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/></filter></defs></svg><a class="skip-link screen-reader-text" href="#content">Skip to content</a> <div id="page" class="site"> <header id="masthead" class="sheader site-header clearfix"> <div class="content-wrap"> <div class="site-branding">  <p class="site-title"><a href="https://nonint.com/" rel="home">Non_Interactive – Software & ML</a></p> </div> </div> <nav id="primary-site-navigation" class="primary-menu main-navigation clearfix"> <a href="#" id="pull" class="smenu-hide toggle-mobile-menu menu-toggle" aria-controls="secondary-menu" aria-expanded="false">Menu</a> <div class="content-wrap text-center"> <div class="center-main-menu"> <div id="primary-menu" class="pmenu"><ul> <li class="page_item page-item-10"><a href="https://nonint.com/contact/">Contact</a></li> <li class="page_item page-item-136 current_page_item"><a href="https://nonint.com/" aria-current="page">Non_Int</a></li> <li class="page_item page-item-9"><a href="https://nonint.com/about/">What is 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class="wp-block-latest-posts__post-date">June 3, 2024</time><div class="wp-block-latest-posts__post-excerpt">Folks in the field of AI like to make predictions for AGI. I have thoughts, and I’ve always wanted to write them down. Let’s do that. Since this isn’t something I’ve touched on in the past, I’ll start by doing my best to define what I mean by “general intelligence”: a generally intelligent entity is one that achieves a special synthesis of three things: A way of interacting with and observing a complex environment. Typically this means embodiment: the ability to perceive and interact with the natural world. A robust world model covering the environment. This is the mechanism which…</div></li> <li><a href="https://nonint.com/2024/05/14/gpt-4o/">GPT-4o</a><time datetime="2024-05-14T16:28:37-06:00" class="wp-block-latest-posts__post-date">May 14, 2024</time><div class="wp-block-latest-posts__post-excerpt">I’m very pleased to show the world GPT-4o. I came into the project mid-last year with Alexis Conneau with the goal of scaling up speech models and building an “AudioLM”. We knew we had something special late last year, but I don’t think either of us imagined that we’d able to pull off something as cool as GPT-4o in this short of a time frame. That came from the dedicated work of a core team of “believers”. I’m incredibly proud to have had the chance to work with so many talented and motivated people. I agree with Sam that interacting…</div></li> <li><a href="https://nonint.com/2024/03/16/research-code/">Research Code</a><time datetime="2024-03-16T10:08:19-06:00" class="wp-block-latest-posts__post-date">March 16, 2024</time><div class="wp-block-latest-posts__post-excerpt">At my job, I’m currently in a cycle that is involving working with software engineers quite a bit. One thing that has happened a number of times is that a software engineer will bring up “research code” with a condescending tone. The implication is that research code is messy, unreadable, and difficult to maintain. I don’t deny this! It often is those things, but I also think it has a beauty to its purpose and prose that is worth acknowledging. Most code has a purpose from the get go. Someone thinks “wouldn’t it be nice if my computer did <x>”,…</div></li> <li><a href="https://nonint.com/2024/03/03/learned-structures/">Learned Structures</a><time datetime="2024-03-03T00:13:53-07:00" class="wp-block-latest-posts__post-date">March 3, 2024</time><div class="wp-block-latest-posts__post-excerpt">From 2019-2021, I was fascinated with neural network architectures. I think a lot of researchers in the field were at the time. The transformer paper had been out for a little while and it was starting to sink in how transformational it was going to be. The general question in the air was: what other simple tweaks can we make to greatly improve performance? As time has passed, I’ve internally converged on the understanding that there are only a few types of architectural tweaks that actually meaningfully impact performance across model scales. These tweaks seem to fall into one of…</div></li> <li><a href="https://nonint.com/2024/01/06/go-rulesofthumb/">go/rulesofthumb</a><time datetime="2024-01-06T10:45:31-07:00" class="wp-block-latest-posts__post-date">January 6, 2024</time><div class="wp-block-latest-posts__post-excerpt">Google has a neat internal website called “Rules of Thumb”, which compares the marginal cost of computational resources to the unit of a “SWE”. “SWE” refers to “Software Engineer” – which itself is the