When privacy matters! Chatbots in data-sensitive businesses

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When privacy matters! A series of challenges for chatbots in data-sensitive businesses such as healthcare and finance by Christophe Willemsen Meetup: Integration of Chatbots in Healthcare and BFSI, Dubai, 1.11.2018

When privacy matters !
Chatbots in data-sensitive businesses
GraphAware, world’s #1 Neo4j consultancy
Creators of Hume - AI for Humans
graphaware.com
@graph_aware
Christophe Willemsen
CTO GraphAware
@ikwattro

● A word on data privacy
● Build your own : expectations vs reality
● Natural Language Processing
● Challenges with NLP
● Sentiment
● Q & A
Outline

● Where data is sent ?
● What is sensitive ?
● Are your users aware ?
Data Privacy

● Creating a Chatbot with Deep Learning, Python, and TensorFlow
● How Chatbots Are Learning Emotions Using Deep Learning
● How I Used Deep Learning To Train A Chatbot To Talk Like Me
● Neuralconvo - Chatting with a Deep learning brain
● ...
Current conversation topics

● It is the identification and categorization of what a user online
intended or wanted when they typed their query
Intent detection

● It is the identification and categorization of what a user online
intended or wanted when they typed their
● Intent detection using semantically enriched word embeddings
● Zero-shot User Intent Detection via Capsule Neural Networks
● A Bi-model based RNN Semantic Frame Parsing Model for Intent
Detection and Slot Filling
Intent detection

● Naive Bayes classifier
Intent detection

● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have a cough
Named Entity Recognition

● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have a cough
Named Entity Recognition
SYMPTOM

Coreference Resolution
● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have pain in the neck, it is really burning

Coreference Resolution
● Bot : Hi, I am your health assistant, how can I help you ?
● User : I have pain in the neck, it is really burning
Refers to

Sentiment Analysis
● Sentiment analysis aims to determine the attitude of a speaker,
writer, or other subject with respect to some topic or the overall
contextual polarity or emotional reaction to a document,
interaction, or event

Sentiment Analysis
● If chatbots are not your primary mean of communication, it
means you failed at other points ( the user could not find the
information online )
● A typical negative, neutral, positive polarity does not reflect the
reality

Sentiment Analysis
angry
Very angry
I’m gonna find your family

Expectations vs reality
● Bot : I am your financial assistant, how can I help you ?
● User : Hi, I could not login into the online banking system

Expectations vs reality
● Bot : I am your financial assistant, how can I help you ?
● User : Hi, I could not login into the online banking system
● The app doesn’t work, dunno man, wtf ?

Expectations vs reality
● Bot : I am your health assistant, how can I help you ?
● User : Hi, I have a cough

Expectations vs reality
● Bot : I am your health assistant, how can I help you ?
● User : Hi, I have a cough
● ‫ﺳﻌﺎل‬ ‫ﻋﻧدي‬ ، ً‫ﺎ‬‫ﻣرﺣﺑ‬

Expectations vs reality
● Bot : I am your health assistant, how can I help you ?
● User : Hi, I have a cough
● ‫ﺳﻌﺎل‬ ‫ﻋﻧدي‬ ، ً‫ﺎ‬‫ﻣرﺣﺑ‬
● Most NLP Processing techniques for English do not work for
Arabic
● Arabic has 40 dialects
● POS tagging is dependant on the domain
● etc...

Build your own :
So what’s the point now ?

Data-privacy is a trade-off
● When you make the choice of data-privacy, you have trade-offs
○ You do not sit on the shoulders on giants anymore
○ The cost of research will exponentially surpass the usage cost
of a third-party solution
○ You have the sole responsibility of the satisfaction of the
customers

“I don’t understand your
question”
is not an answer

Integrate all the things
● Intent Detection
● Natural Language Processing
● Custom Named Entity model training
● Co-reference resolution training
● English, Arabic, German support
● Graph-based context builder

Download software: http://gate.ac.uk/wiki/twitter-postagger.html Original paper: http://derczynski.com/sheffield/papers/twitter_pos.pdf Part-of-speech information is a pre-requisite in many NLP algorithms. However, Twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. We present a detailed error analysis of existing taggers, motivating a series of tagger augmentations which are demonstrated to improve performance. We identify and evaluate techniques for improving English part-of-speech tagging performance in this genre. Further, we present a novel approach to system combination for the case where available taggers use different tagsets, based on vote-constrained bootstrapping with unlabeled data. Coupled with assigning prior probabilities to some tokens and handling of unknown words and slang, we reach 88.7% tagging accuracy (90.5% on development data). This is a new high in PTB-compatible tweet part-of-speech tagging, reducing token error by 26.8% and sentence error by 12.2%. The model, training data and tools are made available.

