Many are wondering how much longer itll be before Capcom unveils the next mainlineResident Evilrelease, but before that happens, it seems like the company is planning to bring back some of the series older instalments.Specifically, it seems like a native re-release for Resident Evil 5is on the cards. The 2009 action horror title has been rated for Xbox Series X/S by the ESRB. Whether a PS5 version is also in the works remains to be seen, but at the very least, it seems like the game is at least coming to Microsofts current-gen platform.Resident Evil 5is, of course, already playable on both Xbox Series X/S and PS5, but only through backward compatibility of its Xbox One and PS4 versions respectively.Interestingly, recent weeks have also seen ESRB ratings for Xbox Series X/S versions ofResident Evil 6andResident Evil Origins Collection. The latter, for those unaware, includes HD remasters of the originalResident Evilremake andResident Evil Zero.Presumably, Capcom will have plenty of re-releases to announce at some point in the future, though at this point, thats little more than speculation. Stay tuned for more details in the coming weeks.

Open-vocabulary object detection (OVD) aims to detect arbitrary objects with user-provided text labels. Although recent progress has enhanced zero-shot detection ability, current techniques handicap themselves with three important challenges. They heavily depend on expensive and large-scale region-level annotations, which are hard to scale. Their captions are typically short and not contextually rich, which makes them inadequate in describing relationships between objects. These models also lack strong generalization to new object categories, mainly aiming to align individual object features with textual labels instead of using holistic scene understanding. Overcoming these limitations is essential to pushing the field further and developing more effective and versatile vision-language models.Previous methods have tried to enhance OVD performance by making use of vision-language pretraining. Models such as GLIP, GLIPv2, and DetCLIPv3 combine contrastive learning and dense captioning approaches to promote object-text alignment. However, these techniques still have important issues. Region-based captions only describe a single object without considering the entire scene, which confines contextual understanding. Training involves enormous labeled datasets, so scalability is an important issue. Without a way to understand comprehensive image-level semantics, these models are incapable of detecting new objects efficiently.Researchers from Sun Yat-sen University, Alibaba Group, Peng Cheng Laboratory, Guangdong Province Key Laboratory of Information Security Technology, and Pazhou Laboratory propose LLMDet, a novel open-vocabulary detector trained under the supervision of a large language model. This framework introduces a new dataset, GroundingCap-1M, which consists of 1.12 million images, each annotated with detailed image-level captions and short region-level descriptions. The integration of both detailed and concise textual information strengthens vision-language alignment, providing richer supervision for object detection. To enhance learning efficiency, the training strategy employs dual supervision, combining a grounding loss that aligns text labels with detected objects and a caption generation loss that facilitates comprehensive image descriptions alongside object-level captions. A large language model is incorporated to generate long captions describing entire scenes and short phrases for individual objects, improving detection accuracy, generalization, and rare-class recognition. Additionally, this approach contributes to multi-modal learning by reinforcing the interaction between object detection and large-scale vision-language models.The training pipeline consists of two primary stages. First, a projector is optimized to align the object detectors visual features with the feature space of the large language model. In the next stage, the detector undergoes joint fine-tuning with the language model using a combination of grounding and captioning losses. The dataset used for this training process is compiled from COCO, V3Det, GoldG, and LCS, ensuring that each image is annotated with both short region-level descriptions and extensive long captions. The architecture is built on the Swin Transformer backbone, utilizing MM-GDINO as the object detector while integrating captioning capabilities through large language models. The model processes information at two levels: region-level descriptions categorize objects, while image-level captions capture scene-wide contextual relationships. Despite incorporating an advanced language model during training, computational efficiency is maintained as the language model is discarded during inference.This approach attains state-of-the-art performance over a range of open-vocabulary object detection benchmarks, with greatly improved detection accuracy, generalization, and robustness. It surpasses prior models by 3.3%14.3% AP on LVIS, with clear improvement in the identification of rare classes. On ODinW, a benchmark for object detection over a range of domains, it shows better zero-shot transferability. Robustness to domain transition is also confirmed through its improved performance on COCO-O, a dataset measuring performance under natural variations. In referential expression comprehension tasks, it attains the best accuracy