RoadMap to the world of AI
Have you ever felt overwhelmed by what latest innovations happening in the world of AI?
Do you need to learn a new language Python? or still stick to our tradition way of preparing for Java interviews , leetcode problems and system design when the world has evolved so much around AI.
Dont worry , we all are in the same phase of figuring out as to when all these started and from where do we start? This post will be useful for those who are interested for an transition to this field and yet stay relevant .
This is to see where we as Java developers can learn and understand what different horizons are present in AI world and how we can advance with these concepts.
I understand its hard to catch up with all the new things happening. But lets take a step back. Lets try to understand where this started and what all the terminologies means?
History- The birth of AI:
Artificial intelligence is a speciality within computer science that is concerned with creating systems that can replicate human intelligence and problem-solving abilities.
Terminologies:
Artificial Intelligence:
Artificial Intelligence (AI) is a transformative technology that enables machines to perform human-like problem-solving tasks. It simulates human intelligence in machines such that , it is programmed to think and learn like humans.
It encompasses the development of computer systems capable of performing tasks that typically require human intelligence.
Machine Learning:
Machine Learning is a specific subset of AI that focuses on providing systems the ability to learn and improve from experience without being explicitly programmed.
ML is a critical component of many AI systems. ML algorithms are used to train AI models by providing them with datasets containing labeled examples or historical data. The model then learns the underlying patterns in the training data, enabling it to make accurate predictions or decisions on new, unseen data. By continuously feeding data to ML models, they can adapt and improve their performance over time.
Usecases: companies like Amazon use machine learning to recommend products to a specific customer based on what they’ve looked at and bought before.
Neural Networks:
Artificial neural networks form the core of artificial intelligence technologies. They mirror the processing that happens in the human brain. A brain contains millions of neurons that process and analyze information.
An artificial neural network uses artificial neurons that process information together. Each artificial neuron, or node, uses mathematical calculations to process information and solve complex problems.
Usecases: It can be used in various applications such as computer vision(Visual recognition in self-driving cars so they can recognize road signs and other road users) , Speech recognition , Natural language processing
Deep Learning:
Deep learning is a subset of machine learning within artificial intelligence (AI) that uses multi-layered artificial neural networks to learn complex patterns directly from large amounts of data, mimicking how the human brain processes information with unprecedented accuracy.
It includes convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data.
Usecases: used in Image and speech recognition, natural language processing, and autonomous vehicles.
Generative AI:
Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.
It can learn human language, programming languages, art, chemistry, biology, or any complex subject matter. It reuses what it knows to solve new problems.
Generative AI is a subset that generates new content meaningfully and intelligently.
Usecases: Organization can use generative AI for various purposes, like chatbots, media creation, product development, and design.
Large language models, also known as LLMs, are very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a set of neural networks that consist of an encoder and a decoder with self-attention capabilities. The encoder and decoder extract meanings from a sequence of text and understand the relationships between words and phrases in it.
Usecases: content generation (e.g., blogs, marketing copy, code) and analysis (e.g., sentiment analysis, financial document review)
NLP ( Natural Language Processing):
Natural language processing (NLP) is the technology that allows computers to interpret, manipulate, and comprehend human language.
Natural language processing is key in analyzing the large volumes of voice and text data from various communication channels like emails, text messages, social media newsfeeds, video, audio, and more for actionable business insights.
Organizations can classify, sort, filter, and understand the intent or sentiment hidden in language data. Natural language processing is a key feature of AI-powered automation and supports real-time machine-human communication.
Neural networks help computers gather insights and meaning from text data and documents.
Usecases:
Automated virtual agents and chatbots
Automatic organization and classification of written data
Analyze customer feedback or call center recordings
Prompt Engineering:
Prompt engineering is the process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs. Even though generative AI attempts to mimic humans, it requires detailed instructions to create high-quality and relevant output.
In prompt engineering, you choose the most appropriate formats, phrases, words, and symbols that guide the AI to interact with your users more meaningfully. Prompt engineers use creativity plus trial and error to create a collection of input texts, so an application’s generative AI works as expected.
Usecases : In the medical field, a physician could use a prompt-engineered language model to generate differential diagnoses for a complex case.
The medical professional only needs to enter the symptoms and patient details. The application uses engineered prompts to guide the AI first to list possible diseases associated with the entered symptoms.
Then it narrows down the list based on additional patient information.
Data science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
It extracts insights and knowledge from data. It encompasses a wide range of techniques and methodologies, including statistical analysis, data mining, predictive modeling, and data visualization.
Data scientists are often described as part statistician, part computer scientist, and part business strategist. They work to collect, process, and analyze vast amounts of structured and unstructured data to uncover patterns, trends, and insights that can inform decision-making and strategy.
Where does all these terminologies fit?
Lets take a simple usecase of Card payment of a customer.
User : My payment failed when I tried to upgrade my subscription.”
Lets see how AI can solve this issue:
Step 1: AI (Artificial Intelligence)
The entire customer support system is considered AI since it mimics human-like reasoning, decision-making, and communication.
