A Comprehensive Guide on Data Lake

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October 4, 2024

Businesses are always looking to explore new information quickly and generate valuable insights. A data lake plays a crucial role in achieving these goals by serving as a centralized repository to store data. It allows businesses to consolidate data from different sources in one place and offers versatility to manage diverse datasets efficiently.

Unlike traditional data storage systems, which focus on storing processed and structured data, data lake stores data in its original format. This approach preserves the data’s integrity and allows for deeper analysis, supporting a wide range of use cases.

This article will discuss data lakes, their need, and their importance in modern-day data management.

What is Data Lake?

A data lake is a centralized repository that stores all structured and unstructured data in its native form without requiring extensive processing or transformation. This flexibility enables you to apply transformations and perform analytics as needed based on specific query requirements.

One of the key features of a data lake is its flat architecture, which allows data to be stored in its original form without pre-defining the schema or data structure. The flat architecture makes the data highly accessible for various types of analytics, ranging from simple queries to complex machine learning, supporting more agile data-driven operations. While data lakes typically store raw data, they can also hold intermediate or fully processed data. This capability can significantly reduce the time required for data preparation, as processed data can be readily available for immediate analysis.

Key Concepts of Data Lake

Here are some of the fundamental principles that define how a data lake operates:

Data Movement

Data lakes can ingest large amounts of data from sources like relational databases, texts, files, IoT devices, social media, and more. You can use stream and batch processing to integrate this diverse data into a data lake.

Schema-on-Read

Unlike traditional databases, a data lake uses a schema-on-read approach. The structure is applied when the data is read or analyzed, offering greater flexibility.

Data Cataloging

Cataloging enables efficient management of the vast amount of data stored within a data lake. It provides metadata and data descriptions, which makes it easier for you to locate specific datasets and understand their structure and content.

Security and Governance

Data lakes support robust data governance and security features. These features include access controls, encryption, and the ability to anonymize or mask sensitive data to ensure compliance with data protection regulations.

Self-Service Access

Data lake provides self-service access to data for different users within an organization, such as data analysts, developers, marketing or sales teams, and finance experts. This enables teams to explore and analyze data without relying on IT for data provisioning.

Advanced Analytics Support

One of the key strengths of a data lake is its support for advanced analytics. Data lake can integrate seamlessly with tools like Apache Hadoop and Spark, which are designed for processing large datasets. It also supports various machine learning frameworks that enable organizations to run complex algorithms directly on the stored data.

Data Lake Architecture

In a data lake architecture, the data journey begins with collecting data. You can integrate data, structured data from relational databases, semi-structured data such as JSON and XML, and unstructured data like videos into a data lake. Understanding the type of data source is crucial as it influences data ingestion and processing methods.

Data ingestion is the process of bringing data into the lake, where it is stored in unprocessed form. Depending on the organization’s needs, this can be done either in batch mode or in real-time.

The data then moves to the transformation section, where it undergoes cleansing, enrichment, normalization, and structuring. This transformed, trusted data is stored in a refined data zone, ready for analysis.

The analytical sandbox is an isolated environment that facilitates data exploration, machine learning, and predictive modeling. It allows analysts to experiment without affecting the main data flow using tools like Jupyter Notebook and RStudio.

Finally, the processed data is exposed to end users through business intelligence tools like Tableau and Power BI, which are used to dive into business decisions.

How Data Is Different from Other Storage Solutions

Data lake offers a distinct approach to storing and managing data compared to other data storage solutions like data warehouses, lakehouses, and databases. Below are the key differences between data lake and these storage solutions.

Data Lake vs Data Warehouse

Below are some of the key differences between a data lake and a data warehouse, showing how each serves a different purpose in data management and analysis.

Aspect	Data Lake	Data Warehouse
Data Structure	Stores raw, unstructured, semi-structured, and structured data.	Stores structured data in predefined schema.
Schema	It uses a schema-on-read approach (the structure of the data is defined at the time of analysis)	It uses a schema-on-write approach (the structure is defined when the data is stored within a warehouse)
Processing	Data Lakes use the ELT process, in which data is first extracted from the source, then loaded into a data lake, and transformed when needed.	The warehouse uses the ETL process, in which data is extracted and transformed before being loaded into the system.
Use Case	Ideal for experiential data analytics and machine learning.	Best for reporting, BI, and structured data analysis.

