In this post, I delve into my experience training an AI agent using Behavior Cloning on the classic arcade game Final Fight. By relying solely on demonstrations, I evaluated the agent's performance in the first stage, navigating challenges like action space remapping and trajectory alignment issues. While the agent shows promise, consistency and survival remain hurdles. I’m eager for community insights on enhancing BC performance, transitioning to PPO, and addressing partial observability.

Hello everyone, I’m seeking assistance with my personal project, an LLM-based CLI tool designed to explore MLflow logs. To enhance my testing, I’m looking for real data since I’ve only been using dummy data I generated myself. Unfortunately, I haven’t found any MLflow database files shared online. If anyone has access to MLflow logs or databases they can share, it would greatly help my project. You can check out my work on GitHub: https://github.com/5aumit/floki. Thank you for your support!

Integrating a Chebyshev filter into a CNN architecture can be a compelling approach to enhance performance, particularly in feature extraction. However, achieving meaningful results can be challenging, as many have experienced similar frustrations. In this discussion, we invite insights and advice from those who have tackled this integration. Have you successfully combined Chebyshev filters with CNNs? What strategies or modifications led to improved accuracy? Share your experiences, tips for tuning, or relevant resources that could help guide this journey toward better outcomes.

Introducing **torch-nvenc-compress**, a Python library designed to optimize GPU NVENC silicon for efficient data management. By leveraging the otherwise-idle NVENC/NVDEC hardware, this solution compresses activations and KV cache on the fly, significantly reducing bandwidth demands across PCIe connections. With measured compression ratios of 6.1× for diffusion and 2.7× for LLM KV cache, it transforms how large models operate on consumer GPUs. Explore the repository for runnable PoCs and contribute to advancing multi-GPU performance. Join the journey toward more efficient data processing with **

The landscape of machine learning PhDs is under scrutiny, with many questioning whether current research is becoming overly incremental. Observations suggest that a substantial portion of modern work follows predictable patterns, often yielding polished results without significant scientific advancement. While incremental progress is valuable, there is concern that these efforts resemble extended master’s theses rather than groundbreaking contributions.

Creating dropdown lists in Excel from non-contiguous cells can be a challenge, especially when you're working with data on a separate sheet that you can't modify. The error message stating, "You may not use reference operators for data validation criteria," can be frustrating, particularly when you know the logic behind your selections. While Excel requires contiguous ranges for data validation, there are workarounds to achieve your goal. Let’s explore some innovative solutions to help you create effective dropdown lists without the hassle.

Naming files using specific cell values from multiple sheets can streamline your workflow and enhance organization. In this example, a beginner VBA user seeks assistance in automatically populating file names with values from cell D21 across all tabs in their workbook. While they have successfully named files from a single sheet, they want to extend this functionality. By leveraging the active workbook's SaveAs method and referencing the desired cells, you can create a dynamic and informative naming convention for your files.

Transitioning to a more analytical role can feel overwhelming, especially when working with Excel. If you're looking to subtract 425 days from a date in one column using a formula, the formula you've used is on the right track. However, it's essential to ensure the dates are formatted correctly for accurate calculations. You may need to adjust your column format to "Date" to see the results. Let's explore how to set this up properly so you can confidently manage your data moving forward.

Creating a paycheck calculator in Excel can streamline your financial planning and help you understand your earnings better. Start by setting up a cell with a drop-down list for pay frequency, including options like Daily, Weekly, and Bi-Weekly. To calculate gross pay based on the selected frequency, consider using the `IF` function for clarity, or the `CHOOSE` function for a more streamlined approach. Don't forget to incorporate state income tax calculations by referencing your location, ensuring a comprehensive overview of your earnings after deductions.

Introducing the Xteink X3, a compact and innovative e-reader designed to transform your reading experience and curb doomscrolling. This MagSafe-compatible device effortlessly attaches to the back of your phone, offering a seamless way to access your favorite books without distraction. With its e-ink technology, the X3 provides a comfortable reading experience that mimics paper, making it easy on the eyes. Discover how this delightful gadget can empower you to reclaim your time and focus on what truly matters—immersive reading.

