Snowflake Cortex is an intelligent, fully-managed service offered by Snowflake. It empowers users to analyze data and build AI applications entirely within the secure environment of Snowflake [1]. Here's a breakdown of its key functionalities:

• Large Language Models (LLMs): Cortex provides access to industry-leading LLMs through its LLM Functions. These functions allow you to leverage the power of LLMs for tasks like understanding text, generating different creative text formats, translating languages, and summarizing information. Imagine having advanced AI models built right into your data platform!

• Machine Learning (ML) Functions: Cortex offers built-in ML functions that use SQL syntax. This makes it easier for data analysts, even those without extensive machine learning expertise, to perform tasks like anomaly detection and classification directly on their data in Snowflake.

• Security and Scalability: Because Cortex functions within the Snowflake environment, it benefits from Snowflake's robust security features and scalability. This ensures your data remains secure while allowing you to handle large datasets efficiently.

Overall, Snowflake Cortex aims to bring the power of AI and machine learning to data analysis within the familiar Snowflake platform. It allows data analysts and business users to leverage cutting-edge AI models and functionalities without needing to become machine learning experts themselves.

You can use all the LLM functions without any additional subscriptions or agreements. This applies even to free trial accounts.

• Pay-per-use model: You only pay for what you use. Snowflake credits are used to cover the cost of LLM functions. These functions are priced based on the number of tokens processed for each task.
• Transparent pricing: Snowflake's documentation provides a clear table showing the cost per 1 million tokens for each LLM function, so you can easily estimate your usage costs.

Snowflake Cortex is expanding its capabilities beyond text-based LLMs! As of recently (March 2024), they've announced support for multimodal LLMs. This means Snowflake is incorporating models that can handle not just text, but also images and potentially other data formats.

Here's a breakdown of what this means:

• Multimodal LLMs: These advanced models go beyond text and can understand the relationship between different data types. For instance, an LLM might analyze an image and its accompanying text description to provide a more comprehensive understanding of the content.
• Snowflake's Partnership: They've partnered with Reka.ai, a company offering powerful multimodal models like Flash and Core [1]. These models can be used within Snowflake Cortex to unlock new possibilities for data analysis.

While the full range of supported data types beyond text might not be explicitly documented yet, the introduction of multimodal LLMs signifies a shift towards handling various data formats within Cortex.

Here are some potential use cases for image-based LLMs in Snowflake Cortex:

• Automated Image Captioning: Generate captions for product images in an e-commerce platform, improving accessibility and searchability.
• Content Moderation: Identify inappropriate content within images based on pre-defined criteria.
• Image Classification and Tagging: Automatically categorize and tag images based on their content, facilitating image organization and retrieval.

Remember, this is a rapidly evolving field. As Snowflake Cortex progresses, we can expect even more capabilities for working with diverse data types using LLMs.

Here are some exciting use cases for LLMs on your enterprise data:

Enhancing Data Analysis and Exploration:

• Automated Summarization: LLMs can analyze vast amounts of data and generate concise summaries, highlighting key trends, insights, and anomalies. This saves analysts time and helps them focus on deeper exploration.
• Data Quality Improvement: LLMs can identify inconsistencies and errors within your data by recognizing patterns and relationships. They can also suggest data cleaning strategies for more reliable analysis.
• Generating Research Questions: LLMs can analyze existing data and research to identify potential new research avenues or questions worth exploring. This can fuel innovation and lead to new discoveries.

Boosting Content Creation and Communication:

• Automated Report Generation: LLMs can take analyzed data and automatically generate reports in a clear and concise format, saving time and resources.
• Personalized Content Creation: LLMs can personalize marketing materials, customer support responses, or internal communications based on user data and preferences.
• Document Summarization and Translation: LLMs can quickly summarize lengthy documents or translate them into different languages, improving accessibility and international communication.

Optimizing Business Processes and Decision Making:

• Customer Service Chatbots: LLMs can power advanced chatbots that understand natural language, answer customer queries effectively, and even personalize interactions.
• Market Research and Trend Analysis: LLMs can analyze social media data, customer reviews, and market research reports to identify customer sentiment, emerging trends, and potential areas of growth.
• Risk Assessment and Fraud Detection: LLMs can analyze financial data and identify patterns that might indicate fraudulent activity or potential financial risks.

Important Considerations:

• Data Security and Privacy: Ensure proper data governance and anonymization techniques when using LLMs on sensitive enterprise data.
• Model Explainability and Bias: Understand how LLMs arrive at their conclusions and be aware of potential biases within the training data.
• Focus on Business Needs: Choose LLM applications that directly address specific business challenges and contribute to measurable goals.

