AI’s Missing Piece: Comprehensive AI Testing
AI’s Missing Piece: Comprehensive AI Testing
AI’s Missing Piece: Comprehensive AI Testing
Gaurav Agarwal
Jan 11, 2024
Introduction
The past ten years have seen AI technology grow exponentially. And with the advent of GenerativeAI, there is huge optimism about the potential of AI across industries. However taking the AI from the lab to production in a safe and reliable manner is still a big challenge.
AI failures in production are more common than we think. Lack of proper testing of AI in the lab setting is a key gap causing these failures. Given the complexity of AI, groundbreaking technology and rigorous process is required to address this to ensure that the potential of AI is realised. Latest advancement in AI technology has now made this possible.
AI Failures are Everywhere
General Motors' Cruise recently suspended Autonomous driving operations in San Francisco due to safety concerns. Earlier, it crashed into a bus. Most of the key technologies in their vehicles were AI driven. In late 2021, Zillow had to shut down its home buying business and reported a huge loss and layoffs due to wrong predictions from their AI algorithms. IBM‘s Watson made wrong predictions about cancer patients which had strong repercussions and eventually ended up with IBM selling this business. Tesla too is in the news often about the accidents involving their self-driving cars. The list can go on!
And these are the examples from the big tech companies who have invested billions of dollars to develop their AI technology. If their AI fails, imagine the challenges faced by tens of thousands of other companies who are new to deploying AI. Even with the advent of LLMs, there is a need to fine tune and test the LLMs to the user's own data.
Reasons behind AI Failures
As an AI practitioner for over a decade, I have seen the challenges of building and deploying AI at scale and the failures that happen. There are several reasons for these failures. Ranging from issues with the data used to train & test including comprehensiveness of the data to address Operational Design Domains (ODDs), bias, fairness, corner cases to even issues with the AI model itself such as overfitting and underfitting. Operation aspects such as model compression tradeoffs, latency, performance on GPUs can also impact the desired outcome.
What is Missing - Comprehensive AI Testing
If we look at the software realm, dedicated testing teams meticulously ensure quality, working independently to ensure that the product is tested comprehensively. However, this best practice hasn't seamlessly transitioned into the AI world. Why? Well, testing approaches for AI vary widely across industries and companies.
As an AI practitioner, I have seen that most often AI testing today is merely scratching the surface. Basic checks involve splitting the datasets for training, testing, and validation. But this isn't remotely enough, and is possibly the core reason behind AI failures.
Why is Testing Hard?
Doing proper testing of AI to avoid AI failures is a non-trivial problem statement for many reasons. Firstly, knowing that AI is failing or not performing is not easy. For example, if a LLM is hallucinating, it's not straightforward to know that. Secondly, unlike traditional software, data plays a pivotal role in ensuring that AI is working as intended and ensuring quality of data is extremely important. For example, a vehicle detection algorithm if not trained for low light condition data, will underperform for low light condition images/videos. Well balanced training data is also key to ensuring unbiased and fair AI. Another important aspect is to look at all different aspects while ensuring that AI is functioning properly, so comprehensive testing is important.
What’s the Solution?
A scientific, methodical, and comprehensive approach to AI testing is needed which is a big gap in the AI development life-cycle. The advent of Generative AI and other recent advancements (e.g. compute), it’s now possible to understand the failures of AI and address them in a timely manner.
Introducing RagaAI
RagaAI (raga.ai) is offering a comprehensive Testing / QA Platform for all types of AI/ML applications. RagaAI’s breakthrough foundation model called RagaAI DNA for testing, brings automation to detect AI issues, diagnoses and fixes them instantly. The platform is comprehensive and it already offers over 300+ different tests to help users triage the issue down to its root cause for AI failures which can be as varied as data drift, edge case detection, poor data labelling quality, bias in the data, lack of model robustness to adversarial attacks.