on RefCOCO, RefCOCO+, and RefCOCOg, affirming its capacity for textual description alignment with object detection. Ablation experiments show that image-level captioning and region-level grounding in combination make significant contributions to performance, especially in object detection for rare objects. As well, incorporating the learned detector into multi-modal models improves vision-language alignment, suppresses hallucinations, and advances accuracy in visual question-answering.By using large language models in open-vocabulary detection, LLMDet provides a scalable and efficient learning paradigm. This development remedies the primary challenges to existing OVD frameworks, with state-of-the-art performance on several detection benchmarks and improved zero-shot generalization and rare-class detection. Vision-language learning integration promotes cross-domain adaptability and enhances multi-modal interactions, showing the promise of language-guided supervision in object detection research.Check outthePaper.All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our75k+ ML SubReddit. Aswin AkAswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.Aswin Akhttps://www.marktechpost.com/author/aswinak/Sundial: A New Era for Time Series Foundation Models with Generative AIAswin Akhttps://www.marktechpost.com/author/aswinak/Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework for Enhancing Reasoning CapabilitiesAswin Akhttps://www.marktechpost.com/author/aswinak/Meta AI Introduces MILS: A Training-Free Multimodal AI Framework for Zero-Shot Image, Video, and Audio UnderstandingAswin Akhttps://www.marktechpost.com/author/aswinak/4 Open-Source Alternatives to OpenAIs $200/Month Deep Research AI Agent [Recommended] Join Our Telegram Channel

How AI is Quietly Eating the Internet 0 like February 11, 2025Share this postAuthor(s): Mukundan Sankar Originally published on Towards AI. Every Website, Every App, Every Piece of Content Youre Already Consuming AI-Generated Information, and You Dont Even Know It.This member-only story is on us. Upgrade to access all of Medium.Image Created by the Author using ChatGPTThe internet is no longer human-driven. Every search result you see, every news article you read, every product recommendation you get its all shaped, ranked, or outright generated by AI. And its happening so seamlessly that you dont even notice.AI isnt just an assistant anymore; its the architect of the digital experience. But how did we get here? And what does it mean for the way we consume and trust online information?When you Google something, youre not getting an organic list of the best results. Youre getting what AI thinks is best for you.Googles search algorithm is powered by AI models like RankBrain and BERT, which predict what you meant to search for not just what you typed. Increasingly, search engines are shifting toward AI-generated answers rather than listing human-written sources. The rise of AI-powered search tools like Perplexity AI and ChatGPTs browsing features means the future of search might not even include traditional links at all.For content creators, this is a seismic shift. If AI decides what gets seen, how do you ensure your content stays relevant?A study published suggests Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningA Neural Sparse Graphical Model for Variable Selection and Time-Series Network Analysis 0 like February 10, 2025Share this postLast Updated on February 11, 2025 by Editorial TeamAuthor(s): Shenggang Li Originally published on Towards AI. A Unified Adjacency Learning and Nonlinear Forecasting Framework for High-Dimensional DataThis member-only story is on us. Upgrade to access all of Medium.Photo by Susan Q Yin on UnsplashImagine a spreadsheet with rows of timestamps and columns labeled x_1, x_2, . Each x_n might represent a products sales, a stocks price, or a genes expression level. But these variables rarely evolve in isolation they often influence one another, sometimes with notable time lags. To handle these interactions, we need a robust Time-Series Network that models how each variable behaves in relation to the others. This paper focuses on precisely that objective.For instance, last months dip in x_1 could trigger a spike in x_2 this month, or perhaps half these columns are simply noise that drowns out the key relationships I want to track.My quest was to figure out how to select the most important variables and build a reliable model of how each x_m depends on the others over time. For example, is x_m mostly driven by x_1 and x_2 from the previous day, or does it depend on all variables from the previous week? I looked into various ideas, like Graph Neural Networks (GNNs) to capture who influences whom, structural modeling for domain-specific equations, or more exotic approaches like Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Apple on Monday released out-of-band security updates to address a security flaw in iOS and iPadOS that it said has been exploited in the wild.Assigned the CVE identifier CVE-2025-24200, the vulnerability has been described as an authorization issue that could make it possible for a malicious actor to disable USB Restricted Mode on a locked device as part of a cyber physical attack.This suggests that the attackers require physical access to the device in order to exploit the flaw. Introduced in iOS 11.4.1, USB Restricted Mode prevents an Apple iOS and iPadOS device from communicating with a connected