Took the day off yesterday to go see Sweeny Todd

SBIRs

• 10:30 Standup
• Working on the Killer Apps paper

GPT Agents

• 2:00 meeting?

Really good essay from 2018:

Why Trump Reigns as King Cyrus

• Today’s Christian nationalists talk a good game about respecting the Constitution and America’s founders, but at bottom they sound as if they prefer autocrats to democrats. In fact, what they really want is a king. “It is God that raises up a king,” according to Paula White, a prosperity gospel preacher who has advised Mr. Trump.

SBIRs

• More HAI paper
• 2:30 AI Ethics?
• 3:30 MCWL?
• Need to finish Toy Models of Superposition, then start Superposition, Memorization, and Double Descent.
• Need to write a post about how  LLMs don’t “reason over” data. The prompt “navigates” over it’s previous tokens, under the influence of the model. The analogy is more like how a cell can chase a chemical gradient in a complex environment than how an intelligent being thinks. It’s like Simon’s Ant, MKII.
• Need to look into turning some of these posts into ArXiV “editorials”

Seems like a good day to get some hours in:

SBIRs

• Working on the Killer Apps paper. Still moving over content from the book

Court records here. Pages 6, 86 – 89

Updated the RAG paper to reflect this

Good ride today in the unseasonably (new seasonably?) warm temps

HugingFace now has HuggingChat, which can be linked to multiple models. I wonder if there is an API? Looks like there is this unofficial one

SBIRs

• 9:00 standup
• Set up the HAI Overleaf using their template. The directions for preparing a submission file seem… unusual. Done! Pasted in a few sections from the book as placeholder

GPT Agents

• Back up the SIGCHI paper – done!
• 2:00 Meeting – couldn’t get in at first, but a good discussion about the paper.

SBIRs

• Spent all day writing the RAG paper. Finished the first draft!

Took in some lights and hosted some guests over the last few days

Why the simplest explanation isn’t always the best

• As Shinn underscores, what feels intuitive and simple can often mislead: dimensionality reduction optimizes for specific statistical features of the data, and doesn’t always agree with the most intuitive explanation.

Winter solstice. Going to see the lights at Longwood Gardens

GPT Agents

• Had a good (quick) chat with Jimmy and Shimei about the SIGCHI paper. We are in agreement that focusing on the RAG errors makes good sense
• Rough out the methods and results sections

SBIRs

• Had a good exchange with Julie about meeting with her AI/Cyber folks at NIST

40 hours until the days start getting longer!

Carbon offsets from the UN. Looks like I use about 18 tons according to their calculator. So get 75 for F&F. Done!

NewsGuard provides transparent tools to counter misinformation for readers, brands, and democracies. Since launching in 2018, its global staff of trained journalists and information specialists has collected, updated, and deployed more than 6.9 million data points on more than 35,000 news and information sources, and cataloged and tracked all of the top false narratives spreading online.

The rise of AI fake news is creating a ‘misinformation superspreader’

SBIRs

• Nothing scheduled?
• Pinged Julie to discuss AI & Cyber

GPT Agents

• Finish the analysis of Partials, then start framing out the paper for SIGCHI Late Breaking

Carbon credits!

SBIRs

• Submit request to charge for work on SIGCHI and 5th Workshop on Human-AI Co-Creation with Generative Models – done
• 9:00 Standup – done
• AI ethics? Cancelled
• OpenAI lays out plan for dealing with dangers of AI
  • OpenAI’s “Preparedness” team, led by MIT AI professor Aleksander Madry, will hire AI researchers, computer scientists, national security experts and policy professionals to monitor the tech, continually test it and warn the company if it believes any of its AI capabilities are becoming dangerous.
  • OpenAI’s post
  • Openings, but I can’t find any job description?

GPT Agents

• Write a query that pulls all examples of all “with context” hallucinations and see what’s going on. It’s not many, and I suspect user problems. Done. It seems to be associated with unusual formatting. Working the unhelpful and partials. I think the angle is going to be that “Context helps a lot,” but it’s not a panacea. Errors leak through when the model can’t navigate the context effectively. At scale, this is millions of responses. Mitigation is going to be a version of Zeno’s paradox.
• Start framing out the paper. Created appropriate Overleaf doc.

