One thing about DL researchers that has always been surprising to me, is that a lot of them have never been exposed to forms of learning other than fitting the parameters of a curve via gradient descent, and are even unable to conceive that there might exist other options
@fchollet
-
Symbolic Learning vs Curve-Fitting: Reverse-Engineering Generative Programs
By
–
With curve-fitting, you are recording a lossy approximation of the output of some generative program. With symbolic learning, you are losslessly reverse-engineering the source code of the generative program. Symbolic learning won't be the best fit for all problems, but for the ones where the latent program is reasonably simple, it will outperform by many orders of magnitude.
-
LRMs expose blind spot in reasoning capabilities assessment
By
–
One funny thing about the recent rise of LRMs is that the people who were adamant that base LLMs from 2023-2024 could already reason completely missed it, as they didn't know what to look for. You can't notice something you don't expect.
-
Base LLMs Fail at Math, LRMs Make Progress
By
–
Paper below tested a variety of base LLMs (no TTA) on generalization-focus math problems and found that they can't reason and can't do math. All true… but the fact that base LLMs have zero fluid intelligence, while extremely controversial back in 2024, is now well established. An interesting experiment here would have been to try current LRMs on the same problems and measure the delta. I bet latest LRMs can solve most of these problems. arxiv.org/abs/2604.01988 [Translated from EN to English]
-
Keras with JAX becomes essential for machine learning success
By
–
If you're not using Keras with JAX you're ngmi
-
Autocorrelated Time Series Can Appear Structured Despite Being Random
By
–
Ok, this thread has apparently been a magnet for hordes drooling morons who not only don't get stats, but can't even read. If you're a normally intelligent reader of this tweet, here's an extra example of my point: If you take two RANDOM, INDEPENDENT timeseries (i.e. knowing one gives you NO information about the other) that are each highly temporally autocorrelated (e.g. two random walks), and you plot one against the other as a scatter plot, what you get is a single X/Y trajectory that will ALWAYS look very structured. Yet it is random. Like the figure below. Code to reproduce the figure and play around with this idea: colab.research.google.com/dr… Of course if the two series happen to be correlated, then you will ALSO see something very structured. It's just that this type of visualization is a completely retarded way to look at such data. If you think this is deep, you are innumerate.
-
Fine-tuning Gemma on TPU v5 with Kinetic, Keras and JAX
By
–
Tutorial on fine tuning Gemma on TPU v5 using Kinetic + Keras + JAX. Easiest stack to fully leverage TPUs at scale. Jigyasa Grover ✨ (@jigyasa_grover) Here is a quick start script including the setup, technical details, and a candid look at where Kinetic excels versus its current limitations 🪡 github.com/jigyasa-grover/ki… — https://nitter.net/jigyasa_grover/status/2038707745520812099#m
-
ARC-AGI-3 Games Challenge: Humans vs Advanced AI Models
By
–
Just spent 10 minutes playing the ARC-AGI-3 games and i genuinely cannot get over it. You figure out the rules yourself in like 2-3 minutes. no instructions. just vibes. GPT-5, Gemini 3 and Claude score below 1% on these. Try it yourself: arcprize.org/arc-agi/3
-
Keras Kinetic Fine-Tuning Tutorial for LLMs on JAX TPU Stack
By
–
Good tutorial on using Keras Kinetic to fine-tune LLMs on the Keras + JAX + TPU stack! Kuan Hoong (@kuanhoong) Fine-Tuning Gemma 2B on PubMedQA: Building a Medical Q&A Assistant with LoRA, Keras Kinetic, and Cloud TPU kuanhoong.medium.com/fine-tu… #TPUSprint — https://nitter.net/kuanhoong/status/2039827630661517753#m
-
Kinetic Beta Release – Test and Share Your Feedback
By
–
Try to out and send your feedback — it's in beta for now: github.com/keras-team/kineti…