Category Archives: AI

AI related blogs

AI, LLM, Technology

The Engineering Mechanics of AI

A new hobby I discovered last year is traditional tabletop puzzles. Building puzzles is a form of Engineering. To illustrate, prompting could be like looking for a puzzle piece. The LLM is trained to search the box for the right puzzle and piece. Let’s shake the box to see what pieces make up an LLM.

What’s in the Box

LLMs, or Large Language Models, are advanced machine learning constructs proficient in handling massive volumes of textual data and producing precise outcomes. Constructed through intricate algorithms, they dissect and comprehend data patterns at the granular level of individual words. This empowers LLMs to grasp the subtleties inherent to human language and its contextual usage. Their virtually boundless capacity to process and create text has fueled their rising prominence across diverse applications, ranging from language translation and chatbots to text categorization.

At their core, Large Language Models (LLMs) serve as fundamental frameworks leveraging deep learning for tasks in natural language processing (NLP) and natural language generation (NLG). These models are engineered to master the intricacies and interconnections of language by undergoing pre-training on extensive datasets. This preliminary training phase facilitates subsequent fine-tuning of models for specific tasks and applications.

LLM Edge Pieces

In a puzzle, the edge pieces are the ones that frame the entire puzzle and give it its shape. Plainly stated, the edges are the most essential pieces of the puzzle. Let’s consider these vital pieces that give LLM its shape and meaning:

Automation and Productivity

Armed with the ability to process large volumes of data, LLMs have become instrumental in automating tasks that once demanded extensive human intervention. Sentiment analysis, customer service interactions, content generation, and even fraud detection are some of the processes that AI has transformed. By assuming these responsibilities, LLMs save time and free up valuable human resources to focus on more strategic and creative endeavors.

Personalization and Customer Satisfaction

The integration of LLMs into chatbots and virtual assistants has resulted in round-the-clock service availability, catering to customers’ needs and preferences at any time. These language models decode intricate patterns in customer behavior by analyzing vast amounts of data. Consequently, businesses can tailor their services and offerings to match individual preferences, increasing customer satisfaction and loyalty.

Enhancing Accuracy and Insights

Meaningful data through insights is an essential attribute of AI. Their capacity to extract patterns and relationships from extensive datasets refines the quality of outputs. These models have demonstrated their abilities to enhance accuracy across various applications, including sentiment analysis, data grouping, and predictive modeling. Their adeptness at extracting intricate patterns and relationships from extensive datasets directly influences the quality of outputs, leading to more informed decision-making.

Language Models Architecture

Autoregressive Language Models

These models predict the next word in a sequence based on preceding words. They have been instrumental in various natural language processing tasks, particularly those requiring sequential context.

Autoencoding Language Models

Autoencoders, conversely, reconstruct input text from corrupted versions, resulting in valuable vector representations. These representations capture semantic meanings and can be used in various downstream tasks.

Hybrid Models

The hybrid models combine the strengths of both autoregressive and autoencoding models. By fusing their capabilities, these models tackle tasks like text classification, summarization, and translation with remarkable precision.

Text Processing

Tokenization

Tokenization fragments text into meaningful tokens, aiding processing. It boosts efficiency, widens vocabulary coverage, and enhances model understanding. This technique increases efficiency and widens the vocabulary coverage, allowing models to understand complex languages better.

Embedding

Embeddings map words to vectors, capturing their semantic essence. These vector representations form the foundation for various downstream tasks, including sentiment analysis and machine translation.

Attention Mechanisms

Attention mechanisms allow models to focus on pertinent information. The mechanisms enable models to focus on relevant information, mimicking human attention processes and significantly enhancing their ability to extract context from sequences.

Pre-training and Transfer Learning

In the pre-training phase, models are exposed to vast amounts of text data, acquiring fundamental language understanding. This foundation is then transferred to the second phase, where transfer learning adapts the pre-trained model to specialized tasks, leveraging the wealth of prior knowledge amassed during pre-training.

