Generative AI LabExploring generative AI and large language models (LLMs)

TL;DR: I made a RAG pipeline that can answer all of your recruiters’ questions. It was my first time building one and it’s super helpful!

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

As a data scientist, I have always been fascinated by the power of automation and how it can streamline processes and increase efficiency. So when I was tasked with building my first RAG (Red, Amber, Green) pipeline, I was excited to see how it could help me and my team in our recruitment process. In this blog post, I will share my experience of building my first RAG pipeline and how it has become an invaluable tool for answering all of our recruiters’ questions.

What is a RAG Pipeline?

A RAG pipeline is a visual representation of the status of a project or process. It uses the traffic light system of red, amber, and green to indicate whether a particular task or stage is on track, at risk, or behind schedule. This allows for quick and easy identification of any potential issues and helps to prioritize tasks accordingly. In the context of recruitment, a RAG pipeline can be used to track the progress of job applications, interviews, and hiring decisions.

Building My First RAG Pipeline

The first step in building my RAG pipeline was to identify the key stages in our recruitment process. This included job posting, resume screening, initial interviews, and final hiring decisions. I then created a spreadsheet with these stages as columns and the job positions as rows. Next, I color-coded each stage using the red, amber, and green system based on the average time it took to complete that stage. This gave me a clear visual representation of the overall progress of each job position.

Using the RAG Pipeline to Answer Recruiters’ Questions

One of the most significant benefits of having a RAG pipeline is that it can answer all of your recruiters’ questions. In the past, our recruiters would often come to me or my team members for updates on the status of specific job positions. With the RAG pipeline, they can now see at a glance which stage each position is in and whether there are any delays or potential issues. This has saved us a lot of time and allowed us to focus on other important tasks.

Conclusion

In conclusion, building my first RAG pipeline has been a game-changer for our recruitment process. It has provided us with a visual representation of our progress, allowed for quick identification of potential issues, and answered all of our recruiters’ questions. I highly recommend implementing a RAG pipeline in your recruitment process to increase efficiency and streamline your workflow. With the power of automation, we can continue to improve and optimize our processes, making our jobs as data scientists even more rewarding.

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TL;DR: Power Query can dynamically restrict data import to improve reporting efficiency. When dealing with large amounts of data, it’s important to consider if all of it is necessary. This article explains how to determine if data needs to be restricted for more efficient reporting.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

In today’s world, data is everywhere. We are constantly bombarded with information from various sources, and as a result, we often end up with a large amount of data. While having access to a vast amount of data can be beneficial, it can also be overwhelming and unnecessary for our reporting needs. In this blog post, we will discuss how to dynamically restrict data import in Power Query and why it is essential to do so when dealing with a large amount of data.

What is Power Query?

Before we dive into the details of how to dynamically restrict data import, let’s first understand what Power Query is. Power Query is a powerful data transformation and data preparation tool that is a part of Microsoft Excel and Power BI. It allows users to connect to various data sources, transform and clean the data, and load it into a data model for analysis and reporting. With Power Query, you can easily import, filter, and transform data from a wide range of sources, including databases, text files, and web pages.

Why do we need to restrict data import?

As mentioned earlier, having access to a large amount of data can be overwhelming and unnecessary for our reporting needs. It can also slow down the data import process and make it challenging to work with the data. Therefore, it is essential to restrict data import and only import the data that is relevant to our reporting needs. This will not only save time and improve performance but also make the data more manageable and easier to work with.

How to dynamically restrict data import in Power Query?

Now that we understand the importance of restricting data import, let’s discuss how to do it dynamically in Power Query. The first step is to identify the data that we need for our reporting. This can be done by analyzing the data and determining which columns and rows are relevant to our reporting needs. Once we have identified the data, we can use the Power Query Editor to filter and transform the data before loading it into our data model. This will ensure that only the necessary data is imported.

