Days and nights have been busy diving deeper into the AI landscape. I've been reading a great book by Tom Taulli called Artificial Intelligence Basics: A Non-Technical Introduction. It's been a huge help in getting down the vocabulary and understanding the background to the technology from the 1950s on. In combination with the book, I've been messing around a lot with Azure's OpenAI Service and looking closely at the infrastructure and security aspects of the service. In my last post I covered the controls available to customers to secure their specific instance of the service.

From fake photos of Donald Trump being arrested by New York City police officers to a chatbot describing a very-much-alive computer scientist as having died tragically, the ability of the new generation of generative artificial intelligence systems to create convincing but fictional text and images is setting off alarms about fraud and misinformation on steroids. Indeed, a group of artificial intelligence researchers and industry figures urged the industry on March 29, 2023, to pause further training of the latest AI technologies or, barring that, for governments to "impose a moratorium." These technologies – image generators like DALL-E, Midjourney and Stable Diffusion, and text generators like Bard, ChatGPT, Chinchilla and LLaMA – are now available to millions of people and don't require technical knowledge to use. Given the potential for widespread harm as technology companies roll out these AI systems and test them on the public, policymakers are faced with the task of determining whether and how to regulate the emerging technology. The Conversation asked three experts on technology policy to explain why regulating AI is such a challenge – and why it's so important to get it right.

In this blog post, we will introduce you to a powerful, user-friendly solution for transcribing YouTube videos: a Flask web app utilizing OpenAI's cutting-edge Whisper API for speech-to-text conversion. We'll walk you through the entire process, from setting up the app to integrating Pytube for downloading YouTube videos and OpenAI's Whisper API for transcription. By the end of this post, you'll have a functional web application that allows users to transcribe YouTube videos with just a few clicks. So, let's dive in and explore the world of automated YouTube video transcription! The app created in this guide is a user-friendly web application that allows users to transcribe YouTube videos effortlessly.

With RunwayML, you can create and experiment with generative models in a matter of minutes, without having to write a single line of code" (RunwayML Website). Demand for innovation: Generative AI has opened up new possibilities and opportunities for innovation in various fields and industries. Generative AI can help to generate new ideas, designs, products, services, etc. that can solve problems or meet needs. In manufacturing, Autodesk and Creo use generative AI to design physical objects. In some cases, they also create those objects through 3D printing or computer-controlled machining and additive manufacturing. NVIDIA's set an example through GauGAN, an AI-powered tool that can transform rough sketches into photorealistic images in real-time. "GauGAN represents a major breakthrough in AI-powered image creation, opening up new possibilities for artists, designers, and creatives.

In the age of technological marvels, Artificial Intelligence (AI) chatbot, ChatGPT, created by OpenAI, has been a game-changer. ChatGPT offers personalized restaurant recommendations, table bookings, travel arrangements, and even grocery orders. But beneath the awe-inspiring capabilities lies a startling revelation. A recent bug in the chatbot has exposed users' payment information, leaving thousands of subscribers vulnerable. You must be wondering who the culprit behind this is.

Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing exponentially in the past few years, there is an increasing need for surveys of diffusion models on specific fields. In this work, we are committed to conducting a survey on the graph diffusion models. Even though our focus is to cover the progress of diffusion models in graphs, we first briefly summarize how other generative modeling methods are used for graphs. After that, we introduce the mechanism of diffusion models in various forms, which facilitates the discussion on the graph diffusion models. The applications of graph diffusion models mainly fall into the category of AI-generated content (AIGC) in science, for which we mainly focus on how graph diffusion models are utilized for generating molecules and proteins but also cover other cases, including materials design. Moreover, we discuss the issue of evaluating diffusion models in the graph domain and the existing challenges.

OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.

Google Cloud offered a glimpse of what it is doing to keep up in the race as the so-called generative AI (software that can create images, text, and videos based on user prompts) buzzes in Silicon Valley. The company demonstrated how cloud customers will be able to use its AI tools to make presentations and documents for sales training, take notes in meetings, and write emails to coworkers. Developers now have access to some of the company's underlying AI models, allowing them to create their applications using Google's technology. Google, which is owned by Alphabet Inc., also stated on Tuesday that it had signed up a slew of AI startups as customers for its cloud service. These startups include Midjourney, which provides a system for image generation, and AI21, which focuses on large language model technology.

Not even OpenAI comes close. Before it became the fastest-growing consumer app in history, before it popularised the phrase "generative pre-trained transformers," and before every company you can think of was racing to adopt its underlying model, ChatGPT debuted in November as a "research preview." In this article, we have explained how ChatGPT sparked a new AI race and revived the popularity of text boxes. Read to know more about ChatGPT sparked a new AI race. The blog post that announced ChatGPT has since become a hilarious case study in underselling.

As Dave has stated, AI is a powerful tool that is driving meaningful value today across multiple retail verticals. Although there is a lot of hype around generative AI and some of the more creative and qualitative aspects of AI, there is no doubt that the quantitative aspects of AI are the most important ones for retailers today. In fact, Gartner specifically calls out 26 different use cases for AI in retail with price optimization, promo optimization and demand forecasting as the clear top use cases for value and feasibility. In a highly inflationary market with shifting brand loyalties and endless disruptions, the data is clear -- retailers using sophisticated AI solutions for their pricing have stolen share and customers profitably from retailers who haven't made the leap. While a lot of AI capabilities may not be a sound investment today, there is no doubt that retailers trying to compete on price without sophisticated tools are at a strategic disadvantage.