Data Lake vs. Lakehouse

Data lakehouse represents a hybrid solution that merges the benefits of both data lake and data warehouse. Here is how it differs from a data lake:

Aspect	Data Lake	Lakehouse
Architecture	Flat architecture with file and object storage and processing layers.	Combines the features of data lakes and data warehouses.
Data Management	Primarily stores raw data without a predefined structure.	Manages raw and structured data with transactional support.
Cost	Cost-effective, as it eliminates the overhead cost for data transformation and cleaning.	Potentially higher cost for data storage and processing.
Performance	Performance may vary depending on the type of tool used for querying.	Optimized for fast SQL queries and transactions

Data Lake vs Database

Databases and data lakes are designed to handle different types of data and use cases. Understanding the differences helps select appropriate storage solutions based on processing needs and scalability.

Aspect	Data Lake	Database
Data Type	Store all types of data, including unstructured and structured.	Stores structured data in tables with defined schemas.
Scalability	Highly scalable	Limited scalable, focused on transactional data.
Schema Flexibility	Schema-on-read, adaptable at analysis time.	Scheme-on-write, fixed schema structure.
Processing	Supports batch and stream processing for large datasets	Primarily supports real-time transactional processing.

Data Lake Vendors

Several vendors offer data lake technologies, ranging from complete platforms to specific tools that help manage and deploy data lakes. Here are some of the key players:

AWS: Amazon Web Services provide Amazon EMR and S3 for data lakes, along with tools like AWS Lake Formation for building and AWS Glue for data integration.
Databricks: It is built on the Apache Spark foundation. This cloud-based platform blends the features of a data lake and a data warehouse, known as a data lakehouse.
Microsoft: Microsoft offers Azure HD Insight, Azure Blob Storage, and Azure Data Lake Storage Gen2, which help deploy Azure data lake.
Google: Google provides Dataproc and Google Cloud storage for data lakes, and their BigLake service further enhances this by enabling storage for both data lakes and warehouses.
Oracle: Oracle provides cloud-based data lake technologies, including big data services like Hadoop/Spark, object storage, and a suite of data management tools.
Snowflake: Snowflake is a known cloud data warehouse vendor. It also supports data lakes and integrates with cloud object stores.

Data Lake Deployments: On-premises or On-Cloud

When deciding how to implement a data lake, organizations have the option of choosing between on-premises and cloud-based solutions. Each approach has its own set of considerations, impacting factors like cost, scalability, and management. Understanding the differences helps businesses make informed decisions aligning with their needs.

On-Premises Data Lake

An on-premises data lake involves setting up and managing a physical infrastructure within the organization’s own data centers. This setup requires significant initial hardware, software, and IT personnel investment.

The scalability of an on-premises data lake is constrained by the physical hardware available, meaning the scaling up involves purchasing and installing additional equipment. Maintenance is also a major consideration; organizations must internally handle hardware upgrades, software patches, and overall system management.

While this provides greater control over data security and compliance, it also demands robust internal security practices to safeguard the data. Moreover, disaster recovery solutions must be implemented independently, which can add to the complexity and cost of the data lake system.

Cloud-Based Data Lake

A cloud data lake leverages the infrastructure provided by cloud service providers. This model offers high scalability, as resources can be scaled up or down on demand without needing physical hardware investments.

Cloud providers manage system updates, security, and backups, relieving organizations of these responsibilities. Access to the cloud data lake is flexible and can be done anywhere with internet connectivity, supporting remote work and distributed teams.

The cloud-based data lake also offers built-in disaster recovery solutions, which enhance data protection and minimize the risk of data loss. However, security is managed by the cloud provider, so organizations must ensure that the provider’s security measures align with the compliance requirements.

Data Lake Challenges

Data Swamps

A poorly managed data lake can easily turn into a disorganized data swamp. If data isn’t properly stored and managed, it becomes difficult for users to find what they need, and data managers may lose control as more data keeps coming in.