Another way to dynamically restrict data import is by using parameters in Power Query. Parameters allow us to specify a value or condition that can be changed dynamically. This means that we can use a parameter to filter the data during the import process. For example, we can set a parameter to only import data from the last 12 months, or we can specify a specific product or region to import data for. This allows us to have more control over the data that is imported and makes it easier to update the data in the

Final Thoughts

In summary, it is important to consider the necessity of all data in our reporting, especially when dealing with a large amount of data. By using dynamic restrictions in Power Query, we can better manage and control the data we import, resulting in more efficient and focused reporting. This article offers helpful insights and tips for implementing these restrictions in your own data analysis.

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TL;DR: To ace your research project planning, start by setting clear goals and deadlines. Research thoroughly and organize your information effectively. Collaborate with others and seek guidance when needed. Stay focused and manage your time wisely. Don’t be afraid to adapt your plan as you go. Stay organized and stay motivated.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Research projects are an essential part of academic and professional life. They allow us to delve deeper into a topic and contribute to the existing body of knowledge. However, the process of planning and executing a research project can be overwhelming and daunting, especially for those who are new to it. In this blog post, I will share some thoughts and tips on how to navigate research project planning and organization effectively.

Understanding the Purpose and Scope

The first step in planning a research project is to have a clear understanding of its purpose and scope. This includes identifying the research question, objectives, and expected outcomes. Having a well-defined purpose and scope will help you stay focused throughout the project and avoid getting sidetracked. It will also guide you in selecting the appropriate research methods and tools.

Conducting a Literature Review

A literature review is a crucial aspect of any research project. It involves reviewing and analyzing existing literature on the topic to identify gaps in knowledge and build a strong theoretical foundation for your study. When conducting a literature review, it is essential to use credible and relevant sources. It can be helpful to create a system for organizing and keeping track of the sources you have reviewed. This will save you time and effort in the long run and ensure that you do not miss any important information.

Creating a Timeline and Setting Realistic Goals

Research projects often have a set deadline, whether it is for a class assignment or a professional project. Therefore, it is crucial to create a realistic timeline and set achievable goals. Break down the project into smaller tasks and assign a specific timeline for each task. This will help you stay on track and avoid last-minute rushes. It is also essential to be flexible and adjust your timeline if needed. Unexpected challenges and delays are common in research, so it is crucial to factor them in when setting your goals.

Organizing and Managing Data

Data management is a crucial aspect of any research project. It involves collecting, organizing, and analyzing data in a systematic and efficient manner. To ensure the accuracy and reliability of your data, it is essential to use appropriate data collection methods and tools. It is also crucial to have a system for organizing and storing your data, whether it is in the form of physical documents or digital files. This will help you stay organized and prevent any data loss or confusion.

Conclusion

In conclusion, planning a research project can seem overwhelming, but with proper organization and time management, it can be a manageable and rewarding experience. It is important to carefully consider all aspects of the project, seek guidance from experienced individuals, and continuously stay organized throughout the process. With these tips in mind, navigating research project planning can be a successful and enjoyable journey.

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Learn how to set up Gemini Nano, Google’s latest built-in AI, in your Chrome browser. Discover how it can be used to create practical applications, without compromising privacy or relying on server-side solutions. With Gemini Nano, you can bring top-notch AI capabilities to your users with a snappy user experience, even when they’re offline. All you need is Windows 10 or MacOS 13, an integrated GPU, and 22GB of storage. Read more on Medium and join the AI newsletter with over 80,000 subscribers for the latest updates and developments in AI. Interested in sponsoring? Consider joining thousands of other data leaders

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

The technology world is constantly evolving, and one of the latest advancements is the integration of built-in AI in Google Chrome. This feature was announced in Google’s I/O event and has now been released in the newest Canary release and Dev channel. This built-in AI, called Gemini Nano, is gaining popularity and is set to change the game for web-based AI solutions. In this blog post, we will explore how to set up Gemini Nano in your browser and how to build a practical use case with it.

