Artificial intelligence keeps on taking the community at large by surprise, especially the large language models with the advanced capabilities they tend to offer, surpassing expectations. Tech giants like OpenAI are increasingly focused on bringing more efficient AI models and pushing the potential of the technology further to do many of the mundane tasks on consumers' behalf. While we have been hearing about the vast application of the tools in varied domains, a recent study evaluated how these models tend to perform on emotional intelligence, and the results are not something you would be expecting. A team of researchers conducted a study with findings suggesting that AI is capable of processing and understanding human emotions even better than humans Companies and researchers are increasingly invested in finding ways in which artificial intelligence can be used to bring more efficiency and different ways it can be used in institutions. OpenAI and many other tech giants are arduously working towards making their models feel more natural with capabilities like contextual understanding. A recent study has been conducted by University of Geneva and University of Bern researchers to find out about AI's empathetic capabilities. The study that has been published in Communications Psychology delivered some interesting findings that pointed towards generative AI models like ChatGPT not only demonstrating emotional intelligence but also outperforming humans in emotional intelligence tests. The study involved a series of tests with the six leading large language models, including ChatGPT-4, ChatGPT-o1, Gemini 1.5 Flash, Claude 3.5 Haiku, Copilot 365, and DeepSeek V3. The study further involved five emotional tests that were generally used in academics and professionally to see emotional understanding, regulation, and management. The situations presented were realistic and emotionally charged ones to see how the models would respond based on the emotional context provided. The results left the researchers baffled as all the LLM models outperformed the human participants significantly. The researchers even went a step ahead by asking ChatGPT-4 to create new EI test items, which were validated by human participants as well, and the results remarkably remained the same, with the AI models demonstrating a high level of contextual understanding. One of the Senior Researchers, Marcello Mortillaro,  had the following to say on the findings: LLMs are therefore not only capable of finding the best answer among the various available options, but also of generating new scenarios adapted to a desired context. This reinforces the idea that LLMs, such as ChatGPT, have emotional knowledge and can reason about emotions. These findings are vital, especially if we see how the technology is increasingly exceeding expectations in domains previously exclusive to humans. This could have great broader implications in terms of augmenting human skills in sensitive fields such as conflict management or coaching. Deal of the Day #new #study #reveals #chatgpt4 #other

Forward-looking: Antimatter consists of particles with properties opposite to those of regular particles. It plays a central role in modern physics research and forms naturally through cosmic collisions or radioactive decay. However, studying it is difficult, as contact with normal matter results in instant annihilation. The European Organization for Nuclear Research, better known as CERN, is one of the few places on Earth capable of routinely producing antimatter from high-energy collisions with particle accelerators. Researchers there have now developed a novel method to transport small quantities of antimatter to external laboratories. This world-first achievement could enable more precise studies of the elusive antiparticles described in the Standard Model of particle physics. CERN researchers developed a two-meter containment device capable of temporarily trapping antimatter particles. They even trucked the device around the facility for four kilometers before returning it to the lab, where they confirmed the antiparticles were still intact. The brief trip required no external power source, proving that antiparticles can theoretically travel far beyond a few kilometers. It also demonstrated that antimatter can be safely transported to distant laboratories using nothing more than a standard vehicle and Europe's public road network. CERN facilities lie near Geneva, on the France – Switzerland border. Judging by the truck's route carrying the experimental containment device, the researchers likely crossed the border from France into Switzerland and back. Physicists have explained the practical application of antimatter transport in a recent study, which revealed limits to precision measurements using low-energy protons produced exclusively at CERN's Antimatter Factory. Magnetic field fluctuations from the facility's decelerators interfere with experiments, while dedicated off-site laboratories could enable more accurate results. // Related Stories Now that CERN has proven it can safely transport antiparticles beyond its grounds, it is preparing the next phase of its antimatter project. A new, state-of-the-art facility at Heinrich Heine University Düsseldorf in Germany will soon receive the first batch of antimatter cargo. The particles will travel nearly 800 kilometers to reach their destination. #cern #researchers #took #few #antimatter