The Untraditional Puzzle

Large Language Models (LLM) have demonstrated their effectiveness in enhancing accuracy across various applications, including sentiment analysis, data grouping, and predictive modeling. Their adeptness at extracting intricate patterns and relationships from extensive datasets directly influences the quality of outputs, leading to more informed decision-making.

LLMs are like a giant puzzle with all the pieces coming together to build the model. The difference between LLMs and the traditional puzzle is that a traditional puzzle stops growing once all the pieces are in place. Unlike a traditional puzzle, technological innovations and data gathering will enable the LLM model to continue learning and growing.

AI, LLM, Technology

Drifting through AI

AI drift refers to a phenomenon in artificial intelligence where sophisticated AI entities, such as chatbots, robots, or digital constructs, deviate from their original programming and directives to exhibit responses and behaviors that their human creators did not intend or anticipate.

The accuracy of data is becoming more and more critical as we move forward in this space. Let’s consider “drift” in AI, why it’s happening, and how to monitor it using Machine Learning.

Factors Leading to AI Drift

  • Loosely Coupled Machine Learning Algorithms: Modern AI systems heavily rely on machine learning algorithms that are more interpretive and adaptable. Unlike traditional technologies focused on rigid computing tasks and quantifiable data, AI now embraces self-correcting and self-evolving tools through machine learning and deep learning strategies. This shift allows AI systems to simulate human thought and intelligence more effectively.
  • Multi-Part Collaborative Technologies: AI drift also stems from collaborative technologies, often called “deep stubborn networks.” These technologies combine generative and discriminative components, allowing them to work together and evolve the AI’s capabilities beyond its original programming. This collaborative approach enables AI systems to produce more accessible results and become less constrained by their initial design.

Understanding AI Drift

AI drift, also known as model drift or model decay, refers to the change in distribution over time for model inputs, outputs, and actuals. In simpler terms, the model’s predictions today may differ from what it predicted in the past. There are different types of drift to monitor in production models:

  • Prediction Drift: This type of drift signifies a change in the model’s predictions over time. It can result in discrepancies between the model’s pre-production predictions and its predictions on new data. Detecting prediction drift is crucial in maintaining model quality and performance.
  • Concept Drift: Concept drift, on the other hand, relates to changes in the statistical properties of the target variable or ground truths over time. It indicates a shift in the relationship between current and previous actuals, making it vital to ensure model accuracy and relevance in real-world scenarios.
  • Data Drift: Data drift refers to a distribution change in the model’s input data. Shifts in customer preferences, seasonality, or the introduction of new offerings can cause data drift. Monitoring data drift is essential to ensure the model remains resilient to changing input distributions and maintains its performance.
  • Upstream Drift: Upstream drift, or operational data drift, results from changes in a model’s data pipeline. This type of drift can be challenging to detect, but addressing it is crucial to manage performance issues as the model moves from research to production.

Detecting AI drift: Key factors to consider.

  • Model Performance: Monitoring for drift helps identify when a model’s performance is degrading, allowing timely intervention before it negatively impacts the customer experience or business outcomes.
  • Model Longevity: As AI models transition from research to the real world, predicting how they will perform is difficult. Monitoring for drift ensures that models remain accurate and relevant even as the data and operating environment change.
  • Data Relevance:  Models trained on historical data need to adapt to the changing nature of input data to maintain their relevance in dynamic business environments.

Here’s a front-runner I discovered in my research on this topic:

Evidentlyai, is a game-changing open-source ML observability platform that empowers data scientists and Machine Learning(ML) engineers to assess, test, and monitor machine learning models with unparalleled precision and ease. 

Evidentlyai rises above the conventional notion of a mere monitoring tool or service; it is a comprehensive ecosystem designed to enhance machine learning models’ quality, reliability, and performance throughout their entire lifecycle.

Three Sturdy Pillars

This product stands on three sturdy pillars: Reporting, Testing, and Monitoring. These distinct components offer a diverse range of applications that cater to varying usage scenarios, ensuring that every aspect of model evaluation and testing is covered comprehensively.