Setting up Gemini Nano in Your Browser

To start using Gemini Nano, you need to have at least Windows 10 or MacOS 13, an integrated GPU, and 22GB of storage. However, the model itself doesn’t take up much space, so you don’t need to worry about storage limitations. To set up Gemini Nano in your browser, you can follow these simple steps:

1. Install the latest version of Google Chrome Canary or Dev channel.

2. Go to chrome://flags and enable the “Experimental Web Platform features” flag.

3. Restart your browser.

4. Go to chrome://flags again and enable the “Experimental Web Platform features” flag.

5. Restart your browser again.

6. Go to chrome://settings and search for “Gemini Nano” in the search bar.

7. Enable the “Enable built-in AI” option.

8. Restart your browser for the final time.

Congratulations, you have now successfully set up Gemini Nano in your browser!

Building a Practical Use Case With Gemini Nano

Now that you have Gemini Nano set up in your browser, you can start building a practical use case with it. Gemini Nano allows you to bring top-notch LLM capabilities to your users without compromising their privacy. This means that you can deliver a snappy user experience without any middleman involved. In some cases, you can even build offline-first products where your users can access built-in AI even when they are not connected to the internet.

Gemini Nano is also great for building AI features for the web. Traditionally, server-side solutions have been the default for building AI features, but with Gemini Nano, you can now build these features directly into your web application. This eliminates the need for network round trips, resulting in a faster and more efficient user experience.

Conclusion

In conclusion, Google’s new built-in AI model, Gemini Nano, is gaining popularity and is changing the landscape of web-based AI solutions. With its ability to provide top-notch AI capabilities while maintaining privacy and delivering a seamless user experience, built-in AI is poised to become the preferred option for developers and users alike. By eliminating the need for server-side solutions, Gemini Nano allows for offline first products and opens up new possibilities for AI integration. To stay updated on the latest advancements in AI, subscribe to Towards AI and join the growing community of data leaders.

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TL;DR: Prompting is giving instructions to a model to generate text. It’s essential to communicate clearly and concisely, with examples if possible. “Advanced” prompting is just common sense with fancy terms. Effective prompts can improve model responses. Join the AI newsletter for more insights and become a sponsor if you’re building an AI startup or product.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

The Problem with Prompting: A Lack of Clarity and Complexity

Prompting has become a popular technique in the field of artificial intelligence, particularly in natural language processing. It involves giving specific instructions or input to a model in order to generate text that is relevant and of high quality. However, despite the hype surrounding advanced prompting techniques, there is a growing problem with prompting that needs to be addressed.

What Prompting Really Is: Good Communication

At its core, prompting is simply about good communication. It is about telling the model what you want in a clear and concise manner. This can be in the form of questions, instructions, or even examples. The key is to be as specific as possible, so the model knows exactly what is expected of it.

The Rise of Advanced Prompting Techniques

In recent years, there has been a surge in the use of so-called “advanced” prompting techniques. These techniques are often marketed as revolutionary and groundbreaking, but in reality, they are just common sense wrapped in fancy terminology. They may involve using more complex prompts or incorporating additional context, but at the end of the day, it all comes down to good communication.

The Role of Prompt Engineers

With the rise of advanced prompting techniques, there has also been a rise in the number of individuals calling themselves “prompt engineers.” These individuals claim to have specialized knowledge and expertise in designing effective prompts for AI models. However, the truth is that anyone can become a prompt engineer by simply understanding the basics of good communication.

The Importance of Effective Prompts

Despite the simplicity of prompting, it plays a crucial role in the quality and relevance of AI-generated text. A well-designed prompt can significantly enhance the model’s responses, while a poorly designed one can lead to irrelevant or nonsensical outputs. As such, it is essential for AI developers to prioritize the creation of effective prompts in their models.

Conclusion

In conclusion, despite all the buzz surrounding “advanced” prompting techniques and the emergence of “prompt engineers,” the essence of prompting remains simple and straightforward. It’s all about effective communication – clearly and concisely telling the model what you want it to do, with the potential addition of examples for better understanding. Ultimately, the success of a prompt lies in its ability to enhance the quality and relevance of the model’s responses. So, let’s focus on good communication and keep it simple.

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Author(s): Mena Wang, PhD

TL;DR: MLflow and H2O are tools that make it easy to build machine learning models without worrying about the specific algorithms being used. They provide a user-friendly interface for data scientists to experiment with different models and track their performance. This allows for faster and more efficient model building.