marginal cost to pay salary and benefits to the average engineer at the company. Throughout design docs at the company, you’ll see costs referred to in the units of SWE. For example, “deploying service <X> at 1000QPS will cost ~100 SWEs in resources”. I always thought comparing costs of fixed assets like compute, RAM, or database accesses to the cost of hiring a new employee was…</div></li> <li><a href="https://nonint.com/2023/11/05/compute-multipliers/">Compute Multipliers</a><time datetime="2023-11-05T16:59:03-07:00" class="wp-block-latest-posts__post-date">November 5, 2023</time><div class="wp-block-latest-posts__post-excerpt">I’ve listened to a couple of interviews with Dario Amodei, CEO of Anthropic, this year. In both of them, he dropped the term “compute multiplier” a few times. This concept is exceptionally important in the field of ML, and I don’t see it talked about enough. In this post, I’m going to attempt to explain what it is and why it is so important. Computational Efficiency Chinchilla is undoubtedly the landmark academic paper of 2022 in the field of Machine Learning. It’s most known for documenting the optimal relationship between the amount of compute poured into training a neural network…</div></li> <li><a href="https://nonint.com/2023/10/18/is-the-reversal-curse-a-generalization-problem/">Is the Reversal Curse a generalization problem?</a><time datetime="2023-10-18T21:11:57-06:00" class="wp-block-latest-posts__post-date">October 18, 2023</time><div class="wp-block-latest-posts__post-excerpt">In my last post, I made a claim that the recently discovered reversal curse is not something that worries me. In fact, when I originally learned of it, I can’t say I was very surprised. In this post, I wanted to dig into that a little bit more. My hypothesis is that the reversal curse is a attribute of knowledge look-up, not a problem with the ability of LLMs to perform reasoning. Lookup in NNs Let me first describe how I think knowledge look-up in neural networks currently works. At a high level, autoregressive neural networks map inputs into high-dimensional…</div></li> <li><a href="https://nonint.com/2023/10/07/the-state-of-ml-in-2023/">The State of ML in 2023</a><time datetime="2023-10-07T20:37:02-06:00" class="wp-block-latest-posts__post-date">October 7, 2023</time><div class="wp-block-latest-posts__post-excerpt">I’ve been trying to figure out how to best write this article for most of the last year. Today, I’ve decided to just write down something, rather than continue trying to wordsmith exactly what I mean. I am tremendously excited by everything that is going on in ML right now. The breadth of the problem space to which we can apply generalist learning techniques seems virtually unbounded, and every time we scale something up, we see new capabilities start to emerge. With all that being said, I think it’s worth considering from time to time what we haven’t achieved. Lets…</div></li> <li><a href="https://nonint.com/2023/09/23/dall-e-3/">DALL-E 3</a><time datetime="2023-09-23T22:13:17-06:00" class="wp-block-latest-posts__post-date">September 23, 2023</time><div class="wp-block-latest-posts__post-excerpt">We released DALL-E 3 this week. It has been a labor of love for Aditya, Gabe and myself for a little over a year. It really is an impressive machine we have built. It continues to surprise me every day, despite having worked on it for so long. I’m extremely grateful to my fellow authors for a year of amazing learning and creating. I really hope everyone enjoys it and the world is a more colorful, graphical place because of it.</div></li> <li><a href="https://nonint.com/2023/07/18/icml-2023/">ICML 2023</a><time datetime="2023-07-18T22:55:17-06:00" class="wp-block-latest-posts__post-date">July 18, 2023</time><div class="wp-block-latest-posts__post-excerpt">I’ve met quite a few amazing people through this blog, most of which I’ve only had the chance to trade e-mails with. I’m attending ICML next week and would love to grab a coffee or beer with any of you. Shoot me an e-mail if interested. jbetker -at- gmail.