AI is evolving... now what

These days, “AI Development” often means a developer is wielding an LLM to generate insights and hook into existing APIs, data, and resources. But it raises a curious question: if you’re training a deep‑learning model for something like malware analysis, does that make you a real AI developer — or is the definition shifting beneath our feet?

I’ve been experimenting with AI agents and LLM integrations. You call the APIs, update the databases, and watch new pipelines .. the exciting part?  Agent figures out what needs to be done, almost as if the intent itself has become an input.

This brings new integrations, richer insights… and fresh security puzzles. Agents can be tricked into doing things they weren’t designed for — so what do positive and negative test cases even look like now?

I don’t have all the answers. Honestly, no one does. We’re in the middle of an evolution — and the definitions, best practices, and even the questions are still taking shape.

Transfer Learning Explained: How Transfer Learning Transforms Neural Network Training

 What is Transfer Learning?

Transfer Learning is a machine learning technique that leverages knowledge gained from solving one problem to improve learning in a related task. This approach is analogous to human learning, where we apply previously acquired skills to new but similar situations.

Bike Analogy

Consider learning to ride a bicycle. The skills you develop—balancing, coordinating body movements, and understanding traffic rules—can be transferred when learning to ride a motorcycle. Some skills, like pedaling, are specific to bicycles and are not needed for motorcycles. Similarly, in neural networks, we aim to reuse generic layers while adapting or replacing task-specific layers.

Why is Transfer Learning Important?

Transfer Learning addresses several challenges in deep learning:

• Data Scarcity: It enables effective learning with limited datasets.
• Computational Efficiency: It reduces the time and resources required for training.
• Performance Boost: It often leads to improved model performance on new tasks.
• Faster Development: It accelerates the development of models for new applications.

How Transfer Learning Works

The process typically involves the following steps:

1. Select a pre-trained model relevant to your task
2. Freeze some or all layers of the pre-trained model
3. Add new layers specific to your task
4. Train the model on your dataset, updating only the new layers
5. Optionally, fine-tune the entire model with a low learning rate

The Pre-trained model has multiple Convolution and Pooling layers , lets look at them

Convolutional Layers

Convolutional layers are the core building blocks of CNNs. They apply learned filters to input data to extract features . Layers have activation functions that use element-wise multiplication and summation

Pooling Layers

Pooling layers follow convolutional layers and downsample the feature maps. They reduce dimensions and minimize the computational load .It may be of Max or Average pooling .Pooling layers provide capability to translational variance and reduces over fitting

Transfer Learning with Pre-trained Models

Transfer learning allows leveraging knowledge from pre-trained models to new tasks. Initial layers from the Pre-trained model are not modified (Frozen) and the output and classification layers are dropped to be retrained .Using Image Classification as the example initial layers pickup patterns, edges and shapes that is useful for classification of any image and the layers close to the output will be responsible for classification and needs to be retrained .

By leveraging pre-trained convolutional and pooling layers through transfer learning, you can significantly improve the efficiency and effectiveness of training models for new tasks, especially when working with limited datasets.

Example 

I have used Transfer Learning for Classification of Flowers . You can find the example here . The initial model is trained for four types of flowers and then  model is used to train for new type of flower . 

Image classification is highly used in Medical field for analyzing X-rays and MRIs

It is also multiple other uses in the field of NLP , Speech Recognition ,Computer Vision and Bioinformatics.

Learning Through Failure: The Parallels of Backpropagation

What's Backpropagation ?

Definition reads 

"Backpropagation is a method used for training neural network models by estimating the gradient of the loss function with respect to the network's weights."

I have covered the description in the other blog , here it is for reference . Model makes predictions based on input data, compares the outcomes with labeled data, and then adjusts its internal parameters to minimize the difference between predicted and actual results.

As I delved deeper into the intricacies of backpropagation, exploring how features are detected and weights are applied,I found myself drawing fascinating parallels to two seemingly unrelated concepts: video game play and philosophy.

Video Game Mastery

The process of mastering a video game level bears a striking resemblance to how neural networks learn through backpropagation. In gaming, players often face multiple failures before successfully clearing a level. Each attempt provides valuable insights, shaping behavior for subsequent tries.This iterative learning process culminates in a successful run that incorporates all the accumulated knowledge.

