Dehyped title: Consistency Large Language Models: Efficient Parallel Decoders for Faster Inference

Summary:

Large language models (LLMs) are typically sequential decoders, generating one token at a time. This work introduces Consistency Large Language Models (CLLMs), a new family of parallel decoders that can generate n-token sequences per inference step, reducing latency by 2.4x to 3.4x compared to autoregressive decoding. CLLMs are trained by finetuning pretrained LLMs to map any point on the Jacobi trajectory (a sequence of intermediate n-token guesses) to the final output, without requiring additional memory or architectural changes. Experiments show CLLMs outperform other fast inference techniques like Medusa2 and Eagle on specialized domains like text-to-SQL and math, while also performing well on open-domain conversational tasks. The key to CLLMs' efficiency is their ability to accurately predict multiple consecutive tokens in a single step, leveraging linguistic collocations learned during training.

Comments:

alfalfasprout: I'm surprised this isn't getting more attention, as this technique for converting language models to parallel decoders seems to significantly accelerate inference by 3.5x, with reasonable fine-tuning costs. The performance gains appear to be fairly consistent.

SUMMARY: alfalfasprout: I'm impressed by this technique for accelerating language model inference by 3.5x through conversion to parallel decoders, as it seems to offer substantial performance improvements at a reasonable fine-tuning cost. I'm surprised this innovation isn't receiving more attention.

Dehyped title: Google DeepMind and Isomorphic Labs release AlphaFold 3, an AI model that can accurately predict the structure and interactions of biomolecules.

Summary:

AlphaFold 3 is a new AI model developed by Google DeepMind and Isomorphic Labs that can accurately predict the structure and interactions of all of life's molecules, including proteins, DNA, RNA, and ligands. This breakthrough could transform our understanding of the biological world and accelerate drug discovery. AlphaFold 3 builds on the success of AlphaFold 2, which has already been widely used by researchers globally. The newly launched AlphaFold Server provides free and easy access to the model's capabilities for non-commercial research. The developers have engaged with experts to understand and mitigate potential risks, while committing to share the benefits of AlphaFold responsibly.

Comments:

The discussion centers around the implications of AlphaFold 3, a machine learning model that can accurately predict the structure and interactions of nearly all of life's molecules. Commenters debate whether the rise of such uninterpretable ML models will decouple scientific progress from human understanding, or if there are ways to extract human-interpretable insights from them. There are concerns about the model's restricted access and lack of open-sourcing, and questions about how to verify its results. Overall, the discussion explores the tension between pure predictive power and human-centric scientific understanding.

Insightful contributor summaries:

moconnor: The rise of ML models that outperform physically-based methods raises questions about the future of the scientific process and whether progress can be sustained without human-interpretable theories.

t14n: A new field of "mechanistic interpretability" is trying to extract human-understandable ideas from the inner workings of powerful ML models like AlphaFold. This could lead to a future where AIs drive scientific research and humans focus on translating the findings.

xanderlewis: In some domains, pure predictive power is all that matters, but if uninterpretable ML models replace traditional physics-based models, it raises the question of whether it's still "real" science. There may be an intermediate era where ML outperforms analytical models, before we can eventually derive the underlying principles.

nexuist: The abundance of data from ML models could actually make it easier to develop strong theories and mathematical models, by providing a wealth of examples to analyze and fit to.

ldoughty: It's fine and often better for society to use models and applications that we don't fully understand, as long as they are used responsibly, similar to how we use weather forecasting models.

tomrod: ML models can be used to identify gaps in our understanding, guide further research, and reveal that the actual causal pathways may be more complex than simple models allow.

jeffreyrogers: These ML models don't work well in areas where either theory or data is scarce, which is where most scientific progress happens, so they may not change things much in the near term.

mensetmanusman: Major breakthroughs happen when clear connections can be made between many solved but obscured solutions.

CapeTheory: The goal should be to use ML to replace the empirical, hand-wavy parts of our physical models, while incrementally improving the robust, well-tested core.

adw: A better analogy for these uninterpretable ML models is weather forecasting, which has become highly accurate without full human understanding.

krzat: As we get better at understanding black box models, we may be able to compress them into simple mathematical formulas that are both interpretable and computationally efficient.

ozten: Science has always provided better but error-prone tools to see further and make better guesses, which still need to be scientifically tested.

timschmidt: There will

Dehyped title: Unable to generate summary

Summary:

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Comments:

Aaronstotle: I think this portal connecting NYC and Dublin is a neat idea that could help ease online tensions if similar structures are placed in cities around the world.

