May 20, 20255 min readNOAA Has ‘Ground to a Halt’ amid Backlog of Unsigned ContractsA NOAA official says that “everything has ground to a halt” at the agency as staffers have waited for Secretary of Commerce Howard Lutnick to review more than 200 agreementsBy Scott Waldman & E&E News Howard Lutnick, US commerce secretary, during an executive order signing in the Oval Office of the White House in Washington, DC, US, on Wednesday, April 23, 2025. Samuel Corum/Sipa/Bloomberg via Getty ImagesCLIMATEWIRE | A growing backlog of hundreds of unsigned NOAA contracts has slowed agency operations to a crawl — so much so that even Sen. Ted Cruz, a staunch ally of the Trump administration, has raised concerns about the gridlock.The bottleneck is due largely to one man: Commerce Secretary Howard Lutnick, whose portfolio includes the National Oceanic and Atmospheric Administration. After taking office in February, Lutnick insisted that he personally review any contract in excess of $100,000.Intended as an attempt to identify waste and redundancy, the policy instead has sown chaos at the nation’s preeminent climate and weather agency, say former and current NOAA officials.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.More than 200 NOAA contracts — including one aimed at helping local communities prepare for extreme weather events — are now stuck in limbo, waiting for Lutnick to make a decision. The impasse has forced NOAA to furlough employees, and it has created a work environment where NOAA staffers spend much of their time trying to justify their work — rather than doing it, they say.“Everything has ground to a halt,” said one NOAA official who was granted anonymity for fear of reprisal. “We prepare briefings and fill out new forms, nothing is addressed until the very last minute (or later), stress and urgency is very high.”The NOAA contracts that do make it through the wicket often are done at the eleventh hour, such as one designed to ensure two polar weather satellites receive the flight software updates they need. Others languish for days or weeks beyond their expiration before any action is taken.Both Cruz and a second NOAA official, who also was granted anonymity for fear of reprisal, say Lutnick typically reviews about two dozen contracts a week — a tiny fraction of the total.And Cruz warned the backlog could get worse — and cause trouble in his home state of Texas.“NOAA alone has 5,700 contracts set to expire this year,” said the Republican lawmaker at a Senate hearing earlier this month.“These contracts include everything from post-hurricane flood assessment to janitorial services,” Cruz said. He added that a data center at Texas A&M University was shut down for days, “depriving Texas emergency and water managers of critical drought forecasts that help them manage reservoirs and track storm surge data and hurricane forecasts in real time.”Cruz's office did not respond to requests for further comment.Commerce Department officials did not make Lutnick available for an interview, nor did they respond to a list of questions from POLITICO’s E&E News. But Kristen Eichamer, a Commerce spokeswoman, defended the agency’s approach in a statement.“NOAA is focused on modernizing the department by implementing cutting-edge modern technology,” Eichamer said. “We are immersed in NOAA’s mission-critical services and this administration will continue on delivering for the American people.”Former NOAA Administrator Rick Spinrad said it’s perfectly reasonable for administration officials to review outside contracts to ensure they are an effective use of taxpayer money — especially if they cost millions of dollars.But he questioned whether it made sense for Lutnick to review every NOAA contract that exceeded the $100,000 mark, especially if he can’t keep pace with the paperwork. NOAA operations rely on a significant number of contractors, he noted.“The agency ceases and stops operations if the contracts are stuck and so that's what you're starting to see,” Spinrad said.He said too that Lutnick’s policy might be sending the wrong message to NOAA employees.“There's an inherent distrust in this, too, if you don't trust your staff to be making the right decisions, you start doing that,” Spinrad said.To be sure, Lutnick isn’t doing it all on his own.To even land on Lutnick’s desk, NOAA contracts must first go through an approval process led in part by Keegan McLaughlin, a former Temple University student who worked as a food hall monitor last year and lists his Eagle Scout award on his LinkedIn résumé.According to internal documents obtained by E&E News, NOAA officials who want to renew outside contracts typically must make their pitch to McLaughlin and Bryton Shang, who was part of the so-called Department of Government Efficiency (DOGE) operation championed by Elon Musk, the tech billionaire and Trump ally.NOAA officials — many with decades of government experience — have been asked to pitch their requests in the form of a slide show or to write a few bullet points, the documents show.“Keegan and Bryton will ask questions and make a ‘next steps’ decision at the end of the meeting, including requesting any follow-up needed (including needing the program manager to request another meeting),” the document states.McLaughlin and Shang reject some contracts, but any they do approve go to Lutnick’s desk for a final sign-off, according to one current NOAA official.And that’s where they sit.The contracts currently in limbo run the gamut. One has to do with shoreline mapping. Another deals with flood inundation modeling and networks for tsunami warning buoys. Others encompass internet maintenance that ensure key weather data can be distributed during critical events.Though Lutnick promised to keep NOAA intact during his Senate confirmation hearing in January, he hasn’t engaged much with the agency in his short tenure.Part of this is by design: NOAA composes only a piece of the Commerce secretary’s portfolio. Other divisions of Commerce include the Census Bureau, the Patent and Trademark Office and the National Institute of Standards and Technology.And Lutnick has taken on other responsibilities too. In recent weeks, he has been a mouthpiece for the Trump administration’s push to implement new tariffs on foreign goods. And he recently joined President Donald Trump on part of his tour of the Middle East.When Lutnick does get a chance to review NOAA contracts, agency officials say his default setting is to either reject them — or demand partial cuts to the ones he does approve.Also notable: The paralysis created by the contract delays is separate from the Trump White House budget proposal to effectively break up and dismantle NOAA.Taken together, it’s a problematic mix, said Spinrad, the former NOAA administrator, not just because potentially vital programs could be cut, but because the officials making those decisions often lack the institutional knowledge to understand the consequences.“If people don't know the history and don't understand the rules and regulations with respect to how you acquire things with contrasts, they're going to make mistakes,” he said. “And so I think the probability of mistakes is going up when you have inexperienced people doing this kind of thing.”Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2025. E&E News provides essential news for energy and environment professionals.