The benefits from the RagaAI Testing Platform are clear - it helps data science teams focus on building the best AI products without getting bogged down with crucial but massive infrastructure development projects. With the promise of 3x faster AI development cycle and at least 90% reduction in AI failures, we believe RagaAI will unlock the next phase of the AI revolution.
Introduction
The past ten years have seen AI technology grow exponentially. And with the advent of GenerativeAI, there is huge optimism about the potential of AI across industries. However taking the AI from the lab to production in a safe and reliable manner is still a big challenge.
AI failures in production are more common than we think. Lack of proper testing of AI in the lab setting is a key gap causing these failures. Given the complexity of AI, groundbreaking technology and rigorous process is required to address this to ensure that the potential of AI is realised. Latest advancement in AI technology has now made this possible.
AI Failures are Everywhere
General Motors' Cruise recently suspended Autonomous driving operations in San Francisco due to safety concerns. Earlier, it crashed into a bus. Most of the key technologies in their vehicles were AI driven. In late 2021, Zillow had to shut down its home buying business and reported a huge loss and layoffs due to wrong predictions from their AI algorithms. IBM‘s Watson made wrong predictions about cancer patients which had strong repercussions and eventually ended up with IBM selling this business. Tesla too is in the news often about the accidents involving their self-driving cars. The list can go on!
And these are the examples from the big tech companies who have invested billions of dollars to develop their AI technology. If their AI fails, imagine the challenges faced by tens of thousands of other companies who are new to deploying AI. Even with the advent of LLMs, there is a need to fine tune and test the LLMs to the user's own data.
Reasons behind AI Failures
As an AI practitioner for over a decade, I have seen the challenges of building and deploying AI at scale and the failures that happen. There are several reasons for these failures. Ranging from issues with the data used to train & test including comprehensiveness of the data to address Operational Design Domains (ODDs), bias, fairness, corner cases to even issues with the AI model itself such as overfitting and underfitting. Operation aspects such as model compression tradeoffs, latency, performance on GPUs can also impact the desired outcome.
What is Missing - Comprehensive AI Testing
If we look at the software realm, dedicated testing teams meticulously ensure quality, working independently to ensure that the product is tested comprehensively. However, this best practice hasn't seamlessly transitioned into the AI world. Why? Well, testing approaches for AI vary widely across industries and companies.
As an AI practitioner, I have seen that most often AI testing today is merely scratching the surface. Basic checks involve splitting the datasets for training, testing, and validation. But this isn't remotely enough, and is possibly the core reason behind AI failures.
Why is Testing Hard?
Doing proper testing of AI to avoid AI failures is a non-trivial problem statement for many reasons. Firstly, knowing that AI is failing or not performing is not easy. For example, if a LLM is hallucinating, it's not straightforward to know that. Secondly, unlike traditional software, data plays a pivotal role in ensuring that AI is working as intended and ensuring quality of data is extremely important. For example, a vehicle detection algorithm if not trained for low light condition data, will underperform for low light condition images/videos. Well balanced training data is also key to ensuring unbiased and fair AI. Another important aspect is to look at all different aspects while ensuring that AI is functioning properly, so comprehensive testing is important.
What’s the Solution?
A scientific, methodical, and comprehensive approach to AI testing is needed which is a big gap in the AI development life-cycle. The advent of Generative AI and other recent advancements (e.g. compute), it’s now possible to understand the failures of AI and address them in a timely manner.
Introducing RagaAI
RagaAI (raga.ai) is offering a comprehensive Testing / QA Platform for all types of AI/ML applications. RagaAI’s breakthrough foundation model called RagaAI DNA for testing, brings automation to detect AI issues, diagnoses and fixes them instantly. The platform is comprehensive and it already offers over 300+ different tests to help users triage the issue down to its root cause for AI failures which can be as varied as data drift, edge case detection, poor data labelling quality, bias in the data, lack of model robustness to adversarial attacks.
The benefits from the RagaAI Testing Platform are clear - it helps data science teams focus on building the best AI products without getting bogged down with crucial but massive infrastructure development projects. With the promise of 3x faster AI development cycle and at least 90% reduction in AI failures, we believe RagaAI will unlock the next phase of the AI revolution.
