AI's Hacking Skills Are Approaching an'Inflection Point' AI models are getting so good at finding vulnerabilities that some experts say the tech industry might need to rethink how software is built. Vlad Ionescu and Ariel Herbert-Voss, cofounders of the cybersecurity startup RunSybil, were momentarily confused when their AI tool, Sybil, alerted them to a weakness in a customer's systems last November. Sybil uses a mix of different AI models --as well as a few proprietary technical tricks--to scan computer systems for issues that hackers might exploit, like an unpatched server or a misconfigured database. In this case, Sybil flagged a problem with the customer's deployment of federated GraphQL, a language used to specify how data is accessed over the web through application programming interfaces (APIs). The issue meant that the customer was inadvertently exposing confidential information.

Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of autonomous vehicle (AV) decisions. This paper evaluates the capability of four multimodal large language models (MLLMs), including relatively small models, to understand scenes in a zero-shot, in-context learning setting. Additionally, we explore whether combining these models using an ensemble approach with majority voting can enhance scene understanding performance. Our experiments demonstrate that GPT-4o, the largest model, outperforms the others in scene understanding. However, the performance gap between GPT-4o and the smaller models is relatively modest, suggesting that advanced techniques such as improved in-context learning, retrieval-augmented generation (RAG), or fine-tuning could further optimize the smaller models' performance. We also observe mixed results with the ensemble approach: while some scene attributes show improvement in performance metrics such as F1-score, others experience a decline. These findings highlight the need for more sophisticated ensemble techniques to achieve consistent gains across all scene attributes. This study underscores the potential of leveraging MLLMs for scene understanding and provides insights into optimizing their performance for autonomous driving applications.

We present an upper bound for the Single Channel Speech Separation task, which is based on an assumption regarding the nature of short segments of speech. Using the bound, we are able to show that while the recent methods have made significant progress for a few speakers, there is room for improvement for five and ten speakers. We then introduce a Deep neural network, SepIt, that iteratively improves the different speakers' estimation. At test time, SpeIt has a varying number of iterations per test sample, based on a mutual information criterion that arises from our analysis. In an extensive set of experiments, SepIt outperforms the state-of-the-art neural networks for 2, 3, 5, and 10 speakers.

"Please note that the official seller in India is Pothi. Other sellers are selling printouts/photocopies for cheaper prices" This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option.

There are a growing number of works out there addressing how to approach machine learning problems, many of them quite good. But how many of them are written by a 4x Kaggle Grandmaster? Abhishek Thakur, the 4x Grandmaster in question -- who now works on NLP at Hugging Face -- wrote and released his book Approaching (Almost) Any Machine Learning Problem (AAAMLP) last year. The book can be purchased through Amazon for a very reasonable price, much more so than most other books of similar content. Additionally, however, Abhishek has recently released the entirety of the book online for free, available in PDF via its Github repo.

Those keeping abreast of the latest medical developments may be aware of the buzz surrounding applications of artificial intelligence (AI) to medical tasks. To date, these have mainly involved application of computer algorithms to clinical data such as x-rays, images or text-based medical records, to diagnose disease. The sensationalism has largely arisen due to the fact that in some instances, these algorithms have met or exceeded capabilities of a specialist physician for particular diagnostic tasks. With these early accomplishments, a question arises as to how the introduction of clinically viable AI may affect the role of human physicians in the future. Being at a primitive stage and lacking widespread real-world application, the topic remains speculative at present.

What happens at the black hole event horizon, where time stands still and intuition breaks down? In the opening chapter of his new book, Einstein's Monsters, astronomer and popular science writer Chris Impey puts the problem succinctly: "It made no sense for a physical object to have zero size and infinite mass density. Einstein's theory had created something monstrous." Despite a few shortcomings, the book will be sure to capture the imagination of most who pick it up, simultaneously convincing the reader that these monsters should be loved and not feared.

Is it worth comparing approaches to the machine learning process? Are there any fundamental differences between such frameworks? Though classical approaches to such tasks exist, and have existed for some time, it is worth taking consult from new and different perspectives for a variety of reasons: Have I missed something? Are there new approaches which had not previously been considered? Should I change my perspective on how I approach machine learning?

Machine learning capabilities are a significant asset in IIoT platforms, assisting in the collection and organization of data between multiple edge devices within the network. FogHorn Systems announced yesterday the availability of Lightning ML, an edge intelligence software platform for the Industrial Internet of Things (IIoT), which the company states is the industry's first IIoT software platform with integrated machine learning capabilities and universal combability across all major IIoT edge systems, i.e. operational technology (OT) systems and IoT sensors. "To date, machine learning is typically done in the cloud," said David C. King, CEO of FogHorn Systems. "FogHorn Lightning ML is designed to deliver the same powerful machine learning (ML) insights on a very tiny footprint, less than 256MB." Lightning ML users can execute proprietary, domain-specific ML models or choose from ML algorithms which plug into streaming data from assets and machines.

Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. We will be using python!