With seemingly no limit to the demand for artificial intelligence, everyone in the energy, AI, and climate fields is justifiably worried. Will there be enough clean electricity to power AI and enough water to cool the data centers that support this technology? These are important questions with serious implications for communities, the economy, and the environment.  This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. But the question about AI’s energy usage portends even bigger issues about what we need to do in addressing climate change for the next several decades. If we can’t work out how to handle this, we won’t be able to handle broader electrification of the economy, and the climate risks we face will increase. Innovation in IT got us to this point. Graphics processing units (GPUs) that power the computing behind AI have fallen in cost by 99% since 2006. There was similar concern about the energy use of data centers in the early 2010s, with wild projections of growth in electricity demand. But gains in computing power and energy efficiency not only proved these projections wrong but enabled a 550% increase in global computing capability from 2010 to 2018 with only minimal increases in energy use.  In the late 2010s, however, the trends that had saved us began to break. As the accuracy of AI models dramatically improved, the electricity needed for data centers also started increasing faster; they now account for 4.4% of total demand, up  from 1.9% in 2018. Data centers consume more than 10% of the electricity supply in six US states. In Virginia, which has emerged as a hub of data center activity, that figure is 25%. Projections about the future demand for energy to power AI are uncertain and range widely, but in one study, Lawrence Berkeley National Laboratory estimated that data centers could represent 6% to 12% of total US electricity use by 2028. Communities and companies will notice this type of rapid growth in electricity demand. It will put pressure on energy prices and on ecosystems. The projections have resulted in calls to build lots of new fossil-fired power plants or bring older ones out of retirement. In many parts of the US, the demand will likely result in a surge of natural-gas-powered plants. It’s a daunting situation. Yet when we zoom out, the projected electricity use from AI is still pretty small. The US generated about 4,300 billion kilowatt-hours last year. We’ll likely need another 1,000 billion to 1,200 billion or more in the next decade—a 24% to 29% increase. Almost half the additional electricity demand will be from electrified vehicles. Another 30% is expected to be from electrified technologies in buildings and industry. Innovation in vehicle and building electrification also advanced in the last decade, and this shift will be good news for the climate, for communities, and for energy costs. The remaining 22% of new electricity demand is estimated to come from AI and data centers. While it represents a smaller piece of the pie, it’s the most urgent one. Because of their rapid growth and geographic concentration, data centers are the electrification challenge we face right now—the small stuff we have to figure out before we’re able to do the big stuff like vehicles and buildings. We also need to understand what the energy consumption and carbon emissions associated with AI are buying us. While the impacts from producing semiconductors and powering AI data centers are important, they are likely small compared with the positive or negative effects AI may have on applications such as the electricity grid, the transportation system, buildings and factories, or consumer behavior. Companies could use AI to develop new materials or batteries that would better integrate renewable energy into the grid. But they could also use AI to make it easier to find more fossil fuels. The claims about potential benefits for the climate are exciting, but they need to be continuously verified and will need support to be realized. This isn’t the first time we’ve faced challenges coping with growth in electricity demand. In the 1960s, US electricity demand was growing at more than 7% per year. In the 1970s that growth was nearly 5%, and in the 1980s and 1990s it was more than 2% per year. Then, starting in 2005, we basically had a decade and a half of flat electricity growth. Most projections for the next decade put our expected growth in electricity demand at around 2% again—but this time we’ll have to do things differently.  To manage these new energy demands, we need a “Grid New Deal” that leverages public and private capital to rebuild the electricity system for AI with enough capacity and intelligence for decarbonization. New clean energy supplies, investment in transmission and distribution, and strategies for virtual demand management can cut emissions, lower prices, and increase resilience. Data centers bringing clean electricity and distribution system upgrades could be given a fast lane to connect to the grid. Infrastructure banks could fund new transmission lines or pay to upgrade existing ones. Direct investment or tax incentives could encourage clean computing standards, workforce development in the clean energy sector, and open data transparency from data center operators about their energy use so that communities can understand and measure the impacts. In 2022, the White House released a Blueprint for an AI Bill of Rights that provided principles to protect the public’s rights, opportunities, and access to critical resources from being restricted by AI systems. To the AI Bill of Rights, we humbly offer a climate amendment, because ethical AI must be climate-safe AI. It’s a starting point to ensure that the growth of AI works for everyone—that it doesn’t raise people’s energy bills, adds more clean power to the grid than it uses, increases investment in the power system’s infrastructure, and benefits communities while driving innovation. By grounding the conversation about AI and energy in context about what is needed to tackle climate change, we can deliver better outcomes for communities, ecosystems, and the economy. The growth of electricity demand for AI and data centers is a test case for how society will respond to the demands and challenges of broader electrification. If we get this wrong, the likelihood of meeting our climate targets will be extremely low. This is what we mean when we say the energy and climate impacts from data centers are small, but they are also huge. Costa Samaras is the Trustee Professor of Civil and Environmental Engineering and director of the Scott Institute for Energy Innovation at Carnegie Mellon University. Emma Strubell is the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. Ramayya Krishnan is dean of the Heinz College of Information Systems and Public Policy and the William W. and Ruth F. Cooper Professor of Management Science and Information Systems at Carnegie Mellon University.

May 20, 2025Ravie LakshmananCloud Security / Vulnerability Cybersecurity researchers have discovered risky default identity and access management (IAM) roles impacting Amazon Web Services that could open the door for attackers to escalate privileges, manipulate other AWS services, and, in some cases, even fully compromise AWS accounts. "These roles, often created automatically or recommended during setup, grant overly broad permissions, such as full S3 access," Aqua researchers Yakir Kadkoda and Ofek Itach said in an analysis. "These default roles silently introduce attack paths that allow privilege escalation, cross-service access, and even potential account compromise." The cloud security firm said it identified security issues in default IAM roles created by AWS services like SageMaker, Glue, EMR, and Lightsail. A similar flaw has also been unearthed in a popular open-source framework called Ray, which automatically creates a default IAM role (ray-autoscaler-v1) with the AmazonS3FullAccess policy. What's concerning about these IAM roles is that while they are intended for something specific, they could be abused to perform administrative actions and break isolation boundaries between services, effectively allowing an attacker who has a foothold in the environment to move laterally across services. These attacks go beyond bucket monopoly attacks, which revolve around a scenario where a threat actor could take advantage of predictable S3 bucket naming patterns to set up buckets in unused AWS regions and ultimately gain control over the contents of the bucket when a legitimate customer starts using services like CloudFormation, Glue, EMR, SageMaker, ServiceCatalog, and CodeStar. "In this case, an attacker who gains access to a default service role with AmazonS3FullAccess doesn't even need to guess bucket names remotely," the researchers explained. "They can use their existing privileges to search the account for buckets used by