America last RFK Jr. calls WHO “moribund” amid US withdrawal; China pledges to give M As the rest of the world signed a pandemic agreement, the US sent an abrasive video. Beth Mole – May 21, 2025 7:07 pm | 27 World Health Organization Director-General Tedros Adhanom Ghebreyesus speaks during the 78th World Health Assembly in Geneva, Switzerland, May 19, 2025. Credit: Getty | Xinhua News Agency World Health Organization Director-General Tedros Adhanom Ghebreyesus speaks during the 78th World Health Assembly in Geneva, Switzerland, May 19, 2025. Credit: Getty | Xinhua News Agency Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more China is poised to be the next big donor to the World Health Organization after Trump abruptly withdrew the US from the United Nations health agency on his first day in office, leaving a critical funding gap and leadership void. On Tuesday, Chinese Vice Premier Liu Guozhong said that China would give an additional million to WHO over the course of five years. Liu made the announcement at the World Health Assemblybeing held in Geneva. The WHA is the decision-making body of WHO, comprised of delegations from member states, which meet annually to guide the agency's health agenda. “The world is now facing the impacts of unilateralism and power politics, bringing major challenges to global health security," Liu told the WHA, according to The Washington Post. "China strongly believes that only with solidarity and mutual assistance can we create a healthy world together." This year, China sent its largest-ever delegation—180—to the WHA, while the US was absent, according to Health Policy Watch. The increased involvement and large donation are seen as clear examples that China is working to take the place of the US. Although the US has cut all ties with the WHO—and reportedly still owes the agency million in 2024–2025 dues—US health secretary and anti-vaccine advocate Robert F. Kennedy Jr. made an unexpected appearance at the WHA via a six-minute video. Isolated In the abrasive, pre-recorded speech, Kennedy described the WHO as "moribund" and "mired in bureaucratic bloatentrenched paradigms." "WHO's priorities have increasingly reflected the biases and interests of corporate medicine," Kennedy said, alluding to his anti-vaccine and germ-theory denialist views. He chastised the health organization for allegedly capitulating to China and working with the country to "promote the fiction that COVID originated in bats." Kennedy ended the short speech by touting his Make America Healthy Again agenda. He also urged the WHO to undergo a radical overhaul similar to what the Trump administration is currently doing to the US government—presumably including dismantling and withholding funding from critical health agencies and programs. Last, he pitched other countries to join the US in abandoning the WHO. "I would like to take this opportunity to invite my fellow health ministers around the world into a new era of cooperation.... we're ready to work with you," Kennedy said. Meanwhile, the WHA embraced collaboration. During the assembly this week, WHO overwhelmingly voted to adopt the world's first pandemic treaty, aimed at collectively preventing, preparing for, and responding to any future pandemics. The treaty took over three years to negotiate, but in the end, no country voted against it—124 votes in favor, 11 abstentions, and no objections."The world is safer today thanks to the leadership, collaboration and commitment of our Member States to adopt the historic WHO Pandemic Agreement,” WHO Director-General Tedros Adhanom Ghebreyesus said. “The Agreement is a victory for public health, science and multilateral action. It will ensure we, collectively, can better protect the world from future pandemic threats. It is also a recognition by the international community that our citizens, societies and economies must not be left vulnerable to again suffer losses like those endured during COVID-19.” Beth Mole Senior Health Reporter Beth Mole Senior Health Reporter Beth is Ars Technica’s Senior Health Reporter. Beth has a Ph.D. in microbiology from the University of North Carolina at Chapel Hill and attended the Science Communication program at the University of California, Santa Cruz. She specializes in covering infectious diseases, public health, and microbes. 27 Comments #rfk #calls #who #moribund #amid

