OpenAI updated its GPT-5.5 Instant model with enhanced intent understanding and better ability to follow complex instructions and user clarifications. PCWorld reports the update includes improved location data usage for more effective local business and product searches. This minor release focuses on making AI interactions more intuitive, though its real-world impact requires further observation. Earlier this week, OpenAI announced via release notes that it has updated its most widely used AI model: GPT-5.5 Instant. According to the company, GPT-5.5 Instant should now be better at understanding the underlying intent of a question and keeping track of context across multiple messages.

Protecting cloud applications is critical in an era where security threats are increasingly sophisticated and persistent. Continuous Integration and Continuous Deployment (CI/CD) pipelines are particularly vulnerable, making innovative security approaches essential. This research explores the application of Natural Language Processing (NLP) techniques, specifically Topic Modelling, to analyse security-related text data and anticipate potential threats. We focus on Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) to extract meaningful patterns from data sources, including logs, reports, and deployment traces. Using the Gensim framework in Python, these methods categorise log entries into security-relevant topics (e.g., phishing, encryption failures). The identified topics are leveraged to highlight patterns indicative of security issues across CI/CD's continuous stages (build, test, deploy). This approach introduces a semantic layer that supports early vulnerability recognition and contextual understanding of runtime behaviours.

Measuring innovation often relies on context-specific proxies and on expert evaluation. Hence, empirical innovation research is often limited to settings where such data is available. We investigate how large language models (LLMs) can be leveraged to overcome the constraints of manual expert evaluations and assist researchers in measuring innovation. We design an LLM framework that reliably approximates domain experts' assessment of innovation from unstructured text data. We demonstrate the performance and broad applicability of this framework through two studies in different contexts: (1) the innovativeness of software application updates and (2) the originality of user-generated feedback and improvement ideas in product reviews. We compared the performance (F1-score) and reliability (consistency rate) of our LLM framework against alternative measures used in prior innovation studies, and to state-of-the-art machine learning- and deep learning-based models. The LLM framework achieved higher F1-scores than the other approaches, and its results are highly consistent (i.e., results do not change across runs). This article equips R&D personnel in firms, as well as researchers, reviewers, and editors, with the knowledge and tools to effectively use LLMs for measuring innovation and evaluating the performance of LLM-based innovation measures. In doing so, we discuss, the impact of important design decisions-including model selection, prompt engineering, training data size, training data distribution, and parameter settings-on performance and reliability. Given the challenges inherent in using human expert evaluation and existing text-based measures, our framework has important implications for harnessing LLMs as reliable, increasingly accessible, and broadly applicable research tools for measuring innovation.

OpenAI, the company behind the popular conversational AI model, ChatGPT, has released an update to improve the chatbot's accuracy. Following an extended period of downtime on Tuesday, ChatGPT is up and running with a new model. This is the first update to ChatGPT this year and the second update to the model since its launch in November. A popup message lists the changes in what OpenAI calls the "Jan 9 version" update. "We made more improvements to the ChatGPT model! It should be generally better across a wide range of topics and has improved factuality."

Tesla's Full-Self Driving is using generative pre-trained transformers (GPT) for vision, Elon Musk tweeted recently. He added that the GPTs are running natively on Tesla TRIP chips versus needing to round trip to iGPU. I think it's important to take a quick deep dive into this, because this is kind of the heart and soul of FSD. Know It All Knows It All" to translate what all of this means. Learning new things is something we all should be open to, and that's why I'm writing this today. Elon Musk's initial tweet was a response to @JeffTutorials, who asked Elon Musk to add software release notes into the Tesla app, adding that it would be nice to see what was new right from the phone. In that thread, Elon Musk noted that the transformers are replacing C heuristics for post-processing of the vision neural networks' giant bag of points. I asked Dr. Know It All to share a bit more about TRIP chips and he pointed me to a project that the Department of Computer Science at The University of Texas at Austin worked on. I think, but am not 100% sure, that Elon was referring to TRIPS chips, which is a type of microprocessor architecture. You can read up on the project here. In the tweet below, KL Manish shared a definition of a TRIP chip and Elon Musk confirmed this. Dr. Know It All noted that Elon Musk revealed a lot of useful information, and his video is a short dive into what exactly Elon Musk is talking about and why it matters. I'm sure Jeff didn't plan on initiating a conversation about artificial intelligence and GPT, and Elon's reply to Jeff is a bit off the topic. What Jeff was referring to was making the release notes available in the Tesla app as well as on the screen of the car. It's a brilliant suggestion and would make taking screenshots of the release notes easier for those who share them on Twitter for us writers to write about. Dr. Know It All explained that GPT is something that OpenAI is working on -- specifically GPT3. GPT3 has 175 billion parameters. Now, I'm not saying GPT3 is what Tesla is using here but I just wanted to put that as a contextual element there."

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. By far PyCaret 2.3.6 is the biggest release in terms of the new features and functionalities. This article demonstrates the use of new functionalities added in the recent release of PyCaret 2.3.6. Check out our official notebooks!

PyTorch 1.4 has been released, and the PyTorch domain libraries have been updated along with it. The popular open source machine learning framework has some experimental features on board, so let's take a closer look. PyTorch Mobile was first introduced in PyTorch 1.3 as an experimental release. It should provide an "end-to-end workflow from Python to deployment on iOS and Android," as the website states. In the latest release, PyTorch Mobile is still experimental but has received additional features.

Tesla has released a new software update with major improvements to its automatic wiper trained with a new deep neural net previously referred to as "Deep Rain." Like most premium vehicles today, Tesla has an automatic wiper system that automatically matches the speed of the wipers to the intensity of the rain or snow. Instead, the automaker is using its Autopilot cameras to feed its computer vision neural net to determine the speed for the wipers. It has been deployed in Tesla vehicles since last year, but some owners have been complaining that it is not as accurate as other systems using rain sensors. Lately, CEO Elon Musk has been talking about Tesla releasing a new "Deep Rain" neural net to improve the automatic wipers.

Machine learning framework TensorFlow 1.15 is now available to download, offering those too shy to make the switch to TF 2.0 a way to emulate the new major version's behaviour, as well as offering additional features such as tensor equality and default GPU support. The release is the last of the 1.x branch, since the revamped TensorFlow 2.0 has already been out since end of September 2019. Moving forward, new features will likely be reserved for the more current series, but according to the release notes, patch releases will keep 1.x users safe from vulnerabilities for at least another year. Before updating, users should be aware of the breaking changes included in the release, especially in tf.keras. In the latter, the number of threads has to be configured using the tf.config.threading

Ok, I can give you some answers based on my experiences as software engineer (over 10 years). I deal also a lot with open-source and I'm the author of dozens of open-source libraries with thousands of stars and millions of installations as well, so I know both sides (author and user) in both private and commercial applications pretty well. Also, many people ask me the question why we use at aetros.com Let's define some properties that define whether a library X is good or not: Let me explain in detail each point. When you use libraries, no matter if open-source or commercial, and you want to continue to develop an application using that library, it's very important that there are no hidden changes and your application doesn't break when you update the library (to get wanted features or bugfixes).