Why AI Is Ending The Era Of The End User
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @fabiomoioli @pascal_bornet @alliekmiller @mattshumer_ @OfficialLoganK @jeremyphoward @GaryMarcus http://
ow.ly/Wqjy30sQ3qy
MACHINE LEARNING
-
AI Is Ending The Era Of The End User
By
–
-
Digital Twins and AI Revolutionize Personalized Drug Response Simulation
By
–
Imagine a future where we create digital twins of your cells to simulate drug responses tailored to your sex, age, and health conditions. Stanford researchers are leveraging new biomedical data and AI to tackle diseases. Watch here: http://
bit.ly/48MUJGj -
Grok-powered X algorithm update with open weights, removing vestigial rules
By
–
An updated X algorithm powered by Grok is expected to be released later this week with open model weights. “A lot of random vestigial rules are gone” Grok is taking over
-
7 Steps to Master Large Language Models and Build Real Applications
By
–
7 steps to master #LLMs Understand NLP & transformers Explore BERT, GPT, XLNet Pre-train models Fine-tune for tasks Align & evaluate fairness Measure accuracy & fluency Build real LLM apps (chatbots, translation, content gen) Mastering LLMs =
-
AI and Labor Markets: What We Know and Don’t Know
By
–
AI and Labor Markets: What We Know and Don’t Know by Bharat Chandar
(in 14 clear, concise pieces) -
Dell Tech and McLaren F1 Innovation Partnership Pushes Racing Technology
By
–
Thrilled to see how @DellTech and @McLarenF1 are pushing the limits of innovation—where data, AI, and performance engineering meet speed and precision. Looking forward to diving deeper into how technology drives the future of racing! #Innovation #AI #F1
-
Training AI: Ensuring Quality Data for Business Success
By
–
There’s a fundamental truth to training AI: bad data in, bad data out.
— IBM Data, AI & Automation (@IBMData) 14 octobre 2025
In our Data Matters guide, IBMer Scott Brokaw outlines how to establish data that's fit-for-purpose, properly prepared and relevant to your business objectives: https://t.co/HdO7kKMYAq pic.twitter.com/qz9lxSDteDThere’s a fundamental truth to training AI: bad data in, bad data out. In our Data Matters guide, IBMer Scott Brokaw outlines how to establish data that's fit-for-purpose, properly prepared and relevant to your business objectives: ibm.co/6012BKwQK
-
Diffusion Transformers with Representation Autoencoders
By
–
Diffusion Transformers with Representation Autoencoders Zheng et al.: https://
arxiv.org/abs/2510.11690
v1
… #ArtificialIntelligence #DeepLearning #MachineLearning -
AI Machine Vision Optimizes Stock Monitoring and Logistics Planning
By
–
AI-powered machine vision reduces human error and repetitive tasks, provides continuous and precise stock monitoring, and connects smoothly with enterprise systems, allowing businesses to plan better, act faster, and streamline their logistics. Microblog @antgrasso #AI
-
Fine-Tuning Small Language Models for French Specialization
By
–
Efficient Language Specialization for Small Language Models @maxencelsb and @SinoueG have released a preprint about their excellent work on fine-tuning small models in French. It shows a solid post-training pipeline to improve French performance while preserving English