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Geeky Gadgets
4 days ago
- Science
- Geeky Gadgets
Humans vs AI : The Surprising Truth About How We Think Differently
What truly separates the way you think from how an AI like a large language model operates? Imagine trying to teach a child to recognize a dog. With just a few examples, they'd quickly grasp the concept, connecting it to their sensory experiences and even emotions. Now compare that to an AI, which would need to analyze thousands—if not millions—of images to achieve a similar result, and even then, it wouldn't 'understand' what a dog is in the way you do. This stark human vs AI thinking difference highlights a deeper truth: while humans and AI can produce similar outputs, the paths they take to get there are worlds apart. Understanding these differences isn't just a matter of curiosity—it's essential for navigating a future where AI plays an increasingly central role in our lives. In this exploration, the IBM Technology team delve into the fascinating contrasts between human cognition and the mechanics of large language models (LLMs). From how we learn and process information to the way we reason and handle errors, the distinctions are both striking and revealing. You'll discover why your brain's dynamic adaptability gives you an edge in creativity and context, while an LLM's raw computational power allows it to process vast amounts of data at lightning speed. By the end, you'll not only grasp how these systems differ but also gain insights into how their unique strengths can complement each other in fantastic ways. After all, understanding these contrasts isn't just about comparing—it's about imagining what's possible when human ingenuity and AI precision work hand in hand. Human vs AI Cognition Learning: Neuroplasticity vs Backpropagation Human learning is driven by neuroplasticity, where your brain adapts and reorganizes its neural connections with relatively minimal exposure to new concepts. This adaptability enables you to generalize knowledge and apply it flexibly across various situations. For example, you can learn a new skill, such as playing a musical instrument, and transfer that understanding to related tasks, like composing music. In contrast, LLMs rely on backpropagation, a computational process that adjusts millions or even billions of parameters to minimize errors during training. This process requires vast datasets and significant computational resources. Unlike your ability to learn incrementally, LLMs cannot adapt to new information without undergoing a complete retraining process. Once trained, their parameters are fixed, limiting their ability to dynamically incorporate new knowledge. Processing: Parallel vs Sequential Your brain processes information in parallel, integrating sensory inputs, emotions, and abstract concepts simultaneously. This parallel processing allows you to quickly grasp the broader context of a situation and make informed decisions. For instance, when navigating a busy street, you simultaneously process visual cues, sounds, and spatial awareness to ensure your safety. LLMs, however, process information sequentially. They break down text into discrete units called tokens and predict the next token based on patterns learned during training. While this sequential approach enables LLMs to generate coherent and contextually appropriate text, it lacks the holistic understanding that your brain naturally applies. This limitation means LLMs excel at tasks requiring linear progression but struggle with tasks demanding multidimensional context. Human vs AI Thinking Styles Compared Watch this video on YouTube. Advance your skills in AI intelligence by reading more of our detailed content. Memory: Context-Driven vs Static Human memory operates dynamically across multiple levels—sensory, working, and long-term. This dynamic system allows you to associate new information with past experiences, recall relevant details, and adapt your understanding as new contexts arise. For example, you might remember a childhood lesson about fire safety and apply it instinctively when faced with a dangerous situation. LLMs, by comparison, have a limited 'context window,' which restricts the amount of information they can actively process at any given moment. Beyond this window, they rely on static knowledge encoded during training. Unlike your memory, which evolves with experience and adapts to new information, LLMs cannot update their knowledge without retraining the entire model. This static nature limits their ability to respond to rapidly changing or nuanced contexts. Reasoning: Intuition and Logic vs Statistical Prediction When reasoning, you engage two complementary systems: intuitive (System 1) and analytical (System 2) thinking. System 1 enables you to make quick, instinctive decisions, such as recognizing a familiar face in a crowd. System 2, on the other hand, allows for deliberate, logical problem-solving, such as solving a complex mathematical equation. Together, these systems help you navigate complex situations with both speed and depth. LLMs simulate reasoning by generating statistically plausible sequences of text based on their training data. However, they lack genuine understanding or the ability to engage in conscious thought. While their outputs may appear logical, they are ultimately the result of pattern recognition rather than true reasoning. This distinction underscores the importance of human oversight when interpreting or applying AI-generated outputs. Error: Confabulation vs Hallucination Humans occasionally confabulate, unknowingly creating false memories or explanations to fill gaps in understanding. This is a natural byproduct of your brain's effort to make sense of incomplete information. For example, you might misremember the details of an event but still retain the general context. Similarly, LLMs 'hallucinate,' producing confident but factually incorrect outputs when their training data lacks sufficient context or accuracy. Unlike humans, LLMs cannot self-correct or verify their outputs. Your ability to reflect and reason often allows you to identify and rectify errors more effectively than an LLM. This difference highlights the need for careful validation of AI-generated information. Embodiment: Sensory Experiences vs Disembodiment Your cognition is deeply influenced by your physical interactions with the world. Sensory experiences—sight, touch, sound, and more—shape your understanding and allow you to learn through direct exploration. For instance, you might learn the concept of 'hot' by touching a warm surface and associating the sensation with the word. LLMs, on the other hand, are disembodied systems. They rely exclusively on textual data and lack sensory inputs. Without physical experiences, LLMs cannot ground their 'understanding' in reality. This disembodiment limits their ability to perceive the world as you do, making them highly effective at processing text but unable to fully replicate human experiential learning. Using Human and AI Strengths While both humans and LLMs can produce similar outputs, the processes driving those outputs are fundamentally different. Human cognition is rooted in comprehension, context, and sensory experiences, while LLMs excel in speed and pattern recognition across vast datasets. By understanding these differences, you can better use the strengths of both systems. Combining human insight with AI efficiency offers opportunities to achieve outcomes that neither could accomplish alone. This synergy has the potential to transform fields such as education, healthcare, and scientific research, where the unique capabilities of humans and AI can complement one another to solve complex challenges. Media Credit: IBM Technology Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
11-06-2025
- Business
- Geeky Gadgets
How RAFT is Making AI Smarter, Faster, and More Accurate Than Ever
What if artificial intelligence could think beyond its training, pulling in fresh insights from the vast expanse of human knowledge? Imagine an AI model that doesn't just rely on static datasets but actively retrieves the latest medical research, legal precedents, or financial trends to inform its decisions. This is no longer a futuristic dream—it's the promise of Retrieval-Augmented Fine-Tuning (RAFT). By blending the precision of fine-tuning with the adaptability of retrieval systems, RAFT redefines how AI learns and evolves, making it a fantastic option for industries where accuracy and context are non-negotiable. But with such fantastic potential comes a critical question: how does this hybrid approach actually work, and what makes it so effective? In this exploration of RAFT, the IBM Technology team uncover the mechanics behind this innovative technique and its ability to bridge the gap between static training data and the ever-changing real world. You'll discover how RAFT enables AI to handle complex, domain-specific challenges with unprecedented accuracy, from diagnosing rare medical conditions to navigating intricate legal frameworks. Along the way, we'll delve into its core components, practical applications, and the challenges that lie ahead. Whether you're curious about the future of machine learning or seeking innovative solutions for your field, RAFT offers a glimpse into a smarter, more adaptable AI. After all, what could be more powerful than an AI that learns not just from the past, but also from the present? Overview of RAFT The Mechanism Behind RAFT RAFT functions as a dynamic and adaptive training process, improving upon traditional fine-tuning by incorporating retrieval systems. These systems enable AI models to access and retrieve relevant external knowledge during training, rather than relying solely on static datasets. This dynamic retrieval ensures that the model remains aligned with the most current and accurate information available. For example, consider training an AI model to address complex medical queries. With RAFT, the model can retrieve the latest medical research, guidelines, or case studies during its training phase. This ensures that the model's responses are not only accurate but also reflective of the most up-to-date knowledge in the field. By integrating external data sources, RAFT bridges the gap between static training data and the ever-evolving nature of real-world information. Core Components Driving RAFT The effectiveness of RAFT lies in its integration of several critical components, each contributing to its ability to generate precise and context-aware outputs: Retrieval Systems: These systems are designed to identify and extract relevant information from extensive datasets or databases, making sure the model has access to the most pertinent knowledge. These systems are designed to identify and extract relevant information from extensive datasets or databases, making sure the model has access to the most pertinent knowledge. Fine-Tuning Techniques: Fine-tuning adjusts the model's internal parameters based on the retrieved knowledge, enhancing its ability to produce