Technological Complexity

Choosing the right technologies for a data lake can be overwhelming. Organizations must pick the right tools to handle their data management and analytics needs. While cloud solutions simplify installation, managing various technologies remains a challenge.

Unexpected Costs

Initial costs for setting up a data lake might be reasonable, but they can quickly escalate if the environment isn’t well-managed. For example, companies might face higher-than-expected cloud bills or increased expenses as they scale up to meet growing demands.

Use Cases of Data Lake

Data lakes provide a robust foundation for analytics, enabling businesses across various industries to harness large volumes of raw data for strategic decision-making. Here is how data lake can be utilized in different sectors:

Telecommunication Service: A telecommunication company can use a data lake to gather and store diverse customer data, including call records, interactions, billing history, and more. Using this data, the company can build churn-propensity models by implementing machine learning alogrithms that identify customers who are likely to leave. This helps reduce churn rates and save money on customer acquisition costs.

Financial Services: An investment firm can utilize a data lake to store and process real-time market data, historical transaction data, and external indicators. The data lake allows rapid ingestion and processing of diverse datasets, enabling the firm to respond quickly to market fluctuations and optimize trading strategies.

Media and Entertainment Service: By leveraging a data lake, a company offering streaming music, radio, and podcasts can aggregate massive amounts of user data. This data can include a single repository’s listening habits, search history, and user preferences.

Conclusion

Data lakes have emerged as pivotal solutions for modern data management, allowing businesses to store, manage, and analyze vast amounts of structured and unstructured data in their raw form. They provide flexibility through schema-on-read, support robust data governance, and use cataloging to avoid pitfalls such as data swamps and effectively manage data.

Why is Vector Search Becoming So Critical?

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October 2, 2024

Modern society is increasingly using and relying on generative AI models.

A report from The Hill noted that generative AI “could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 15 percentage points over a 10-year period.” Generative AI describes algorithms that can be used to create new audio, code, images, text, videos, and simulations. The importance of generative AI for modern business is increasing at such a rate that Amazon CEO Andy Jassy disclosed that generative AI projects are now being worked on by every single one of Amazon’s divisions.

With this rise in generative AI use cases comes a massive increase in the amount of data. The International Data Corporation predicts that by 2025, the global data sphere will grow to 163 zettabytes, 10 times the 16.1 zettabytes of data generated in 2016. In response to this increasing amount of data, more companies and developers who work in advanced fields are turning to vector searches as the most effective way to leverage this information.

This article will examine what a vector search is and the critical ways it is being used by developers.

How Do Vector Searches Work?

A vector search compiles a wide range of information from a vector database to create results outside of what would be expected from a regular search.

These vector databases are an ultramodern solution for storing, swiftly retrieving, and processing high-dimensional numerical data representations at scale.

Compared to a traditional SQL database, where a developer could use keywords to find what they are looking for, a vector database can effortlessly enable multimodal use cases from information of all types, ranging from text and images to statistics and music. This is done by turning the information into vectors.

As explained by MongoDB, a vector can be broken down into components, which means that it can represent any type of data. The vector is then usually characterized as a list of numbers where each number in the list represents a specific feature or attribute of that data. When a user does a vector search, it doesn’t just look for exact matches but recognizes content based on semantic similarity.

This means the database works better for identifying and retrieving information that is not just identical but similar to the request. A simple example of this would be that a keyword search for documents would only point to documents with that exact keyword, while a vector search would find similarities between documents, creating a much broader search.

Critical Use Cases For Vector Searches

Helping Clients Manage Large Datasets

Vector databases are being offered to a wide range of clients to help efficiently manage and query large datasets in modern applications. A good example of this is Amazon Web Services (AWS), which has heavily invested in generative AI to help its clients. The services offer vector databases like Amazon OpenSearch, which can be used by clients for full-text search, log analytics, and application monitoring, allowing clients to get insights from their data in real time.

Recommendations for Customers

Customer service is the cornerstone of every business, and ecommerce platforms are implementing vector searches to help their customers by using the data collected on them. In an article titled Why Developers Need Vector Search, The New Stack details how vector databases and vector searches can build a recommendation engine for their customers. This is done by seeking similarities across data in order to develop meaningful relationships. When a customer searches for a particular item, the vector database will also find and recommend similar items, improving the company’s customer service and increasing the chance of more sales.

