New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
TensorFlow is an open-source library for numerical computation in which it uses data flow graphs. The Google Brain Team researchers developed this with the Machine Intelligence research organization by Google. TensorFlow is open source and available to the public. It is also good for distributed computing. A minimalist, modular, Neural Network library, Keras uses Theano or TensorFlow as a backend.
NVIDIA Isaac platform with Jetson Xavier, a computer designed specifically for robotics.NVIDIA Robots are a well-established part of manufacturing but have the opportunity to unlock new efficiencies in industries such as retail, food service and healthcare. To date, robots have primarily been enclosed or segmented into specific areas to protect people from possible injuries. Today, companies want to integrate robotics into various types of workplaces, but this requires a new design paradigm for robotics. Allowing a robot to move freely in an unpredictable environment requires fast, reliable, intelligent computing within the robot. The ability to deliver this level of complex computing at within a small component, at a low price point has held the robotics industry back.
Deep Learning is a subset of Machine Learning,which itself a subset of Artificial Intelligence. Artificial Intelligence (AI): Any algorithm or technique that allows computing devices to make decisions or solve problems that previously required humans to perform them manually can be called an AI algorithm or technique. An AI can be either a huge stack of simple if-else statements or a very complex algorithm. If you have studied Artificial Intelligence in your school or college,you would read something titled "Rule Based Systems", which is nothing but a collection of IF THEN statements,to perform a task. An example of a Rule based system is MYCIN,which is developed to identify bacteria causing severe infections and to recommend antibiotics by Stanford,which has basically nothing but IF THEN statements.
Judging by the presentations at the 2018 Symposium on VLSI Technology, held in Honolulu this summer, the semiconductor industry has a challenge ahead of it: how to develop the special low-power hardware needed to support artificial intelligence-enabled networks. To meet society's needs for low-power-consumption machine learning (ML), "we do need to turn our attention to this new type of computing," said Naveen Verma, an associate professor of electrical engineering at Princeton University." While introducing intelligence into engineering systems has been what the semiconductor industry has been all about, Verma said machine learning represents a "quite distinct" inflection point. Accustomed as it is to fast-growing applications, machine learning is on a growth trajectory that Verma said is "unprecedented in our own industry" as ML algorithms have started to outperform human capabilities in a wide variety of fields. Faster GPUs driven by Moore's Law, and combining chips in packages by means of heterogenous computing, "won't be enough as we proceed into the future.
Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Deep Learning on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.
Artificial intelligence tools will never be widely used if it takes decades of expertise to put them into action, which is why cloud companies have been working hard to make them easier to use and more accessible. Microsoft took another step in that direction Thursday with the acquisition of San Francisco-based Lobe. Terms of the deal were not disclosed in a blog post written by Kevin Scott, Microsoft's executive vice president and chief technology officer, announcing the acquisition. Founded by Mike Matas, Adam Menges, and Markus Beissinger in 2015, Lobe created visual tools that can build deep-learning models with a drag-and-drop user interface, rather than lines of code. Lobe will continue to operate its service under Microsoft, according to its web site.
In the weeks and months following a major earthquake, the surrounding area is often wracked by powerful aftershocks that can leave an already damaged community reeling and can significantly hamper recovery efforts. While scientists have developed empirical laws to describe the likely size and timing of those aftershocks, such as Bath's Law and Ohmori's Law, forecasting their location has been harder. But sparked by a suggestion from researchers at Google, Brendan Meade, professor of earth and planetary sciences, and Phoebe DeVries, a postdoctoral fellow working in his lab, are using artificial intelligence technology to try to get a handle on the problem. Using deep-learning algorithms, the pair analyzed a database of earthquakes from around the world to try to predict where aftershocks might occur, and developed a system that, while still imprecise, was able make significantly better forecasts than random assignment. The work is described in an Aug. 30 paper published in the journal Nature.
In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.
Researchers delivered a 15-petaflop deep-learning software and ran it on Cori, a supercomputer at the National Energy Research Scientific Computing Center, a Department of Energy Office of Science user facility. Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that situation.
This is an eclectic collection of interesting blog posts, software announcements and data applications I've noted over the past month or so. ONNX Model Zoo is now available, providing a library of pre-trained state-of-the-art models in deep learning in the ONNX format. In the 2018 IEEE Spectrum Top Programming Language rankings, Python takes the top spot and R ranks #7. Julia 1.0 has been released, marking the stabilization of the scientific computing language and promising forwards compatibility. Google announces Cloud AutoML, a beta service to train vision, text categorization, or language translation models from provided data.