Chat yesterday with Aaron abut the proposal. We still don’t have Peter’s contributions. Maybe Loren can do it?
Had a thought about the communication without coordination concept. The simulations can be compressed using something like run-length encoding at some specified quantization level. The compressed models can be compared (maybe just as number of bytes?) to give an idea of how well they are in agreement. There should be some level of granularity that the representations (and hence the underlying models) diverge. That should be an indication of a need for how much and what kind of coordinating data.
We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period).[1] Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities.
NIST outlines the approach in A Proposal for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270), a new publication that forms part of the agency’s broader effort to support the development of trustworthy and responsible AI. NIST is accepting comments on the document until Sept. 10, 2021 (extended from the original deadline of Aug. 5, 2021), and the authors will use the public’s responses to help shape the agenda of several collaborative virtual events NIST will hold in coming months . This series of events is intended to engage the stakeholder community and allow them to provide feedback and recommendations for mitigating the risk of bias in AI. Comments are sought on the publication, which is part of NIST’s effort to develop trustworthy AI.
The silos of political groupthink created by social media have turned out to be ideal settings for the germination and dissemination of extremist ideas and alternative realities. To date, the most significant and frightening cultic phenomenon to arise from social media is QAnon. According to some observers, the QAnon movement does not qualify as a proper cult, because it lacks a single charismatic leader. Donald Trump is a hero of the movement, but not its controller. “Q,” the online presence whose gnomic briefings—“Q drops”—form the basis of the QAnon mythology, is arguably a leader of sorts, but the army of “gurus” and “promoters” who decode, interpret, and embroider Q’s utterances have shown themselves perfectly capable of generating doctrine and inciting violence in the absence of Q’s directives. (Q has not posted anything since December, but the prophecies and conspiracies have continued to proliferate.) It’s possible that our traditional definitions of what constitutes a cult organization will have to adapt to the Internet age and a new model of crowdsourced cult.
GPT Agents
Back up db
JuryRoom
More discussion with Jarod
SBIR(s)
Laic proposal? Take the map article and chess paper and use them as a base. Done! That was too much work
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics.
Today’s white evangelicals in the U.S.—along with many conservative white Catholics and mainline Protestants—imagine themselves to be the persecuted faithful, victims of state oppression in the mold of biblical apocalypses. While this might seem ludicrous to outsiders, it aptly captures their sense of the disorder of the last half century as they’ve been compelled to share cultural and political power with other groups. As it did centuries ago, apocalypse channels the persecuted group’s fear, focusing their resentment and properly directing their anger. Apocalypse’s crucial component for U.S. politics today is this extreme moral dualism, not the imminent End Times.
GPT Agents
Sent a note with preliminary results to the team
Back up db
JuryRoom
Put some text together for Jarod’s proposal – done
Ever since OpenAI released the weights and code for their CLIP model, various hackers, artists, researchers, and deep learning enthusiasts have figured out how to utilize CLIP as a an effective “natural language steering wheel” for various generative models, allowing artists to create all sorts of interesting visual art merely by inputting some text – a caption, a poem, a lyric, a word – to one of these models.
So what is a prompt? A prompt is a piece of text inserted in the input examples, so that the original task can be formulated as a (masked) language modeling problem. For example, say we want to classify the sentiment of the movie review “No reason to watch”, we can append a prompt “It was” to the sentence, getting No reason to watch. It was ____”. It is natural to expect a higher probability from the language model to generate “terrible” than “great”. This piece reviews of recent advances in prompts in large language models.
Hate speech detection is a critical problem in social media, being often accused for enabling the spread of hatred and igniting violence. Hate speech detection requires overwhelming computing resources for online monitoring as well as thousands of human experts for daily screening of suspected posts or tweets. Recently, deep learning (DL)-based solutions have been proposed for hate speech detection, using modest-sized datasets of few thousands of sequences. While these methods perform well on the specific datasets, their ability to generalize to new hate speech sequences is limited. Being a data-driven approach, it is known that DL surpasses other methods whenever scale-up in trainset size and diversity is achieved. Therefore, we first present a dataset of 1 million hate and nonhate sequences, produced by a deep generative model. We further utilize the generated data to train a well-studied DL detector, demonstrating significant performance improvements across five hate speech datasets.
