Emerging Trends in Artificial Intelligence - A deeper review

1. © 2020 MUST India
Gopi Krishna Nuti
MS, MBA
Vice President
MUST Research
ngopikrishna@gmail.com, vp@must.co.in
+91-9036005121
Emerging Trends in Artificial Intelligence
Mar 2021

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• Humble Origins
• Grand dreams
• Painful reality (twice) a.k.a AI Winters
• Early adopters
• Marketing Hype (So far)
• Mainstay of life (Future)
• Maturity (Far out)
Artificial Intelligence

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• Deep Learning is taking over
• Explainability of Deep learning
• Commoditization of AI
• AI on the edge
• Exploitation using AI
• Frauds
• Standards
• Expansion of job opportunities because of AI
Trends

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• 1950 - Alan Turing, Turing Test
• 1951 - Christopher Strachey, first successful AI program to play Checkers
• 1952 - Anthony Oettinger, Shopper – first machine learning program using rote learning.
• 1954 - Belmont Farley and Wesley Clark of MIT, first Artificial Neural Network
• 1956 - John McCarthy coined the term AI
• 1956-1974 – Golden years. Lot of funding
• 1974-1980 – First AI winter
• 1980-1987 – Boom
• 1987-1993 – Second AI Winter
• 1993 – 2011- Improvements
• 11-May-1997 : Deep blue beats Garry Kasparov
• Mostly behind the scenes
• 2011 – present
• Deep learning, Artificial General Intelligence
• Narrow AI, Weak AI, Strong AI
• Narrow Ai – Siri, Cortana, Google Assistant etc. Most of the personal assistants, chatbots etc we see today
Road so far

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• Sophia
• social humanoid robot developed by Hong Kong-based company
Hanson Robotics.
• First robot to receive citizenship
• First non-human to receive a title from UNO (Innovation
Champion)
Marketing Hype

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Reality
It is "not ideal" that some think of Sophia as
having human-equivalent intelligence
- Ben Goertzel, Chief Scientist who built Sophia
A chatbot with a face
- Expert review of Sophia
Sophia is complete bullshit
- Yann LeCun,
Winner of Turing Award, Godfather of Deep Learning

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• Narrow AI -AI algorithms applied for a specific problem.
• Deepface – 2015 – Facebook’s Face recognition algorithm
• Resnet, Inception, YOLO – Image classification, localization etc
• GPT2, GPT3 – Content generation
• Weak AI
• No clarity on what this is
• Strong AI – Artificial General Intelligence
• Very much in a research stage
So Where is everything at?

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Select milestones
•Linear
Regression
1805
•Neural
Networks
1943 •K-NN
1951
•Perceptron
•Logistic
Regression
1958 •Support
Vector
Machine
1963
•K-Means
Clustering
1967 •Decision
Trees
1968
•RNN
1986 •LeNet
1990
•Random
Forest
1995 •LSTM
1997
•GANs
2014
Supervised Learning
- Regression
Supervised Learning
- Classification
Neural Networks and
Deep Learning
Unsupervised
Learning Content creation

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Select milestones
Year Caption
1805 Least Square
1812 Bayes' Theorem
1913 Markov Chains
1950 Turing's Learning Machine
1951 First Neural Network Machine
1952 Machines Playing Checkers
1957 Perceptron
1967 Nearest Neighbor
1970 Automatic Differentiation (Backpropagation)
1976 Transfer Learning
1982 Recurrent Neural Network
1986 Backpropagation
1989 Reinforcement Learning
1995 Random Forest Algorithm
1995 Support Vector Machines
1997 LSTM
2005 RankNet
Year Caption
2014 Leap in Face Recognition
2014 Sibyl
2014 XgBoost
2014 GANs
2014 Regions with CNN features
2015 Fast R-CNN and Faster R-CNN, Inception V3
2016 YOLO, SSD
2017 Google AI – Attention is all you need
2018 ULMfit fast.ai
2018 BERT
2019 Stanford NLP
2019 Open AI GPT2
2020 Open AI GPT3 beta

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Current Growth

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• Third Generation Neural Networks – Spiking networks
• Ranknet – Deep learning based implementation for Learning to Rank (Not too recent)
• Transfer Learning along with CNNs (relatively recent)
• Generative AI (quite recent)
(Some) New algorithms