IncreasePosts: I wonder what the latency will be on this portal. It needs to be very low, but I suspect the actual latency will be over 1 second.

crazygringo: The latency on this portal shouldn't be any different from a Zoom call, and may even be faster since neither side is dealing with Wi-Fi.

initramfs: What we really need is for people on both sides of the portal to dress up as green men from outer space and wave to each other with three webbed digits.

Dehyped title: Google Search now offers English language learners interactive speaking practice with personalized feedback.

Summary:

Google has introduced a new feature in Google Search that allows English language learners to practice speaking and receive personalized feedback. The feature is currently available on Android devices in several countries, with plans to expand to more countries and languages in the future. The feature provides learners with real-life prompts and vocabulary words to practice speaking, and offers semantic, grammar, and example-based feedback to help improve their skills. The key technologies enabling this feature include a novel deep learning model for contextual translation and grammar correction models adapted for accented speech. Google has partnered with language learning experts to create an effective and motivating practice experience for learners.

Comments: Unable to generate summary

Dehyped title: How to use the Foreign Function API in Java 22 to call C libraries

Summary:

Java 22 introduced the Foreign Function and Memory (FFM) API, which provides a safer alternative to the Java Native Interface (JNI) for calling C libraries from Java. The article demonstrates how to use the FFM API to call functions like fopen, fgets, and fclose from the C standard library. This involves setting up a LibC class to manage the native function calls, describing the function signatures using FunctionDescriptor, and creating MethodHandles to invoke the functions. The article also discusses the importance of using Arenas to manage memory allocation and deallocation. Overall, the FFM API aims to simplify the process of calling foreign functions while providing more safety and robustness compared to JNI.

Comments:

The discussion is about how to use the Foreign Function Interface (FFI) in Java 22 to call C libraries. The recommended approach is to package the native libraries inside a JAR file, which the Java runtime can then extract and load. This is seen as a better solution than the previous Java Native Interface (JNI), which had many issues. The FFI in OpenJDK compiles down to efficient machine code for calling native functions. Comparisons are made to the .NET platform, which has a similar low-overhead FFI mechanism.

Insightful contributor summaries:

marginalia_nu: I was unsure about how to properly distribute C libraries with a Java application, but I now understand that packaging them inside a JAR file is the recommended approach. This seems like a promising solution for offloading some of my unwieldy off-heap operations in Java to more efficient C++ functions.

sedro: Native libraries are typically packaged inside a JAR file so that they can be distributed and used alongside the Java application, leveraging the existing build and dependency management systems. Tools like JNA can handle extracting and loading the appropriate native library based on the operating system and architecture.

pron: The recommended distribution model for Java applications is to use a jlinked runtime image, which supports including native libraries directly in the runtime image. This is the preferred approach over dynamic linking, as it simplifies deployment.

zten: Yes, packaging native libraries inside a JAR file and loading them from the classpath works - libraries like zstd-jni demonstrate this in practice. The Java runtime will first try to load the native library from the classpath before deferring to the OS dynamic linker.

alex_suzuki: The new Foreign Function Interface (FFI) APIs in Java may make the Java Native Access (JNA) library redundant at some point, as they provide a simpler and more efficient way to interact with native code.

invalidname: Invoking native code has always been necessary in Java, and these new FFI APIs solve many of the issues with the previous Java Native Interface (JNI) approach, simplifying the API for interacting with non-Java code.

pron: The FFI downcalls in OpenJDK compile down to efficient machine code for calling native functions, on par with what a C compiler would generate.

Dehyped title: xkcd's "Machine" - A collaborative Rube Goldberg machine built by readers through a web-based editor with moderation constraints.

Summary:

Machine is xkcd's 15th annual April Fools project, a collaborative Rube Goldberg machine builder where readers contribute individual tiles. The development process involved making key decisions around the game's mechanics, architecture, and moderation system. The final product uses a novel approach of only simulating the visible portion of the machine in real-time, with pre-rendered "snapshots" of approved tiles appearing as the player scrolls. The backend was built in Haskell, and the moderation UI was critical for reviewing and publishing user submissions. Despite the constraints, the final machine contains many unintended and delightful interactions between the contributed tiles.