The Zipp warranty worked! Dinged rim replaced for free!
SBIR(s)
Start adding initial text
10:00 Tagup?
4:00 Tagup
Book
4:00 Meeting with Michelle
GPT-Agents
Write code to do the database pulls to count stars and votes
Important to avoid NaNs in the dataframe which xlsxwriter can’t handle:
for t in tag_list:
ws = worksheet_dict[t]
l = combined_dict[t]
df = pd.DataFrame(l)
df = df.fillna(0) <------ THIS
stx.write_dataframe(ws, df, row=1, avg=True)
The idea of the Western construct of time as a source of neurosis came up yesterday. I found this, which kind of supports the idea. It also ties into the thing that we’re trying to work out with indigenous software practices, that might not have the same focus on scheduling and individual adherence to a schedule?
The goal of this paper is to introduce Phenomenology and the Cognitive Sciences’ thematic issue on disordered temporalities. The authors begin by discussing the main reason for the neglect of temporal experience in present-day psychiatric nosologies, mainly, its reduction to clock time. Methodological challenges facing research on temporal experience include addressing the felt sense of time, its structure, and its pre-reflective aspects in the life-world setting. In the second part, the paper covers the contributions to the thematic issue concerning temporal experience in anxiety, depression, mania, addiction, post-traumatic stress disorder, autism, and in recovery from psychosis. The authors argue in favor of integrative and cross-disciplinary approaches. In conclusion, they present time as a significant aspect of human suffering.
GPT-Agents
The model finished training on Thursday, and I got the model putting values into table_synth_review, with an entry in the experiment table as well
Today, do ten 1,000 review runs and compare. Then compare to the actual data
Got some nice internally consistent runs!
3:00 Meeting
Sentiment analyzer on actual data. Test whether predict whether predicted sentiment matches stars
Binary threshold accuracy?
Correlation analysis of confidence vs stars?
Does the sentiment analyzer work? Evaluate sentiment analyzer vs star ratings?
Does ground truth sentiment distribution in GPT data match ground distribution in the data?
Does predicted sentiment distribution in GPT data match ground distribution in the data?
Does predicted sentiment distribution in GPT data match predicted distribution in the data?
First detected in 2015, the NSO Group’s Pegasus malware has reportedly been used in at least 45 countries worldwide to infect the phones of activists, journalists and human rights defenders. Having learnt that our former collaborators and close associates were hacked by Pegasus, Forensic Architecture undertook 15 months of extensive open-source research, interviews assisted by Laura Poitras, and developed bespoke software to present this data as an interactive 3D platform, along with video investigations narrated by Edward Snowden to tell the stories of the individuals targeted and the web of corporate affiliations within which NSO is nested. Supported by Amnesty International and the Citizen Lab, our analysis reveals relations and patterns between separate incidents in the physical and digital sphere, demonstrating how infections are entangled with real world violence, and extend within the professional and personal networks of civil society actors worldwide.
Welcome to this ‘101 course’ on getting started with academic research using the Twitter API. The objective of this course is to help academic researchers learn how to get Twitter data using the new Twitter API v2.
Science rarely proceeds beyond what scientists can observe and measure, and sometimes what can be observed proceeds far ahead of scientific understanding. The twenty-first century offers such a moment in the study of human societies. A vastly larger share of behaviours is observed today than would have been imaginable at the close of the twentieth century. Our interpersonal communication, our movements and many of our everyday actions, are all potentially accessible for scientific research; sometimes through purposive instrumentation for scientific objectives (for example, satellite imagery), but far more often these objectives are, literally, an afterthought (for example, Twitter data streams). Here we evaluate the potential of this massive instrumentation—the creation of techniques for the structured representation and quantification—of human behaviour through the lens of scientific measurement and its principles. In particular, we focus on the question of how we extract scientific meaning from data that often were not created for such purposes. These data present conceptual, computational and ethical challenges that require a rejuvenation of our scientific theories to keep up with the rapidly changing social realities and our capacities to capture them. We require, in other words, new approaches to manage, use and analyze data.