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• Considered a natural successor to current Artificial Neural Networks
• More closely resemble the brain’s synapses and biological function.
• Theoretically, more powerful than the current 2nd generation networks
Spiking Neural Networks
Artificial Neural Networks Spiking Neural Networks
Neurons are fully connected Neurons are spatially locally connected (similar to CNNs)
Neuron outputs a continuous value Neuron outputs a discrete value yes/no
Every neuron fires every time during propagation • Neuron fires only when its value reaches a pre-set threshold
(similar to ReLU activation function)
• Until the threshold is not met, incoming synapses either
increase or decrease the value (membrane potential)
• Once the neuron fires, its value is reset
Sigmoid, ReLU, Leaky ReLU are commonly used activation
functions
• Leaky Integrate and Fire (LIF) model
Loss function is differentiable entirely. For single layer
networks, cost function needs to be convex
Loss function is not differentiable. So, Cost function is not
necessarily convex

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• Machine Learning to Rank
• Another branch of Supervised Learning (in addition to Regression and Classification)
• Idea first took shape in 1989
• Ranknet
• Developed by Microsoft in 2005 and used by Bing
• Won “ICML’s Test of Time” award in 2015
• Application in
• Information retrieval (combined with NLP)
• Decision making
• Recommender Systems
• Please see more details at https://www.slideshare.net/gopikrishnanuti/classification-vis-avis-ranking-gopi
Ranknet

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• Subfield of ML and AI
• Applies knowledge gained from one task to another similar task.
• Avoids having to reinvent the wheel
• Example:
• Knowledge gained in analysing sentiment of movie review text can be used for analysing sentiments of
consumer electronics product review
• Knowledge gained in identifying birds in a photo can be used for identifying cars
• Ideated in 1993 by Lorien Pratt
• Need a pretrained model and dataset of our requirement. The model is repurposed for our dataset.
• Allowed major strides in computer vision and NLP
• Example:
• InceptionV3 is image classification model trained on ImageNet. This has been repurposed to identify Diabetic
Retinopathy in medical images of eyes.
Transfer Learning

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• Traditional - Market Research, Finance, Engineering, Education, Medicine, Astronomy
• New Domains
• Robotic Process Automation
• Warfare and Defence - Drones, Quadcopters
• Cyber security – Filtering content, identifying attack vectors, Threat exposure, incident response etc.
• Media and Entertainment - Deep fakes, GANs, Deep Nostalgia
• Advertising and Marketing- Mass Customization
• Hybrid Workforce
• Sub disciplines
• ML Ops
Newer Domains

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Gartner’s AI Hype Cycle for 2020
Image courtesy Gartner.com

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AI Market places
• A multi-sided-platform where AI providers and sellers can exchange services. NB: Seller is not the Cloud
provider!!!
• Clever blend of PaaS & SaaS. Can be thought of as Algorithm as a Service
• Build an AI algorithm for a specific use case. Sell it for a license on top of cloud Services.
• Examples
• AWS Marketplace
• Azure Marketplace

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AI Marketplace examples
Demonstration of the Mask Detector for Epidemiological Safety by Vitech Lab.
Picture courtesy Sandro Luck
Demonstration of the Vehicle Damage Inspection developed by Persistent on
Amazon Market Place

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• Anything is game
• AI models
• Datasets
• Data pipeline management
• ML Ops
AI Marketplace examples
GluonCV YOLOv3 Object Detector
By: Amazon Web Services YOLOv3 is a powerful network for fast and
accurate object detection, powered by GluonCV.
Emotion Analysis API
Sold by:Twinword Inc.
Deep Vision API
Sold by:Deep Vision AI, Inc
Deep Vision API is a computer vision platform allowing you to easily
integrate AI-based technology into your products, services and
applications. You can automatically understand and analyze images and
videos.Pay directly from your AWS account, quick and seamless
integration with your AWS workflow.