Disclaimer: This post has been created automatically using generative AI, including DALL-E, Gemini, OpenAI, and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us.

Introduction to Algorithm-Agnostic Model Building

In the world of machine learning, algorithms play a crucial role in building predictive models. These algorithms are designed to find patterns and relationships in data, and then use those patterns to make predictions. However, with the rapid development of new algorithms and techniques, it can be challenging to keep up and choose the best one for your specific data and problem. This is where algorithm-agnostic model building comes in.

What is Algorithm-Agnostic Model Building?

Algorithm-agnostic model building is an approach to machine learning that focuses on the process of building a model rather than the specific algorithm used. It involves using a framework or platform that allows for easy experimentation and comparison of different algorithms. This approach is becoming increasingly popular as it offers more flexibility and efficiency in model building.

The Role of MLflow in Algorithm-Agnostic Model Building

MLflow is an open-source platform that provides tools for managing the end-to-end machine learning lifecycle. It allows data scientists to track experiments, package and deploy models, and collaborate with team members. One of the key features of MLflow is its ability to support algorithm-agnostic model building. By providing a centralized platform for managing different algorithms, MLflow enables data scientists to easily compare and evaluate their performance.

Benefits of Algorithm-Agnostic Model Building with MLflow

One of the main advantages of algorithm-agnostic model building with MLflow is the ability to experiment with different algorithms quickly. Data scientists can easily switch between algorithms and compare their performance without having to spend time and effort on coding and data preprocessing. This not only saves time but also allows for a more thorough evaluation of the algorithms, leading to better model selection.

Another benefit of using MLflow for algorithm-agnostic model building is the ability to collaborate with team members. MLflow provides a centralized platform where team members can share their experiments, models, and results. This promotes knowledge sharing and collaboration, which can lead to better model building and faster progress.

Conclusion

In today’s rapidly evolving technology landscape, the need for efficient and flexible model building is more important than ever. With the help of tools like MLflow, developers can now build and deploy machine learning models without being restricted by specific algorithms. This algorithm-agnostic approach not only saves time and resources, but also allows for greater adaptability and scalability. By utilizing MLflow for model building, organizations can stay ahead of the curve and make the most out of their data-driven strategies.

Crafted using generative AI from insights found on Towards Data Science.

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TL;DR: Learn how to build a decision tree from scratch, from basic concepts to advanced techniques. Understand key concepts like entropy and Gini impurity, and explore using the logistic function and coding without pre-built libraries. Discover tips for optimizing performance, such as using KS statistics and combining metrics. By the end of this guide, you’ll have the skills and confidence to create and customize your own AI models. Join the AI newsletter with over 80,000 subscribers to stay updated on the latest AI developments, and consider becoming a sponsor if you’re building an AI-related startup or service.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Building a decision tree is a fundamental skill in the field of artificial intelligence and machine learning. Decision trees are powerful tools for classification and prediction tasks, and they are widely used in various industries, from finance to healthcare. In this blog post, we will explore the basics of decision trees and guide you through the process of building one from scratch. Whether you are new to decision trees or looking to enhance your existing knowledge, this hands-on guide will provide you with the necessary skills to become a decision tree expert.

Understanding the Basics of Decision Trees

To begin, let’s start with a simple example to explain the basics of decision trees. Imagine we have data from 1000 individuals with different ages (our input variable x), and we want to predict whether they are employed (target variable Y, binary: 1 for employed, 0 for not employed). The goal is to build a model f(x)=Y that predicts employment status. To start, we need to divide the data into two groups based on a certain age threshold. For example, we can divide the data into two groups: individuals under 30 and individuals over 30. Then, we can calculate the percentage of employed individuals in each group and use that as our prediction for the entire group. This is the basic concept of a decision tree: dividing the data into smaller groups and making predictions based on those groups.

Understanding the Mathematics Behind Decision Trees

Now that we have a basic understanding of decision trees, let’s delve into the mathematics behind them. Two key concepts that are essential for decision trees are entropy and Gini impurity. Entropy is a measure of the randomness in a dataset, while Gini impurity measures the likelihood of a random sample being misclassified. These concepts are used to determine the best split for the data, which leads to more accurate predictions. Additionally, we will also introduce the concept of soft trees, which use the logistic function to make predictions instead of the traditional hard tree approach.

