Akbar Solo Researchers in Moscow and America have discovered how to use machine learning to grow artificial organs, especially to tackle blindness Researchers from the Moscow Institute of Physics and Technology, Ivannikov Institute for System Programming, and the Harvard Medical School-affiliated Schepens Eye Research Institute have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish. Unlike humans, the algorithm achieves this without the need to modify cells, making the method suitable for growing retinal tissue for developing cell replacement therapies to treat blindness and conducting research into new drugs. The study was published in Frontiers in Cellular Neuroscience. How would this enable easier organ growth? This would allow to expand the applications of the technology for multiple fields including the drug discovery and development of cell replacement therapies to treat blindnessIn multicellular organisms, the cells making up different organs and tissues are not the same.
'All models are wrong, but some are useful' As this famous quote by George Box (known as the Box Theorem) shows, no model is ever going to be 100% accurate. If one is, run for the hills! Rather, models should be evaluated by their impact on the bottom line, or how useful they are to the business. In this blog post, we will explore a way in which models can be more useful, by embracing and leveraging uncertainty to maximize business results. Much of the time, business users want a single number to represent the'goodness' of a model, but machine learning models can tell us so much more than just a single number (like accuracy).
Robustness of machine learning models to various adversarial and non-adversarial corruptions continues to be of interest. In this paper, we introduce the notion of the boundary thickness of a classifier, and we describe its connection with and usefulness for model robustness. Thick decision boundaries lead to improved performance, while thin decision boundaries lead to overfitting (e.g., measured by the robust generalization gap between training and testing) and lower robustness. We show that a thicker boundary helps improve robustness against adversarial examples (e.g., improving the robust test accuracy of adversarial training) as well as so-called out-of-distribution (OOD) transforms, and we show that many commonly-used regularization and data augmentation procedures can increase boundary thickness. On the theoretical side, we establish that maximizing boundary thickness during training is akin to the so-called mixup training.
In recent years, there has been a surge in demand for AI-driven big data analysis in various business fields. AI is also expected to help support the detection of anomalies in data to reveal things like unauthorized attempts to access networks, or abnormalities in medical data for thyroid values or arrhythmia data. Data used in many business operations is high-dimensional data. As the number of dimensions of data increases, the complexity of calculations required to accurately characterize the data increases exponentially, a phenomenon widely known as the "Curse of Dimensionality"(1). In recent years, a method of reducing the dimensions of input data using deep learning has been identified as a promising candidate for helping to avoid this problem. However, since the number of dimensions is reduced without considering the data distribution and probability of occurrence after the reduction, the characteristics of the data have not been accurately captured, and the recognition accuracy of the AI is limited and misjudgment can occur (Figure 1). Solving these problems and accurately acquiring the distribution and probability of high-dimensional data remain important issues in the AI field.
Underfitting means that our ML model can neither model the training data nor generalize to new unseen data. A model that underfits the data will have poor performance on the training data. For example, in a scenario where someone would use a linear model to capture non-linear trends in the data, the model would underfit the data. A textbook case of underfitting is when the model's error on both the training and test sets (i.e. during training and testing) is very high. It is obvious that there is a trade-off between overfitting and underfitting.
It's not an exaggeration to say that when it comes to the future of human progress, nothing is more important than Artificial Intelligence (AI). Although often thought to only be associated with everyday entities such as self-driving cars and Google search rankings, AI is in fact the driving force behind virtually every major and minor technology that's bringing people together and solving humanity's problems. You'd be hard-pressed to find an industry that hasn't embraced AI in some shape or form, and our reliance on this field is only going to grow in the coming years--as microchips become more powerful and quantum computing begins to be more accessible. So it should go without saying that if you're truly interested in staying ahead of the curve in an AI-driven world, you're going to have to have at least a baseline understanding of the methodologies, programming languages, and platforms that are used by AI professionals around the world. This can be an understandably intimidating reality for anyone who doesn't already have years of experience in tech or programming, but the good news is that you can master the basics and even some of the more advanced elements of AI and all of its various implications without spending an obscene amount of time or money on a traditional education.
Why? Existing tools are not well-suited to time series tasks and do not easily integrate together. Methods in the scikit-learn package assume that data is structured in a tabular format and each column is i.i.d. Packages containing time series learning modules, such as statsmodels, do not integrate well together. Further, many essential time series operations, such as splitting data into train and test sets across time, are not available in existing python packages. To address these challenges, sktime was created.
Google CEO: Sundar Pichai - A.I. is more important than fire or electricity Artificial Intelligence (AI) and Machine Learning (ML) are changing the world around us. From functions to industries, AI and ML are disrupting how we work and how we function. Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.
Fujitsu Laboratories has developed what it believes to be the world's first AI technology that accurately captures essential features, including the distribution and probability of high-dimensional data in order to improve the accuracy of AI detection and judgment. High-dimensional data, which includes communications networks access data, types of medical data, and images remain difficult to process due to its complexity, making it a challenge to obtain the characteristics of the target data. Until now, this made it necessary to use techniques to reduce the dimensions of the input data using deep learning, at times causing the AI to make incorrect judgments. Fujitsu has combined deep learning technology with its expertise in image compression technology, cultivated over many years, to develop an AI technology that makes it possible to optimize the processing of high-dimensional data with deep learning technology, and to accurately extract data features. It combines information theory used in image compression with deep learning, optimising the number of dimensions to be reduced in high-dimensional data and the distribution of the data after the dimension reduction by deep learning.
Dimensionality reduction is an unsupervised learning technique. Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms. There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good idea to explore a range of dimensionality reduction algorithms and different configurations for each algorithm. In this tutorial, you will discover how to fit and evaluate top dimensionality reduction algorithms in Python.