A novel vision sensor inspired by the neural transmission mechanisms of the human brain has been developed to efficiently and accurately extract object outlines even under fluctuating lighting environments. This advancement promises to significantly

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An agency-wide LLM called Elsa was released weeks ahead of schedule.

Check out the new features in the 3D modeling and animation software, including AI animation and modular character rigging in Bifrost.

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Brian Armstrong, the billionaire CEO of the cryptocurrency exchange Coinbase, says he’s ready to fund a US startup focused on gene-editing human embryos. If he goes forward, it would be the first major commercial investment in one of medicine’s most

Crowdfunding a game you like is a common practice, but be careful with where you send your money. Zarya is an upcoming cozy narrative simulator telling the story of a rural driver in a post-Soviet setting, clearly inspired by Disco Elysium. Developed by SBSR, it needs support like any other game, but the creators don't ask for money online, so if you've seen their game on Indiegogo, be aware that this is a scam.One of the game's developers, Anton Surkov, went to Reddit to warn potential players about this fake page, saying that SBSR didn't create it and never planned to run a crowdfunding campaign. "Someone just stole our game name, visuals, and text, and started collecting money under our name. It’s not just upsetting – it’s terrifying."The worst part is that they might have never learned about it if not for emails offering “advice” and “coaching” to improve crowdfunding results that they started receiving after the page's launch.SBSR has already contacted Indiegogo but hasn't received a reply yet. "We’re just three people working late nights on something we care about. Watching someone try to exploit that… it sucks. But we’re not giving up."If you're interested in Zarya, wishlist it on Steam and find all the relevant information there, not on some crowdfunding site. Here is some information about the game so you can decide if this is something you'd like to play when it gets released.SBSR"Immerse yourself in the life of Vasily, a local driver striving to breathe new life into his village. Assist the villagers, transport packages, tow cars. Do everything within your power! Deliver cargo across endless fields, country roads, and overgrown paths. Upgrade your vehicle, turning it into an indefatigable iron steed."SBSRYou'll get to talk to people living far from civilization and see their views on the world. "Grill kebabs with the guys in the garages, go fishing with Petrovich, take the harvest to the market, tow cars, and try not to end up in a pit, as the last time a tractor was seen in the village was 30 years ago."There are plenty of "authentic post-Soviet clichés" and the warm atmosphere of the good old days.SBSRSign in for a playtest and join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more.

The US Department of Commerce has renamed its AI Safety Institute to the Center for AI Standards and Innovation (CAISI), shifting its focus from overall safety to combating national security risks and preventing “burdensome and unnecessary regulation” abroad. Secretary of Commerce Howard Lutnick announced the change on June 3rd, calling the agency’s overhaul a way to “evaluate and enhance US innovation” and “ensure US dominance of international AI standards.” The AI Safety Institute was announced in 2023 under former President Joe Biden, part of a global effort to create best practices for governments mitigating AI system risk. It signed memorandums of understanding with major US AI companies, including OpenAI and Anthropic, to get access to new models and suggest improvements before release. Near the end of Biden’s term in early 2025, it released draft guidelines for managing AI risks that included using systems to create biological weapons or other clear threats to national security, but also more common categories of harmful content like child sexual abuse material (CSAM). Lutnick’s statement says that the new institute will “focus on demonstrable risks, such as cybersecurity, biosecurity, and chemical weapons” in its evaluations. It will also investigate “malign foreign influence arising from use of adversaries’ AI systems,” a category that likely includes DeepSeek, a Chinese large language model that shook up the American AI industry earlier this year. The move is part of a larger Trump administration effort to accelerate the expansion of American AI companies. On his first day in office Trump rescinded a Biden executive order that ordered new safety standards for large AI systems and a report evaluating the potential risks for US consumers and the labor market. His own executive orders have encouraged increasing generative AI adoption in fields like education and promoting coal as a source of power for energy-hungry AI data centers. And the current Republican budget bill includes a 10-year moratorium on state-level AI regulations — a provision even some in Trump’s party have come to oppose.

