AI Applications

In the fast-evolving landscape of AI-driven innovation, businesses are turning to neocloud providers to power their computational needs. Unlike traditional hyperscalers like AWS, Azure, or Google Cloud, neocloud platforms specialize in GPU-as-a-Service (GPUaaS), offering tailored infrastructure for AI and machine learning workloads. With a focus on cost efficiency, high performance,

Imagine rolling out a customer-service chatbot that, within minutes, can switch from handling billing inquiries to troubleshooting technical issues without rebuilding its entire knowledge base. Or picture a warehouse robot that, after a handful of interactions, masters a new assembly line configuration without weeks of retraining. That’s the promise

In a rapidly evolving AI landscape, businesses are under growing pressure to deploy agents that can understand and respond across text, vision, and audio channels. Yet collecting, annotating, and curating real-world multimodal datasets is often costly, labor-intensive, with compliance hurdles. Synthetic data offers a compelling alternative, enabling businesses to accelerate

The earliest chatbot models were goldfish: every new prompt wiped their memory clean; 2025 is the year they finally start acting more like elephants. By fusing large‑context language models with external memory stores, developers can now build stateful agents that remember users, projects and decisions for days, or even

Building successful AI solutions isn’t just about powerful models, it’s also about how those models and processes are organized. The promise of AI, streamlined operations, predictive insights, and hyper-personalized customer experiences, is undeniable. But the reality? Many organizations are grappling with rigid, siloed AI systems that are tough

Artificial intelligence has come a long way since its early days. Today, we’re witnessing a convergence of two seemingly distinct paradigms, symbolic reasoning and subsymbolic learning. Symbolic AI, with its roots in logic and rule-based systems, offers clarity and explicit reasoning. In contrast, subsymbolic methods, such as deep neural

Artificial Intelligence (AI) is rapidly evolving from narrow, single-task models to more sophisticated systems capable of perceiving and reasoning across multiple types of inputs. This evolution is embodied in multimodal AI, a powerful class of AI that understands and synthesizes information from diverse data types like text, images, audio, video,

In our previous blog, we introduced the fundamentals of Retrieval-Augmented Generation (RAG)—a hybrid architecture that combines the strengths of large language models (LLMs) with retrieval mechanisms to enhance factual accuracy and ground responses in external knowledge. While the basic RAG framework opens the door to powerful AI applications, production-grade

As artificial intelligence continues to evolve rapidly, the need for better coordination, interoperability, and contextual understanding among models has never been more urgent. Enter the Model Context Protocol (MCP), an emerging specification that has quickly gained traction in AI communities and tech giants alike. By early 2025, MCP has become

The open-source AI landscape has seen a significant advancement with the release of Llama 4 by Meta on April 5. This latest iteration of Meta's powerful large language models (LLMs) brings substantial improvements in reasoning, instruction-following, and overall performance, marking the most notable upgrade in the Llama LLM

In an era of rapid information development and increasingly intense competition, a company's success may hinge on how quickly it responds to external changes—or even on how efficiently users perceive its solutions. Ultimately, the ability to extract the most relevant and optimal information from vast amounts of

March 2025 marked a turning point for AI image generation, unveiling tools that are transforming how we create and use visual content. These advancements go far beyond technical upgrades, they’re opening doors for businesses and individuals to innovate like never before. Leading the charge is OpenAI’s GPT-4o, but

Agentic AI represents the next evolution in autonomous systems, going beyond reactive language models to perform complex, multi-step tasks with minimal human oversight. In practice, building such systems requires a deep integration of advanced model training strategies and robust data engineering. Today, we’ll explore the core technical components necessary

In the era of rapid AI advancement, AI agents have become a focal point of innovation. Unlike traditional AI models that rely on continuous human input, AI agents operate autonomously, leveraging real-time data and advanced foundation models to make decisions and execute tasks. These intelligent systems are transforming industries—enhancing

In the realm of machine learning (ML), crafting a powerful model is just the first step. To truly unlock its potential, you need to fine-tune its configuration – a process known as hyperparameter tuning. Imagine a car engine. You can build a powerful engine, but without fine-tuning the carburetor, spark plugs,

The growing popularity of artificial intelligence (AI) has led more people to become aware of it, with the large language model being one of the most familiar aspects. In LLMs (such as DeepSeek or ChatGPT), at the core, there is something called a "token." However, you may not

Artificial intelligence is everywhere, from powering customer service chatbots to driving complex data analysis. But when it comes to picking an AI model, one of the hottest debates is whether to go closed source or open source. Today, we will dive into what sets these two approaches apart, sharing real-world

As AI models continue to scale in complexity and capability, one of the biggest challenges researchers and enterprises face is balancing performance with efficiency. The Mixture of Experts (MoE) architecture has emerged as a game-changing solution, enabling large AI models to achieve higher performance while reducing computational costs. In this

Imagine you're the CTO of a startup migrating to the cloud. Faced with challenges in team collaboration and cost management, you need to answer two key questions: How can team members safely share resources? How do you avoid confusion in financials and permissions? In this case, the three

In the rapidly evolving landscape of Large Language Models (LLMs), two techniques have emerged as game-changers: Reinforcement Learning (RL) and Chain-of-Thought (CoT) reasoning. Models like DeepSeek R1 and OpenAI's o1 leverage these methods to achieve advanced reasoning capabilities, outperforming traditional LLMs in complex tasks. This article delves into

The Bitdeer AI Cloud platform now supports multiple versions of the DeepSeek models including R1 and JanusPro. You can deploy an instance with a single click via the instance console, customize the management mode, and flexibly choose the model image for rapid deployment. This guide uses the DeepSeek-R1 671B image

Artificial intelligence is evolving at an unprecedented pace, and the recent launches of DeepSeek V3, R1 have taken the AI landscape by storm. DeepSeek V3 rivals GPT-4o and LLaMa 3-405B while using ~10x less compute, trained on just 2,000 Nvidia H800 GPUs for $5.58 million. Meanwhile, DeepSeek R1,

Businesses in all sectors are jumping on the bandwagon for this game-changing technology as artificial intelligence (AI) leaps from science fiction to reality. Artificial intelligence (AI) might significantly impact business processes, spur innovation, and help organizations acquire a competitive advantage. Learn how to use AI in businesses and how it

We can't imagine modern life without artificial intelligence (AI), which has changed the game in many sectors and altered our strategy for tackling problems. This incredible technology relies on training AI models for machines to learn, adapt, and make smart decisions. Discover the various training methods accessible, learn