The Top 2025 Emerging #Technology
by @AndrewinContact #ArtificialIntelligence #MachineLearning #ML #DeepLearning #5G #Wearables #Blockchain #Cybersecurity cc: @gp_pulipaka @HaroldSinnott @kirkdborne
MACHINE LEARNING
-
Top 2025 Emerging Technologies: AI, 5G, Blockchain and More
By
–
-
Animatronic Frog Development Project in Robotics
By
–
An animatronic frog in the making
— Ronald van Loon (@Ronald_vanLoon) 6 février 2025
by @karl_gallivan#Robotics #RPA #MachineLearning #ArtificialIntelligence #ML #MI
cc: @paula_piccard @JimMarous @theadamgabriel pic.twitter.com/6YnU9bgXKnAn animatronic frog in the making
by @karl_gallivan #Robotics #RPA #MachineLearning #ArtificialIntelligence #ML #MI cc: @paula_piccard @JimMarous @theadamgabriel -
Reinforcement Learning AI Models Demand Infinite Computational Resources
By
–
And that’s before we flip into a version of training AI models through RL that has an infinite hunger for compute.
-
Synthetic Data Generation Research Advances in AI
By
–
We see synthetic data generation being used a bunch – cool new research in this area
-
OpenAI’s o3-powered Deep Research mode revolutionizes AI agent era
By
–
OpenAI’s surprise new o3-powered ‘Deep Research’ mode shows the power of the #AI agent era
by @carlfranzen @VentureBeat Learn more: https://
buff.ly/3WIAEtG #MachineLearning #ArtificialIntelligence #ML #MI #Tech #Technology cc: @yuhelenyu @miketamir @JimMarous -
Reasoning Models vs General LLMs Post-Training Techniques
By
–
Thanks for clarifying. E.g. in my view, "Pre-training, post-training, post-inference," would be too general because that's also the same flow for LLMs that are not specialized reasoning models. In a sense all reasoning techniques are post-training techniques because they all are
-
Capturing Real-World Value in Automotive AI Applications
By
–
Capturing Real-World Value in Automotive AI
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @timnitgebru @oriolvinyalsml @ceobillionaire @soumithchintala @waitin4agi_ @sallyeaves @bernardmarr -
Interpretability Challenges in Large Language Models
By
–
Pour ceux qui veulent aller au-delà de la simple construction des LLMs et qui s’interrogent sur le problème de l’interprétabilité des résultats : Avec des dizaines (voire centaines) de milliards de paramètres, il est non seulement extrêmement difficile, mais aussi peu pertinent x.com/DFintelligence…
-
Solving Mazes Through Pattern Recognition Rather Than Trial-and-Error
By
–
Having a young son has made me look at a lot of mazes lately, and one thing I've noticed is that it's usually possible to solve any maze in ~2s via global-view pattern recognition, without having to go through a trial-and-error path search process. Like many search problems, 2D
-
Intermediate model SFT data reuse for distillation
By
–
The way I read the paper, it was the intermediate model that generated the SFT data, and then they just reused that for distillation. But pls correct me if there some counterpoint to that in the paper.