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.

December 1, 2022McKinsey has been named a Leader, the highest designation, in The Forrester Wave: AI Service Providers, Q4 2022 report. Forrester evaluated 12 firms, assessing them on 29 criteria grouped into the categories of current offering, strategy, and market presence. We received the highest possible rating in criteria including AI talent, vision, and market approach. "McKinsey & Company leads enterprises with end-to-end AI transformation," the Forrester report notes, also recognizing that "McKinsey addresses AI holistically: as a technology, an operational model, and a strategic asset." The report also notes that McKinsey "[places] a heavy emphasis on ROI." McKinsey acquired the AI arm of our firm, QuantumBlack, in 2015, and the Forrester report points out that this move "continues to deliver top-notch data science talent."

The foundation of enterprise intelligence is technology platform that is increasingly being driven by artificial intelligence (AI). IDC has identified three pillars that drive enterprise intelligence: 1) an organization's ability to synthesize information, 2) its capacity to learn and 3) its ability to apply insights at scale. AI has immense potential to super-charge all three of these pillars. However, most enterprises still struggle with AI, and achieving enterprise intelligence at scale remains a challenge for most organizations. According to IDC's 2020 survey of analytics, AI, and RPA services buyers, 80% of respondents said they were at some stage of AI adoption, though most were only in pilots or using AI for limited business functions.

ICT industry veteran Andile Ngcaba's inq., a Pan-African digital service provider, wants to be Africa's number one artificial intelligence (AI) service provider. The company has points of contacts in 12 African cities, Johannesburg, Gaborone, Lusaka, Ndola, Blantyre, Lilongwe, Mzuzu, Lagos, Abuja, Port Harcourt, Kanu and Abidjan. It has concluded the 100% acquisition of Vodacom Business Africa's operations in Nigeria, Zambia and Cote d'Ivoire with a further planned acquisition in Cameroon pending regulatory approvals. At the time of the announcement of the transaction last June, inq. said this deals represents a significant milestone to its vision to be a leading provider of cloud and digitally based services in key markets across sub-Saharan Africa and provides additional vital assets in its build-out of a regional footprint. Today, inq. said this landmark transaction grows inq.'s regional footprint to 13 cities in 7 countries across Africa including its existing operations in Botswana, Malawi and Mozambique.

This guidance is part of a wider collection about using artificial intelligence (AI) in the public sector. The SQ team is responsible for reviewing the sale of financial products for regulatory compliance. Currently the team is required to check a sample of 10% to 15% of completed sales. A team of 120 reviewers had to look at more than 10 different data sources and 180 data points to find and extract the information they needed to complete the audit. Each review took around 4 hours.