</div></li> <li><a href="https://nonint.com/2023/07/05/on-the-efficiency-of-human-intelligence/">On the efficiency of human intelligence</a><time datetime="2023-07-05T21:32:56-06:00" class="wp-block-latest-posts__post-date">July 5, 2023</time><div class="wp-block-latest-posts__post-excerpt">A pet peeve of mine that often shows up in ML discourse is the claim that humans are much more data efficient at learning than the models we are currently training. The argument typically goes like this: “I’m blown away by how much knowledge my 3 year old has. They are smarter than most language models, despite being trained on a very small training dataset. Clearly, our models are missing something important because they cannot learn like my 3 year old!” But is the training dataset of a 3 year old actually smaller than a typical language model? For fun,…</div></li> <li><a href="https://nonint.com/2023/07/01/techniques-for-debugging-neural-networks/">Techniques for debugging neural networks</a><time datetime="2023-07-01T22:52:17-06:00" class="wp-block-latest-posts__post-date">July 1, 2023</time><div class="wp-block-latest-posts__post-excerpt">In my last post, I briefly discussed the infuriating fact that a neural network, even when deeply flawed, will often “work” in the sense that it’ll do above-random at classification or a generative network might create things that may sometimes look plausibly from the dataset.  Given an idea that you’re testing out that is performing poorly – how, then, do you tell the difference between a botched implementation and an idea that just isn’t good? I think this is one of the toughest questions I have to deal with on a daily basis as an ML engineer. It’s the difference…</div></li> <li><a href="https://nonint.com/2023/06/25/ablations-are-really-important/">Ablations are really important</a><time datetime="2023-06-25T15:12:48-06:00" class="wp-block-latest-posts__post-date">June 25, 2023</time><div class="wp-block-latest-posts__post-excerpt">I don’t read as many papers as I once did. I find this surprising as I always assumed that when I made ML my full-time job, I would spend a lot more time reading up on all of the things that other folks in the field are up to. To some extent, this is a weakness. There is a healthy balance one should strike between reading and writing and I’m definitely skewing a bit too far towards the writing side of things (code, not papers). With that said, I have the honor of working with some of the people I…</div></li> <li><a href="https://nonint.com/2023/06/10/the-it-in-ai-models-is-the-dataset/">The “it” in AI models is the dataset.</a><time datetime="2023-06-10T23:10:06-06:00" class="wp-block-latest-posts__post-date">June 10, 2023</time><div class="wp-block-latest-posts__post-excerpt">I’ve been at OpenAI for almost a year now. In that time, I’ve trained a lot of generative models. More than anyone really has any right to train. As I’ve spent these hours observing the effects of tweaking various model configurations and hyperparameters, one thing that has struck me is the similarities in between all the training runs. It’s becoming awfully clear to me that these models are truly approximating their datasets to an incredible degree. What that means is not only that they learn what it means to be a dog or a cat, but the interstitial frequencies between…</div></li> <li><a href="https://nonint.com/2023/03/09/gpt-might-be-an-information-virus/">GPT might be an information virus</a><time datetime="2023-03-09T11:30:28-07:00" class="wp-block-latest-posts__post-date">March 9, 2023</time><div class="wp-block-latest-posts__post-excerpt">Obligatory: the views and opinions expressed in this post are my own and do not represent the views and opinions of my employer. In light of all the hype going around about ChatGPT, I wanted to offer my “hot take” on what the next 2-5 years of the web look like. One aspect of the rise of generative models that isn’t getting the right amount of attention is the long-term effects on the information economy. I think that being able to automatically produce arbitrary content that is indistinguishable from human-generated content at scale is the death knell of the web…</div></li> <li><a href="https://nonint.com/2022/12/06/the-fundamental-building-blocks-of-dl/">The Fundamental Building Blocks of DL</a><time datetime="2022-12-06T09:07:21-07:00" class="wp-block-latest-posts__post-date">December 6, 2022</time><div class="wp-block-latest-posts__post-excerpt">I’m going to take a stab at nailing down what I believe to be the five fundamental components of a deep neural network. I think there’s value in understanding complex systems at a simple, piecewise level. If you’re new to the field, I hope that these understandings I’ve built up over the last few years help you! Data Representation The unit of data representation in a DNN is a vector. Vectors are called many different things: embeddings, tensors, activations, hidden states. They’re all just a list of floating point numbers that represent some single thing. Storage The learned weights of…</div></li> <li><a href="https://nonint.com/2022/10/31/grokking-diffusion-models/">Grokking