Remember, LLMs are a powerful tool but require careful integration and ongoing monitoring to ensure responsible and effective use within your enterprise data landscape.

Snowflake created a link on tips on which Large Languange Model you should use. Check the following guide: Large Language Model (LLM) Functions (Snowflake Cortex) | Snowflake Documentation

The complete() function in the tidyr package for R actually doesn't involve downloading or running any model for inference. It focuses on handling missing values within your existing data frame.

Here's a breakdown to clarify the difference:

• Complete Function:

  • Purpose: This function addresses missing values (NA or NULL) in your data.
  • Functionality: It helps you identify missing entries and potentially fill them in based on various strategies (e.g., mean imputation, forward fill). You don't choose a model in this context.

• Model for Inference:

  • Purpose: This refers to machine learning models trained on separate data to make predictions on new data.
  • Functionality: You would download or use a pre-trained model for tasks like image classification, text generation, or sentiment analysis. This process often involves running inference on the model with your input data.

Here's an analogy:

Imagine you have a table with some missing entries.

• complete() is like a tool to fill in those missing gaps in the table itself.
• A model for inference is like a separate tool that analyzes the completed table and makes predictions about something else entirely, based on patterns it learned from other data.

In summary, complete() focuses on data cleaning within your existing data set, while models for inference involve separate, pre-trained models used for making predictions.

The main difference between the complete() function and the summarize() function lies in their purpose and level of detail:

• Complete Function:

  • Goal: Checks for missing values (often represented by NA or NULL) in a data set.
  • Output: Typically a logical value (TRUE or FALSE) indicating if there are any missing values in the entire data set or specific columns.
  • Focus: Provides a high-level overview of data completeness.

• Summarize Function:

  • Goal: Creates a summary of the data set based on user-defined calculations.
  • Output: A new data frame with one row for each group (if used with group_by beforehand) containing summary statistics like mean, median, count, etc. for specified columns.
  • Focus: Offers a detailed look at various aspects of the data set.

Here's an analogy:

Imagine you have a library with books.

• complete() is like checking if any books are missing from the shelves.
• summarize() is like calculating the average number of pages per book, the number of books in each genre, or the most popular author.

In short, complete() gives a yes/no answer about missing data, while summarize() provides a rich analysis of the data's characteristics.

In general, no, the LLMs you use through Snowflake Cortex likely won't learn directly from your data. Here's why:

• Pre-Trained Models: LLMs are typically pre-trained on massive datasets before being deployed for use. These datasets encompass a vast amount of text and code, aiming to give the LLM a strong foundation for understanding language.
• Cortex as an Access Point: Snowflake Cortex acts as an intermediary, providing a secure environment to run pre-trained LLMs on your data. Your data is used for the specific task at hand (like translation or text analysis) but isn't incorporated into the core LLM itself.

However, there are some nuances to consider:

• Privacy Preserving Techniques: There's a possibility that Snowflake Cortex might use privacy-preserving techniques to leverage your data while protecting its confidentiality. This could involve anonymized versions of your data used to improve the LLM's performance within Cortex without compromising sensitive information.
• Future Advancements: The field of LLMs is constantly evolving. As the technology progresses, there might be future scenarios where LLMs can be fine-tuned on user data within secure environments like Cortex. But this is not the current standard practice.

Here are the supported LLMs as of March 2024.

 

No, everything happens within Snowflake's secure environment. This means your data remains under your control and adheres to your existing security and governance policies.

Also, there's no need to worry about setting up separate environments, APIs, integrations, or managing different governance rules. Snowflake takes care of everything for a smooth experience.

Snowflake doesn't necessarily have its own large language models (LLMs) but it offers a platform to leverage them. Here's a breakdown of what they do:

• Snowflake Cortex (Private Preview): This service allows you to securely run LLMs from various providers directly within the Snowflake environment. You can think of it as a one-stop shop to access cutting-edge AI models for tasks like text translation. For instance, Snowflake mentions using Meta's Llama 2 model for such purposes [3].
• Focus on Integration: Their approach seems to be integrating industry-leading LLMs rather than creating their own. This gives users flexibility and access to a wider range of capabilities.

Overall, Snowflake acts as a facilitator for using LLMs for data analysis and manipulation tasks. They provide the cloud infrastructure and user interface for you to leverage these powerful language models.

Here are some resources for further reading:

• Snowflake's blog on their vision for LLMs: [Snowflake Vision for Generative AI and LLMs]
• Snowflake's page on the role of LLMs in Machine Learning: [The Role of Large Language Models (LLMs) in Machine Learning]

LLMs are computer programs trained on massive amounts of text data to communicate and generate human-like text in response to a wide range of prompts and questions.