Tracking Down Copyright Infringement

Due to the vast amount of unstructured data available online, developers are increasingly using vector searches to track and enforce copyright infringement. The example The New Stack gives is social media companies like Facebook. Every media that is uploaded to the platform creates a vector, which is then cross-checked against copyrighted vectors. Because a vector search can find similar data points in unstructured data like videos, it allows the user to filter through a much wider database with greater accuracy. This makes it much harder for those who want to share material they don’t have the rights to.

As more companies rely on data to reorganize and develop their businesses, vector searches will become increasingly more critical.

LambdaTest Launches KaneAI for End-to-End Software Testing

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Analytics Drift

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September 2, 2024

LambdaTest Launches KaneAI — Image Source: LambdaTest

LambdaTest, a California-based company, is known for its cross-platform app testing services. It has launched KaneAI, an AI-driven tool for testing purposes. This AI-powered agent simplifies end-to-end software testing. Using natural language, you can write, execute, debug, and refine automated tests. This marks a shift away from complex coding and low-code workflows.

KaneAI is available to select partners as an extension of LambdaTest’s platform. It allows you to write test steps in natural language. You can also record actions interactively, which the AI converts into test scripts. These scripts run on LambdaTest’s cloud, which is used for speed and scalability.

KaneAI uses OpenAI models and LambdaTest’s technology for a smooth testing experience. It integrates with existing LambdaTest tools, which provides detailed insights into test performance and supports continuous integration processes.

A key feature of KaneAI is its ability to manage the entire testing process within a single platform. KaneAI covers multiple processes, including test creation, execution, and analysis. This feature reduces the need for various tools, simplifying processes and boosting productivity.

CEO Asad Khan said that KaneAI addresses the problems of using various tools by offering simple and unified solutions. However, only a few users use KaneAI, and LambdaTest plans to make it available to more people soon. They will also add features to connect with popular platforms like Slack and Microsoft Teams. It will allow you to start and manage tests from these tools, making the process even easier.

More than 10,000 organizations, including Nvidia and Microsoft, use KaneAI to make software testing even more efficient. It offers a more complete and integrated platform, which puts KaneAI ahead of competitors such as BrowserStack and Sauce Labs. As KaneAI develops, it will become a key tool for QA teams wanting to make their testing processes easier and faster.

Google Launches Zero-shot Voice Transfer Technology

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September 2, 2024

Google Launches Zero-shot Voice Transfer

Google has launched a new voice transfer module for its text-to-speech systems. This module aims to help people who have lost their voices or have unique speech patterns. It works by restoring their original voice, making communication easier.

People lose their voices due to conditions such as ALS, muscular dystrophy, or any hereditary diseases. Losing one’s voice can impact one’s identity. Google’s technology aims to bring back that vital part of one’s identity.

The system works with either few-shot or zero-shot training. Few-shot training adapts the model using samples from the speaker’s past voice recordings. On the other hand, zero-shot training uses short audio samples, even if the person has never had a typical voice. It makes zero-shot training ideal for those who have never recorded a speech.

One of the key strengths of Google’s VT module is its seamless integration with existing TTS systems. It can be easily plugged into these systems to restore voices from small speech samples, whether typical or atypical. This multilingual technology can transfer voices across different languages, making it versatile and applicable in various fields.

With such powerful technology, there are security measures to prevent its misuse. Google has incorporated audio watermarking into the system. This technique embeds hidden information within the synthesized audio, allowing you to detect the unauthorized use of voice transfer technology.

Google’s zero-shot voice transfer module represents a significant leap forward in personalized voice technology. It allows people with speech impairments to communicate more effectively, opening up new possibilities.

Salesforce Releases xGen-MM Open-source Multimodal AI Models

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September 2, 2024

Salesforce has introduced xGen-MM, a powerful new AI model, as open-source. By providing public access to these advanced AI tools, Salesforce fosters innovation and promotes a culture of transparency in AI development. This open-source approach helps you build and improve these models, driving the evolution of AI.

xGen-MM has been introduced to handle tasks that require integrating images and text. These models can combine and process these two types of data simultaneously, enabling them to perform complex tasks, such as answering questions that include multiple images. This capability of xGen-MM makes it efficient for a wide range of applications, from healthcare to autonomous systems.

xGen-MM’s capabilities lie in its training on MINT-1T datasets. It is a dataset of enormous data collections comprising a trillion tokens of mixed text and image content. This vast dataset equips the models with a deep understanding of how text and image data interact with each other. The diversity of xGen-MM reaches new levels of performance in multimodal AI.