other services using the naming patterns, modify assets like CloudFormation templates, EMR scripts, and SageMaker resources, and move laterally across services within the same AWS account." Put differently, an IAM role within an AWS account with AmazonS3FullAccess permissions has read/write access to every S3 bucket and modifies various AWS services, effectively turning the role into a powerful method for lateral movement and privilege escalation. Some of the identified services with the permissive policy are listed below - Amazon SageMaker AI, which creates a default execution role named AmazonSageMaker-ExecutionRole-<Date&Time> when setting up a SageMaker Domain that comes with a custom policy equivalent to AmazonS3FullAccess AWS Glue, which creates a default AWSGlueServiceRole role with the AmazonS3FullAccess policy Amazon EMR, which creates a default AmazonEMRStudio_RuntimeRole_<Epoch-time> role that's assigned the AmazonS3FullAccess policy In a hypothetical attack scenario, a threat actor could upload a malicious machine learning model to Hugging Face that, when imported into SageMaker, can result in the execution of arbitrary code, which could then be used to seize control of other AWS services like Glue by injecting a backdoor capable of stealing IAM credentials of the Glue job. The adversary could then escalate their privileges within the account, ultimately breaching the entire AWS environment by looking for buckets used by CloudFormation and injecting a malicious template to escalate privileges further. In response to the disclosure, AWS has addressed the issues by modifying the AmazonS3FullAccess policy for default service roles. "Default service roles must be tightly scoped and strictly limited to the specific resources and actions they require," the researchers said. "Organizations should proactively audit and update existing roles to minimize risk, rather than relying on default configurations." The findings come as Varonis detailed a vulnerability in a utility used for mounting Azure Storage that comes preinstalled on Microsoft Azure AI and High-Performance Computing (HPC) workloads and allows an unprivileged user on a Linux machine with this utility installed to escalate their privileges to root. "It involves a classic privilege escalation method involving a SUID binary that is part of the installation of AZNFS-mount, a utility for mounting Azure Storage Account NFS endpoints," security researcher Tal Peleg said. "For example, a user could elevate permissions to root and use those permissions to mount additional Azure Storage containers, install malware or ransomware on the machine, and attempt to move laterally in the network or cloud environments." The flaw, which affects all versions of the utility up to 2.0.10, has been addressed in version 2.0.11 released on January 30, 2025. Found this article interesting? Follow us on Twitter  and LinkedIn to read more exclusive content we post. SHARE    

Language models (LMs) have great capabilities as in-context learners when pretrained on vast internet text corpora, allowing them to generalize effectively from just a few task examples. However, fine-tuning these models for downstream tasks presents significant challenges. While fine-tuning requires hundreds to thousands of examples, the resulting generalization patterns show limitations. For example, models fine-tuned on statements like “B’s mother is A” struggle to answer related questions like “Who is A’s son?” However, the LMs can handle such reverse relations in context. This raises questions about the differences between in-context learning and fine-tuning generalization patterns, and how these differences should inform adaptation strategies for downstream tasks. Research into improving LMs’ adaptability has followed several key approaches. In-context learning studies have examined learning and generalization patterns through empirical, mechanistic, and theoretical analyses. Out-of-context learning research explores how models utilize information not explicitly included in prompts. Data augmentation techniques use LLMs to enhance performance from limited datasets, with specific solutions targeting issues like the reversal curse through hardcoded augmentations, deductive closure training, and generating reasoning pathways. Moreover, synthetic data approaches have evolved from early hand-designed data to improve generalization in domains like linguistics or mathematics to more recent methods that generate data directly from language models. Researchers from Google DeepMind and Stanford University have constructed several datasets that isolate knowledge from pretraining data to create clean generalization tests. Performance is evaluated across various generalization types by exposing pretrained models to controlled information subsets, both in-context and through fine-tuning. Their findings reveal that in-context learning shows more flexible generalization than fine-tuning in data-matched settings, though there are some exceptions where fine-tuning can generalize to reversals within larger knowledge structures. Building on these insights, researchers have developed a method that enhances fine-tuning generalization by including in-context inferences into the fine-tuning data. Researchers employ multiple datasets carefully designed to isolate specific generalization challenges or insert them within broader learning contexts. Evaluation relies on multiple-choice likelihood scoring without providing answer choices in context. The experiments involve fine-tuning Gemini 1.5 Flash using batch sizes of 8 or 16. For in-context evaluation, the researchers combine training documents as context for the instruction-tuned model, randomly subsampling by 8x for larger datasets to minimize interference issues. The key innovation is a dataset augmentation approach using in-context generalization to enhance fine-tuning dataset coverage. This includes local and global strategies, each employing distinct contexts and prompts. On the Reversal Curse dataset, in-context learning achieves near-ceiling performance on reversals, while conventional fine-tuning shows near-zero accuracy as models favor incorrect celebrity names seen during training. Fine-tuning with data augmented by in-context inferences matches the high performance of pure in-context learning. Testing on simple nonsense reversals reveals similar patterns, though with less pronounced benefits. For simple syllogisms, while the pretrained model performs at chance level (indicating no data contamination), fine-tuning does produce above-chance generalization for certain syllogism types where logical inferences align with simple linguistic patterns. However, in-context learning outperforms fine-tuning, with augmented fine-tuning showing the best overall results. In conclusion, this paper explores generalization differences between in-context learning and fine-tuning when LMs face novel information structures. Results show in-context learning’s superior generalization for certain inference types, prompting the researchers to develop methods that enhance fine-tuning performance by incorporating in-context inferences into training data. Despite promising outcomes, several limitations affect the study. The first one is the dependency on nonsense words and implausible operations. Second, the research focuses on specific LMs, limiting the results’ generality. Future research should investigate learning and generalization differences across various models to expand upon these findings, especially newer reasoning models. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Salesforce AI Researchers Introduce UAEval4RAG: A New Benchmark to Evaluate RAG Systems’ Ability to Reject Unanswerable QueriesSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Google Researchers Introduce LightLab: A Diffusion-Based AI Method for Physically Plausible, Fine-Grained Light Control in Single ImagesSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/DanceGRPO: A Unified Framework for Reinforcement Learning in Visual Generation Across Multiple Paradigms and TasksSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Reinforcement Learning, Not Fine-Tuning: Nemotron-Tool-N1 Trains LLMs to Use Tools with Minimal Supervision and Maximum Generalization 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! (Promoted)

