Tag Archives: #ai

AI, LLM, Technology

A Model for Mental Health Using LLM

We are currently facing one of the largest mental health crises of this century. The pressures and challenges of living day to day exceed the current mental health resources that are available and accessible for most. Data has a substantial role within healthcare, especially when it comes to developing treatment plans for individuals facing mental health challenges. Technologists are looking at new ways to provide resources by securely utilizing this foundational data in this arena.

Large language models (LLMs) have made strides in the healthcare domain by leveraging their capacity to analyze text data effectively. By employing natural language processing (NLP) techniques, LLMs can sift through sources such as records, therapy transcripts, and social media content to unveil patterns and connections pertaining to mental health issues. Such capabilities enable them to uncover insights that traditional methods may have missed, thereby offering avenues for comprehending and predicting health outcomes. This type of data processing and retrieval may prompt individuals to reconsider what is shared on social media channels.

Nevertheless, it is crucial to recognize that LLMs have limitations, including biases in their training data and challenges in interpreting emotions accurately. There is a greater risk for underrepresented communities where limited resources are available; the training data will also be limited. Mental health is not a one-size-fits-all issue. By examining language patterns associated with conditions like anxiety, depression, and other mental disorders, LLMs can support healthcare professionals in identifying problems and providing interventions that could potentially enhance outcomes while also contributing to destigmatizing health matters.

Advanced Language Models (ALMs) have also revolutionized the industry by processing and generating text that mirrors human language patterns using extensive datasets. One area where ALMs have demonstrated promise is healthcare. Analyzing large amounts of text data allows Advanced Language Models (ALMs) to uncover patterns, predict outcomes, and offer insights that were previously hard to obtain. Let’s explore how ALMs are transforming our understanding of the healthcare sector:

Patient Data Review
ALMs can examine records, therapy notes, and patient communications to identify symptoms, track progress, and suggest treatments. This data interpretation capability helps mental health professionals better understand conditions.

Diagnosis
By analyzing interactions, social media posts, and other digital footprints, ALMs can spot signs of health issues. This timely detection can facilitate interventions. Enhance patient outcomes.

Personalized Treatment Plans
ALMs can help develop tailored treatment plans by analyzing data and comparing it with databases containing similar cases. This personalized approach holds promise for improving treatment effectiveness.

Combatting Stigma
Language Learning Models (LLMs) can help reduce the stigma surrounding seeking health support by providing assistance through chatbots and virtual assistants.

Company Insights
Let’s consider two companies that are trying to address this shortage with a technical solution. Woebot Health is utilizing LLMs for healthcare support. They have introduced an agent called Woebot that delivers therapy (CBT) through interactive chat sessions. Woebot uses language models to understand and communicate with users, facilitating conversations that help in managing symptoms of depression and anxiety.

Features

  • Live Chats. Engages with users in a timely manner, providing support and interventions.

  • Personalized Guidance. Offers advice and coping mechanisms through analyzing user interactions.
  • Accessibility. 24/7 availability model, assistance is always at hand.

Wysa
Has a healthcare app powered by AI featuring a coach who guides users through evidence-based practices. The Wysa chatbot allows users to express their emotions and receive guidance on handling stress, anxiety, depression, and other mental health issues. It tailors activities to suit the individual’s needs.

Features

  • AI Coaching. Leveraging advanced language models to create user profiles that guide them through cognitive behavioral therapy (CBT), mindfulness exercises, and other therapeutic activities.
  • Data Security: The app encrypts and securely stores data in AWS Cloud and MongoDB Atlas.

Utilizing large language models will allow these companies and others to analyze various sources such as records, scientific papers, and social media content datasets. This presents an opportunity for health research by uncovering correlations and treatment approaches that may pave the way for future advancements in mental health care. Let’s consider the following data points:

Predictive Analysis

Language models have the ability to predict health trends and outcomes using data, which helps in planning and allocating resources for healthcare services.

Understanding Natural Language

By understanding the language used during therapy sessions, language models can assess the effectiveness of methods and provide suggestions for improvement.

