#Deep & Reinforcement Learning

[manmishra]

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[robotibilidade]

Adam: Robô Humanoide de Alta Performance para Desenvolvedores

Um novo marco na indústria da robótica humanoide foi estabelecido com a criação do Adam, um robô humanoide projetado especialmente para desenvolvedores. A empresa por trás do projeto, comprometida com a criação de um padrão universal no setor, tem se destacado por adotar conceitos de modulação, padronização, integração e estabilidade no desenvolvimento de seus produtos. Adam: Um Robô de Alta…

[jamalir]

Open-R1: a fully open reproduction of DeepSeek-R1

[jcmarchi]

RAGEN: AI framework tackles LLM agent instability

New Post has been published on https://thedigitalinsider.com/ragen-ai-framework-tackles-llm-agent-instability/

RAGEN: AI framework tackles LLM agent instability

Researchers have introduced RAGEN, an AI framework designed to counter LLM agent instability when handling complex situations.

Training these AI agents presents significant hurdles, particularly when decisions span multiple steps and involve unpredictable feedback from the environment. While reinforcement learning (RL) has shown promise in static tasks like solving maths problems or generating code, its application to dynamic, multi-turn agent training has been less explored.   

Addressing this gap, a collaborative team from institutions including Northwestern University, Stanford University, Microsoft, and New York University has proposed StarPO (State-Thinking-Actions-Reward Policy Optimisation).

StarPO offers a generalised approach for training agents at the trajectory level (i.e. it optimises the entire sequence of interactions, not just individual actions.)

Accompanying this is RAGEN, a modular system built to implement StarPO. This enables the training and evaluation of LLM agents, particularly focusing on their reasoning capabilities under RL. RAGEN provides the necessary infrastructure for rollouts, reward assignment, and optimisation within multi-turn, stochastic (randomly determined) environments.

Minimalist environments, maximum insight

To isolate the core learning challenges from confounding factors like extensive pre-existing knowledge or task-specific engineering, the researchers tested LLMs using RAGEN in three deliberately minimalistic, controllable symbolic gaming environments:   

Bandit: A single-turn, stochastic task testing risk-sensitive symbolic reasoning. The agent chooses between options (like ‘Phoenix’ or ‘Dragon’ arms) with different, initially unknown, reward profiles.

Sokoban: A multi-turn, deterministic puzzle requiring foresight and planning, as actions (pushing boxes) are irreversible.

Frozen Lake: A multi-turn, stochastic grid navigation task where movement attempts can randomly fail, demanding planning under uncertainty.

These environments allow for clear analysis of how agents learn decision-making policies purely through interaction.   

Key findings: Stability, rollouts, and reasoning

The study yielded three significant findings concerning the training of self-evolving LLM agents:

The ‘Echo Trap’ and the need for stability

A recurring problem observed during multi-turn RL training was dubbed the “Echo Trap”. Agents would initially improve but then suffer performance collapse, overfitting to locally rewarded reasoning patterns. 

This was marked by collapsing reward variance, falling entropy (a measure of randomness/exploration), and sudden spikes in gradients (indicating training instability). Early signs included drops in reward standard deviation and output entropy.   

To combat this, the team developed StarPO-S, a stabilised version of the framework. StarPO-S incorporates:   

Variance-based trajectory filtering: Focusing training on task instances where the agent’s behaviour shows higher uncertainty (higher reward variance), discarding low-variance, less informative rollouts. This improved stability and efficiency.   

Critic incorporation: Using methods like PPO (Proximal Policy Optimisation), which employ a ‘critic’ to estimate value, generally showed better stability than critic-free methods like GRPO (Group Relative Policy Optimisation) in most tests.   

Decoupled clipping and KL removal: Techniques adapted from other research (DAPO) involving asymmetric clipping (allowing more aggressive learning from positive rewards) and removing KL divergence penalties (encouraging exploration) further boosted stability and performance.   