accurate and contextually appropriate outputs. Fine-tuning adjusts the model's internal parameters based on the retrieved knowledge, enhancing its ability to produce accurate and contextually appropriate outputs. External Knowledge Integration: By incorporating external data sources, RAFT ensures that models are not limited to static training datasets, allowing them to adapt to dynamic, real-world information. By incorporating external data sources, RAFT ensures that models are not limited to static training datasets, allowing them to adapt to dynamic, real-world information. Contextual Reasoning: RAFT improves the model's capacity to understand and process complex relationships within data, resulting in nuanced and precise outputs. RAFT improves the model's capacity to understand and process complex relationships within data, resulting in nuanced and precise outputs. Domain-Specific Knowledge: This approach is particularly effective in specialized fields where accurate and context-aware information is essential for success. What is Retrieval-Augmented Fine-Tuning (RAFT)? Watch this video on YouTube. Here are additional guides from our expansive article library that you may find useful on AI learning. Practical Applications of RAFT The versatility of RAFT makes it applicable across a wide range of industries and use cases. In natural language processing (NLP), RAFT enhances tasks such as question answering, text summarization, and conversational AI. For instance, customer support chatbots equipped with RAFT can retrieve real-time product information, allowing them to provide more precise and contextually relevant responses to user queries. In the realm of scientific research, RAFT can analyze vast datasets by retrieving relevant studies or data, helping researchers draw accurate and insightful conclusions. Similarly, in legal and regulatory fields, RAFT ensures that AI models remain updated with the latest laws, regulations, and guidelines, thereby improving compliance and decision-making accuracy. These applications highlight RAFT's ability to adapt to the specific needs of various domains, making it a valuable tool for tackling complex challenges. Advantages and Potential of RAFT RAFT offers a range of benefits that extend beyond traditional fine-tuning approaches. By integrating external knowledge retrieval, RAFT enables AI models to: Handle Complex Queries: RAFT equips models to process intricate and multi-faceted queries that require deep contextual understanding. RAFT equips models to process intricate and multi-faceted queries that require deep contextual understanding. Adapt to Evolving Information: By incorporating up-to-date knowledge during training, RAFT ensures that models remain relevant in dynamic environments. By incorporating up-to-date knowledge during training, RAFT ensures that models remain relevant in dynamic environments. Excel in Specialized Fields: RAFT is particularly effective in domains such as medicine, law, and finance, where static training data often falls short of capturing the complexity of real-world scenarios. RAFT is particularly effective in domains such as medicine, law, and finance, where static training data often falls short of capturing the complexity of real-world scenarios. Produce Contextually Relevant Outputs: By retrieving and integrating external knowledge, RAFT ensures that the outputs generated are tailored to the specific context of a given query or task. Challenges and Future Prospects While RAFT offers significant advantages, it also presents certain challenges. The retrieval process can be computationally intensive, requiring robust infrastructure to manage and process large-scale data efficiently. Additionally, making sure the quality and relevance of the retrieved information is critical to maintaining the accuracy and reliability of the model's outputs. Looking ahead, ongoing research aims to optimize retrieval mechanisms and incorporate more diverse data sources into the RAFT framework. These advancements are expected to enhance the efficiency and adaptability of RAFT, allowing AI models to tackle increasingly complex tasks with greater precision. As the field of machine learning continues to evolve, RAFT's ability to integrate external knowledge and improve contextual reasoning will play a pivotal role in addressing the growing demands of AI applications. Media Credit: IBM Technology Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
03-06-2025
- Business
- Geeky Gadgets
Unlock Hidden Data Insights with GraphRAG: The Future of AI Retrieval
What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for semantic searches, often miss the intricate connections buried within complex datasets. Enter GraphRAG, a new approach that combines the power of knowledge graphs and Cypher queries to transform how we retrieve and interpret information. By transforming unstructured text into structured data, GraphRAG offers a way to explore deeper insights and relationships that traditional methods simply can't match. Imagine not just finding the right answer but understanding the web of connections that brought you there. In this exploration of GraphRAG, the IBM Technology team explain how it uses the structured nature of graph databases to provide context-rich insights and unparalleled depth in data retrieval. From understanding the mechanics of entity and relationship extraction to seeing how natural language queries are transformed into precise Cypher commands, this overview will guide you through the core principles that