When we set out to write a story on the best available estimates for AI’s energy and emissions burden, we knew there would be caveats and uncertainties to these numbers. But, we quickly discovered, the caveats are the story too.  This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. Measuring the energy used by an AI model is not like evaluating a car’s fuel economy or an appliance’s energy rating. There’s no agreed-upon method or public database of values. There are no regulators who enforce standards, and consumers don’t get the chance to evaluate one model against another.  Despite the fact that billions of dollars are being poured into reshaping energy infrastructure around the needs of AI, no one has settled on a way to quantify AI’s energy usage. Worse, companies are generally unwilling to disclose their own piece of the puzzle. There are also limitations to estimating the emissions associated with that energy demand, because the grid hosts a complicated, ever-changing mix of energy sources.  It’s a big mess, basically. So, that said, here are the many variables, assumptions, and caveats that we used to calculate the consequences of an AI query. (You can see the full results of our investigation here.) Measuring the energy a model uses Companies like OpenAI, dealing in “closed-source” models, generally offer access to their  systems through an interface where you input a question and receive an answer. What happens in between—which data center in the world processes your request, the energy it takes to do so, and the carbon intensity of the energy sources used—remains a secret, knowable only to the companies. There are few incentives for them to release this information, and so far, most have not. That’s why, for our analysis, we looked at open-source models. They serve as a very imperfect proxy but the best one we have. (OpenAI, Microsoft, and Google declined to share specifics on how much energy their closed-source models use.)  The best resources for measuring the energy consumption of open-source AI models are AI Energy Score, ML.Energy, and MLPerf Power. The team behind ML.Energy assisted us with our text and image model calculations, and the team behind AI Energy Score helped with our video model calculations. Text models AI models use up energy in two phases: when they initially learn from vast amounts of data, called training, and when they respond to queries, called inference. When ChatGPT was launched a few years ago, training was the focus, as tech companies raced to keep up and build ever-bigger models. But now, inference is where the most energy is used. The most accurate way to understand how much energy an AI model uses in the inference stage is to directly measure the amount of electricity used by the server handling the request. Servers contain all sorts of components—powerful chips called GPUs that do the bulk of the computing, other chips called CPUs, fans to keep everything cool, and more. Researchers typically measure the amount of power the GPU draws and estimate the rest (more on this shortly).  To do this, we turned to PhD candidate Jae-Won Chung and associate professor Mosharaf Chowdhury at the University of Michigan, who lead the ML.Energy project. Once we collected figures for different models’ GPU energy use from their team, we had to estimate how much energy is used for other processes, like cooling. We examined research literature, including a 2024 paper from Microsoft, to understand how much of a server’s total energy demand GPUs are responsible for. It turns out to be about half. So we took the team’s GPU energy estimate and doubled it to get a sense of total energy demands.  The ML.Energy team uses a batch of 500 prompts from a larger dataset to test models. The hardware is kept the same throughout; the GPU is a popular Nvidia chip called the H100. We decided to focus on models of three sizes from the Meta Llama family: small (8 billion parameters), medium (70 billion), and large (405 billion). We also identified a selection of prompts to test. We compared these with the averages for the entire batch of 500 prompts.  Image models Stable Diffusion 3 from Stability AI is one of the most commonly used open-source image-generating models, so we made it our focus. Though we tested multiple sizes of the text-based Meta Llama model, we focused on one of the most popular sizes of Stable Diffusion 3, with 2 billion parameters.  The team uses a dataset of example prompts to test a model’s energy requirements. Though the energy used by large language models is determined partially by the prompt, this isn’t true for diffusion models. Diffusion models can be programmed to go through a prescribed number of “denoising steps” when they generate an image or video, with each step being an iteration of the algorithm that adds more detail to the image. For a given step count and model, all images generated have the same energy footprint. The more steps, the higher quality the end result—but the more energy used. Numbers of steps vary by model and application, but 25 is pretty common, and that’s what we used for our standard quality. For higher quality, we used 50 steps.  We mentioned that GPUs are usually responsible for about half of the energy demands of large language model requests. There is not sufficient research to know how this changes for diffusion models that generate images and videos. In the absence of a better estimate, and after consulting with researchers, we opted to stick with this 50% rule of thumb for images and videos too. Video models Chung and Chowdhury do test video models, but only ones that generate short, low-quality GIFs. We don’t think the videos these models produce mirror the fidelity of the AI-generated video that many people are used to seeing.  Instead, we turned to Sasha Luccioni, the AI and climate lead at Hugging Face, who directs the AI Energy Score project. She measures the energy used by the GPU during AI requests. We chose two versions of the CogVideoX model to test: an older, lower-quality version and a newer, higher-quality one.  We asked Luccioni to use her tool, called Code Carbon, to test both and measure the results of a batch of video prompts we selected, using the same hardware as our text and image tests to keep as many variables as possible the same. She reported the GPU energy demands, which we again doubled to estimate total energy demands.  Tracing where that energy comes from After we understand how much energy it takes to respond to a query, we can translate that into the total emissions impact. Doing so requires looking at the power grid from which data centers draw their electricity.  Nailing down the climate impact of the grid can be complicated, because it’s both interconnected and incredibly local. Imagine the grid as a system of connected canals and pools of water. Power plants add water to the canals, and electricity users, or loads, siphon it out. In the US, grid interconnections stretch all the way across the country. So, in a way, we’re all connected, but we can also break the grid up into its component pieces to get a sense for how energy sources vary across the country.  Understanding carbon intensity The key metric to understand here is called carbon intensity, which is basically a measure of how many grams of carbon dioxide pollution are released for every kilowatt-hour of electricity that’s produced.  To get carbon intensity figures, we reached out to Electricity Maps, a Danish startup company that gathers data on grids around the world. The team collects information from sources including governments and utilities and uses them to publish historical and real-time estimates of the carbon intensity of the grid. You can find more about their methodology here.  The company shared with us historical data from 2024, both for the entire US and for a few key balancing authorities (more on this in a moment). After discussions with Electricity Maps founder Olivier Corradi and other experts, we made a few decisions about which figures we would use in our calculations.  One way to measure carbon intensity is to simply look at all the power plants that are operating on the grid, add up the pollution they’re producing at the moment, and divide that total by the electricity they’re producing. But that doesn’t account for the emissions that are associated with building and tearing down power plants, which can be significant. So we chose to use carbon intensity figures that account for the whole life cycle of a power plant.  We also chose to use the consumption-based carbon intensity of energy rather than production-based. This figure accounts for imports and exports moving between different parts of the grid and best represents the electricity that’s being used, in real time, within a given region.  For most of the calculations you see in the story, we used the average carbon intensity for the US for 2024, according to Electricity Maps, which is 402.49 grams of carbon dioxide equivalent per kilowatt-hour.  Understanding balancing authorities While understanding the picture across the entire US can be helpful, the grid can look incredibly different in different locations.  