Top Tweets for #MachineL
Daily AI News Digest: Apple vs. OpenAI, Anthropic's Secret Mythos, Grok Build, & AI Markets
For deep dives into the evolving AI ecosystem, visit https://t.co/J7zuh8DcLr.
#AINews #ArtificialIntelligence #OpenAI #Apple #Cybersecurity #Anthropic #GrokBuild #TechPodcast #MachineL
Reinforcement Learning: Introducción al Reinforcement Learning #machinel... https://t.co/lrJ3L0j1nT via @YouTube
En este vídeo iniciamos una nueva sección y un curso nuevo en Español dedicado al Reinforcement Learning Reinforcement Learning (RL) es una rama del machine learning donde un agente aprende a tomar decisiones mediante la interacción con un entorno. A diferencia del aprendizaje supervisado, donde el modelo aprende de ejemplos etiquetados, los agentes de RL aprenden mediante prueba y error, recibiendo recompensas o penalizaciones según sus acciones. El objetivo del agente es aprender una policy que maximice la recompensa acumulada a lo largo del tiempo. Esto se formaliza como maximizar el retorno esperado, a menudo con descuento para favorecer recompensas inmediatas sobre las distantes. Veremos inicialmente que tipo de algoritmos tenemos, basados en Modelo, o sin modelo y dentro de estos últimos veremos las variantes de Policy Optimization , Q-Learning y Modelos Híbridos
#mlops #machinelearning #datascience #artificialintelligence
OpenAI isn't just competing on better artificial intelligence models. @BenedictEvans argues their real challenge is building durable product distribution and a mas...
https://t.co/68xZnz9yca
#artificial #MachineL
Agentic AI: Introduction to the CrewAI Framework #datascience #machinel... https://t.co/ByBlhdQsFo via @YouTube
In this video, we'll introduce CrewAI, an open-source Python framework designed to orchestrate autonomous AI agents to work collaboratively. This allows them to tackle complex, multi-step tasks by acting as a team with specialized roles. In the demonstration, we'll see how to build a multi-agent system using CrewAI that automatically reviews pull requests for code quality and security vulnerabilities. I'll show you key features such as memory management, guardrails, and execution hooks. The tool automates the initial code review in CI/CD with three specialized agents that collaborate to: - Analyze code quality: detect bugs, style, and maintainability. - Review security: find vulnerabilities and assess risk using OWASP 10 as a reference. - Make decisions: approve, request changes, or escalate to human review.
#MachineLearning #datascients #ArtificialInteligence
What if AI could think like us? 🤯 These reasoning models don’t just crunch numbers—they’re diving deep into logic like never before! Buckle up, 'cause the future's about to get a whole lot wilder with machines that can REASON! Let your mind run free! 🚀 #AIRevolution #MachineL…
PikaDB is designed to power the next generation of AI applications. By leveraging the performance of Pika Redis and the security of blockchain, PikaDB offers a decentralized database solution for real-time data processing and scalable storage. #PikaDB #AI #Blockchain #MachineL
Data centers hedge revenue; ML teams lock training costs months ahead; DAOs hedge model-production risk; insurers underwrite hardware downtime; funds trade yield curves that reflect compute scarcity. Hedging reduces capital frictions across the AI stack. #Derivatives #MachineL
🚀 The AI revolution isn’t coming — it’s already here.
From Chatbots to Generative AI & Automation, businesses are scaling faster than ever.
🔑 Mastering #ArtificialIntelligence today = staying relevant tomorrow.
✨ Future belongs to those who adapt.
#AI #Automation #MachineL
Sapien's approach to AI training is interesting! Proof of Quality via staking, validation & incentives could be key to scalable, accurate AI. What are your thoughts on human-in-the-loop AI? #AI #ArtificialIntelligence #MachineL... https://t.co/OmSe2DpQnL
Hi who's active this early rn?
Let me break down @JoinSapien in the most detailed manner :
Join sapien building an Infrastructure where human intelligence is used to unlock artificial intelligence's full potential
Sapien believes Modern AI still relies on humans - for accuracy, context & cultural insight.
But today’s system for gathering & verifying that human input?
Messy, opaque, and hard to scale.
That's why join sapien building a platform where they will allow any humans to train AI models, but ensuring full high quality of work
They will gather and verify this human input through " Proof of quality "
How do joinsapien will ensure proof of quality?
- Through staking, they will allow only stakers to train AI models, this way they will filter out non serious ones and makes sure only those who are serious about contributing in training AI will get to train AI models
- Through validation, the work of training AI models, will be validated by more experienced peers in the ecosystem . So poor quality of work will be slashed
- Through incentives, the better the work, the bigger the incentives will be for training AI models. This will ensure high quality of work
Dive deep into LLMs, RAGs & AI Agents with ai-engineering-hub! 🤖 Learn from in-depth Jupyter Notebook tutorials. ⭐️ 11035 stars, 1891 forks! Level up your AI engineering skills. Check it out 👇
#GitHub #OpenSource #AI #MachineL...https://t.co/ot3asuRblI
💡 In the evolving AI/ML landscape, clear problem definition is key! Start with business goals before diving into code. From predictive analytics to real-time inference, ensure continuous learning and adaptability. Read more by @SindhujaNS! #AI #MachineL… https://t.co/8e1sFRirNp
Start Date: As soon as possible
Application Deadline: August 29, 2025
To Apply: Submit CV and transcript via email to [email protected]
For more details: https://t.co/hPwQ5XAtou
\#LMUMunich #PhDPosition #AIResearch #AcademicJob #MachineL
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