Ethical Considerations

Although language models hold potential in healthcare, there are challenges and ethical considerations that need to be considered. Safeguarding the privacy and security of health information is essential to maintain user trust and confidentiality. Addressing any biases in the training data used for language models is a step towards ensuring unbiased support in mental health contexts. Prioritizing fairness reassures audiences about considerations in health support. It’s important to validate and update advice and insights from language models regularly to uphold their accuracy and reliability. This validation process plays a role in building confidence in the reliability of health support. While language models can serve as tools, they should complement rather than replace mental health professionals, enhancing human judgment instead of replacing it.

Future Directions

Incorporating tools based on language models into existing healthcare systems can streamline decision-making processes, with AI serving as an element within treatment procedures. Advancements in LLM technology could bring about health support tailored to individual backgrounds, preferences, and responses to previous treatments. By analyzing text and data sources like speech patterns and facial expressions, a comprehensive picture of a person’s well-being can be obtained.

Companies such as Woebot Health and Wysa are at the forefront of using AI to provide customized health guidance. This underscores a sense of duty and dedication towards their use to ensure that these technologies can fulfill their potential. Despite facing obstacles, the potential benefits of LLMs in understanding and treating health conditions are significant. As advancements continue, we can look forward to solutions that enhance our grasp on supporting mental well-being.

Ethical considerations surrounding LLMs in healthcare are crucial. Prioritizing data privacy and addressing any present biases is essential. The responsible use of AI-generated insights is key to implementing these technologies. Despite challenges, the profound transformative impact of LLMs on healthcare opens up avenues for diagnosing, treating, and supporting individuals dealing with health issues in the future.

AI, Tech Fix, Technology

Learning Through Reverse Engineering: Guided by AI

Learning is an evolving journey in the changing realm of technology. While traditional education holds significance, exploring approaches can deepen our understanding of technologies. One effective method is engineering, which not only unveils the inner workings of a product but also lays the groundwork for expanding our skills. Combining engineering and artificial intelligence (AI) can revolutionize your learning experience.

The Basis of Learning

Similar to mathematics, technology relies on a knowledge base. Just as mathematical principles build upon each other, technology requires skills that can be honed and broadened. To venture into mastering technologies, it’s crucial to acknowledge that emerging products or services often come with a model and comprehensive documentation – providing a valuable starting point for our exploration.

The Process of Reverse Engineering

At the core of engineering lies dissecting an existing product or service to grasp its mechanisms. For instance, when presented with a model like a software application or service, the initial step involves replicating it and running the code as is. However, the real magic unfolds when deliberate alterations are made.

Introducing changes and observing their effects gives you insights into how the system operates.
I’ve named this technique the Two Clicks Rule, which helps you grasp the cause-and-effect dynamics in technology. If something goes left during your learning process, you’re a step away from reverting to the original working model. I’ll delve deeper into this concept in a future blog post.

Documenting Progress

An essential aspect of engineering is documentation. Document your experiences – note the modifications made, errors encountered, and solutions applied. This record guides your learning journey, allowing you to retrace your steps if needed and serving as a record of your knowledge.

Discoveries with AI

As technology progresses, so does the involvement of AI in enriching the learning process. Integrating AI tools into engineering can expedite the understanding of systems. AI can detect patterns, recognize dependencies, and propose enhancements or optimizations. The collaboration between intelligence and artificial intelligence can enhance the effectiveness of engineering as a learning approach.

Reverse engineering isn’t about breaking things but digging into layers and grasping underlying complexities. By harnessing AI alongside this method, you can accelerate your learning journey. Gain insights into emerging technologies.

When you come across a product, consider experimenting, analyzing, and embracing AI as your tech buddy in exploring knowledge.

AI, Technology

Rest and Repeat AI for 2024

The winter season brings a sense of slowing down, if only for a moment. However, this doesn’t apply to AI. AI continues to captivate us with its capabilities and the potential for collaboration. With each innovation, I’ve contemplated the challenges and opportunities it presents to humans. After researching, I’ve gathered a few highlights from 2023 that are still relevant today.

Open-source AI development reshaped the landscape of AI frameworks and models. The introduction of PyTorch 2.0 did not establish an industry standard. Also equipped researchers and developers with powerful tools. Ongoing enhancements to Nvidias Modulus and Colossal AIs’ PyTorch-based framework have further enriched the open-source ecosystem, fostering innovation.