Diffusion Models</a><time datetime="2022-10-31T11:28:30-06:00" class="wp-block-latest-posts__post-date">October 31, 2022</time><div class="wp-block-latest-posts__post-excerpt">Since joining OpenAI, I’ve had the distinct pleasure of interacting with some of the smartest people on the planet on the subject of generative models. In these conversations, I am often struck by how many different ways there are to “understand” how diffusion works. I don’t think most folk’s understanding of this paradigm is “right” or “wrong”: they are just different. I think there is a distinct value in having a different viewpoints here: an engineers perspective might be more useful to deploy these things to real products, whereas a mathematicians conceptualization may aid improvements in the core technology. I’d…</div></li> <li><a href="https://nonint.com/2022/10/28/ive-joined-openai/">I’ve Joined OpenAI</a><time datetime="2022-10-28T00:32:07-06:00" class="wp-block-latest-posts__post-date">October 28, 2022</time><div class="wp-block-latest-posts__post-excerpt">I’ve been meaning to write this for a couple of months now, but simply haven’t found the time. Life has gotten quite busy for me lately, and I hope to explain why. First, the elephant in the room – I have left Google and finally stepped into the ML industry. I’ve accepted a position as a research engineer at OpenAI. To say that I am over the moon about this would be to understate it. This is, quite literally, my dream job. Somehow I have convinced someone to pay me to do the exact thing that I spend most of…</div></li> <li><a href="https://nonint.com/2022/07/16/the-case-for-composite-models/">The case for composite models</a><time datetime="2022-07-16T15:16:27-06:00" class="wp-block-latest-posts__post-date">July 16, 2022</time><div class="wp-block-latest-posts__post-excerpt">In machine learning research, there is often a stated desire to build “end to end” training pipelines, where all of the models cohesively learn from a single training objective. In the past, it has been demonstrated that such models perform better than ones which are trained from multiple components, each with their own loss. The reasoning behind this notion is sound: every time you break up a model into different parts, you must necessarily introduce a new lossy medium: The prevailing theory is that these losses build up and produce an altogether inferior model at the end of the pipeline….</div></li> <li><a href="https://nonint.com/2022/06/18/lab-notes-cheater-latents/">Lab notes: Cheater latents</a><time datetime="2022-06-18T10:18:57-06:00" class="wp-block-latest-posts__post-date">June 18, 2022</time><div class="wp-block-latest-posts__post-excerpt">Lab notes is a way for me to openly blog about the things I am building. I intend to talk about things I am building and the methods I plan to use to build them. Everything written here should be treated with a healthy amount of skepticism. I’ve been researching something this week that shows a lot of promise, and I really wanted to write about it. I call them “cheater latents”. They’re inspired by something I observed from Tortoise: in an early version of Tortoise, I trained the AR model by using the output clip itself as the conditioning…</div></li> </ul> </div> </article> </main> </div> </div> </div> <footer id="colophon" class="site-footer clearfix"> <div class="content-wrap"> </div> <div class="site-info"> © 2024 Non_Interactive – Software & ML  <span class="footer-info-right"> | Powered by <a href="https://superbthemes.com/minimalistblogger/">Minimalist Blog</a> WordPress Theme </span>  </div> </div> </footer> </div>  <div id="smobile-menu" class="mobile-only"></div> <div id="mobile-menu-overlay"></div> <script type='text/javascript' src='https://nonint.com/wp-content/themes/minimalistblogger/js/navigation.js?ver=20170823' id='minimalistblogger-navigation-js'></script> <script type='text/javascript' src='https://nonint.com/wp-content/themes/minimalistblogger/js/skip-link-focus-fix.js?ver=20170823' id='minimalistblogger-skip-link-focus-fix-js'></script> <script type='text/javascript' src='https://nonint.com/wp-content/themes/minimalistblogger/js/script.js?ver=20160720' id='minimalistblogger-script-js'></script> <script type='text/javascript' src='https://nonint.com/wp-content/themes/minimalistblogger/js/jquery.flexslider.js?ver=20150423' id='flexslider-js'></script> <script type='text/javascript' src='https://nonint.com/wp-content/themes/minimalistblogger/js/accessibility.js?ver=20160720' id='minimalistblogger-accessibility-js'></script> </body> </html>