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Top 7 Benefits of Using an...

Introduction In today’s fast-paced digital landscape, content is king and the demand for high-quality, engaging and optimized content has never been higher. Whether you’re a business owner, blogger, or digital marketer, creating compelling content consistently can be a time-consuming and challenging process. This is where an Online Content Generator comes into play. AI-powered tools like Yatter AI are revolutionizing content creation by streamlining the writing process, enhancing creativity, and ensuring digital success. These advanced tools leverage artificial intelligence to generate well-structured, engaging, and SEO-friendly content within minutes, helping businesses stay ahead in the competitive digital space. With content marketing playing a crucial role in brand awareness and lead generation, having an Online Content Generator at your disposal can significantly boost productivity while maintaining quality. Whether you need blog posts, social media captions, product descriptions, or marketing copy, AI-driven content generators provide a seamless and efficient solution. What is an Online Content Generator? An Online Content Generator is a powerful digital tool that leverages artificial intelligence (AI) and natural language processing (NLP) to create high-quality written content automatically. These tools are designed to assist businesses, marketers, content creators, and bloggers by generating well-structured and engaging content with minimal effort. Whether you...

As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s block settling into the field when it comes to dealing with large images. Large images are no longer rare—the cameras we carry in our pockets and those orbiting our planet snap pictures so big and detailed that they stretch our current best models and hardware to their breaking points when handling them. Generally, we face a quadratic increase in memory usage as a function of image size. Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present in an image. We take another look at these approaches and introduce $x$T, a new framework to model large images end-to-end on contemporary GPUs while effectively aggregating global context with local details. Architecture for the $x$T framework.

February 11, 2017 Vasilis Vryniotis . 6 Comments The dstat is an awesome little tool which allows you to get resource statistics for your Linux box. It has a modular architecture which allows you to develop additional plugins and it’s easy to use. Recently I was profiling a Deep Learning pipeline developed with Keras and Tensorflow and I needed detailed statistics about the CPU, Hard Disk and GPU usage. The first two are available out-of-the-box by dstat, nevertheless as far as I know there is no plugin for monitoring GPU usage for NVIDIA graphics cards. Thankfully it is super easy to write a python plugin for dstat. I have already sent a pull-request on the official repo but since new versions are released relatively rarely here are some instructions on how to set up the dstat NVIDIA GPU usage plugin on your box. Installation The following commands are tested on Ubuntu 16.04 and they will help you install dstat, the Python NVIDIA Management Library and my dstat nvidia plugin: sudo apt-get install dstat #install dstat sudo pip install nvidia-ml-py #install Python NVIDIA Management Library wget https://raw.githubusercontent.com/datumbox/dstat/master/plugins/dstat_nvidia_gpu.py sudo mv dstat_nvidia_gpu.py /usr/share/dstat/ #move file to the plugins directory of dstat To...

Precision and recall are commonly used metrics to measure the performance of machine learning models or AI solutions in general. It helps understand how well models are making predictions. Let’s use an email SPAM prediction example. Say you have a model that looks at an email and decides whether it’s SPAM or NOT SPAM. To see how well it’s doing, you want to compare it with human-generated labels, which we will call the actual labels. To demonstrate this, the table below shows you some actual labels and the machine (model) predicted labels. Now we’ll assume that the spam prediction is positive, and the not spam prediction is negative. Email ID Actual Label Machine Predicted Label Email 1 Spam (positive) Spam (positive & correct) Email 2 Spam (positive) Not Spam (negative & incorrect) Email 3 Not Spam (negative) Spam (positive & incorrect) Email 4 Spam (positive) Not Spam (negative & incorrect) Email spam predictions with corresponding true predictions. What is Precision in ML? Given this, intuitively, precision measures the proportion of correct positive predictions. How precision is computed As you can see from the table above, out of the 2 spam (positive) machine predictions, only 1 is correct. So the precision...

Have you ever wondered just how many decisions you will make today? In your lifetime? Though difficult to measure, researchers have estimated that the average adult makes anywhere from a few thousand to 35,000 decisions in a single day. While the estimates range tremendously due to the complexity of deciphering between decisions and automatic reflexes, it is undisputed that people make a lot of decisions every single day. A different way to understand just how active our brains are is to look at a more easily measured metric, the energy consumption of the brain—specifically the amount of glucose and oxygen burned each day. Researchers have found that although the brain accounts for only 2% of adult body weight, it burns roughly 20% of the body’s calories. Those watching The Queen’s Gambit on Netflix will be interested to know that grandmasters in chess can burn up to 6,000 calories per day, which is on par with elite athletes. We make thousands of decisions each day and our bodies use a disproportionate amount of our energy resources to fuel our brain power. Thanks to advances in behavioral science and neuroimaging technologies, we better understand what is happening under the hood. So how...