AI models have revolutionized content generation. Redefined natural language processing as we know it. OpenAIs GPT 4 a language model at the forefront of this transformation has pushed the boundaries of AI capabilities. GPT 4 showcases its proficiency in text-based writing, coding, and complex problem-solving applications. Additionally, Jina AI’s 8K Text Embedding Model and Mistral AI’s Mistral 7B exemplify the growing expertise within the AI community regarding handling amounts of data.

There is a trend towards increased collaboration between AI systems and humans to achieve desired outcomes. This highlights the importance of utilizing AI to enhance capabilities and improve efficiency and effectiveness.

AI has made progress by introducing code-based and no-code solutions. These advancements have made AI more accessible to individuals without expertise, promoting inclusivity and diversity within the AI community. There is still work to be done in this space.

Cybersecurity solutions leveraging AI technology have been developed to address the growing threat of cyberattacks. These solutions provide defense mechanisms against evolving cyber threats, bolstering security measures. This topic is on my shortlist. Stay tuned.

Digital twinning has become a tool for simulating real-world situations and improving processes. It allows businesses and industries to create replicas that assist decision-making and boost efficiency. This technology leverages machine learning algorithms to analyze sensor data and identify patterns. Through artificial intelligence and machine learning (AI/ML), organizations gain insights to enhance performance, streamline maintenance operations, measure emissions, and improve efficiency.

AI-driven personalization has gained traction across domains. Systems can now tailor products and services to users, offering a customized experience that aligns with their preferences. Customizations are always a plus in my book. This personalized approach has significantly improved user experiences in e-commerce and entertainment domains.
The use of AI in voice technology has constantly changed, leading to the development of voice assistants. This advancement has improved voice recognition, language comprehension, and interaction between users and AI-driven voice systems. I have taken advantage of this by scripting my presentation when I lost my voice and had it delivered through AI.

AI has also made progress within the healthcare industry. It is now utilized for disease diagnosis and treatment development. Integrating AI into healthcare showcases its potential to transform care and medical research. The bias in medical data is a real concern, so how this data is used may put certain communities at risk. This is a space I am following very closely.

AI continues to challenge the boundaries of creatives, but the community is strong. It will be interesting to see how AI will begin acknowledging and accepting that creatives are here to stay. Creatives are also acknowledging the same for AI.

In the coming years, expect a rise in similar services and products. Instead of viewing this repetition as a drawback, it should be embraced as an advantage. The increasing array of AI options indicates a dynamic ecosystem that provides opportunities and choices for developers, businesses, and users. This wealth of options fosters competition fuel innovation and empowers individuals to customize AI solutions according to their requirements. As the AI landscape continues to evolve, the presence of repeated services and products validates the growth of this field. It offers us endless possibilities that contribute significantly to the evolution and accessibility of artificial intelligence.

I appreciate you reading my blog and look forward to sharing more in this space.

AI, LLM, Technology

PyGraft – A Python-Based AI Tool for Generating Knowledge Graphs

Visualization

Telling a story with data is an effective way to share information. I’ve spent years developing data stories with standard reporting tools as the only option. Visualizing information has become incredibly important today, especially when representing and analyzing relationships between entities and concepts. Knowledge graphs (KGs) capture these relationships through triples (s, p, o) where s represents the subject, ‘o’ represents the object, and ‘p’ defines their relationship. KGs are often accompanied by schemas or ontologies defining the data’s structure and meaning. They have proven valuable in recommendation systems and natural language understanding.

Challenges Associated with Mainstream KGs

While knowledge graphs are tools, there are limitations when relying solely on mainstream knowledge graphs for evaluating AI models. These mainstream KGs often share properties that can lead to evaluations, particularly in tasks like node categorization. Additionally, some datasets used for link prediction may contain biases and inference patterns that can result in assessments of models.

