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Squint AI

The Squint AI platform is divided into three parts, tackling each phase of an AI-pipeline's life-cycle:

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  • Discovery Phase: Aimed at the development phase of capable AI models. Debugging AI models is not so difficult with Squint AI.

  • Deployment Phase: Aimed at monitoring runtime predictions for signs of mistakes, and providing measures to improve each decision.

  • Maintenance Phase: Ensures that the performance of deployed models does not degrade over time.

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Squint AI is necessary when the cost of a mistake is high, e.g: autonomous vehicles, healthcare automation, financial projections.

Autonomous-Vehicle

Whether you are driving on a country road, or navigating a busy inner-city block, taking a second look at the evolving scene, squinting when you are unsure, is what makes you a safer driver.

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Squint AI equips vision pipelines for autonomous vehicles, with the capability to measure the quality of each decision, and refine the decision when necessary.

Medical Domain

Use Case Example

X-Ray

Cancer Detection

Hospitals and medical laboratories are investing heavily in solutions that can automate the process of detecting different types of cancers by analyzing data collected from patients. 

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Automation is important in cancer detection use cases as it can significantly reduce the time that a patient has to wait before receiving a diagnosis. The sooner that signs of cancer are confirmed, the sooner that the patient can begin to receive treatment.

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A team of researchers recently trained a neural network model on a dataset of breast cancer images. They then used the Squint Insights™ platform to investigate the types of images leading to mistakes by the model.

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In the dataset of 130,000 images, the original model made ~15,000 mistakes. Although an error rate of ~10% is considered much better than a human pathologist's, these results were achieved in testing. In production environments the error rate might increase if the types of images leading to mistakes occur with a higher frequency.

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The Insights platform was used to identify markers in the data, as well as the model's decision-making process that are correlated with prediction errors. These markers were then added to a watchdog module for runtime prediction monitoring.

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The Insights platform then produced a Squinting™ model specifically trained to analyze the types of images leading to mistakes. Finally, the Squinting model and the watchdog were organized into a more capable vision pipeline.

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The Squint pipeline led to an improved performance of 97% accuracy, compared to the original 90%. The improvement was due to the watchdog module flagging 75% of the predictions leading to mistakes as requiring further analysis. The Squinting model performed this analysis and automatically corrected 2% of the mistakes, leaving the rest to a human pathologist to correct.

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Highlights: 

  • The watchdog module correctly flagged 75% of the mistakes in production.

  • Overall improvement of 7% in accuracy.

  • The Squinting model produced 177 false negatives less than the original model

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How Does Squint Help?

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Results After Deploying the Squint Pipeline

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