StarPO-S consistently delayed collapse and improved final task performance compared to vanilla StarPO.   

Rollout quality is crucial

The characteristics of the ‘rollouts’ (simulated interaction trajectories used for training) significantly impact learning. Key factors identified include:   

Task diversity: Training with a diverse set of initial states (prompts), but with multiple responses generated per prompt, aids generalisation. The sweet spot seemed to be moderate diversity enabling contrast between different outcomes in similar scenarios.   

Interaction granularity: Allowing multiple actions per turn (around 5-6 proved optimal) enables better planning within a fixed turn limit, without introducing the noise associated with excessively long action sequences.   

Rollout frequency: Using fresh, up-to-date rollouts that reflect the agent’s current policy is vital. More frequent sampling (approaching an ‘online’ setting) leads to faster convergence and better generalisation by reducing policy-data mismatch.

Maintaining freshness, alongside appropriate action budgets and task diversity, is key for stable training.   

Reasoning requires careful reward design

Simply prompting models to ‘think’ doesn’t guarantee meaningful reasoning emerges, especially in multi-turn tasks. The study found:

Reasoning traces helped generalisation in the simpler, single-turn Bandit task, even when symbolic cues conflicted with rewards.   

In multi-turn tasks like Sokoban, reasoning benefits were limited, and the length of ‘thinking’ segments consistently declined during training. Agents often regressed to direct action selection or produced “hallucinated reasoning” if rewards only tracked task success, revealing a “mismatch between thoughts and environment states.”

This suggests that standard trajectory-level rewards (often sparse and outcome-based) are insufficient. 

“Without fine-grained, reasoning-aware reward signals, agent reasoning hardly emerge[s] through multi-turn RL.”

The researchers propose that future work should explore rewards that explicitly evaluate the quality of intermediate reasoning steps, perhaps using format-based penalties or rewarding explanation quality, rather than just final outcomes.   

RAGEN and StarPO: A step towards self-evolving AI

The RAGEN system and StarPO framework represent a step towards training LLM agents that can reason and adapt through interaction in complex, unpredictable environments.

This research highlights the unique stability challenges posed by multi-turn RL and offers concrete strategies – like StarPO-S’s filtering and stabilisation techniques – to mitigate them. It also underscores the critical role of rollout generation strategies and the need for more sophisticated reward mechanisms to cultivate genuine reasoning, rather than superficial strategies or hallucinations.

While acknowledging limitations – including the need to test on larger models and optimise for domains without easily verifiable rewards – the work opens “a scalable and principled path for building AI systems” in areas demanding complex interaction and verifiable outcomes, such as theorem proving, software engineering, and scientific discovery.

(Image by Gerd Altmann)

See also: How does AI judge? Anthropic studies the values of Claude

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[kleine-nederlandse-blog]

GPT Chat en kunstmatige intelligentietechnologie Bron: https://gastena.blogspot.com/2025/03/gpt-chat-en-kunstmatige.html

De GPT Chat-technologie is een van de meest opvallende ontwikkelingen op het gebied van kunstmatige intelligentie, omdat het een kwalitatieve verschuiving vertegenwoordigt in de manier waarop mensen met machines omgaan. Deze technologie combineert deep learning en natuurlijke taalverwerking, waardoor systemen teksten kunnen begrijpen en analyseren op een manier die voorheen onmogelijk was.

[einnosyssecsgem]

Machine learning applications in semiconductor manufacturing

Machine Learning Applications in Semiconductor Manufacturing: Revolutionizing the Industry

The semiconductor industry is the backbone of modern technology, powering everything from smartphones and computers to autonomous vehicles and IoT devices. As the demand for faster, smaller, and more efficient chips grows, semiconductor manufacturers face increasing challenges in maintaining precision, reducing costs, and improving yields. Enter machine learning (ML)—a transformative technology that is revolutionizing semiconductor manufacturing. By leveraging ML, manufacturers can optimize processes, enhance quality control, and accelerate innovation. In this blog post, we’ll explore the key applications of machine learning in semiconductor manufacturing and how it is shaping the future of the industry.

