Marine debris management is entering a new phase of data-driven applications. Converting years of accumulated coastal imagery into actionable tools for surveying, identification, and monitoring has emerged as a critical challenge in the digitalization of ocean governance.Under the guidance of Taiwan's Ocean Affairs Council (OAC), the National Academy of Marine Research (NAMR) is hosting the "2026 International Marine Debris Image Recognition AI Challenge." Featuring a dataset of over 20,000 real-world marine debris images, the competition invites AI, data science, computer vision, and marine science teams from Taiwan and abroad to participate.The competition is supported by Amazon Web Services (AWS) as the AI technology partner, with model evaluation and competition operations managed through the Industrial Technology Research Institute's (ITRI) AIdea AI Co-Creation Platform. Registration is now open.NAMR sets the challenge: bringing AI to the frontlines of ocean governanceMarine debris has long been a fundamental issue in coastal environmental governance - and one of the most difficult to address in the field. Coastal debris is diverse in type, scattered in distribution, and frequently degraded by sun exposure, seawater erosion, sand burial, and physical damage, making manual surveys and image interpretation highly labor- and time-intensive.To accelerate digital transformation, NAMR has established MDImageNet, an AI-Ready marine debris image dataset covering the ICC19+1, NAMR26+1, and NAMR33+1 marine debris category schemes. The competition draws on NAMR's existing marine debris image dataset, comprising over 20,000 images annotated with YOLO-format bounding boxes. The dataset covers common coastal waste categories including plastic litter, fishing-related debris, and other anthropogenic waste.A "post-mapping" strategy is adopted: participants first train models using the original class labels provided, then map predictions into 20 official recognition categories during inference, with final scoring based on 19+1 primary marine debris target classes.The competition design confronts teams with the real-world constraints of field data - cluttered backgrounds, diverse object classes, and significant appearance variations. By requiring quantitatively evaluated object detection models, the challenge goes beyond open data sharing: it validates whether AI can be transformed into deployable tools built upon existing survey infrastructure.The competition comprises preliminary and final rounds, evaluated primarily on mean Average Precision (mAP) at an IoU threshold of 0.5. A "Best Lightweight Optimization Award" is also offered, using NetScore to balance detection accuracy, model size, and computational complexity-encouraging models suitable for practical deployment in coastal patrols, UAV-based image analysis, and long-term environmental monitoring.AWS cloud resources and AIdea platform power hands-on AI developmentThe competition integrates resources from AWS and ITRI's AIdea AI Co-Creation Platform to support teams throughout model development, training, testing, and evaluation. AWS provides the cloud development environment, offering Amazon SageMaker AI and computing resources for machine learning workflows. Technical workshops are also scheduled to familiarize participants with cloud-based AI development tools and model training pipelines.ITRI's AIdea Platform handles competition execution, dataset support, and automated scoring. The standardized cloud environment and evaluation framework ensures all teams operate under identical conditions, maintaining fairness and reproducibility.The competition is open to high school students, college students, and professionals, with teams of two to five members. Cross-institutional and interdisciplinary collaboration is encouraged, combining expertise in AI, computer vision, data science, and marine science to produce evaluated model outcomes for marine debris recognition.Registration for the "2026 International Marine Debris Image Recognition AI Challenge" is open until August 10. The total prize pool is NT$300,000, with two additional Best Lightweight Optimization Awards.NAMR aims to bring together the AI community, research institutions, academia, and industry to convert marine debris imagery into deployable environmental monitoring models-building Taiwan's AI application experience and marine data foundation for ocean governance.For competition details and registration, visit the official website (link).

Communication – for whatever purpose, be it trying to persuade the electorate to vote for you, to convince your audience to buy whatever product you're selling, to manage client relationships, or simply to become famous online through creating engagement – is, at its heart, an exercise in storytelling. In today's visual-first world plagued with short attention spans and what can seem like an infinite number of e-mails and ads diverting our concentration, being able to fashion compelling video-based stories is increasingly crucial to successful communication. Attempts to automate communication can also seem generic and devoid of empathy and personalisation, consigning them to failure.EQX Lab and its product, EQX Flow, aim to use artificial intelligence to enable the masses to successfully grab others' attention. By inputting a very simple text prompt, users will be able to generate videos that have the potential to go viral. Corporate sales teams will also be able to automate the creation of personalised, cinema-quality videos for client retention purposes.EQX Lab is the brainchild of Benjamin Cheung. He studied computer animation at the Savannah College of Art and Design in the state of Georgia and then moved to Hollywood, where he worked for many of the major animation studios – Square, Disney, DreamWorks, Sony, and Lucasfilm – for roughly 15 years. He worked on Square's Final Fantasy: The Spirits Within, which came out in 2001 and was the world's first photorealistic