Welcome back to TechCrunch Mobility, your central hub for news and insights on the future of transportation. In this edition, we explore the intriguing question: how do you issue a ticket to a robotaxi? As autonomous vehicles reshape our urban landscapes, understanding the mechanisms behind their operation becomes essential. We’ll delve into the technology and regulations that govern this emerging sector, providing you with a comprehensive view of how ticketing systems are evolving in tandem with innovations in mobility.

A recent Harvard study reveals that AI outperformed emergency room doctors in accuracy when diagnosing real medical cases. This groundbreaking research evaluates the performance of large language models across various medical contexts, highlighting their potential to enhance clinical decision-making. As healthcare increasingly integrates innovative technologies, the findings suggest that AI can serve as a valuable tool for medical professionals, empowering them to deliver more precise diagnoses and ultimately improving patient outcomes. This development invites further exploration into the transformative role of AI in medicine.

When tackling the RFM clustering problem in your furniture and decor enterprise, it's essential to address the low silhouette score of 0.3, indicating weak cluster separation. Simplifying to just frequency and monetary value has led to concentration around specific values, which compromises the effectiveness of your analysis. Consider refining your feature selection by experimenting with additional variables, such as customer demographics or preferences, alongside tenure and interpurchase metrics.

In "Time Series Foundation Models: A Deep Dive into Strengths and Limitations," the article offers a candid examination of Time Series Foundation Models (TSFMs), stripping away the hype to reveal their true capabilities and constraints. It thoughtfully assesses which limitations can be addressed, which remain inherent, and identifies ongoing challenges in the field. This analysis not only enhances understanding of TSFMs but also empowers readers to navigate the complexities of time series modeling with greater clarity and confidence.

In the realm of climate discourse, misinformation can significantly distort public perception. A recent video promoting climate denial illustrates this issue by utilizing cherry-picked charts that mislead viewers about climate trends. By selectively presenting data, the video obscures the broader context necessary for informed understanding. This analysis, submitted by /u/rhiever, delves into the tactics employed in the video and highlights the importance of scrutinizing the sources and representations of data in shaping our understanding of climate change. Explore the full discussion [here].

After a decade as a Data Scientist, I’ve learned that you don’t need to be a PhD or work at FAANG to make a meaningful impact. My journey has taken me through medium and small companies, often with outdated tech, and I’ve thrived by translating complex data into accessible insights. While I may not fit the mold of a traditional expert, my passion for data and continuous learning has driven my success.

Introducing Evolving Deep Learning Optimizers [R], a groundbreaking framework that leverages genetic algorithms to automatically discover innovative optimization algorithms for deep learning. By encoding optimizers as genomes, we explore various combinations of update terms and hyperparameters through evolutionary search across 50 generations. Our findings reveal an evolved optimizer that surpasses Adam by 2.6% in aggregate fitness and achieves a 7.7% improvement on CIFAR-10.

In the recent discussion titled "UAI Reviews Disappeared [D]," submitted by user /u/No_Language165, the community grapples with a perplexing issue: the sudden disappearance of reviews associated with submissions. This development has left many users questioning whether their feedback has been affected as well. As participants seek clarity and share their experiences, this thread serves as an important platform for addressing concerns, exploring potential causes, and finding solutions together. Join the conversation to uncover the truth behind this unexpected situation.

In my latest project, I trained an AI agent using Behavior Cloning to tackle the classic arcade game Final Fight. The initial phase involved learning from demonstrations without reward shaping, and I assessed its performance in the first stage. While the agent shows potential, it faces challenges with action space remapping and trajectory alignment. I plan to enhance its capabilities by integrating GAIL and PPO methods. I welcome community feedback on improving performance, transitioning techniques, and addressing partial observability.

Stripe, Visa, Mastercard, Microsoft, and Meta are all innovating to create a more seamless and efficient digital payment ecosystem. Each company brings its unique strengths to the table, from Stripe's developer-friendly payment solutions to Visa and Mastercard's extensive global networks. Microsoft enhances payment integration through its software offerings, while Meta explores new avenues in digital currency and social commerce.