AI is the umbrella concept under which everything else fits.
Step 2: ML (Machine Learning)
The system uses ML models trained on historical support tickets to classify the intent: Billing issue , Technical issue or General inquiry
In this case, ML tags it as Billing → Payment Issue.
Step 3: Neural Networks
The ML classifier uses a Neural Network (multi-layer perceptron or transformer-based intent classifier).
This helps detect that “payment failed” relates to billing errors, not login or password issues.
Step 4: Deep Learning
For understanding the sentence context and variations (e.g., “card declined”, “transaction error”), the system uses Deep Learning, usually Transformer models.
Deep learning helps handle the complex unstructured text of human queries.
Step 5: NLP (Natural Language Processing)
NLP extracts key entities: “payment” (object) , “failed” (problem) or “subscription” (context)
This gives structure to the raw text, so the system knows the issue is with subscription payment failure.
Step 6: LLM (Large Language Model)
The LLM (e.g., GPT) generates a fluent, natural response like:
“I see that your payment didn’t go through. Let’s check your saved card details and try updating them.”
Step 7: Generative AI
Here, the LLM is acting as Generative AI because it’s not just retrieving templates — it’s creating a fresh, context-aware response tailored to this customer’s problem.
Step 8: Prompt Engineering
Developers design prompts to control the AI’s behavior:
“If it’s a billing issue, suggest updating the card first.”
This ensures responses are consistent and helpful instead of random.
Step 9: Chatbot
The Chatbot is the user-facing interface.
The LLM’s output is shown here as a conversational message.
Step 10: AI Agent
Beyond answering, the AI Agent can take action:
It can call the billing API to attempt a new charge. Or send a link for updating payment details.
Example: “I’ve sent you a secure link where you can update your payment card.”
Step 11: Agentic AI
If the problem isn’t solved, Agentic AI kicks in: Runs a diagnostic to confirm card decline reason.
Creates a support ticket with the finance team.
Notifies the customer about next steps.
Logs the interaction in CRM automatically.
This is autonomous multi-step reasoning and planning, not just reacting.
Courses:
Everyone is busy these days selling tons of courses. Do you really need to buy these courses? Well , it depends on individual. But I personally will not recommed anyone to spend so much money in learning this when there are lot of information available for free on internet. All you need to do is to figure out which are the right ones to get started.
Some of the courses I found useful:
Introduction to AI (Elements of AI)
AI for Everyone by Andrew Ng (DeepLearning.AI)
Introduction to AI (IBM)
Google AI for Anyone (Google)
CS50’s Introduction to Artificial Intelligence with Python(Hardvard)
Generative AI with LLMs (AWS & DeepLearning.AI)
Introduction to Generative AI — Art of the Possible (AWS)
Prompt Engineering for ChatGPT (Vanderbilt University)
AI Fundamentals for Non-Data Scientists (University of Pennsylvania)
Machine Learning crash course (Google)
some of the courses do offer some certifications.
Leaders to follow:
I personally felt their contents are useful that i could relate to live coding experiences.
Aishwarya srinivasan : https://www.linkedin.com/in/aishwarya-srinivasan
Aishwarya Naresh : https://www.linkedin.com/in/areganti/
Sreedath panat : https://www.linkedin.com/in/sreedath-panat/
krish Naik : https://www.youtube.com/@krishnaik06
Please comments if there are any other good leaders to follow. I will be happy to follow and learn from them.
RoadMap
Conference & Networking:
Try to follow the AI leaders in Linkedln , watch out for few updates.
Check in your network if anyone is working in these fields. Try to ask how their work is as an AI engineer.
Try to attend few bootcamps or any conferences happening in your cities to meet the leaders in these fields.
some of conference details listed here.
Generative AI developer
Data scientist
AI/ML engineer
MLOps Engineer
Robotics Engineer etc
The median salary ranges for these roles starts from $100,000 to $150,000. Salaries are significantly higher for AI engineers, averaging $171,715 with the top 25% earning above $200,000.
References :
1. AI Jobs
2. Indeed
3. Glassdoor
4. Talent
The job opportunities and salary is good in this field. AI is the future.
can Java outperform python for AI development ? The answer is clearly No. Java is good for API, micro services, web applications . Python has good tools for numerical analysis like pandas , numPy and provides great framework for machine learning (tensorflow, PyTorch and scikit learn)
will Java still remain? Yes , Java is still be used for enterprise applications , to build microservices architecture.
some of the Java AI frameworks
- Spring AI — Easy LLM integration for Spring applications
- LangChain4j — Java version of Python’s LangChain
- Jlama — Pure Java LLM implementation
- Deeplearning4j (DL4J) — Deep learning library
- Weka — Machine learning algorithms
- Smile — Statistical ML library
- Apache Mahout — Scalable ML for big data
Java excels at scaling AI to production and integrating AI into existing enterprise systems but Python is still strongly used for research and development activities in the field of AI.
We will cover in next articles how these cover some of the concepts in Java.
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