To run AI, you need hardware that can process and analyze large amounts of data quickly and efficiently. AI is a computationally intensive process that requires specialized hardware optimized for complex calculations and data processing.

The hardware you need to run AI depends on the specific application and the scale of the project. For smaller projects, you can get by with a basic computer equipped with a powerful processor and a high-end graphics card that can handle the computational load. However, for larger and more complex projects, you may need specialized hardware such as high-performance GPUs or dedicated AI processors.

One key component of AI hardware is the graphics processing unit (GPU). GPUs are designed for parallel processing and can perform many calculations simultaneously, making them ideal for running AI algorithms. High-end GPUs have thousands of cores, which enable them to process large amounts of data in parallel without slowing down.

Another important component is memory. AI algorithms require fast access to large amounts of data, so you need a lot of RAM to keep up with the computational demands. Additionally, you need fast storage to read and write data quickly.

Finally, you need a reliable and scalable infrastructure to support the hardware. This may include cloud-based services that provide on-demand access to powerful computing resources, or on-premise data centers with specialized hardware.

Overall, to run AI, you need hardware that is optimized for complex calculations and data processing, including high-performance GPUs, large amounts of RAM, fast storage, and a reliable infrastructure.

AI, also known as Artificial Intelligence, can run on both CPU (Central Processing Unit) and GPU (Graphics Processing Unit). The choice of which one to use depends on the specific task and the complexity of the AI model.

In general, CPUs are better suited for handling small to medium-sized datasets and simpler AI models. They are optimized for sequential processing, making them ideal for tasks that require a lot of branching and decision-making. CPUs also have a larger cache and memory capacity than GPUs, which can be beneficial for some AI applications.

On the other hand, GPUs are better suited for handling large datasets and complex AI models. They are optimized for parallel processing, which allows them to perform many calculations simultaneously. This parallel processing capability is necessary for deep learning applications, such as neural networks, which involve large amounts of matrix multiplication.

In recent years, GPUs have become increasingly popular for AI due to their high processing power and relatively low cost. Many companies have developed specialized GPUs, such as Nvidia's Tensor Cores, specifically for deep learning applications.

In conclusion, AI models can run on both CPUs and GPUs, and the choice of which one to use depends on the specific task and the complexity of the AI model. CPUs are better suited for simpler tasks with smaller datasets, while GPUs excel at handling large datasets and complex deep learning models.

Artificial intelligence can run on various hardware, depending on the specific application and requirements. AI requires powerful processing capabilities, large amounts of memory, and efficient data storage and retrieval systems. In addition, specialized hardware may be needed to accelerate specific AI tasks, such as image or speech recognition.

Traditionally, AI has been run on high-performance computing systems, such as clusters of servers or supercomputers. These systems are capable of performing massive amounts of processing in parallel, allowing AI algorithms to analyze and learn from vast amounts of data. However, as AI becomes more integrated into everyday applications, smaller and more specialized hardware solutions are becoming more common.

One of the most popular hardware platforms for AI is the graphics processing unit (GPU). Originally developed for rendering images and video in video games, GPUs are highly parallelized and can perform massive amounts of processing in parallel. This makes them ideal for running AI algorithms, which also require parallel processing.

Another hardware solution for AI is the field-programmable gate array (FPGA). These are programmable hardware chips that can be customized to perform specific tasks, such as neural network processing. FPGAs are highly efficient and can perform specific AI tasks much faster than traditional CPUs or GPUs.

Finally, specialized hardware, such as application-specific integrated circuits (ASICs), are becoming more common for AI applications. These chips are designed specifically for AI tasks, such as image or speech recognition, and can provide significant performance boosts over traditional hardware solutions.

In summary, AI can run on a variety of hardware solutions, depending on the specific application and requirements. High-performance computing systems, GPUs, FPGAs, and specialized hardware are all commonly used to run AI algorithms.

The number of GPUs that one needs for AI depends on the specific application and the size of the data being worked with. Generally, the more GPUs that are available, the faster the computation will be.

For small-scale projects, a single GPU may be sufficient. However, for larger projects that involve processing large datasets or complex neural networks, multiple GPUs may be needed to achieve optimal performance.

It's also important to consider the type of GPU being used. Not all GPUs are created equal, and some may be better suited for AI workloads than others. Nvidia's Tensor Cores, for example, are designed specifically for deep learning and can provide significant performance improvements compared to traditional GPUs.

Another factor to consider is whether to use GPUs in a distributed system. By distributing the workload across multiple machines that each have multiple GPUs, it's possible to achieve even faster computation times. However, setting up a distributed system can be complex and requires an in-depth understanding of the underlying hardware and software components.

