Open-world learning is a problem where an autonomous agent detects things that it does not know and learns them over time from a non-stationary and never-ending stream of data; in an open-world environment, the training data and objective criteria are never available at once. The agent should grasp new knowledge from learning without forgetting acquired prior knowledge. Researchers proposed a few open-world learning agents for image classification tasks that operate in complex scenarios. However, all prior work on open-world learning has all labeled data to learn the new classes from the stream of images. In scenarios where autonomous agents should respond in near real-time or work in areas with limited communication infrastructure, human labeling of data is not possible. Therefore, supervised open-world learning agents are not scalable solutions for such applications. Herein, we propose a new framework that enables agents to learn new classes from a stream of unlabeled data in an unsupervised manner. Also, we study the robustness and learning speed of such agents with supervised and unsupervised feature representation. We also introduce a new metric for open-world learning without labels. We anticipate our theories and method to be a starting point for developing autonomous true open-world never-ending learning agents.

The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limited so far. In this paper, we first prove that the focal loss is classification-calibrated, i.e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified. However, we also prove a negative fact that the focal loss is not strictly proper, i.e., the confidence score of the classifier obtained by focal loss minimization does not match the true class-posterior probability and thus it is not reliable as a class-posterior probability estimator. To mitigate this problem, we next prove that a particular closed-form transformation of the confidence score allows us to recover the true class-posterior probability. Through experiments on benchmark datasets, we demonstrate that our proposed transformation significantly improves the accuracy of class-posterior probability estimation.

Hyperspectral Imaging is an important technique in remote sensing, which collects the electromagnetic spectrum ranging from the visible to the near-infrared wavelength. Hyperspectral imaging sensors often provide hundreds of narrow spectral bands from the same area on the surface of the earth. In hyperspectral images (HSI), each pixel can be regarded as a high-dimensional vector whose entries correspond to the spectral reflectance in a specific wavelength. With the advantage of distinguishing subtle spectral differences, HSIs have been widely applied in diverse areas such as Crop Analysis, Geological Mapping, Mineral Exploration, Defence Research, Urban Investigation, Military Surveillance, Flood Tracking, etc. In this article, we are going to use the Pavia University Hyperspectral Image for classification purposes.

AI has made incredible progress over the last decade, and better tools and models are being developed every day. From GPU-Acceleration to Natural Language Processing progress, we have seen accelerators and enablers taking shape and move huge amounts of investments in the most recent past. Deepmind showed us just this week again that things thought to be impossible for another decade can become a reality in no time. Ranging from Smart Robots to Neuromorphic Hardware, we will have a look at the top 13 AI trends that will be on everyone's mind from now until 2025. I am in no way affiliated with any of the following companies.

Deep learning is often used to make predictions for data driven analysis. But what are the meanings of these predictions? This post explains how neural networks used in deep learning provide the parameters of a statistical model describing the probability of the occurrence of events. Data, observables, events or any other way of describing the things we can see and/or collect is absolute: we roll two sixes on a pair of six-sided dice or we get some other combination of outcomes; we toss a coin 10 times and we get heads each time or we get some other mixture of heads and tails; our universe evolves some way and we observe it, or it doesn't -- and we don't. We do not know, a priori, whether we will get two sixes with our dice roll or heads each time we toss a coin or what possible universes could exist for us to come into being and observe it.

On Monday morning, organizers of NeurIPS, the largest annual gathering of AI researchers in the world, gave Best Paper awards to the authors of three pieces of research, including one detailing OpenAI's GPT-3 language model. The week also started with AI researchers refusing to review Google AI papers until grievances were resolved after firing Ethical AI team co-lead Timnit Gebru. Googlers describe it as an instance of "unprecedented research censorship," raising questions of corporate influence. According to one analysis, Google publishes more AI research than any other company or institution. Tension between corporate interests, human rights, ethics, and power could be seen at workshops throughout the week.

For those with not much time to read through the intuition of how sharded works, I'm going to explain up-front how to use sharded with your PyTorch code. But I encourage you to read through the end of the article to understand how sharded works. Sharded is meant to be used with multiple GPUs to gain all the benefits. But, training on multiple GPUs can be intimidating and a huge pain to set up. The easiest way to supercharge your code with sharded is to convert your model to PyTorch Lightning (which is just a simple refactor).

Python is an abundant source of libraries. A Python library is a gathering of functions that assist one to perform many actions. It has myriad inbuilt libraries. Python contains ample libraries for data science. This tutorial covers python libraries for data scientist. Let's see Python libraries for data scientist: Pandas is one of the most popular data analysis and data manipulation libraries.

Deep neural networks are one of the most fundamental aspects of artificial intelligence (AI), as they are used to process images and data through mathematical modeling. They are responsible for some of the greatest advancements in the field, but they also malfunction in various ways. These malfunctions can have either a small to non-existent impact, such as a simple misidentification, to a more dramatic and deadly one, such as a self-driving malfunction. New research coming out of the University of Houston suggests that our common assumptions on these malfunctions may be wrong, which could help evaluate the reliability of the networks in the future. The paper was published in Nature Machine Intelligence in November.

JAX is a Python library developed by Google researchers for high-performance numerical computing. Its API is based on NumPy. NumPy is a collection of functions applied in scientific computing. Developers extensively adopt Python and NumPy, making JAX simple, flexible, and easy to use. JAX and its developing ecosystem of open source libraries have assisted and accelerated numerous machine learning projects.