Introduction
The past ten years have seen AI technology grow exponentially. And with the advent of GenerativeAI, there is huge optimism about the potential of AI across industries. However taking the AI from the lab to production in a safe and reliable manner is still a big challenge.
AI failures in production are more common than we think. Lack of proper testing of AI in the lab setting is a key gap causing these failures. Given the complexity of AI, groundbreaking technology and rigorous process is required to address this to ensure that the potential of AI is realised. Latest advancement in AI technology has now made this possible.
AI Failures are Everywhere
General Motors' Cruise recently suspended Autonomous driving operations in San Francisco due to safety concerns. Earlier, it crashed into a bus. Most of the key technologies in their vehicles were AI driven. In late 2021, Zillow had to shut down its home buying business and reported a huge loss and layoffs due to wrong predictions from their AI algorithms. IBM‘s Watson made wrong predictions about cancer patients which had strong repercussions and eventually ended up with IBM selling this business. Tesla too is in the news often about the accidents involving their self-driving cars. The list can go on!
And these are the examples from the big tech companies who have invested billions of dollars to develop their AI technology. If their AI fails, imagine the challenges faced by tens of thousands of other companies who are new to deploying AI. Even with the advent of LLMs, there is a need to fine tune and test the LLMs to the user's own data.
Reasons behind AI Failures
As an AI practitioner for over a decade, I have seen the challenges of building and deploying AI at scale and the failures that happen. There are several reasons for these failures. Ranging from issues with the data used to train & test including comprehensiveness of the data to address Operational Design Domains (ODDs), bias, fairness, corner cases to even issues with the AI model itself such as overfitting and underfitting. Operation aspects such as model compression tradeoffs, latency, performance on GPUs can also impact the desired outcome.
What is Missing - Comprehensive AI Testing
If we look at the software realm, dedicated testing teams meticulously ensure quality, working independently to ensure that the product is tested comprehensively. However, this best practice hasn't seamlessly transitioned into the AI world. Why? Well, testing approaches for AI vary widely across industries and companies.
As an AI practitioner, I have seen that most often AI testing today is merely scratching the surface. Basic checks involve splitting the datasets for training, testing, and validation. But this isn't remotely enough, and is possibly the core reason behind AI failures.
Why is Testing Hard?
Doing proper testing of AI to avoid AI failures is a non-trivial problem statement for many reasons. Firstly, knowing that AI is failing or not performing is not easy. For example, if a LLM is hallucinating, it's not straightforward to know that. Secondly, unlike traditional software, data plays a pivotal role in ensuring that AI is working as intended and ensuring quality of data is extremely important. For example, a vehicle detection algorithm if not trained for low light condition data, will underperform for low light condition images/videos. Well balanced training data is also key to ensuring unbiased and fair AI. Another important aspect is to look at all different aspects while ensuring that AI is functioning properly, so comprehensive testing is important.
What’s the Solution?
A scientific, methodical, and comprehensive approach to AI testing is needed which is a big gap in the AI development life-cycle. The advent of Generative AI and other recent advancements (e.g. compute), it’s now possible to understand the failures of AI and address them in a timely manner.
Introducing RagaAI
RagaAI (raga.ai) is offering a comprehensive Testing / QA Platform for all types of AI/ML applications. RagaAI’s breakthrough foundation model called RagaAI DNA for testing, brings automation to detect AI issues, diagnoses and fixes them instantly. The platform is comprehensive and it already offers over 300+ different tests to help users triage the issue down to its root cause for AI failures which can be as varied as data drift, edge case detection, poor data labelling quality, bias in the data, lack of model robustness to adversarial attacks.
The benefits from the RagaAI Testing Platform are clear - it helps data science teams focus on building the best AI products without getting bogged down with crucial but massive infrastructure development projects. With the promise of 3x faster AI development cycle and at least 90% reduction in AI failures, we believe RagaAI will unlock the next phase of the AI revolution.