accessory if it has not been unlocked and connected to an accessory within the past hour.The feature is seen as an attempt to prevent digital forensics tools like Cellebrite or GrayKey, which are mainly used by law enforcement agencies, from gaining unauthorized entry to a confiscated device and extracting sensitive data.In line with advisories of this kind, no other details about the security flaw are currently available. The iPhone maker said the vulnerability was addressed with improved state management.However, Apple acknowledged that it's "aware of a report that this issue may have been exploited in an extremely sophisticated attack against specific targeted individuals."Security researcher Bill Marczak of The Citizen Lab at The University of Toronto's Munk School has been credited with discovering and reporting the flaw.The update is available for the following devices and operating systems -iOS 18.3.1 and iPadOS 18.3.1 - iPhone XS and later, iPad Pro 13-inch, iPad Pro 12.9-inch 3rd generation and later, iPad Pro 11-inch 1st generation and later, iPad Air 3rd generation and later, iPad 7th generation and later, and iPad mini 5th generation and lateriPadOS 17.7.5 - iPad Pro 12.9-inch 2nd generation, iPad Pro 10.5-inch, and iPad 6th generationThe development comes weeks after Cupertino resolved another security flaw, a use-after-free bug in the Core Media component (CVE-2025-24085), that it revealed as having been exploited against versions of iOS before iOS 17.2.Zero-days in Apple software have been primarily weaponized by commercial surveillanceware vendors to deploy sophisticated programs that can extract data from victim devices.While these tools, such as NSO Group's Pegasus, are marketed as "technology that saves lives" and combat serious criminal activity as a way to get around the so-called "Going Dark" problem, they have also been misused to spy on members of the civil society.NSO Group, for its part, has reiterated that Pegasus is not a mass surveillance tool and that it's licensed to "legitimate, vetted intelligence and law enforcement agencies."In its transparency report for 2024, the Israeli company said it serves 54 customers in 31 countries, of which 23 are intelligence agencies and another 23 are law enforcement agencies.Found this article interesting? Follow us on Twitter and LinkedIn to read more exclusive content we post.

A new pavilion and performance space has been installed in a temporary public garden in Camden, co-designed by young people in collaboration with social enterprise MATT+FIONA and Fitzrovia Youth in Action. The six-metre-tall structure, called Reflect, is intended as a focal point for the spaceSource: Nick TurpinSource: Nick TurpinSource: Nick Turpin1/3show captionA new public pavilion, designed in collaboration with young people, has been installed in a temporary green space created by HS2 in Camden. The structure, named Reflect, was developed through a youth-led co-design process led by social enterprise MATT+FIONA in partnership with Fitzrovia Youth in Action, a local charity.The site, previously occupied by the National Temperance Hospital, is one of several plots in the Euston area designated for temporary use by HS2. This garden is the only one in the series to have been co-designed with local young people and the community.The project follows a community engagement programme on the Regents Park Estate, which identified a need for recreational and safe green spaces for young people. Forty-eight participants initially contributed ideas, with a core group of 18 young placemakers developing the final design over 12 weeks. The structure was fabricated by the young designers during a practical workshop at the Euston Skills Centre.Ellie Rudd, youth leadership and Regents Park community champions manager at Fitzrovia Youth in Action, said: It has been really exciting to see a renewed commitment to youth-led decision making and co-production, in not only planning and design, but also in the physical creation of this meanwhile-use space.Rising six metres, the structure comprises a blue timber stage with seating and has been designed to accommodate wheelchair users. It incorporates steel, timber, and stainless steel, with shaped panels fabricated by the young participants. The upper section features mirrored surfaces intended to create visual effects, reflecting performers back to the audience.Young participants engaged in the design processSource: Jon ShmulevitchA young participant engaged in the build processSource: Jon Shmulevitch1/2show captionMatthew Springett, director at MATT+FIONA, said: The youth-led co-design process for this project brought the design ideas and vision for a new playable landscape to the fore. This unique project demonstrates the true value that comes from trusting young people to contribute to the shaping of the public spaces that we all share.The wider garden has been designed by LDA Design and includes a maze of long grasses, parterre gardens, and naturalistic planting aimed at increasing biodiversity. The design also reuses material from the former construction site, integrating the concrete foundation slabs of the HS2 compound into the landscape.Dafydd Warburton, design director and project lead at LDA Design, said: We wanted to create a fun but also high-quality space, reusing and recycling wherever possible.The pavilion is intended to be relocated to the Regents Park Estate when the site is redeveloped as part of HS2s masterplan.