One way we can break things up is by looking at balancing authorities. These are independent bodies responsible for grid balancing in a specific region. They operate mostly independently, though there’s a constant movement of electricity between them as well. There are 66 balancing authorities in the US, and we can calculate a carbon intensity for the part of the grid encompassed by a specific balancing authority. Electricity Maps provided carbon intensity figures for a few key balancing authorities, and we focused on several that play the largest roles in data center operations. ERCOT (which covers most of Texas) and PJM (a cluster of states on the East Coast, including Virginia, Pennsylvania, and New Jersey) are two of the regions with the largest burden of data centers, according to research from the Harvard School of Public Health.  We added CAISO (in California) because it covers the most populated state in the US. CAISO also manages a grid with a significant number of renewable energy sources, making it a good example of how carbon intensity can change drastically depending on the time of day. (In the middle of the day, solar tends to dominate, while natural gas plays a larger role overnight, for example.) One key caveat here is that we’re not entirely sure where companies tend to send individual AI inference requests. There are clusters of data centers in the regions we chose as examples, but when you use a tech giant’s AI model, your request could be handled by any number of data centers owned or contracted by the company. One reasonable approximation is location: It’s likely that the data center servicing a request is close to where it’s being made, so a request on the West Coast might be most likely to be routed to a data center on that side of the country.  Explaining what we found To better contextualize our calculations, we introduced a few comparisons people might be more familiar with than kilowatt-hours and grams of carbon dioxide. In a few places, we took the amount of electricity estimated to be used by a model and calculated how long that electricity would be able to power a standard microwave, as well as how far it might take someone on an e-bike.  In the case of the e-bike, we assumed an efficiency of 25 watt-hours per mile, which falls in the range of frequently cited efficiencies for a pedal-assisted bike. For the microwave, we assumed an 800-watt model, which falls within the average range in the US.  We also introduced a comparison to contextualize greenhouse gas emissions: miles driven in a gas-powered car. For this, we used data from the US Environmental Protection Agency, which puts the weighted average fuel economy of vehicles in the US in 2022 at 393 grams of carbon dioxide equivalent per mile.  Predicting how much energy AI will use in the future After measuring the energy demand of an individual query and the emissions it generated, it was time to estimate how all of this added up to national demand.  There are two ways to do this. In a bottom-up analysis, you estimate how many individual queries there are, calculate the energy demands of each, and add them up to determine the total. For a top-down look, you estimate how much energy all data centers are using by looking at larger trends.  Bottom-up is particularly difficult, because, once again, closed-source companies do not share such information and declined to talk specifics with us. While we can make some educated guesses to give us a picture of what might be happening right now, looking into the future is perhaps better served by taking a top-down approach. This data is scarce as well. The most important report was published in December by the Lawrence Berkeley National Laboratory, which is funded by the Department of Energy, and the report authors noted that it’s only the third such report released in the last 20 years. Academic climate and energy researchers we spoke with said it’s a major problem that AI is not considered its own economic sector for emissions measurements, and there aren’t rigorous reporting requirements. As a result, it’s difficult to track AI’s climate toll.  Still, we examined the report’s results, compared them with other findings and estimates, and consulted independent experts about the data. While much of the report was about data centers more broadly, we drew out data points that were specific to the future of AI.  Company goals We wanted to contrast these figures with the amounts of energy that AI companies themselves say they need. To do so, we collected reports by leading tech and AI companies about their plans for energy and data center expansions, as well as the dollar amounts they promised to invest. Where possible, we fact-checked the promises made in these claims. (Meta and Microsoft’s pledges to use more nuclear power, for example, would indeed reduce the carbon emissions of the companies, but it will take years, if not decades, for these additional nuclear plants to come online.)  Requests to companies We submitted requests to Microsoft, Google, and OpenAI to have data-driven conversations about their models’ energy demands for AI inference. None of the companies made executives or leadership available for on-the-record interviews about their energy usage. This story was supported by a grant from the Tarbell Center for AI Journalism.

The Solar Energy Industries Association released an assessment of how the budget reconciliation bill currently under review in Congress would have a negative impact on the economy. The legislation cuts incentives around solar power investment and adoption, such as the Section 25D residential tax credit. The group's analysis found that the bill, as it stands, would lead to the loss of nearly 300,000 current and future jobs in the US. It also said removal of incentives could mean a loss of ​​$220 billion in investment in the sector by 2030. It also pointed to a future energy shortage, claiming that solar was on course to be responsible for about 73 percent of the 206.5 GW of new energy capacity needed in the country by 2030. “Passing this bill would create a catastrophic energy shortfall, cede AI and tech leadership to China, and damage some of the most vital sectors of the U.S. economy,” SEIA President and CEO Abigail Ross Hopper said. It's the type of reaction we expect to see when an industry is under threat from federal action. It's also the type of researched data that doesn't seem to have much influence on the current administration, particularly when it comes to the environment and sustainability.This article originally appeared on Engadget at https://www.engadget.com/science/solar-trade-association-warns-of-devastating-energy-shortages-if-incentives-are-cut-214607526.html?src=rss

Hello, my name is Oleksandr, I’m a Senior UX/UI Designer at Ubisoft. At Games Gathering Kyiv ’24, I gave a presentation titled “Layers of Player Experience. Why is everyone on the team a UXer”. This article is a supplement and an alternative form of that presentation.Tutorials Menu in The Last of Us Part IITo have an experience, we first need a user (player). As a result, users interact with the product and gain that experience.A game is a tool — a generator of experiences, emotions, and memories for the audience. And that’s why games are bought, loved and hated. It is for the experience they provide. Exceptions, when we tend to accumulate: we buy games during some Steam sale, so we will never launch them.You can’t design an experience; you can design a product through which people gain an experience. That is, our Figma prototype is UI, and as soon as we let people use it. They share thoughts, impressions, expectations, feelings… this is UX. And even memes talk about it.UX MemesOur goal is not a game, but the player’s experience. I came across a thought that stood out to me:“The Game Direction is the concrete manifestation of the Target Game Experience and is the first thing you should aim for. This makes game development a sort of “reverse-engineering process”At the moment, in the essence of UX, and in our context of Player Experience, I have identified 4 layers-components that influence the perception of the product.UX LayersFunctionalityLet’s start with the first one: functionality is the basic and most objective part. It answers the question: Does the game work properly?For example, does it meet such requirements as load time, memory usage, number of frames per second (FPS), etc. In an ideal world, there are no crashes, friezes, softlocks (inability to progress in the game, although the game technically works), servers can withstand a large number of players, excellent optimization for different platforms and adaptation for input/output devices, various edge cases, Poka-yoke protection, etc. are also considered and covered. And of course, it affects the audience experience. Some critical moments can make the game completely unplayable, while others become memes.Assassin’s Creed UnityUsabilityThe next level is usability — it defines how easy and convenient the product is to use.The main task of this area is to minimize cognitive (attention, memory, …) and physical (muscles, vision, hearing, fine motor