A compelling illustration of this concept can be seen in deep learning algorithms trained to play Super Mario. 

Watching these AI players evolve is remarkable – their progress from initial failures to eventual mastery mirrors the human learning process. The visual representation of the algorithm's numerous attempts and gradual improvement offers an intuitive understanding of how backpropagation works in practice.

Philosophical

Interestingly, this learning process also echoes a profound philosophical sentiment captured by Samuel Beckett:

 "Ever tried. Ever failed. No matter. Try again. Fail again. Fail better."

While Beckett likely didn't have backpropagation in mind, his words encapsulate the essence of this machine learning technique. The quote speaks to the iterative nature of improvement – each failure is a stepping stone towards eventual success. In the context of neural networks, each "failure" (or error) leads to adjustments that bring the model closer to its goal.

Both these parallels highlight a fundamental truth about learning, whether in artificial neural networks, video games, or life itself. Progress is achieved through repeated attempts, each building upon the lessons of the last. In backpropagation, as in life, we continuously adjust our approach based on past experiences, gradually moving towards our objectives with increasing precision.

From Tennis Courts to Neural Networks: An Introduction to LSTM

Deep Learning concepts seem hard to grasp at first, but I am going to attempt to make that easy. This article will focus on LSTM.It stands for Long Short-Term Memory. Wait? It's both Long and Short-Term? What is it? Even the name is confusing until you understand it.

Definition and Explanation - Academic 

"A recurrent neural network (RNN) designed to address the vanishing gradient problem in traditional RNNs."

When dealing with sequential data, the gradient (change) of data is lost as the data processing technique has no information on what was processed before. There is a special memory unit called Cell State that handles the memory useful for processing.

Processing involves Gates that continuously address the gradient issue.

Gates: Three gates (input, forget, and output) that regulate the flow of information:

• Input gate: Controls what new information is added to the cell state
• Forget gate: Decides what information to discard from the cell state
• Output gate: Determines action based on information from the cell state to output

These gates use sigmoid and tanh activation functions to control information flow and update the cell state at each time step. 

In essence, the problem is addressed on an ongoing basis. Take in what is working and reject what isn't working. This is very familiar, and we apply it in real life, from running a successful business to playing a Tennis match. Concepts are way easier to grasp when they can be illustrated using an analogy, this will help illustrate the key concepts of LSTM in a more relatable way.

Explanation - Using Tennis Game Plan!

Imagine you're a tennis player in a match, and your LSTM network is your strategic thinking process throughout the game.

The Setup

You are the LSTM Network, each point is a time step in the sequence. The game plan is the cell state. The decision-making process represents the gates in LSTM, and finally, the match is the entire input sequence.

Workflow

1. Initialization

   You begin with a general game plan, like an LSTM's initialized cell state.

2. Playing each point 

• Forget Gate: After each point, you decide what part of your strategy is no longer relevant. Maybe you forget about targeting your opponent's backhand because it's improving.
• LSTM: This gate decides what information to discard from the cell state.
• Input Gate: You observe new information during the point - your opponent's serve placement, their movement patterns, etc.
• LSTM: This gate decides what new information to add to the cell state.
•  Cell State Update: You update your overall game plan, combining your existing strategy with new observations.
• LSTM: This is updating the cell state with new information while retaining relevant past information.
•  Output Gate: You decide which aspects of your updated game plan are most relevant for the next point.
• LSTM: This gate determines what information to output from the cell state.

3. Next Point (Making predictions)

• Based on your updated game plan and current state of play, you predict how to approach the next point.
•  LSTM: This is equivalent to making a prediction based on the processed sequence.  

4. Match Continues 

• You repeat this process for each point, constantly refining your strategy.
• LSTM: This represents processing each time step in the sequence.

Just as you might remember a successful strategy from early in the match even after many points (long-term memory), LSTM can retain important information from much earlier in the sequence.

Your ability to forget irrelevant tactics (like a failed approach shot) while remembering important ones (like exploiting your opponent's weak serve) mirrors how LSTM selectively retains or forgets information.

The Final Game (Output)

By the end of the match, your game plan has evolved based on all the points played, with some strategies being more influential than others.Similarly, LSTM's final output is based on processing the entire sequence, with the ability to give more weight to certain parts of the sequence than others.