Introduction
The past ten years have seen AI technology grow exponentially. And with the advent of GenerativeAI, there is huge optimism about the potential of AI across industries. However taking the AI from the lab to production in a safe and reliable manner is still a big challenge.
AI failures in production are more common than we think. Lack of proper testing of AI in the lab setting is a key gap causing these failures. Given the complexity of AI, groundbreaking technology and rigorous process is required to address this to ensure that the potential of AI is realised. Latest advancement in AI technology has now made this possible.
AI Failures are Everywhere
General Motors' Cruise recently suspended Autonomous driving operations in San Francisco due to safety concerns. Earlier, it crashed into a bus. Most of the key technologies in their vehicles were AI driven. In late 2021, Zillow had to shut down its home buying business and reported a huge loss and layoffs due to wrong predictions from their AI algorithms. IBM‘s Watson made wrong predictions about cancer patients which had strong repercussions and eventually ended up with IBM selling this business. Tesla too is in the news often about the accidents involving their self-driving cars. The list can go on!
And these are the examples from the big tech companies who have invested billions of dollars to develop their AI technology. If their AI fails, imagine the challenges faced by tens of thousands of other companies who are new to deploying AI. Even with the advent of LLMs, there is a need to fine tune and test the LLMs to the user's own data.
Reasons behind AI Failures
As an AI practitioner for over a decade, I have seen the challenges of building and deploying AI at scale and the failures that happen. There are several reasons for these failures. Ranging from issues with the data used to train & test including comprehensiveness of the data to address Operational Design Domains (ODDs), bias, fairness, corner cases to even issues with the AI model itself such as overfitting and underfitting. Operation aspects such as model compression tradeoffs, latency, performance on GPUs can also impact the desired outcome.
What is Missing - Comprehensive AI Testing
If we look at the software realm, dedicated testing teams meticulously ensure quality, working independently to ensure that the product is tested comprehensively. However, this best practice hasn't seamlessly transitioned into the AI world. Why? Well, testing approaches for AI vary widely across industries and companies.
As an AI practitioner, I have seen that most often AI testing today is merely scratching the surface. Basic checks involve splitting the datasets for training, testing, and validation. But this isn't remotely enough, and is possibly the core reason behind AI failures.
Why is Testing Hard?
Doing proper testing of AI to avoid AI failures is a non-trivial problem statement for many reasons. Firstly, knowing that AI is failing or not performing is not easy. For example, if a LLM is hallucinating, it's not straightforward to know that. Secondly, unlike traditional software, data plays a pivotal role in ensuring that AI is working as intended and ensuring quality of data is extremely important. For example, a vehicle detection algorithm if not trained for low light condition data, will underperform for low light condition images/videos. Well balanced training data is also key to ensuring unbiased and fair AI. Another important aspect is to look at all different aspects while ensuring that AI is functioning properly, so comprehensive testing is important.
What’s the Solution?
A scientific, methodical, and comprehensive approach to AI testing is needed which is a big gap in the AI development life-cycle. The advent of Generative AI and other recent advancements (e.g. compute), it’s now possible to understand the failures of AI and address them in a timely manner.
Introducing RagaAI
RagaAI (raga.ai) is offering a comprehensive Testing / QA Platform for all types of AI/ML applications. RagaAI’s breakthrough foundation model called RagaAI DNA for testing, brings automation to detect AI issues, diagnoses and fixes them instantly. The platform is comprehensive and it already offers over 300+ different tests to help users triage the issue down to its root cause for AI failures which can be as varied as data drift, edge case detection, poor data labelling quality, bias in the data, lack of model robustness to adversarial attacks.
The benefits from the RagaAI Testing Platform are clear - it helps data science teams focus on building the best AI products without getting bogged down with crucial but massive infrastructure development projects. With the promise of 3x faster AI development cycle and at least 90% reduction in AI failures, we believe RagaAI will unlock the next phase of the AI revolution.
