15 JULY 2023 - 15 JAN 2024VOL 02 - ISSUE 01

NEWSLET TER

REFLECTIONS: FROM THE VICE CHANCELLOR’S DESK

As we usher in a new year of progress and

innovation, we center this edition of 1729

on the theme of ‘Tech for All’. In these

pages, we delve into the diverse and

impactful ways in which technology must

be harnessed to address societal challen-

ges and promote inclusivity.

These days conversations around us are

centered on data science and its transfor-

mative impact. But how many of us know

about the historical and scientiﬁc dimen-

sions of the subject? In the third issue of

1729, Professor Rajesh Gupta, a luminary

in the ﬁeld, gives us a refresher with exem-

plary insights.

Our campus is a microcosm of society

where we nurture young individuals and

prepare them to not only face real-world

challenges but also address them and make

a diﬀerence. Professor M Balakrishnan’s

journey in assistive technology and social

entrepreneurship serves as an inspiration

for all our faculty and students.

With such academic scholars and outstand-

ing faculty guiding our academic commu-

nity, our students are inspired and are

making strides in leveraging tech in addres-

sing societal problems. These tangible out-

comes highlight our rich academic tapestry

and embody Plaksha’s spirit of innovation,

inclusivity and meaningful impact.

I envision a future where technology serves

all, and Plaksha continues to lead the way.

Christened ‘1729', after Ramanujan - Hardy number, the Plaksha newsletter is a window into our

thriving, interconnected, and learner centered environment where Plakshans look beyond the

obvious, just like Srinivas Ramanujan did with the seemingly dull number ‘1729'.

Through this newsletter we share the contribution of each member of our vibrant community of

learners, researchers, leaders, innovators and problem solvers to reimagining technology education.

Prof. Rudra Pratap

Founding Vice Chancellor

Plaksha University

T E C H F O R A L L

Tech must address

societal challenges

and promote

inclusivity

Modeled after the visual cortex, the resulting

convolutional neural networks provided a

systematic means of optimization that auto-

matically identiﬁed spatial ‘features’ in images.

By early 2000, advances in computing hard-

ware demonstrated measurable progress in

the ability of convolutional neural networks to

classify images and handwritten text.

Driven by a standard set of benchmark chal-

lenges, the progress in automatic image classi-

ﬁcation was rapid and dramatic. While it is still

not very clear as to why these networks - the

so-called deep neural networks - worked, a

combination of happy coincidences, including

the availability of large amounts of labeled

images over the internet, cloud computing,

and signiﬁcant parallel processing using

graphical computing co-processors enabled

advances in pattern recognition that will soon

expand beyond recognizing images to speech

and text. In its latest incarnation, AI had literally

and metaphorically become synonymous with

the ability to see or the ‘eyes of the machine’.

In our attempt to understand images, mankind

had walked into replicating the processes by

which we understood images. Another branch

of research in neural networks put these into

the feedback loop, and challenged the network

to generate an image given the feature/label,

the outcome of this network was then fed to

another neural network that tried to classify it.

Thus, modeled as an adversarial game

between a generation and a discrimination

network, the roots of generative AI took place.

It was only a matter of time before the atten-

tion shifted from two-dimensional images to

one-dimensional input of text, speech. Machine

translation was, for instance, the task of trans-

forming one such serial input into a serial

output, the so-called, pre-trained transformer.

A signiﬁcant result about six years ago pointed

out that convolution was not needed for such

transformation on text, and a simpler mecha-

nism provided the necessary capability to

identify semantically relevant text pieces

regardless of their distance in the source text.

These advances are now at the root of the

continuing evolution of neuromorphic comput-

ing architectures in ‘generative AI’.

As we close the circle from images to text, we

also make another important transition from

engineering, which was all about numbers, to

text and symbolic processing. While numbers

DATA SCIENCE AND INTELLIGENCE: SEEING THROUGH THE ‘EYES’

OF HISTORY AND SCIENCE

The Bastille fell in 1789, 13 years after the

American Declaration of Independence, a period

marked by war and upheaval on both sides of the

Atlantic, one that demanded technological advan-

tages to win the war. Among the two areas that

Napoleon sought to overcome the British were

steel and the conversion of heat to work/motion.

He tasked a young Sadi Carnot to build these

capabilities in a small military school that gave us

the polytechnic legacy, a precursor to engineering

as a discipline perfected in the United States.

This led to the peacetime dividend of the 1840s,

a period also marked as the European Spring

before we entered the next phase of upheaval

between the two world wars. Our engineering

legacy deﬁned a century of enormous progress.

It raised a talent pool trained to be engineers

from the beginning, for no amount of scientiﬁc

knowledge alone would make an engineer.

A century after the rise of the ﬁrst engineering

schools in America, we saw the emergence of

computing as a discipline with its roots in engi-

neering and math and an early glimpse into

computation and computing machines. Almost

immediately, it gave a glimpse into how human

thought could be emulated and the vision of

‘thinking machines’. Thus, the ﬁrst forays into

artiﬁcial intelligence (AI) rose in the early 1950s.

In the six decades since, AI has seen three

generations of technological advances, each

with its own ‘winters’ as the early excitement

eventually led to disappointments in putting

these advances to meaningful use, at least in

competition to more targeted domain knowl-

edge. The vision of intelligent machines was

contrasted by the reality of failures: machine

translation, speech recognition, expert systems.

Yet, progress was being made, slowly and

steadily. Optical character recognition was being

put into practical use, and it became a driver

for limited image classiﬁcation eﬀorts such as

handwriting recognition on a network of

‘neurons’ as multiplicative weight elements that

updated their weights based on known outputs

by back-propagation of error correction, a

version of chain rule that eﬃciently computed

gradient of a loss function. In the meantime,

driven by discoveries by neuroscientists in how

the optical nervous system was organized, the

single-layer network of neurons in neural net-

works transitioned to alternating layers of

complex/simple neural networks interspersed

with normalization/non-linear functions.

T E C H F O R A L L

Plaksha Think Tank