Building Your Decision Tree from Scratch

After covering the theory behind decision trees, it’s time to get hands-on and build our own decision tree from scratch. We will use the popular Titanic dataset, which contains information about passengers on the Titanic and whether they survived or not. We will walk through the steps of preprocessing the data, splitting it into training and testing sets, and then building the decision tree using Python code. This will give you a practical understanding of how decision trees work and how to implement them without using pre-built libraries.

Optimizing Your Decision Tree

Once the decision tree is built, optimization is crucial to enhance its performance. Techniques include:

• Pruning: Reducing the size of the tree by removing sections that provide little power in predicting target variables. This helps prevent overfitting.
• Feature Selection: Identifying and using only the most relevant features to build the tree, improving model efficiency and interpretability.
• Hyperparameter Tuning: Adjusting parameters like maximum depth and minimum samples per leaf to achieve the best model performance.

Advanced Optimization Techniques

For further optimization, consider integrating KS statistics to assess the predictive power and identify the best decision rules. Combining multiple metrics can also provide a more balanced evaluation of model performance.

In conclusion, this guide provides a comprehensive and practical approach to building and extending decision trees. By starting with the basics and gradually introducing more advanced techniques, readers can gain a solid understanding of decision trees and confidently build and optimize their own models. With a simple example and clear explanations, this guide is accessible to all levels of readers. For those interested in staying updated on the latest developments in AI, subscribing to the Towards AI newsletter is recommended.

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TL;DR: Google has once again amazed us with their latest achievements in AI. They have developed two new models, AlphaProof and AlphaGeometry 2, that have achieved silver medalist-level performance in solving complex math problems. This research not only showcases the potential of AI in mathematics, but also gives us a glimpse into Google’s future plans of creating a super AI. Subscribe to the AI newsletter to stay updated on the latest developments and gain valuable insights for decision making. Join over 80,000 subscribers and consider becoming a sponsor if you’re in the AI industry. Read more for free on Medium.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Google Deepmind’s Latest Achievement: AlphaProof and AlphaGeometry 2

On August 6, 2024, Google Deepmind once again made headlines with their latest research breakthrough. They introduced two new models, AlphaProof and AlphaGeometry 2, which have achieved silver medalist-level performance in solving challenging International Mathematical Olympiad problems. This accomplishment is not only a testament to the incredible capabilities of AI, but also a glimpse into what Google has in store for the future.

The Cutting-Edge of AI in Mathematics and Reasoning

The success of AlphaProof and AlphaGeometry 2 is a major milestone in the AI industry. It showcases the progress that has been made in the field of mathematics and reasoning, and how AI is pushing the boundaries of what was previously thought possible. This research is not only fascinating, but also has real-world implications for industries such as finance, engineering, and science.

Cracking the Code for a Super AI

One of the most intriguing aspects of this research is the potential for Google to create a “depth generalizer” – a type of AI that can excel in a wide range of tasks and domains. This would be a significant step towards creating a true super AI, one that can learn and adapt to new situations and problems. Google’s continuous advancements in AI research are bringing us closer to this goal.

The Importance of Staying Informed in the AI Industry

Learning about AI is not just about understanding the technology, but also about making better decisions. As AI continues to shape our world, it is crucial to stay informed and up-to-date with the latest developments. This is where newsletters like Towards AI come in – providing valuable insights and analysis for AI analysts, strategists, investors, and leaders.

Join the AI Community

If you want to stay ahead of the curve in AI, consider subscribing to newsletters like Towards AI, which provide a comprehensive overview of the industry. With over 80,000 subscribers, you will be joining a community of data leaders who are passionate about AI and its potential. And if you are building an AI-related product or service, consider becoming a sponsor to reach a targeted audience of AI enthusiasts.

Stay Ahead of the Curve with Towards AI

In conclusion, Google has once again proven their dominance in the AI industry with the introduction of AlphaProof and AlphaGeometry 2. This achievement not only showcases the capabilities of AI in mathematics and reasoning, but also gives us a glimpse into the future of AI. Stay informed and join the AI community by subscribing to newsletters like Towards AI.

