In recent years we have seen a growing number of edge devices adopted by consumers, in their homes (e.g., smart cameras and doorbells), in their cars (e.g., driver assisted systems), and even on their persons (e.g., smart watches and rings). Similar growth is reported in industries including aerospace, agriculture, healthcare, transport, and manufacturing. At the same time that devices are getting smaller, Deep Neural Networks (DNN) that power most forms of artificial intelligence are getting larger, requiring more compute power, memory, and bandwidth. This creates a growing disconnect between advances in artificial intelligence and the ability to develop smart devices at the edge. In this paper, we present a novel approach to running state-of-the-art AI algorithms at the edge. We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks (BWN) and XNOR-Networks. In BWN, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a BWN version of AlexNet is the same as the full-precision AlexNet.

Since 2012, the field of artificial intelligence (AI) has reported remarkable progress on a broad range of capabilities including object recognition, game playing, speech recognition, and machine translation.43 Much of this progress has been achieved by increasingly large and computationally intensive deep learning models.a Figure 1, reproduced from Amodei et al.,2 plots training cost increase over time for state-of-the-art deep learning models starting with AlexNet in 201224 to AlphaZero in 2017.45 The chart shows an overall increase of 300,000x, with training cost doubling every few months. An important paper47 has estimated the carbon footprint of several NLP models and argued this trend is both environmentally unfriendly and prohibitively expensive, raising barriers to participation in NLP research. We refer to such work as Red AI. The amount of compute used to train deep learning models has increased 300,000x in six years. Figure taken from Amodei et al.2 This trend is driven by the strong focus of the AI community on obtaining "state-of-the-art" results,b as exemplified by the popularity of leaderboards,53,54 which typically report accuracy (or other similar measures) but omit any mention of cost or efficiency (see, for example, leaderboards.allenai.org).c Despite the clear benefits of improving model accuracy, the focus on this single metric ignores the economic, environmental, and social cost of reaching the reported results.

In the past year, lockdowns and other COVID-19 safety measures have made online shopping more popular than ever, but the skyrocketing demand is leaving many retailers struggling to fulfill orders while ensuring the safety of their warehouse employees. Researchers at the University of California, Berkeley, have created new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments. The technology is described in a paper published online today (Wednesday, Nov. 18) in the journal Science Robotics. Automating warehouse tasks can be challenging because many actions that come naturally to humans -- like deciding where and how to pick up different types of objects and then coordinating the shoulder, arm and wrist movements needed to move each object from one location to another -- are actually quite difficult for robots. Robotic motion also tends to be jerky, which can increase the risk of damaging both the products and the robots.

Technology continues to advance at impressive rates. And while the novelty of technologies such as self-parking cars and robotic vacuums have worn off, we are still many years away from the age of computers capable of human thought. Well, that was true before the development of Artificial Neural Networks (ANN), of course. ANN is one of the only techniques currently available for training machines to truly think like people, and it is a tool used within the deep learning space. Artificial intelligence, defined broadly, is the field of training machines to autonomously perform tasks normally thought to require intelligence. Beneath that umbrella is machine learning, in which machines autonomously learn new tasks, and deep learning is a further subcategory of machine learning.

A recent research paper published by InterDigital AI Lab introduces CompressAI. CompressAI is a platform that provides custom operations, layers, models, and tools to research, develop, and evaluate end-to-end image and video compression codecs. It uses pre-trained models and evaluation tools to compare learned methods with traditional codecs. Various models have been trained on learned end-to-end compression from scratch and re-implemented in PyTorch. Artificial Neural Network (ANN) based codecs have shown remarkable outcomes for compressing images.

The renewed interest in artificial intelligence in the past decade has been a boon for the graphics cards industry. Companies like Nvidia and AMD have seen a huge boost to their stock prices as their GPUs have proven to be very efficient for training and running deep learning models. Nvidia, in fact, has even pivoted from a pure GPU and gaming company to a provider of cloud GPU services and a competent AI research lab. But GPUs also have inherent flaws that pose challenges in putting them to use in AI applications, according to Ludovic Larzul, CEO and co-founder of Mipsology, a company that specializes in machine learning software. The solution, Larzul says, are field programmable gate arrays (FPGA), an area where his company specializes. FPGA is a type of processor that can be customized after manufacturing, which makes it more efficient than generic processors.

Some visual recognition datasets have set benchmarks for supervised learning (Caltech101, Caltech256, CaltechBirds, CIFAR-10 andCIFAR-100) and unsupervised or self-taught learning algorithms(STL10) using deep learning across different object categories for various researches and developments. Under visual recognition mainly comes image classification, image segmentation and localization, object detection and various other use case problems. Many of these datasets have APIs present across some deep learning frameworks. I'll be mentioning some of them in this article which can be directly imported and used to train models. Cifar(Canadian Institute of Advanced Research) is a subset of 80 million tiny images dataset which has been collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.

Last month, the deep learning powered online tool Toonify Yourself! Designed "for fun and amusement using deep learning and Generative Adversarial Networks," the system was developed by a pair of independent researchers, Justin N. M. Pinkney and Doron Adler, and let anyone change selfies or portraits into impressive animation-style images. Demand for the high-performance homemade model caused the site to crash, but it quickly returned thanks to support from user donations. In a paper submitted to the NeurIPS 2020 Machine Learning for Creativity and Design workshop, Pinkney and Adler present their research, which enables image generation in novel domains and with a degree of creative control on the output. The team's resolution dependant GAN interpolation method combines high resolution layers of an FFHQ model with low resolution layers from a model transferred to animated character faces to enable the combination of realistic facial textures with the structural characteristics of a cartoon.

The broad applicability of artificial intelligence in today's society necessitates the need to develop and deploy technologies that can build trust in emerging areas, counter asymmetric threats, and adapt to the ever-changing needs of complex environments. As part of a new collaboration to advance and support AI research, the MIT Stephen A. Schwarzman College of Computing and the Defense Science and Technology Agency in Singapore are awarding funding to 13 projects led by researchers within the college that target one or more of the following themes: trustworthy AI, enhancing human cognition in complex environments, and AI for everyone. The 13 research projects selected are highlighted below. Emerging machine learning technology has the potential to significantly help with and even fully automate many tasks that have confidently been entrusted only to humans so far. Leveraging recent advances in realistic graphics rendering, data modeling, and inference, Madry's team is building a radically new toolbox to fuel streamlined development and deployment of trustworthy machine learning solutions.

Financial firms are putting more resources towards using complex forms of machine learning. And that means they'll also need to develop ways to better understand the tech and explain it both internally and to regulators. A recent survey by data giant Refinitiv on the use of artificial intelligence and machine learning in financial services found that 75% of respondents were using some form of deep learning, a type of ML that includes a series of complex, ever-evolving calculations. Those surveyed included data scientists, quants, and executives at a variety of financial firms. Geoffrey Horrell, head of Refinitiv Labs in London, told Business Insider that the surge of interest in deep learning means firms will need to invest resources in understanding these complex algorithms.