skills…) loads during interaction. In many products and services, teams aim to track and remove all friction and difficulties users may face. In games, however, we look for and remove only those obstacles that are not there by design (intended challenge).For example, in the VR shooter Half Life Alyx, reloading is implemented as follows: you need to press a button on the controller to eject the empty magazine from the weapon, then reach back to grab a new one and carefully install it. From a usability perspective, this is not ideal, requiring a certain physical dexterity, and it would technically be simpler to assign these actions to a single button. But this would reduce the challenge in the game, the immersion in the world, and would likely not improve the overall gaming experience.Half Life AlyxOn the other hand, if players get lost in the menu, cannot perform the actions they need, do not understand why their character dies every time, fails a mission, or cannot master a certain mechanic due to lack of and poor tutorials. Something definitely needs to be done about this. Otherwise, players may feel frustrated — like the game is being unfair or not entirely honest with them.Usability in games relies on the same methods and research as other software development. Covers the following sections and disciplines:Interaction Design, IxDVisual DesignHeuristics, for example Jakob Nielsen’s in gamesPlatform Guidelines (PC, Xbox, PS, Switch, Steam Deck…)Accessibility Standards: Web Content Accessibility Guidelines (WCAG) and Xbox Accessibility Guidelines (XAG)Primary research:Card sorting (information architecture development)Moderated and unmoderated usability tests (find interaction issues in a product at the current stage of development)Eye-tracking (heat maps)A/B testing (can say which of 2 solutions works better, but won’t say why)Secondary research:Market and competitor analysis (definition of patterns, interaction standards, glossary of terms…)Open source data analysis (streams, articles, social networks, forums…)Closed source data analysis (telemetry)Usability is an important aspect of a game. Moreover, even for the entire platform or industry. The market is full of direct and indirect competitors (apps, streaming, social networks…), all fighting for our time and attention.Let’s look at the Quick Resume function on Xbox Series, which allows you to instantly switch between multiple games, preserving their state, like Alt-Tab between windows. Moreover, thanks to it, after turning off the console, players can continue the game exactly from where they left off, just by pressing one button:I press the Xbox logo on the gamepad → the console turns on → the TV automatically turns on via HDMI-CEC and selects the HDMI console source → the console launches the last session game → I get into the game exactly where I stopped.This greatly simplifies interaction, reduces the number of steps for players to reach Value, and bypasses competitors, as the operating system screen of the TV often contains preview sections for Netflix, YouTube, etc. Our brain already finds this action, playing a game, more appealing because it requires less effort.In the book “Show Your Work” by Austin Kleon, author of the bestsellers “Steal Like an Artist” and “Show Your Work”, writes:“Your attention is one of the most valuable things you have, so everyone wants to steal it from you.”https://medium.com/media/d464918f88fb6d2a57eeedab67211cc7/hrefAestheticsLet’s go further — there’s a branch of philosophy called aesthetics. It’s the study of sensory, non-utilitarian ways of understanding the world, a critical reflection on art, culture, and nature.In our field, aesthetics relates to the physical and digital form of the product. It encompasses sensory aspects such as appearance, sound, tactility, taste, and smell. Accordingly, I have identified 5 types of aesthetics:VisualMusic/AuditoryTactileTasteScentVisual AestheticsIt doesn’t always mean “beautiful”, “pleasant to the eye”. It’s about how the game looks… what impressions it evokes because of this. It can be “beautiful” as is usually considered, and “ugly”, “terrible”, or even “disgusting”, “repulsive”. Each game has its own proportion, somewhere more beauty — the Ori series, Ghost of Tsushima, Just Dance, … Somewhere ugliness, horror, and disgust — Scorn, Resident Evil, Dead Space, Little Nightmares…It’s the same in art — just look at the works of Zdzisław Beksiński, H.R. Giger, or Ukrainian artist Ivan Marchuk, the founder of the plentanism technique.Paintings by Zdzislaw BeksinskiPaintings “Awakening” and “Sorrow” by Ivan MarchukAesthetics should correspond to the game’s setting and lore, support immersion in this fictional world. An interesting unique visual style is another way to stand out in the market. For example, the creators of Cuphead were inspired by the aesthetics of 1930s cartoons, surrealism, and the jazz era.CupheadThe game is known for its distinctive crafted animation and soundtracks, which make it recognizable and stand out among others. The loud title of the marketing book “Differentiate or Die” comes to mind.In design and development, there is often a challenge: to find a balance between functionality, convenience, and aesthetics. You can overdo it with visual effects, animations, which will reduce performance and optimization. Get carried away with graphic elements, and this will add “visual noise”, lower readability, and accordingly, worsen usability.This dilemma is present in other subject areas as well. For example, chess sets. Pieces can be extremely detailed, made of expensive materials, but not very usable because players confuse their roles due to a certain unique, non-standard appearance. These obstacles do not contribute to the game and can ruin the entire experience, especially if the mistake leads to critical consequences in the match. Or take industrial design: for some time, Apple devices did not have as many ports/interfaces as their competitors’ devices. That is, for the sake of aesthetics, they neglect certain convenience and practicality. Maybe too convenient things form “convenient” people? ;)Auditory AestheticsIt greatly influences the atmosphere, emotions, and mood of the players. These include all the sounds that accompany the game: soundtracks, ambient sounds, sound design, dialogues, dubbing, and more. This has a significant impact on the gaming experience, even down to the goosebumps.This game component can be reflected in other products, such as vinyl records. It offers a new experience for users, providing auditory, tactile, and visual aesthetics.Alan Wake 2 Vinyl RecordsTactile AestheticsThese refer to the sensations related to physical perception during the game — from how we interact with peripheral devices.A few examples: gamepad vibration (Haptic Feedback), Dual Sense adaptive triggers, steering wheel and pedals, special gloves or suits with tactile feedback. All this serves to provide greater immersion and inform about the game state.Special game editions with additional materials, such as 3D models, figurines, maps, or other items from the game world, can also be considered part of tactile aesthetics. This adds a physical aspect to the game interaction experience, can enhance the sense of involvement and value for players. It provides a certain tactile contact with the game universe, even when our PC or console is turned off due to a power outage.S.T.A.L.K.E.R. 2: Heart of Chornobyl Ultimate EditionTaste AestheticsHmm, can we try a game by taste? “Of course!” — marketing and business will joyfully exclaim. But no one will force us to gnaw on consoles or game discs. The approach is more gentle and courteous.So, on the shelves of grocery stores, we’ll find Nuka-Cola from the Fallout universe, Butterbeer, or Beans with different flavors from the Potterverse. The line between the game world and reality is blurring, and here we enter the realm of product placement: Calorie Mate (energy bars popular in Japan) appear in Metal Gear Solid, Monster Energy drinks in Death Stranding, and Nonstop in S.T.A.L.K.E.R..Nuka-Cola from the Fallout universeScent AestheticsI began exploring this topic and discovered GameScent — a device that reacts to game events such as shooting, explosions, racing, storms, and forests, releasing corresponding smells. It’s a form of 4D gaming, an attempt to expand the gaming experience to another sense. But I think we should be careful with this — who knows what it might emit when burning cannabis fields in Far Cry 3.GameScentBut in fact, if we delve deeper, we can think about certain new niches. The Moth and Rabbit perfume brand, founded in 2016, is known for its unique approach. They create unisex perfumes inspired by movies and stories, such as Parasite, The Lobster, La Haine, and The Color of Pomegranates. The focus is not only on the scent but also on the emotional and sensory experience.I feel like perfumers and marketers could easily craft fragrance concepts for games like GTA, RDR2, or The Last of Us… Thus adding a new dimension of experience for the audience. I will share my own associations — what perfumes remind me of different parts of Far Cry.FUN (Engagement)Now we have reached the core, the center that creates the desire to use this or that entertainment product. You can fix bugs, work on optimization, improve usability, and create aesthetics as much as you want. However, if a game has no fun, it is not interesting to play, and it is most likely doomed to fail.There is an expression Usable yet Useless. Because many products appear on the market that don’t find their purpose, have no value for the customers. And of course, we know a lot of such examples among published games. Or those that were canceled in development because the team never found the “FUN recipe” in practice. That said, projects can also be canceled for many other reasons — finances, the market, team issues, tech limitations, shifting priorities, or strategic decisions…Well, fun is a rabbit hole — a vast and complex topic that touches many areas. A person’s impression of it is individual, personal, and subjective.