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AI Services Commoditization
Image courtesy : Gartner.com

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• ML algorithms are run on the hardware device/sensor itself.
• This is in direct contrast to cloud/server based processing of data.
• Processing of data happens close to the user.
• Can be on IoT senser or dedicated Edge Server
• Benefits
• Latency time is reduced my many orders of magnitude
• CapEx and OpEx costs are reduced significantly
• Increased data privacy and security
• Examples
• Alexa, Google Assistant,
• Self driving cars
• Edge AI Camera from Avinton, VIA Mobile360 Dash Cam
Edge AI

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• Originally thought of as unrelated/competing technologies
• Now complementing one another
• Robotic Process Automation uses specially developed programs to automate repeatable business processes. There
is no learning. Repeats the same set of actions every time.
• Great for automating simple tasks.
• Typically, suitable for structured data
• For complex tasks, AI comes handy because
• Learns from data/past
• Handles unstructured data
Robotic Process Automation and AI

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Scenario : A TV program asks the audience “Which candidate do you support?
Send your answer as SMS to 56789 in the next 2 minutes”
RPA only:
SMS MUST be in the below format –
SMALLBOSS <your mobile number> <your name> <candidate name> <your date of
birth> <candidate id> <your village> <your parrot name>
RPA + AI
Details can be in any sequence and some details can perhaps be skipped too
RPA and AI – Over simplified example

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• Automation Anywhere released IQBot in 2019
• Could read low resolution documents and read 190 languages
• Blue Prism released Cogito
• Offers NLP and ML algorithms
• UiPath released Intelligent Automation
• NLP and Computer Vision
RPA and AI – real examples

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• Specialized hardware to accelerate the processing of AI algorithms particularly, Deep Neural Networks
• GPUs
• Nvidia, Intel
 Vision Processing Units
• Intel Neural Computing Stick, Qualcomm Snapdragon
 Tensor Processing Units
 FPGAs and ASICs
 Special Purpose programming languages
 CUDA, OpenCL
 OpenVino Kit from Intel, SNPE SDK from Qualcomm
 For more details, please refer to https://www.slideshare.net/gopikrishnanuti/inferene-trends-in-industry
GPU Accelerators

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Multi-media and Entertainment
https://www.youtube.com/watch?v=cQ54GDm1eL0&t=1s
https://www.youtube.com/watch?v=4GdWD0yxvqw&t=2s

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Multi-media and Entertainment
https://www.youtube.com/watch?v=JSEdBNslGOk https://www.youtube.com/watch?v=251pfVoGBUA&t=1s

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• Natural Language Processing
• Natural Language Understanding
NLP
•Breakthrough in Language Translation
•seq2seq models
•Transformer
Google AI - 2017
•Transfer learning introduced to NLP
ULMFiT fast.ai 2018
•Uses both Transformers and Transfer learning
•State of the art for 11 NLP tasks
•Pre-trained on English Wikipedia with 2.5 billion words
•Upgraded to RoBERTa by facebook in July 2019
BERT Google AI 2018
•Python NLP package
•Pre-trained neural models for 53 languages
•Replaced by Stanza also from Stanford supporting 66 languages
Stanford NLP – Jan 2019
•State of the art for Text Generation
•GPT3 is out in beta in June 2020. Rave reviews
Open AI GPT2 – Feb 2019
•State of the art models made available on Torch and with Python bindings
PyTorch Transformers from Hugging
face – July 2019

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• For a topic-wise review on Computer Vision and recent trends, please visit
https://www.slideshare.net/gopikrishnanuti/computer-vision-old-problems-new-solutions
Computer Vision
• Used selective search
• Very slow and complicated
2014 – Regions with CNN features
• Designed to solve problems with R-CNN
2015 – Fast R-CNN, Faster R-CNN from
Microsoft
• Image classification with very deep network
2015 – Inception from Google
• You Only Look Once
• Object Localization
• Significantly fast and a radically different approach.
2016 – YOLO
• Faster and more accurate than YOLO
2016 - SSD
• Mask R-CNN
• Pixelwise Instance Segmentation
2017

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Some stunning implementations
https://www.youtube.com/watch?v=uhND7Mvp3f4 https://www.youtube.com/watch?v=bs9Lm3ss39o

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https://www.youtube.com/watch?v=AewM3TsjoBk
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MUST Research – Our publications
• A book introducing Machine Learning from basics through Supervised and
Unsupervised learning for beginners
https://www.amazon.in/Machine-Learning-Engineers-Gopi-
Krishna/dp/9389024870/ref=sr_1_2?dchild=1&keywords=machine+learning+for
+engineers&qid=1616195333&sr=8-2
• Multiple research papers and publications

33. © 2020 MUST India
Gopi Krishna Nuti
Vice President
MUST Research
Thanks