In this Q&A, get a glimpse into the future of artificial intelligence (AI) and computer vision through the lens of longtime Unity user Gerard Espona, whose robot digital twin project was featured in the Made with Unity: AI series. Working as simulation lead at Luxonis, whose core technology makes it possible to embed human-level perception into robotics, Espona uses his years of experience in the industry to weigh in on the current state and anticipated progression of computer vision.During recent years, computer vision (CV) and AI have become the fastest-growing fields both in market size and industry adoption rate. Spatial CV and edge AI have been used to improve and automate repetitive tasks as well as complex processes.This new reality is thanks to the democratization of CV/AI. Increasingly affordable hardware access, including depth perception capability as well as improvements in machine learning (ML), has enabled the deployment of real solutions on edge CV/AI systems.Spatial CV using edge AI enables depth-based applications to be deployed without the need of a data center service, and also allows the user to preserve privacy by processing images on the device itself.Along with more accessible hardware, software and machine learning workflows are undergoing important improvements. Although they are still very specialized and full of technical challenges, they have become much more accessible, offering tools that allow users to train their own models.Within the standard ML pipeline/workflow, large-scale edge computing and deployment can still pose issues. One of the biggest general challenges is to reduce the costs and timelines currently required to create and/or improve machine learning models on real-world applications. In other words, the challenge is how to manage all these devices to enable a smooth pipeline for continuous improvement.Also, the implicit limitations in terms of compute processing need extra effort on the final model deployed on the device (that is, apps need to be lightweight, performant, etc.). That said, embedded technology evolves really fast, and each iteration is a big leap in processing capabilities.Spatial CV/AI is a field that still requires a lot of specialization and systems. Workflows are often complicated and tedious due to numerous technical challenges, so a lot of time is devoted to smoothing out the workflow instead of focusing on value-added tasks.Creating datasets (collecting and filtering images and videos), annotating the images, preprocessing/augmentation process, training, deploying and closing the feedback loop for continuous improvement is a complex process. Each step of the workflow is technically difficult and usually involves time and financial cost, and more so for systems working in remote areas with limited connectivity.At Luxonis, we help our customers build and deploy solutions to solve and automate complex tasks at scale, so we’re facing all these issues directly. Our mission, “Robotic vision made simple,” provides not only great and affordable depth-capable hardware, but also a solid and smooth ML pipeline with synthetic datasets and simulation.Another important challenge is the work that needs to be done on the interpretability of models and the creation of datasets from an ethical, privacy and bias point of view.Last but not least, global chip supply issues are making it difficult to get the hardware into everybody’s hands.Data-centric AI is potentially useful when a working model is underperforming. Investing a large amount of time to optimize the model often leads to almost zero real improvement. Instead, with data-centric AI the investment is in analysis, cleaning and improving of the dataset.Usually when a model is underperforming, the issue is within the dataset itself, as there is not enough data for the model to outperform. This could be the result of two possible reasons: 1) the model needs a much larger amount of data, which is difficult to collect in the real world, or 2) the model doesn’t have enough examples of rare cases, which take a lot of time to happen in the real world.In both situations, synthetic datasets could help.Thanks to Unity’s computer vision tools, it is very easy to create photorealistic scenes and randomize elements like materials, light conditions and object placement. The tools come with common labels like 2D bounding boxes, 3D bounding boxes, semantic and instance segmentation, and even human body key points. Additionally, these can be easily extended with custom randomizers, labelers and annotations.Almost any task you want to automate or improve using edge CV/AI very likely involves detecting people for obvious safety and security reasons. It’s critical to guarantee user safety around autonomous systems or robots when they’re working, requiring models to be trained on data about humans.That means we need to capture a large amount of images, including information like poses and physical appearance, that are representative of the entire human population. This task raises some concerns about privacy, ethics and bias when starting to capture real human data to train the model.Fortunately, we can use synthetic datasets to mitigate some of these concerns using human 3D models and poses. A very good example is the work done by the Unity team with PeopleSansPeople.PeopleSansPeople is a human-centric synthetic dataset creator using 3D models and standard animations to randomize human body poses. Also, we can use a Unity project template, to which we add our own 3D models and poses to create our own human synthetic dataset.At Luxonis, we’re using this project as the basis for creating our own human synthetic dataset and training models. In general, we use Unity’s computer vision tools to create large and complex datasets with a high level of customization on labelers, annotations and randomizations. This allows our ML team to iterate faster with our customers, without needing to wait for real-world data collection and manual annotation.Since the introduction of transformer architecture, CV tasks are more accessible. Generative models like DALL-E 2 could also be used to create synthetic datasets, and NeRF as a neural approach to generate novel point of views of known objects and scenes. It’s clear all these innovations are catching the attention of audiences.On the other hand, having access to better annotation tools and model zoos and libraries with pre-trained, ready-to-use models are helping drive wide adoption.One key element contributing to the uptick in computer vision use is the fast evolution of vision processing units (VPUs) that currently allow users to perform model inferences on device (without the need for any host) at 4 TOPS of processing power (current Intel Movidius Myriad X). The new generation of VPUs promises a big leap in capabilities, allowing even more complex CV/AI applications to be deployed on edge.Any application related to agriculture and farming always captures my attention. For example, there is now a cow tracking and monitoring CV/AI application using drones.Our thanks to Gerard for sharing his perspective with us – keep up with his latest thoughts on LinkedIn and Twitter. And, learn more about how Unity can help your team generate synthetic data to improve computer vision model training with Unity Computer Vision.

