OpenAI recently released a model for automatic speech recognition called Whisper. I decided to reimplement the inference of the model from scratch using C/C . To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C . The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies.

Engineers at the Massachusetts Institute of Technology have developed robotic lightning bugs that emit light when they fly. Think fireflies, but with what the robot's creators call electroluminescent soft artificial muscles for flying. Their tiny artificial muscles control the robot's wings and emit colored light during flight, which provides a low-cost way to track the robots and also could enable them to communicate. Their light, which offers a way for researchers to track the robots, could one day make the machines useful for search-and-rescue missions. In dangerous locations, the robots could signal for help by flashing their lights.

A new artificial intelligence chatbot could make it much easier for students to cheat on tests and homework that require written answers. Billed by technologists and industry watchers as the most powerful AI chatbot ever released, ChatGPT is the latest effort from OpenAI, a San Francisco-based company that also made tools like DALL-E 2, the image generator that made a splash earlier this year. ChatGPT, which has been trained on a gigantic sample of text from the internet, can understand human language, conduct conversations with humans and generate detailed text that many have said is human-like and quite impressive. 'We've trained a model called ChatGPT which interacts in a conversational way,' OpenAI said in a statement. 'The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.'

"Our lab is always trying to make robots more enduring and agile, so they operate longer with more capabilities," said Rob Shepherd, associate professor of mechanical and aerospace engineering. "If you make robots operate for a long time, they're going to accumulate damage. And so how can we allow them to repair or deal with that damage?" Shepherd's Organic Robotics Lab has developed stretchable fiber-optic sensors for use in soft robots and related components -- from skin to wearable technology. For self-healing to work, Shepard says the key first step is that the robot must be able to identify that there is, in fact, something that needs to be fixed.

Uber on Wednesday announced it's launching its first-ever autonomous vehicle rides, beginning in Las Vegas. For now, the autonomous vehicles will still have a driver, but the plan is to launch a driverless service in 2023. If all goes according to plan, Uber will then expand its driverless ride offering to Los Angeles. Uber is offering the rides with its partner Motional -- the autonomous vehicle (AV) company founded in March 2020 as a joint venture between Hyundai Motor Group and auto supplier Aptiv. Motional actually partnered with Lyft to begin offering rides in AVs (with a driver) in Las Vegas back in 2018.

Dean Cheng, senior fellow at the Potomac Institute for Policy Studies, joined'Fox & Friends First' to discuss the report and a former Wuhan scientist claiming COVID was manmade. Apple has reportedly scaled back the debut of its self-driving electric vehicle, delaying the car's targeted launch date to 2026. Citing people with knowledge of the matter, Bloomberg reported Monday that the project -- known as Project Titan -- has been in limbo for the past several months. Apple is now planning a "less-ambitious design," the sources told the outlet, that will include a steering wheel and pedals and only support full autonomous capabilities on highways. In addition, Bloomberg said that the tech giant plans to develop a vehicle that lets drivers conduct other tasks on a freeway, and be alerted with time to switch over to manual control if they reach city streets or encounter inclement weather.

Current software documentation practices don't adequately serve developers. The ultimate goal of software documentation is to help developers find and fix code quickly and efficiently. Still, in most cases, code comments are difficult to understand, incomplete, out of date and untrustworthy to many developers, often resulting in significant additional work and unintended business risks. Traditionally, supplying detailed documentation and comments in code can help developers quickly get the context surrounding the code they are working on, resulting in increased productivity. While documentation and comments are an important part of software engineering, poor or insufficient documentation is a widespread problem that can ultimately create more problems for developers and negatively impact the business.

Sometimes as engineers and scientists, we think of data only as bytes on RAM, matrices in GPUs, and numeric features that go into our predictive black-box. We forget they represent changes in some real-world patterns. For example, when real world events and trends arise, we tend to defer to Google first to acquire related information (i.e where to go for a hike, what does term X mean) -- which makes Google Search Trends a very good source of data for interpreting and understanding what is going on live around us. This is why we decided to study a complex interplay between Google Search trends using it to predict other temporal data, and see if perhaps it could be used as features for a temporal machine learning model, and any insights we can draw from it. In this project, we looked at how Google Trends data could be used as features for times series models or regression models.

To explore the limits of deep learning–based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold along a single phase-encoded direction for two-dimensional [2D] sections) on the fastMRI test set collected at New York University, consisting of 558 image volumes. In a sample of 69 volumes, reconstructions were classified by radiologists for identification of two clinical thresholds: (a) general-purpose diagnostic imaging and (b) potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data.

AI can help identify biases in news reporting that we wouldn't otherwise see. Researchers from McGill University have recently directed a computer program to generate news coverage of COVID-19 using headlines from CBC articles as prompts. They then compared the simulated news coverage to the actual reporting at the time and found that CBC coverage was less focused on the medical emergency and more positively focused on personalities and geo-politics. "Reporting on real-world events requires complex choices, including decisions about which events and players take center stage. By comparing what was reported with what could have been reported, our study provides perspective on the editorial choices made by news agencies," says Professor Andrew Piper of the Department of Languages, Literatures, and Cultures at McGill University.