  • Reporting: Visualization is paramount in reporting. Love this part. The reporting provides data scientists and ML engineers with a user-friendly interface to delve into the intricacies of their models. By translating complex data into insightful visualizations, Reports empower users to deeply understand their model’s behavior, uncover patterns, and make informed decisions. It’s more than just data analysis; it’s a journey of discovery.
  • Testing: Testing is the cornerstone of model reliability. Evidentlyai’s testing redefines this process by introducing automated pipeline testing. This revolutionary approach allows rigorous model quality assessment, ensuring every tweak and modification is evaluated against a comprehensive set of predefined benchmarks. Evidentlyai streamlines the testing process through automated testing, accelerating model iteration and evolution.
  • Monitoring:  Real-time monitoring is the key to preemptive issue detection and performance optimization. Evidentlyai’s monitoring component is poised to revolutionize model monitoring by providing continuous insights into model behavior. By offering real-time feedback on model performance, Monitoring will empower users to identify anomalies, trends, and deviations, allowing for swift corrective action and continuous improvement.

Evidentlyai

At the heart of Evidentlyai lies its commitment to open-source collaboration. This level of commitment always makes me smile. The platform’s Python library opens up a world of possibilities for data scientists and ML engineers, enabling them to integrate Evidentlyai seamlessly into their workflows. This spirit of openness fosters innovation, accelerates knowledge sharing, and empowers the AI community to collectively elevate model monitoring and evaluation standards.

Evidentlyai is a beacon of innovation, redefining how we approach model monitoring and evaluation. Its comprehensive suite of components, ranging from insightful Reports to pioneering automated Tests and real-time Monitors, showcases a commitment to excellence that is second to none. As industries continue to harness the power of AI, Evidentlyai emerges as a vital companion on the journey to model reliability, performance, and success. Experience the future of model observability today, and embrace a new era of AI confidence with Evidentlyai.

AI drift is an essential aspect of machine learning observability that cannot be overlooked. By understanding and monitoring different types of drift, data scientists and AI practitioners can take proactive measures to maintain the performance and relevance of their AI models over time. As AI advances, staying vigilant about drift will be critical in ensuring the success and longevity of AI applications in various industries. Evidentlyai will play a large part in addressing this issue in the future.

GitHub Test ML Models with Evidentlyai.

image credit – suspensions

AI, LLM, Technology

Reasoning via Planning (RAP) the LLM Reasoners

In my research, I have discovered a variety of LLMs. I am always fascinated by the complexity and capabilities of these models. I follow several startups, founders, researchers, and data scientists in this growing space.

The role of research in AI is critical as they drive the progress and understanding of intelligence technologies. Researchers have a role in exploring state-of-the-art techniques creating algorithms, and discovering new applications for AI. Their work contributes to natural language processing, computer vision, robotics, and more advancements.

They investigate AI systems’ potentials and limitations to ensure their responsible use. Moreover, researchers share their findings through publications promoting collaboration and propelling the field forward. Their commitment to pushing AI’s boundaries improves capabilities and shapes its impact on society. Therefore researchers play a role in shaping the future of AI-driven innovations.

Let’s consider some of the advancements in LLM and how researchers use it to advance their work with Reasoning via Planning(RAP).

Large Language Models (LLMs) are witnessing progress leading to groundbreaking advancements and tools. LLMs have displayed capabilities in tasks such as generating text-classifying sentiment and performing zero-shot classification. These abilities have revolutionized content creation, customer service, and data analysis by boosting productivity and efficiency.

In addition to their existing strengths, researchers are now exploring the potential of LLMs in reasoning. Transformer architecture could accurately respond to reasoning problems in the training space but struggled to generalize to examples drawn from other distributions within the same problem space. They discovered that the models had learned to use statistical features to make predictions rather than understanding the underlying reasoning function.

  • These models can comprehend information and make logical deductions, making them valuable for question-answering, problem-solving, and decision-making. However, despite their skills, LLMs still need help with tasks requiring an internal world model like humans. This limitation hampers their ability to generate action plans and perform reasoning effectively.
  • Researchers have developed a reasoning framework called Reasoning via Planning (RAP) to address these challenges. This framework equips LLMs with algorithms for reasoning, enabling them to tackle tasks more efficiently.