Multimodal modeling focuses on building systems to understand and generate content across visual and textual formats. These models are designed to interpret visual scenes and produce new images using natural language prompts. With growing interest in bridging vision and language, researchers are working toward integrating image recognition and image generation capabilities into a unified system. This approach eliminates the need for separate pipelines and opens the path to more coherent and intelligent interactions across modalities. A key challenge in this field is to develop architectures that handle both understanding and generation without compromising the quality of either. Models need to grasp complex visual concepts and produce high-quality images matching user prompts. The difficulty lies in identifying suitable picture representations and training procedures that support both tasks. This problem becomes more evident when the same model is expected to interpret detailed text descriptions and generate visually accurate outputs based on them. It requires alignment of semantic understanding and pixel-level synthesis. Previous approaches have generally used Variational Autoencodersor CLIP-based encoders to represent images. VAEs are efficient for reconstruction but encode lower-level features, often leading to less informative representations. CLIP-based encoders provide high-level semantic embeddings by learning from large-scale image-text pairs. However, CLIP was not built for image reconstruction, making it challenging to use for generation unless paired with models like diffusion decoders. In terms of training, Mean Squared Erroris widely used for simplicity but tends to produce deterministic outputs. To improve generation diversity and quality, researchers have turned to Flow Matching, which introduces controlled stochasticity and better models the continuous nature of image features. Researchers from Salesforce Research, in collaboration with the University of Maryland and several academic institutions, introduced BLIP3-o, a family of unified multimodal models. The model adopts a dual-stage training strategy where image understanding is learned first, followed by image generation. The proposed system leverages CLIP embeddings to represent images and integrates them with a diffusion transformer to synthesize new visual outputs. Unlike previous joint training methods, the sequential approach maintains the strength of each task independently. The diffusion module is trained while keeping the autoregressive backbone frozen, avoiding task interference. To improve alignment and visual fidelity, the team also curated BLIP3o-60k, a high-quality instruction-tuning dataset created by prompting GPT-4o across varied visual categories, including scenes, objects, gestures, and text. They developed two model versions: an 8-billion parameter model trained with proprietary and public data, and a 4-billion version using only open-source data. The image generation pipeline of BLIP3-o is built on Qwen2.5-VL large language models. Prompts are processed to produce visual features refined through a Flow Matching diffusion transformer. This transformer is based on the Lumina-Next architecture, optimized for speed and quality with 3D rotary position embedding and grouped-query attention. The model encodes each image into 64 fixed-length semantic vectors, regardless of resolution, which supports compact storage and efficient decoding. The research team used a large-scale dataset of 25 million images from sources like CC12M, SA-1B, and JourneyDB to train the models. They extended it with 30 million proprietary samples for the 8B model. They also included 60k instruction-tuning samples covering challenging prompts such as complex gestures and landmarks, generated via GPT-4o. In terms of performance, BLIP3-o demonstrated top scores across multiple benchmarks. The 8B model achieved a GenEval score of 0.84 for image generation alignment and a WISE score of 0.62 for reasoning ability. Image understanding scored 1682.6 on MME-Perception, 647.1 on MME-Cognition, 50.6 on MMMU, and 83.1 on both VQAv2 and TextVQA datasets. A human evaluation comparing BLIP3-o 8B with Janus Pro 7B showed that BLIP3-o was preferred 50.4% of the time for visual quality and 51.5% for prompt alignment. These results are supported by statistically significant p-values, indicating the superiority of BLIP3-o in subjective quality assessments. This research outlines a clear solution to the dual challenge of image understanding and generation. CLIP embeddings, Flow Matching, and a sequential training strategy demonstrate how the problem can be approached methodically. The BLIP3-o model delivers state-of-the-art results and introduces an efficient and open approach to unified multimodal modeling. Check out the Paper, GitHub Page and Model on Hugging Face. 