The document discusses the role and impact of artificial intelligence (AI) in various sectors, highlighting its potential to enhance productivity and transform daily tasks by 2030. It outlines AI's capabilities, such as voice recognition and chatbots, and its implications for job automation, including the projection that AI could replace 16% of jobs within a decade. Additionally, it emphasizes the importance of understanding AI's limitations and the ethical considerations surrounding its use.

1. Artificial Intelligence

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3. Glimpse into the future - AI by 2030
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80%executives
say AI boosts
productivity
Mobiles using
voice
assistants
Virtual Assistants:
Google, Cortant, Siri,
Alexa
6billion devices need AI
support
Customer / Employee interactions driven by
Chat-bots
$ 5.4 Bn invested in AI start ups
Robots to outnumber humans
AI to be powered by
GPUsnot CPUs
AI to replace 16%
jobs in a decade

4. AI- part our lives today
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Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts
of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to
and from work.
AI-powered personal assistant that accepts voice commands to create to-do lists, order items online, set reminders,
and answer questions (via internet searches)
Google uses artificial neural networks to power voice search. Microsoft claims to have developed a speech-
recognition system that can transcribe conversation slightly more accurately than humans.
Voice – to – text
“Ok Google” “Hey Siri”
Smart Personal Assistants
AI-Powered Predictions
Source(s):
• https://emerj.com/ai-sector-overviews/everyday-examples-of-ai/

5. Starters
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What is AI ?

6. Starters
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What is AI ?
Where have you seen / experienced AI ?

7. What is AI ?
Where have you seen / experienced AI ?
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Starters
- Programs that behave externally like humans ?
- Programs that operate internally as humans do ?
- Computational systems that behave intelligently ?
- Rational behaviour ?
- Human beings are intelligent
- To be called intelligent, a machine must produce
responses that are indistinguishable from those of a
human
- Autonomous planning and scheduling of tasks aboard a spacecraft
- Beating Gary Kasparov in a chess match
- Steering a driver-less car
- Understanding language
- Robotic assistants in surgery
- Monitoring trade in the stock market to see if insider trading is going on
Source: https://www.coursehero.com/file/47104889/Week-8-Artificial-Intelligencepdf/

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Are there tasks which cannot be automated ?
If so, what are the limitations ?
OR
How do computers’ ability compare to that of humans?

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What is Artificial Intelligence
Branch of computer science concerned with building smart machines
capable of performing tasks the typically require human intelligence
Interdisciplinary science with multiple approaches, but advancements
in machine learning and deep learning are creating a paradigm shift in
virtually every sector of industry

10. A 70 year Rich History
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“Can machines think?” “Study of artificial intelligence”
1950 1956
John McCarthyAlan TuringTuring test
A test to empirically determine whether a
computer has achieved intelligence
It simply shows that it generated behaviour consistent with thinking.
Weak equivalence: the two systems (human and computer) are equivalent in results (output), but
they do not necessarily arrive at those results in the same way
Strong equivalence: the two systems use the same internal processes to produce results
Source(s) / Picture Credit:
• https://cs.slu.edu/~fritts/CSCI140_F12/schedule/csci140_artificial_intelligence.pdf

11. Compare the way Humans and Machines Work
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Artificial Intelligence
Machine Learning
Deep Learning
Any technique that enables machines
to mimic human behaviour
Subset of AI technique which uses
statistical methods to enable
machine to improve with experience
Subset of ML which make the
computation of multi-layer neural
networks feasible
Source(s) / Picture Credit:
• https://www.slideshare.net/grigorysapunov/artificial-intelligence-past-present-and-future-64586944

12. Typical Machine Learning Tasks
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Classification Regression Clustering
Dimensionality
reduction
Ranking
Recommendatio
n
Predict a class of an
object (cat / dog
from an image;
Male / female from
user’s web activity;
Spam / Ham from
an email contents
etc.
Predicts a
continuous value
for an object (Sales
in the next month,
price of a house
with specific
features, energy
consumption for a
household
Group similar
objects together
find user groups
different by their
behaviour on site
cluster customers
into meaningful
groups etc.
“Compress” the
data from a high
dimensional
representation into
a lower-
dimensional one
(useful for
visualization)
Arrange a list of
objects to
maximize some
utility (i.e. by click
probably, or by
relevance to a
query)
Filter a small
subset of objects
from a large
collection and
recommend them
to a user (increase
hits / user loyalty;
time spent on
service etc.)

