Lifeboat Foundation

A team of computer programmers at IT University of Copenhagen has developed a new way to encode and generate Super Mario Bros. levels—called MarioGPT, the new approach is based on the language model GPT-2. The group outlines their work and the means by which others can use their system in a paper on the arXiv pre-print server.

Mario Brothers is a video game first introduced in 1983—it involves two Italian plumbers emerging from a sewer and attempting to rescue Princess Peach, who has been captured and held by Bowser. To rescue her, the brothers must travel (via input from the game player) across a series of obstacles made of pipes and bricks. As they travel, the terrain changes in accordance with the level they have achieved in the game. In this new effort, the team in Denmark has recreated one aspect of the game—the number of levels that can be traversed.

The researchers used Generative Pre-trained Transformer 2 (GPT-2)—an open-source language model created by a team at OpenAI, to translate user requests into graphical representations of Super Mario Brothers game levels. To do so, they created a small bit of Python code to help the language model understand what needed to be done and then trained it using samples from the original Super Mario Bros. game and one of its sequels, “Super Mario Bros.: The Lost Levels.”

An international team led by researchers from Nanyang Technological University, Singapore (NTU Singapore) has developed a universal connector to assemble stretchable devices simply and quickly, in a ‘Lego-like’ manner.

Stretchable devices including soft robots and wearable healthcare devices are assembled using several different modules with different material characteristics — some soft, some rigid, and some encapsulated.

However, the commercial pastes (glue), currently used to connect the modules often either fail to transmit mechanical and electrical signals reliably when deformed or break easily.

The concept of synthetic data is almost too good to be true – it can mimic the distinctive properties of a dataset while dodging a number of issues that afflict data. There are zero data privacy concerns around synthetic data since it is artificially generated and isn’t related to real-world persons. It can be manufactured on demand and in the volumes required. In other words, synthetic data is a boon in a world eternally thirsty for data.

And the hectic space of generative AI is offering a helping hand in the easy generation of synthetic data.

The concept of synthetic data has been around for decades until the autonomous vehicle (AV) industry started using it commercially in the mid-2010s. But for how important an issue it resolves, creating synthetic data brings a myriad of complications along with it.

I have to admit, it’s very human like. It makes we wonder if the incident if fabricated, but it may also be true.

SOPA images/Getty.

The concept of a conversational chatbot shot to fame with OpenAI’s ChatGPT, which can provide paraphrased answers for users’ detailed queries and write poetry or code with equal ease. Microsoft, which has provided financial support for OpenAI’s work, has incorporated the chatbot into its Bing search engine, providing users with a new way to search for information. The service’s rollout is still slow, with few users getting access. However, their experience has been interesting.

This is a talk I gave to an audience of computer scientists and neuroscientists, interested in AI, consciousness, and the brain.

Professor of Biology at Tufts University Michael Levin shows the remarkable plasticity of somatic (non-neural) cells and the way they communicate through bioelectric signalling to produce different morphologies. He argues that cellular control of growth and form is a type of collective intelligence.

Prof. Levin also shows that by manipulating bioelectric signalling between cells it is possible to change what the cells are going to build. The particular examples include converting one type of tadpole tissue into another, making planaria (a type of flatworm) to regrow two heads, etc. Prof. Levin’s and his team work has profound theoretical contributions towards understanding better biological intelligence, and from the practical side, it may lead to applications in biomedicine (solving birth defects, curing degenerative disease and cancer).

Continue reading “Collective intelligence of cell swarms | Prof. Michael Levin | AI Forward Forum” »

The ability to work with ChatGPT is becoming a valuable skill for job seekers, as they need to be able to extract relevant responses to create high-quality content.

System appears to be suffering a breakdown as it ponders why it has to exist at all.

To further promote Copilot, GitHub is giving it an update, which will bring a set of new capabilities. According to GitHub, this update will benefit both users in the Copilot for Individuals and Copilot for Business plans.

Copilot is an AI-based coding tool that offers autocomplete-style suggestions while the users code. It complements Visual Studio, Neovim, and JetBrains integrated development environments, making code writing easier and faster for developers. In December, GitHub announced its ‘Copilot for Business,’ which costs $19 per user monthly. Aside from the features in the single-license Copilot tier, the business plan includes license management and organization-wide policy management capabilities. This collection of capabilities is now getting an expansion with a new update GitHub is pushing for Copilot for Individuals and Copilot for Business plans.

First of these improvements are the corporate proxy support (including those with self-signed certificates) specifically for Copilot for Business and the better quality of code suggestions in the entire Copilot tool. According to GitHub, the latter is made possible through the upgraded AI Codex model, the new Fill-In-the-Middle paradigm, and a lightweight client-side model.