If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Intel's open-source oneDNN library, which was formerly known as MKL-DNN and DNNL for this deep neural network library now living under the oneAPI umbrella, continues working on some big performance advancements for its 2.0 release. Intel on Thursday released oneDNN 2.0 Beta 7 and with it comes more Intel CPU performance optimizations around convolutional neural networks, binary primitive performance for the broadcast case, BFloat16 and FP32 weights gradient convolutions, INT8 convolutions with 1x1 kernel and spatial strides, and a variety of other specific areas within this deep learning library seeing optimizations. This is also the first release beginning to see initial performance optimizations for Intel's Xe Graphics architecture - benefiting both the likes of Tiger Lake laptops and the DG1 discrete graphics card. OneDNN 2.0 is also adding AArch64 support and other non-x86 processor support and a variety of other improvements.
Artificial Intelligence (AI) and smart devices are gaining more and more traction in the manufacturing market. AI can be used to automate multiple things, and the technologies behind it keep getting better, and smarter. And combining AI with the IoT means fewer people will be required to take decisions and to execute those decisions. If things keep evolving as they have been so far, one thing is certain: the manufacturing industry will never be the same. But, how can AI and IoT affect the manufacturing job market? How can they improve it?
Voice search is on the rise. We all have known this for ages but the voice search market may actually be growing faster than we expected. Throughout the last few years, smart speakers have been taking the market by storm. With the emergence of Amazon's Alexa (powered by Bing search), Google's Homepod, and Apples' Homepod (both powered by Google), voice search is naturally seeing unprecedented growth. And the growth is likely to surge in the next few years. Companies have already started to fine-tune their marketing and SEO strategies to accommodate this new technology.
The second conference on learning for dynamics and control (L4DC) was held on 11-12 June. In their introduction to the conference, the organisers write that "over the next decade, the biggest generator of data is expected to be devices which sense and control the physical world." They note that this explosion of real-time data from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. The overall goal for the conference is to create a new community of people that think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area. The live streams of the conference were made available to all and included invited talks and contributed talks.
CVPR 2020 is yet another big AI conference that takes place 100% virtually this year. Here we've picked up the research papers that started trending within the AI research community months before their actual presentation at CVPR 2020. These papers cover the efficiency of object detectors, novel techniques for converting RGB-D images into 3D photography, and autoencoders that go beyond the capabilities of generative adversarial networks (GANs) with respect to image generation and manipulation. Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries. If you'd like to skip around, here are the papers we featured: Model efficiency has become increasingly important in computer vision.
The evolution of technology is taking artificial intelligence (AI) to the fore in nearly every industry. As AI gradually becomes mature, it is being applied in the energy management sector. A number of Internet of Things (IoT) companies are using AI to help businesses reduce energy consumption and expenses. U.S.-based BuildingIQ is one of these companies that aim to improve energy efficiency in large, complex building structures. BuildingIQ's Predictive Energy Optimization (PEO) service uses cloud-based software to calculate heating, ventilation and air conditioning (HVAC) related utility expenses.
These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know! Welcome back to deep learning! So, let's continue with our lecture.
In this episode, we hear from Luca Colasanto, Senior Robotic Scientist at Realtime Robotics, about real-time robot motion planning in dynamic and complex environments with human-robot collaboration. Realtime Robotics focuses on accelerating conventional motion planning through optimization of algorithms and hardware to allow safe use of robotic tools in work areas with humans. Luca spoke to our interviewer Kate about Realtime Robotic's fast motion planning technology, including key aspects, such as perception, algorithms and custom hardware. Luca Colasanto is a Sr. Scientist at Realtime Robotics focusing on AI-based grasping and multi-robot optimization. Luca completed his PhD in Humanoid Robotics at Italian Institute of Technology, focusing on control systems for bipedal walking machines and compliant actuators.
Health care languishes in data dissonance. A fundamental imbalance between collection and use persists across systems and geopolitical boundaries. Data collection has been an all-consuming effort with good intent but insufficient results in turning data into action. After a strong decade, the sentiment is that the data is inconsistent, messy, and untrustworthy. The most advanced health systems in the world remain confused by what they've amassed: reams of data without a clear path toward impact.
The aforementioned tools provide the necessary elements to obtain proper gradients for the network parameter updates. Ultimately we needed to devise an effective strategy to utilize these gradients. This time, the inspiration came from physics in the form of momentum. One of the most commonly used optimizers is Stochastic gradient descent (SGD). Unfortunately, SGD is inherently limiting as it employs first-order information only.