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Deep Learning / Neural Nets: Overview
Ability to learn hierarchical representations of features (and for images they do resemble
some neuroscience data on how does visual system work
Source(s) / Picture Credit:
• https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148

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Deep Learning Tasks
Speech, Images, Video Text/NLP Reinforcement Learning Other Research
Simple tasks (classification,
localization) almost done on
super-human level
Complex tasks (semantic
segmentation, style transfer,
image generation, …) are in
progress with exciting
examples
Robot perception is in
progress
Some good basic technologies
are in use (word embeddings,
language models, sequence to
sequence learning for machine
translation, etc)
Even more in progress
(translation, q&a and
conversational nets, etc)
Great achievements exist: Go
playing, Atari playing
One-shot learning, multi-
modal and multi-task learning,
memory and attention,
unsupervised learning, and
many many more
Source(s) / Picture Credit:
• https://www.slideshare.net/grigorysapunov/deep-learning-and-the-state-of-ai-2016

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Use Cases
Source(s) / Picture Credit:
• https://www.datacamp.com/community/tutorials/object-detection-guide

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Use Cases
Source(s) / Picture Credit:
• https://azati.ai/image-detection-recognition-and-classification-with-machine-learning/

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Use Cases
Source(s) / Picture Credit:
• https://www.slideshare.net/grigorysapunov/deep-learning-and-the-state-of-ai-2016

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Use Cases
Source(s) / Picture Credit:
• https://www.slideshare.net/grigorysapunov/deep-learning-cases-text-and-image-processing

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Use Cases
Source(s) / Picture Credit:
• https://www.slideshare.net/grigorysapunov/deep-learning-cases-text-and-image-processing

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Use Cases
Source(s) / Picture Credit:
• https://www.slideshare.net/grigorysapunov/deep-learning-cases-text-and-image-processing

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Use Cases
Source(s) / Picture Credit:
• https://www.slideshare.net/grigorysapunov/deep-learning-and-the-state-of-ai-2016

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AI- part our lives today
Mobile Use
Voice – to – text “Ok Google” “Hey Siri”
Google uses artificial neural networks to power voice search. Microsoft claims to have developed a speech-
recognition system that can transcribe conversation slightly more accurately than humans.
Smart Personal Assistants
AI-powered personal assistant that accepts voice commands to create to-do lists, order items online, set reminders,
and answer questions (via internet searches)
AI-Powered Predictions
Maps can more easily incorporate user-reported traffic incidents like construction and
accidents. Access to vast amounts of data being fed to its proprietary algorithms
means Maps can reduce commutes by suggesting the fastest routes to and from work.
Source(s) / Picture Credit:
• https://emerj.com/ai-sector-overviews/everyday-examples-of-ai/

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Glimpse into the future
social messaging tool
typically used in the
workplace,
also allows third parties
to incorporate AI-
powered chatbots
Soon, our day-to-
day tasks may be
completed within
a conversation
with an AI chatbot
Source(s) / Picture Credit:
• https://emerj.com/ai-sector-overviews/everyday-examples-of-ai/

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Source(s) / Picture Credit:
• https://emerj.com/ai-sector-overviews/everyday-examples-of-ai/
Glimpse into the future

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Glimpse into the future
90%reduction
in accidents
75%reduction
in number of cars
on road through
ride sharing
40%reduction
in wait time
through smart
traffic lights
26%reduction
in overall travel
time through
smart traffic
lights
2028 is the
year for mass
production of
such vehicles
- UBER CEO
Self Driving Cars

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How does the future look like ? ? ?
His decisions aren’t any better than yours
- But they are WAY faster
The good news is I have discovered inefficiencies.
The bad news is that you’re one of themSource(s) / Picture Credit:
• https://timoelliott.com/blog/cartoons/artificial-intelligence-cartoons

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Key Contact details for any queries:
Ashutosh Basotia
NEI Digital Office
(E) ashutosh.basotia@nbcbearings.in
(P) +91 141 2223221