In summary, the number of GPUs that one needs for AI depends on the specific requirements of the project. For small-scale projects, a single GPU may be sufficient, while larger projects may require multiple GPUs or a distributed system. It's important to also consider the type of GPU being used and whether it's optimized for AI workloads.

Generative AI and AI are two different subsets of Artificial Intelligence, and they serve different purposes. AI, in general, refers to the ability of machines to carry out tasks that would typically require human intelligence. This can include tasks such as visual perception, speech recognition, decision-making, and language translation, among others.

Generative AI, on the other hand, is a subset of AI that involves the use of machine learning algorithms to generate new content. This can include generating images, videos, music, text, and more. Generative AI uses neural networks to learn patterns in existing data and then creates new content based on those patterns.

The main difference between generative AI and AI is that generative AI involves the creation of new content while AI involves performing tasks. For example, a chatbot that is programmed to answer customer questions is an AI application, while a program that generates new lyrics based on existing songs is an example of generative AI.

Another key difference between the two is the level of human intervention required. AI systems are typically programmed by humans to perform specific tasks, while generative AI systems can generate new content without explicit human input.

In summary, while AI and generative AI are both subsets of Artificial Intelligence, they serve different purposes. AI involves the performance of tasks that require human intelligence, while generative AI involves the creation of new content using machine learning algorithms.

GPT stands for "Generative Pre-trained Transformer." It is a type of artificial intelligence language model created by OpenAI that is capable of generating high-quality human-like text.

The model is pre-trained on a vast corpus of text data, allowing it to learn and understand the nuances of language. This pre-training allows GPT to generate coherent and grammatically correct text, making it a powerful tool for various natural language processing tasks such as language translation, text summarization, and conversational AI.

GPT's ability to generate human-like text has been a significant breakthrough in the field of AI, and it has many potential applications. For example, it can be used to generate news articles, product descriptions, and even creative writing such as poetry or short stories.

One notable feature of GPT is its ability to perform language tasks without explicit instruction. This means that the model can understand the meaning and context of a sentence, even if it contains ambiguous or complex phrases. GPT can also generate text that is specific to a particular domain or subject matter, making it a valuable tool for businesses and researchers.

In summary, GPT is a groundbreaking AI language model that can generate high-quality human-like text. Its ability to understand language nuances, perform language tasks without explicit instruction, and generate domain-specific text has made it a valuable tool for various natural language processing applications.

The most advanced NVIDIA AI chip currently available is the NVIDIA A100 Tensor Core GPU. This GPU was designed specifically for large scale artificial intelligence and machine learning workloads, delivering unprecedented levels of computational power and efficiency.

The A100 GPU is built on NVIDIA's Ampere architecture, which features 54 billion transistors and 6912 CUDA cores. It also includes 432 Tensor Cores, which are specifically designed for deep learning applications. The A100 also boasts 40 GB or 80 GB of high-bandwidth memory (HBM2), which allows for faster data transfer and processing.

One of the key features of the A100 is its ability to scale out across multiple GPUs and nodes, making it ideal for large-scale deep learning and AI workloads. It also includes new features such as multi-instance GPU (MIG), which allows a single A100 GPU to be partitioned into up to seven independent instances to support multiple users or workloads.

Overall, the NVIDIA A100 Tensor Core GPU is a significant leap forward in AI chip technology, delivering unparalleled performance, scalability, and efficiency. Its advanced architecture, massive memory, and deep learning capabilities make it the go-to choice for developers and researchers working on the most demanding AI and machine learning projects.

NVIDIA Corporation is a publicly traded company, which means that it is owned by its shareholders. Any individual or institutional investor can purchase shares of NVIDIA stock, which represent a portion of ownership in the company. As of 2021, NVIDIA has over 2 billion outstanding shares of common stock.

The largest individual shareholders of NVIDIA are its co-founders, Jensen Huang, Chris Malachowsky, and Curtis Priem. Combined, they own approximately 5% of the company's outstanding shares. However, the majority of NVIDIA's stock is owned by institutional investors, such as mutual funds, pension funds, and hedge funds.

As of the latest available data, the top institutional shareholders of NVIDIA are The Vanguard Group, Inc., BlackRock, Inc., and Capital Research and Management Company. These companies own large portions of NVIDIA's outstanding shares and have significant influence on the company's decision-making processes.

It's worth noting that ownership of a publicly traded company like NVIDIA can be fluid, as shareholders can buy and sell shares on the open market. As a result, the ownership structure of the company can change over time. Nevertheless, NVIDIA will always be ultimately owned by its shareholders, who have the power to elect the company's board of directors and vote on important matters at annual shareholder meetings.