AI人工智能的兴起

在这个快节奏的世界里,顾客在与公司交谈时需要轻松和高效。这是“会话智能服务”,一个自动对话代理,通过文本或语音进行对话。我将从会话智能(Conversational Intelligence)的当前状态以及用于构建聊天机器人的一些常见Python库开始。本文将讨论建立会话引擎的各种方法,如模式匹配、单词嵌入和长期短期记忆(LSTM)模型。同时,我将介绍下一代会话式人工智能,其重点是问题回答,并执行一个这样的系统的实时演示。
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1.The Rise of Conversational AI David Low Co-founder / CDS

2.Bio ● Research Urban Mobility | Social Media ● Public Service GovTech(IDA) Data Science Division ● Teach Deep Learning Masterclass at National University of Singapore (NUS) ● Startup Conversational AI | Deep Natural Language Processing

3.Overview ● Why Conversational AI? ● What is NLP? ● Current state of Conversational AI ● Why is NLP difficult? ● Open Source Framework ● Recent advancements ● What have we learnt? ● Demo of Question Answering System

4.Rising trend of mobile (& messaging) usage

5.As messaging apps have become indispensable parts of our lives, Enterprises are determined to be where their customers are.

6.What is Natural Language Processing?

7.What is Natural Language Processing? ● is a field at the intersection of Computer Science, Artificial Intelligence (AI) and Linguistics. ● Goal For computers to process or “understand” natural language in order to perform tasks that are useful ● Not to be confused with “Computational Linguistics” ● Deep NLP = Deep Learning based Natural Language Processing

8.Example I - Sentiment Analysis

9.Example II - Machine Translation Source: Google AI

10.NLU vs NLP Source: Stanford NLP Group

11.Current State of Conversational AI

12.“While Siri, Alexa and the likes can follow simple spoken or typed commands and answer basic questions; they can’t hold a conversation and have no real understanding of the words they use. ” Will Knight

13.In the past... Chatbot A Chatbot B

14.Now Source: Salemove

15.Why is NLP difficult? “Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo” - This is a grammatically correct sentence in American English.

16.What is Homonym? (In linguistics) Words with identical pronunciation and spelling, whilst maintaining different meanings.

17.Buffalo - a homonym 1 - Noun: The city of Buffalo, at western New York state, US. 2 - Noun: The animal, Buffalo. 3 - Verb: To confuse, deceive or intimidate.

18.Three groups of buffalo “Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo”

19.Why is NLP difficult? [cont] ● Sheer complexity of sentence structure ● Ambiguity ○ Eg: “I made her duck” ● Meaning is context sensitive ○ Depends on the people present e.g. “How far is it?” (miles, km?) ○ Depends on the time of day, e.g. “Let's go eat” ○ Depends on prior sentences: “The third one” ● Recognizing named entities (people/places/…) ● Slang, jargon, humour, sarcasm, spelling mistakes, grammar mistakes and abbreviations… Source: Stanford CS224

20.Dialog research ● Purpose of language ○ Used to accomplish communication goals. ● Hence, solving dialog is a fundamental goal for NLP. ● Dialog can be seen as a single task (learning how to talk) OR as thousands of related tasks that require different skills, all using the same input and output format ● Example ○ Booking a restaurant, ○ Chatting about sports/news ○ Answering factual questions ○ …almost anything can be posed as Question Answering.

21.Open Source Framework ● To provide an unified framework for the training and testing of dialog models ● Integration of Amazon Mechanical Turk & Facebook Messenger ● Multi-task training ● Datasets for over 20 tasks Source: FAIR

22.ParlAI Tasks Source: FAIR

23.SQuAD Tasks

24.bAbI Tasks Source: FAIR

25.bAbI Tasks Source: FAIR

26.bAbI SOTA result GS Ramachandran et al 2017

27.Visual Question Answering Source: FAIR

28.Amazon Mechanical Turk Live Chat Source: FAIR

29.Multi-Task Learning (MTL) ● Convention ML ○ Optimize for a particular metric (RMSE, AUC etc) ○ Train a single model or an ensemble of models ○ Fine-tune and tweak ● Multi-task Learning ○ Also known as “joint learning”, “learning to learn” etc ○ Optimize for more than one loss function ○ Why is it better? ■ It improves generalization by leveraging the domain-specific info contained in the training signals of related tasks.