In the evolving landscape of AI, reasoning models have emerged as powerful tools for enhancing decision-making processes. However, their implementation can significantly impact operational costs, particularly in terms of token usage, latency, and infrastructure demands. This article delves into the intricacies of inference scaling during test-time compute, illuminating how these models can inadvertently elevate your compute bill. By understanding these dynamics, you can make informed decisions that balance performance with cost-effectiveness, ensuring your data management strategies remain both innovative and sustainable.

In the realm of deep learning, the Cross-Stage Partial Network (CSPNet) presents a compelling approach that enhances model performance without compromising efficiency. This walkthrough demystifies the CSPNet paper, highlighting its innovations and practical applications. You'll discover how its design effectively balances complexity and usability, enabling robust performance in various tasks. Additionally, a from-scratch PyTorch implementation is provided, empowering you to explore these concepts hands-on. Dive into this review to transform your understanding of CSPNet and elevate your deep learning projects.

It seems you are experiencing a discrepancy between your Named Table, tblLabInfo, and the dropdown box, ddLabInfo. While the dropdown shows entries for specific laboratories, these entries may not be visible in tblLabInfo due to several potential issues. It’s possible that the data was inadvertently added to a different range or table, or there may be filters applied that hide certain information. Let’s explore these possibilities to ensure your data is accurately reflected and easily accessible in your workbook.

If you’re diving into Excel and want to leverage the Google API for distance calculations, you’re embarking on an exciting journey. By setting up a spreadsheet where you input start and finish addresses, you can automate distance and time calculations tailored to your needs. While utilizing Power Query and a Google route API key is essential, making your solution native to Excel will enhance efficiency.

Struggling to plot bedtime data in Excel can be incredibly frustrating, especially after investing hours in finding the right information. If you’re encountering issues with Co-Pilot and can only display generic numbers, you’re not alone. Many users face challenges when trying to visualize time-based data effectively. Consider sharing your spreadsheet for community insights—collaboration often leads to innovative solutions. Let’s explore how to unlock the potential of your data visualization and transform your experience with charts. Your journey toward clearer insights starts here!

Sorting rows from least to most repeating values in a spreadsheet can streamline your data analysis, especially with large datasets like your inventory of over 15,000 rows. To achieve this, you can use a combination of functions, such as COUNTIF and SORT. By counting the occurrences of each value and then sorting based on these counts, you can easily display the least repeating values at the top.

If you're experiencing frustrating freezing issues with Excel on your new Windows 11 HP I7 G8 laptop, you're not alone. Despite having robust specifications, including 64GB of RAM and a 1TB hard drive, some users have reported similar problems when navigating files or performing basic tasks like removing rows. This seems especially perplexing given that your previous laptop, while less powerful, handled Excel functionalities like Power Query and VBA without issues. Have others encountered this challenge, and what solutions have you found? [link] [comments]

If your TMLR paper has been under review since October and you’ve addressed reviewer feedback, it's reasonable to follow up with the Action Editor. While two months may not seem excessive in the peer-review landscape, TMLR's reputation for a swift process warrants a gentle reminder. Consider sending a concise email rather than an official comment on OpenReview, as it allows for a more personal touch.

Introducing KMRI [P], an efficient and fast MRI compression program designed for .nii files, utilizing Python, Zstd, and C++. This innovative chunk-based compression format achieves remarkable results, especially with smooth synthetic MRI-like volumes, boasting compression ratios of up to ~900× in optimal scenarios through zero-block skipping. KMRI [P] empowers users to manage their MRI data more effectively, streamlining workflows without sacrificing quality. Explore the project on GitHub at https://github.com/Kiamehr5/KMRI, and share your thoughts on this transformative solution.

When considering the credibility of research papers, independent researcher affiliation can raise questions among reviewers and readers alike. However, the value of your work should ultimately depend on its quality and the insights it provides, rather than solely on your affiliation. Publishing as an independent researcher, especially with a proven track record at A* venues, demonstrates your expertise. While using a Gmail domain might seem informal, it should not diminish the impact of your research.

