New Delhi, NFAPost: Sophos, a global leader in innovating and delivering cybersecurity as a service, released new research on how the cybersecurity industry can leverage GPT-3, the language model behind the now well-known ChatGPT framework, as a co-pilot to help defeat attackers. The latest report, "GPT for You and Me: Applying AI Language Processing to Cyber Defenses," details projects developed by Sophos X-Ops using GPT-3's large language models to simplify the search for malicious activity in datasets from security software, more accurately filter spam, and speed up analysis of "living off the land" binary (LOLBin) attacks. Sophos Principal Threat Researcher Sean Gallagher said Since OpenAI unveiled ChatGPT back in November, the security community has largely focused on the potential risks this new technology could bring. "Can the AI help wannabee attackers write malware or help cybercriminals write much more convincing phishing emails? Perhaps, but, at Sophos, we've long seen AI as an ally rather than an enemy for defenders, making it a cornerstone technology for Sophos, and GPT-3 is no different. The security community should be paying attention not just to the potential risks, but the potential opportunities GPT-3 brings," said Sophos Principal Threat Researcher Sean Gallagher.

Maintaining accurate and interesting interactions is essential for a successful user experience as chatbots continue to gain popularity across different industries. However, chatbots frequently experience conversation drift, in which over time, their responses become less pertinent and comprehensible. Users may find it annoying and may also reduce the chatbot's efficiency. A simple and straightforward method can be applied to stop conversation drift and maintain the focus of the chatbot convo. We will examine this method in this post and show how it works using ChatGPT, a highly robust large language model (LLM) that has recently gained a lot of popularity in different specializations.

Jensen Huang getting carried away about an emerging technology is nothing new. This time last year, the charismatic and excitable co-founder and CEO of chip design giant Nvidia was telling anyone who'd listen about the potential of the metaverse (or the Omniverse, as Nvidia's marketing department prefers to call it). Since then, the metaverse bubble has suffered a slow puncture, and Huang is back to evangelising about one of his favourite topics: artificial intelligence. Describing the growth in power of generative AI systems like GPT-4 – the model that powers OpenAI's tools such as ChatGPT – as a "new era of computing", Huang told investors on his company's most recent earnings call that AI was at an "inflection point", stating that businesses have "an urgency to develop and deploy new AI strategies". However, Huang added that he believes many companies face "an insurmountable obstacle" in getting access to the resources and skills needed to make AI work, which is why, he says, Nvidia is getting into the services business.

As the world becomes increasingly digital, the need for secure and decentralized communication channels has never been greater. This book examines the ways in which integrating ChatGPT with blockchain technology can provide a powerful solution to this problem. The book begins with an overview of the basics of artificial intelligence and blockchain technology, providing a foundation for readers who may be unfamiliar with these concepts. From there, it delves into the specific ways in which ChatGPT, a state-of-the-art language model, can be integrated with blockchain technology to create a secure and decentralized communication platform. Readers will learn about the technical details of integrating these two technologies, as well as the potential applications for such a platform.

Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models. At its core, the goal of prompt engineering is about alignment and model steerability. Check my previous post on controllable text generation.

Powerful new artificial-intelligence software is already shaking up the travel industry, but it has a long way to go until it can plan a seamless trip. I want to hit a history museum and an amusement park -- and then I'd like 7 p.m. dinner reservations near the hotel at a restaurant with vegan options and a great wine list." But for now, travelers using ChatGPT -- the powerful new A.I. software that is already offering creative cocktail recipes and writing college papers -- may have to temper their expectations. Oded Battat, the general manager at Traveland, a travel agency in Bridgeport, Conn., asked ChatGPT for outings he might offer his clients going to Tuscany to see if it could help him with his work. He got a list of 14 activities, including winery tours and museum visits, with a stop for gelato in the town square of the medieval hill town San Gimignano.

In just a few months, you'll be able to ask a virtual assistant to transcribe meeting notes during a work call, summarize long email threads to quickly draft suggested replies, quickly create a specific chart in Excel, and turn a Word document into a PowerPoint presentation in seconds. Over the past week, a rapidly evolving artificial intelligence landscape seemed to leap ahead again. Microsoft and Google each unveiled new AI-powered features for their signature productivity tools and OpenAI introduced its next-generation version of the technology that underpins its viral chatbot tool, ChatGPT. Suddenly, AI tools, which have long operated in the background of many services, are now more powerful and more visible across a wide and growing range of workplace tools. Google's new features, for example, promise to help "brainstorm" and "proofread" written work in Docs.

ChatGPT is gaining immense popularity and that too in a very short span of time. It can get a lot done in a very short span of time. From automobile companies to social media platforms like Snapchat, elements of ChatGPT are being adopted by all. But have you ever thought of earning money with the help of ChatGPT? Yes, you can do it too.

Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of classes defined at training time. In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method. Our method builds on the insight that we can fuse image features from a vision-language model into a neural implicit representation. We show that the resulting feature field can be segmented into different classes by assigning points to natural language text prompts. The implicit volumetric representation enables us to segment the scene both in 3D and 2D by rendering feature maps from any given viewpoint of the scene. We show that our method works on noisy real-world data and can run in real-time on live sensor data dynamically adjusting to text prompts. We also present quantitative comparisons on the ScanNet dataset.

AI documentation is a rapidly-growing channel for coordinating the design of AI technologies with policies for transparency and accessibility. Calls to standardize and enact documentation of algorithmic harms and impacts are now commonplace. However, documentation standards for AI remain inchoate, and fail to match the capabilities and social effects of increasingly impactful architectures such as Large Language Models (LLMs). In this paper, we show the limits of present documentation protocols, and argue for dynamic documentation as a new paradigm for understanding and evaluating AI systems. We first review canonical approaches to system documentation outside the context of AI, focusing on the complex history of Environmental Impact Statements (EISs). We next compare critical elements of the EIS framework to present challenges with algorithmic documentation, which have inherited the limitations of EISs without incorporating their strengths. These challenges are specifically illustrated through the growing popularity of Model Cards and two case studies of algorithmic impact assessment in China and Canada. Finally, we evaluate more recent proposals, including Reward Reports, as potential components of fully dynamic AI documentation protocols.