Page 10 – Advance Aii

Clustering documents and gaussian data with...

June 30, 2014 Vasilis Vryniotis . No comments This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we will try to link the theory with the practice by introducing two models DPMM: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model which is used to cluster documents. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.clustering to see the implementation of Dirichlet Process Mixture Models in Java. 1. The Dirichlet Multivariate Normal Mixture Model The first Dirichlet Process mixture model that we will examine is the Dirichlet Multivariate Normal Mixture Model which can be used to perform clustering on continuous datasets. The mixture model is defined as follows: Equation 1: Dirichlet Multivariate Normal Mixture Model As we can see above, the particular model assumes that the Generative Distribution is the Multinomial Gaussian Distribution and uses the Chinese Restaurant process as prior for the cluster assignments. Moreover for the Base...

July 7, 2014 Vasilis Vryniotis . 1 Comment In the previous articles we discussed in detail the Dirichlet Process Mixture Models and how they can be used in cluster analysis. In this article we will present a Java implementation of two different DPMM models: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model which is used to clustering documents. The Java code is open-sourced under GPL v3 license and can be downloaded freely from Github. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.clustering to see the implementation of Dirichlet Process Mixture Models in Java. Dirichlet Process Mixture Model implementation in Java The code implements the Dirichlet Process Mixture Model with Gibbs Sampler and uses the Apache Commons Math 3.3 as a matrix library. It is licensed under GPLv3 so feel free to use it, modify it and redistribute it freely and you can download the Java implementation from Github. Note that you can find all the theoretical parts of the clustering method in the previous 5 articles and detailed Javadoc comments for implementation in the source code. Below we list...

🔘 Paper page: arxiv.org/abs/2108.02497?fbclid=IwAR3MNl5qa5ysUoNlkEQE4hSXNGoEGwtCClMNcJDXH1etKHNcCweDRTXW_tY Abstract «This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions. It covers five stages of the machine learning process: what to do before model building, how to reliably build models, how to robustly evaluate models, how to compare models fairly, and how to report results». Authors Michael A. Lones Liked this post? Follow this blog to get more.

FBI Warns of AI Voice Scams The FBI warns of AI voice scams, a chilling alert that highlights how artificial intelligence is being used to exploit trust and manipulate emotion. In its latest advisory, the FBI has expressed growing concern over criminals using generative AI to create convincing voice clones of loved ones, executives, or colleagues. These voices often prompt victims to hand over money or sensitive data. This reflects a major change in social engineering threats, as synthetic media like deepfake audio is making scams more personalized and effective. With the rise of AI-powered deception tactics, individuals and organizations must learn to spot warning signs and implement protective strategies. Key Takeaways Criminals are using AI-driven tools to clone voices and execute convincing scams. The FBI has issued a public warning and urges extreme caution when receiving urgent voice requests. These voice cloning scams often prompt financial actions or data transfers under false pretenses. The growing availability of AI tools increases the threat level for both individuals and businesses. Also Read: Protecting Your Family from AI Threats How AI Voice Scams Work AI voice scams involve the use of generative artificial intelligence to synthesize and replicate a person’s voice based...

As the tech world buzzes with the unveiling of Apple Intelligence, expectations are soaring. The leap from iPhone to AI-Phone paints a picture of a future where our devices aren’t just tools but partners capable of anticipating our needs and actions. Yet, amidst this enthusiastic anticipation, it’s crucial to examine the potential pitfalls that might cause Apple Intelligence to fall short of these lofty expectations. The very ambition that makes Apple Intelligence seem revolutionary could also be its Achilles’ heel. Apple plans to seamlessly integrate advanced AI across its suite of devices, promising an ecosystem where your iPad, iPhone, and Mac work together more intelligently than ever. However, the complexity of implementing such deep, cross-platform integration without glitches, privacy issues, or user frustration is immense. Could Apple be promising more than current technology realistically allows? Apple has long championed privacy as a cornerstone of its brand, yet the increased data processing required by Apple Intelligence could strain this commitment. With features like real-time language and image processing touted, the volume of data analyzed by AI will be vast. Even with promises of on-device processing, the potential for privacy breaches grows as more personal information is constantly analyzed by AI. Will...

For the past decade and a half, I’ve been exploring the intersection of technology, education, and design as a professor of cognitive science and design at UC San Diego. Some of you might have read my recent piece for O’Reilly Radar where I detailed my journey adding AI chat capabilities to Python Tutor, the free visualization tool that’s helped millions of programming students understand how code executes. That experience got me thinking about my evolving relationship with generative AI as both a tool and a collaborator. I’ve been intrigued by this emerging practice called “vibe coding,” a term coined by Andrej Karpathy that’s been making waves in tech circles. Simon Willison describes it perfectly: “When I talk about vibe coding I mean building software with an LLM without reviewing the code it writes.” The concept is both liberating and slightly terrifying—you describe what you need, the AI generates the code, and you simply run it without scrutinizing each line, trusting the overall “vibe” of what’s been created. My relationship with this approach has evolved considerably. In my early days of using AI coding assistants, I was that person who meticulously reviewed every single line, often rewriting significant portions. But as...

