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Contemplating possible future scenarios should be left to the field of philosophy, not to futurology – or so claims Luciano Floridi in this somewhat harsh but fair editor letter. Floridi examines so-called Artificial Intelligence (AI) winters and their impact on the development of AI. AI winters are periods where the hype for AI wanes and often the result of disillusionment when – inevitably – promises concerning AI applications fail to deliver. The drawbacks of hype for AI are twofold: on the one hand, it makes people more skeptical towards useful applications, and on the other hand alarmism blinds people to the actual risks associated with AI applications. Floridi mentions these instances of alarmism during his discussion of hype, seemingly categorizing alarmism as an instance of hype rather than a different variant of an exaggerated claim.

AiM Future Joins the Edge AI and Vision Alliance


AiM Future, a leader in embedded machine learning intellectual property (IP) for edge computing devices, announced it has joined the Edge AI and Vision Alliance. AiM Future is accelerating the transition from centralized cloud-native AI to the distributed intelligent edge. Its market-proven NeuroMosAIc Processor (NMP) family of machine learning hardware accelerators and software, NeuroMosAIc Studio, enables the efficient execution of deep learning models common to computer vision applications. "It is our company's pleasure to join the Edge AI and Vision Alliance," said ChangSoo Kim, founder, and CEO of AiM Future. "As a premier organization for technology innovators revolutionizing artificial intelligence across the edge computing spectrum, the partnership is a natural fit. It is clear AiM Future's vision of bringing the impossible to reality is shared by the Alliance and its ecosystem. The field of edge AI is rapidly advancing and partnerships are fundamental to addressing the many challenges and limitations of today's edge devices."

The Application of AI Technology in GPU Scheduling Algorithm Optimization


The Application of AI Technology in GPU Scheduling Algorithm Optimization | Zhancai Yan, Yaqiu Liu, Hongrun Shao | Artificial intelligence, Computer science, CUDA, GPU cluster, nVidia, Task scheduling

GrAI Matter Labs Launches Life-Ready AI 'GrAI VIP', A Full-Stack AI System-On-Chip Platform


GrAI Matter Labs unveils life-ready AI with GrAI VIP at GLOBAL INDUSTRIE. GrAI Matter Labs is a company in brain-inspired ultra-low latency computing that specializes in Life-Ready AI. Artificial Intelligence is the closest thing to natural intelligence. Artificial intelligence that feels alive. They make brain-inspired chips that act like people.

Steve Blank Artificial Intelligence and Machine Learning– Explained


Hundreds of billions in public and private capital is being invested in Artificial Intelligence (AI) and Machine Learning companies. The number of patents filed in 2021 is more than 30 times higher than in 2015 as companies and countries across the world have realized that AI and Machine Learning will be a major disruptor and potentially change the balance of military power. Until recently, the hype exceeded reality. Today, however, advances in AI in several important areas (here, here, here, here and here) equal and even surpass human capabilities. If you haven't paid attention, now's the time. Artificial Intelligence and the Department of Defense (DoD) The Department of Defense has thought that Artificial Intelligence is such a foundational set of technologies that they started a dedicated organization- the JAIC – to enable and implement artificial intelligence across the Department. They provide the infrastructure, tools, and technical expertise for DoD users to successfully build and deploy their AI-accelerated projects. Some specific defense related AI applications are listed later in this document. We're in the Middle of a Revolution Imagine it's 1950, and you're a visitor who traveled back in time from today. Your job is to explain the impact computers will have on business, defense and society to people who are using manual calculators and slide rules. You succeed in convincing one company and a government to adopt computers and learn to code much faster than their competitors /adversaries. And they figure out how they could digitally enable their business – supply chain, customer interactions, etc. Think about the competitive edge they'd have by today in business or as a nation. That's where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies.

Baidu Research: 10 Technology Trends in 2021 - KDnuggets


While global economic and social uncertainties in 2020 caused significant stress, progress in intelligent technologies continued. The digital and intelligent transformation of all industries significantly accelerated, with AI technologies showing great potential in combatting COVID-19 and helping people resume work. Understanding future technology trends may never have been as important as it is today. Baidu Research is releasing our prediction of the 10 technology trends in 2021, hoping that these clear technology signposts will guide us to embrace the new opportunities and embark on new journeys in the age of intelligence. In 2020, COVID-19 drove the integration of AI and emerging technologies like 5G, big data, and IoT.

6 Artificial Intelligence Frameworks to Learn


By using this framework, anyone can build neural networks with graphs. This also depicts operations as nodes. PyTorch is one of the most important frameworks in artificial intelligence. However, it is super adaptable in terms of integrations and languages. It was released by Facebook's AI research lab. This also acts as an open source library useful in deep learning, computer vision and natural language processing software. Another feature is its greater affinity with iOS as well as Android etc. It uses debugging tools like IPDB and PDB.

AI in robotics: Problems and solutions


We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Robotics is a diverse industry with many variables. Its future is filled with uncertainty: nobody can predict which way it will develop and what directions will be leading a few years from now. According to the International Federation of Robotics data, 3 million industrial robots are operating worldwide – the number has increased by 10% over 2021. The global robotics market is estimated at $55.8 billion and is expected to grow to $91.8 billion by 2026 with a 10.5% annual growth rate.

Skills and security continue to cloud the promise of cloud-native platforms


Joe McKendrick is an author and independent analyst who tracks the impact of information technology on management and markets. As an independent analyst, he has authored numerous research reports in partnership with Forbes Insights, IDC, and Unisphere Research, a division of Information Today, Inc. The KubeCon and CloudNativeCon events just wrapped up in Europe, and one thing has become clear: the opportunities are outpacing organizations' ability to leverage its potential advantages. Keith Townsend, who attended the conference, observed in a tweet that "talent and education is the number one challenge. I currently don't see a workable way to migrate thousands of apps without loads of resources. Information technology gets more complex every day, and there is no shortage of demand for monitoring and automation capabilities the build and manage systems. Cloud-native platforms are seen as remedies for not only improved maintenance, monitoring, and automation, but also for modernizing ...

Traditional vs Deep Learning Algorithms in the Telecom Industry -- Cloud Architecture and Algorithm Categorization


The unprecedented growth of mobile devices, applications and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research and development of 5G systems have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experiences challenges due to computational and battery power limitations. ML models employed at the edge-servers are constrained to light-weight to boost model performance by achieving a trade-off between model complexity and accuracy. Also, model compression, pruning, and quantization are largely in place.