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👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program: https://bit.ly/Automation_Student_Kickstart In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC. 📕 Detailed agenda: What is RPA? Benefits of RPA? RPA Applications The UiPath End-to-End Automation Platform UiPath Studio CE Installation and Setup 💻 Extra training through UiPath Academy: Introduction to Automation UiPath Business Automation Platform Explore automation development with UiPath Studio 👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/

Automation Student Developers Session 3: Introduction to UI Automation

UiPathCommunity

👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program: http://bit.ly/Africa_Automation_Student_Developers After our third session, you will find it easy to use UiPath Studio to create stable and functional bots that interact with user interfaces. 📕 Detailed agenda: About UI automation and UI Activities The Recording Tool: basic, desktop, and web recording About Selectors and Types of Selectors The UI Explorer Using Wildcard Characters 💻 Extra training through UiPath Academy: User Interface (UI) Automation Selectors in Studio Deep Dive 👉 Register here for our upcoming Session 4/June 24: Excel Automation and Data Manipulation: https://community.uipath.com/events/details

MySQL InnoDB Storage Engine: Deep Dive - Mydbops

Mydbops

This presentation, titled "MySQL - InnoDB" and delivered by Mayank Prasad at the Mydbops Open Source Database Meetup 16 on June 8th, 2024, covers dynamic configuration of REDO logs and instant ADD/DROP columns in InnoDB. This presentation dives deep into the world of InnoDB, exploring two ground-breaking features introduced in MySQL 8.0: • Dynamic Configuration of REDO Logs: Enhance your database's performance and flexibility with on-the-fly adjustments to REDO log capacity. Unleash the power of the snake metaphor to visualize how InnoDB manages REDO log files. • Instant ADD/DROP Columns: Say goodbye to costly table rebuilds! This presentation unveils how InnoDB now enables seamless addition and removal of columns without compromising data integrity or incurring downtime. Key Learnings: • Grasp the concept of REDO logs and their significance in InnoDB's transaction management. • Discover the advantages of dynamic REDO log configuration and how to leverage it for optimal performance. • Understand the inner workings of instant ADD/DROP columns and their impact on database operations. • Gain valuable insights into the row versioning mechanism that empowers instant column modifications.

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

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HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable. In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed: Key Takeaways: Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement. Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers. Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.

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"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

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This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application. In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy. We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.

ScyllaDB Tablets: Rethinking Replication

ScyllaDB

ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.

"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk

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At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience

TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...

TrustArc

Global data transfers can be tricky due to different regulations and individual protections in each country. Sharing data with vendors has become such a normal part of business operations that some may not even realize they’re conducting a cross-border data transfer! The Global CBPR Forum launched the new Global Cross-Border Privacy Rules framework in May 2024 to ensure that privacy compliance and regulatory differences across participating jurisdictions do not block a business's ability to deliver its products and services worldwide. To benefit consumers and businesses, Global CBPRs promote trust and accountability while moving toward a future where consumer privacy is honored and data can be transferred responsibly across borders. This webinar will review: - What is a data transfer and its related risks - How to manage and mitigate your data transfer risks - How do different data transfer mechanisms like the EU-US DPF and Global CBPR benefit your business globally - Globally what are the cross-border data transfer regulations and guidelines

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leebarnesutopia

So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation. In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.

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When privacy matters! Chatbots in data-sensitive businesses

1. When privacy matters ! Chatbots in data-sensitive businesses GraphAware, world’s #1 Neo4j consultancy Creators of Hume - AI for Humans graphaware.com @graph_aware Christophe Willemsen CTO GraphAware @ikwattro

2. ● A word on data privacy ● Build your own : expectations vs reality ● Natural Language Processing ● Challenges with NLP ● Sentiment ● Q & A Outline

3. A word on data privacy

4. ● Where data is sent ? ● What is sensitive ? ● Are your users aware ? Data Privacy

5. Build your own : expectations

6. Business guy

7. Business guy I WANT A CHATBOT

8. IT Department

9. IT Department

10. DataScience ARMY

11. Build your own : expectations

12. ● Creating a Chatbot with Deep Learning, Python, and TensorFlow ● How Chatbots Are Learning Emotions Using Deep Learning ● How I Used Deep Learning To Train A Chatbot To Talk Like Me ● Neuralconvo - Chatting with a Deep learning brain ● ... Current conversation topics