Microsoft has introduced the generative AI model Phi-4 with fully open weights on the Hugging Face platform. Since its presentation in December 2024, Phi-4 has garnered attention for its enhanced performance in mathematical computations and multitask language understanding, while requiring fewer computational resources than larger models. Phi-4, boasting 14 billion parameters, is designed to compete with models like GPT-4o mini, Gemini 2.0 Flash, and Claude 3.5 Haiku. This Small Language Model (SLM) is optimized for complex mathematical calculations, logical reasoning, and efficient multitasking. Despite its smaller size, Phi-4 delivers high performance, processes long contexts, and is ideal for applications that demand precision and efficiency. Another standout feature is its MIT license, allowing free use, modification, and distribution, even for commercial purposes. Microsoft has further enhanced the model using synthetic data and fine-tuning techniques, improving its accuracy in tasks requiring reasoning. An example of Phi-4’s mathematical reasoning capabilities is demonstrated in the figure below. In April 2023, Microsoft introduced Phi-3 Mini, the first in the Phi-3 series of small language models. It featured 3.8 billion parameters and was trained on a smaller dataset compared to larger models like GPT-4. This was followed in August by Phi-3.5 models, including Phi-3.5-vision and Phi-3.5-MoE,...

Abstract «The rapid recent progress in machine learning (ML) has raised a number of scientific questions that challenge the longstanding dogma of the field. One of the most important riddles is the good empirical generalization of overparameterized models. Overparameterized models are excessively complex with respect to the size of the training dataset, which results in them perfectly fitting (i.e., interpolating) the training data, which is usually noisy. Such interpolation of noisy data is traditionally associated with detrimental overfitting, and yet a wide range of interpolating models – from simple linear models to deep neural networks – have recently been observed to generalize extremely well on fresh test data. Indeed, the recently discovered double descent phenomenon has revealed that highly overparameterized models often improve over the best underparameterized model in test performance. Understanding learning in this overparameterized regime requires new theory and foundational empirical studies, even for the simplest case of the linear model. The underpinnings of this understanding have been laid in very recent analyses of overparameterized linear regression and related statistical learning tasks, which resulted in precise analytic characterizations of double descent. This paper provides a succinct overview of this emerging theory of overparameterized ML (henceforth abbreviated as TOPML)...

I have been learning through Andrew Ng’s Deep Learning specialization on Coursera. I have completed the 1st of the 5 courses in the specialization (Neural Networks and Deep Learning). I am onto the 2nd one which is Improving Deep Learning. This one is a very interesting course which goes deep into Hyper-parameter tuning, Regularization and Optimization techniques. 1. What are Optimization Algorithms? They enable you to train your neural network much faster since Applied Machine Learning is a bery empirical process these algorithms help reach optimized results efficiently. Let’s start looking into Optimization Algorithms with a more sophisticated version of Gradient Descent. 1.1 Batch v/s Mini-Batch Gradient Descent In general, Gradient Descent goes over the entire set of training examples (#m), and takes one step towards the global minima. This is also called Batch Gradient Descent. This is rather a little inefficient because it requires us to go through all the training examples before taking a tiny step towards the minima. How about we use a smaller chunk/sample of the training set to take a step at a time? This is nothing but Mini-Batch Gradient Descent. This means that we divide the input training set (X) and the target set...

Like it or not, technology is everywhere. We have long ago passed a time where computers were relegated to men in white coats in distant rooms. It’s now in our pockets, in our faces, and mediates many of our relationships. Yet, as Shoshana Zuboff explains in The Age of Surveillance Capitalism, recent years were characterized by AI and other manipulative technologies monitoring and controlling us, rather than the other way around. As an AI professional who cares about the problems raised by AI systems, not just at the individual level but at the interface between technology and whole-systems change, I ask myself, what can be done? Let’s start with the obvious: Democracy is about empowering people, yet technology is increasingly disempowering us. Would it make sense, then, to regain our agency by taking back control of AI? Many have realized that the internet and social media created a false-or at least greatly flawed-democratization. Vyacheslav W. Polonski, writing in Newsweek, summarizes the situation: “Instead of creating a digitally-mediated agora which encourages broad discussion, the internet has increased ideological segregation. It filters dissent out of our feeds and grants a disproportionate amount of clout to the most extreme opinions...