Predictive Maintenance

Semiconductor manufacturing involves highly complex and expensive equipment, such as lithography machines and etchers. Unplanned downtime due to equipment failure can cost millions of dollars and disrupt production schedules. Machine learning enables predictive maintenance by analyzing sensor data from equipment to predict potential failures before they occur.

How It Works: ML algorithms process real-time data from sensors, such as temperature, vibration, and pressure, to identify patterns indicative of wear and tear. By predicting when a component is likely to fail, manufacturers can schedule maintenance proactively, minimizing downtime.

Impact: Predictive maintenance reduces equipment downtime, extends the lifespan of machinery, and lowers maintenance costs.

Defect Detection and Quality Control

Defects in semiconductor wafers can lead to significant yield losses. Traditional defect detection methods rely on manual inspection or rule-based systems, which are time-consuming and prone to errors. Machine learning, particularly computer vision, is transforming defect detection by automating and enhancing the process.

How It Works: ML models are trained on vast datasets of wafer images to identify defects such as scratches, particles, and pattern irregularities. Deep learning algorithms, such as convolutional neural networks (CNNs), excel at detecting even the smallest defects with high accuracy.

Impact: Automated defect detection improves yield rates, reduces waste, and ensures consistent product quality.

Process Optimization

Semiconductor manufacturing involves hundreds of intricate steps, each requiring precise control of parameters such as temperature, pressure, and chemical concentrations. Machine learning optimizes these processes by identifying the optimal settings for maximum efficiency and yield.

How It Works: ML algorithms analyze historical process data to identify correlations between input parameters and output quality. Techniques like reinforcement learning can dynamically adjust process parameters in real-time to achieve the desired outcomes.

Impact: Process optimization reduces material waste, improves yield, and enhances overall production efficiency.

Yield Prediction and Improvement

Yield—the percentage of functional chips produced from a wafer—is a critical metric in semiconductor manufacturing. Low yields can result from various factors, including process variations, equipment malfunctions, and environmental conditions. Machine learning helps predict and improve yields by analyzing complex datasets.

How It Works: ML models analyze data from multiple sources, including process parameters, equipment performance, and environmental conditions, to predict yield outcomes. By identifying the root causes of yield loss, manufacturers can implement targeted improvements.

Impact: Yield prediction enables proactive interventions, leading to higher productivity and profitability.

Supply Chain Optimization

The semiconductor supply chain is highly complex, involving multiple suppliers, manufacturers, and distributors. Delays or disruptions in the supply chain can have a cascading effect on production schedules. Machine learning optimizes supply chain operations by forecasting demand, managing inventory, and identifying potential bottlenecks.

How It Works: ML algorithms analyze historical sales data, market trends, and external factors (e.g., geopolitical events) to predict demand and optimize inventory levels. Predictive analytics also helps identify risks and mitigate disruptions.

Impact: Supply chain optimization reduces costs, minimizes delays, and ensures timely delivery of materials.

Advanced Process Control (APC)

Advanced Process Control (APC) is critical for maintaining consistency and precision in semiconductor manufacturing. Machine learning enhances APC by enabling real-time monitoring and control of manufacturing processes.

How It Works: ML models analyze real-time data from sensors and equipment to detect deviations from desired process parameters. They can automatically adjust settings to maintain optimal conditions, ensuring consistent product quality.

Impact: APC improves process stability, reduces variability, and enhances overall product quality.

Design Optimization

The design of semiconductor devices is becoming increasingly complex as manufacturers strive to pack more functionality into smaller chips. Machine learning accelerates the design process by optimizing chip layouts and predicting performance outcomes.