13. ● Creating a Chatbot with Deep Learning, Python, and TensorFlow ● How Chatbots Are Learning Emotions Using Deep Learning ● How I Used Deep Learning To Train A Chatbot To Talk Like Me ● Neuralconvo - Chatting with a Deep learning brain ● … ● Training your Machine Learning models on the blockchain ● Conversational Artificial Intelligence in the FinTech Current conversation topics

14. Build your own : reality

15. Conversation Flow

16. ● It is the identification and categorization of what a user online intended or wanted when they typed their query Intent detection

17. Conversation Flow

18. Business guy

19. ● It is the identification and categorization of what a user online intended or wanted when they typed their ● Intent detection using semantically enriched word embeddings ● Zero-shot User Intent Detection via Capsule Neural Networks ● A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling Intent detection

20. Business guy

21.

22. ● Naive Bayes classifier Intent detection

23. Intent detection

24. Build your own : Challenges with NLP

25. ● Bot : Hi, I am your health assistant, how can I help you ? ● User : I have a cough Named Entity Recognition

26. ● Bot : Hi, I am your health assistant, how can I help you ? ● User : I have a cough Named Entity Recognition

27. ● Bot : Hi, I am your health assistant, how can I help you ? ● User : I have a cough Named Entity Recognition SYMPTOM

28. Named Entity Recognition Finding data

29. Named Entity Recognition Finding data

30. Named Entity Recognition Finding data

31. Named Entity Recognition Finding data

32. hume.ga/ner

33. Build your own : Challenges with NLP

34. Coreference Resolution ● Bot : Hi, I am your health assistant, how can I help you ? ● User : I have pain in the neck, it is really burning

35. Coreference Resolution ● Bot : Hi, I am your health assistant, how can I help you ? ● User : I have pain in the neck, it is really burning Refers to

36. Coreference Resolution

37. Coreference Resolution

38. Build your own : Challenges with NLP

39. Sentiment Analysis ● Sentiment analysis aims to determine the attitude of a speaker, writer, or other subject with respect to some topic or the overall contextual polarity or emotional reaction to a document, interaction, or event

40. Sentiment Analysis

41. Sentiment Analysis

42. Sentiment Analysis ● If chatbots are not your primary mean of communication, it means you failed at other points ( the user could not find the information online ) ● A typical negative, neutral, positive polarity does not reflect the reality

43. Sentiment Analysis angry Very angry I’m gonna find your family

44. Build your own : Challenges with NLP

45. Expectations vs reality ● Bot : I am your financial assistant, how can I help you ? ● User : Hi, I could not login into the online banking system

46. Expectations vs reality ● Bot : I am your financial assistant, how can I help you ? ● User : Hi, I could not login into the online banking system ● The app doesn’t work, dunno man, wtf ?

47. Expectations vs reality ● Bot : I am your health assistant, how can I help you ? ● User : Hi, I have a cough

48. Expectations vs reality ● Bot : I am your health assistant, how can I help you ? ● User : Hi, I have a cough ● ‫ﺳﻌﺎل‬ ‫ﻋﻧدي‬ ، ً‫ﺎ‬‫ﻣرﺣﺑ‬

49. Expectations vs reality ● Bot : I am your health assistant, how can I help you ? ● User : Hi, I have a cough ● ‫ﺳﻌﺎل‬ ‫ﻋﻧدي‬ ، ً‫ﺎ‬‫ﻣرﺣﺑ‬ ● Most NLP Processing techniques for English do not work for Arabic ● Arabic has 40 dialects ● POS tagging is dependant on the domain ● etc...

50. Build your own : So what’s the point now ?

51. Data-privacy is a trade-off ● When you make the choice of data-privacy, you have trade-offs ○ You do not sit on the shoulders on giants anymore ○ The cost of research will exponentially surpass the usage cost of a third-party solution ○ You have the sole responsibility of the satisfaction of the customers

52. It has to work 100% of the times

53. “I don’t understand your question” is not an answer

54. It’s an insult !

55. Integrate all the things ● Intent Detection ● Natural Language Processing ● Custom Named Entity model training ● Co-reference resolution training ● English, Arabic, German support ● Graph-based context builder

56. hume.ga graphaware.com

57.

58. Questions ?

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