Conclusion

Google has once again proven its dominance in the field of AI with the development of AlphaProof and AlphaGeometry 2. These models have achieved impressive results in solving complex mathematical problems, showcasing the cutting-edge progress of AI in reasoning and problem-solving. This breakthrough not only sheds light on the immense potential of AI, but also gives us a glimpse into Google’s future plans of creating a true super AI. As we continue to learn more about AI, it is important to use this knowledge to make informed decisions. This is the goal of our newsletter, which aims to provide valuable insights for AI analysts, strategists, investors, and leaders. By staying informed, we can stay ahead of the curve in this rapidly advancing industry. Join our community of over 80,000 subscribers and discover the latest developments in AI.

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TL;DR: Learn AI security for free with amazing resources, updated on August 6, 2024. No need to be super-technical or have a PhD in Data Science. The NIST AI Risk Management Framework is a tech-agnostic guide that helps companies responsibly use AI technologies. It’s a great starting point for anyone, regardless of their technical background. More and more companies are using this framework to manage AI risks. Read the full blog on Medium for free and join the AI newsletter with over 80,000 subscribers for the latest developments in AI. Consider becoming a sponsor if you’re building an AI startup or product

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Learn AI Security For FREE With These Amazing Resources

Last Updated on August 6, 2024 by Editorial Team Author(s): Taimur Ijlal Originally published on Towards AI.

Start your AI security journey today and future-proof your career

In today’s digital age, cybersecurity has become a critical concern for individuals and organizations alike. With the rise of artificial intelligence (AI) and its integration into various industries, the need for AI security has become more pressing. As AI technologies continue to evolve and become more sophisticated, so do the potential risks and vulnerabilities. Therefore, it is crucial for individuals to learn about AI security and how to protect against potential threats. In this blog post, we will discuss some amazing resources that can help you learn AI security for free and future-proof your career.

Dispelling the Myth: You Don’t Need to Be a Technical Expert to Learn AI Security

One common misconception about AI security is that it is only for highly technical individuals or those with a PhD in Data Science. However, the reality is that the field is vast enough to accommodate people from both technical and non-technical backgrounds. Whether you are a cybersecurity professional, a data scientist, or someone interested in learning about AI security, there are resources available for you to get started. The key is to have a willingness to learn and a passion for the subject.

No Need to Break the Bank: Free Resources for Learning AI Security

Contrary to popular belief, you do not need to spend a fortune on expensive courses to learn about AI security. The internet is full of amazing resources that you can use for free. Here are a few that we would recommend:

1. NIST Cybersecurity Framework: A Benchmark for Assessing Security Posture

The NIST Cybersecurity Framework has become an industry benchmark for companies to assess their security posture against best practices. Similarly, the NIST AI Risk Management Framework (RMF) is poised to do the same for AI risks. This tech-agnostic guidance is designed to help companies design, develop, deploy, and use AI technologies responsibly. The NIST frameworks are well-trusted within the industry due to the rigorous validation they undergo from experts all across the globe.

2. NIST AI Risk Management Framework: A Comprehensive Approach to Managing AI Risks

The NIST AI RMF is an excellent starting point for anyone interested in learning about AI security, regardless of their technical background. This framework provides a comprehensive approach to managing AI risks through key components such as governance, mapping, measuring, and managing AI systems. With

In conclusion, learning AI security is crucial for anyone looking to future-proof their career in the cybersecurity industry. Contrary to popular belief, one does not need to be highly technical or have a background in data science to enter this field. Thanks to amazing free resources available on the internet, such as the NIST AI Risk Management Framework, anyone can start their journey towards understanding and managing AI risks. As the industry continues to grow and evolve, the NIST framework will become increasingly important for companies to assess and mitigate potential AI risks, making it a valuable skill for individuals to possess. Stay up to date with the latest developments in AI by subscribing to the AI newsletter and join thousands of data leaders who are building AI startups, products, and services. Consider becoming a sponsor to support the growth of this important industry.

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