Furthermore, in fields like education, law enforcement, and healthcare, where data privacy is a concern, publicly accessible knowledge graphs are not always readily available. Researchers and practitioners in these domains often have requirements for their knowledge graphs. It is crucial to create graphs replicating real-world graphs’ characteristics.
To tackle these challenges, a group of researchers from Université de Lorraine and Université Côte d’Azur has developed PyGraft, a Python-based AI tool that’s the source. PyGraft aims to generate customized schemas and knowledge graphs that apply to domains.

Contributions to this space:

  • Pipeline for Knowledge Graph Generation;
    PyGraft introduces a pipeline for generating schemas and knowledge graphs, allowing researchers and practitioners to customize the generated resources according to their requirements. This ensures flexibility and adaptability.
  • Domain Neutral Resources;
    One remarkable feature of PyGraft is its ability to create domain schemas and knowledge graphs. This means the generated resources can be used for benchmarking and experimentation across fields and applications. It eliminates the necessity for domain KGs, making it an invaluable tool for domain research.
  • Expanded Range of RDFS and OWL Elements;
    PyGraft uses RDF Schema (RDFS) and Web Ontology Language (OWL) elements to construct knowledge graphs with semantics. This technology allows resource descriptions while adhering to accepted standards of the Semantic Web.
  • Ensuring Logical Coherence through DL Reasoning;
  • The tool uses a reasoning system based on Description Logic (DL) to ensure that the resulting schemas and knowledge graphs are coherent. This process guarantees that the generated knowledge graphs follow the principles of ontology.

Accessibility in a Tool

PyGraft is an open-source project with available code and documentation. It also includes examples to make it user-friendly for beginners and experienced users.

PyGraft is a Python library that researchers and practitioners can use to generate schemas and knowledge graphs (KGs) according to their requirements. It enables the creation of schemas and KGs, on demand with knowledge of the desired specifications. The resources generated are not tied to any application field, making PyGraft a valuable tool for data-limited research domains.

Features:

  • It can generate schemas, knowledge graphs, or both.
  • The generation process is highly customizable through user-defined parameters.
  • Schemas and KGs are constructed using a range of RDFS and OWL constructs.
  • Logical consistency is guaranteed by employing a DL reasoner called HermiT.
  • A generator of synthesizing both schemas and knowledge graphs using a single pipeline.
  • Creates generated schemas and KGs(Knowledge Graphs) with a set of RDFS(Resource Description Framework Schema) and OWL (W3C Web Ontology Language) constructs, ensuring compliance with used Semantic Web standards.


PyGraft is an advancement in the field of knowledge graph generation. It overcomes the limitations of mainstream KGs by offering a customizable solution for researchers, practitioners, and engineers.

PyGraft enables users to create KGs that accurately reflect real-world data by adopting a domain approach and adhering to Semantic Web standards.

Pygraft bridges the gap between data privacy and the need for high-quality knowledge graphs.

The beauty of this open-source tool, is that it encourages collaboration and innovation within the AI and Semantic Web communities, opening up possibilities for knowledge representation and reasoning. This type of technical collaboration is priceless.

Resources:

https://pygraft.readthedocs.io/en/latest/

https://github.com/nicolas-hbt/pygraft

AI, Technology

Human Engineering in AI

Engineers are tasked with comprehending the layers of artificial intelligence (AI), including its strengths and limitations. Engineering plays a role in the development of AI as it is indispensable in harnessing its power. However, it’s essential to acknowledge and respect AI’s boundaries. Let’s consider some of AI’s characteristics, capabilities, and limitations that we are aware of today.

The effectiveness of AI models heavily relies on the data they are trained on. This reliance on training data can introduce biases or limitations in that data itself. Engineers need to be aware of these biases and actively work towards addressing them through representative training data—a practice commonly referred to as Responsible AI.

It is crucial to remember that AI models lack emotions, intentions, or subjective experiences as humans do. Their operations are based on algorithms and logical rules, requiring engineers’ understanding and firsthand knowledge. Therefore caution should be exercised when interpreting AI-generated content since bias can inadvertently seep into its output.

Despite its capabilities, an AI model cannot truly engage in cognition or attain consciousness comparable to human beings. It has the capability to process and analyze data generate responses, and imitate human behavior. However, it operates based on predefined algorithms and statistical patterns than possessing human qualities. I want to emphasize that it is not a being. The AI model lacks experiences, emotions, and the ability to be conscious like humans. Instead, its functionality relies on processes rather than humans’ complex cognitive abilities.

