TITLE: ARTIFICIAL INTELLIGENGE AND MACHINE LEARNING
Name of Author: Prerana Rajendra Shimpi
Sakshi Sanjay Joshi
Pinky Sanjay Todkari
Name of Institute: Government Polytechnic, Thane
❖ ABSTRACT: and predicting stock market trends. Together,
Artificial Intelligence (AI) and Machine AI and ML are transforming industries by
Learning (ML) are two closely related fields making processes more efficient and opening
that are revolutionizing the way we think new possibilities for innovation. These
about technology. At a basic level, AI is all technologies don’t just push the boundaries
about creating machines that can do things of what's possible in tech—they’re also
that typically require human intelligence, like helping to solve complex problems in
making decisions, solving problems, creative, dynamic ways.
perceiving the world and understanding
language. Machine Learning is a key part of I. WHAT IS AI?
AI which focuses on teaching machines, how
to learn from data? Rather than being
programmed for every specific task,
machines with ML capabilities can analyze
data, spot patterns, and improve themselves
over time.
The relationship between AI and ML is
essential to their success. AI represents the
big picture of building smart systems, while
ML provides the tools like algorithms that
make these systems smarter, more adaptable Fig.1. Layers of AIML
and able to handle new situations without
constant updates. This ability to learn and Artificial Intelligence (AI): Think of AI as
adapt is crucial in areas like self-driving cars, the effort to build machines that can do tasks
medical diagnoses, translating languages, we typically associate with human
intelligence. For example, recognizing faces, objects without us having to give it step-by-
making decisions, solving problems, and step instructions.
understanding language. It’s the dream of ML is what makes modern AI systems more
creating computers that "think" like we do, flexible and powerful. It’s not like traditional
even though they don’t have brains! programming, where you have to code each
➢ Types of AI rule. With ML, we focus on the data—the
1.Narrow AI: This is AI that specializes in more examples a system gets, the smarter it
one task. It's everywhere! From your voice becomes.
assistant like Siri or Alexa to ➢ Types of Machine Learning:
recommendation engines on Netflix or 1.Supervised Learning: In supervised
YouTube, Narrow AI focuses on specific learning, the machine is trained using data
problems, like helping you find a movie to that’s already labelled—so the system knows
watch. the "right answer." For example, if you want
2.General AI: This is still a theoretical to teach a computer to recognize cats and
concept. It refers to machines that could do dogs, you’d show it lots of pictures with
anything a human can do—learning, labels telling it which is which. Over time, the
reasoning, understanding, and even problem- machine gets better at identifying new
solving across all tasks. We don’t have this pictures based on what it learned from the
yet, but it's the goal. labelled ones.
2.Unsupervised Learning: This is when we
II. MACHINE LEARNING don’t give the machine labelled data. Instead,
Machine Learning (ML): Now, Machine it must figure out patterns or relationships on
Learning is like a tool within the AI toolkit. its own. For example, in retail, unsupervised
It's a way for machines to "learn" from data learning might be used to group customers
without being explicitly told what to do. based on their shopping behaviour—without
Instead of programming every single ever being told what the groups should look
decision, we give the machine data, and it like.
learns patterns and improves over time. It’s 3.Reinforcement Learning: Think of
like teaching a child to recognize objects by reinforcement learning like teaching a pet
showing them lots of examples. Over time, new trick. The machine learns by interacting
the machine gets better at recognizing these with its environment and getting feedback—
like rewards or penalties. For example, in a 3.Self-driving Cars: These cars rely on AI
video game, a machine might learn how to and ML to process data from cameras,
play by receiving points (rewards) or losing a sensors, and other sources to navigate roads,
game (penalties) based on its actions recognize obstacles, and make driving
4.Deep Learning decisions.
Deep learning is a subset of ML, and it uses
complex neural networks (inspired by the IV. APPLICATIONS OF AI AND ML:
human brain) to learn from large amounts of Example
data. These systems are good at handling Field Application Technologie
complicated tasks. s
Healthcare AI-driven Medical
III. HOW AI AND ML WORK? diagnostics, imaging,
While AI is about creating machines that can personalized NLP,
simulate intelligent behaviour, ML is the treatment, drug predictive
engine that drives it forward. ML allows AI discovery. analytics
systems to learn from experience, get better Fraud detection, Anomaly
with time, and adapt to new challenges. In Finance algorithmic detection,
short, ML powers AI to make decisions or trading, risk deep
predictions based on past data, without management. learning,
needing to be explicitly programmed for reinforceme
every situation. nt learning
Examples of AI powered by ML: Retail Product Recommend
1.Recommendation Systems: When Netflix recommendatio er systems,
or YouTube suggests what to watch next, it’s ns, customer chatbots,
using machine learning algorithms to analyze service predictive
your preferences and recommend something automation, modelling
you'll likely enjoy. demand
2.Speech Recognition: Siri or Google forecasting.