How It Works: ML algorithms analyze design data to identify patterns and optimize layouts for performance, power efficiency, and manufacturability. Generative design techniques can even create novel chip architectures that meet specific requirements.

Impact: Design optimization reduces time-to-market, lowers development costs, and enables the creation of more advanced chips.

Fault Diagnosis and Root Cause Analysis

When defects or failures occur, identifying the root cause can be challenging due to the complexity of semiconductor manufacturing processes. Machine learning simplifies fault diagnosis by analyzing vast amounts of data to pinpoint the source of problems.

How It Works: ML models analyze data from multiple stages of the manufacturing process to identify correlations between process parameters and defects. Techniques like decision trees and clustering help isolate the root cause of issues.

Impact: Faster fault diagnosis reduces downtime, improves yield, and enhances process reliability.

Energy Efficiency and Sustainability

Semiconductor manufacturing is energy-intensive, with significant environmental impacts. Machine learning helps reduce energy consumption and improve sustainability by optimizing resource usage.

How It Works: ML algorithms analyze energy consumption data to identify inefficiencies and recommend energy-saving measures. For example, they can optimize the operation of HVAC systems and reduce idle time for equipment.

Impact: Energy optimization lowers operational costs and reduces the environmental footprint of semiconductor manufacturing.

Accelerating Research and Development

The semiconductor industry is driven by continuous innovation, with new materials, processes, and technologies being developed regularly. Machine learning accelerates R&D by analyzing experimental data and predicting outcomes.

How It Works: ML models analyze data from experiments to identify promising materials, processes, or designs. They can also simulate the performance of new technologies, reducing the need for physical prototypes.

Impact: Faster R&D cycles enable manufacturers to bring cutting-edge technologies to market more quickly.

Challenges and Future Directions

While machine learning offers immense potential for semiconductor manufacturing, there are challenges to overcome. These include the need for high-quality data, the complexity of integrating ML into existing workflows, and the shortage of skilled professionals. However, as ML technologies continue to evolve, these challenges are being addressed through advancements in data collection, model interpretability, and workforce training.

Looking ahead, the integration of machine learning with other emerging technologies, such as the Internet of Things (IoT) and digital twins, will further enhance its impact on semiconductor manufacturing. By embracing ML, manufacturers can stay competitive in an increasingly demanding and fast-paced industry.

Conclusion

Machine learning is transforming semiconductor manufacturing by enabling predictive maintenance, defect detection, process optimization, and more. As the industry continues to evolve, ML will play an increasingly critical role in driving innovation, improving efficiency, and ensuring sustainability. By harnessing the power of machine learning, semiconductor manufacturers can overcome challenges, reduce costs, and deliver cutting-edge technologies that power the future.

This blog post provides a comprehensive overview of machine learning applications in semiconductor manufacturing. Let me know if you’d like to expand on any specific section or add more details!

[lemoonata]

GPT-Chat-Technologie und künstliche Intelligenz Quelle: https://colorscandles1.blogspot.com/2025/01/gpt-chat-technologie-und-kunstliche.html

Die GPT-Chat-Technologie ist eine der bedeutendsten Entwicklungen im Bereich der künstlichen Intelligenz, da sie einen qualitativen Wandel in der Art und Weise darstellt, wie Menschen mit Maschinen interagieren. Diese Technologie konnte Deep Learning und natürliche Sprachverarbeitung kombinieren, wodurch Systeme Texte auf eine Weise verstehen und analysieren konnten, die das bisher Mögliche übertraf.