computer-animated film (where the animated images appear as real-to-life as possible); and when he was at Sony Imageworks, on 2007's Beowulf.But Cheung always knew that he wanted to return to Asia, and so he moved to Taiwan in 2010 to work for an animation company that counted some of his former employers as major clients. He was then poached by a cloud-computing company in China whose president at the time was Taiwanese. Cheung stayed with this company, which specialised in rendering (using a computer to generate [usually 3-D] images) for almost seven years. During this time, he grew the company’s staff count by roughly seven times and its revenue by a factor of 30.Cheung had been the technical director for most of his projects in Hollywood – he was constantly using new tools and had to develop their back-end integration himself. This gave him enough confidence in his technical abilities to launch EQX Lab. He realised that even though there were already plenty of AI video-generation models, they all required a detailed prompt and only generated a somewhat passable video.Instead, what if you could make a professional-quality video with just a simple click, or a single word, or a short sentence? That's what EQX Flow aims to do.The tool is powered by the company's NEXES Engine, an AI pipeline that, from a single piece of user input, generates prompts, writes a script, composes a storyboard, and finally outputs a video. Chain-of-thought reasoning is used to come up with an emotional arc and narrative to the story. The system handles everything internally, so that the user doesn't need any technical knowledge to produce a cinematic result. Moreover, the engine creates five stories and, again using AI, measures how gripping they will be, choosing the best one to increase the chances of the chosen story going viral. (The engine pays particular attention to optimising the first three seconds of the clip, as these are the seconds that decide whether users continue watching or not.)In the current prototype, users input a story idea (something as simple as "play tennis"), and then specify from a list of options the gender of the protagonist; the type of video (e.g., a sports/fitness video); its niche (e.g., a glow-up video); the length of the clip; the type of person that the creator himself/herself is (e.g., a no-nonsense expert); and the 'look' of the clip (e.g., commercial realism, cinematic realism, cyberpunk, vintage film, or anime). Cheung intends to add a 'director's cut' option in a later incarnation of the story engine, where users will be able to emulate, for example, Christopher Nolan's aesthetic.EQX Flow will have two modes: fast and professional. The latter will be more sophisticated and, as a result, also more expensive (in his current prototype, it is about 2.5 times as expensive as the fast model). A tennis ball hitting a player in the face would look passably realistic in the professional-mode video; the fast mode would instead produce stylised visuals in a fraction of the time. Cheung says that directors often prefer the fast mode because it can produce unexpected scenes that they consider to be spontaneous.The tool is also modular: users will be able to stop after each stage (e.g., script generation), tweak things, and then move onto the next stage. Directors who already have a script and storyboard might not need the story engine at all – instead, they will only use EQX Flow’s back end to generate the shots and make the actual video. EQX Flow currently uses Google's generative AI models. This does not allow for the creation of violent, graphic, or adult material, a limitation that is in line with EQX Flow's business model and vision. They are also developing a model-agnostic pipeline, and plan to integrate additional leading AI video models to expand their creative possibilities and to reduce platform dependency.The company's Face-Lock technology can also incorporate photos that users upload of themselves or their desired characters into the videos.If non-specialists can now produce videos, should directors be worried about the future of their profession? Cheung doesn't think so. He says that average directors who don't learn how to use AI tools and don't improve the quality of their work might very well be replaced – but a good director can't be replaced by AI because directing is an art form and good films are unique products that cannot simply be reassembled from pre-existing films. Although Cheung is appreciative of the award, he is aware that he needs more than just the money it brings to fully launch EQX Flow. He is drawing on his network to expand his core technical team, growing his corporate presence in Taiwan, and actively seeking venture capital investors. In particular, he would like to raise three years of funding for research and development, which he will carry out in Taiwan. Cheung says that Taiwan provides a stable environment for his company, with a robust legal framework for intellectual property rights, top-tier engineering talent, and unfettered access to services such as Google.But despite its physical presence in Taiwan, Cheung is focussed on the North American market. Hollywood has the most advanced technology and the most resources for storytelling; Google works best in English; and the story engine has also been developed in English.The Best AI Awards celebrate global excellence in artificial intelligence and IC design, welcoming submissions from innovative companies and brilliant student teams. Following the success of the 2026 edition—advised by the MOEA, organized by DoIT, and executed by TCA—the prestigious competition is officially transitioning into an annual tradition.Offering substantial grand prizes and unmatched industry exposure, the countdown to Best AI Awards 2027 has already begun. Details on the next submission cycle, prize tiers, and eligibility rules will be released soon. Connect with us on LinkedIn for the latest official updates and application alerts.

