SaaS AI Tools is a collection of AI-powered tools to help supercharge your creativity to the next level

We'll implement a naive semantic video search using OpenAI's CLIP model (ignoring audio) in Python. By the end of the post, we'll get results like this: Note that dog has the highest value, which is what we would hope for since the image is of a dog. But do the cat and misc values seem low enough compared to the dog value? Well, looking at the CLIP codebase we can see that softmax with a temperature parameter (i.e. So we can see that the model is pretty certain that "a photo of a dog" is the best of the options it was presented with to describe the image.

To understand how radically gaming is about to be transformed by Generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion Dreambooth, popular 2D Generative AI models, to generate images of potions for a hypothetical game. What's transformative about this work is not just that it saves time and money while also delivering quality – thus smashing the classic "you can only have two of cost, quality, or speed" triangle. Artists are now creating high-quality images in a matter of hours that would otherwise take weeks to generate by hand. What's truly transformative is that: There hasn't been a technology this revolutionary for gaming since real-time 3D. Spend any time at all talking to game creators, and the sense of excitement and wonder is palpable. So where is this technology going? And how will it transform gaming?

Blockchain enables creators to package and monetize their digital content in new ways. However, it's not the only tech stack that is doing so. Artificial intelligence (AI) is also redefining creativity in the digital space, and here's how. DALL-E, Stable Diffusion, and Midjourney are all generative AI models. They use AI algorithms to automatically generate digital content based on a simple prompt that would otherwise take a human a long time to complete.

Picture Lee Unkrich, one of Pixar's most distinguished animators, as a seventh grader. Some of the magic wears off, however, when Lee learns that the image had not appeared simply by asking for "a picture of a train." Instead, it had to be painstakingly coded and rendered--by hard-working humans. Now picture Lee 43 years later, stumbling onto DALL-E, an artificial intelligence that generates original works of art based on human-supplied prompts that can literally be as simple as "a picture of a train." As he types in words to create image after image, the wow is back.

The Governance Team's mission is to develop late-stage, credible AGI strategy and governance plans. Pursuing this mission involves developing, analyzing, and ultimately taking action toward concrete and realistic interventions that could contribute to ensuring that the development and deployment of increasingly powerful, general AI models go well for humanity. If you enjoy owning research projects end-to-end, thrive in roles where the ambitious entrepreneurial pursuit of open-ended goals is rewarded, and are strongly motivated to contribute to the roll-out of advanced general AI systems going well, you will find our work here uniquely challenging and rewarding. We are looking for experienced research scientists with a proven track record of delivering high-quality research autonomously and collaboratively. In this role, you will produce novel insights, ideas, and proposals that shift the needle on the clarity of our roadmap toward governing increasingly powerful, advanced AI models.

Proteins are the molecules that get work done in nature, and there's a whole industry emerging around successfully modifying and manufacturing them for various uses. But doing so is time consuming and haphazard; Cradle aims to change that with an AI-powered tool that tells scientists what new structures and sequences will make a protein do what they want it to. The company emerged from stealth today with a substantial seed round. AI and proteins have been in the news lately, but largely because of the efforts of research outfits like DeepMind and Baker Lab. Their machine learning models take in easily collected RNA sequence data and predict the structure a protein will take -- a step that used to take weeks and expensive special equipment.

One of the latest applications of Artificial Intelligence (AI) is to generate images from natural language descriptions. These generators are now becoming available and achieve impressive results that have been used for example in the front cover of magazines. As the input to the generators is in the form of a natural language text, a question that arises immediately is how these models behave when the input is written in different languages. In this paper we perform an initial exploration of how the performance of three popular text-to-image generators depends on the language. The results show that there is a significant performance degradation when using languages other than English, especially for languages that are not widely used. This observation leads us to discuss different alternatives on how text-to-image generators can be improved so that performance is consistent across different languages. This is fundamental to ensure that this new technology can be used by non-native English speakers and to preserve linguistic diversity.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. For Tome, which calls itself the "new storytelling format for work and important ideas," integrating OpenAI's DALL-E into its flexible, interactive slide options -- which it announced today -- was a natural fit to add a generative AI dimension to decks. When OpenAI announced the release of the DALL-E API in early November, the San-Francisco-based startup had its chance. "Making that a part of the storytelling creation experience just felt really natural," Tome CEO Keith Peiris told VentureBeat. "It felt so much more powerful than looking for a stock photo or clip art -- it's kind of giving us a first look at what generative storytelling can look like."

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Salesforce's AI research is heavily focused on generative AI techniques to provide a fully conversational workflow, according to Silvio Savarese, EVP and chief scientist at Salesforce. In a world with increasing workloads -- where even highly trained experts are expected to do more with less -- as well as constant information overload and the need to master complex tools, harnessing the power of simple conversation is incredibly useful, he says. In a recent Salesforce Research blog post, Saverase called conversation "a kind of universal interface for human collaboration." That's why Salesforce developed its open-source large-scale language model, CodeGen, which is competitive with OpenAI's Codex (which, in turn, powers GitHub Copilot) and turns simple English prompts into executable code.