Mark S may have no idea who Jake from State Farm is, but a new ad featuring the insurance company and Apple TV+ hit "Severance" brings them together.Jake from State Farm with Mark S from 'Severance' and new recruit Jamie S. Image source: State FarmApple is known for its well-shot advertisements, especially the ones that air during the holidays, but those aren't the only ads featuring Apple products. The company works with carriers and resellers to promote iPhones, its other products, and sometimes, its services.An ad shared by State Farm on Thursday places a newly severed Jamie S with Mark S, played by Adam Scott, on the Severed Floor featured in Apple TV+ drama "Severance." She's shown a video of her "outie" reading a note saying she's been severed... from her parents' car insurance. Continue Reading on AppleInsider | Discuss on our Forums

Apple only just revealed the C1 modem with iPhone 16e on Wednesday, but there's already a rumor about the C2 being tested internally.iPhone 16e has the C1 modem. Image source: AppleApple has spent years trying to distance itself from Qualcomm and use its own in-house modem. The company has finally landed on the C1 modem used in iPhone 16e, calling it a "platform for generations."According to a report from MacRumors sourced from an accurate leaker that uses a private account on X, Apple is already working on the future of the C-series platform. The leaker called it the C2 and shared it has the identifier C4020. Rumor Score: Likely Continue Reading on AppleInsider | Discuss on our Forums

Saber Interactive delivered one of 2024s best games in the form ofWarhammer 40,000: Space Marine 2,and we now know more about what project that games development team is moving on to next.Reported by Polygon, Hasbro announced during its recent quarterly earnings call that it has partnered with Saber Interactive for a AAA project based on one of its IP, to be developed by theSpace Marine 2team. Hasbro owns several major properties, includingDungeons and Dragons, Transformers, Magic: The Gathering, G.I. Joe, and others.According to Hasbro CEO Chris Cocks, the Saber title will be based on one of Hasbros tentpole IPs, and will use the Swarm engine, which was utilized forSpace Marine 2sdevelopment as well.We have many new digital collaborations in the works, but Im especially excited to announce this one today, being a personal fan of many of this teams games, he said. Hasbro and Saber Interactive will be collaborating on an all-new video game partnership developed by the team behind 2024s megahit, Warhammer 40,000: Space Marine 2. Combining high-octane single-player action and amazing multiplayer with Sabers Swarm tech, this new AAA title, based on one of our tentpole IPs, is sure to be a hit.What we donotknow yet is which IP specifically Saber and Hasbro have chosen to work on together. Cocks did confirm, however, that the game will be co-published by the two companies.Saber Interactive currently has a couple other AAA licensed titles in development as well, includingJurassic Park: SurvivalandStar Wars: Knights of the Old Republic Remake.

The open beta for SNKs Fatal Fury: City of the Wolves is officially live on PC, PS4, PS5, and Xbox Series X/S. Available until February 11:59 PM PST, it features eight characters, from returning fighters like Kain R. Heinlein and Terry Bogard to newcomers like Preecha and Vox Reaper.Players can participate in Casual and Ranked modes or set up Room Matches, with four stages available. Unfortunately, theres no Training mode, and the tutorial is the only offline content. Nevertheless, this allows players to go hands-on with the new REV System, including Rev Arts, Rev Blows, and more. Of course, fans are encouraged to share their thoughts regarding the quality of online play.Fatal Fury: City of the Wolves will be available on April 24th for $59.99, with only the Special Edition available. It contains the first Season Pass, which adds characters like Ken and Chun-Li from Street Fighter 6, Joe Hisashi, Andy Bogard, and Mr. Big. Head here for more details on their release windows.[CotW OBT]Open beta is now live! Time to REV IT UP!Share your feedback and report issues from the OBT with the forms linked in the PDF below. Alternatively, use the hashtag #CotWobt on X to get the word out.Dont forget to let us know your connection strength (level) pic.twitter.com/eRTRQTfnKN SNK GLOBAL (@SNKPofficial) February 20, 2025

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreVery small language models (SLMs) can outperform leading large language models (LLMs) in reasoning tasks, according to a new study by Shanghai AI Laboratory. The authors show that with the right tools and test-time scaling techniques, an SLM with 1 billion parameters can outperform a 405B LLM on complicated math benchmarks.The ability to deploy SLMs in complex reasoning tasks can be very useful as enterprises are looking for new ways to use these new models in different environments and applications.Test-time scaling explainedTest-time scaling (TTS) is the process of giving LLMs extra compute cylces during inference to improve their performance on various tasks. Leading reasoning models, such as OpenAI o1 and DeepSeek-R1, use