Read More: Google Launches Gemini Live

Addressing your needs, xGen-MM offers different model variants, such as instruction-tuned and safety-tuned models. The instruction-tuned model follows specific tasks or directions, and the safety-tuned model is designed to minimize unethical outputs. This versatility highlights Salesforce’s dedication to building AI technology that can be used responsibly in real-world scenarios.

Salesforce’s decision to make xGen-MM an open-source marks a shift towards maintaining transparency in AI environments. This move could inspire other companies to adopt similar practices, promoting a more open and collaborative environment.
As the community embraces xGen-MM, its impact on real-world applications and research will grow significantly. This progress will create new opportunities for future innovations in artificial intelligence technology.

Microsoft Announced New Cutting-Edge Phi-3.5 Model Series

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August 30, 2024

Microsoft’s Three New Phi-3.5 Model Series

Microsoft expanded its Small Langauge Models (SLMs) lineup by launching the Phi-3 collection in April 2024. Phi-3 models delivered advanced capabilities and cost efficiency, surpassing similar and larger models across key language, reasoning, coding, and math benchmarks. These models received valuable customer and community feedback, driving further AI adoption.

https://t.co/dFfyktuEUL

In August 2024, Microsoft proudly introduced its latest AI innovation, the Phi-3.5 series. This cutting-edge collection features three open-source SLMs: a 3.82 billion parameter mini-instruct, a 4.15 billion vision-instruct, and a 41.9 billion MoE-instruct. These models support a 128k token context length and show that performance is not solely determined by size in the world of Generative AI.

https://t.co/I8NiWTh5Q2

The lightweight AI model Phi-3.5-mini-instruct is well suited for code generation, mathematical problem-solving, and logic-based reasoning tasks. Despite its small size, the mini version surpasses the Llama-3.1-8B-instruct and Mistral-7B-instruct models on the RepoQA benchmark for long context code understanding.

Read More: Top Robots in India in 2024

Microsoft’s Mixture of Experts (MoE) merges multiple model types, each focusing on various reasoning tasks. According to Hugging Face documentation, MoE runs only with 6.6B parameters instead of the entire 42 billion active parameters. MoE model provides robust performance in code, math, and multilingual language understanding. It outperforms GPT-4o mini on the different benchmarks across various subjects, such as STEM, social sciences, and humanities, at different levels of expertise.

The advanced multimodal Phi-3.5 vision model integrates text and vision processing capabilities. It is designed for general image understanding, chart and table comprehension, optical character recognition, and video summarization.

For ten days, the Phi-3.5 mini model was trained on 3.4 trillion tokens using 512 H100-80G GPUs. MoE underwent training on 4.9 trillion tokens over 23 days, and the vision model used 500 billion tokens with 256 A100-80G GPUs for six days.

All three models are free for developers to download, use, and customize on Hugging Face under Microsoft’s MIT license. By providing these models with open-source licenses, Microsoft enables developers to incorporate advanced AI features into their applications.

Midjourney’s AI-Image Generator Is Now Open to Everyone

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August 30, 2024

Midjourney’s Web-Based AI-Image Generator Open to Public

On August 21, 2024, Midjourney, an AI image generation service and company, announced on X that its website is now available for everyone worldwide. According to Midjourney co-founder and CEO David Holz in a Discord message, new users can generate around 25 images at zero cost.

Previously, users needed to generate ten images on Discord to access the Midjourney’s web version. The long-awaited move away from Discord is here, and you no longer need it to try Midjourney. You can now sign up directly through its website to explore the platform’s features without any upfront investment.

To begin, register with your Google or Discord account. Once logging in, you can easily start creating AI-generated art by entering text descriptions on the web-based interface. The platform will automatically build four images based on your prompts.