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 90k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Georgia Tech and Stanford Researchers Introduce MLE-Dojo: A Gym-Style Framework Designed for Training, Evaluating, and Benchmarking Autonomous Machine Learning EngineeringAgentsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Investigates Test-Time Scaling of English-Centric RLMs for Enhanced Multilingual Reasoning and Domain GeneralizationNikhilhttps://www.marktechpost.com/author/nikhil0980/PwC Releases Executive Guide on Agentic AI: A Strategic Blueprint for Deploying Autonomous Multi-Agent Systems in the EnterpriseNikhilhttps://www.marktechpost.com/author/nikhil0980/Multimodal AI Needs More Than Modality Support: Researchers Propose General-Level and General-Bench to Evaluate True Synergy in Generalist Models #salesforce #releases #blip3o #fully #opensource

Recent advances in generative models, especially diffusion models and rectified flows, have revolutionized visual content creation with enhanced output quality and versatility. Human feedback integration during training is essential for aligning outputs with human preferences and aesthetic standards. Current approaches like ReFL methods depend on differentiable reward models that introduce VRAM inefficiency for video generation. DPO variants achieve only marginal visual improvements. Further, RL-based methods face challenges including conflicts between ODE-based sampling of rectified flow models and Markov Decision Process formulations, instability when scaling beyond small datasets, and a lack of validation for video generation tasks. Aligning LLMs employs Reinforcement Learning from Human Feedback, which trains reward functions based on comparison data to capture human preferences. Policy gradient methods have proven effective but are computationally intensive and require extensive tuning, while Direct Policy Optimizationoffers cost efficiency but delivers inferior performance. DeepSeek-R1 recently showed that large-scale RL with specialized reward functions can guide LLMs toward self-emergent thought processes. Current approaches include DPO-style methods, direct backpropagation with reward signals like ReFL, and policy gradient-based methods such as DPOK and DDPO. Production models primarily utilize DPO and ReFL due to the instability of policy gradient methods in large-scale applications. Researchers from ByteDance Seed and the University of Hong Kong have proposed DanceGRPO, a unified framework adapting Group Relative Policy Optimization to visual generation paradigms. This solution operates seamlessly across diffusion models and rectified flows, handling text-to-image, text-to-video, and image-to-video tasks. The framework integrates with four foundation modelsand five reward models covering image/video aesthetics, text-image alignment, video motion quality, and binary reward assessments. DanceGRPO outperforms baselines by up to 181% on key benchmarks, including HPS-v2.1, CLIP Score, VideoAlign, and GenEval. The architecture utilizes five specialized reward models to optimize visual generation quality: Image Aesthetics quantifies visual appeal using models fine-tuned on human-rated data. Text-image Alignment uses CLIP to maximize cross-modal consistency. Video Aesthetics Quality extends evaluation to temporal domains using Vision Language Models. Video Motion Quality evaluates motion realism through physics-aware VLM analysis. Thresholding Binary Reward employs a discretization mechanism where values exceeding a threshold receive 1, others 0, specifically designed to evaluate generative models’ ability to learn abrupt reward distributions under threshold-based optimization. DanceGRPO shows significant improvements in reward metrics for Stable Diffusion v1.4 with an increase in the HPS score from 0.239 to 0.365, and CLIP Score from 0.363 to 0.395. Pick-a-Pic and GenEval evaluations confirm the method’s effectiveness, with DanceGRPO outperforming all competing approaches. For HunyuanVideo-T2I, optimization using the HPS-v2.1 model increases the mean reward score from 0.23 to 0.33, showing enhanced alignment with human aesthetic preferences. With HunyuanVideo, despite excluding text-video alignment due to instability, the methodology achieves relative improvements of 56% and 181% in visual and motion quality metrics, respectively. DanceGRPO uses the VideoAlign reward model’s motion quality metric, achieving a substantial 91% relative improvement in this dimension. In this paper, researchers have introduced DanceGRPO, a unified framework for enhancing diffusion models and rectified flows across text-to-image, text-to-video, and image-to-video tasks. It addresses critical limitations of prior methods by bridging the gap between language and visual modalities, achieving superior performance through efficient alignment with human preferences and robust scaling to complex, multi-task settings. Experiments demonstrate substantial improvements in visual fidelity, motion quality, and text-image alignment. Future work will explore GRPO’s extension to multimodal generation, further unifying optimization paradigms across Generative AI. Check out the Paper and Project Page. 