In the ever-evolving landscape of AI, not every investment yields the anticipated returns. The $60M AI Win That Wasn't explores a high-profile case where expectations clashed with reality, shedding light on the complexities and challenges of implementing AI solutions. This analysis invites you to discover the lessons learned from this experience, emphasizing the importance of aligning technology with user needs and practical outcomes. Join us as we unpack this intriguing story and consider how it shapes our understanding of AI's potential in real-world applications.

In the evolving landscape of AI, it’s essential to recognize that technology can sometimes excel in areas that may not align with our true intentions. The concept of Intent Engineering sheds light on this phenomenon, emphasizing the importance of directing AI capabilities toward meaningful outcomes. By understanding the nuances of AI’s strengths and limitations, we can harness its potential effectively. Join us as we explore the implications of AI working too well at the wrong things and how to navigate this challenge for better results.

Cloudflare has unveiled a new infrastructure tailored for running large AI language models across its expansive global network. Recognizing the demands of these models, which require substantial hardware and must efficiently manage high volumes of text traffic, Cloudflare has innovatively separated input processing from output generation. This strategic optimization enhances performance and scalability, positioning Cloudflare as a key player in the evolving landscape of AI technology. This advancement exemplifies Cloudflare's commitment to empowering users with robust, future-focused solutions in the realm of artificial intelligence.

If you're exploring effective question-answering solutions without the need for training data, the Transformers library offers several pre-trained models. However, it seems the pipeline you referenced has transitioned away from the "question-answering" feature, now focusing on tasks like document-question-answering and text classification. To address this, consider utilizing the "text-classification" or "table-question-answering" features if they align with your goals. Alternatively, explore other frameworks like Hugging Face's model hub for additional options that can handle text-based question answering effectively.

In exploring the decision-making process around Flyte, Prefect, and Temporal, it's essential to understand whether teams genuinely trialed these platforms or simply dismissed them after a cursory review of documentation. What were the critical factors that led to their rejection? Was it the complexity of setup, ongoing maintenance challenges, or perhaps other limitations that influenced their choice? Engaging in this discussion can uncover valuable insights into user experiences and the specific hurdles that may deter teams from adopting these innovative workflow orchestration tools.

If you have experience submitting machine learning articles to ACM journals like TOPML or TIST, your insights could be invaluable. Share your experiences regarding the submission process, including how long it took and the quality of the reviews you received. Additionally, how does the submission experience compare to other journals, such as TMLR, in terms of difficulty and overall satisfaction? Your contributions can help others navigate the complexities of academic publishing in the field of machine learning. Thank you for sharing your thoughts.

Explore the evolving landscape of AI with our guide to 15+ solved agentic AI projects, complete with GitHub links. As the focus shifts from generative models to systems capable of step-by-step reasoning and purposeful action, these projects serve as vital bridges between understanding AI and practical application. Each project is designed to empower you to build innovative solutions that address real-world challenges. Dive in to discover how these examples can inspire your own journey into the transformative world of agentic AI.

Ask.com, once a prominent player in the search engine landscape, is officially shutting down its search business, as announced by its owner, IAC. This decision marks the end of an era for a platform that sought to provide users with a unique question-and-answer experience. As the digital landscape evolves, the discontinuation reflects a broader trend of consolidation and innovation in online search. Users are encouraged to explore alternative search solutions that continue to meet their needs in an increasingly competitive market.

In a significant shift for the film industry, the Academy of Motion Picture Arts and Sciences has announced that AI-generated actors and scripts will no longer be eligible for Oscars. This decision marks a pivotal moment for creatives navigating the intersection of technology and artistry. For Tilly Norwood, this news presents a unique challenge, as her innovative work in the realm of AI-driven performance and storytelling now faces new limitations. As the industry evolves, the implications for creators and audiences alike will be profound.

Navigating the distinction between a Data Scientist role and an analytics support position can be challenging, especially in a specialized field like aerospace and supply chain. Your new responsibilities—focused on service parts planning, forecasting, and stakeholder engagement—suggest a practical application of data science principles. While the absence of model-building from scratch may raise questions, your efforts in analysis, system support, and innovative projects like Monte Carlo simulations indicate a commitment to skill development.