Despite a flurry of new AI engines just released by OpenAI, the current default engine for the chatbot – ChatGPT-4o – is still the best, overall solution for writing. That said, users may want to give the new OpenAI-o3 model a run if they’re looking for better reasoning and overall better performance when engaging in math, coding, science and other hard sciences. Ditto for OpenAI-o4-mini and OpenAI-o4 mini-high. Both are designed to be less expensive to run than OpenAI-o3, but are touted as nearly as good as Open-AI-o3. Plus, there is also a separate set of new AI engines – dubbed the GPT-4.1 family – also specially designed for help with computer coding. Last, but not least, there’s also a GPT-4.5, which is sometimes better for writing than ChatGPT-4o. But the AI engine is generally only available for limited use. In other news and analysis on AI writing: *ChatGPT Beats TikTok and Instagram in Downloads: In another popularity wars head-turner, ChatGPT became the most downloaded non-gaming app in March – beating-out TikTok and Instagram. Observes writer Sarah Perez: “This is the first time the app has topped the monthly download charts and ChatGPT’s biggest month ever. ”According to new data,...

🔘 Laboratory page: github.com/koaning/human-learn Summary Machine learning covers a lot of ground but it is also capable of making bad decision. We’ve also reached a stage of hype that folks forget that many classification problems can be handled by natural intelligence too. This package contains scikit-learn compatible tools that should make it easier to construct and benchmark rule based systems that are designed by humans. You can also use it in combination with ML models. Author Vincent Warmerdam Liked this post? Follow this blog to get more.

In the 2017 Report “Water for Sustainable Food and Agriculture“, the UN Food and Agriculture Organization (FAO) has remarked that, on average, agriculture accounts for 70% of global freshwater withdrawals. Only 0.003% of the World water are “fresh water resources”, and an even smaller fraction is actually accessible and can be used for drinking, hygiene, agriculture and industry. Water is essential for every form of life, for all aspects of socio-economic development, and for the maintenance of healthy ecosystems UN Food and Agriculture Organization, 2017 As the food requirements of the growing global population increase, with an estimate of 60% more food needed by 2050 worldwide, farmers are struggling to improve irrigation practices and increment yields. Water accounts for more than 70% of of global freshwater withdrawals. AI-enabled smart and precision farming can help at several different levels, and many of the emerging problems in prevision farming and smart irrigation have been addressed in the last 15 years through advanced Artificial Intelligence methodologies at the ELEDIA Research Center. Smart farming technology has been widely developed by ELEDIA in the last 15 years starting from applications in the cultivation of apples, grapes, and berries. Members of the ELEDIA team are continuously...

Neural radiance fields (NeRFs) slowly become the next hot topic in the world of Deep Learning. Since they were originally proposed in 2020, there is an explosion of papers as it can be seen from CVPR’s 2022 submissions. Time magazine recently included a variation of NeRFs, called instant graphics neural primitives, in their best inventions of 2022 list. But what exactly are NeRFs they and what are their applications? In this article, I will try to demystify all the different terminologies such as neural fields, NeRFs, neural graphic primitives etc. To give you a preview, they all stand for the same thing depending on who you ask. I will also present an explanation of how they work by analyzing the two most influential papers. What is a neural field? The term neural field was popularized by Xie et al. and describes a neural network that parametrizes a signal. This signal usually is a single 3D scene or object but that’s not mandatory. We can also use neural fields to represent any type of signals (discrete or continuous) such as audio or images. Their most popular use is in computer graphics applications such as image synthesis and 3D reconstruction, which is...

Generative AI is a rapidly evolving field with a plethora of fascinating applications, from creating realistic images and videos to generating human-like text and beyond. As the technology advances, the demand for skilled professionals who can harness the power of generative AI is growing exponentially. However, navigating the myriad of tutorials and courses available can be overwhelming, especially when trying to acquire these critical skills quickly. To help you on your journey, we have curated a list of some of the highest-quality courses from respected providers such as DeepLearning.ai, Google Cloud, AWS, IBM, and more. These courses are designed with a strong practical focus, ensuring that you gain real-world skills needed to build applications powered by large language models (LLMs). The best part? Most of these courses are available for free, making it easier than ever to dive into the world of generative AI. In this article, we provide an overview of these top courses, highlighting their key features and content to help you find the best fit for your learning needs. Whether you’re a beginner just starting out or an advanced developer looking to deepen your expertise, there’s something here for everyone. Here are the courses we cover: Generative...