In this episode, the hosts focus on the basics of anomaly detection in machine learning and AI systems, including its importance, and how it is implemented. They also touch on the topic of large language models, the (in)accuracy of data scraping, and the importance of high-quality data when employing various detection methods. You'll even gain some techniques you can use right away to improve your training data and your models.

Intro and discussion (0:03)

• Questions about Information Theory from our non-parametric statistics episode.
• Google CEO calls out chatbots (WSJ)
• A statement about anomaly detection as it was regarded in 2020 (Forbes)
• In the year 2024, are we using AI to detect anomalies, or are we detecting anomalies in AI? Both? 

Understanding anomalies and outliers in data (6:34)

• Anomalies or outliers are data that are so unexpected that their inclusion raises warning flags about inauthentic or misrepresented data collection. 
• The detection of these anomalies is present in many fields of study but canonically in: finance, sales, networking, security, machine learning, and systems monitoring
• A well-controlled modeling system should have few outliers
• Where anomalies come from,  including data entry mistakes, data scraping errors, and adversarial agents 
• Biggest dinosaur example: https://fivethirtyeight.com/features/the-biggest-dinosaur-in-history-may-never-have-existed/

Detecting outliers in data analysis (15:02)

• High-quality, highly curated data is crucial for effective anomaly detection. 
• Domain expertise plays a significant role in anomaly detection, particularly in determining what makes up an anomaly.

Anomaly detection methods (19:57)

• Discussion and examples of various methods used for anomaly detection 
  • Supervised methods
  • Unsupervised methods
  • Semi-supervised methods
  • Statistical methods

Anomaly detection challenges and limitations (23:24)

• Anomaly detection is a complex process that requires careful consideration of various factors, including the distribution of the data, the context in which the data is used, and the potential for errors in data entry
• Perhaps we're detecting anomalies in human research design, not AI itself?
• A simple first step to anomaly detection is to visually plot numerical fields. "Just look at your data, don't take it at face value and really examine if it does what you think it does and it has what you think it has in it." This basic practice, devoid of any complex AI methods, can be an effective starting point in identifying potential anomalies.

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