internal TTS, which means they are trained to think slowly by generating a long string of chain-of-thought (CoT) tokens.An alternative approach is external TTS, where model performance is enhanced with (as the name implies) outside help. External TTS is suitable for repurposing exiting models for reasoning tasks without further fine-tuning them. An external TTS setup is usually composed of a policy model, which is the main LLM generating the answer, and a process reward model (PRM) that evaluates the policy models answers. These two components are coupled together through a sampling or search method.The easiest setup is best-of-N, where the policy model generates multiple answers and the PRM selects one or more best answers to compose the final response. More advanced external TTS methods use search. In beam search, the model breaks the answer down into multiple steps. For each step, it samples multiple answers and runs them through the PRM. It then chooses one or more suitable candidates and generates the next step of the answer. And, in diverse verifier tree search (DVTS), the model generates several branches of answers to create a more diverse set of candidate responses before synthesizing them into a final answer.Different test-time scaling methods (source: arXiv)What is the right scaling strategy?Choosing the right TTS strategy depends on multiple factors. The study authors carried out a systematic investigation of how different policy models and PRMs affect the efficiency of TTS methods.Their findings show that efficiency is largely dependent on the policy and PRM models. For example, for small policy models, search-based methods outperform best-of-N. However, for large policy models, best-of-N is more effective because the models have better reasoning capabilities and dont need a reward model to verify every step of their reasoning.Their findings also show that the right TTS strategy depends on the difficulty of the problem. For example, for small policy models with fewer than 7B parameters, best-of-N works better for easy problems, while beam search works better for harder problems. For policy models that have between 7B and 32B parameters, diverse tree search performs well for easy and medium problems, and beam search works best for hard problems. But for large policy models (72B parameters and more), best-of-N is the optimal method for all difficulty levels.Why small models can beat large modelsSLMs outperform large models at MATH and AIME-24 (source: arXiv)Based on these findings, developers can create compute-optimal TTS strategies that take into account the policy model, PRM and problem difficulty to make the best use of compute budget to solve reasoning problems.For example, the researchers found that a Llama-3.2-3B model with the compute-optimal TTS strategy outperforms the Llama-3.1-405B on MATH-500 and AIME24, two complicated math benchmarks. This shows that an SLM can outperform a model that is 135X larger when using the compute-optimal TTS strategy.In other experiments, they found that a Qwen2.5 model with 500 million parameters can outperform GPT-4o with the right compute-optimal TTS strategy. Using the same strategy, the 1.5B distilled version of DeepSeek-R1 outperformed o1-preview and o1-mini on MATH-500 and AIME24.When accounting for both training and inference compute budgets, the findings show that with compute-optimal scaling strategies, SLMs can outperform larger models with 100-1000X less FLOPS.The researchers results show that compute-optimal TTS significantly enhances the reasoning capabilities of language models. However, as the policy model grows larger, the improvement of TTS gradually decreases.This suggests that the effectiveness of TTS is directly related to the reasoning ability of the policy model, the researchers write. Specifically, for models with weak reasoning abilities, scaling test-time compute leads to a substantial improvement, whereas for models with strong reasoning abilities, the gain is limited.The study validates that SLMs can perform better than larger models when applying compute-optimal test-time scaling methods. While this study focuses on math benchmarks, the researchers plan to expand their study to other reasoning tasks such as coding and chemistry.Daily insights on business use cases with VB DailyIf you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.Read our Privacy PolicyThanks for subscribing. Check out more VB newsletters here.An error occured.