The central concept behind RAP is to approach multi-reasoning as a process of planning, where we search for the most optimal sequence of reasoning steps while considering the balance between exploration and exploitation. To achieve this, RAP introduces the idea of a “World Model” and a “Reward” function.

  • In the RAP framework, the concept of a “World Model” is introduced that treats solutions as states. It then adds actions or thoughts to these states as transitions. The “Reward” function plays a role in evaluating the effectiveness of each reasoning step giving rewards to reasoning chains that are more likely to be correct.
  • Apart from the RAP paper, researchers have proposed LLM Reasoners, an AI library designed to enhance LLMs (language models) with reasoning capabilities. LLM Reasoners perceive step reasoning as a planning process and utilize sophisticated algorithms to search for the most efficient reasoning steps. They strike a balance between exploring options and exploiting information. With LLM Reasoners, you can define reward functions. Optionally include a world model streamlining aspects of the reasoning process such as Reasoning Algorithms, Visualization tools, LLM invocation, etc.
  • Extensive experiments on challenging reasoning problems have demonstrated that RAP outperforms CoT-based (Commonsense Transformers) approaches. In scenarios, it even surpasses models like GPT 4.
  • Through evaluation of the steps in reasoning, the LLM utilizes its world model to create a reasoning tree. RAP allows it to simulate outcomes estimate rewards, and improve its decision-making process.

The versatility of RAP in designing reward states and actions showcases its potential as a framework for addressing reasoning tasks. Its innovative approach that combines planning and reasoning brings forth opportunities for AI systems to achieve thinking and planning at a human level. The advanced reasoning algorithms, user visualization, and compatibility with LLM libraries contribute to its potential to revolutionize the field of LLM reasoning.

Reasoning via Planning (RAP) represents an advancement in enhancing the capabilities of Language Models (LLMs) by providing a robust framework for handling complex reasoning tasks. As AI systems progress, the RAPs approach could be instrumental in unlocking level thinking and planning to propel the field of AI toward an era of intelligent decision-making and problem-solving. The future holds possibilities, with RAP leading the way on this journey. I will continue to share these discoveries with the community.

Resources

Source code Paper for Reasoning via Planning (RAP)

GitHub for RAP

AI, LLM, Technology

GPT4 LLAMA 2 and Claude 2 – by Design

A Large Language Model (LLM) is a computer program that has been extensively trained using a vast amount of written content from various sources such as the internet, books, and articles. Through this training, the LLM has developed an understanding of language closely resembling our comprehension.

LLM can generate text that mimics writing styles. It can also respond to your questions, translate text between languages, assist in completing writing tasks, and summarize passages.

The design of these models has acquired the ability not to recognize words within a sentence but also to grasp their underlying meanings. They comprehend the context and relationships among words and phrases, producing accurate and relevant responses.

LLMs have undergone training on millions or even billions of sentences. This extensive knowledge enables them to identify patterns and associations that may go unnoticed by humans.

Let’s take a closer look at a few models:

Llama 2

Picture a multilingual language expert that can fluently speak over 200 languages. That’s Llama 2! It’s the upgraded version of Llama jointly developed by Meta and Microsoft. Llama 2 excels at breaking down barriers enabling effortless communication across nations and cultures. This model is ideal for both research purposes and businesses alike. Soon you can access it through the Microsoft Azure platform catalog as Amazon SageMaker.

The Lifelong Learning Machines (LLAMA) project’s second phase, LLAMA 2, introduced advancements:

  • Enhanced ability for continual learning; Expanding on the techniques employed in LLAMA 1, the systems in LLAMA 2 could learn continuously from diverse datasets for longer durations without forgetting previously acquired knowledge.
  • Integration of symbolic knowledge; Apart from learning from data, LLAMA 2 systems could incorporate explicit symbolic knowledge to complement their learning, including utilizing knowledge graphs, rules, and relational information.
  • The design of LLAMA 2 systems embraced a modular and flexible structure that allowed different components to be combined according to specific requirements. By design, LLAMA 2 enabled customization for applications.
  • The systems exhibited enhanced capability to simultaneously learn multiple abilities and skills through multi-task training within the modular architecture.
  • LLAMA 2 systems could effectively apply acquired knowledge to new situations by adapting more flexibly from diverse datasets. Their continual learning process resulted in generalization abilities.
  • Through multi-task learning, LLAMA 2 systems demonstrated capabilities such as conversational question answering, language modeling, image captioning, and more.