[indirezioneostinata]

Dal Pre-training all'Expert Iteration: Il Percorso verso la Riproduzione di OpenAI Five

Il Reinforcement Learning (RL) rappresenta un approccio distintivo nel panorama del machine learning, basato sull’interazione continua tra un agente e il suo ambiente. In RL, l’agente apprende attraverso un ciclo di azioni e ricompense, con l’obiettivo di massimizzare il guadagno cumulativo a lungo termine. Questa strategia lo differenzia dagli approcci tradizionali come l’apprendimento…

[sanjanabia]

From Chatbots to Autonomous Systems: How AI is Evolving Through Reinforcement Learning

Artificial Intelligence (AI) is revolutionizing numerous industries, and one of the most significant advancements driving this evolution is reinforcement learning (RL). This dynamic branch of machine learning focuses on how agents can learn optimal behaviors through trial and error by interacting with their environments. From enhancing chatbots to enabling autonomous systems, reinforcement learning is at the forefront of AI innovation. For those interested in mastering these concepts, enrolling in a data analytics course in Kolkata can provide the essential skills needed to understand and apply RL techniques effectively.

What is Reinforcement Learning?

Reinforcement learning is a learning paradigm where agents learn to make decisions by performing actions in an environment to achieve specific goals. Unlike traditional machine learning methods that rely on labeled data, RL uses a reward system to encourage desired behaviors. Here’s how it works:

Agent: The learner or decision-maker.

Environment: The context within which the agent operates.

Actions: The choices available to the agent.

Rewards: Feedback from the environment that evaluates the success of the agent’s actions.

Through this feedback loop, agents learn to maximize their cumulative rewards, making RL particularly suited for complex decision-making tasks.

Applications of Reinforcement Learning

Reinforcement learning has a wide range of applications that demonstrate its capabilities. Two notable areas where RL is making significant impacts are chatbots and autonomous systems.

Chatbots:

Personalization: Modern chatbots use RL to improve user interactions by learning from past conversations. This allows them to adapt their responses based on user preferences and behavior.

Efficiency: By optimizing conversation pathways, RL enables chatbots to provide faster and more accurate responses, enhancing user satisfaction.

Learning from Feedback: Chatbots can adjust their strategies in real-time, learning which types of responses yield the best outcomes in terms of user engagement.

Autonomous Systems:

Robotics: In robotics, RL empowers machines to navigate environments, making real-time decisions to avoid obstacles and accomplish tasks. This is crucial for applications in warehouses, factories, and even space exploration.

Self-Driving Cars: Autonomous vehicles rely on RL to make split-second decisions based on sensory data. By continuously learning from driving experiences, these systems become safer and more efficient.

Game Playing: RL has gained fame through its success in games like Go and chess, where agents learn to play at superhuman levels by exploring vast action spaces and optimizing strategies.

The Importance of Data Analytics in Reinforcement Learning

Understanding reinforcement learning requires a solid foundation in data analytics, which is where a data analytics course in Kolkata can be beneficial. Here are some key areas covered in such a course that are directly applicable to RL:

Data Preprocessing: Preparing data for analysis is essential in RL, as it often involves large and complex datasets.

Statistical Analysis: Understanding the principles of statistics is crucial for interpreting reward signals and evaluating agent performance.

Machine Learning Algorithms: A solid grasp of various machine learning techniques is necessary for implementing RL algorithms effectively.

Model Evaluation: Learning how to assess the performance of RL models is vital for improving their effectiveness and reliability.

By acquiring these skills through a data analytics course in Kolkata, individuals can position themselves at the cutting edge of AI technology.

Challenges in Reinforcement Learning

While reinforcement learning offers tremendous potential, it also faces several challenges:

Sample Efficiency: RL often requires a vast amount of data to learn effectively, making it resource-intensive.

Stability and Convergence: Ensuring that RL algorithms converge to optimal solutions can be complex, particularly in dynamic environments.

Exploration vs. Exploitation: Balancing the need to explore new strategies while exploiting known successful ones is a critical aspect of RL that can affect learning outcomes.

Addressing these challenges is crucial for advancing the field and enabling RL applications in real-world scenarios.

The Future of Reinforcement Learning

The future of reinforcement learning is promising, with ongoing research aimed at overcoming current limitations and expanding its applications. As industries increasingly adopt AI technologies, the integration of RL into more complex systems will likely lead to breakthroughs in automation, personalization, and efficiency.