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreWhen DeepSeek-R1 first emerged, the prevailing fear that shook the industry was that advanced reasoning could be achieved with less infrastructure.As it turns out, thats not necessarily the case. At least, according to Together AI, the rise of DeepSeek and open-source reasoning has had the exact opposite effect: Instead of reducing the need for infrastructure, it is increasing it.That increased demand has helped fuel the growth of Together AIs platform and business. Today the company announced a $305 million series B round of funding, led by General Catalyst and co-led by Prosperity7. Together AI first emerged in 2023 with an aim to simplify enterprise use of open-source large language models (LLMs). The company expanded in 2024 with the Together enterprise platform, which enables AI deployment in virtual private cloud (VPC) and on-premises environments. In 2025, Together AI is growing its platform once again with reasoning clusters and agentic AI capabilities.The company claims that its AI deployment platform has more than 450,000 registered developers and that the business has grown 6X overall year-over-year. The companys customers include enterprises as well as AI startups such as Krea AI, Captions and Pika Labs.We are now serving models across all modalities: language and reasoning and images and audio and video, Vipul Prakash, CEO of Together AI, told VentureBeat.The huge impact DeepSeek-R1 is having on AI infrastructure demandDeepSeek-R1 was hugely disruptive when it first debuted, for a number of reasons one of which was the implication that a leading edge open-source reasoning model could be built and deployed with less infrastructure than a proprietary model.However, Prakash explained, Together AI has grown its infrastructure in part to help support increased demand of DeepSeek-R1 related workloads.Its a fairly expensive model to run inference on, he said. It has 671 billion parameters and you need to distribute it over multiple servers. And because the quality is higher, theres generally moredemand on the top end, which means you need more capacity.Additionally, he noted that DeepSeek-R1 generally has longer-lived requests that can last two to three minutes. Tremendous user demand for DeepSeek-R1 is further driving the need for more infrastructure.To meet that demand, Together AI has rolled out a service it calls reasoning clusters that provision dedicated capacity, ranging from 128 to 2,000 chips, to run models at the best possible performance.How Together AI is helping organizations use reasoning AIThere are a number of specific areas where Together AI is seeing usage of reasoning models. These include:Coding agents: Reasoning models help break down larger problems into steps.Reducing hallucinations: The reasoning process helps to verify the outputs of models, thus reducing hallucinations, which is important for applications where accuracy is crucial.Improving non-reasoning models: Customers are distilling and improving the quality of non-reasoning models.Enabling self-improvement: The use of reinforcement learning with reasoning models allows models to recursively self-improve without relying on large amounts of human-labeled data.Agentic AI is also driving increased demand for AI infrastructureTogether AI is also seeing increased infrastructure demand as its users embrace agentic AI.Prakash explained that agentic workflows, where a single user request results in thousands of API calls to complete a task, are putting more compute demand on Together AIs infrastructure.To help support agentic AI workloads, Together AI recently has acquired CodeSandbox, whose technology provides lightweight, fast-booting virtual machines (VMs) to execute arbitrary, secure code within the Together AI cloud, where the language models also reside. This allows Together AI to reduce the latency between the agentic code and the models that need to be called, improving the performance of agentic workflows.Nvidia Blackwell is already having an impactAll AI platforms are facing increased demands.Thats one of the reasons why Nvidia keeps rolling out new silicon that provides more performance. Nvidias latest product chip is the Blackwell GPU, which is now being deployed at Together AI.Prakash said Nvidia Blackwell chips cost around 25% more than the previous generation, but provide 2X the performance. The GB 200 platform with Blackwell chips is particularly well-suited for training and inference of mixture of expert (MoE) models, which are trained across multiple InfiniBand-connected servers. He noted that Blackwell chips are also expected to provide a bigger performance boost for inference of larger models, compared to smaller models.The competitive landscape of agentic AIThe market of AI infrastructure platforms is fiercely competitive.Together AI faces competition from both established cloud providers and AI infrastructure startups. All the hyperscalers, including Microsoft, AWS and Google, have AI platforms. There is also an emerging category of AI-focussed players such as Groq and Samba Nova that are all aiming for a slice of the lucrative market.Together AI has a full-stack offering, including GPU infrastructure with software platform layers on top. This allows customers to easily build with open-source models or develop their own models on the Together AI platform. The company also has a focus on research developing optimizations and accelerated runtimes for both inference and training.For instance, we serve the DeepSeek-R1 model at 85 tokens per second and Azure serves it at 7 tokens per second, said Prakash. There is a fairly widening gap in the performance and cost that we can provide to our customers.Daily insights on business use cases with VB DailyIf you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.Read our Privacy PolicyThanks for subscribing. Check out more VB newsletters here.An error occured.