Introduction
The past ten years have seen AI technology grow exponentially. And with the advent of GenerativeAI, there is huge optimism about the potential of AI across industries. However taking the AI from the lab to production in a safe and reliable manner is still a big challenge.
AI failures in production are more common than we think. Lack of proper testing of AI in the lab setting is a key gap causing these failures. Given the complexity of AI, groundbreaking technology and rigorous process is required to address this to ensure that the potential of AI is realised. Latest advancement in AI technology has now made this possible.
AI Failures are Everywhere
General Motors' Cruise recently suspended Autonomous driving operations in San Francisco due to safety concerns. Earlier, it crashed into a bus. Most of the key technologies in their vehicles were AI driven. In late 2021, Zillow had to shut down its home buying business and reported a huge loss and layoffs due to wrong predictions from their AI algorithms. IBM‘s Watson made wrong predictions about cancer patients which had strong repercussions and eventually ended up with IBM selling this business. Tesla too is in the news often about the accidents involving their self-driving cars. The list can go on!
And these are the examples from the big tech companies who have invested billions of dollars to develop their AI technology. If their AI fails, imagine the challenges faced by tens of thousands of other companies who are new to deploying AI. Even with the advent of LLMs, there is a need to fine tune and test the LLMs to the user's own data.
Reasons behind AI Failures
As an AI practitioner for over a decade, I have seen the challenges of building and deploying AI at scale and the failures that happen. There are several reasons for these failures. Ranging from issues with the data used to train & test including comprehensiveness of the data to address Operational Design Domains (ODDs), bias, fairness, corner cases to even issues with the AI model itself such as overfitting and underfitting. Operation aspects such as model compression tradeoffs, latency, performance on GPUs can also impact the desired outcome.
What is Missing - Comprehensive AI Testing
If we look at the software realm, dedicated testing teams meticulously ensure quality, working independently to ensure that the product is tested comprehensively. However, this best practice hasn't seamlessly transitioned into the AI world. Why? Well, testing approaches for AI vary widely across industries and companies.
As an AI practitioner, I have seen that most often AI testing today is merely scratching the surface. Basic checks involve splitting the datasets for training, testing, and validation. But this isn't remotely enough, and is possibly the core reason behind AI failures.
Why is Testing Hard?
Doing proper testing of AI to avoid AI failures is a non-trivial problem statement for many reasons. Firstly, knowing that AI is failing or not performing is not easy. For example, if a LLM is hallucinating, it's not straightforward to know that. Secondly, unlike traditional software, data plays a pivotal role in ensuring that AI is working as intended and ensuring quality of data is extremely important. For example, a vehicle detection algorithm if not trained for low light condition data, will underperform for low light condition images/videos. Well balanced training data is also key to ensuring unbiased and fair AI. Another important aspect is to look at all different aspects while ensuring that AI is functioning properly, so comprehensive testing is important.
What’s the Solution?
A scientific, methodical, and comprehensive approach to AI testing is needed which is a big gap in the AI development life-cycle. The advent of Generative AI and other recent advancements (e.g. compute), it’s now possible to understand the failures of AI and address them in a timely manner.
Introducing RagaAI
RagaAI (raga.ai) is offering a comprehensive Testing / QA Platform for all types of AI/ML applications. RagaAI’s breakthrough foundation model called RagaAI DNA for testing, brings automation to detect AI issues, diagnoses and fixes them instantly. The platform is comprehensive and it already offers over 300+ different tests to help users triage the issue down to its root cause for AI failures which can be as varied as data drift, edge case detection, poor data labelling quality, bias in the data, lack of model robustness to adversarial attacks.
The benefits from the RagaAI Testing Platform are clear - it helps data science teams focus on building the best AI products without getting bogged down with crucial but massive infrastructure development projects. With the promise of 3x faster AI development cycle and at least 90% reduction in AI failures, we believe RagaAI will unlock the next phase of the AI revolution.