For individuals eager to be part of this evolving landscape, enrolling in a data analytics course in Kolkata can provide the necessary training to understand and implement reinforcement learning techniques. This education can open doors to exciting career opportunities in AI and data science.

Conclusion

Reinforcement learning is a game-changing technology that is reshaping the landscape of artificial intelligence. From enhancing chatbots to powering autonomous systems, its applications are vast and impactful. As the field continues to evolve, understanding the principles of RL through a data analytics course in Kolkata becomes increasingly valuable. With the right skills and knowledge, individuals can contribute to the advancements in AI and play a pivotal role in the future of technology. Embracing reinforcement learning not only enhances career prospects but also fosters innovation across various industries.

[fuerst-von-plan1]

Neuartige KI-Modelle: Optimierung von Echtzeit-Entscheidungen

In der heutigen schnelllebigen Welt spielt Künstliche Intelligenz (KI) eine entscheidende Rolle bei der Optimierung von Echtzeit-Entscheidungen in verschiedenen Branchen. Die neuesten Entwicklungen in der KI-Technologie ermöglichen es Unternehmen, nicht nur ihre Effizienz zu steigern, sondern auch genaue und zeitgerechte Entscheidungen zu treffen. Durch den Einsatz neuartiger KI-Modelle können…

[thedevmaster-tdm]

STOP Using Fake Human Faces in AI

[jcmarchi]

DeepSeek-R1 reasoning models rival OpenAI in performance

New Post has been published on https://thedigitalinsider.com/deepseek-r1-reasoning-models-rival-openai-in-performance/

DeepSeek-R1 reasoning models rival OpenAI in performance

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DeepSeek has unveiled its first-generation DeepSeek-R1 and DeepSeek-R1-Zero models that are designed to tackle complex reasoning tasks.

DeepSeek-R1-Zero is trained solely through large-scale reinforcement learning (RL) without relying on supervised fine-tuning (SFT) as a preliminary step. According to DeepSeek, this approach has led to the natural emergence of “numerous powerful and interesting reasoning behaviours,” including self-verification, reflection, and the generation of extensive chains of thought (CoT).

“Notably, [DeepSeek-R1-Zero] is the first open research to validate that reasoning capabilities of LLMs can be incentivised purely through RL, without the need for SFT,” DeepSeek researchers explained. This milestone not only underscores the model’s innovative foundations but also paves the way for RL-focused advancements in reasoning AI.

However, DeepSeek-R1-Zero’s capabilities come with certain limitations. Key challenges include “endless repetition, poor readability, and language mixing,” which could pose significant hurdles in real-world applications. To address these shortcomings, DeepSeek developed its flagship model: DeepSeek-R1.

Introducing DeepSeek-R1

DeepSeek-R1 builds upon its predecessor by incorporating cold-start data prior to RL training. This additional pre-training step enhances the model’s reasoning capabilities and resolves many of the limitations noted in DeepSeek-R1-Zero.

Notably, DeepSeek-R1 achieves performance comparable to OpenAI’s much-lauded o1 system across mathematics, coding, and general reasoning tasks, cementing its place as a leading competitor.

DeepSeek has chosen to open-source both DeepSeek-R1-Zero and DeepSeek-R1 along with six smaller distilled models. Among these, DeepSeek-R1-Distill-Qwen-32B has demonstrated exceptional results—even outperforming OpenAI’s o1-mini across multiple benchmarks.

MATH-500 (Pass@1): DeepSeek-R1 achieved 97.3%, eclipsing OpenAI (96.4%) and other key competitors.  

LiveCodeBench (Pass@1-COT): The distilled version DeepSeek-R1-Distill-Qwen-32B scored 57.2%, a standout performance among smaller models.  

AIME 2024 (Pass@1): DeepSeek-R1 achieved 79.8%, setting an impressive standard in mathematical problem-solving.