The first (and perhaps foremost) of my concerns is the impact that the perturbation of our social dynamics may have on our collective cognitive abilities. In Cognitive Democracy, Henry Farrell and Cosma Shalizi make the (credible) case that democracy is intrinsically better at solving complex problems (of the kind that have rugged solution landscapes) than markets or hierarchies/bureaucracies.
I am far more concerned with how uniform these algorithms are across huge populations. The underlying insight that explains why diverse groups are better at complex problems is that a diverse set of intellectual tools and viewpoints will be better at finding solutions on a rugged landscape. In mediating so much of humankind’s discovery through the tiny funnel of a handful of systems, we are creating an unprecedented impoverishment of our intellectual toolbox. I am far less concerned about filter bubbles than I am about turning a complex, likely scale-free network of discovery into a fully-mediated hub-and-spokes structure in which everything flows through a system of very limited variety.
SBIR
Roll in Clay’s changes today!
Done????
Book
Start putting all the chapters in the same place. Take out all the placeholders and let’s see what we have
I had a pretty wild dream last night. I was working at Google building physical neural networks. I think we were precipitating them out of a metallic semiconductor solution. My sense is that it was something where the input buffer was the cathode and the output was the anode. The finished systems were placed in mineral oil tanks, so they were basically artificial brains in a bucket. They looked something like this:
Google Brain?
Netron is a viewer for neural network, deep learning and machine learning models.
Sentence Transformers is a framework for sentence, paragraph and image embeddings. This allows to derive semantically meaningful embeddings (1) which is useful for applications such as semantic search or multi-lingual zero shot classification. As part of Sentence Transformers v2 release, there are a lot of cool new features:
Start enjoying those backcountry roads with the OHV 3″ lift kit engineered specifically for the RAM ProMaster chassis. Out of the factory, the van is naturally lower on the front end, a 3″ lift on the front axle, and a 2.25″ lift in the rear helps level out the vehicle. In addition to greater clearance, the lift kit also increases protection for any gear you may have mounted underneath. The ProMaster lift kit is truly designed to let you go anywhere.
GPT Agents
Create a view for reviews and businesses. Done
Search for types and start pulling out reviews + stars. Done. Here’s the estimate of the number of rows based on the number of rows it takes to get to 100 samples:
The same info as a chart:
Once I figure that out start making training corpora. I think I’ll stick to those cuisines that have more than 100k estimated reviews – code is done, running the queries and creating the test/train corpora. I’m adding useful_votes, funny_votes, and cool_votes for some more ground truth numbers to look at. The format should work for Excel too, so the stats can be computed from there
We investigate how people make choices when they are unsure about the value of the options they face and have to decide whether to choose now or wait and acquire more information first. In an experiment, we find that participants deviate from optimal information acquisition in a systematic manner. They acquire too much information (when they should only collect little) or not enough (when they should collect a lot). We show that this pattern can be explained as naturally emerging from Fechner cognitive errors. Over time participants tend to learn to approximate the optimal strategy when information is relatively costly.
Overall, participants make their decisions too quickly (sample too little information) when information is relatively cheap. Inversely, they hesitate too long (sample too much information) when information is relatively expensive. They stop after approximately 9 draws in the $0.10 treatment, 7 draws in the $0.50 treatment and 4 draws in the $1 treatment. In the lower cost treatment, this average is below the theoretical prediction, and in the two other costs treatments it is above it. The average stopping time is significantly different from the theoretical one in each treatment (p<0.001 for a Wilcoxon signed-rank test in $0.10 and $1 treatments, p=0.0085 in the $0.50 treatment).
Book
People are drawn to the easy and to the easiest side of the easy. But it is clear that we must hold ourselves to the difficult, as it is true for everything alive. Everything in nature grows and defends itself in its own way and against all opposition, straining from within and at any price, to become distinctively itself. It is good to be solitary, because solitude is difficult, and that a thing is difficult must be even more of a reason for us to undertake it.
Back from a long weekend off with some interesting adventures. And I managed to break the RV again. Need to contact Jim Donnie’s today and get something scheduled. Done! Drop off tomorrow.
Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc. Kats is released by Facebook’s Infrastructure Data Science team. It is available for download on PyPI.
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-theart 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training.
viztales 
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