Some Meta workers impacted by Monday's job cuts were surprised since they said they had strong track records.Meta's layoffs targeted 5% of low performers. Some higher-rated staff said they were "blindsided."Meta CEO Mark Zuckerberg has been pushing to streamline the company's workforce.Several Meta employees who said they received positive performance ratings in their mid-year reviews last year had their jobs cut Monday, as the company let go of nearly 4,000 workers in its latest round of job reductions.Business Insider spoke to eight terminated employees, who said they received "At or Above Expectations" ratings the middle tier in Meta's three-level mid-year review system in their 2024 assessments. These employees said they were surprised to learn their ratings had been downgraded to "Meets Most," one of the lower tiers in Meta's year-end performance system that refers to meeting most, but not all, expectations and could make them eligible for Monday's cuts. They asked to be anonymous because they were not authorized to discuss internal company matters.The job cuts stem from Meta's push to let go of roughly 5% of its lowest-performing employees, according to internal guidance sent to managers in January. While Meta framed these cuts as targeting underperforming workers, internal guidance sent last month by Hillary Champion, Meta's director of people experience, and viewed by BI, allowed managers to include employees from higher performance tiers if they couldn't meet their reduction targets from lower-rated employees alone.Some employees said they were caught off guard by their inclusion in the cuts, as this guidance had previously only been shared with managers, not with the broader workforce."When I received the email I was surprised by it mostly because I have a very solid performance history and no indicators of the last six months of performance problems," one affected employee told BI.Meta began its year-end performance review process for 2024 in December, although most employees wouldn't learn their final ratings until the coming weeks.Meta CEO Mark Zuckerberg has been pushing to streamline Meta's workforce as the company pours billions into artificial intelligence and virtual reality. The cuts could become an annual event as Meta aims to regularly trim what it considers its lowest performers. Meanwhile, Meta plans to ramp up the hiring of machine learning engineers to work on AI.Meta did not respond to a request for comment from BI.Meta downgraded some employees' ratingsMultiple employees told BI that they felt frustrated that Meta had publicly framed the layoffs as targeting consistently low performers when some of those affected had previously received strong performance reviews.In posts on Workplace, Meta's internal communications platform, several laid-off employees shared their performance histories, according to screenshots viewed by BI. One employee who said they were "unexpectedly" terminated posted documentation showing they had consistently met or exceeded expectations for four years before being downgraded to "Meets Most" in late 2024. Another employee reported being cut shortly after returning from parental leave, despite receiving an "At or Above Expectations" rating in early 2024."I am super confused how I got terminated," they wrote. "I still think this is an error."The sudden downgrade in performance ratings left many employees feeling misrepresented by Meta's public stance on the layoffs. Some employees worried that being branded as a "low performer" publicly could harm future employment prospects."The hardest part is Meta publicly stating they're cutting low performers, so it feels like we have the scarlet letter on our backs," another employee told BI. "People need to know we're not underperformers.""I would certainly challenge Meta's narrative about cutting only low performers," another affected employee said. "I have a really, really difficult time believing I was a low performer based on past feedback I was given by my manager."Another employee said their manager had given them no indication that their job was at risk."We were told by leadership that if we would be impacted by this then we would already be expecting it, based on conversations our managers should have been having with us in our weekly one-on-ones," one former employee said. "But I was completely blindsided by this. My manager had been telling me that I have been doing great and did not provide any areas to be worked on. My manager even said that I would be fine and not impacted."Likewise, another worker who received an "Exceeds Expectations" rating in their mid-year review said they were surprised to be "dropped two ratings" to "Meets Most" without explanation."We are not even able to see the feedback that our manager wrote for us," they said.If you're a current or former Meta employee, or have an insight to share about the company, contact Pranav Dixit from a nonwork device securely on Signal at or email him at .Reach Jyoti Mann via email at or via Signal at jyotimann.11. Get in touch with Hugh Langley at hlangley@businessinsider.com or reach him on Signal at hughlangley.01

Looking for help with today's NYT Mini Crossword puzzle? Here are some hints and answers for the ... [+] puzzle.Credit: NYTIn case you missed Mondays NYT Mini Crossword puzzle, you can find the answers here:The NYT Mini is a smaller, quicker, more digestible, bite-sized version of the larger and more challenging NYT Crossword, and unlike its larger sibling, its free-to-play without a subscription to The New York Times. You can play it on the web or the app, though youll need the app to tackle the archive.Spoilers ahead!ACROSS1- Yoga discipline with a name from Sanskrit HATHA6- ___ run (testing-out stage) TRIAL7- ___ run (jog in the woods) TRAIL8- Deflect an attack, in fencing PARRYMORE FOR YOU9- "Woo-hoo!" YAYDOWN1- Internet address starter HTTP2- Matrixlike grid ARRAY3- Headwear for a princess TIARA4- Like Chewbacca and Mr. Snuffleupagus HAIRY5- Supporter of L.G.B.T.Q. rights ALLYToday's MINICredit: Erik KainThis one was pretty easy, though I had no idea about 1-ACROSS. That wasnt a big deal, as I quickly filled in the rest of the blanks until HATHA became apparent. The whole thing took 44 seconds.How did you do? Let me know on Twitter, Instagram or Facebook.If you also play Wordle, I write guides about that as well. You can find those and all my TV guides, reviews and much more here on my blog. Thanks for reading!

