Career Building in AI - Technologies, Trends and Opportunities

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Career Building in AI
Technologies, Trends and Opportunities
Renganathan Sekar
Product Manager, AI (Voice & Natural Language)
Samsung R&D Institute India, Bangalore (SRIB)

Introduction

1 3 5
6
4
2
BE Mech Engg.,
PSG Tech, Coimbatore
Senior Engineer,
VOLVO Trucks, India
M.Sc, RWTH Aachen Germany &
Research Associate, WZL, Germany
Research Engineer
MFRC, South Korea
MBA, IIM Bangalore
Product Manager, AI
Voice & Natural Language, Samsung
2 years 5 years 1.5 years & ….
Renganathan Sekar
Renga
Outside Work:
Android app development
Course creator in Udemy
Self-publisher in Amazon
Small Cap Investor
Football Player

Contents
Section 1: Overview of AI
Section 2: Is it all hype?
Section 3: What is Samsung doing?
Section 4: AI: Layers, Components & Tech Stack
Section 5: Emerging Trends in AI
Section 6: Skills & Opportunities in AI

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Overview of AI
Section 1

Machine learning
Enable computers to act without
explicit programming
by
designing new learning algorithms
and improving existing ones
These algorithms allow computers to analyze large volumes
of complex data to recognize patterns and make predictions
and adjustments.

Supervised Learning Unsupervised Learning Reinforcement Learning
Description
The learning algorithm is fed
with a series of inputs as well as
with the corresponding outputs.
The algorithm then applies this
same set of rules in the future.
The system learns by itself by exploring the
data on its own to find some sort of structure
or patterns.
In other words, the AI system uses its
experience of solving one problem to solve
another related problem.
The algorithm learns through a trial-and-
error process in which the actions are
either virtually ‘rewarded’ or ‘punished’.
It then forms a memory of each
experience and uses this learning for
subsequent experiences.
Use cases
▪ Predicting real estate prices,
▪ Bank fraud
▪ Health risks
▪ Loan risks
▪ industrial maintenance
▪ Identify consumers with similar purchasing
behaviors to deliver personalized
marketing,
▪ Group inventory according to sales
▪ determine associations in customer data.
▪ Autonomous driving
▪ Training robots
▪ Teaching cars to park themselves
Common
algorithms
Neural networks, decision trees, linear
regression, and support vector machines
Hidden Markov models, k-means,
,hierarchical clustering, and Gaussian mixture models.
Markov decision process, Dynamic programming

OpenAI
Scale AI
Anyscale
Inflection AI
Weights & Biases
Cohere.ai
Hugging Face
OctoML
AI21 Labs
InstaDeep
Funding in million U.S. Dollars
Leading Machine Learning Operations/Platform startups worldwide in 2023,
by funding raised
Funding of Machine Learning Operations/Platform startups worldwide 2023

Global AI market Projections

Adoption of AI in businesses globally from 2017 to 2022
2017 2018 2019 2020 2021 2022
Share
of
respondents

Leading AI capabilities
Robotic process automation
Computer vision
Natural-language text understanding
Virtual agents or conversational interfaces
Deep learning
Knowledge graphs
Recommender systems
Digital twins
Natural language speech understanding
Physical robotics
Reinforcement learning
Facial recognition
Natural language generation
Transfer learning
Generative adverserial networks (GAN)

Robotic Process Automation (RPA)

Key Players in Robotic Process Automation

Artificial Neural Networks
Built to mimic a human brain’s
working
by
Organizing artificial neurons in layers
to process information.

Is it all hype?
Section 2

Gartner Hype Cycle

Robotic process automation
Natural-language text understanding
Deep learning
Recommender systems
Natural language speech understanding
Reinforcement learning
Natural language generation
Generative adverserial networks (GAN)
Share of respondents
9
Primary artificial intelligence (AI) capability adaption rate in businesses
globally in 2022
Leading AI capabilities in business worldwide 2022

Increase somewhat /
significant investment
Stay mostly the same Decrease somewhat /
significant devestment
Unsure / NA
Share
of
respondents
17
Change in Artificial Intelligence (AI) investments worldwide in the fiscal year
2023
Global AI investment change expected in 2023

ASAPP
Observe.ai
Ada
Cresta
Woeboth Health
Forethought
Kasisto
PolyAI
Balto
Ushur
Funding
19
Leading chatbot/conversational AI startups worldwide in 2023, by funding
raised
Funding of chatbot/conversational AI startups worldwide 2023

Increasing efficiency in
resolving persistent
challenges
Better asset utilization More accurate demand
forecasts
Reduction in structural costs
Share
of
respondents
24
Business benefits of supply chains transformation with artificial intelligence
(AI) worldwide in 2022
Supply chain transformation benefits for business worldwide 2022

What is Samsung doing?
Section 3

What is Samsung doing?
Samsung recently launched its own Gen AI Model - Samsung Gauss
Gauss Language
to understand human language, craft
natural responses
Gauss Image
to allow the creation of and the
modification of images
Gauss code
to assist software development for code
descriptions and create test cases
https://news.samsung.com/global/samsung-ai-forum-2023-day-2-discussing-technological-trends-and-the-future-of-generative-ai
Samsung Gauss is currently used on employee productivity but will be expanded to a variety of Samsung product applications to provide new user experience soon.