No matter how large or intricate the AI model is, it may be unable to have conversations or engage in self-reflection. While it can process input and generate responses accordingly, its system has no mechanism for introspection or self-awareness. Its primary focus is interacting with users or external systems by utilizing its knowledge and adaptive methods to provide insights and responses. Let’s consider the importance and necessity for Humans in AI.

The roles of Humans in the field of AI:

  1. Data Collection and Annotation; The process of training AI systems heavily relies on amounts of data. Humans are instrumental in collecting, cleaning, and annotating this data to ensure its quality and relevance. They meticulously label the data verify its accuracy and strive to create representative datasets for training AI models.
  2. Model Training and Tuning; Developing AI models requires making decisions regarding architecture design, selecting hyperparameters, and training the models using suitable datasets. Human expertise is indispensable in making these decisions. Their intuition and domain knowledge contribute significantly to tuning models for tasks.
  3. Ethical and Moral Considerations; Given the impact of AI systems. Both positive and negative. Humans are responsible for ensuring ethical development and using AI technology. Upholding values such as bias mitigation, fairness, transparency, and privacy requires judgment.
  4. Interpreting and Understanding AI Outputs; AI models can generate outputs that may sometimes be unexpected or difficult to comprehend. Human interpretation is essential to grasp these healthcare, finance, or law outputs. Humans provide insights into understanding the implications of AI-generated results.

Human oversight plays a role in preventing complete reliance on AI systems and mitigating potential harmful consequences.

  1. Adaptability to Changing Situations; AI systems often struggle to adapt when confronted with situations that differ from their training data. Humans can quickly adapt to scenarios, exercise common sense judgments and respond flexibly to novel situations that might be challenging for AI.
  2. Approach to Problem-Solving; While AI excels at pattern recognition and optimization, human creativity remains unparalleled. Creative problem-solving, thinking, and the ability to think “outside the box” are areas where human intelligence truly shines and complements the capabilities of AI.
  3. Development and Enhancement of AI Models; humans are responsible for designing and developing AI models. The evolution of AI algorithms and architectures relies heavily on ingenuity to create advanced and efficient models.
  4. Human AI Collaboration; than aiming for replacement, the goal of AI is often focused on augmenting abilities. Collaborative efforts between humans and AI can lead to effective outcomes. Humans provide overarching guidance while leveraging AI’s capability to handle data-intensive tasks.
  5. Navigating Ambiguity and Uncertainty; Many real-world situations involve ambiguity and uncertainty. Humans are more adept at handling situations as they rely on their intuition and experience to navigate ambiguous scenarios.
  6. Ensuring Safety and Control; humans must lead in establishing safeguards and mechanisms that guarantee the operation of AI systems within defined parameters. This involves implementing tools and incorporating human oversight for critical decision-making.

Human involvement in AI remains indispensable due to its abilities, ethical considerations, adaptability, creativity, and aptitude for intricate decision-making. While AI technologies continue to advance, humans provide the supervision and guidance to ensure that AI is developed and deployed in ways that benefit society.

As engineers understand AI’s capabilities and limitations, it becomes essential to harness its power. AI models process amounts of data. Rely on engineers’ assistance in integrating safeguards. However, human intervention is necessary to foster cognition, internal dialogue, and the generation of original ideas. Engineers acknowledge that AI models lack emotions, intentions, or subjective experiences. Therefore they must make decisions. Responsibly utilize the potential in their respective fields. The engineers’ role is pivotal. Contributes significantly to the development of AI.

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

Technology

What is a Focused Transformer (FOT)?

Language models have witnessed remarkable advancements in various fields, empowering researchers to tackle complex problems with text-based data. However, a significant challenge in these models is effectively incorporating extensive new knowledge while maintaining performance. The conventional fine-tuning approach is resource-intensive, complex, and sometimes short of fully integrating new information. To overcome these limitations, researchers have introduced a promising alternative called Focused Transformer (FOT), which aims to extend the context length in language models while addressing the distraction issue.