Midjourney also lets you fine-tune your creations by configuring elements like stylization levels, aspect ratios, etc, using its intuitive slider bars. Your work stays active in the Organize tab, while the Chat tab allows you to connect with other users. This is a great way to discuss image-generating ideas with fellow creatives.

The top AI platform lets anyone benefit from the offer, even if you already have an account. Midjourney recommends logging into your existing Discord account to retain your image history. You can also merge your Discord and Google accounts under the Account tab for seamless access to your work across both platforms.

Midjourney, acclaimed for its top-tier AI text-to-image generation and image editing, is widely regarded as the “gold standard” by several early AI users. The platform competes with Elon Musk’s xAI company and its Grok 2 chatbot. However, it faces copyright issues from artists who allege that the platform uses their work without permission or payment.

Midjourney strengthens its position by building a vibrant, inclusive, creative community for experienced designers and newcomers in this rapidly growing AI-generated art field. So, enjoy Midjourney’s free features and explore its pricing plans if you want more. Now is a great time to dive in, build, and let your creativity flourish.

OpenAI Enhances GPT-4o With New Fine-tuning Feature

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August 30, 2024

OpenAI announced that it will now allow third-party software developers to fine-tune the custom version of its large multimodal model (LMM), GPT-4o. Earlier, the company introduced fine-tuning in the GPT-4o mini model, which is cheaper and less powerful than the full GPT-4o.

To know more about fine-tuning in GPT-4o mini, read here.

Fine-tuning is a machine learning technique for modifying a pre-trained AI model for specific use cases or tasks. Now, developers can use this to train GPT-4o with custom datasets to use the LLM to perform specific tasks as per their requirements.

OpenAI said that this is just a start. It will continue introducing model customization options for its users. The fine-tuning will greatly impact the model’s performance across domains, such as businesses, coding, or creative writing.

GPT-4o services are available in all the paid versions of the model. To use them, developers can go to the fine-tuning dashboard, click Create, and select gpt-4o-2024-08-06 from the base model drop-down list. The cost of fine-tuning training is $25 per million tokens. The cost of inference is $3.75 per million input tokens and $15 per million output tokens.

To encourage the use of fine-tuning in GPT-4o, OpenAI is offering 1M tokens per day for free to every organization until September 23. For the GPT-4o mini model, it is offering 2M training tokens per day for free till September 23.

Tokens are the numerical representation of words, characters, combinations of words, and punctuations for concepts learned by LLM or LMM. Tokenization is the first step in the AI model training process.

OpenAI worked with some industry partners for a couple of months to test the efficiency of fine-tuning services. Cosine, an AI software engineering company, used fine-tuned GPT-40 for its AI agent Genie. It achieved a SOTA score of 43.8% on the new SWE-bench verified benchmark.

Another firm, Distyl, an AI service partner to Fortune 500 companies, was ranked first on the BIRD-SQL benchmark, the leading text-to-SQL benchmark. Distyl’s fine-tuned GPT-4o model achieved an execution accuracy of 71.83%. It excelled in query reformulation, intent classification, chain-of-thought, self-correction, and SQL generation.

OpenAI has stated that it will ensure the data privacy of businesses as they will have complete control over their datasets. These datasets will not be shared or used to train other models. The fine-tuned models will be safeguarded through automated evaluations and usage monitoring mechanisms.

The introduction of fine-tuning features in the GPT-4o model is a significant step by OpenAI to enhance the capabilities of its AI model. The feature will allow users to leverage the high performance of GPT-4o along with customization to develop specialized applications securely. It will also help OpenAI to gain an edge in the highly competitive AI landscape.

NVIDIA Introduces a Miniaturized Version of Mistral NeMo 12B

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August 30, 2024

NVIDIA Mistral-NeMo-Minitron 8B Miniaturized Version Mistral NeMo 12B

On August 21, 2024, NVIDIA announced the release of Mistral-NeMo-Minitron 8B, a small language model(SLM), and a miniature version of the earlier released Mistral NeMo 12B model. NVIDIA unveiled the Mistral NeMo 12B model, a cutting-edge LLM, on July 18, 2024. It was developed through a collaboration between NVIDIA and Mistral AI. The model can be deployed in enterprise applications to support chatbots, summarization, and multilingual tasks.

