This document discusses and compares several next generation artificial intelligence techniques, including capsule networks, transfer learning, deep reinforcement learning, unsupervised/semi-supervised deep learning, meta-learning, swarm intelligence, and differentiable neural computers. It provides brief descriptions of each technique and potential applications, such as using capsule networks for text analytics, transfer learning for robotics, deep reinforcement learning for banking product recommendations, and swarm intelligence for robotics and fraud analytics. Examples and diagrams are included to help explain how some of the techniques work.

1. Deep Learning Howlers: Why Learning
Surface Statistical Regularities is not
Enough
Dr. Vijay Srinivas Agneeswaran,
Senior Director and Head of Data
Sciences, SapientRazorfish

2. C O P Y R I G H T S A P I E N T R A Z O R F I S H | C O N F I D E N T I A L 2
Microsoft Computer Vision API: Image Captioning and Tagging
a group of giraffe standing next to a rock wall a statue of a bear in a pool of water

3. C O P Y R I G H T S A P I E N T R A Z O R F I S H | C O N F I D E N T I A L 3
Microsoft Computer Vision API: Image Captioning and Tagging
a group of brown cows walking along a wooden fence
a group of giraffe standing next to a tree

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More DL Howlers
a monkey sitting on a rock a group of pink flowers on top of a grass covered field

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More DL Howlers
a flock of seagulls at sunset a herd of sheep standing on top of a grass covered
field

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More DL Howlers
herd of sheep grazing on a lush green field
a woman wearing a green shirt

7. Even other pre-
built models make
mistakes
a man walking down a street holding an
umbrella(Neuraltalk2)

8. § a man is sitting on a bench in a park-(Neuraltalk2 demo)

9. Detection of Errors in CNN:
For error detection, we have compared two neural networks outputs on the same input data-
set.
§ Network1 – Microsoft’s captionbot.ai
§ Network2 – Cloudsight.ai bot
Comparison is done quantitatively between the outputs of the network.
Claim:
§ “If the networks are correct most of the time, then their output’s frequency distributions must
almost be equal”
§ “If the frequency distribution varies too much, then there must be some irregularities
associated with at-least one network”

10. Results:
Frequency Distributions of words
File 1 File 2
• The Euclidean Distance between two files is: 37.33
• The Cosine Distance between two files is: 0.1385

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Generalization and Surface Statistical Regularities
Claim 1:
• Deep CNNs
are
generalizing
extremely
well to an
unseen test
set.
Claim 2:
• Deep CNNs
have extreme
sensitivity to
adversarial
examples.
Train and Test
data sets
• Have similar
image
statistics
• Superficial
cues –
generalization
behaviour
• Perturbations
on images

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Fourier Filtering
J. Jo and Y. Bengio, “Measuring the tendency of cnns to learn surface statistical regularities,” arXiv preprint arXiv:1711.11561, 2017.

13. C O P Y R I G H T S A P I E N T R A Z O R F I S H | C O N F I D E N T I A L 13
DLs only learn Surface Statistical Regularities?
J. Jo and Y. Bengio, “Measuring the tendency of cnns to learn surface statistical regularities,” arXiv preprint arXiv:1711.11561, 2017.
Network1
• unfiltered
data set for
training
• Generalization
gap of 18-20%
Network2
• randomly
filtered data
for training
• Generalization
gap of 28%
Network3
• radially
filtered data
for training
• Generalization
gap of 10%

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Next Gen Artificial Intelligence Algorithms
Capsule Networks
Transfer Learning
Deep
Reinforcement
Learning
Unsupervised/
Semi-supervised
Deep Learning
Meta-Learning
Swarm Intelligence
Differentiable
Neural Computers
Privacy-preserving
Analytics

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Capsule Networks
CNN – these 2 images are the same!
§ Capture spatial relationships between objects –
represented as pose (rotation+translation)
§ Handles data sparsity
§ Capsule outputs a vector, whereas a neuron
outputs a scalar.
§ Equivariance VS Invariance
§ Applications: text analytics and multi-modal data
https://www.youtube.com/watch?v=pPN8d0E3900

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Privacy Preserving Analytics: Federated Learning
§ Data is non-iid, massively distributed across
multiple clients/organizations and imbalanced
§ Trusted curator – collects parameters for
model.
§ Joint representative model without data
sharing.
§ Applications : Insurance domain for banks.
How federated learning works ?

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Transfer Learning
§ Typical application of multi task
learning can be found in Robotics,
where robots can learn from each
other.
§ Applications: RPA
How transfer learning works ?

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Unsupervised/Semi-supervised Deep Learning
Image source https://arxiv.org/abs/1511.06430
§ Approaches to Semi-Supervised Learning(SSL)
§ Pseudo Labeling
§ Ladder Network
• Uses Encoder and Decoder approach
• Uses two encoders paths(one with noise and another without
noise) to get noisy hidden activations and clean hidden
activations respectively
• Decoder reconstructs the clean hidden activations at each layer
from noisy activations
• Ladder Network provide close to 99% accuracy on MNIST test
data with just 100 labeled samples
§ Applications : Customer Behavior Prediction, Credit Scoring, Fraud
Detection, Image Classification, Topic Modeling, Text Summarization
How ladder network works ?

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Differentiable Neural Computing
Source: https://deepmind.com/blog/differentiable-neural-computers/
§ Read and write learnings to external memory
§ Selective Attention
§ Able to combine and use controller and memory
results in order to make predictions
§ Applications: Predictive process analytics –
optimization of business processes.
Next GEN AI

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Meta-Learning (Learning to Learn)
§ Meta Learning monitors the automatic learning process
itself, in the context of the learning problems it encounters,
and tries to adapt its behavior to perform better.
§ Supervised learning with data points that are entire
datasets
§ Applications: Few-shot image recognition
How meta-learning works ?

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Deep Reinforcement Learning
Source: http://www.tmansi.net/wp-content/uploads/2016/12/dsl.jpg
§ Applications in game theory, control
theory, operations research, information
theory, simulation-based optimization,
multi-agent systems, genetic algorithms
etc.
§ Applications: Banking Products
Recommendation System

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Swarm Intelligence
§ What is Swarm Intelligence?
§ AI technique based on the collective
behavior of decentralized, self-organized
systems
§ Uses a number of agents (particles) that
constitute a swarm moving around in the
search space looking for the best solution
§ Each particle in search space adjusts its
“flying” according to its own flying
experience as well as the flying experience
of other particles
§ Applications: Robotics, fraud analytics
http://www.techferry.com/articles/swarm-intelligence.html