Photo Credit: Unsplash/Solen Feyissa Connectors on ChatGPT is not available in European Union countries, China, and the UK Highlights With Record Mode, ChatGPT can transcribe and summarise meetings ChatGPT’s Connector feature works only with Deep Research It is available to all the paid subscribers of ChatGPT Advertisement OpenAI released two new utility features for ChatGPT users on Wednesday. The artificial intelligence (AI) app on MacOS now has a Record Mode that can capture meetings, brainstorming sessions, and voice notes, and transcribe and summarise the main discussion points. This feature is currently only available to the ChatGPT Team subscribers. Additionally, the San Francisco-based AI firm is also introducing Connectors, which is a tool that lets the chatbot connect to the user's internal cloud-based data sources such as Gmail, Outlook, Google Drive, and more.ChatGPT Can Now Record Your MeetingsIn a series of posts on X (formerly known as Twitter), the official handle of OpenAI announced the new ChatGPT features. The company also hosted a live stream on YouTube to provide a demonstration of these business-focused features. Both of these features are exclusive to the company's paid subscribers, however, the Record Mode is only aimed at the Team users. Additionally, Record Mode is not available in the European Economic Area (EEA), China, and the UK.Record Mode is a new capability available on ChatGPT's macOS desktop app. Team users can now tap the new Record button at the bottom of any chat. Once the user has given permission for microphone, the chatbot will begin capturing the meeting. It can also record voice notes. Once the session has ended, it can provide an editable summary of the conversation as well as its recording.OpenAI says users will be able to search for past meetings, references to them during conversations, and bring relevant context. The transcripts of the meetings also get saved as a canvas in the user's chat history. This transcript can also be rewritten as an email, project plans, or code scaffold. Notably, the tool can record up to 120 minutes per session.Separately, the AI firm also released Connectors. The tool allows ChatGPT to connect to third-party internal data sources and retrieve information in real-time. The feature works with Outlook, Teams, Google Drive, Gmail, Linear, and more. The Team, Enterprise, and Edu subscribers can also connect to SharePoint, DropBox, and Box. Connectors will only work when using Deep Research. OpenAI is also letting workspace admins build custom Deep Research Connectors using Model Context Protocol (MCP) in beta. For the latest tech news and reviews, follow Gadgets 360 on X, Facebook, WhatsApp, Threads and Google News. For the latest videos on gadgets and tech, subscribe to our YouTube channel. If you want to know everything about top influencers, follow our in-house Who'sThat360 on Instagram and YouTube. Further reading: OpenAI, ChatGPT, AI, Artificial Intelligence, Apps Akash Dutta Akash Dutta is a Senior Sub Editor at Gadgets 360. He is particularly interested in the social impact of technological developments and loves reading about emerging fields such as AI, metaverse, and fediverse. In his free time, he can be seen supporting his favourite football club - Chelsea, watching movies and anime, and sharing passionate opinions on food. More Related Stories