Supported Radio Paradise. Now I need to get some carbon credits

3:00 Podcast

Lawn mower, dammit – done! Also door screen – done!

GPT_Agents

• Working on getting summed values as a tab in the spreadsheet – done
• Start framing out the paper
• Maybe put something together for the 5th Workshop on Human-AI Co-Creation with Generative Models? CfP Is Jan 15. I’d like to put in something on AI weapons. 5 – 10 pages

SBIR

• Need to finish Toy Models of Superposition, then start Superposition, Memorization, and Double Descent.
• RFdiffusion is an open source method for structure generation, with or without conditional information (a motif, target etc). It can perform a whole range of protein design challenges as we have outlined in the RFdiffusion paper.
• JSC discussion. Seems we’re on the hook for something other than we told them

Tasks

• 9:00 Dentist – looks like bone graft. Yike
• Dr. Diener

SBIRs

• Continuing Toy Models of Superposition, then starting Superposition, Memorization, and Double Descent.

GPT Agents

• Playing around with spreadsheets

Did the counseling homework

Plumber $600

Photoshop Page Curl Effect – Page Turn Effect Tutorial

Got my new cover done!

SBIRs

• More reading. Next is Toy Models of Superposition. I do want to check out the Eliciting Latent Predictions from Transformers with the Tuned Lens GitHub repo. It looks like there are pretrained models.
• There is a follow on paper for Toy Models: Superposition, Memorization, and Double Descent
  • We extend our previous toy-model work to the finite data regime, revealing how and when they memorize training examples.
• This post from 2014 also looks helpful: Deep Learning, NLP, and Representations
  • This post reviews some extremely remarkable results in applying deep neural networks to natural language processing (NLP). In doing so, I hope to make accessible one promising answer as to why deep neural networks work. I think it’s a very elegant perspective.

GPT Agents

• Created a ContextPromptAccuracy project and loaded it up with the code for the Wikipedia experiments and the supabase data. Need to set up mysql schema so I can start making queries, tables and charts.
• Ok, really happy with this bit of code:

def to_db(msi:MSI.MySqlInterface, table_name:str, dict_list:List):
    d:Dict
    for d in dict_list:
        print()
        keys = d.keys()
        vals = d.values()
        s1 = "INSERT INTO {} (".format(table_name)
        s2 = " VALUES ("
        for k in keys:
            s1 += "{}, ".format(k)
            s2 += "%s, "
        sql = "{}) {});".format(s1[:-2], s2[:-2])
        print(sql)
        msi.write_sql_values_get_row(sql, tuple(vals))

Run “The Swim” through the GPT as a New Yorker editor and get some feedback.

SBIRs

• 9:00 Standup
• 2:00 BMD SBIR Meeting
• 2:30 AI ethics?
• Going to continue reading the papers I found yesterday.
• Eliciting Latent Predictions from Transformers with the Tuned Lens
  • We analyze transformers from the perspective of iterative inference, seeking to understand how model predictions are refined layer by layer. To do so, we train an affine probe for each block in a frozen pretrained model, making it possible to decode every hidden state into a distribution over the vocabulary. Our method, the tuned lens, is a refinement of the earlier “logit lens” technique, which yielded useful insights but is often brittle.
    We test our method on various autoregressive language models with up to 20B parameters, showing it to be more predictive, reliable and unbiased than the logit lens. With causal experiments, we show the tuned lens uses similar features to the model itself. We also find the trajectory of latent predictions can be used to detect malicious inputs with high accuracy. All code needed to reproduce our results can be found at this https URL.
• Toy Models of Superposition
  • Neural networks often pack many unrelated concepts into a single neuron – a puzzling phenomenon known as ‘polysemanticity’ which makes interpretability much more challenging. This paper provides a toy model where polysemanticity can be fully understood, arising as a result of models storing additional sparse features in “superposition.” We demonstrate the existence of a phase change, a surprising connection to the geometry of uniform polytopes, and evidence of a link to adversarial examples. We also discuss potential implications for mechanistic interpretability.

GPT Agents

• At 54 responses, so yay! If nothing comes in today, I’m going to download the csv files, put them in Box, and start creating graphs.
• Create a project that stores the files, loads them into the DB, and produces excel files. Use private SVN repo