The convergence of edge computing and AI gives rise to Edge-AI, which enables the deployment of real-time AI applications and services at the network edge. One of the fundamental research issues in Edge-AI is edge inference acceleration, which aims to realize low-latency high-accuracy DNN inference services by leveraging the fine-grained offloading of partitioned inference tasks from end devices to edge servers. However, existing research has yet to adopt a practical Edge-AI market perspective, which would systematically explore the personalized inference needs of AI users (e.g., inference accuracy, latency, and task complexity), the revenue incentives for AI service providers that offer edge inference services, and multi-stakeholder governance within a market-oriented context. To bridge this gap, we propose an Auction-based Edge Inference Pricing Mechanism (AERIA) for revenue maximization to tackle the multi-dimensional optimization problem of DNN model partition, edge inference pricing, and resource allocation. We investigate the multi-exit device-edge synergistic inference scheme for on-demand DNN inference acceleration, and analyse the auction dynamics amongst the AI service providers, AI users and edge infrastructure provider. Owing to the strategic mechanism design via randomized consensus estimate and cost sharing techniques, the Edge-AI market attains several desirable properties, including competitiveness in revenue maximization, incentive compatibility, and envy-freeness, which are crucial to maintain the effectiveness, truthfulness, and fairness of our auction outcomes. The extensive simulation experiments based on four representative DNN inference workloads demonstrate that our AERIA mechanism significantly outperforms several state-of-the-art approaches in revenue maximization, demonstrating the efficacy of AERIA for on-demand DNN inference in the Edge-AI market.

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design.

A new study from Accenture says just 12% of firms have figured out how to deploy AI to "achieve superior growth and business transformation." In other words, there's quite a bit of work yet to be done when it comes to AI success. The correlation between AI use and business achievement is still positive. As far back as 2019, Accenture noted that top AI achievers see 50% better revenue growth than their peers who are not AI experts. No one is calling for an AI rethink, yet progress seems to be coming painfully slowly in the real-world branch of the AI discussion.

Between the COVID-19 pandemic, a mental health crisis, rising healthcare costs, and aging populations, industry leaders are rushing to develop healthcare-specific artificial intelligence (AI) applications. One signal comes from the venture capital market: over 40 startups have raised significant funding--$20M or more --to build AI solutions for the industry. But how is AI actually being put to use in healthcare? The "2022 AI in Healthcare Survey" queried more than 300 respondents from across the globe to better understand the challenges, triumphs, and use cases defining healthcare AI. In its second year, the results did not change significantly, but they do point to some interesting trends foreshadowing how the pendulum will swing in years to come.

Of the seemingly inexhaustible uses of artificial intelligence (AI) in the financial sector, its applications around managing credit risk and optimizing payment services are among the most promising. The proliferation of "smart agents" that handle these tasks is a glimpse of more innovation to come, as AI proves its worth to financial institutions (FIs) in the great reopening. That FIs and enterprises are pouring millions into AI development is not surprising, and where they've been focusing that development shines a light on where AI can do the most good. The April 2020 Unlocking AI Playbook: Credit Risk And Payments edition, done in collaboration with Brighterion, notes that "… banks appear to be applying AI with greater specificity than in the past, suggesting that strong use cases have emerged. Our research shows that 92.9 percent of AI-using FIs are applying it to payment services, and 71.4 percent are doing so in credit underwriting. The latter finding is a marked shift from our 2018 study, in which just 27.3 percent of FIs reported using AI in credit underwriting."

You're likely an AI user, whether you realize it or not. Don't want to use artificial intelligence (AI)? Afraid to try it, or suspicious of it taking over the world? Too bad… you've probably already employed it, and don't even know it. But don't worry, it's not ready to enslave us all yet, at least according to a pair of CEOs… one from an artificial intelligence company and another from a live streaming company.

This sponsored post from Lenovo's Bhushan Desam covers how new HPC tools like Lenovo's LiCO (Lenovo Intelligent Computing Orchestration) are working to address the growing popularity of AI and to simplify the convergence of HPC and AI. Artificial Intelligence (AI) is coming for your HPC cluster – and while there are no autonomous robots taking over the data center, some days it might feel that way to cluster administrators. The HPC cluster looks very attractive to the "outside world", particularly to those who will need performance beyond a single system or workstation. That is, until they try to use it and realize there is a learning curve they have to overcome. AI workloads are well suited for running on a cluster – but is your cluster management ready for AI users?

Artificial intelligence (AI) assistants, like Apple's Siri and Microsoft's Cortana, can help internet users with a variety of activities, especially when they're on the go. According to June 2016 research, nearly two-thirds of AI users in the US use these personal assistants to answer general questions. San Francisco, CA–based AYTM Market Research surveyed 1,000 US internet users about their dealings with an AI assistant. Overall, 58% had never used one, while about a quarter used AI assistants on at least a monthly basis. According to the survey, the majority (64.5%) of AI users said they used AI assistants to ask general questions, followed by getting directions while driving (39.7%) and making calls (25.2%). Other research confirms, though, that few mobile phone owners in the US actually use a voice-controlled personal assistant regularly.