Workplace pixabayFor all the buzz about artificial intelligence reshaping work, concrete data on how its happening has been scarce. That is starting to change. Anthropic, the AI company behind the Claude assistant, has introduced the Anthropic Economic Index, an initiative to rigorously track AIs impact on jobs and tasks over time.Released this week, the index offers data-driven insight into how businesses and professionals incorporate AI into their daily work. It is based on millions of actual AI interactions instead of hypothetical surveys.The goal is straightforward: Provide executives with an empirical baseline for strategic decisions regarding AI, cutting through the hype with actual data on where AI is making an impact.Here are 10 key takeaways from the Anthropic Economic Index report:1. AI Augmentation Dominates Over AutomationThe index reveals that 57% of AI usage is for augmentation (AI assisting humans with tasks), compared to 43% for automation (AI handling tasks with minimal human input). This suggests that AI primarily enhances human capabilities rather than replacing jobs.Impact: Businesses should focus on integrating AI as a collaborative tool to boost employee productivity rather than viewing it solely as a means to reduce headcount.2. Widespread AI Adoption Across OccupationsMORE FOR YOUAI isnt broadly replacing jobs wholesale instead, its augmenting specific tasks across a wide range of roles. Roughly 36% of occupations in the analysis used AI for at least a quarter of their tasks, whereas only about 4% saw AI used in most tasks.Impact: AI adoption is becoming mainstream across industries. Companies that lag in AI integration may find themselves at a competitive disadvantage.3. Concentration in Software Development and Technical WritingThe analysis shows particularly strong AI integration in software development and technical writing, where AI tools have become nearly ubiquitous. These sectors serve as valuable case studies for other industries, demonstrating both the potential and limitations of AI integration.Impact: Businesses in these sectors should prioritize AI integration to stay competitive. Other industries can learn from these early adopters to identify potential AI applications.4. Mid-to-High Wage Occupations See Higher AI UseInterestingly, the highest and lowest-paid positions show relatively limited AI adoption, indicating that highly specialized expertise and hands-on manual work remain primarily human domains. This pattern helps organizations understand where to focus their AI investment for maximum impact.Impact: Companies should focus on upskilling their mid-level workforce to effectively collaborate with AI tools, potentially leading to increased productivity and value creation.5. Limited AI Use in Lowest and Highest-Paid RolesHighly paid professionals like senior physicians or executives arent using AI as much, likely due to their works specialized, sensitive nature. At the same time, many lower-wage service and manual jobs have limited applicability to current AI tools.Impact: This suggests that very specialized or manual labor jobs may be less affected by AI in the short term. However, businesses should monitor AI advancements that could impact these roles.6. Computer and Mathematical Fields Lead AI AdoptionThe computer and mathematical fields have the maximum AI adoption, accounting for 37.5% of all AI interactions. This concentration in technical domains demonstrates AI's particular strength in enhancing logical and analytical tasks.Impact: Businesses in tech-related fields should prioritize AI integration to maintain a competitive edge. Other sectors can explore how to leverage AI for computational and analytical tasks.7. Arts and Media Sectors Show Significant AI UseAfter science, the second-biggest slice, around 10%, came from creative and media-related tasks think marketing content creation, editing, and design assistance. Business and financial operations, education, and administrative roles also showed healthy adoption in the mid-single digits of overall usage share.Impact: Creative industries should explore AI tools to enhance productivity and innovation while maintaining the unique human elements of their work.8. AI as a Collaborative PartnerThe index highlights AIs role as a collaborative partner in digital work environments. The data shows employees collaborating with AI to get work done more efficiently, whether by offloading tedious tasks or improving the quality of outputs.Impact: Business leaders should foster a culture that embraces AI as a tool for enhancing human capabilities rather than a threat to job security.9. Potential for Economic Growth and Productivity GainsThe most obvious trend from the report is that AI-facilitated work processes promise to heighten productivity and drive economic