To learn more about Mistral-NeMo-Minitron 8B, click here.

Mistral-NeMo-Minitron 8B, with 8 billion parameters, is a scaled-down version of Mistral NeMo 12B, which has 12 billion parameters. It is a small language model, a specialized AI model trained on datasets smaller than those used for LLMs.

SLMs are usually curated to perform specific tasks like sentiment analysis, basic text generation, and classification. They can run in real-time on workstations and laptops. Small organizations with limited resources for LLM infrastructure can easily deploy SLMs to leverage generative AI capabilities at lower costs.

Mistral-NeMo-Minitron 8B is small enough to run on an NVIDIA RTX-powered workstation. At the same time, it excels across various benchmarks for virtual assistants, chatbots, coding, and education applications.

Bryan Catanzaro, NVIDIA’s vice president of applied deep learning research, said, “We have combined two AI optimization methods in this model. One is pruning to reduce parameters from 12 billion to 8 billion, and the other is distillation to transfer learnings of the Mistral NeMo 12B model to the Mistral-NeMo-Minitron 8B model. This helps the model to deliver accurate results similar to LLM at lower computational costs.”

The model development team first performed the pruning process, which condenses the size of the neural network by removing model weights that contribute the least to its accuracy. The pruned model was then retrained during distillation to compensate for the reduction in accuracy caused by pruning.

The distillation process for the Mistral-NeMo-Minitron 8B model has been performed using NVIDIA NeMo, a platform for developing generative AI applications. Developers can further compress this model for smartphone use by performing distillation and pruning using NVIDIA AI Foundry. This compressed model is built using a fraction of parent models’ training data and infrastructure but offers high accuracy.

NVIDIA has emerged as a significant player among the companies offering AI services. Its products, especially AI chips, are increasingly being adopted for various applications, resulting in a surge in the company’s share value by 170% in the current year. With the launch of Mistral-NeMo-Minitron 8B, NVIDIA’s strategy to diversify its AI services will gain further momentum.

Google Launches Gemini Live for Hands-Free AI Conversation

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August 30, 2024

Google Gemini Live Hands-free AI Conversation Experience

On August 13, 2024, Google introduced Gemini Live, a voice assistant for Android mobile devices, in its annual Made by Google event. The event also saw the launch of Google’s mobile phone, Pixel 9 series, Pixel Buds Pro 2 and Pixel Watch 3.

To know more about Gemini Live, read here.

Gemini Live is a chat assistant that will provide users with a free-flowing conversational experience with Gemini, Google’s AI-powered assistant. It works on the new Gemini 1.5 Pro and 1.5 Flash AI models, which utilize advanced text-to-speech technology.

Fully integrated with Android, Gemini Live can be used in English on mobile devices such as Pixel and Samsung. Google plans to make it multilingual and expand it further for iOS devices. To provide a natural interaction experience, Gemini Live offers ten voices to choose from according to the user’s preference for tone and style.

Currently, only users who have subscribed to Gemini Advance can use Gemini Live. To start a conversation with Gemini Live, users can tap the live button at the bottom right of the Gemini app and enter text-based or hands-free input. One can interrupt the conversation and change topics, just like having a conversation on a phone call.

The assistant works in the background even when the phone is locked. Users can turn off the microphone by tapping the Hold or End buttons or saying “Stop.”

To further enhance Gemini Live’s capabilities, Google is set to launch new extensions in a few weeks. These extensions include Keep, Tasks, Utilities3, and expanded features on YouTube Music, which will strengthen Gemini’s integration with other Google apps and make them more efficient.

Sissie Hsiao, Google’s vice president for Gemini experience, told WIRED, “This chatbot is not just revamped Google Assistant, but its interface has been completely rebuilt using generative AI. Over the years, users have asked us for two things repeatedly. One, an assistant with whom they can talk naturally without changing their tone, and two, the assistant should be more capable of solving real-life problems. Gemini can now be your personal assistant and manage your calendar appointments and email invites.”
Google’s AI strategy emphasizes improving user experience through the responsible use and development of artificial intelligence. The launch of Gemini Live aligns with the tech giant’s resolve and can give users a more personalized AI experience.