This is an abbreviated transcript of my FARI “Happy Hour” talk titled “Using AI to Detect the Unusual”, held at BeCentral on the 25th of November, 2024. The slides I used during my talk can be found here. This transcript was created using OpenAI’s Whisper model and edited using GPT-4o.

Introduction

Tonight, I’m going to talk about how we can use artificial intelligence to detect the unusual. If I knew nothing about artificial intelligence, I would have three main questions regarding this title:

1. What is artificial intelligence and how do we use it in general?
2. What is the unusual, and how would we traditionally detect this without using artificial intelligence?
3. How would we then use AI to detect the unusual?

What is Artificial Intelligence?

Artificial intelligence (AI) is a field of research. It is a subset of mathematics and computer science, and it overlaps with philosophy and linguistics, depending on the specific subset of AI being studied. This interdisciplinary nature makes it a fascinating field because you often collaborate with a diverse group of people across various domains.

AI has been researched since the 1950s, and although we’ve heard a lot about it in media recently, the increase in computing power over the last few decades has enabled us to perform remarkable tasks based on the research conducted during this time.

Examples of AI Applications

I have some examples of how we use artificial intelligence:

• Search:

  • In 1996, an AI algorithm named Deep Blue managed to beat the world chess champion, Garry Kasparov.
  • More recently, AlphaGo competed against the second-best player in Go, a game with greater complexity than chess. For those who haven’t seen it, I recommend the excellent documentary about it.
  • Day-to-day applications like Google Maps and Waze help you navigate from point A to point B in the most efficient way possible. You might not think of this as AI, but it results from extensive AI research.

• Classification: If you have a large dataset containing labeled images of cats and dogs, for instance, you can use AI to assign the correct label to new images as they come in. Google offers “teachable machines” that allows users to train classification models using their webcam, requiring no programming experience.

• Generative AI: Tools like ChatGPT for text generation and DALL-E for image generation have gained popularity recently. While impressive, it’s crucial to recognize that generative AI is not the only area of AI research. There are also significant studies in classification, search, and reinforcement learning, for example.

What AI Isn’t

Now that we’ve discussed what artificial intelligence is, it’s also important to clarify what AI isn’t.

First, AI isn’t about creating conscious machines, a notion often propagated in science fiction. While artificial general intelligence—AI with capabilities akin to human intelligence—is a goal for some researchers, most focus on narrow AI tasks. When companies vaguely claim they “use an AI” for significant goals, it raises red flags.

For example, it would be like a toothpaste manufacturer saying they used “a chemistry” to create better toothpaste—it doesn’t quite make sense. It’s important not to fall for the hype surrounding AI in the media. AI has nothing to do with Terminators or humanoid robots; it’s grounded in real-world applications.

Examples of AI Misuse

I’ve put together some examples of how AI can be misused:

• The Smartschool Case: Smartschool is a platform where parents can communicate with their child’s teachers and access their test results. Recently, the company behind the platform announced plans to use AI to predict student dropout risks. This admirable goal (trying to reduce student dropout) was poorly executed, essentially reducing these students to test results and vaguely implementing AI in their product without public dialogue, leading to significant backlash.

• Social Media Companies: Social media platforms are designed to maximize user engagement for profit through targeted advertisement. However, this focus can pose risks to younger users: vulnerable youth are presented with content that romanticizes self-harm, for example. Internal Facebook reports revealed that toxic content on Instagram negatively affects teenagers, but the company plays this down in public.

• Dutch Child Care Benefit Scandal: Thousands of families in the Netherlands were wrongly accused of fraud when applying for child care benefits, based on algorithmic predictions. This led to severe financial hardships for these families, illustrating how false positives can have disastrous impacts.

Detecting the Unusual

Let’s now discuss the unusual and how we detect it.

Gathering Data

Everything around us—every process, system, or activity—produces data, which we can collect through observation and measurement. Here are some examples:

1. Health Monitoring: Tracking heart rate and blood pressure.
2. Financial Activity: Every transaction you make with a credit card—transaction date, time, location, and amount.
3. Social Network Activity: Actions like sending messages, making friend requests, and liking posts.

Understanding Anomalies

Anomalies are deviations from what we consider to be normal behavior, and these deviations leave traces in our observations. We are interested in understanding the underlying causes of these deviations. For example:

• Health Monitoring: An ECG can show if you have arrhythmia, indicating an underlying health issue.
• Financial Activity: If you generally conduct transactions in Belgium, an unexpected transaction from Nigeria would likely be labeled as anomalous.
• Social Network Activity: You can have spam messages or friend requests, also classifiable as anomalies.

Anomalies vs. Outliers

It’s essential to distinguish between anomalies and outliers. Some sources claim they are the same, but this is misleading.

Traditional examples of anomalies often show high peaks and obvious outliers in time series data. However, context matters significantly in determining whether a data point is an anomaly. For instance, a large peak in time series data could be normal if it occurs at regular intervals, suggesting that context is critical.

Using AI to Detect the Unusual

Now that we’ve reached the point of understanding what anomalies are, we can discuss using AI for detecting these unusual patterns.

Anomaly Detection

Anomaly detection refers to finding patterns in data that do not conform to expected behavior. The goal of identifying anomalies is to alert domain experts that something deviates from the norm and to provide actionable information to address the situation.

For instance, in the medical field, detecting arrhythmia would alert healthcare professionals to investigate further.

The Role of Context

Detecting anomalies is highly context-dependent. While classification involves labeling items, anomaly detection focuses on recognizing deviations from expected patterns—often rare occurrences.

Traditional approaches to identifying anomalies are rule-based, which can be cumbersome and require constant updating. Relying solely on these rules may lead to missed anomalies or false triggers.

A Rule-Based Approach

If we would have a manual rule-based configuration for anomaly detection:

• Some anomalies would be missed if predefined rules do not trigger: no rule for certain anomalies means that these anomalies go undetected
• Rule-based trigger check on a “per event” basis, no “bigger picture”: context matters, and anomalies can involve multiple events
• Expert time would be wasted on hard-coded rule design instead of tackling the root cause of anomalies

Challenges in Anomaly Detection

In my research, I previously focused on developing a pattern-based series framework. Instead of relying on hard-coded rules, my framework allows us to apply various AI algorithms to learn a model of normal behavior and test new data against it. However, challenges exist, including:

1. Complexity of Normal Behavior: Context and variation can make defining ’normal’ difficult.
2. Domain-Specific Definitions of Anomalies: Definitions of anomalies can vary significantly depending on the domain (e.g., cybersecurity vs. healthcare).
3. Ambiguity of Artifacts: Different events might present similarly in data, making it challenging to differentiate between normal and anomalous behavior.
4. Impact of Mistakes: Algorithms can produce both false positives and false negatives, leading to significant consequences.

Conclusion

In summary:

1. AI is a research field: It focuses on narrow tasks and is not about conscious machines.
2. Anomaly detection aims to identify unusual patterns in various domains. These patterns are often context-dependent deviations from what is deemed normal.
3. Algorithms can make mistakes, resulting in false positives and negatives that can have disastrous consequences.

Thank you all for your attention. Slides and transcripts are available on my website. If you have any questions, please feel free to ask. Thank you!