This is the slide deck for the talk at https://youtu.be/nNAmEMi3MAs

2. Aman Sharma
❏ Founder CTO Twimbit
❏ Lead Mobile-web.dev
❏ Deeplearning.ai & OSI Member
@amanintech
🌐 amansharma.dev

3. Rabimba Karanjai
AI Engineer Twimbit
GDE WebXR
Mozilla tech speaker
@rabimba

4. The Agenda
● What is AI ?
● AI on Web
● Benefits
● What is ML ?
● ML in JS
● Applications

5. Whats AI ?
Tendency to create
intelligence artificially
Computers thinking like
humans

8. The Circle of AI
AI
ML
NN
DL

9. Recommendation
Systems
Uses Machine learning to
generate results based on
your interest history

10. Spam Filter
Uses Machine learning
based on biasing to
predict probability of
spam

11. Speech
Recognition
Uses Neural Nets and NLP
to process analog sounds
waves to Digital Data

12. Computer Vision
Labeling Data based on
object detection based on
neural net using
classification

13. Self Driving
Ability to learn to drive
like human using lot of
data and deep learning
technique beyond human
capabilities

14. Traditional
Application
on device
Backend
API
AI Algo
running on
server
Data
Warehouse
ML Model
Model
Training

15. That’s too
long
Normal
AI
App
Ai on Web

16. Application
on device
Backend
API
AI Algo
running on
server
Data
Warehouse
ML Model
Model
Training
Run AI on
the device

17. Benefits

18. Lesser turnaround

19. AI based UX

20. Personalised
content

21. Voice Search

22. WHAT IS MACHINE
LEARNING

23. DIFFERENCE FROM TRADITIONAL PROGRAMMING
Example: Recognising spam emails
if(email.includes('viagra')){
filterAsSpam(email);
} else if(email.includes('v!agra')){
filterAsSpam(email);
} else if(...){
....
}

24. DIFFERENCE FROM TRADITIONAL PROGRAMMING
Example: Recognising spam emails
if(email.includes('viagra')){
filterAsSpam(email);
} else if(email.includes('v!agra')){
filterAsSpam(email);
} else if(...){
....
}
import * as spamModel from "model.json"
const model = load(spamModel);
const newEmail = getEmail();
const prediction =
model.predict(newEmail);
prediction === 0 ? spam : not spam

25. ALGORITHMS

26. ● Naive Bayes
● K-Nearest Neighbor (KNN)
● Linear regression
● Convolutional Neural Networks (CNN)
● LSTM - Long Short Term Memory
● ...
EXAMPLES OF ALGORITHMS

27. Supervised
learning
Unsupervised
learning
TYPES OF MACHINE LEARNING
ALGORITHMS
Reinforcement
learning

28. Supervised
learning
TYPES OF MACHINE LEARNING
ALGORITHMS
Reinforcement
learning
Unsupervised
learning

29. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.

30. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.

31. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.

32. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.

33. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.
Example: Predicting the price of a house

34. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.
How you would classify each
entry.
Example: Predicting the price of a house
Price

35. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.
How you would classify each
entry.
Example: Predicting the price of a house
Price
Characteristics of the entries
in your dataset.
Number of rooms, floors,
location, type of property, ...

36. SUPERVISED LEARNING
Create predictive model based on a set of features
and labels.
How you would classify each
entry.
Example: Predicting the price of a house
Price
Characteristics of the entries
in your dataset.
Number of rooms, floors,
location, type of property, ...
Mathematical representation of
the outcome of your training.
Function that takes features
as parameters and is able to
predict a label as output.

37. The ability to create predictions based only on a set of
features.
UNSUPERVISED LEARNING

38. The ability to create predictions based only on a set
of features.
UNSUPERVISED LEARNING

39. UNSUPERVISED LEARNING
The ability to create predictions based only on a set
of features.
Example: Predicting customer behaviour

40. MACHINE LEARNING
IN JAVASCRIPT

41. WHY
● Accessible, easier learning curve.

42. WHY
● Accessible, easier learning curve.
● Rapid prototyping.

43. WHY
● Accessible, easier learning curve.
● Rapid prototyping.
● Big ecosystem.

44. WHAT CAN YOU DO
Import an existing,
pre-trained model.

45. WHAT CAN YOU DO
Import an existing,
pre-trained model.
Re-train an imported
model (transfer learning)

46. WHAT CAN YOU DO
Import an existing,
pre-trained model.
Re-train an imported
model (transfer learning)
Define, train and run models
entirely in the browser.

47. TOOLS

48. APPLICATIONS

49. Sarah Drasner’s experiment using ML to dynamically generate alt text
GENERATING ALT TEXT

50. FILTERING UNWANTED CONTENT
Nsfw.js by Gant Laborde

51. ACCESSIBILITY
Making Amazon Alexa respond to sign language using AI - Abhishek Singh

52. DEMOS & CODE

53. USING A PRE-TRAINED MODEL

54. TRANSFER LEARNING

55. LIMITS

56. LIMITS
● Need large amount of data (except if using pre-trained model).

57. LIMITS
● Need large amount of data (except if using pre-trained model).
● Can take a lot of time to train your own model.

58. LIMITS
● Need large amount of data (except if using pre-trained model).
● Can take a lot of time to train your own model.
● Think about the mobile experience.

59. LIMITS
● Need large amount of data (except if using pre-trained model).
● Can take a lot of time to train your own model.
● Think about the mobile experience.
● Liability (some models are black boxes).

60. LIMITS
● Need large amount of data (except if using pre-trained model).
● Can take a lot of time to train your own model.
● Think about the mobile experience.
● Liability (some models are black boxes).
● Bias / ethics.

61. ALGORITHMS ARE NOT BIASED,
WE ARE.

62. ETHICS

63. SURPRISE!!!

64. Minko Gevech’s Mortal Kombat game
INSPIRATION

65. @devdevcharlie

66. RESOURCES7

67. Magenta.js
NeuroJS
ConvNetJS
WebDNN
Ml.js
AIJS
Keras.js
Tensorflow.js examples
Brain.js
Synaptic.js
Natural
Neataptic
Google Colab
Teachable machine boilerplate
PoseNet
machine_learning
Tensorflow playground
Jupyter Notebooks
ML5.js
Amazon ML
Microsoft ML APIs
Google Cloud AI
ONNX.js
TOOLS