Introduce Deep learning & A.I. ApplicationsMario Cho
This document provides an overview of deep machine learning and its applications. It introduces Mario Cho and his experience with image recognition, medical data processing, and open source software development. The document then discusses neural networks, deep learning techniques, and how deep learning can be applied to areas like image recognition, natural language processing, and autonomous vehicles. Examples of deep learning applications include image captioning, machine translation, and generating synthetic images from sketches.
Introduce Deep learning & A.I. ApplicationsMario Cho
This document provides an overview of deep machine learning and its applications. It introduces Mario Cho and his experience with image recognition, medical data processing, and open source software development. The document then discusses neural networks, deep learning techniques, and how deep learning can be applied to areas like image recognition, natural language processing, and autonomous vehicles. Examples of deep learning applications include image captioning, machine translation, and generating synthetic images from sketches.
1. The document discusses AIoT and edge computing.
2. It introduces Microsoft's Azure IoT platform and services for connecting, processing, analyzing and acting on IoT data.
3. Edge computing with Azure IoT Edge is described which analyzes data locally on IoT devices to reduce latency and cloud requirements.
Artificial intelligence (AI) is the field of computer science that develops machines or software with human-like intelligence. AI can perform tasks like humans or even better than humans through activities like speech recognition, decision making, and translation. There are two main categories of AI: narrow AI, which is dedicated to a specific task, and strong/general AI, which does not currently exist but is being researched to allow machines to think like humans through their own intelligence and self-awareness. AI has many applications across industries like healthcare, transportation, education, and more. The evolution of AI began in the 1940s and important milestones include the invention of the Turing test in 1950, the development of machine learning in the 1950
CV分野での最近の脱○○系論文3本を紹介します。
・脱ResNets: RepVGG: Making VGG-style ConvNets Great Again
・脱BatchNorm: High-Performance Large-Scale Image Recognition Without Normalization
・脱attention: LambdaNetworks: Modeling Long-Range Interactions Without Attention
This presentation contains a brief review of the topic VIRTUAL REALITY, its definition, features, systems, applications, concern and challenges, and example of its implementation.
(The slide have notes added to them for more information )
This document outlines the syllabus for an Advanced Artificial Intelligence course. The course objectives are to learn the differences between optimal and human-like reasoning, understand state space representation and complexity, learn methods for solving problems using AI, be introduced to machine learning concepts, and learn probabilistic reasoning techniques. The syllabus covers topics like search strategies, constraint satisfaction problems, games, knowledge representation, planning, and uncertainty. Recommended textbooks are also listed.
Artificial intelligence refers to the simulation of human intelligence in machines. The goals of artificial intelligence include learning, reasoning, and perception. AI is being used across different industries including finance and healthcare.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
1. The document discusses AIoT and edge computing.
2. It introduces Microsoft's Azure IoT platform and services for connecting, processing, analyzing and acting on IoT data.
3. Edge computing with Azure IoT Edge is described which analyzes data locally on IoT devices to reduce latency and cloud requirements.
Artificial intelligence (AI) is the field of computer science that develops machines or software with human-like intelligence. AI can perform tasks like humans or even better than humans through activities like speech recognition, decision making, and translation. There are two main categories of AI: narrow AI, which is dedicated to a specific task, and strong/general AI, which does not currently exist but is being researched to allow machines to think like humans through their own intelligence and self-awareness. AI has many applications across industries like healthcare, transportation, education, and more. The evolution of AI began in the 1940s and important milestones include the invention of the Turing test in 1950, the development of machine learning in the 1950
CV分野での最近の脱○○系論文3本を紹介します。
・脱ResNets: RepVGG: Making VGG-style ConvNets Great Again
・脱BatchNorm: High-Performance Large-Scale Image Recognition Without Normalization
・脱attention: LambdaNetworks: Modeling Long-Range Interactions Without Attention
This presentation contains a brief review of the topic VIRTUAL REALITY, its definition, features, systems, applications, concern and challenges, and example of its implementation.
(The slide have notes added to them for more information )
This document outlines the syllabus for an Advanced Artificial Intelligence course. The course objectives are to learn the differences between optimal and human-like reasoning, understand state space representation and complexity, learn methods for solving problems using AI, be introduced to machine learning concepts, and learn probabilistic reasoning techniques. The syllabus covers topics like search strategies, constraint satisfaction problems, games, knowledge representation, planning, and uncertainty. Recommended textbooks are also listed.
Artificial intelligence refers to the simulation of human intelligence in machines. The goals of artificial intelligence include learning, reasoning, and perception. AI is being used across different industries including finance and healthcare.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
AI Home lighting control system
專題介紹:
利用Linebot方式控制居家燈光,在語音轉成文字後 ,我們的Linebot程式會處理所輸入的文字(NLP) 利用JIEBA斷詞再用word embedded 進行正面(開燈)或負面(關燈)語句的判斷....
Linebot 程式分析完語句話後, ,會透過MQTT方式送給對應號碼的Pi。在每個燈光下都包含一個Pi,,主要用來接收來自Linebot的命令並判斷是否要開啓或關閉Relay。
#2018 艾鍗學院 AIoT工程師訓練班
http://bit.ly/2MGsRXE
11. 硬體的進步
● AI 需要耗費大量的運算資源,例如:Google 可以使用 AI 成功辨識照片上
的貓,在成功之前讓 AI 觀看了 20000000 張有貓的照片,沒有高效能硬體
的幫助,這樣的訓練過程必須耗費 10 年以上。
● 由於 CPU 製程的進步,再加上用來產生3D圖形的 GPU,使得 AI 獲得了空
前的成功。例如:AlphaGo 從國小的棋力進步到打敗世界冠軍,只花了短短
2 年的時間,當時使用了 176 顆 GPU,是一台超級電腦。
● 2017 年 Google 發明了專門為 AI 優化的 TPU 來取代 GPU,目前只要一台
搭載 4 TPU 的個人電腦,搭載 AlphaZero AI,訓練 3 天就可以打敗
AlphaGo。
12. AI 與運算思維
AI 是運算思維的一種應用,分述如下:
● 拆解問題:分治法是解決問題的基本方法,AI 研究中是把智慧區分成各種
不同層次的問題,分別加以研究。
● 樣式識別:AI 利用抽取特徵的方法,來進行學習和推論。例如:語音識別
、圖像識別。
● 抽象化:知識必須先建立抽象的表示法,然後才能夠用 AI 進行分析和預測
。例如:OWL 即是一種知識的抽象化語言。
● 演算法:透過演算法,AI 得以快速搜尋找到最佳解。例如:AlphaGo 使用
的改良式蒙地卡羅演算法
13. AI 與程式設計
實現 AI 需要仰賴大量的運算處理,這些都必須透過程式設計來實現,而隨著 AI
技術的進步,程式已經可以由 AI 自動產出,也就是說,AI 具備了有限度的自我
進化能力。
程式設計裡有部分牽涉到設計理論,例如:物件導向分析,目前還需要人類介入
,當人類完成設計後輸入藍圖(UML),即可由電腦產出程式碼。未來若 AI 具
備這樣的分析能力,或許也可以取代低階程式設計師的工作。