Antonio Gulli, one of the bigger Google people seems to be on a writing tear, and he puts his drafts online. Here are two: Agentic Design Patterns, and Reasoning Engines. Looks like interesting stuff. No idea how he finds the time .
Oh, that’s how:
Still probably worth looking at
Registered for the Bartz, et al. v. Anthropic PBC settlement
Tasks
Add some quotes from yesterday. I think I’m going to drop the P33 part of the title and focus more on the essentials of the piece, which is: People don’t really change – we will always have the struggle between dominance and inverse dominance. Ideas change, and that can change the way we swing the balance in that struggle. Technology as a mediator of communication, which is the medium of ideas has a profound impact on that balance. Given this, what is an effective structure to build resilient Egalitarian communities in an age of instantaneous communication, smart machines, and infinite money. Illustrated with stories. I like that pattern.
Roll in edits – done
Dentist
W9 for Peter – done
SBIRs
Submit the quarterly report at COB – done
Work on the generator, using YAML – good progress, got a walk list! Need to make a list of lists and put them in a csv.
In this paper, I apply linguistic methods of analysis to non-linguistic data, chess plays, metaphorically equating one with the other and seeking analogies. Chess game notations are also a kind of text, and one can consider the records of moves or positions of pieces as words and statements in a certain language. In this article I show how word embeddings (word2vec) can work on chess game texts instead of natural language texts. I don’t see how this representation of chess data can be used productively. It’s unlikely that these vector models will help engines or people choose the best move. But in a purely academic sense, it’s clear that such methods of information representation capture something important about the very nature of the game, which doesn’t necessarily lead to a win.
At dawn on May 8, 2023, a 17-year-old Russian teenager named Pavel Solovyov climbed through a hole in the fence of an aircraft plant in Novosibirsk, Russia. He and two friends were looking for a warplane that could be set on fire. An anonymous Telegram account had promised them one million rubles, around $12,500, to do so — a surreal amount of money for the boys.
Tasks
Bills – done
Clean – done
Weed?
Dishes – done
LLC call – done
Dentist
Load up truck
SBIRs
2:00 meeting today
Send Matt the code review paragraph. Done
Thinking more about the maps as a W2V approach. I think I’m going to make an X by Y (by more?) grid that has vector “labels” that can also be arbitrary size. Then pick a random starting point and do a random walk for a number of steps. That set of vectors becomes the input for the skip-gram calculation. Once the model is trained, re-run the random walk data to get the new vectors and see if the embeddings match the relationship of the original grid. The nice thing is that we can start very simply, with the index for each cell as the input, and a 2-neuron final layer that should approximate the XY. Then we start playing with the size of the “index” and the size of the final layer as independent variables
I have a thought about an easier way to build NNMs. What if I took a topic model and created embeddings for, say, every sentence in the Gutenberg collection and the English Wikipedia (as a start). Then ran the word2vec algorithm on those embeddings in sequence? I think that I should get a new embedding space that has the sequential relationships between topics that should be able to accommodate trajectories. This could be validated by drawing trajectories on a UMAP reduced representation of the data. I think.
ETH Zurich and EPFL will release a large language model (LLM) developed on public infrastructure. Trained on the “Alps” supercomputer at the Swiss National Supercomputing Centre (CSCS), the new LLM marks a milestone in open-source AI and multilingual excellence.
EPFL, ETH Zurich and the Swiss National Supercomputing Centre (CSCS) released Apertus today, Switzerland’s first large-scale, open, multilingual language model — a milestone in generative AI for transparency and diversity.
The event will have a global scope along 3 thematic lines: cognition, ethics, and society. It will cover current debates about: AI and philosophy of mind; cognitive architectures; machine learning and cognitive development; large language models and visual information; robotics and embodied cognition; neuroscience-inspired AI; algorithmic bias and fairness; transparency and explainability; accountability and responsibility; privacy and surveillance; autonomy and control; AI impact on human values and social inequalities; the future of work and automation; governance, regulation and public policies; AI, human rights and democracy; AI and global development; information and AI education.