A pipeline to benefit the wider industry

DeepSeek has shared insights into its rigorous pipeline for reasoning model development, which integrates a combination of supervised fine-tuning and reinforcement learning.

According to the company, the process involves two SFT stages to establish the foundational reasoning and non-reasoning abilities, as well as two RL stages tailored for discovering advanced reasoning patterns and aligning these capabilities with human preferences.

“We believe the pipeline will benefit the industry by creating better models,” DeepSeek remarked, alluding to the potential of their methodology to inspire future advancements across the AI sector.

One standout achievement of their RL-focused approach is the ability of DeepSeek-R1-Zero to execute intricate reasoning patterns without prior human instruction—a first for the open-source AI research community.

Importance of distillation

DeepSeek researchers also highlighted the importance of distillation—the process of transferring reasoning abilities from larger models to smaller, more efficient ones, a strategy that has unlocked performance gains even for smaller configurations.

Smaller distilled iterations of DeepSeek-R1 – such as the 1.5B, 7B, and 14B versions – were able to hold their own in niche applications. The distilled models can outperform results achieved via RL training on models of comparable sizes.

🔥 Bonus: Open-Source Distilled Models!

🔬 Distilled from DeepSeek-R1, 6 small models fully open-sourced 📏 32B & 70B models on par with OpenAI-o1-mini 🤝 Empowering the open-source community

🌍 Pushing the boundaries of **open AI**!

🐋 2/n pic.twitter.com/tfXLM2xtZZ

— DeepSeek (@deepseek_ai) January 20, 2025

For researchers, these distilled models are available in configurations spanning from 1.5 billion to 70 billion parameters, supporting Qwen2.5 and Llama3 architectures. This flexibility empowers versatile usage across a wide range of tasks, from coding to natural language understanding.

DeepSeek has adopted the MIT License for its repository and weights, extending permissions for commercial use and downstream modifications. Derivative works, such as using DeepSeek-R1 to train other large language models (LLMs), are permitted. However, users of specific distilled models should ensure compliance with the licences of the original base models, such as Apache 2.0 and Llama3 licences.

(Photo by Prateek Katyal)

See also: Microsoft advances materials discovery with MatterGen

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, artificial intelligence, benchmark, comparison, deepseek, deepseek-r1, large language models, llm, models, reasoning, reasoning models, reinforcement learning, test

[mitsde123]

How to Choose the Right Machine Learning Course for Your Career

As the demand for machine learning professionals continues to surge, choosing the right machine learning course has become crucial for anyone looking to build a successful career in this field. With countless options available, from free online courses to intensive boot camps and advanced degrees, making the right choice can be overwhelming. 

[edsonjnovaes]

Curso de Inteligência Artificial para todos - Aula 1

Curso de Inteligência Artificial para todos – Aula 1. Diogo Cortiz – 2020 23 mar Este primeiro vídeo é para discutir o panorama de IA e as principais abordagens existentes. Vou apresentar a história da inteligência artificial e a sopa de letrinhas que confunde muita gente: ia, machine learning, deep learning. Também explico as principais abordagens de aprendizado e treinamento: aprendizado…

[alphalesbian]

Youll just be minding your own business when all of a sudden the inherant intimacy of solo instrumental music is realized upon you. Like youre just supposed to proceed normally after

[michaeldemanega]

Künstliche Intelligenz: Trends

Derzeit wird der Begriff der Künstlichen Intelligenz extrem exzessiv verwendet, etwa im Bereich von: Beim Bloggen über Künstliche Intelligenz (KI) gibt es viele relevante Schlüsselwörter, die dir helfen können, mehr Klicks zu generieren. Hier sind einige Vorschläge: Allgemeine Schlüsselwörter zu KI Künstliche Intelligenz Maschinenlernen Deep Learning AI Trends AI Forschung Zukunft der…