#50 Why Do Neural Networks Hallucinate?

Towards AI’s latest course teaches you how to reduce hallucinations in your LLM systems!?

Good morning, AI enthusiasts! This week, we have a small gift for our community. Use the coupon "learnaitogether" to get 10% off all courses, including From Beginner to LLM Developer and the ebook Building LLMs for Production.

We also have very interesting articles on AI essentials like casual inference, AI agents, and predictive modeling, along with exciting discussions and products from our community.

More About From Beginner to Advanced LLM Developer

This course is the product of a year of work from our ~15-course writers and has resulted in the most in-depth LLM developer course out there, with 85+ lessons and many, many code notebooks! We brought together everything we learned from building over the past two years to teach the full stack of LLM skills. It is a one-stop conversion course for software developers, machine learning engineers, data scientists, aspiring founders, or AI/Computer Science students- junior or senior alike.

We cover the full stack of learning to build on top of foundation LLMs - from choosing a suitable LLM application to collecting data, iterating on many advanced techniques (RAG, fine-tuning, agents, audio, caching, and more), integrating industry expertise, and deploying. Right now, this means working with Python, OpenAI, Llama 3, Gemini, Perplexity, LlamaIndex, Langchain, Hugging Face, Gradio, and many other amazing tools (we are unaffiliated and will introduce all the best LLM tool options). It also means learning many new non-technical skills and habits unique to the world of LLMs. The only skill required for the course is some knowledge of Python (or basic programming). Our students will create a working product, which we certify, and we also provide instructor support in our dedicated Discord channel. This could become the seed of a startup, a new tool at your company, or a portfolio project for landing an LLM Developer job.?

You can find all the lesson titles, syllabus, and more information via a free preview here !?

— Louis-Fran?ois Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community section!

Featured Community post from the Discord

.fetze has created a platform that uses smart search functions and personalized recommendations to help you find the AI tools that best suit your projects. You only need to describe the task for which the AI tool is required, and it will display the top AI options. Check it here and support a fellow community member. If you have any questions or feedback, share it in the thread !?

AI poll of the week!

Most of you consider yourself a beginner in the field, and this is true not just for our community but also for the AI community in general. LLMs have only been around for a few years, and a lot has happened since; it’s truly difficult to keep up and let’s say you did keep up, but it’s still very difficult to predict what to prepare for. Where is the friction with LLM expertise for you? Tell me in the thread !?

P.S. That’s why we created the ‘Beginner to Advanced LLM Developer” Course, to help make this journey a little easier and more future-proof for you.?

Meme of the week!

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TAI Curated section

Article of the week

Empirical Techniques for Enhanced Predictive Modeling: Beyond Traditional ARMA By Shenggang Li

This article explores the use of empirical techniques in predictive modeling, offering alternatives to traditional ARMA models. The author argues that empirical methods, being non-parametric and data-driven, are more robust to outliers and complex patterns found in real-world datasets. Two approaches are explored in this article: empirical transformation, which ranks data to smooth irregularities before applying ARMA, and empirical likelihood, which modifies the ARMA model's loss function to accommodate unknown data distributions. Backtesting using energy consumption data demonstrates that both methods significantly improve forecasting accuracy and stability compared to standard ARMA, particularly in handling outliers and capturing nuanced data patterns. It concludes by highlighting the broad applicability of empirical techniques across diverse fields, including fraud detection and financial modeling.

Our must-read articles

1. AI Research Agents: Set to Transform Knowledge Research in 2025 (Plus Top 3 Free Tools) By Alden Do Rosario

This article discusses the transformative potential of AI research agents, predicting a significant impact on knowledge research by 2025. These agents, unlike traditional AI tools, autonomously conduct research, analyze vast datasets, and generate insights faster than humans. It highlights the use of Retrieval Augmented Generation (RAG) and anti-hallucination algorithms for accuracy and source citation. It introduces three free tools: Stanford STORM, CustomGPT.ai Researcher, and GPT Researcher, each with its strengths and limitations regarding ease of use and data sources. It underscores that these agents are designed to complement, not replace, human researchers, enabling them to focus on strategic, high-level tasks.

2. Why Do Neural Networks Hallucinate (And What Are Experts Doing About It)? By Vitaly Kukharenko

This article discusses AI hallucinations and explains that this phenomenon stems from the probabilistic nature of large language models (LLMs), which operate on patterns rather than true understanding, mirroring Godel's incompleteness theorems. Several examples of hallucinations, ranging from misinformation to fabricated data, are provided, highlighting the potential risks. Then, it explores current research efforts to mitigate hallucinations, including improved data processing, statistical methods like entropy analysis, and techniques like Fully-Formatted Facts. Finally, it offers practical strategies for developers to minimize hallucinations in their applications.

3. A Modern Approach To The Fundamental Problem of Causal Inference By Andrea Berdondini

This article explores the fundamental problem of causal inference, highlighting the fallacy of assuming correlation implies causality. It argues that the statistical approach, unlike the experimental, offers a solution by considering tested hypotheses as interdependent. Generating numerous hypotheses creates a composite hypothesis that can fit any dataset, leading to false correlations. The probability of a random correlation is accurately calculated only by considering all prior attempts, a challenging task in practice. Also, it emphasizes the importance of conscious statistical practice and the risks associated with readily available computational power that lead to false correlations and irreproducible results.

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