Assistant can understand and respond to your Natural Speech Transformer
voice because ML models have learned from Language recognition, text s (e.g., GPT,
thousands of hours of voice data. analysis, BERT),
Processing language speech-to- and reskilling workers for new roles in the
(NLP) translation. text digital economy.
Manufactu Predictive Predictive 4.Accountability: When an AI system makes
ring maintenance, analytics, a mistake—say, in a healthcare diagnosis or a
quality control, computer financial transaction—who’s responsible?
supply chain vision, This is an ongoing debate, especially as AI
optimizes robotics systems become more autonomous.
V. ETHICAL AND SOCIAL VI. CHALLENGES AND
IMPLICATIONS: LIMITATIONS:
1.Bias in AI: If the data we train AI systems 1.Data Dependency: For AI and ML to be
on is biased, the outcomes can be unfair. For effective, they need high-quality data.
example, AI used in hiring or law Without enough good data, these systems
enforcement might unfairly favour one group might not work as intended. This is especially
over another if the training data reflects challenging in fields like healthcare, where
biased human decisions. That's why it's so data privacy and access can be barriers.
important to ensure fairness and diversity in 2.Computational Power: Training advanced
the data we use. AI models, especially deep learning models,
2.Privacy Concerns: AI often relies on large requires immense computing power. This can
amounts of personal data. Whether it’s be costly and environmentally taxing, raising
healthcare, banking, or just browsing the concerns about sustainability.
web, our data is being used by AI systems to 3.Explainability: Many AI systems,
personalize services. This raises questions especially deep learning, are “black boxes.”
about how much of our personal information We know they work, but it’s hard to explain
should be used, and who controls it. how or why they make certain decisions. This
3.Job Displacement: As machines take over lack of transparency can be a problem,
repetitive tasks, there’s concern about job particularly in sensitive areas like healthcare
loss, particularly in industries like or criminal justice.
manufacturing, retail, and transportation. 4, General AI vs. Narrow AI: Right now,
This makes it crucial to focus on retraining most AI is narrow—it can do specific tasks
but can't adapt to new, unforeseen situations.
Creating General AI (machines that can do could revolutionize every industry, but we
anything a human can do) is still a long way need to carefully consider the ethical
off. implications.
AI in Emerging Fields: AI will have an
VII. THE FUTURE OF AI AND ML: enormous impact in areas like space
exploration, climate change modelling, and
robotics. As these technologies advance, the
possibilities for AI are virtually endless.
❖ CONCLUSION :
To wrap up, AI and ML are already changing
the world, and their influence will only grow.
As we embrace these technologies, we must
be mindful of the ethical challenges, ensure
transparency, and prioritize fairness in AI
The future of AI and ML is bright, and
systems. With the right guidance, AI and ML
here’s what we can expect:
have the potential to revolutionize industries
Advancements in AI: We’ll see AI continue
and improve our quality of life. The future is
to improve, especially with innovations like
exciting, but it’s up to us to shape it
deep learning, quantum computing, and
responsibly.
neural networks. AI will get better at
This version makes the content more
handling complex, real-world problems.
relatable and easier to digest while retaining
AI and Human Collaboration: Instead of
key technical concepts and insights. It’s
replacing humans, AI will augment our
structured to engage the audience while
abilities. In fields like healthcare, for
highlighting the importance, applications,
instance, AI will help doctors make faster,
and challenges of AI and Ml.
more accurate diagnoses. Think of it as a tool
to enhance human potential, not replace it.
Artificial General Intelligence (AGI):
While we're not there yet, AGI—the idea of
machines that can think and reason like
humans—is an exciting area of research. It
❖ REFERENCES:
➢ Machine Learning (ML)
:https://en.wikipedia.org/wiki/Machi
ne_learning
➢ YouTube link :
https://youtu.be/C6YtPJxNULA?si=
m9NR0mH-31aHiqhC
➢ Chatgpt.