growth.Impact: Companies that effectively integrate AI could see significant improvements in efficiency and output, potentially leading to increased profitability and market share.10. Need for Continuous Learning and AdaptationThe index reveals that successful AI adoption requires substantial investment in workforce development, with the most significant returns coming from enabling skilled workers to leverage AI tools effectively rather than attempting to replace workers entirelyImpact: Businesses should invest in continuous learning programs to ensure thei workforce remains skilled in AI-human collaboration, potentially following the lead of major corporations like Apple and Google.Looking ForwardThe index strongly suggests that AIs role in the workplace will continue to evolve and expand. Organizations that view AI as a collaborative partner rather than a replacement technology will be best positioned to capture its benefits. Success will require ongoing investment in workforce training, careful attention to emerging AI capabilities, and a commitment to fostering a culture that embraces AI-human collaboration.The key to thriving in this new landscape lies not in wholesale automation but in thoughtful integration that enhances human capabilities while preserving the unique value that human workers bring to their roles. Organizations that master this balance will be best positioned to compete in an increasingly AI-enhanced business environment. Copy

Terry Crews said he and his wife managed to rebuild their marriage.They have been married for 36 years but nearly split for good years ago due to his infidelity and porn addiction."It's work. It's really work. You have to get better at it, it's a skill," Crews said.Terry Crews and his wife, Rebecca King-Crews, know just how much effort goes into making a marriage work.In an interview with People, the actor spoke about how they overcame all the challenges they faced in their years together."Me and my wife have been married 36 years and, at year 20 though, it was over. And we totally rebuilt our relationship," Crews told People. "And we decided we were going to be stronger together. It was a decision that we decided to make."The couple got married in 1989, several years before Crews joined theNFL, and two decades before he made the transition to acting. They have five children together.The "White Chicks" star added that love isn't just about feelings."It's work. It's really work. You have to get better at it, it's a skill," Crews said. "There's a lot of things I had to relearn."He says he thinks of him and his wife as a "testament" to the fact that two people can make a relationship work as long as they are both willing to tough it out."It's wild because there was a point when, when I wanted to quit, she didn't want to, and then when she wanted to quit, I didn't want to," he said. "And I was just glad we didn't want to quit at the same time."This isn't the first time that Crews has spoken about the challenges he faced in his marriage.Crews shared how his past infidelity and porn addiction had affected his family during an episode of Dax Shepard's"Armchair Expert" podcast in 2023.Crews said he would "start an argument" with his wife whenever she tried to ask him questions about where he was when he was feeding his porn addiction at adult bookstores near truck stops. He would also "be angry" at his kids "for getting in the way."He also said he cheated on his wife by getting a handjob in a massage parlor while filming his first-ever movie in 2000, and "kept that secret for years."He eventually decided to confess to his wife about the cheatingporn addiction. Although she left him at that time, they eventually reconciled after Crews went to rehab.Crews and his wife aren't the only celebrities who have spoken about the steps they take to build and maintain a healthy relationship.Robert Downey Jr. and Susan Downey don't go more than two weeks without seeing each other and their family.Rob Lowe, who has been married to Sheryl Berkoff for over 30 years, said they go to couples therapy regularly because "it's like taking your car in and making sure the engine's running great."Barbara Grossman, a couples counselor, previously told Business Insider that her tips for a successful marriage include sharing emotional baggage, carving time out to be together, and actively communicating grievances."I encourage people to talk about their past because it usually reveals the historical reasons for their behavior, opinions, and attitudes," Grossman said. "If you open up about situations including unresolved feelings toward a family member, friend, or lover it develops understanding, trust, and connection."A representative for Crews did not immediately respond to a request for comment sent by Business Insider outside regular hours.