I conducted an extensive analysis of the Kentucky Derby, running one trillion simulations on a 1,000-vCPU cloud cluster. This model, built using a combination of historical data and machine learning techniques, offers insights into race dynamics and potential outcomes. With the top pick, *Further Ado*, showing a significant win probability of 27.9%, this approach highlights the power of data-driven decision-making in horse racing. While not a guarantee of success, these findings provide a fresh perspective for enthusiasts and bettors alike.

In this toy experiment, I explore the capabilities of a frozen Pythia-70M transformer model using forward-derived fast memory to achieve contextual one-shot symbolic recall. By leveraging the model's inherent geometry without updating its weights, I examine how an external memory can facilitate the binding of invented symbolic meanings. The findings suggest that even in a constrained setup, the model effectively distinguishes between conflicting meanings based on context, showcasing the potential for fast memory systems to enhance data handling in AI-driven applications.

Landing your first machine learning (ML) job can feel daunting, especially as a student navigating a complex field. Start by building a solid foundation in key concepts like algorithms, data manipulation, and model evaluation. Participate in projects, internships, or competitions to gain practical experience and showcase your skills. Networking is also vital; connect with professionals in the field through events or online platforms. Remember, persistence and a willingness to learn are crucial.

Managing variable-dependent items in Excel can be a challenge, especially when each parent document type requires a different number of child items. This workaround utilizes a mapping table to automate the assignment process without macros, allowing users to bypass manual selection and maintain the integrity of their workflows. By employing a COUNTA pointer and INDEX formulas, the solution dynamically assigns the next valid child item based on the chosen parent. This method enhances productivity, prevents errors, and simplifies complex structures while ensuring user-friendliness.

Hello fellow Excel enthusiasts! At 27 and currently jobless, I’ve relied on my Excel skills to land two positions in the past. However, the job market has become competitive, with many roles requiring a bachelor’s degree I don’t possess. I’m contemplating launching an Excel course for beginners to advanced users, but I'm unsure if people will invest in a course when free resources are abundant. I’d appreciate any suggestions on how to leverage my Excel expertise to generate income in today’s landscape. Thank you!

While Lovable and Mistral AI have garnered notable attention, they are just the tip of the iceberg in Europe’s vibrant startup ecosystem. Numerous innovative companies are making strides in various sectors, capturing the interest of industry insiders and investors alike. This article highlights 21 European startups that are poised for success, showcasing their unique approaches and transformative potential.

In today's fast-paced digital landscape, AI-powered dictation apps are transforming how we communicate and manage tasks. These innovative tools enable users to reply to emails, take notes, and even code—all through the power of their voice. This guide explores the best AI dictation apps, rigorously tested and ranked to help you discover solutions that enhance your productivity. Whether you're looking to streamline your workflow or simplify everyday tasks, these apps offer accessible and effective ways to harness the potential of voice technology.

Netflix has announced a delay for Greta Gerwig’s highly anticipated adaptation of "The Magician's Nephew," pushing its release to 2027. This strategic decision marks a significant step in Netflix's evolving relationship with traditional movie theaters, as the streaming giant aims for a robust theatrical release. By prioritizing a big-screen debut, Netflix is signaling its commitment to enhancing the cinematic experience while tapping into the nostalgia and excitement surrounding C.S. Lewis's beloved Narnia series.

In today's competitive hiring landscape, the integrity of candidates is increasingly under scrutiny. As a hiring manager for a Fortune 500 company, I’ve witnessed a troubling rise in dishonest applications, including fake candidates and those manipulating the interview process. This discussion will highlight the challenges we face, from misleading responses about sponsorship to the alarming prevalence of AI-assisted cheating. By understanding these issues, we can work together to foster a transparent hiring environment and empower genuine talent to shine through.

I am excited to share my implementation of the Meta AI paper titled "Scaling Test Time Compute for Agentic Coding," available on GitHub at genji970/Scaling-Test-Time-Compute-for-Agentic-Coding. This project features a minimal research implementation of the core PDR+RTV pipeline, a crucial contribution as there are currently no public implementations of this paper. The project runs the Gemini-3.1-pro model and tests it on the SWE benchmark, among others. To get started, you will need a Gemini API key. Explore this innovative approach to enhance your coding efficiency!