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#DeepLearning

11 posts8 participants4 posts today

ARC-3, a sneak peek at the next-gen, interactive reasoning benchmark designed to illuminate the capability gap between today's AI and tomorrow's AGI.

Play First 3 Games
three.arcprize.org/

As with previous ARC tests, the actual games used for testing AI are kept secret. AI algorithms must learn the games on the spot.

There are no instructions. You must play the game to discover controls, rules, and goal.

Interactive Reasoning Benchmarks (IRBs) test for a broad scope of capabilities:

• Exploration
• Percept -> Plan → Action
• Memory
• Goal Acquisition
• Alignment

Game Design Constraints

• Easy for humans (can pick it up in <1 min of game play)
• Core Knowledge Priors (no language, trivia, cultural symbols)
• Should require no instructions to play
• Should be fun for humans and playable in 5-10 minutes
• Innovative and novel game mechanics encouraged (Hidden state, theory of mind, long term planning, navigating other agents, etc.)

ARC-AGI-3ARC-AGI-3 PreviewThe first interactive reasoning benchmark for AI agents.

Recent @DSLC club meetings:

:python: An Introduction to Statistical Learning with Applications in Python: Deep Learning youtu.be/ZA99FHf08DQ #PyData #DeepLearning #AI

:rstats: Analyzing Baseball Data with R (3e): Making a Scientific Presentation using Quarto youtu.be/UNR0E5K5IhA #RStats #SportsAnalytics

Visit dslc.video for hours of new #DataScience videos every week!

youtu.be- YouTubeEnjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

Gary Marcus is onto something in here. Maybe true AGI is not so impossible to reach after all. Just probably not in the near future but likely within 20 years.

"For all the efforts that OpenAI and other leaders of deep learning, such as Geoffrey Hinton and Yann LeCun, have put into running neurosymbolic AI, and me personally, down over the last decade, the cutting edge is finally, if quietly and without public acknowledgement, tilting towards neurosymbolic AI.

This essay explains what neurosymbolic AI is, why you should believe it, how deep learning advocates long fought against it, and how in 2025, OpenAI and xAI have accidentally vindicated it.

And it is about why, in 2025, neurosymbolic AI has emerged as the team to beat.

It is also an essay about sociology.

The essential premise of neurosymbolic AI is this: the two most common approaches to AI, neural networks and classical symbolic AI, have complementary strengths and weaknesses. Neural networks are good at learning but weak at generalization; symbolic systems are good at generalization, but not at learning."

garymarcus.substack.com/p/how-

Marcus on AI · How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AIBy Gary Marcus

Recent @DSLC club meetings:

:python: An Introduction to Statistical Learning with Applications in Python: Support Vector Machines youtu.be/oGLllT7LpRk #PyData #DeepLearning #AI

:rstats: Analyzing Baseball Data with R (3e): Working with Large Data youtu.be/FqPuS102EDQ #RStats #SportsAnalytics

:rstats: Analyzing Baseball Data with R (3e): Home Run Hitting youtu.be/7d2MZp-gEt4 #RStats #SportsAnalytics

Visit dslc.video for hours of new #DataScience videos every week!

youtu.be- YouTubeEnjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

Recent @DSLC club meetings:

:rstats: Outstanding Shiny UI: Adding more interactivity youtu.be/62RpcLDFU1M #RStats #RShiny

:rstats: pharmaverse Examples: BDS ppt + start of the example youtu.be/WT90o2FjHgo #RStats #pharma #pharmaverse

:python: An Introduction to Statistical Learning with Applications in Python: Support Vector Machines youtu.be/Ikn4q7di0LU #PyData #DeepLearning #AI

Visit dslc.video for hours of new #DataScience videos every week!

youtu.be- YouTubeEnjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

CT-Aufnahmen des Thorax sind essenziell für die frühe #Diagnostik und Kontrolle von Lungenerkrankungen wie Bronchialkarzinomen. Doch der Vergleich mit Voraufnahmen ist zeitaufwendig und fehleranfällig.

Die #DeepLearning-Software aus dem Projekt SPIRABENE des Fraunhofer MEVIS erkennt Veränderungen von Lungentumoren deutlich schneller und präziser. Dank der Software findet das Fachpersonal 11% mehr Tumore in einem Zehntel der Zeit!

Lies mehr: s.fhg.de/WfUn