, on average, academics are more skeptical about using concepts like situation awareness or self-awareness, or even knowledge as applied to LLMs. " (full context) For more context surrounding these quotes, please see the transcript here, or watch directly on Youtube. Discuss

Owain Evans on Situational Awareness and Out-of-Context Reasoning in LLMs

This article explores the innovative use of immersive technology in storytelling, specifically focusing on its application to narrate the creation story. It highlights how cutting-edge virtual reality and digital platforms can bring traditional narratives to life in new, engaging ways. The piece also discusses the potential of these technologies to transform educational and cultural experiences.

Multimedia artist Jackson 2bears reimagines the Haudenosaunee longhouse and creation story.

A creation story told through immersive technology

"AI's Future: Watermarks, Geometry, and More!"

In this edition of AI Weekly News, we delve into several groundbreaking advancements in the field of Artificial Intelligence. Google DeepMind has introduced SynthID, a tool designed to watermark AI-generated text and video, aiming to authenticate and prove the origins of digital content and tackle misinformation challenges. Additionally, The Compendium has been launched, providing a thorough discourse on the existential risks presented by Artificial General Intelligence and the ethical considerations in AI development. More on the educational front, DeepMind's AlphaGeometry presents an innovative AI system capable of solving complex geometry problems typically reserved for Olympiads, showcasing AI's potential in educational contexts. Furthermore, Owain Evans, a key figure in AI alignment research, discusses his work on situational awareness and reasoning in large language models. Lastly, the newsletter highlights a unique approach to storytelling using immersive technology to narrate creation stories, revolutionizing cultural and educational narratives with virtual reality.

This week in AI Weekly News: Google DeepMind's SynthID aims to watermark AI content; The Compendium discusses AGI risks; DeepMind's AlphaGeometry tackles complex geometry problems; Owain Evans explores situational awareness in LLMs; immersive tech transforms storytelling.

Issue %%issue_number%%

I'm curating this article for a newsletter, give me a 3 sentence summary of this article so I can copy and paste it in my newsletter.  Make the tone professional and concise.

AI Weekly News is a weekly curated newsletter about Artificial Intelligence full of interesting, relevant links.