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 90k+ ML SubReddit. 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/Reinforcement Learning, Not Fine-Tuning: Nemotron-Tool-N1 Trains LLMs to Use Tools with Minimal Supervision and Maximum GeneralizationSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/RL^V: Unifying Reasoning and Verification in Language Models through Value-Free Reinforcement LearningSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Offline Video-LLMs Can Now Understand Real-Time Streams: Apple Researchers Introduce StreamBridge to Enable Multi-Turn and Proactive Video UnderstandingSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/AI That Teaches Itself: Tsinghua University’s ‘Absolute Zero’ Trains LLMs With Zero External Data #dancegrpo #unified #framework #reinforcement #learning

Prime Video has renewed its hit series Fallout for a third season, ahead its Season 2 premiere slated for December of this year. The upcoming season will pick up in the aftermath of the Season 1 finale, and travel into the post-apocalyptic city of New Vegas. “We are absolutely thrilled that our global Prime Video customers will be able to delve deeper into the wonderfully surreal and captivating world of Fallout,” said Vernon Sanders, global head of television, Amazon MGM Studios. “Jonah, Lisa, Geneva, and Graham have done an exceptional job bringing this beloved video game franchise to vivid life on Prime Video. Together with our amazing partners at Bethesda Games and Bethesda Softworks, we are delighted to announce a third season of Fallout, well ahead of the much-anticipated debut of Season 2.” “The holidays came a little early this year - we are thrilled to be ending the world all over again for a third season of Fallout,” said executive producers Jonathan Nolan and Lisa Joy. “On behalf of our brilliant cast and crew, our showrunners Geneva and Graham, and our partners at Bethesda, we’re grateful to our incredible collaborators at Amazon MGM Studios and to the amazing fans as we continue our adventures in the wasteland together.” Based on Bethesda’s beloved video game series, Fallout tells the story of haves and have-nots in a world in which there’s almost nothing left to have. 200 years after the apocalypse, the gentle denizens of luxury fallout shelters are forced to return to the irradiated hellscape their ancestors left behind - and are shocked to discover an incredibly complex, gleefully weird, and highly violent universe waiting for them. The series stars Ella Purnell (Yellowjackets, Sweetpea), Aaron Moten (Emancipation, Father Stu), Walton Goggins (The White Lotus, The Righteous Gemstones), Kyle MacLachlan (Twin Peaks), Moisés Arias (The King of Staten Island), and Frances Turner (The Boys). Fallout is produced by Kilter Films, with executive producers Nolan, Joy and Athena Wickham. Geneva Robertson-Dworet and Graham Wagner also serve as executive producers, creators, and showrunners. Todd Howard, Bethesda Game Studios, executive produces along with James Altman for Bethesda Softworks. Amazon MGM Studios and Kilter Films produce in association with Bethesda Game Studios and Bethesda Softworks. BlackGinger, CoSA VFX, Framestore, FutureWorks Media Ltd., Important Looking Pirates, Magnopus, Mavericks, One of Us, Refuge, and Yafka collaborated to create the VFX for Season 1. Grant Everett served as visual effects supervisor, with Brannek Gaudet as visual effects supervisor for Mavericks, Fred Ruff for Refuge, and Antonis Kotzias for Yafka. No word yet on Season 2 work. Fallout Season 1 is now streaming on Prime Video. Source: Prime Video Journalist, antique shop owner, aspiring gemologist—L'Wren brings a diverse perspective to animation, where every frame reflects her varied passions. Source: https://www.awn.com/news/fallout-renewed-third-season-ahead-season-2-december-premiere #fallout #renewed #for #third #season #ahead #december #premiere