Fine-Tuned OpenLLaMA Models

An exemplary manifestation of FOT in action is the fine-tuned OpenLLaMA models known as LONGLLAMAs. Designed explicitly for tasks that require extensive context modeling, such as passkey retrieval, LONGLLAMAs excel where traditional models falter. What once presented challenges is now efficiently handled by these powerful language models.

An Obstacle to Context Length Scaling

As the number of documents increases, the relevance of tokens within a specific context diminishes. This leads to overlapping keys related to irrelevant and relevant values, creating the distraction issue. The distraction issue poses a challenge when attempting to scale up context length in Transformer models, potentially hindering the performance of language models in various applications.

Training FOT Models

The process of training FOT models is a game-changer in itself. Inspired by contrastive learning, FOT enhances the sensitivity of language models to structural patterns. Teaching the model to distinguish between keys associated with different value structures improves their understanding of language structure and results in a more robust language model.

Extending Context Length in FOT

The Focused Transformer (FOT) technique breaks through the obstacle by effectively extending the context length of language models. By allowing a subset of attention layers to access an external memory of (key, value) pairs using the k-nearest neighbors (kNN) algorithm, FOT enhances the model’s ability to maintain relevance and filter out irrelevant information within a broader context.

Unveiling Focused Transformers (FOT)

The Focused Transformer method emerges as an innovative solution to the distraction dilemma. By extending the context length of language models, FOT directly targets the root cause of distraction. The magic of FOT lies in its mechanism that enables a portion of attention layers to access an external memory of (key, value) pairs, leveraging the power of the kNN (k-Nearest Neighbors) algorithm.

Contrastive Learning for Improved Structure

The training procedure of Focused Transformer is inspired by contrastive learning. During training, the memory attention layers are exposed to relevant and irrelevant keys, simulating negative samples from unrelated documents. This approach encourages the model to differentiate between keys connected to semantically diverse values, enhancing the overall structure of the language model.

Augmenting Existing Models with FOT

To demonstrate the effectiveness of FOT, researchers introduce LONGLLAMAs, which are fine-tuned OpenLLaMA models equipped with the Focused Transformer. Notably, this technique eliminates the need for long context during training and allows the application of FOT to existing models. LONGLLAMAs exhibit significant improvements in tasks that require long-context modeling, such as passkey retrieval, showcasing the power of extending the context length in language models.

Research Contributions

Several notable research contributions have paved the way for FOT’s success. Overcoming the distraction dilemma, the development of the FOT model, and the implementation techniques integrated into existing models are milestones achieved by these brilliant minds. The result of these contributions, epitomized by LONGLLAMAs, has revolutionized tasks reliant on extensive context.

Contributions of Focused Transformer (FOT) are threefold:

  • Identifying the Distraction Issue: FOT highlights the distraction issue as a significant obstacle to scaling up context length in Transformer models.
  • Addressing the Distraction Issue: FOT introduces a novel mechanism to address the distraction issue, allowing context length extension in language models.
  • Simple Implementation Method: FOT provides a cost-effective and straightforward implementation method that augments existing models with memory without modifying their architecture.

The resulting models, LONGLLAMAs, are tested across various datasets and model sizes, consistently demonstrating improvements in perplexity over baselines in long-context language modeling tasks. This validates the effectiveness of FOT in boosting language model performance for tasks that benefit from increased context length.

The Focused Transformer (FOT) technique presents an innovative solution to address distraction and extend context length in language models. By training the model to differentiate between relevant and irrelevant keys, FOT enhances the overall structure and significantly improves long-context modeling tasks. The ability to apply FOT to existing models without architectural modifications makes it a cost-effective and practical approach to augment language models with memory. As language models continue to evolve, FOT holds the potential to unlock new possibilities and push the boundaries of text-based AI applications.

GitHub Resource Find: LONGLLAMA

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.

Technology

From Digital Divide to AI Gap

Will AI decrease the Digital Divide or create a larger gap in access to Technology?

What is the Digital Divide? It is the gap between individuals, communities, or countries regarding accessing and using information and communication technologies such as computers, the Internet, and mobile devices. This divide includes disparities in physical access to technology infrastructure and the skills, knowledge, and resources needed to use these technologies effectively.

