Microsoft CTO Kevin Scott believes understanding AI in the future will help people be better citizens. "I think to be a well-informed citizen in the 21st century, you need to know a little bit about this stuff [AI] because you want to be able to participate in the debates. You don't want to be someone to whom AI is sort of this thing that happens to you. You want to be an active agent in the whole ecosystem," he said. In an interview with VentureBeat in San Francisco this week, Scott shared his thoughts on the future of AI, including facial recognition software and manufacturing automation.
Dr Jacques Ludik is a smart technology entrepreneur, AI expert, investor & ecosystem builder and currently Founder & President of Machine Intelligence Institute of Africa (MIIA), Founder & CEO of Cortex Logic, Founder of Bennit AI, Founder of SynerG, The Talent Index, & aiTRADE Systems, and investor in The Student Hub (ERAOnline). He holds a Ph.D. in Computer Science (AI) with many publications and has 25 years' experience in Machine / Artificial Intelligence (AI) & Data Science and its applications. MIIA is an innovative community & accelerator for Machine Intelligence & Data Science Research & Applications to help transform Africa, whereas Cortex Logic is an AI company that provides an AI Engine for Business, advances AI and builds end-to-end AI solutions for a range of industries. Bennit A.I. is an intelligent virtual production assistant/advisor for manufacturing. For businesses to thrive in the smart technology era, they need to be agile, innovative and adapt quickly to stay relevant, given the swift pace of change and disruption to business and society.
Artificial intelligence has become a booming trend in the industrial sector these days, as automation and optimization continue to be the primary focus of the digital revolution. In this article, we will take a look at the state of the art computer vision techniques that have generated a lot of excitement in AI community in the last few years, and are considered to be industry-ready and are sure to have a significant and practical impact for industrial use cases. Some of these techniques demonstrate incremental yet incredible advancements in performance, surpassing human level performance and thus surpassing precision and reliability standards expected by most industries. The incredible advancement in basic computer vision tasks, such as image classification, have made it feasible to reliably combine multiple techniques to create new, compound techniques that enable entirely new use cases that have never been explored in industrial environments before. That being said, these new techniques have demonstrated that is it possible to obtain precision and reliability results comparable to those that would otherwise only be obtainable with specialized systems that are very hardware intensive.
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. The codebase is implemented in Python 3.5.2.
This is the second installment of a three-part piece on the advances made in artificial intelligence in 2018, by Yves Bergquist, founder and CEO of AI company Corto, and director of the AI and Neuroscience in Media Project at the Entertainment Technology Center at the University of Southern California (ETC@USC). Part one can be read here. With one new academic paper publisher every half hour or so in 2018, machine learning is still -- and by far -- the most vigorous domain of AI. And within machine learning, Deep Learning (also called Deep Neural Networks) still dominates the field. This year saw a lot of extensions of DL to new areas, especially natural language.
Last week, we released Databricks Runtime 5.1 Beta for Machine Learning. As part of our commitment to provide developers with the latest deep learning frameworks, this release includes the best of these libraries. In particular, our PyTorch addition makes it simple for a developer to simply import the appropriate Python torch modules and start coding, without installing all of its myriad dependencies. In this blog, we briefly cover these additions. PyTorch project is a popular deep learning Python package that provides GPU accelerated tensor computation and high-level functionalities for building deep learning networks.
Spell recently announced a new, end-to-end deep learning and artificial intelligence platform that is designed to help teams and businesses across various industries to build with artificial intelligence. Besides unveiling the new product offerings, the company also announced that it has closed a $15 million funding round that was led by Two Sigma Ventures and Eclipse Ventures. The company said it will utilizing this investment to integrate more advancements and drive even bigger organizations, while still bringing artificial intelligence and deep learning to more and more members of the global workforce. Serkan Piantino, Co-founder and Chief Executive Officer, Spell, said, "AI may seem so advanced, but behind the scenes, 90 percent of the work is often spent on the basic mechanics of getting data, software and computation in the right place. Rather than having to start from scratch to build dozens of machines or physically move data from place to place, teams and businesses can run real experiments from a laptop at a coffee shop. The level of access and collaboration offered by Spell invites both greater productivity and adoption, as far more people will be able to harness the power of machine learning. It'll no longer be reserved for the biggest of companies."
"Machine learning and robotics are a perfect match," suggests HP Fellow Will Allen. Although experts in the one field rarely stray into the other, Allen says, their potential synergies are real. "Machine learning is very applicable to robotics, and robotics--by which I mean working with physical robots--needs some of the things that machine learning is good at," he argues. Now Allen, who has a background as a distinguished innovator in imaging and printing technologies, is co-leading a research team with colleague David Murphy in HP's Emerging Compute Lab that aims to understand, and potentially harness, those synergies to create a new generation of what the team are calling "Smart Machines." One of the main challenges in robotics--where you want electro-mechanical machines to perform specific tasks with some degree of autonomy--is to have the machines move both precisely and efficiently in 3D space.
Nvidia is not the only pioneer who helped AI algorithms gain momentum with revolutionary hardware architecture. Google is the frontrunner of the international AI market, sharing its spot with cloud providers AWS and Microsoft. Google offers the framework TensorFlow as well as its own hardware Tensor Processor Unit (TPU). This year, SAP partner Atos announced its cooperation with Google in Paris. Meanwhile, SAP obviously prefers Nvidia, evidenced by its first-time attendance at Sapphire in Orlando last year.
A live demonstration uses artificial intelligence and facial recognition in dense crowd spatial-temporal technology at the Horizon Robotics exhibit at the Las Vegas Convention Center during CES 2019 in Las Vegas on January 10, 2019. Venture capitalists are warning the Trump administration not to overly restrict the export of new technology such as artificial intelligence -- insisting that could make it much harder for American start-ups to sell their products abroad. The Commerce Department is considering whether to slap tighter export controls on a long list of new technologies, including AI and quantum computers, to prevent U.S. technology from falling into the hands of foreign adversaries. But the National Venture Capital Association, in public comments on the potential rule last week, voiced concerns that the list of technology the government defines as critical to national security is far too broad. The venture capitalists only want to see the department limit the export of technology specific to defense -- not a whole category of technology so broad it could include consumer products such as self-driving cars and voice assistants.