“We are not many standard selves, but many different universes” — Vasyl Symonenko.Making a game that works for everyone is possible (Functionality). Making a game that is interesting and liked by everyone — I don’t think so. Here, I will try to describe certain components of fun as simply as possible.MotivationAgain, it’s not easy because there is no single theory of motivation. But the dominant framework, which is also described in management books, is the self-determination theory (SDT). It suggests that our motivation comes from both intrinsic drivers (internal desires) and extrinsic drivers (external rewards).Let’s start with the three intrinsic ones:Competence is a need to feel successful, skilled, and to see yourself progress and develop. It’s about growth and providing players with the right level of challenge to showcase their skills. Competence is one of the key drivers. A nice real-life example would be when you give a riddle to your friends. Time passes, and they don’t want you to give them the answer — they want to solve it themselves to experience the feeling of “We did it!”Autonomy is a need for self-expression, to feel that you have an impact, that your choices matter. This is a certain level of freedom in the game, the ability to customize characters, their belongings, make decisions, participate in dialogues that change the further course of events…Relatedness is a need to have meaningful social connections, whether through cooperation or competition, because we are social beings. Accordingly, these are all kinds of coop or competitive modes in online or offline game mods.Extrinsic drivers are external rewards. Ideally, they also immerse and help progress, providing some recognition, feedback on the players’ competence. Goals and rewards should be meaningful to the players (make sense, have value, logical justification).More on player motivation in this talk by Celia Hodent, former Director of User Experience at Epic Games.EmotionEmotions are very important, they affect our perception, decision-making and memory — the more emotional the experience, the better we will remember it. Here I have highlighted:Immersion, which is also shaped by aesthetics.The feeling of the game — 3C (Camera, Characters and Controls)Linking Narrative (how the story is presented) — Story (what happens in the game world) — Plot (how it is revealed to the players)Surprises and new content: locations, seasons, tournaments, modes, etc.So to speak, we ride the emotional roller coaster. In any case, it is better in the game than in life. So as not to complain like Chris Isaak in the song Wicked Game “What a wicked game you play to make me feel this way”.FlowThe state of flow, a concept introduced by psychologist Mihaly Csikszentmihalyi, occurs at the appropriate level of challenge and skills. The mental state in which a person is fully engaged in what they are doing is a feeling of focus, concentration, and success during the activity. Flow can occur during various activities such as sports, work, daily life, creativity, and others.State of FlowThe main components and conditions in a game are:Difficulty curve — the level of challenges, “match of skills and challenges”Learning curve — onboarding players and step-by-step distributed tutorials (learn by doing);Pacing — a rhythm of stress and rest. The best rest is a change of activity.The state of flow is so captivating that we lose track of time.Ethical Aspects and ResponsibilityThis is about the conscious and unconscious use of deceptive design patterns (aka Dark), to increase business revenue. It is an exploitation of our biases and perception. Some companies have lawsuits regarding these decisions in certain countries.There is also a book written in a simple, concise form “Hooked: How to Build Habit-Forming Products” — Nir Eyal, Ryan Hoover. It allowed me to look at various products, services from a different angle and notice, recognize their methods of engagement.Summary about FunIn the book “A Theory of Fun for Game Design” by Raph Koster, there is a phrase “Learning is the drug”. The feeling of fun is the brain’s emotional reaction to learning. Even curiosity is enjoyable in itself, which is why we dislike spoilers — or at least, part of us does.Just as in games we solve puzzles, learn combos, scripts, and timings for further progress, so is it in life. A cat that drops things from the table for its learning (or just to annoy) or scientists trying to solve the mystery of the universe. Our brain always wants to find a pattern, a cause-and-effect relationship, to create structure in chaos. And as soon as it finds it, it becomes uninteresting and boring. As Dua Lipa sings in her song “New Rules”, “Now I’m standin’ back from it, I finally see the pattern.”Brand ConnectionWe can combine these layers and add another one around that also affects the game’s perception by the audience and its loyalty. This is the company’s brand, its philosophy, position, previous experience with its products and services. As well as personal brand: Neil Druckmann (The Last of Us), Dan & Sam Houser (GTA), Hideo Kojima (Death Stranding), etc.https://medium.com/media/f9db10894c4401ea10cc154898d1c065/hrefTeamEach of these components of the Game UX is handled by specific teams and stakeholders. In the image below, I have only indicated a part of the job titles that came to mind. In game development, especially for large projects, there are significantly more roles, and the credits are proof of that. Management is responsible for effective coordination and cooperation of these teams. Moreover, the R&D process of the game is impossible without the support of other company departments — HR, infrastructure, operational team, finance, etc.UX Layers & TeamAll these layers-components are closely interconnected, influencing and depending on each other. People from “Fun” tell we need a game world without seams and loading, while people from “Functionality”, having studied this issue, say that at the moment it is impossible, or requires additional resources, technologies. The search for a solution, a compromise is ongoing.Although my main focus is usability and aesthetics. It is necessary to collaborate with a large number of different teams. For example, when I worked on the High Contrast Mode feature, I first aligned with the Game Design team and then shared prototypes with the Render team, which made its implementation possible. All these layers-components are important for players’ experiences.On the FinalA game can bring not just fun, but also a deeper experience, raising and revealing important themes. It is an important medium for exploring social, ethical, and psychological issues. Games evoke emotions and empathy, allowing players to experience stories, situations that raise questions about morality, responsibility, and human values.Valiant HeartsWe love games with all their advantages and disadvantages. After all, there are no perfect products, as they are created by imperfect people, and perhaps a bit by hallucinating AI.“There is a crack in everything, that’s how the light gets in.” — Leonard Cohen, Anthem.Thank you!Why is everyone on the team a UXer? Layers of player experience was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