make GraphRAG so powerful. Along the way, we'll compare it to VectorRAG, explore its advantages, and even touch on hybrid systems that combine the best of both worlds. By the end, you'll not only grasp how GraphRAG works but also why it's reshaping the future of AI-powered knowledge retrieval. Could this be the key to unlocking the full potential of your data? GraphRAG Overview What is GraphRAG? GraphRAG is a retrieval method that uses knowledge graphs to store and manage structured data, serving as an alternative to VectorRAG (Vector Retrieval Augmented Generation). While vector databases rely on embeddings to identify semantic similarities, knowledge graphs represent data as nodes (entities) and edges (relationships). This structure provides a more interconnected and holistic view of the dataset, allowing for the retrieval of information with greater depth and context. By focusing on structured data, GraphRAG enables you to explore relationships and patterns that are often missed by traditional vector-based methods. This makes it particularly useful for tasks requiring detailed exploration and analysis of complex datasets. How Does GraphRAG Work? GraphRAG operates by transforming unstructured text into a structured format and storing it in a graph database. The process involves several key steps: Entity and Relationship Extraction: A large language model (LLM) identifies entities and their relationships within unstructured text. A large language model (LLM) identifies entities and their relationships within unstructured text. Data Structuring: The extracted information is organized into nodes (entities) and edges (relationships) to form a knowledge graph. The extracted information is organized into nodes (entities) and edges (relationships) to form a knowledge graph. Querying: Natural language queries are converted into Cypher, a graph database query language, to retrieve relevant data. Natural language queries are converted into Cypher, a graph database query language, to retrieve relevant data. Result Interpretation: The retrieved data is translated back into natural language for easy understanding. This structured approach allows you to explore complex interconnections within datasets, offering insights that traditional vector-based methods often overlook. By using the power of knowledge graphs, GraphRAG provides a more nuanced and comprehensive understanding of the data. GraphRAG Explained: AI Retrieval with Knowledge Graphs & Cypher Watch this video on YouTube. Advance your skills in AI knowledge by reading more of our detailed content. System Setup Implementing GraphRAG requires a combination of tools and technologies to create and manage the knowledge graph effectively. Here's how you can set it up: Knowledge Graph Creation: Use an LLM to extract entities and relationships from unstructured text and populate a graph database like Neo4j. Use an LLM to extract entities and relationships from unstructured text and populate a graph database like Neo4j. Containerized Environments: Tools such as Docker or Podman ensure scalability and simplify deployment. Tools such as Docker or Podman ensure scalability and simplify deployment. Programming Libraries: Python libraries like LangChain and IBM are essential for configuring and managing the system. This setup ensures a scalable, efficient, and user-friendly environment for implementing GraphRAG. By combining these tools, you can streamline the process of transforming unstructured data into actionable insights. Transforming Data: From Unstructured to Structured A cornerstone of GraphRAG is its ability to transform unstructured text into structured data. This transformation process involves several steps: Entity Identification: The LLM identifies key entities (nodes) within the text. The LLM identifies key entities (nodes) within the text. Relationship Mapping: Relationships (edges) between entities are extracted to form meaningful connections. Relationships (edges) between entities are extracted to form meaningful connections. Controlled Structuring: By limiting the types of nodes and relationships, you can improve the graph's accuracy and relevance. This structured representation enhances data retrieval and allows for the exploration of intricate patterns and relationships within the dataset. By converting unstructured text into a graph format, GraphRAG enables you to uncover hidden connections and gain a deeper understanding of the data. Querying the Knowledge Graph Natural language processing (NLP) plays a pivotal role in querying knowledge graphs. When you input a query in plain language, the system converts it into Cypher, a specialized query language for graph databases. The process involves: Query Conversion: The LLM translates your natural language query into a Cypher query. The LLM translates your natural language query into a Cypher query. Data Retrieval: The Cypher query retrieves relevant information from the graph database. The Cypher query retrieves relevant information from the graph database. Result Translation: The retrieved data is converted back into natural language for easy interpretation. Prompt engineering ensures that the generated Cypher queries are accurate and the responses are well-structured. This process improves the overall user experience by making complex data retrieval tasks more intuitive and accessible. Advantages of GraphRAG GraphRAG offers several distinct advantages over traditional retrieval methods: Holistic Retrieval: Unlike vector-based methods, GraphRAG retrieves information