GPT 4

GPT 4 stands out as the most advanced version of the GPT series. Unlike its predecessor, GPT 3.5, this model excels at handling text and image inputs. Let’s consider some of its attributes.

Parameters

Parameters dictate how a neural network processes input data and produces output data. They are acquired through training. Encapsulate the knowledge and abilities of the model. As the number of parameters increases, so does the complexity and expressiveness of the model, enabling it to handle amounts of data.

  • Versatile Handling of Multimodal Data: Unlike its previous version, GPT 4 can process text and images as input while generating text as output. This versatility empowers it to handle diverse and challenging tasks such as describing images, answering questions with diagrams, and creating imaginative content.
  •  Addressing Complex Tasks: With a trillion parameters, GPT 4 demonstrates problem-solving abilities. Possesses extensive general knowledge. It can achieve accuracy in demanding tasks like simulated bar exams and creative writing challenges with constraints.
  • Generating Coherent Text: GPT 4 generates coherent and contextually relevant texts. The vast number of parameters allows it to consider a context window of 32,768 tokens, significantly improving the coherence and relevance of its generated outputs.
  • Human-Like Intelligence: GPT 4s, creativity, and collaboration capabilities are astonishing. It can compose songs, write screenplays and adapt to users writing styles. Moreover, it can. Follow nuanced instructions provided in a language, such as altering the tone of voice or adjusting the output format.

Common Challenges with LLM 

  • High Computing Costs: Training and operating a model with such an enormous number of parameters requires resources. OpenAI has invested in a designed supercomputer tailored to handle this workload, estimated to cost around $10 billion.
  •  Extended Training Time: The process of training GPT 4 takes time, although the exact duration has not been disclosed. However OpenAIs ability to accurately predict training performance indicates that they have put effort into optimizing this process.
  •  Alignment with Human Values: Ensuring that GPT 4 aligns with values and expectations is an undertaking. While it possesses capabilities, there is still room for improvement. OpenAI actively seeks feedback from experts and users to refine the model’s behavior and reduce the occurrence of inaccurate outputs.

GPT has expanded the horizons of machine learning by demonstrating the power of learning. This approach enables the model to learn from data and tackle new tasks without extensive retraining.

Claude 2

What sets this model apart is its focus on intelligence. Claude 2 not only comprehends emotions but also mirrors them, making interactions with AI feel more natural and human-like.

Let’s consider some of the features:

  • It can handle, up to 100,000 tokens, analyzing research papers, or extracting data from extensive datasets. The fact that Claude 2 can efficiently handle amounts of text sets it apart from many other chatbot systems available.
  • Emotional intelligence enables it to recognize emotions within the text and effectively gauge your state during conversations. 
  • Potential to improve health support and customer service. Claude 2 could assist in follow-ups and address non-critical questions regarding care or treatment plans. It can understand emotions and respond in a personal and meaningful way.
  • Versatility. Claude 2’s versatility enables processing text from various sources, making it valuable in academia, journalism, and research. Its ability to handle complex information and make informed judgments enhances its applicability in content curation and data analysis.

Both Claude 2 and ChatGPT employ intelligence. They have distinct areas of expertise. Claude 2 specializes in text processing and making judgments, while ChatGPT focuses on tasks. The decision to choose between these two chatbots depends on the needs of the job you have at hand.

Large Language Models have become tools in Artificial Intelligence. LLAMA 2 has enhanced lifelong learning capabilities. The ongoing development of GPT 4 continues to be at the forefront of natural language processing due to the parameter size that enables it. Claude 2’s launch signifies the ongoing evolution of AI chatbots, aiming for safer and more accountable AI technology.

These models have been designed to demonstrate how AI systems can gather information and enhance intelligence through learning. LLMs are revolutionizing our interactions with computers. Transforming how we use language in areas of our lives.