Reddit is experiencing international outages, NetBlocks, a global internet monitor, said in a post Thursday evening. The organization notes that the incident is not related to country-level internet disruptions or filtering. Theres also been a big spike of reports on Downdetector, with the site showing a peak of more than around 47,000 reports as of this writing. Ive seen many posts on X, Threads, and Bluesky indicating that Reddit is down for them, too.But that being said, I personally havent run into any problems myself, so I cant describe my own experience with what might be going on. The platform has worked fine as Ive browsed from my logged-in account that uses Old Reddit, an incognito window, and on my mobile browser on my iPhone.Reddits status page also currently says that all systems are operational, so its unclear what the extent of these issues may be. Reddit didnt immediately reply to a request for comment.

Adidass site and app will soon get Buy with Prime Amazon fulfillment, allowing Prime members to receive free shipping and streamlined returns when ordering directly from the three-stripe brand. Beginning in the spring, paying US-based Amazon Prime subscribers will see Prime-eligible items for sale on adidas.com and through the Adidas app. By logging into their Amazon account during checkout, those items will be fulfilled by Amazon. In addition to faster free shipping, Prime members who make purchases this way will be able to view and track the purchase through their Amazon account.If you do the bulk of your shopping on Amazon then Buy with Prime may be a handy way to centralize your purchase history into one easy-to-find location, or at least make your subscription fee go a little further on the websites of other brands.While Adidas is joining thousands of other companies registered in the direct-to-consumer Buy with Prime program, it seems to be a notable score for Amazon when it comes to brand clout. Other notable brands linked up with the program include Belkin, Steve Madden, Laura Mercier, Izod, MrBeast, and more.

Hypothesis validation is fundamental in scientific discovery, decision-making, and information acquisition. Whether in biology, economics, or policymaking, researchers rely on testing hypotheses to guide their conclusions. Traditionally, this process involves designing experiments, collecting data, and analyzing results to determine the validity of a hypothesis. However, the volume of generated hypotheses has increased dramatically with the advent of LLMs. While these AI-driven hypotheses offer novel insights, their plausibility varies widely, making manual validation impractical. Thus, automation in hypothesis validation has become an essential challenge in ensuring that only scientifically rigorous hypotheses guide future research.The main challenge in hypothesis validation is that many real-world hypotheses are abstract and not directly measurable. For instance, stating that a specific gene causes a disease is too broad and needs to be translated into testable implications. The rise of LLMs has exacerbated this issue, as these models generate hypotheses at an unprecedented scale, many of which may be inaccurate or misleading. Existing validation methods struggle to keep pace, making it difficult to determine which hypotheses are worth further investigation. Also, statistical rigor is often compromised, leading to false verifications that can misdirect research and policy efforts.Traditional methods of hypothesis validation include statistical testing frameworks such as p-value-based hypothesis testing and Fishers combined test. However, these approaches rely on human intervention to design falsification experiments and interpret results. Some automated approaches exist, but they often lack mechanisms for controlling Type-I errors (false positives) and ensuring that conclusions are statistically reliable. Many AI-driven validation tools do not systematically challenge hypotheses through rigorous falsification, increasing the risk of misleading findings. As a result, a scalable and statistically sound solution is needed to automate the hypothesis validation process effectively.Researchers from Stanford University and Harvard University introduced POPPER, an agentic framework that automates the process of hypothesis validation by integrating rigorous statistical principles with LLM-based agents. The framework systematically applies Karl Poppers principle of falsification, which emphasizes disproving rather than proving hypotheses. POPPER employs two specialized AI-driven agents:The Experiment Design Agent which formulates falsification experimentsThe Experiment Execution Agent which implements themEach hypothesis is divided into specific, testable sub-hypotheses and subjected to falsification experiments. POPPER ensures that only well-supported hypotheses are advanced by continuously refining the validation process and aggregating evidence. Unlike traditional methods, POPPER dynamically adapts its approach based on prior results, significantly improving efficiency while