across the entire dataset, not just the top results. Unlike vector-based methods, GraphRAG retrieves information across the entire dataset, not just the top results. Contextual Insights: The structured nature of knowledge graphs provides deeper contextual understanding and reveals hidden connections. The structured nature of knowledge graphs provides deeper contextual understanding and reveals hidden connections. Enhanced Exploration: Relationships and patterns that are difficult to capture with vector-based methods become accessible through GraphRAG. These benefits make GraphRAG a powerful tool for tasks requiring comprehensive data retrieval and analysis. Its ability to provide context-rich insights sets it apart from traditional methods. GraphRAG vs. VectorRAG The key difference between GraphRAG and VectorRAG lies in their approach to data retrieval: VectorRAG: Relies on embeddings and semantic similarity to retrieve the most relevant results. Relies on embeddings and semantic similarity to retrieve the most relevant results. GraphRAG: Uses structured data and Cypher queries to explore the entire dataset, uncovering deeper connections. While VectorRAG excels at quick semantic searches, GraphRAG is better suited for tasks requiring detailed exploration and summarization of complex datasets. Each method has its strengths, and the choice between them depends on the specific requirements of your use case. HybridRAG Systems: Combining Strengths HybridRAG systems integrate the strengths of both GraphRAG and VectorRAG to create a more versatile retrieval framework. By combining vector-based semantic search with the structured insights of knowledge graphs, HybridRAG systems offer: Enhanced Retrieval: Use the best of both methods for diverse datasets and complex queries. Use the best of both methods for diverse datasets and complex queries. Improved Flexibility: Adapt to a wide range of use cases, from quick searches to in-depth analysis. This hybrid approach ensures a robust and comprehensive retrieval system. By balancing the speed of vector-based methods with the depth of graph-based insights, HybridRAG systems provide a powerful solution for modern data retrieval challenges. Media Credit: IBM Technology Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
09-04-2025
- Business
- Yahoo
IBM unveils AI-integrated z17 mainframe
Tech major IBM has unveiled the latest version of its mainframe hardware engineered to support artificial intelligence (AI) across software, hardware, and systems operations. Powered by the IBM Telum II processor, the IBM z17 is designed to extend beyond transactional AI workloads, aiming to deliver real-time analysis for 100% of enterprise transactions. The system introduces capabilities that process up to 50% more AI inference operations per day than its predecessor, the IBM z16. The z17 incorporates feedback from more than 100 enterprise clients and reflects a five-year development process, the company said. The company revealed that it filed more than 300 patent applications with the US Patent and Trademark Office during this period. The new system integrates AI to support over 250 use cases such as risk assessment for loans, chatbot management, among others. At the core of the z17 is a second-generation on-chip AI accelerator within the Telum II processor, offering 40% more cache and the ability to perform over 450 billion inferencing operations daily with one millisecond response times. Concurrently, the tech major previewed the IBM Spyre Accelerator which will offer additional AI compute capacity via a PCIe card. The accelerator is intended to enable generative AI capabilities on the mainframe, such as AI assistants using enterprise data. The z17 introduces tools like IBM watsonx Code Assistant for Z and IBM watsonx Assistant for Z to enhance developer and IT operations productivity. For the first time, watsonx Assistant for Z will integrate with Z Operations Unite to provide chat-based AI incident detection and resolution using live system data, the company said. IBM also introduced IBM Z Operations Unite, a solution designed to unify key performance metrics and logs using OpenTelemetry. Scheduled for general availability in May 2025, it is aimed at improving incident detection, impact assessment, and resolution times. The z17 also includes features such as IBM Vault for secrets management across hybrid cloud, leveraging technology from HashiCorp. Additionally, the system introduces AI-powered tools for data classification and threat detection, including IBM Threat Detection for z/OS. Support for the z17 will be provided through IBM Technology Lifecycle Services, offering AI-driven processes to enhance case resolution and operational efficiency. The z17 will be generally available from 18 June 2025, with the IBM Spyre Accelerator to follow in late 2025. IBM LinuxONE and IBM Z general manager Ross Mauri said: 'The industry is quickly learning that AI will only be as valuable as the infrastructure it runs on. 'With z17, we're bringing AI to the core of the enterprise with the software, processing power, and storage to make AI operational quickly. Additionally, organisations can put their vast, untapped stores of enterprise data to work with AI in a secured, cost-effective way.' Recently, IBM and Tokyo Electron (TEL) renewed their partnership to advance semiconductor technologies for the next five years. "IBM unveils AI-integrated z17 mainframe" was originally created and published by Verdict, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
