Low-power microcontroller (MCU) hardware is currently evolving from single-core architectures to predominantly multi-core architectures. In parallel, new embedded software building blocks are more and more written in Rust, while C/C++ dominance fades in this domain. On the other hand, small artificial neural networks (ANN) of various kinds are increasingly deployed in edge AI use cases, thus deployed and executed directly on low-power MCUs. In this context, both incremental improvements and novel innovative services will have to be continuously retrofitted using ANNs execution in software embedded on sensing/actuating systems already deployed in the field. However, there was so far no Rust embedded software platform automating parallelization for inference computation on multi-core MCUs executing arbitrary TinyML models. This paper thus fills this gap by introducing Ariel-ML, a novel toolkit we designed combining a generic TinyML pipeline and an embedded Rust software platform which can take full advantage of multi-core capabilities of various 32bit microcontroller families (Arm Cortex-M, RISC-V, ESP-32). We published the full open source code of its implementation, which we used to benchmark its capabilities using a zoo of various TinyML models. We show that Ariel-ML outperforms prior art in terms of inference latency as expected, and we show that, compared to pre-existing toolkits using embedded C/C++, Ariel-ML achieves comparable memory footprints. Ariel-ML thus provides a useful basis for TinyML practitioners and resource-constrained embedded Rust developers.

Demand for AI chips is booming--and so is the need for software to run them. Chris Lattner's startup Modular just raised $250 million to build the best developer tools for AI hardware. At a certain point between building Apple's developer tools, leading a core part of Google's AI infrastructure team, and clashing with Elon Musk during a stint as Tesla's Autopilot chief, Chris Lattner's vision for his life's work started to come into focus. AI was taking over the world, and demand was growing for the chips that powered it. But the software stack for those chips was dominated by just a few big companies.

Now that Apple's recent slew of hardware releases are behind us, we got some news on the software side last week. First, the company publicly announced that it was delaying the smarter, more personal version of Siri that'll be powered by Apple Intelligence. Then, rumors sprang up again that Apple was giving an extensive visual update to its software platforms, including iOS 19 and macOS 16 which are expected to be revealed at WWDC in June. The sources for this redesign rumor are solid. Jon Prosser dropped a video on his YouTube channel Front Page Tech back in January where he said that he had seen a redesigned Camera app for the next version of iOS that had a number of interface changes that made it feel more like a visionOS app. His thinking is that Apple wouldn't redesign a core app like Camera without bringing changes to some of the rest of the OS, as well.

Full-stack autonomous driving system spans diverse technological domains-including perception, planning, and control-that each require in-depth research. Moreover, validating such technologies of the system necessitates extensive supporting infrastructure, from simulators and sensors to high-definition maps. These complexities with barrier to entry pose substantial limitations for individual developers and research groups. Recently, open-source autonomous driving software platforms have emerged to address this challenge by providing autonomous driving technologies and practical supporting infrastructure for implementing and evaluating autonomous driving functionalities. Among the prominent open-source platforms, Autoware and Apollo are frequently adopted in both academia and industry. While previous studies have assessed each platform independently, few have offered a quantitative and detailed head-to-head comparison of their capabilities. In this paper, we systematically examine the core modules of Autoware and Apollo and evaluate their middleware performance to highlight key differences. These insights serve as a practical reference for researchers and engineers, guiding them in selecting the most suitable platform for their specific development environments and advancing the field of full-stack autonomous driving system.

General Motors is studying the possibility of an artificial intelligence voice assistant in future vehicles, according to the company. GM Chair and CEO Mary Barra, who was asked for details Tuesday by Fox Business channel anchor Liz Claman, referenced the company's Ultifi "end-to-end" vehicle software platform. "It's one of many things we can put on the vehicle. The vehicle really is a software platform and starting in 2019, General Motors started rolling out vehicles where you could do over-the-air updates for almost every module in the vehicle," Barra said, in an interview that touched on artificial intelligence, self-driving vehicles and a current production shutdown tied to supply chain issues at one of GM's truck plants. "Having an assistant with a voice that's clear enough where you can ask questions and get answers, I think that's what the artificial intelligence will enable us to do," Barra said, noting that "we'll be able to make your car better as you own it."

Artificial intelligence is taking over almost every industry. The investment and finance industry is no exception. In Deloitte's 2019 report, the firm reveals that AI is transforming the financial ecosystem to reduce costs and make operations more efficient by providing automated insights and alternative data, analysis and risk management. Technology such as AI has digitized the finance sector, ranging from payments and remittances to lending. However, asset management is still in the nascent stage of digitization, according to the chief strategy officer and co-founder of Akros Technologies, Jin Chung.

Afresh Technologies Inc., a startup using artificial intelligence to help grocery store operators reduce food waste, on Thursday announced that it has closed a $115 million funding round. The Series B round was led by Spark Capital. More than a half-dozen other backers participated as well, including Walter Robb, the former co-chief executive officer of Whole Foods. The investment brings Afresh's total outside funding to $148 million. Founded in 2017, Afresh provides a software platform that enables grocery store operators to track fresh food sales.

Today's cloud was made possible by virtualization technology, which creates a software-based representation of hardware equipment. Virtual machines, such as those popularized by VMWare and the hypervisor technology that manages VM execution, make it possible to run different software on the same machine. This concept is now expanding beyond the cloud to the physical world through the use of software that controls autonomous robots. I call this software-defined X: any physical task (X), from cleaning the floor at an airport terminal to delivering an item from one end of a warehouse to the other, can now be controlled through software. This is really taking "digital transformation" to its logical conclusion.

Updated An oral medication designed by scientists with the help of AI algorithms could one day treat patients with COVID-19 and other types of diseases caused by coronaviruses. Insilico Medicine, a biotech startup based in New York, announced on Tuesday it had nominated a drug candidate for preclinical trials – the stage before you start testing it on humans. Today's mRNA vaccines boost the body's immunity to COVID-19 by aiding the generation of antibodies capable of blocking the virus's spike protein, stopping the bio-nasty from infecting cells. The small molecule developed by Insilico, however, is used to treat people already infected, and works by preventing the coronavirus from replicating. The preclinical candidate has a specialized structure to target the 3C-like (3CL) protease, an enzyme involved in the viral reproduction of the SARS-CoV-2 coronavirus, Feng Ren, Insilico's chief scientific officer, explained.

Like any good buzzwordy phrase, "Industry 4.0" has its high and low points. An example of a low point, for instance, would be that it's a subtle way of presenting our dystopian future as an exciting new idea, rather than simply the inevitable working out of market dynamics. Contrarily, what is perhaps its highest point is that it instantly suggests the inextricable relationship between the trajectories of what could otherwise seem like very distinct economic trends. A French-Swedish startup, for instance, ADAXIS (founded in 2021), is predicated entirely on developing a software platform that recognizes the necessarily overlapping objectives between three of the sectors most closely associated with the phrase "Industry 4.0": artificial intelligence (AI), additive manufacturing (AM), and robotics. Earlier this week, ADAXIS announced that its software platform, currently in development and called AdaOne, has just received over €1 million in its first, pre-seed round of funding.