PlantVillage, a research and development project, based at Penn State University, is beginning to bring artificial intelligence to these smaller farms. Scientists at PlantVillage, in collaboration with international organizations, local farm extension programs and engineers at Google, is working to tailor A.I. technology for farmers in Tanzania who have inexpensive smartphones. The initial focus is on cassava, a hearty crop that can survive droughts and barren soil. But plant disease and pests can reduce crop yields by 40 percent or more. PlantVillage and International Institute of Tropical Agriculture have developed a simple A.I. assistant, called Nuru ("light" in Swahili).
As you plan your agenda, artificial intelligence (AI) is undoubtedly a hot topic on your list. This year we have a lot of great technical content on AI, machine learning (ML), and deep learning (DL)--with over 200 breakout sessions, hands-on workshops, deep-dive chalk talks, and more. You'll hear success stories about machine learning on AWS firsthand from customers and partners such as Sony, Moody's, NFL, Intuit, 21st Century Fox, Toyota, and more. This year's re:Invent also includes the AI Summit, where thought leaders in the academic community will share their perspectives on the future of AI. Here are a few highlights of this year's lineup from the re:Invent session catalog to help you plan your event agenda.
Penguin Computing, a subsidiary of SMART Global Holdings, Inc. and leading provider of high-performance computing (HPC), artificial intelligence (AI), enterprise data center, and cloud solutions, today announced the formation of the Penguin Computing Artificial Intelligence Practice, a full service consultancy dedicated to working with organizations to achieve their AI goals. Penguin Computing is launching this practice in response to increased customer demand for guidance and support on building and deploying AI and machine learning (ML) projects. With technology advancements making AI a more practical option to extract value from massive data sets, research from Goldman Sachs indicates that AI will become a $109 billion market by 2025 as an increasing number of senior technology officers move their organizations to this new computing paradigm. The new AI Practice will be led by Penguin Computing Chief Technology Officer Philip Pokorny and will operate as a full-service consultancy, delivering system design expertise, building custom technology solutions, and providing professional services (including management and hosting of AI clusters) and advanced level support. Penguin Computing has already designed, built, managed, and supported many large AI systems, resulting in Penguin Computing being named Americas HPC Partner of the Year for 2017 by NVIDIA, an AI leader.
"Once a new technology rolls over you, if you're not part of the steamroller, you're part of the road." Some technologies fizz out over a period of time while some stay on the sidelines and then gain traction after startups, SMEs, and other MNCs fund it or integrate it in their operations. Regardless of changing trends, technology is inevitable. As time passes by, technology gets more and more advanced and pervades every facet of our lives from the way we live to the way we work. Driverless electric cars, AR and VR technology, and robot surgeons are some talk of the town technologies that have created a revolution that will grow for as long as humans continue to advance in their capabilities.
Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2019. Analysts presented their findings during Gartner Symposium/ITxpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years. "The Intelligent Digital Mesh has been a consistent theme for the past two years and continues as a major driver through 2019. Trends under each of these three themes are a key ingredient in driving a continuous innovation process as part of a ContinuousNEXT strategy," said David Cearley, vice president and Gartner Fellow.
I feel that there was a sort of explosion a couple of years ago after which the whole topic of Artificial Intelligence (AI) suddenly sprang into a wider audience's consciousness. All of a sudden we had Siri, Amazon's Alexa and we started talking about self-driving cars. Jaan Tallinn, how did it happen? There were two different explosions. I believe that a lot of the latter had to do with the works of Elon Musk and Stephen Hawking. Most importantly, the former was the revolution of deep learning.
Huawei has announced two new chips for artificial intelligence applications. The Ascend AI IP and chip series, the world's first AI IP and chip series that natively serves all scenarios, providing optimal TeraOPS per watt. The Ascend series delivers excellent performance per watt in every scenario, whether it's minimum energy consumption or maximum computing power in data centers. Their unified architecture also makes it easy to deploy, migrate, and interconnect AI applications across different scenarios. The Ascend 910 and Ascend 310 chips, which were announced at today's event, mark Huawei's leading AI capabilities at the chip level – the bottom layer of the stack.
Huawei has unveiled an AI strategy and a portfolio of supporting products built on two new microchips, which the telecoms giant claims is the world's first AI IP and chip series designed for a full range of deployment scenarios. The Ascend 310 is designed for the low-power computing needs of smart devices and the Ascend 910 for cloud computing. Huawei's rotating chairman Eric Xu said the Ascend 910 has "greatest computing density in a single chip," but denied that it was designed to provide direct competition for the likes of Qualcomm and Nvidia. "As Huawei does not sell chips directly to third party companies, if you're talking about pure chip-vendors, there is never competition between Huawei and them," Xu explained at the Huawei Connect conference in the Shanghai World Expo Exhibition and Convention Center. "We provide hardware and cloud services so if it's a hardware company or a cloud service company, there is competition here."
Huawei has unveiled its artificial intelligence (AI) strategy and full-stack portfolio, including a series of chips, cloud services, and products. Announced at Huawei Connect 2018 in Shanghai by rotating chair Eric Xu, Huawei's Ascend AI chip series includes the Ascend 910 and Ascend 310, with the company also unveiling the Compute Architecture for Neural Networks (CANN), a chip operators library and automated operators development toolkit, and MindSpore, a device, edge, and cloud training and inference framework. The latter includes "full-pipeline services (ModelArts), hierarchical APIs, and pre-integrated solutions", Huawei said, with the Chinese networking giant to later expand its AI stack to include an AI acceleration card, AI server, AI appliance, and other AI products. "Huawei's AI strategy is to invest in basic research and talent development, build a full-stack, all-scenario AI portfolio, and foster an open global ecosystem," Xu said during his Huawei Connect keynote.
The development of smart cities and their fast-paced deployment is resulting in the generation of large quantities of data at unprecedented rates. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards that benefit from the availability of such data. Moreover, the high dynamical nature of smart cities calls for new generation of machine learning approaches that are flexible and adaptable to cope with the dynamicity of data to perform analytics and learn from real-time data. In this article, we shed the light on the challenge of under utilizing the big data generated by smart cities from a machine learning perspective. Especially, we present the phenomenon of wasting unlabeled data. We argue that semi-supervision is a must for smart city to address this challenge. We also propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstractions. The proposed framework is scalable to meet the needs of smart city services. Fundamentally, the framework benefits from semi-supervised deep reinforcement learning where a small amount of data that has users' feedback serves as labeled data while a larger amount is without such users' feedback serves as unlabeled data. This paper also explores how deep reinforcement learning and its shift toward semi-supervision can handle the cognitive side of smart city services and improve their performance by providing several use cases spanning the different domains of smart cities. We also highlight several challenges as well as promising future research directions for incorporating machine learning and high-level intelligence into smart city services.