maintaining statistical integrity.POPPER functions through an iterative process in which falsification experiments sequentially test hypotheses. The Experiment Design Agent generates these experiments by identifying the measurable implications of a given hypothesis. The Experiment Execution Agent then carries out the proposed experiments using statistical methods, simulations, and real-world data collection. Key to POPPERs methodology is its ability to strictly control Type-I error rates, ensuring that false positives are minimized. Unlike conventional approaches that treat p-values in isolation, POPPER introduces a sequential testing framework in which individual p-values are converted into e-values, a statistical measure allowing continuous evidence accumulation while maintaining error control. This adaptive approach enables the system to refine its hypotheses dynamically, reducing the chances of reaching incorrect conclusions. The frameworks flexibility allows it to work with existing datasets, conduct new simulations, or interact with live data sources, making it highly versatile across disciplines.POPPER was evaluated across six domains: biology, sociology, and economics. The system was tested against 86 validated hypotheses, with results showing Type-I error rates below 0.10 across all datasets. POPPER demonstrated significant improvements in statistical power compared to existing validation methods, outperforming standard techniques such as Fishers combined test and likelihood ratio models. In one study focusing on biological hypotheses related to Interleukin-2 (IL-2), POPPERs iterative testing mechanism improved validation power by 3.17 times compared to alternative methods. Also, an expert evaluation involving nine PhD-level computational biologists and biostatisticians found that POPPERs hypothesis validation accuracy was comparable to that of human researchers but was completed in one-tenth the time. By leveraging its adaptive testing framework, POPPER reduced the time required for complex hypothesis validation by 10, making it significantly more scalable and efficient.Several Key Takeaways from the Research include:POPPER provides a scalable, AI-driven solution that automates the falsification of hypotheses, reducing manual workload and improving efficiency.The framework maintains strict Type-I error control, ensuring that false positives remain below 0.10, critical for scientific integrity.Compared to human researchers, POPPER completes hypothesis validation 10 times faster, significantly improving the speed of scientific discovery.Unlike traditional p-value testing, using e-values allows accumulating experimental evidence while dynamically refining hypothesis validation.Tested across six scientific fields, including biology, sociology, and economics, demonstrating broad applicability.Evaluated by nine PhD-level scientists, POPPERs accuracy matched human performance while dramatically reducing time spent on validation.Improved statistical power by 3.17 times over traditional hypothesis validation methods, ensuring more reliable conclusions.POPPER integrates Large Language Models to dynamically generate and refine falsification experiments, making it adaptable to evolving research needs.Check outthePaper and GitHub Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Steps to Build an Interactive Text-to-Image Generation Application using Gradio and Hugging Faces DiffusersAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-MakingAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Moonshot AI Research Introduce Mixture of Block Attention (MoBA): A New AI Approach that Applies the Principles of Mixture of Experts (MoE) to the Attention MechanismAsif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces NSA: A Hardware-Aligned and Natively Trainable Sparse Attention Mechanism for Ultra-Fast Long-Context Training and Inference

If youve been waiting for an opportunity to plug your PlayStation VR2 headset into a proper gaming PC and dive face first into SteamVRs rich back catalog of games, your options have been disappointingly limited. Previously console-bound PS VR2 owners are in luck, however, as Sony released a $60 adapter last fall that enables the PlayStation VR2 to be used with any modern gaming PC that is, as long as your PC meets the headsets minimum specs. But connecting the PS VR2 to a PC isn't as simple as just plugging in the adapter and calling it a day. Despite being marketed as a plug-and-play device, there are some tricky omissions in its built-in features that may make it require some additional setup, depending on your existing PC configuration. How to Connect to Your PC With the AdapterBefore you dive into the step-by-step setup instructions, its important to make sure that you have everything you need. Via the adapter, the PS VR2 is fully compatible with most SteamVR games, but youre going to want to make sure your PC has sufficient Bluetooth 4.0 connectivity, a spare DisplayPort 1.4 cable, a free AC power outlet nearby, and both the PlayStation VR2 and SteamVR apps installed