Tag Archives: data

AI, Technology

It’s a Family Affair with Claude 3

Anthropic announced the Claude 3 model family last month, which sets new industry benchmarks across various cognitive tasks. I am always excited to see what comes from Anthropic, so I was eager to see this group arrive.

The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each successive model offers increasingly powerful performance, allowing users to select the optimal balance of intelligence, speed, and cost for their specific application.

Opus, Haiku, and Sonnet are now available in claude.ai, and the Claude API is generally available in 159 countries. All Claude 3 models show increased capabilities in analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages like Spanish, Japanese, and French.

Let’s take a look at each member of the Claude 3 family:

Opus

Opus is considered the most intelligent model. It outperforms its peers on most of the standard evaluation benchmarks for AI systems, including undergraduate-level expert knowledge (MMLU), graduate-level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits near-human comprehension and fluency levels on complex tasks, leading the frontier of general intelligence. It can navigate open-ended prompts and sight-unseen scenarios with remarkable fluency and human-like understanding. Opus shows us the outer limits of what’s possible with generative AI.

Haiku

Claude 3 Haiku is the fastest, most compact model for near-instant responsiveness. With state-of-the-art vision capabilities, it caters to various enterprise applications, excelling in analyzing large volumes of documents. Its affordability, security features, and availability on platforms like Amazon Bedrock and Google Cloud Vertex AI make it transformative for developers and users alike.

Sonnet

Sonnet balances intelligence, speed, and cost, making it well-suited for various applications. Notably, it is approximately twice as fast as its predecessor, Claude 2.1. Sonnet excels in tasks requiring rapid responses, such as knowledge retrieval and sales automation. Additionally, it demonstrates a unique understanding of requests and is significantly less likely to refuse answers that push system boundaries. With sophisticated vision capabilities, including the ability to process visual formats like photos, charts, and technical diagrams, Claude 3 Sonnet represents a significant advancement in AI language models.

Let’s Talk Capabilities

Near-instant results

The Claude 3 models can power live customer chats, auto-completions, and data extraction tasks where responses must be immediate and real-time.

Haiku is the fastest and most cost-effective model in its intelligence category. It can read an information- and data-dense research paper on arXiv (~10k tokens) with charts and graphs in less than three seconds. Following its launch, Anthropic is expected to improve performance even further.

For the vast majority of workloads, Sonnet is 2x faster than Claude 2 and Claude 2.1 and has higher levels of intelligence. It excels at tasks demanding rapid responses, like knowledge retrieval or sales automation. Opus delivers similar speeds to Claude 2 and 2.1 but with much higher levels of intelligence.

Strong vision capabilities

The Claude 3 models have sophisticated vision capabilities that are on par with other leading models. They can process various visual formats, including photos, charts, graphs, and technical diagrams. Anthropic is providing this new modality to enterprise customers, some of whom have up to 50% of their knowledge bases encoded in PDFs, flowcharts, or presentation slides.

Fewer refusals

Previous Claude models often made unnecessary refusals that suggested a need for more contextual understanding. Anthropic has made substantial progress in this area: Opus, Sonnet, and Haiku are significantly less likely to refuse to answer prompts that border on the system’s guardrails than previous generations of models. The Claude 3 models show a more nuanced understanding of requests, recognize actual harm, and refuse to answer harmless prompts much less often.

Improved accuracy

Businesses of all sizes rely on models to serve their customers, making it imperative for model outputs to maintain high accuracy at scale. To assess this, Anthropic uses many complex, factual questions that target known weaknesses in current models. Anthropic categorizes the responses into correct answers, incorrect answers (or hallucinations), and admissions of uncertainty, where the model says it doesn’t know the answer instead of providing inaccurate information. Compared to Claude 2.1, Opus demonstrates a twofold improvement in accuracy (or correct answers) on these challenging open-ended questions while exhibiting reduced incorrect answers.

In addition to producing more trustworthy responses, Anthropic will soon enable citations in their Claude 3 models so they can point to precise sentences in reference material to verify their answers. This is a plus for any AI tool.

Extended context and near-perfect recall

The Claude 3 family of models initially offered a 200K context window upon launch. However, all three models can accept inputs exceeding 1 million tokens and may make this available to select customers who need enhanced processing power.

To process long context prompts effectively, models require robust recall capabilities. The ‘Needle In A Haystack’ (NIAH) evaluation measures a model’s ability to recall information from a vast corpus of data accurately. Anthropic enhanced the robustness of this benchmark by using one of 30 random needle/question pairs per prompt and testing on a diverse crowdsourced corpus of documents. Claude 3 Opus not only achieved near-perfect recall, surpassing 99% accuracy, but in some cases, it even identified the limitations of the evaluation itself by recognizing that the “needle” sentence appeared to be artificially inserted into the original text by a human.

Responsible design

Anthropologists developed the Claude 3 family of models to be as trustworthy as they are capable. They have several dedicated teams that track and mitigate various risks, ranging from misinformation and CSAM to biological misuse, election interference, and autonomous replication skills. These efforts are much appreciated in a space where misinformation is often overlooked. Anthropologists continue to develop methods such as constitutional AI that improve the safety and transparency of their models, and they have tuned their models to mitigate privacy issues that could be raised by new modalities.

Addressing biases in increasingly sophisticated models is an ongoing effort, and Anthropic has made strides with this new release. They remain committed to advancing techniques that reduce biases and promote greater neutrality in their models.

Easier to use

The Claude 3 models are better at following complex, multi-step instructions. They are particularly adept at adhering to brand voice and response guidelines and developing customer-facing experiences. This is a plus for UX developers. In addition, the Claude 3 models are better at producing popular structured output in formats like JSON, making it more straightforward to instruct Claude on use cases like natural language classification and sentiment analysis.