The term “digital divide” was first used in the mid-1990s to describe unequal technological access and its potential consequences. It emerged during the early stages of the Internet’s tech bubble as policymakers and scholars recognized disparities in technology adoption and their implications for social and economic development. Since its inception. The digital divide has expanded to include various dimensions encompassing hardware accessibility, internet connectivity, digital literacy, affordability, relevant content, and services. This divide can exist at different levels: globally between countries, nationally within a country, and even individuals within communities or households.

Artificial intelligence (AI) and related technologies have brought about transformative changes in our lives. With applications ranging from healthcare to transportation AI is revolutionizing industries and enhancing efficiency. However, acknowledging that not everyone has equal access to these technologies is crucial. Consider the growing concern regarding unrepresented communities lacking access to AI and related technologies. Without responsible AI initiatives, this technology gap will inevitably widen existing inequalities while impeding progress toward a more inclusive future. The technology gap refers to differences in access to and adoption of technology among various societal groups.

Historically marginalized communities such as low-income households, racial and ethnic minorities, women, and rural populations face significant barriers when accessing emerging technologies like AI. These communities often require greater infrastructure support and educational resources to participate fully in the AI revolution.

One of the primary reasons behind this technology gap is the economic disparity prevalent within society. The development, implementation, and maintenance of AI technologies can be expensive; costs disproportionately burden unrepresented communities with limited financial resources. The high price of AI software, hardware, and specialized training hinders their ability to embrace these technologies and reap their potential benefits.

Access to AI is closely linked with education and awareness; unfortunately, many communities that are not adequately represented lack the necessary understanding of AI and its potential applications. Limited access to quality education and training further hampers their ability to participate in the AI revolution. As a result. These communities are unable to reap the benefits of AI. Missing out on economic opportunities and advancements. Another critical aspect of the AI divide is the biases embedded within the technology.

AI systems can only be as unbiased as the data they are trained on. Historically marginalized communities have been underrepresented in data sets, leading to biased algorithms perpetuating discriminatory outcomes. This bias further deepens social inequalities and hinders fair access to AI-driven services and opportunities.
The consequences of this technology gap are far-reaching for individuals and society. We risk perpetuating and exacerbating existing inequalities by failing to address this issue. Economic disparities worsen due to the technology gap. Creating a cycle of limited opportunities for unrepresented communities.

Access to AI and related technologies is crucial for these communities as it provides them with reduced access to job opportunities, higher wages, and economic growth. The resulting inequality ultimately impedes social mobility and perpetuates poverty.
AI technologies impact various aspects, such as healthcare, criminal justice, and education. Without adequate representation or responsible practices in place. Unrepresented communities face biased outcomes that reinforce social injustice. Biased algorithms can lead to disparities in healthcare access or contribute to racial profiling within criminal justice systems.
Excluding diverse voices and perspectives from the development and application of AI means missing out on its immense innovation potential. Lack of representation hinders the creation of technologies that cater specifically to different community needs. Consequently. Society misses out on reaping the full range of benefits that AI can bring, impeding progress. Responsible AI practices and initiatives must be implemented to bridge this technology gap and ensure equal access for all communities.

Several steps can be taken to address the issue at hand. Firstly it is imperative to promote inclusivity within the AI industry. This can be achieved by increasing diversity and representation among professionals in this field. One way to accomplish this task is to encourage individuals from underrepresented communities to pursue AI-related careers through educational programs. It is essential to recognize that diverse perspectives are vital in building unbiased and inclusive AI systems.

Another crucial aspect of addressing this matter involves ethical considerations in AI development. Developers and researchers should give precedence to responsible AI development practices. This includes taking action to address bias in data sets. Promoting transparency and explainability of algorithms. As well as involving community stakeholders in decision-making processes. These ethical considerations should be integrated throughout the entire life cycle of AI development.

Furthermore, efforts must be made to provide accessible training and resources for individuals from underrepresented communities. This may involve forming partnerships with educational institutions, non-profit organizations, and government initiatives that offer scholarships and mentorship programs. As well as workshops focused on AI literacy and skills development. By making training programs more affordable and accessible. We can ensure everyone has an equal opportunity to benefit from AI technology.

