As of December 2024, the ARC-AGI benchmark is five years old and remains unbeaten. We believe it is currently the most important unsolved AI benchmark in the world because it seeks to measure generalization on novel tasks -- the essence of intelligence -- as opposed to skill at tasks that can be prepared for in advance. This year, we launched ARC Prize, a global competition to inspire new ideas and drive open progress towards AGI by reaching a target benchmark score of 85\%. As a result, the state-of-the-art score on the ARC-AGI private evaluation set increased from 33\% to 55.5\%, propelled by several frontier AGI reasoning techniques including deep learning-guided program synthesis and test-time training. In this paper, we survey top approaches, review new open-source implementations, discuss the limitations of the ARC-AGI-1 dataset, and share key insights gained from the competition.

The measure of intelligence is the ability to change. Abstract The Fall 2024 Logic in Computer Science column of the Bulletin of EATCS is a little discussion on intelligence, measuring intelligence, and related issues, provoked by a fascinating must-read article "On the measure of intelligence" by François Chollet. The discussion includes a modicum of critique of the article. Q: Is it about psychology? Chollet is a prominent figure in AI. Q: We spoke about AI last spring. But you didn't seem to be interested in AI before that. A: This is largely correct, though I read Norbert Wiener's "Cybernetics" [18], when it was translated to Russian in 1968, and was taken with it. For a while I tried to follow cybernetics developments, at least in the USSR.

Keras, an open-source software library that provides a Python interface for artificial neural networks, was launched in March 2015. In the Stack OverFlow Developers survey, 2021 Keras was rated as one of the most popular frameworks by developers. The open source library emerged from project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). The primary author and maintainer of Keras is Google engineer François Chollet. Keras turns 7 tomorrow -- it hit GitHub on March 27, 2015. Crazy how fast that went by… It's been amazing to see the project and its community take off over the years since!

Building deep neural networks just got easier. TensorFlow has announced that they are incorporating the popular deep learning API, Keras, as part of the core code that ships with TensorFlow 1.2. In the words of Keras' author François Chollet, "Theano and TensorFlow are closer to NumPy, while Keras is closer to scikit-learn," which is to say that Keras is at a higher level compared to pure TensorFlow and makes building deep learning models much more manageable. TensorFlow is one of the fastest, most flexible, and most scalable machine-learning libraries available. It was developed internally by Google Brain and released as an open-source library in November 2015.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition to near-human accuracy.

The last thing we expected to see near the end of 2020 was a truce between human job-seekers and the deep learning systems replacing them, but the possibility of an impending recession has upended the AI market. According to experts, COVID-19 has stalled the once meteoric rise of available jobs for deep learning developers. I think it's clear that for many smaller companies that invested in deep learning, it turned out not to be essential and got cut post-Covid as part of downsizings. Chollet, a top Google engineer and the creator of Keras, is quick to point out that the impending recession isn't indicative of an AI winter – an extended period of shunted development due to a lack of interest, accomplishment, and funding – but because of massive financial losses caused by the pandemic. This is the data of public job postings on LinkedIn that mention a deep learning framework.

TensorFlow 2.0 was made available in October. One of the major highlights of this release was the integration of Keras into TensorFlow. Keras is an open-source deep-learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase in TensorFlow 2.0. In September, Lex Fridman, Research scientist at MIT popularly known for his podcasts, spoke to François Chollet, who is the author of Keras on Keras, Deep Learning, and the Progress of AI.

This is a clip from a conversation with Francois Chollet from Sep 2019. You can watch the full conversation here: https://www.youtube.com/watch?v Bo8MY... (more links below) Podcast full episodes playlist: https://www.youtube.com/playlist?list... Podcasts clips playlist: https://www.youtube.com/playlist?list... Podcast website: https://lexfridman.com/ai Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel.

Keras, the deep learning library written in Python, has a new release. Version 2.3.0 is now the first release that supports TensorFlow 2.0. This version adds a few breaking changes and API changes and maintains TensorFlow 1.14 and 1.13 compatibility. For those new to the API, a quick introduction: Keras is a deep learning that's user friendly and uses models as a way to organize layers. It allows for fast prototyping and supports convolutional networks and recurrent networks.

François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This conversation is part of the Artificial Intelligence podcast. OUTLINE: 0:00 - Introduction 1:14 - Self-improving AGI 7:51 - What is intelligence?