Use Case 1

Use Case 2
Automatic Speech Recognition
Convert speech to text
Natural Language Understanding
Make sense of text
Task Execution
Perform an action
Text-To-Speech
Acknowledge the action
Users enjoy the song
Hi Bixby,
Play Kesariya

Use Case 3

AI: Layers, Components & Tech Stack
Section 4

AI Stack: Conceptual Understanding of Layers
05
The applications layer
encompasses the specific use
cases and applications that
leverage AI technologies.
This layer includes chatbots,
recommendation systems,
autonomous vehicles, and
other AI-powered systems.
04
The platforms layer provides
the tools and frameworks
necessary for developing,
deploying, and managing AI
applications.
This layer includes
programming languages,
libraries, and frameworks such
as TensorFlow, PyTorch, and
scikit-learn.
03
The infrastructure layer
comprises the hardware and
software components that
support the AI system's
operation.
This layer includes CPUs, GPUs,
and other specialized
hardware, as well as operating
systems, virtualization, and
containerization tools.
02
The algorithms layer involves
the development of
mathematical models and
algorithms that allow an AI
system to extract patterns and
insights from the data.
This layer includes machine
learning, deep learning, and
other statistical models.
01
This is the foundation of the AI
stack, as AI systems require
large amounts of data to learn
from and make decisions.
This layer includes data
collection, cleaning,
preprocessing, and storage.
Data Algorithms Infrastructure Platforms Applications
https://www.linkedin.com/pulse/artificial-intelligence-stack-rajoo-

Data layer includes collections, storage, and
management of databases that are mandated for
training and testing AI models.
AI Stack: Layers
This machine-learning layer includes algorithms, models,
and data to predict and make informed decisions based on
learning.
https://blocktechbrew.com/artificial-intelligence-stack-guide/
This functions as a subset of machine learning, including
artificial neural networks by permitting a sizable amount of
the database.
This layer in AI uses algorithms and models that help in
processing to understand human inputs and their language.
This layer includes the use of algorithms to analyze and
interpret visual information from images and videos.
This robotics layer ensures the right physical mechanism
of AI technologies by controlling and automating
This layer consists of hardware, software, and cloud services
that require building, training, and deploying AI models and
regular applications.

AI Stack: Technology
https://markovate.com/blog/ai-tech-
Not just mere libraries. Ecosystems:
▪ TensorFlow
▪ PyTorch
▪ Keras
Dependent on user, model efficiency:
▪ Python
▪ R
▪ Julia
Dependent on resources, scalability:
▪ Amazon Web Services (AWS)
▪ Google Cloud Platform (GCP)
▪ MS Azure
Dependent on efficient data processing:
▪ Apache Spark
▪ Apache Hadoop
▪ …..

Emerging Trends in AI
Section 5

Emerging Trends in AI and Machine Learning
Source: https://datasciencedojo.com/blog/ai-and-machine-learning-trends/

Skills & Opportunities in AI
Section 6

Software
Engineering
Business
Requirem
ents
Data
Science
Can you position yourself here?

Some Career Paths
Career Path Description
Big Data Analyst
Find meaningful patterns in data by looking at the past to help make predictions about the
future.
User Experience (UX)
Designer/Developer
Work with products to help customers understand their function and can use them easily.
Understand how people use equipment and how computer scientists can apply that
understanding to produce more advanced software.
Natural Language Processing
Engineer
Explore the connection between human language and computational systems; this includes
working on projects like chatbots and virtual assistants.
Researcher Work with computer science and AI research Discover ways to advance AI technology
Software Engineer
Develop programs in which AI tools function. The role may also be referred to as a
Programmer or Artificial Intelligence Developer.
AI Engineer
Build AI models from scratch and help product managers and stakeholders understand
results.
Data Mining and Analysis Finding anomalies, patterns, etc. within large data sets to predict outcomes.
Machine Learning Engineer Using data to design, build and manage ML software applications.
Data Scientist Collect, analyze and interpret data sets.
Business Intelligence (BI) Developer Analyze complex data sets to identify business and market trends
Big Data Engineer/Architect Develop systems that allow businesses to communicate and collect data
Robotics Engineer Design, build and test robots or robotic systems.
Computer Vision Engineer Develop and work on projects and systems involving visual data.

Getting
Started….
All entry-level roles will expect:
• Graduate degree in computer science, mathematics, or statistics
• Familiarity with Python and SQL
• Knowledge of data analysis, processing, and visualization
• Understanding of cloud technologies
• Business acumen about the industry, market, competition, etc.

https://dailyinfographic.com/habits-of-highly-effective-people

Thank You
Renganathan Sekar
renganathan.sekar@gmail.com
https://www.linkedin.com/in/renganathansekar/

Stay connected
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