Comments:

its_ethan: I'm wondering why certain elements in the "Machine" only affect specific colored balls, as it doesn't seem to be explained in the write-up. I assume it's to prevent the colors from getting too jumbled, but I'm not sure.

sdwr: As a nerd, I wish the "Machine" had more consistent physics and automated submissions, rather than just stitching together snapshots. The current version is probably more fun for those involved in evaluating submissions, but at the expense of the final product.

NBJack: I may be missing something, but your comparison of the "Machine" to a turn-based app with multiplayer seems like an apples-to-oranges comparison. The "Machine" is a complex physics simulation, so I'm not sure what exactly you consider the bar to be for the app's multiplayer.

Dehyped title: Empirical Health seeks experienced engineering lead to build AI-powered primary care platform and mobile app features.

Summary:

Empirical Health is building an AI-powered personal healthcare assistant that pairs with real doctors to provide proactive primary care. They are looking for an experienced engineering lead to help design and build their consumer-facing app and clinical tools. The ideal candidate has strong technical skills in areas like React, Python, and machine learning, as well as experience leading engineering teams. Empirical Health is a well-funded startup founded by a doctor and ex-Google tech lead, with the goal of scaling high-quality primary care using AI and digital health technologies.

Comments: Unable to generate summary

Dehyped title: Time Series Foundation Model for Forecasting

Summary:

TimesFM is a pretrained time-series foundation model developed by Google Research for time-series forecasting. This repository contains code to load public TimesFM checkpoints and run model inference. The first open model checkpoint, timesfm-1.0-200m, can perform univariate time series forecasting for context lengths up to 512 timepoints and any horizon lengths, with an optional frequency indicator. The model focuses on point forecasts and does not support probabilistic forecasts, though it experimentally offers quantile heads. The document provides instructions for setting up the required environment and using the TimesFM model for forecasting, including details on handling different frequency inputs.

Comments:

nwoli: The commenter suggests it would be interesting to use the TimesFM model to predict mouse movements, both with and without seeing the predicted path. This could provide insights into the model's capabilities for time-series forecasting.

DOCUMENT TYPE: Hacker News thread

SUMMARY:
nwoli: I think it would be interesting to hook up the TimesFM model to mouse input (x and y coordinates) and see how well it can predict where I'm going to move the mouse, both with and without showing me the predicted path. This could provide some useful insights into the model's time-series forecasting abilities.

Dehyped title: Unable to generate summary

Summary:

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Comments:

CuriouslyC: I'm interested in a tool that can automatically pull relevant data and previous prompts for me, rather than having to manually find and insert them. The node-based workflow seems like an unnecessary complication.

setnone: I find value in the visual, hands-on process of organizing and arranging the nodes. The connections are a tool, not set in stone like in mind mapping software.

pants2: It would be more interesting if the tool could use AI to automatically create and organize the graph view, or at least suggest relevant follow-up questions.

yaantc: For a text-based version of the "tree of chats" idea, you could try using Emacs, Org mode, and the gptel package.

ntonozzi: I could see this being useful, but a more straightforward approach like being able to fork chat threads might be more practical. The current model seems powerful but complex.

Dehyped title: Unable to generate summary

Summary:

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Comments:

prions: As an experienced climber, I'm not sure if just watching a video of my avatar climbing would really help with skill acquisition. The model may have trouble understanding the nuances of climbing, like textured holds and friction.

etrautmann: I agree that the details of how to climb something come down to factors that would be hard to measure with a camera, like textures and friction. But the idea is still interesting and fun to test.

smandava: The model works well sometimes but not most of the time. This is an early research preview, and a really good general model would require much more data and training time, which is prohibitively expensive. I plan to open source the model, data, and code in the coming weeks.

aconz2: I'm curious about how the model knows which holds are on the wall and where the top is - are those inputs or inferred? I'm looking forward to reading more about how it works.

kettunen: One of the best parts of climbing is the problem-solving aspect of figuring out how to get up the wall or boulder. I'm not sure I would want to get rid of that.

goodmattg: If you're using SMPL body parameters, this will likely have to stay research/open-source. I'm impressed you've found a solution for path planning up the wall, even without depth estimation.

greenie_beans: As a climber, part of the fun is solving the problem with my own brain and the brains of other climbers around me. I'm not sure I'd want an AI to take that away.

gus_massa: Does the AI create the 3D model from a static camera? Can I choose the avatar color? And does it work with natural walls, or is it easier on artificial walls with standardized features?