A new GamesBeat event is around the corner! Learn more about what comes next. Uizard, a startup developing AI-powered tools to build apps and websites, today announced that it raised $15 million in funding from Insight Partners with participation from Mariano Suarez-Battan, byFounders, LDV Capital, and av8 Ventures. According to CEO Tony Beltramelli, the proceeds will be used to grow Uizard's marketing and commercial teams and hire new talent globally, with a focus on Europe. Driven by the consumerization of software and increasing customer demand for good design, companies now live or die by the design of their products.

We've created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today. Codex is the model that powers GitHub Copilot, which we built and launched in partnership with GitHub a month ago. Proficient in more than a dozen programming languages, Codex can now interpret simple commands in natural language and execute them on the user's behalf--making it possible to build a natural language interface to existing applications. We are now inviting businesses and developers to build on top of OpenAI Codex through our API. OpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.

OpenAI has already made some big changes to Codex, the AI-powered coding assistant the company announced last month. The system now accepts commands in plain English and outputs live, working code, letting someone build a game or web app without so much as naming a variable. A few lucky coders (and, one assumes, non-coders) will be able to kick the tires on this new Codex API in a free private beta. Codex is best thought of as OpenAI's versatile language engine, GPT-3, but trained only on code instead of ordinary written material. That lets it do things like complete lines of code or entire sections, but when it was announced it wasn't really something a non-coder would be able to easily interact with.

Every one is talking about the mighty, great, futuristic language model by OpenAI, founded by Tesla CEO Elon Musk, Y Combinator partner Sam Altman and other Silicon Valley big shots like Google researchers and ex CTO of Stripe. It is truly eye opening. We also want to tell you how exciting it is. Why is GPT-3 so hyped right now? Probably because GPT-3 has the coolest video demos ever: based on just a few English sentences it can generate a TODO app (write code by itself), generate Excel spreadsheets, automatically translate, generate quizzes based on content. Every one is writing about GPT-3, but because we are technical, our article will give you the important technical details and background you need to understand OpenAI's GPT-3.

Google LLC said today that its Recommendations AI tool is now publicly available in public beta test mode. Recommendations AI was first announced in private beta in April 2019, and relies on Google's sophisticated artificial intelligence tools to help retailers deliver highly personalized recommendations to their customers. In a blog post, Google Product Manager Pallav Mehta said retailers can use the new tools to shift the emphasis from individual products to each customer, by serving each one with personalized product recommendations. Read more about Google Recommendations AI tool that was announced in private beta in April 2019, is now available in public beta on Silicon Angle.

Originally, the artificial intelligence (AI) development and research organization, OpenAI, was founded as a nonprofit with the ambitious mission of ensuring artificial general intelligence would benefit all humanity. Since then, much has happened, starting with original founder Elon Musk leaving OpenAI's board in 2018. In July of 2019, the narrative changed yet again after the company received a $1 billion investment from Microsoft. The company exists today as a "capped-profit" organization. Most notably during this time, the company developed a text-generating language system it chose to not release "due to our concerns about malicious applications of the technology," then subsequently released said system.

We're releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose "text in, text out" interface, allowing users to try it on virtually any English language task. You can now request access in order to integrate the API into your product, develop an entirely new application, or help us explore the strengths and limits of this technology. Given any text prompt, the API will return a text completion, attempting to match the pattern you gave it. You can "program" it by showing it just a few examples of what you'd like it to do; its success generally varies depending on how complex the task is.

Phiar, a Palo Alto-based startup that is using artificial intelligence and augmented reality to enhance vehicular navigation, is finally making its long-awaited AR driving app available for the first time -- albeit in a limited fashion. The company today announced the start of a private beta program for iPhone users, and plans to expand the program to Android devices later in 2020. Founded by experts in computer vision and machine learning, Phiar spent 2018 and 2019 raising funds and developing its mobile app, which is designed to serve as an augmented navigational head-up display (HUD) that sits on a car's dashboard. Instead of filling a phone's screen with a 2D or 3D map, the app can superimpose guidance lines, arrows, and icons atop live camera feeds of streets. Phiar notes that the app automatically picks visible AR overlays by looking at the road, lighting, weather, and objects present in the live video, drawing pathways using either lines or higher contrast patterns that will be more visible as you drive.

It's been eight weeks since we announced our upcoming service MachineLabs to the world. We've opened the gates to apply for the private beta which is just around the corner. Today we would like to reflect on our recent progress and the road ahead. We are super happy to unveil that we just founded MachineLabs, Inc. After we've implemented a first prototype of MachineLabs, we knew we are onto something and decided pretty quickly after that, to bootstrap a new company as soon as possible. Here's our brand new company logo, carefully crafted with 3 by Judith Kutscheid.

Optimail uses AI to optimize drip email marketing campaigns automatically and continuously while learning directly from customers' behaviors. Under the hood, Optimail uses a suite of algorithms concentrating on message sequence optimization, message delivery optimization, and customer profiling. These algorithms have changed a lot since we first thought of automating email marketing optimization using AI… and, in fact, we weren't always sure that they would work. In this post, I wanted to share the story of how the idea of Optimail was born, how we built and tested our algorithms through several iterations, and what gave us the confidence to bring Optimail to market. The idea that became Optimail first came to light about a year ago.