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How AI can help us clean up our land, air, and water

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The next industrial revolution is already happening. Artificial intelligence (AI) is ushering in an era of technologies that are faster, more adaptable, more efficient, and making the world more digitally connected. AI is best described as complementary to human intelligence, delivering the computing power to crunch numbers too big for people and recognize patterns too tedious for the human eye. In a Harvard Business Review study of 1,500 companies, it was found that the most significant performance improvements were made when humans and machines worked together. As AI becomes one of society's greatest assets, it's especially helpful for solving problems that seem larger than life -- like protecting our natural environment.


The Shape of Tensor

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Tensors are the primary data structures used by neural networks. And they are rather fascinating as well. Machine learning and by extension deep learning is an interdisciplinary field. Its interesting to note how many different people from many different fields came to same concepts. The concept of tensor is a mathematical generalization of more specific concepts, vectors and matrices in particular. In neural networks transformations, input, output etc are performed via tensors.


Artificial intelligence can help protect orchids and other species

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Orchids are more than just decorative - they are also economically important in horticulture, in the pharmaceutical industry and even in the food industry. For example, vanilla orchids are grown commercially for their seed pods, and the economy on the northeast of Madagascar centers around the vanilla trade. But many of the approximately 29,000 orchid species face immediate threats by land conversion and illegal harvesting, resulting in an urgent need to identify the most endangered species and protect them from extinction. The global Red List of the International Union for the Conservation of Nature (IUCN) is the most widely used scheme to evaluate species' risk of extinction. The assessments are based on rigorous criteria and the best available scientific information, making them resource-intensive and, therefore, only available for a fraction of the species worldwide.


The 5 Most Amazing Computer Vision Techniques to Learn

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At this point, computer vision is the hottest research field within deep learning. It fits in many academic subjects such as Computer science, Mathematics, Engineering, Biology, and psychology. Computer vision represents a relative understanding of visual environments. Therefore, due to its cross-domain mastery, many scientists believe the field paves the way towards Artificial General Intelligence. Recent developments in neural networks and deep learning approaches have immensely advanced the performance of state-of-the-art visual recognition systems. Let's look at what are the five primary computer vision techniques.


Focal Loss in Object Detection

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So Focal Loss reduces the loss contribution from easy examples and increases the importance of correcting misclassified examples.) So, let's first understand what Cross-Entropy loss for binary classification. The idea behind Cross-Entropy loss is to penalize the wrong predictions more than to reward the right predictions.


Artificial intelligence: deep learning

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In last month's column, I tackled the subject of machine learning (ML) in Artificial intelligence: machine learning and I suppose one obvious question would be "What's the difference between machine and deep learning?" Well, both terms are subsets of artificial intelligence (AI), although deep learning is a subset of machine learning. In the meantime, let's touch upon machine learning so as to re-establish what we have already understood and, that is, ML is a broad field of study, where the development of software (a computer program) automatically improves through experience, based on the data that it has received. We nowadays associate the term with "big data," "data modelling" or "data science," where retailers, for example, like to collect information about your shopping habits so, in turn, they can more accurately target their advertising. And then there are the likes of Amazon and Netflix, who use data analytics to predict or suggest what you might be interested in viewing or purchasing next.


Adobe's DL-Based 'HDMatt' Handles Image Details Thinner Than Hair

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Image matting plays a key role in image and video editing and composition. Although existing deep learning approaches can produce acceptable image matting results, their performance suffers in real-world applications, where the input images are mostly high resolution. To address this, a group of researchers from UIUC, Adobe Research and the University of Oregon have proposed HDMatt, the first deep learning-based image matting approach for high-resolution image inputs. Generally, deep learning approaches take an entire input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such methods however may fail when dealing with high-resolution input images in sizes of 5000 5000 pixels or higher due to hardware limitations. The researchers designed HDMatt to crop an input image and trimap into patches, then estimate the alpha values of each patch.


Deep learning to estimate RECIST in patients with NSCLC treated with PD-1 blockade – IAM Network

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This article was originally published here Cancer Discov. ABSTRACT Real-world evidence (RWE) – conclusions derived from analysis of patients not treated in clinical trials – is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE may be facilitated through machine learning techniques to integrate and interrogate large and otherwise underutilized data sets. In cancer research, an ongoing challenge for RWE is the lack of reliable, reproducible, scalable assessment of treatment-specific outcomes. We hypothesized a deep learning model could be trained to use radiology text reports to estimate gold-standard Response Evaluation Criteria in Solid Tumors (RECIST)-defined outcomes.


10 Best Python Libraries For Computer Vision Tasks

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One of the most favourite languages amongst the developers, Python is well-known for its abundance of tools and libraries available for the community. The language also provides several computer vision libraries and frameworks for developers to help them automate tasks, which includes detections and visualisations. Below here, we are listing down 10 best Python libraries that developers can use for Computer Vision. It also provides researchers with low-level components that can be mixed and matched to build new approaches. IPSDK is an image processing library in C and Python.


Transformers in NLP: Creating a Translator Model from Scratch

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Transformers have now become the defacto standard for NLP tasks. Originally developed for sequence transduction processes such as speech recognition, translation, and text to speech, transformers work by using convolutional neural networks together with attention models, making them much more efficient than previous architectures. And although transformers were developed for NLP, they've also been implemented in the fields of computer vision and music generation. However, for all their wide and varied uses, transformers are still very difficult to understand, which is why I wrote a detailed post describing how they work on a basic level. It covers the encoder and decoder architecture, and the whole dataflow through the different pieces of the neural network.