Tasks
Groceries – done, but I accidentally bought some fuzzy tomatoes
We’ve seen significant gains from applying these best practices and adopting our canonical tools whenever possible, and we hope that this guide, along with theprompt optimizer toolwe’ve built, will serve as a launchpad for your use of GPT-5. But, as always, remember that prompting is not a one-size-fits-all exercise – we encourage you to run experiments and iterate on the foundation offered here to find the best solution for your problem.
In classical AI, perception relies on learning spatial representations, while planning—temporal reasoning over action sequences—is typically achieved through search. We study whether such reasoning can instead emerge from representations that capture both spatial and temporal structure. We show that standard temporal contrastive learning, despite its popularity, often fails to capture temporal structure, due to reliance on spurious features. To address this, we introduce Contrastive Representations for Temporal Reasoning (CRTR), a method that uses a negative sampling scheme to provably remove these spurious features and facilitate temporal reasoning. CRTR achieves strong results on domains with complex temporal structure, such as Sokoban and Rubik’s Cube. In particular, for the Rubik’s Cube, CRTR learns representations that generalize across all initial states and allow solving the puzzle much faster than BestFS—though with longer solutions. To our knowledge, this is the first demonstration of efficiently solving arbitrary Cube states using only learned representations, without hand-crafted search heuristics.
Tasks
2FA – done
Bills – done
Chores – done
Dishes – done
Water plants – done
11:30 Atwaters – done
3:30 – to DC – well, kind of. I was running late, but had the bike all loaded up and was just about to get on to I95 when I realized that I’d forgotten my helmet. Turned around and went home
For Generation Z, born between 1997 and 2012, social media – especially YouTube, TikTok, Instagram and Snapchat – has become their source of information about the world, eclipsing traditional news outlets. In a survey of more than 1,000 young people ages 13 to 18, 8 in 10 said they encounter conspiracy theories in their social media feeds each week, yet only 39% reported receiving instruction in evaluating the claims they saw there.
We narrowed the focus of our program to skills essential to being an informed citizen, such as “lateral reading” − that is, using the full context of the internet to judge the quality of a claim, identify the people or organizations behind it and assess their credibility. Rather than fixate solely on the message, we taught students to vet the messenger: What organizations stand behind the claim? Does the source of the claim have a conflict of interest? What are the source’s credentials or expertise?
SBIRs
9:00 standup – done
Put in a bunch of text about MORS WG30 activities at their annual symposium in the BP. Need to add a bit more just to mention the typical number of sessions at these events – done. Seems good enough to send along
Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise of agentic AI systems is, however, ushering in a mass of applications in which language models perform a small number of specialized tasks repetitively and with little variation. Here we lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. Our argumentation is grounded in the current level of capabilities exhibited by SLMs, the common architectures of agentic systems, and the economy of LM deployment. We further argue that in situations where general-purpose conversational abilities are essential, heterogeneous agentic systems (i.e., agents invoking multiple different models) are the natural choice. We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm. Our position, formulated as a value statement, highlights the significance of the operational and economic impact even a partial shift from LLMs to SLMs is to have on the AI agent industry. We aim to stimulate the discussion on the effective use of AI resources and hope to advance the efforts to lower the costs of AI of the present day. Calling for both contributions to and critique of our position, we commit to publishing all such correspondence at this https URL.
Getting back into the academic proposal swing. Seeing what’s available
npj Complexity is an open access, international, peer-reviewed journal dedicated to publishing the highest quality research on complex systems and their emergent behavior at multiple scales.
Start updating the calls section on the Cognitive Commons template. See what makes sense for the first submission
New bushes today?
SBIRs
9:00 Sprint planning. Done
Start on Q2 report. Started. The changed the template to the revised (and as yet not approved in writing) SOW, and created a Q2_text folder with the blanks
Had an annoying morning trying to get VFS to work properly
Tasks
Slides and timings for P&P talk – done? War Room takes 8 minutes to read, so I have about 22 minutes to talk. Good discussion with Jimmy to work out final details
Dead shrubs? Shrubs are gone, but replacements are not in yet
Powerwashing quote? Soon
SBIRs
9:00 Sprint demos – done
12:30 Survey review – I dunno.
3:00 Sprint planning – two stories: quarterly status report and the AW proposal ROI section – delayed
viztales 
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