Introduce Microsoft's Generative AI application tools and provide examples of their use in the medical field. The presentation is given by Raymond Tsai, a Principal Technical Program Manager of Azure HPC & AI Engineering group.
Introduce Microsoft's Generative AI application tools and provide examples of their use in the medical field. The presentation is given by Raymond Tsai, a Principal Technical Program Manager of Azure HPC & AI Engineering group.
The product release plan outlines the company's planned releases of hardware and software products over the next few quarters. It includes the scheduled release of new workstation hardware and software, edge hardware and software, AI edge devices, vision and IoT kits, tools, and new Alpha workstation models. The plan provides a quarterly roadmap for introducing new products from Q3 to Q2 the following year.
This document discusses using an M48X MCU and Jetson Nano for a home automation project. The M48X MCU controls lights and other devices via UART commands from a Jetson Nano or PC. Example commands are given to turn lights on/off, change colors, and start automated light shows or routines. Additional commands allow controlling videos, music and messages played on the Jetson Nano through the M48X MCU.
This document describes the design of an autonomous guided vehicle (AGV) system. It outlines the main components including the order system, dispatcher, universal clock, AGVs, broker, grid map database. It then describes the functions of various components like the naive dispatcher, path planner using A* algorithm, discrete time simulation with the universal clock, AGV main flow, grid map GUI, and traffic control strategies to handle conflicts between AGVs.
The document discusses microservices and provides definitions, examples, and considerations around this architectural style. It defines microservices as small, independent processes communicating via APIs to compose complex applications. It notes microservices allow for increased modularity, independence, and scalability compared to traditional monolithic architectures. The document also shares perspectives from experts on microservices and examples of companies using this approach.
20160217 - Overview of Vortex Intelligent Data Sharing PlatformJamie (Taka) Wang
?
Vortex Intelligent is an intelligent data sharing platform that provides several product suites including Vortex OpenSplice, Vortex Starter Kit, and Vortex Device. Vortex OpenSplice is a DDS library that implements DDS 1.2 and DDSI 2.1 standards. It has both free and paid versions. Vortex Starter Kit and Vortex Device target different developer types. Additional products like Vortex Insight, Vortex Cafe, Vortex Web provide visualization, Java DDS library, and JavaScript DDS library capabilities. Vortex also includes options for cloud, fog, and gateway integration.
This document discusses strategies for IoT development. It proposes three levels of architecture - short, mid, and long term - to develop an IoT kernel and services. It also examines Advantech's IoT products and architectures, including their WebAccess SCADA system and WISE-Cloud platform. Challenges in IoT solutions are identified around connectivity, costs, reliability and time to market. Two proof-of-concept projects are proposed using edge cloud services and Modbus TCP.
This document discusses microservices architecture and how Docker has changed application development. It covers:
- The rise of microservices architecture which breaks applications into independently deployable small services.
- How Docker brings development and operations teams closer together through standardized deployment of services.
- How Docker provides consistency for continuous integration testing by allowing identical environments.
- How Docker enables collaboration through sharing of pre-built application containers.
This document summarizes the design of a proactive service. The key aspects covered include:
1. The service uses microservices architecture with services like modbusd, psmb-srv, and azure-srv. It follows design principles of pluggable packages, test-driven development, and API-first design.
2. Configuration is handled through environment variables, JSON/YAML files, or a remote configuration server. Services discover each other through Consul.
3. Logging is done through a structured logger that supports files, color console output, and JSON format. It follows a singleton pattern.
This case study document discusses GoStation, an electric vehicle battery swapping system. It provides an overview of GoStation's business model, stakeholders, proposed solution involving battery reservation and exchange, technical infrastructure, system architecture, and deployment diagrams. The document contains analysis of GoStation's problem statement, solution sketches, use cases, mockups, diagrams and comparisons to other systems like Youbike.
4. Deep Learning Chipset Revenue
($
Millions)
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Data Center (Training)
Edge (Inference)
Source: Tractia & Intel AI summit 2019
5. AI in Vision Market
USD
25.32
Billion
USD
2.37
Billion
2017 2023
CAGR
47.54%
The overall market of AI in computer vision was valued at USD 3.62 billion
in 2018 and is expected to reach USD 25.32 billion by 2023, at a CAGR of
47.54% during the forecast period.
Launches, partnerships, and collaborations by large firms, such as NVIDIA
Corporation and Qualcomm Technologies, Inc, provide key opportunities in
the AI in computer vision market.
The market growth is attributed to the growing demand for edge
computing in mobile devices.
Development of machine learning in vision technology creates several
opportunities for the AI in computer vision market. Source: Markets&Markets
6. AI in Manufacturing Market
USD
17.2
Billion
USD
1.0
Billion
2018 2025
CAGR
49.5%
AI in manufacturing market estimated to be valued at USD 1.0 billion
in 2018 and further expected to reach USD 17.2 billion by 2025, at
CAGR of 49.5% during forecast period.
Machine Learning technology to hold major share of AI in
manufacturing market in 2018.
Among machine learning technologies, deep learning to hold largest
size of AI manufacturing market.
Availability of big data and increase in industrial automation are the
factors driving the growth of AI in manufacturing market. Source: Markets&Markets
7. – Mike Leach, Solution portfolio manager at Lenovo
“AI is a big market and huge talking point,
and it starts on a workstation”
26. Vision Starter Kit
(Classification, Object Detection)
1 Gather & Label 2 Train 3 Export 4 Integration
Gather, label and group your
examples into classes, that
you want the computer to
learn
Train your model, then
instantly test is out to see
whether it can correctly
classify or detect new
examples
Export and convert your
model with pre-configured
codes for your projects
Seamless integration with
your own business logics
without coding experiences
on your edge devices