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The model hallucinated! It was giving me OK answers and then it just started hallucinating. We’ve all heard or experienced it. Natural Language Generation models can sometimes hallucinate, i.e., they start generating text that is not quite accurate for the prompt provided. In layman’s terms, they start making stuff up that’s not strictly related to […]
Retrieval Augmented Generation (RAG) — An Introduction Read More »
Recently, I’ve been interviewing Computer Science students applying for data science and engineering internships with a 4-day turnaround from CV vetting to final decisions. With a small local office of 10 and no in-house HR, hiring managers handle the entire process. This article reflects on the lessons learned across CV reviews, technical interviews, and post-interview
Beyond the Code: Unconventional Lessons from Empathetic Interviewing Read More »
Introduction EVALUATE is the statement to query tabular models. Unfortunately, knowing SQL or any other query language doesn’t help as EVALUATE follows a different concept. EVALUATE has only two “Parameters”: A table to show A sort order (ORDER BY) You can pass a third parameter (START AT), but this one is rarely used. However, a
How to Write Queries for Tabular Models with DAX Read More »
Deploying your Large Language Model (LLM) is not necessarily the final step in productionizing your Generative AI application. An often forgotten, yet crucial part of the MLOPs lifecycle is properly load testing your LLM and ensuring it is ready to withstand your expected production traffic. Load testing at a high level is the practice of
Load-Testing LLMs Using LLMPerf Read More »
DISCLAIMER: This is not financial advice. I’m a PhD in Aerospace Engineering with a strong focus on Machine Learning: I’m not a financial advisor. This article is intended solely to demonstrate the power of Physics-Informed Neural Networks (PINNs) in a financial context. When I was 16, I fell in love with Physics. The reason was
When Physics Meets Finance: Using AI to Solve Black-Scholes Read More »
Imagine going to the doctor with a baffling set of symptoms. Getting the right diagnosis quickly is crucial, but sometimes even experienced physicians face challenges piecing together the puzzle. Sometimes it might not be something serious at all; others a deep investigation might be required. No wonder AI systems are making progress here, as we
Google’s New AI System Outperforms Physicians in Complex Diagnoses Read More »
Could Shopify be right in requiring teams to demonstrate why AI can’t do a job before approving new human hires? Will companies that prioritize AI solutions eventually evolve into AI entities with significantly fewer employees? These are open-ended questions that have puzzled me about where such transformations might leave us in our quest for Knowledge and ‘truth’
The Good-Enough Truth Read More »
In regression models, the independent variables must be not or only slightly dependent on each other, i.e. that they are not correlated. However, if such a dependency exists, this is referred to as Multicollinearity and leads to unstable models and results that are difficult to interpret. The variance inflation factor is a decisive metric for
When Predictors Collide: Mastering VIF in Multicollinear Regression Read More »
As data professionals, we’re comfortable with tabular data… Tabular data. Image by Author. We can also handle words, json, xml feeds, and pictures of cats. But what about a cardboard box full of things like this? (Image by Annie Spratt, Unsplash) The info on this receipt wants so badly to be in a tabular database
An Unbiased Review of Snowflake’s Document AI Read More »
Is there anything more frustrating than building a powerful data model but then struggling to turn it into a tool stakeholders can use to achieve their desired outcome? Data Science has never been short on potential but is also never short on complexity. You can refine algorithms that shine on curated datasets but still face
Plotly’s AI Tools Are Redefining Data Science Workflows Read More »