Claude 3

Now that you’ve been introduced to the Claude 3 model family, the next question is, where do you begin to explore? Haiku, Sonnet, Opus—there isn’t a wrong choice with Claude 3. Each is like a polished gem with different characteristics, intelligence, speed, and versatility. I envision long hours pondering documentation and building with each one of them.

I’m looking forward to the upcoming feature, citations. It’s like adding footnotes to the grand library of AI. Imagine these models pointing to precise sentences in reference material, like scholars citing ancient scrolls. Seriously, I can’t wait for this feature to come out! Claude 3 creates trust and transparency, a solid foundation for AI innovations. The Claude family is a welcome addition to this space. I looked forward to the next chapter with Anthropic. 

Claude 3

Google Cloud and Claude 3

AI, LLM, open source, Technology

Claude 2.1 Lets Go!

One of the things I appreciate and respect about Anthropic, the creators of Claude, is the transparency of their messaging and content. The content is easy to understand, and that’s a plus in this space. Whenever I visit their site, I have a clear picture of where they are and the plans for moving forward. OpenAI’s recent shenanigans have piqued my curiosity to revisit other chatbot tools. Over a month ago, I wrote a comparative discussion about a few AI tools. One of the tools I discussed was Claude 2.0. Now that Claude 2.1 has been released, I wanted to share a few highlights based on my research. Note most of these features are by invitation only (API Console)or fee-based (Pro Access only) and are not generally available now in the free tier. There is a robust documentation library for Claude to review.

The Basics

  • Claude 2.1 is a chatbot tool developed by Anthropic. The company builds large language models (LLM) as a cornerstone of its development initiatives and its flagship chatbot, Claude.
  • Claude 2.1 manages the API console in Anthropics’s latest release. This AI machine powers the claude.ai chat experience.
  • In the previous version, Claude 2.0 could handle 100,000 tokens that translated to inputs of around 75,000 words.
  • A token is a unit measurement of text AI models use to represent and process natural language. The unit can be code, text, or characters, depending on the method of tokenization used. The unit of text is assigned a numeric value fed into the model.
  • Claude 2.1 delivers an industry-leading 200K token context window, translating to around 150,000 words, or about 500 pages.
  • A significant reduction in rates of model hallucination and system prompts in version 2.1 means more consistent and accurate responses.

200k Tokens Oh My!

Why the increase in the number of tokens? Anthropic is listening to their growing community of users. Based on use cases, Claude was used for application development and analyzing complex plans and documents. Users wanted more tokens to review large data sets. Claude aims to produce more accurate outputs when working with larger data sets and longer documents.

With this increase in tokens, users can now upload technical documentation like entire codebases, technical documentation, or financial reports. By analyzing detailed content or data, Claude can summarize, conduct Q&A, forecast trends, spot variations across several revisions of the same content, and more.

Processing large datasets and leveraging the benefits of AI by pushing the limit up to 200,000 tokens is a complex feat and an industry first. Although AI cannot replace humans altogether, it can allow humans to use time more efficiently. Tasks typically requiring hours of human effort to complete may take Claude a few minutes. Latency should decrease substantially as this type of technology progresses.

Decrease in Hallucination Rates

Although I am interested in the hallucination aspects of AI, for most this is not ideal in business. Claude 2.1 has also made significant gains in credibility, with a decrease in false statements compared to the previous Claude 2.0 model. Companies can build high-performing AI applications that solve concrete business problems and deploy AI with the goal of greater trust and reliability.

Claude 2.1 has also made meaningful improvements in comprehension and summarization, particularly for long, complex documents that demand high accuracy, such as legal documents, financial reports, and technical specifications. Use cases have shown that Claude 2.1 demonstrated more than a 25% reduction in incorrect answers and a 2x or lower rate of mistakenly concluding a document supports a particular claim. Claude continues to focus on enhancing their outputs’ precision and dependability.

API Tool Use

I am excited to hear about the beta feature that allows Claude to integrate with users’ existing processes, products, and APIs. This expanded interoperability aims to make Claude more useful. Claude can now orchestrate across developer-defined functions or APIs, search over web sources, and retrieve information from private knowledge bases. Users can define a set of tools for Claude and specify a request. The model will then decide which device is required to achieve the task and execute an action on its behalf.

The Console

New consoles can often be overwhelming, but Claude made the commendable choice to simplify their developer Console experience for Claude API users while making it easier to test new prompts for faster learning. The new Workbench product will enable developers to iterate on prompts in a playground-style experience and access new model settings to optimize Claude’s behavior. The user can create multiple prompts and navigate between them for different projects, and revisions are saved as they go to retain historical context. Developers can also generate code snippets to use their prompts directly in one of our SDKs. Access to the console is by invitation only based on when this content was published.

Anthropic will empower developers by adding system prompts, allowing users to provide custom instructions to Claude to improve performance. System prompts set helpful context that enhances Claude’s ability to assume specified personalities and roles or structure responses in a more customizable, consistent way that aligns with user needs.

Claude 2.1 is available in their API and powers the chat interface at claude.ai for both the free and Pro tiers. This advantage is for those who want to test drive before committing to Pro. Usage of the 200K token context window is reserved for Claude Pro users, who can now upload larger files.

Overall, I am happy to see these improvements with Claude 2.1. I like having choices in this space and more opportunities to learn about LLM in AI as a technology person interested in large data sets. Claude is on my shortlist.

Originally published at https://mstechdiva.com on November 23, 2023.

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.