Key Takeaways The US and China have agreed to lower tariffs, with the US now charging a 30% tariff, whereas China will charge 10%. Actual prices may not come down in the long run, but we may see an increase at a much lower pace. Lower tariffs may also not see massive domestic production shifts, so the trade deficit may close more slowly than expected. After a long-fought tariff war, the US and China seem to have reached an understanding. The two countries’ representatives met at Geneva and decided to lower the tariffs for 90 days. The US agreed to bring down its tariffs from 145% to 30%, while China will now only impose a 10% tariff on US goods instead of the previous 125%. It’s worth noting that the 30% tariff by the US comprises a 10% universal tariff imposed by Trump on all countries and an additional 20% tariff for failing to curb fentanyl flows by the Chinese. Trump used the entire tariff weapon to reduce the US’s massive $295.4B trade deficit with China as of 2024. He accused China of taking ‘unfair advantage’ of the US for years. This is also the largest trade deficit the US has had with any trading partner. Will Lower Tariffs Lead to Lower Prices? One important thing to understand is that while the tariffs have been reduced, the overall average effective tariff still stands at 17.8%, which is the highest since 1934. Even if you consider the shift in consumption due to the increased tariffs, the average tariff rate would come to 16.4%, the highest since 1937.  This means that consumers might not actually see lower prices. At most, we might see slower price increases. Companies like Nvidia have already pumped up the prices of their components and products by 10 to 15%. Acer has also announced a 10% price increase as a result of the tariffs. Also, a lot of products have already been shipped to the US, effective with the 145% tariff rates. Almost 12,000 containers with products for companies like Amazon, Ikea, Tractor Supply, and Home Depot have hit the shores of the US. The PC hardware inventory turnover is 30-90 days on average, which means that the price changes may be deferred by almost six weeks. So, it might take some time for the reduced tariffs to come into effect. Even then, you can expect the prices to be more than in the pre-tariff era. This will also depend on the time required to get rid of the high-tariffed inventory sitting on the US shelves. Fast-moving consumer goods usually have a faster inventory turnover, which means you can expect a price drop soon. However, high-value products like PCs may sit on the shelves for a good couple of months. The expectation of reduced prices is also based on the assumption that no further escalations will occur. This is only a 90-day pause, and the tariff scenario remains somewhat uncertain. The overall average price levels are expected to increase by 1.7% in the short run. This means that the average American household may lose $2,800 in purchasing power when compared to 2024. Even after consumption adjustment, the loss stands at around $2,300. Production Moving Back to the US? In the wake of increasing tariffs, several companies have announced moving production back to the US to save costs. For instance, Nvidia said that it will move $500 billion worth of AI server supply chain to the US. Along with partners like TSMC, Foxconn, Wistron, Amkor, and SPIL, Nvidia plans to set up AI servers and server assembly plants in the US. Apple, too, has announced a $500 billion investment in the U.S., including a Houston manufacturing facility. Trump even went on to say that he talked with Tim Cook after the tariff reduction announcement, and ‘he’s going to be building a lot of plants in the United States for Apple.’ Trump also hinted that the actual value of these investments may be way more than $500 billion. It’s worth noting that these announcements were made when the tariffs were as high as 145%. In such a situation, it wasn’t possible for these companies to shift the entire increase in cost to the customers through price hikes. For instance, Apple said that it would lose $900 million as a result of the tariffs. However, now the situation has eased off massively, with the tariffs standing at just 30%. So, do these manufacturers still have the motivation to shift production units to domestic shores? Sure, 30% is still nothing to sniff at, but a part of this cost can be passed on to the customers while the rest can be recovered through better production planning and cost-cutting. Bottom Line All in all, this seems to be a Catch-22 situation for Trump. The entire point of imposing heavy tariffs was to lower imports and encourage domestic production. While tariffs as high as 145% met these purposes, a 30% tariff may not deter the US manufacturers. However, increasing tariffs beyond a certain point would essentially bring trading to a halt between the US and China, which doesn’t serve the purpose either. So, it now all comes down to how well Trump can negotiate with the US companies. His talks with Cook seem to be reassuring as of now. We’ll know in due course whether production sees a ‘differentiable’ shift in the next few months. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors. Source: https://techreport.com/news/us-china-tariff-understanding-price-impact/ #and #china #reach #tariff #understanding #but #prices #may #not #decrease #immediately