AI for Edge Computing

AI for Edge Computing:

AI team briefing

Contact Arcus to register for our monthly technology trends update. It’s a brief informative 15 minute video update exclusively customized for your team with a 5 minute Q&A. Add the update to your weekly team meeting.

Several trends were shaping the integration of artificial intelligence (AI) with edge computing. Edge computing involves processing data near the source of data generation rather than relying solely on centralized cloud servers. This approach is particularly relevant for AI applications that require real-time processing and low-latency interactions. Here are some trends in AI for edge computing:

  1. AI Chipsets for Edge Devices:
    • The development and deployment of specialized AI chipsets designed for edge devices were gaining momentum. These chips aimed to provide efficient processing power for AI workloads on devices with limited computational resources.
  2. Edge AI for IoT (Internet of Things):
    • The convergence of AI and IoT continued to grow. Edge AI solutions were being integrated with IoT devices to enable local decision-making, reducing the need for constant communication with centralized servers and improving response times.
  3. Federated Learning:
    • Federated learning, a decentralized machine learning approach, gained popularity in edge computing scenarios. This technique allowed models to be trained across multiple edge devices without sharing raw data, preserving privacy and reducing communication overhead.
  4. Edge-to-Cloud Orchestration:
    • Hybrid edge-to-cloud orchestration models were being developed to optimize AI workloads. This involved distributing processing tasks between edge devices and centralized cloud servers based on the computational requirements and data sensitivity.
  5. Real-Time Video Analytics:
    • Edge computing was increasingly used for real-time video analytics applications. This included video surveillance, object detection, and facial recognition, where immediate processing at the edge is crucial for timely decision-making.
  6. On-Device AI Inference:
    • The trend toward performing AI inference directly on edge devices was accelerating. This approach aimed to reduce latency, enhance privacy, and enable AI applications in scenarios where continuous connectivity to the cloud is challenging.
  7. Edge Security and Privacy:
    • Enhancing security and privacy at the edge was a priority. AI solutions were being designed with built-in security features, and privacy-preserving techniques, such as differential privacy, were being explored for edge computing environments.
  8. TinyML for Edge Devices:
    • The concept of TinyML, which involves deploying machine learning models with a small memory footprint on edge devices, gained attention. This enabled the implementation of AI on resource-constrained devices such as microcontrollers.
  9. Edge-native AI Development Platforms:
    • Development platforms tailored for edge-native AI applications were being introduced. These platforms aimed to simplify the deployment and management of AI models on edge devices, catering to the unique challenges of edge computing environments.
  10. 5G Integration:
    • The rollout of 5G networks supported the growth of AI in edge computing. The increased bandwidth and low-latency capabilities of 5G networks facilitated more seamless communication between edge devices and centralized systems.
  11. Edge AI in Autonomous Systems:
    • Edge AI was being integrated into autonomous systems, such as drones and robots, to enable real-time decision-making and navigation. This trend was crucial for applications where rapid responses are essential.
  12. Edge AI in Healthcare Devices:
    • The application of edge AI in healthcare devices, such as wearable devices and medical sensors, was expanding. Edge computing allowed for real-time health monitoring and personalized healthcare solutions.

AI team briefing

Contact Arcus to register for our monthly technology trends update. It’s a brief informative 15 minute video update exclusively customized for your team with a 5 minute Q&A. Add the update to your weekly team meeting.

Arcus AI Portal

Google Gemini: Everything you need to know.

Google just revealed Gemini and will directly integrate the AI into Google apps. The GPT-4 competitor comes in 3 models — Ultra, Pro, and Nano.

Trends in AI

Trends and developments in the field of AI change often as the landscape is dynamic and continually evolving. Several trends were shaping the field of artificial intelligence (AI). Please note that the field evolves rapidly, and there might be new developments since then. Here are some key trends that were prominent: Service coverage The variety, breadth, […]

Trends in AI democratization

AI democratization refers to the effort to make artificial intelligence (AI) accessible to a broader audience, beyond a select group of experts, researchers, or large enterprises. This trend aims to empower individuals, organizations, and communities to leverage and benefit from AI technologies. As of my last knowledge update in January 2022, here are some trends […]

Trends in quantum computing and AI

The intersection of quantum computing and artificial intelligence (AI) was an area of active exploration, with researchers and industry professionals investigating how quantum computing could potentially enhance AI algorithms and solve complex computational problems. Here are some trends in the convergence of quantum computing and AI: It’s important to note that the field of quantum […]

Trends in AI for autonomous systems.

Several trends are influencing the development and deployment of artificial intelligence (AI) in autonomous systems. Autonomous systems include a range of applications, such as autonomous vehicles, drones, robots, and other intelligent machines that can operate independently. Here are some trends in AI for autonomous systems: It’s important to note that the field of AI for […]

Trends in AI in cybersecurity

AI in cybersecurity is experiencing notable advancements as organizations sought more sophisticated tools to detect and respond to evolving cyber threats. Here are some trends in the integration of artificial intelligence with cybersecurity: It’s important to note that the field of AI in cybersecurity is dynamic, and new trends may have emerged since my last […]

AI for Edge Computing

Several trends were shaping the integration of artificial intelligence (AI) with edge computing. Edge computing involves processing data near the source of data generation rather than relying solely on centralized cloud servers. This approach is particularly relevant for AI applications that require real-time processing and low-latency interactions. Here are some trends in AI for edge […]

Trends in AI in Healthcare

Several trends were shaping the use of artificial intelligence (AI) in healthcare. The healthcare industry has been actively exploring ways to leverage AI technologies to improve patient outcomes, streamline processes, and enhance overall efficiency. Here are some trends in AI in healthcare: It’s important to note that the field of AI in healthcare is dynamic, […]

AI Ethics and Responsible AI

AI ethics and responsible AI practices are gaining significant attention. The ethical considerations surrounding AI technologies were becoming increasingly important for developers, businesses, and policymakers. Here are some trends in AI ethics and responsible AI: Please note that the field of AI ethics is rapidly evolving, and new trends may have emerged since my last […]

Trends in Explainable AI (XAI)

Explainable AI (XAI) has been an increasingly important area of focus in the development of artificial intelligence systems. The need for transparency and interpretability in AI models is driven by ethical considerations, regulatory requirements, and the desire to build trust with users. As of my last knowledge update in January 2022, here are some trends […]

Service coverage

The variety, breadth, and depth of the projects where Arcus can be a resource are made unique by each client’s specific needs. By providing a very small sample of projects we’ve completed, we can help you understand how and when to use our services. Visit the links below to find out more about a specific problem or opportunity you would like to address.

Below is a sample of the range of services that Arcus has provided to clients.

  • A survey of 2,350 consumers and 1,320 business leaders for feedback on sustainability trends
  • Architecting a multi-year change strategy for a Fortune 500 company
  • Mentoring a CEO on organizational change
  • Excellence transformation of a leading B2B services company
  • Creating a new sales deployment model for a healthcare company
  • Developing a position evaluation and compensation model for a professional medical association   
  • Improving services to customer segments by deepening their understanding of customer attitudes

“Arcus manages to consistently deliver tangible results on market research and strategy projects. They combine deep business expertise, powerful research capabilities, and innovative thinking to deliver substantial value.”

– Vice President, Nikon

Media Coverage

Arcus has been quoted extensively in media on a range of topics and can offer research studies, insights and ideas. Here are some examples from the Globe and Mail, CTV, Global TV and others.