Flutter Google cross-platform UI framework has released a new version 1.20 stable. Flutter is Google's UI framework to make apps for Android, iOS, Web, Windows, Mac, Linux, and Fuchsia OS. Since the last 2 years, the flutter Framework has already achieved popularity among mobile developers to develop Android and iOS apps. In the last few releases, Flutter also added the support of making web applications and desktop applications. Last month they introduced the support of the Linux desktop app that can be distributed through Canonical Snap Store(Snapcraft), this enables the developers to publish there Linux desktop app for their users and publish on Snap Store.

The AI wars are heating up. Smelling blood in the water, Microsoft has been working tirelessly to bake ChatGPT AI models into virtually all of its products. So far, the most prolific example of that has been Bing Chat. Bing Chat is a new section on Microsoft's oft-derided number 2 search engine that allows users to search with natural human language, and receive natural human language results in return. Powered by OpenAI's ChatGPT-4, Bing has exclusive rights to commercial use of the machine learning models from the firm, prompting a furor of panic at search leader Google.

Originally published on Towards AI. In the last few years, if you google healthcare or clinical NLP, you would see that the search results are blanketed by a few names like John Snow Labs (JSL), Linguamatics (IQVIA), Oncoustics, BotMD, Inspirata. All of these companies were founded between 2013–2016 in various parts of the world. Soon to be followed by large general language models like BERT (Bidirectional Encoder Representations from Transformers). In hindsight, these companies might have been too early in their evolution, as they missed the boat by being early on what groundbreaking advances OpenAI brought to the world.

Originally published on Towards AI. Artificial Intelligence surely is at a great pace these days. Many big tech firms and individuals are diving into the pool of artificial intelligence to keep up with the technologies and launch their versions of AI models. Not very long ago only, ChatGPT, an OpenAI's creation, was launched. This AI model gained popularity immediately after its launch.

As artificial intelligence (AI) continues to evolve, so do the capabilities of Large Language Models (LLMs). These models use machine learning algorithms to understand and generate human language, making it easier for humans to interact with machines. Microsoft Research Asia has taken this technology a step further by introducing VisualGPT. This AI model incorporates Visual Foundation Models (VFM) to enhance the understanding, generation, and editing of visual information. ChatGPT uses natural language processing (NLP) techniques to generate responses to user input.

As a Unity XR prototyper, I'm always eager to explore new technologies and tools that can help me push the boundaries of what's possible. Recently, I have had the pleasure of trying out Auki Lab's ConjureKit SDK and experimenting with its monocular hand tracker (by the way, neither LIDaR nor depth sensors are required, the hand tracker just works with one RGB camera.) In my test scene, I spawned a few spheres, the user can touch the spheres which will deform a bit and move. I needed to create a grid pattern that would make the deformation more visible. Since I'm not an expert in shader programming, I decided to try using ChatGPT (OpenAI) to generate an HLSL script.

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses.

Auto-GPT, also known as Automatic Generative Pre-training Transformer, is a state-of-the-art technology that can generate high-quality, human-like text based on a given prompt or input. Setting up Auto-GPT can be a complex process, but with the right tools and guidance, anyone can get started with this powerful technology. In this article, we will walk you through the steps of setting up Auto-GPT, from choosing a model to selecting a cloud service provider, to fine-tuning your model for optimal results. Step 1: Choose Your Auto-GPT Model The first step in setting up Auto-GPT is to choose a model that best fits your needs. There are several different models available, each with different capabilities and tradeoffs.

While being acutely aware of the potential harm involved in using Artificial Intelligence (AI), I can't help but be intrigued by the possibilities. After listening to academics and following ChatGPT fans on social media, it is no secret that ChatGPT is not a good option for finding the best peer-reviewed academic literature. ChatGPT will "hallucinate" articles that don't even exist. This AI weakness is a real cause for alarm. This weakness has the potential to perpetuate misinformation.

Insomnia is a blessing and a curse: a blessing, because in these times of tumult we need new ideas; a curse, because I’d rather sleep better and leave these worries to someone else. But leaving it to…