Additionally, policy and regulation play a crucial role in ensuring equitable access to AI technologies. Governments and policymakers are responsible for implementing policies that address bias, protect privacy rights and ensure fair distribution of benefits derived from AI systems. Legislation should also be enacted to prevent discriminatory use of AI while promoting transparency and accountability. In doing so, we can bridge the technology gap between different communities and work towards a future where everyone has equal access to the benefits of AI.

It is essential to acknowledge that unrepresented communities face significant barriers when embracing AI’s transformative power due to their already marginalized status. Therefore. By promoting inclusivity through diversity efforts. Ethical considerations drive responsible development practices. As well as accessible resources such as affordable training and partnerships. We can bridge the technology gap and create a society where AI is a tool for empowerment and societal progress.

Ultimately it is our collective responsibility to strive toward a future where AI is accessible, unbiased, and beneficial for all individuals.

Technology

Data with Document AI and Snowflake

The star behind AI is the data, an excellent incentive for me to take a closer look. Snowflake recently shared an announcement at a recent Annual Summit Conference. So when I come across anything or any company in this space that speaks to it, I must share that discovery here. 

Data management is at the heart of every organization’s success. Document processing is a core aspect of many business operations, from finance and legal to human resources and customer service. Document AI, (based on a recent acquisition) powered by artificial intelligence brings automation and efficiency to this process, allowing organizations to extract valuable data from documents accurately and quickly. Snowflake, a cloud-based data platform, stands out as a game-changer. 

Snowflake enables businesses to store, analyze, and share massive amounts of data seamlessly and securely. Its ability to scale on demand, coupled with its high performance, makes it an ideal choice for enterprises of all.

Managing and processing vast amounts of data and documents is always a challenge. Enter Snowflake, and Document AI, offering cutting-edge solutions to revolutionize data management and document processing. In this blog, we explore the incredible capabilities of these technologies and how they can enhance productivity and drive business growth.

Document processing is a core aspect of many business operations, from finance and legal to human resources and customer service. Document AI, powered by artificial intelligence, brings automation and efficiency to this process, allowing organizations to extract valuable data from documents accurately and quickly.

Let’s consider a few of the highlights:

Security

  • Secure and compliant: Snowflake provides robust security measures and ensures compliance with industry regulations, giving you peace of mind.

Shared Data

  • Snowflake’s data-sharing capabilities enable seamless collaboration with partners, customers, and suppliers, empowering data-driven decision-making across the ecosystem.

Advanced analytics and AI integration

  • Snowflake integrates seamlessly with advanced analytics and AI tools, creating valuable insights to make data-driven decisions quickly.

Machine Learning

  • Document AI can leverage machine learning algorithms to extract critical information from various document types, saving time and reducing human error.

Accuracy

  •  Document AI ensures consistent and accurate results by automating document processing, improving operational efficiency, and reducing manual effort.

Data Validation

  • Document AI can cross-reference extracted data with existing databases, enabling businesses to validate the information and ensure data integrity.

Integration

  • Document AI integrates seamlessly with existing systems and workflows, making it easy to implement and adopt within your organization.

Integrating Snowflake and Document AI creates a powerful synergy that enables organizations to streamline their data management and document processing workflows. Snowflake’s scalability and performance facilitate the storage and retrieval of documents, while Document AI automates the extraction of valuable data from these documents, enhancing productivity and accuracy.

Leveraging advanced technologies like Snowflake and Document AI will be crucial for businesses aiming to stay competitive. By harnessing the power of Snowflake’s data management capabilities and Document AI’s intelligent document processing, organizations can unlock new levels of efficiency, accuracy, and productivity. 

The future is data-driven.

If you want to dive deep, excellent resources from Google are available for Document AI.

Currently in private preview for Snowflake customers. I am very interested in seeing this when it goes public.

Snowflake continues forward-moving with recent data-centric acquisitions and partnerships to enhance its AI efforts in deep learning.

Resources:

GitHub repositories for Document AI

Document AI Workbench Google

Google Jupyter Notebook for Document AI