on Steam. The two Sense controllers packed in with the PS VR2 are charged through USB-C, so youll need two USB-C charging ports and USB-C cables to keep both controllers charged between uses though there is a Sense controller charging station available on Sonys website for $50, which is much simpler to use.What You'll NeedBack in StockPlayStation VR2 PC AdapterSee it at AmazonFirst off, we recommend taking account of whether or not your gaming PC is able to work with the PlayStation VR2 headset. An easy way to find that out is by visiting Sonys official PS VR2 PC Adapter preparation page. Assuming your system is up to snuff, heres everything else youll need:A PlayStation VR2 headsetThe PlayStation VR2 PC adapter (AC adapter and male USB 3.0 Type-A cable included)A DisplayPort 1.4 cable (sold separately)A free USB 3.0 Type-A port on your PC (note: Sony warns against using an extension cable or external hub in the adapters pack-in quickstart manual; in our review, we relied on a powered external hub which worked perfectly in practice, despite the warning)Bluetooth 4.0 capability on your PC (either built-in or via an external Bluetooth adapter)Steam and SteamVR installed on your PCThe PlayStation VR2 app installed inside of SteamHow to Connect: Step-by-Step InstructionsOnce you have everything together, follow these steps to connect your PS VR2 to your PC:Install SteamVR and the PlayStation VR2 appIf you dont already have it, youll need to download and install the Steam Windows client.Once Steam is installed, open it and install the SteamVR app.Download and install the PlayStation VR2 app.Set up your PCs Bluetooth and pair your Sense ControllersFrom your PCs start menu, navigate to Settings > Bluetooth & devices > toggle Bluetooth to On.Now that your PCs Bluetooth radio is activated, its time to pair your Sense controllers. On each controller, hold down the PlayStation button and Create button until the white light at the bottom starts to blink.Once both controllers are discoverable, you can scan them into your PCs known Bluetooth devices by clicking the Add device button to the right of Devices on the Bluetooth & devices page of your PCs Settings menu:Select Bluetooth from the menuSearch for PlayStation VR2 Sense Controller (L) and PlayStation VR2 Sense Controller (R) in the dropdown menu. Connect both devices.If your PC doesnt have built-in Bluetooth 4.0 or higher, you can use a compatible Bluetooth adapter like the Asus BT500:If youre using an external Bluetooth adapter on a system with a built-in Bluetooth radio, theres an extra process to follow. Open the Device Manager from your start menu, look under the Bluetooth tab for an internal Bluetooth driver such as Intel(R) Wireless Bluetooth(R), right-click the driver, and click the Disable device option.Set up the adapter and connect it to your PCPlug the PS VR2 adapter into an unused USB 3.0 Type-A port on your PC.Use a DisplayPort 1.4 cable (sold separately) to connect the adapter to a free DisplayPort slot on your GPU.Connect the AC power adapter to the PS VR2 adapters DC IN connector.Plug the power adapter into an electrical outlet with or without a grounding port. Once powered on, the adapters status indicator will turn solid red.Connect the PlayStation VR2 to the the PC adapter via the USB-C port on the front of the adapter.Turn off Hardware-accelerated GPU scheduling (optional)If your PC is equipped with a newer GPU, such as a 40-series Nvidia RTX card, it may be necessary to disable Hardware-accelerated GPU scheduling for a stable experience while playing certain VR games:Navigate to Settings > System > Display > Graphics.On the Graphics page, click Default graphics settings.On the next page, turn the Hardware-accelerated GPU scheduling slider to the left.Restart your PC.Launch the PlayStation VR2 App and SteamVRBoot up the PlayStation VR2 headset by holding down the central button underneath the visor until you feel the headset rumble.Turn on SteamVR and set it as your default OpenXR runtime.From your desktop, open the PlayStation VR2 app to wirelessly update your Sense controllers firmware and begin the process of setting up your PS VR2 headset, including setting up your Play Area and other preferences.Follow all instructions on screen and within the headset as you set up your IPD and display distance. The included instructions also help you tighten the headsets fit to a comfortable level around your head.Once the setup is complete, youre free to play SteamVR games to your hearts content!Can You Connect to PC Without an Adapter?At the moment, whether or not you can connect the PS VR2 to a PC without an adapter is a bit shaky. The short answer is: no. However, according to a report on Road to VR, some GPUs released around 2018 included a USB-C port and a feature called VirtualLink, which some users have reported allows a direct connection to the PS VR2 as long as the PlayStation VR2 app is installed, bypassing the need for the PC adapter.Looking for other ways to play VR games on your PC? Check out our guide to the best VR headsets for PC gaming.