In an era where convenience often comes at the expense of quality or style, Dynaudio is aiming to break the mold. The Danish high-end audio manufacturer has unveiled its most ambitious product yet: the Symphony Opus One soundbar. Priced at $20,000 and slated for release in late 2025, this is not your typical slim speaker that fades into the background. It’s a piece of design-forward technology that redefines what a soundbar can be – both visually and acoustically. Partnering with Copenhagen-based design studio Swift Creatives, Dynaudio set out to create a soundbar that would feel as luxurious as it sounds. The result is a 73-inch-wide audio centerpiece crafted from premium materials: a robust aluminum alloy frame for durability, sound-optimized Nordic acoustic fabric for texture and warmth, and perhaps most striking of all, a front panel composed of white oak wood fins. These aren’t static embellishments – they’re motorized. The fins shift automatically depending on the selected audio mode, subtly transforming the look of the soundbar as it adapts to your listening preferences. This feature isn’t just for show. It serves to visually indicate different sound profiles while reinforcing Dynaudio’s commitment to seamless integration of design and function. At the core of the Symphony Opus One’s performance is an intricate sound engine backed by Dynaudio’s own audio processing algorithms. It offers several sound modes tailored for different experiences: Authentic Mode to deliver natural, transparent sound for purists and audio enthusiasts; Soundstage Mode broadens the perceived width of audio making it ideal for music listening and wide-format films; Immersive Mode leverages Dolby Atmos and other spatial audio technologies to create a 3D-like experience; and Deep Dive Mode uses Dynaudio’s proprietary processing to simulate surround sound from standard stereo sources – perfect for streaming content or older media. These profiles aren’t just equalizer tweaks – they fundamentally reshape how audio is projected into your room, giving the Opus One a level of acoustic versatility that rivals full home theater setups. Internally, the soundbar is as formidable as its price tag suggests. It packs 24 drivers in total, including 6 high-precision tweeters, 14 midrange drivers, and 4 powerful subwoofers. Together, they are driven by a 1500-watt amplification system – technology derived from Dynaudio’s top-tier studio monitors and home theater components. This means the Symphony Opus One is just as capable of delivering crystal-clear dialogue in a TV drama as it is reproducing the nuanced dynamics of an orchestral performance or the deep bass thump of a blockbuster explosion. The Opus One isn’t just acoustically powerful – it’s also physically imposing. Its 73-inch width makes it a perfect aesthetic and functional match for 83-inch televisions, but it can be paired with smaller displays as well. Despite its size, it’s designed with versatility in mind. Whether placed on a media console or mounted on the wall, the soundbar can adapt its output to suit your space. This adaptability is further enhanced by a clever calibration system that uses embedded microphones – not in the soundbar itself, but in the remote control. Simply placing the remote in your listening position during setup allows the system to fine-tune its performance based on your room’s acoustics. Though the built-in subwoofers deliver substantial low-end performance, Dynaudio has future-proofed the Opus One with plans for additional accessories. A wireless subwoofer and a pair of surround speakers are in development and will be able to integrate seamlessly into the system. This modular approach offers flexibility for users who want to expand their setup over time. The Symphony Opus One is expected to start at $20,000, positioning it firmly in the ultra-premium audio category. Dynaudio has indicated that additional finishes and customization options may be available, which could impact final pricing. To learn more about the Symphony Opus One soundbar, visit dynaudio.com. Photography courtesy of Dynaudio.

If you’ve been waiting for the right moment to showcase your newest innovation to a massive AI community, this is it! You have until this Friday, May 23 at 11:59 p.m. PT, to secure one of the few remaining exhibit tables at TechCrunch Sessions: AI and position your brand at the center of the conversation shaping the future of the AI industry. TC Sessions: AI is bringing together the brightest builders, boldest thinkers, and sharpest investors to UC Berkeley’s Zellerbach Hall on June 5. At a venue nestled right in the heart of the AI community, your company will get the chance to rub elbows with a crowd of investors and innovators looking for the next standout partner, which could be you! Benefits of exhibiting Even if you’ve built something powerful, the market is noisy, and visibility is everything. Exhibiting gives you: Direct access to 1,000+ engaged AI decision-makers. Powerful positioning as a startup or company on the cutting edge. Face time with VCs, enterprise buyers, and press looking for what’s next. And beyond access to the Sessions: AI community, you get the following benefits: 6′ x 3′ exhibit space with a branded tabletop sign. 5 event passes for your team or guests. Company logo placement on the event website. Wi-Fi, chairs, and lead capture tools to keep networking friction-free. And much, much more. Deadline: Friday, May 23 at 11:59 p.m. PT Image Credits:Halo Creative Don’t miss your golden opportunity to make a brand impact This is your moment to go from being seen to being remembered. Exhibit at TC Sessions: AI and earn the attention your innovation deserves. Reserve your table now before the opportunity closes. Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW

Lead Graphics EngineerCD ProjektBoston MA us2 hours agoApplyJob DescriptionCD PROJEKT RED is seeking a Lead Graphics Engineer to lead the development and optimization of cutting-edge graphics systems for our next title, Project Sirius, a multiplayer game set within the world of The Witcher. In this role, you will collaborate closely with artists and engineers to deliver industry-leading visuals through advanced rendering, shaders, lighting, and graphical enhancements. Your expertise will ensure that our graphics solutions are performant, efficient, and aligned with the game's creative vision.RESPONSIBILITIES:Design, implement, and optimize rendering systems to meet high visual fidelity goals.Collaborate with the art team to create performant tools and workflows for impactful visuals.Work across disciplines to develop innovative rendering solutions for large-scale, open-world environments.Maintain, debug, and enhance existing graphics systems for improved performance and memory utilization.Mentor junior engineers and provide technical guidance to the team.Ensure adherence to the team's coding standards and contribute to overall code quality.Actively solicit and integrate feedback to enhance system performance and design.Qualifications7+ years of experience in game development, with at least 1 year as a lead.Proficiency in C++ with a robust foundation in graphics programming.Expertise in rendering techniques, shaders, and GPU/CPU architecture.Experience with Unreal Engine 4/5, ideally with a shipped title using the engine.Familiarity with low-level graphics APIs (DirectX12, Vulkan, etc.) and debugging tools (RenderDoc, Pix).Extensive multithreaded programming skills.Demonstrated ability to profile and optimize performance at a low level.Exceptional problem-solving skills, communication, and a collaborative mindset.NICE TO HAVES:Experience working on AAA console titles, particularly with Unreal Engine.Familiarity with memory management and platform-specific optimizations.Additional InformationThis job can be hybrid/in-office from Boston or fully remote.Health & Well-beingCompany paid medical healthcare (dental vision & mental).Paid Leave — 20 days of holiday for every team member per year (26 days after two years).No dress-code — we like to keep it casual.401K with employer matching.Gym Membership Reimbursement.Perks & Professional GrowthFlexible working hours.Trainings, lectures and courses — internal workshops, knowledge-sharing initiatives, online tutorials, and e-learning classes are all available. There’s always something to learn!Merch — gifts for newborn rebels and regular chances to nab some cool swag!A welcome pack filled with goodies — to help you feel right at home once you join the team.Life in the OfficeDog-friendly office — dog owner? Bring your pooch with you and look after them while working! There’s always a furry friend or two to meet at the studio.Truly international working environment — a chance to meet and work with a diverse selection of people from all around the world.If you have observed any irregularities in our recruitment process please click here to report them.Company DescriptionTo create revolutionary, story-driven RPGs which go straight to the hearts of gamers — this is our mission. Want to dive deeper into our company’s culture? Explore our social media and check out our YouTube channel where we share behind-the-scenes insights and stories direct from our team members! Create Your Profile — Game companies can contact you with their relevant job openings. Apply