"Integrated circuit" is a fancy term for the chips that control the electronics that make our modern life possible. They are composed of hundreds of thousands or even millions of tiny components, such as transistors, capacitors, and resistors. These components are so tightly linked together on a tiny piece of silicon that, for all intents and purposes, the chip is indivisible – you can't separate it into its individual components.Any mistake in a chip's circuitry could thus render the entire chip unusable. The process of transferring the as-yet theoretical chip design to the fabrication plant (also known as a foundry or chip fab) to produce a photomask (the template that will later be used to create chips en masse) is known as the tape-out, and costs in the tens of millions of dollars.So chip designers have a strong financial incentive to get their designs right on paper before any silicon is involved.But research conducted for Siemens in 2024 showed that only 14 per cent of chips experienced what is called first-silicon success – when the first tape-out results in a photomask and a physical chip that reliably works. This research also noted that this was the lowest success rate over the past two decades. One reason for that might be that many companies are now bringing chip design in-house instead of contracting out to experienced, specialised chip designers.That's where Vincent Bligny's company, Aniah, steps in.Based in Grenoble in the French Alps, Aniah uses artificial intelligence to conduct electrical rule checks (ERCs) for integrated circuits. These checks examine the electrical connections between different components and verify that the chip design can be made, will work, and will continue to work over time. For example, ERCs look for short circuits, when current flows with very little resistance between two components that it was not supposed to flow between; and they look for open circuits, the opposite of a short circuit, where the circuit is not a closed loop and current therefore cannot flow.ERCs also look for large voltage drops (often because of high resistance in the circuit), leading to overheating, noise, and reduced efficiency; and they look for voltage violations, where two components operating at different voltages are connected, potentially damaging the chip (level shifters are necessary to get around this problem). Traditional checkers, such as the industry-standard simulator SPICE (Simulation Program with Integrated Circuit Emphasis), examine how the chip might behave under different voltage, current, or temperature conditions. However, they are unable to sample every potential configuration of the circuit’s components and might miss corner cases (the rare cases where the parameters are at the limits of their expected range). Aniah's OneCheck, on the other hand, covers all of these potential configurations. If there are N components that can take on either high or law values (e.g., N power domains that can be switched on or off), OneCheck will examine each of the 2N potential states to make sure that the chip will still run correctly. It will go over all possibilities, catching problems such as conditional high-impedance (floating) nodes and electrical-overstress violations in chips that mix high- and low-voltage domains. Unlike traditional checkers, OneCheck doesn't depend on luck: it is a static, simulation-free method. Moreover, traditional checkers might find the first instance of a particular type of violation and immediately report back that this violation is present. The designer might fix this one violation, but many other instances of this violation could still remain. OneCheck, on the other hand, will look for all instances of the violation, so that the designer can fix them all at once and not have to rerun the checker.Not only is OneCheck more comprehensive than traditional electrical rule checkers but it is also faster, giving results in minutes instead of days. There is a trade-off, of course: OneCheck finds more false positives than traditional checkers. In other words, it flags more violations that really aren’t violations at all. However, this is something that most chip designers can accept, especially because of another product that Aniah released earlier this year, Amigo.Amigo is an AI agent that goes hand-in-hand with OneCheck. OneCheck flags potential violations and groups them into roughly fifty clusters, organised by their root cause. Amigo then provides users with an explanation of these violations, and takes advantage of natural language processing – users can simply enter "Explain this violation" – to allow users to query and thus debug these violations. But Amigo's support for chip designers goes further than that. It also provides concrete suggestions for how to fix the violations that it has identified, which the chip designer can then validate. It also tells chip designers which violations are the 'lowest-hanging fruit,' whose correction would lead to the greatest increase in the likelihood that the chip will be ready for tape-out. In fact, Amigo quantifies for users just how ready a design is for tape-out, and can quickly adjust this valuation when changes are made (instead of running the entire checking process from scratch).OneCheck and Amigo are therefore a newer, AI-enabled system for electronic design automation (EDA), and Aniah has ambitious plans for developing this EDA technology over the next two years.  They intend to implement parallel fixing by the end of 2026, where the checker can make corrections as it checks. This is a prelude to delivering results in just seconds by next year.Aniah next wants to go beyond OneCheck and simply electrical rule checking to full circuit sign-off – in other words, it wants to be able to verify that physical design constraints are respected. In the long term, Aniah wants to enable continuous sign-off, whereby all electrical rules and physical constraints are constantly satisfied. Currently, sign-off is a hectic scramble at the end of the design process, where bugs are easy to overlook.Their bronze award at the 2026 Best AI Awards came with a prize of NTD 500,000 (USD ~$16,000). The money itself won't help Aniah a lot – they've already raised more than USD $11 million in funding and expect to open a new funding round soon – but the award will open doors with Taiwanese companies and investors, said Allen Chen, Aniah's director of applications engineering. Chen, who is based in Taipei, added that Bligny, Aniah's CEO, has long held Taiwan's technical prowess in semiconductor manufacturing – its world-leading foundries and design houses – in high esteem. This is why Bligny plans on expanding their Taiwanese team, said Chen, who joined the company three years ago and is currently Aniah’s only employee on the island.Aniah currently has one Taiwanese customer, Novatek, but also supports the 200-strong Taiwanese team of Nvidia, an American company. These clients are loyal to Aniah because OneCheck and Amigo offer the fastest running time and the smallest number of false positive alerts, and because Aniah is quickly adopting AI to improve its service.The Best AI Awards celebrate global excellence in artificial intelligence and IC design, welcoming submissions from innovative companies and brilliant student teams. Following the success of the 2026 edition—advised by the MOEA, organized by DoIT, and executed by TCA—the prestigious competition is officially transitioning into an annual tradition.Offering substantial grand prizes and unmatched industry exposure, the countdown to Best AI Awards 2027 has already begun. Details on the next submission cycle, prize tiers, and eligibility rules will be released soon. Connect with us on LinkedIn for the latest official updates and application alerts.