This tennis game plan captures the essence of how LSTM processes sequential data, makes decisions at each step, and uses its "memory" to make predictions based on both recent and long-past information. It demonstrates how LSTM can adapt to changing patterns in the data, just as a tennis player adapts their strategy throughout a match.

Now if you read the Academic portion again , it will make more sense 😀

How do you Apply this ?

There are multiple frameworks Keras being the most popular. I have created a demo of using it to predict stock market prices. Here is an example.

As a next step, you can dive into the statistical approach and explore the activation functions used for the gates.

Leveraging Deep Learning for Advanced Security

Should you consider Deep Learning for Security ?

ML models can analyze large volumes of data to detect potential threats and anomalies much faster and more accurately than traditional rule-based systems. This allows security teams to  quickly identify suspicious activities and behavior patterns and focus on genuine threats .Unless Machine learning techniques that have the accuracy plateau over time Deep Learning is continuously learn improving accuracy of the results 

The Power of Automatic Feature Detection

Deep Learning has revolutionized cybersecurity by offering significant advantages over traditional Machine Learning techniques. One of the key differentiators is its ability to perform automatic feature detection, which allows for more efficient and accurate processing of complex security data.

In traditional Machine Learning approaches to cybersecurity, feature engineering is a crucial step that often requires extensive domain expertise and manual intervention. Security analysts must carefully select and extract relevant features from raw data to feed into their models. This process can be time-consuming and may inadvertently introduce human bias.

Deep Learning, on the other hand, excels at automatic feature extraction in security contexts. Through its multi-layered neural network architecture, Deep Learning algorithms can autonomously identify and learn important features from raw security data.

Model selects the relevant features that need to be applied for the detection

 

Example: Malware Detection

Consider a malware detection task. In traditional Machine Learning, a security analyst might manually define features like file size, entropy, or specific byte sequences. With Deep Learning, the neural
network automatically learns to recognize these distinguishing characteristics of malicious software without explicit programming.

Translation Variance: Adapting to Evolving Threats

Another significant advantage of Deep Learning in cybersecurity is its ability to handle translation variance. This means that the model can recognize patterns and threats regardless of their position or variation within the input data.

Example: Network Intrusion Detection

In a network intrusion detection system, a Deep Learning model can identify malicious network traffic patterns regardless of their position in the packet stream or slight variations in the attack signature. This flexibility is crucial for detecting evolving cyber threats and zero-day attacks.

Backpropagation: The Learning Engine for Security Models

Deep Learning's power in cybersecurity comes from its ability to learn and improve through a process called backpropagation. During training, the model makes predictions based on input security data, compares the outcomes with labeled data, and then adjusts its internal parameters to minimize the difference between predicted and actual results.

This iterative process allows the model to fine-tune its threat detection and decision-making capabilities, leading to increasingly accurate classifications of security events over time.

Unless Machine learning techniques that have the accuracy plateau over time Deep Learning is continuously learning as data size grows 

Classification Accuracy: Measuring Security Performance

The effectiveness of a Deep Learning model in cybersecurity is often measured by its classification accuracy – the percentage of correct predictions made on a test dataset of security events. By comparing the model's outputs with labeled data, security researchers and practitioners can assess how well the model has learned to recognize and categorize potential threats.

As Deep Learning models process more security data and undergo multiple training iterations, they typically achieve higher classification accuracies compared to traditional Machine Learning methods, especially for complex tasks involving large datasets of diverse cyber threats.

Use Cases: Deep Learning in Cybersecurity Action

Deep Learning's unique capabilities have led to breakthrough applications across various areas of cybersecurity:

• Malware Detection: Analyzing binary files and network behavior to identify new and sophisticated malware variants with higher accuracy than signature-based methods
• Phishing Detection: Automatically identifying phishing websites and emails by learning complex patterns in URLs, email content, and website structures.
• Anomaly Detection: Detecting unusual patterns in network traffic or user behavior that may indicate a security breach or insider threat.
• Threat Intelligence: Analyzing vast amounts of security data to identify emerging threats and attack patterns across multiple sources.
• Automated Incident Response: Powering intelligent security orchestration systems that can automatically prioritize and respond to security incidents based on learned patterns.

Deep Learning's ability to automatically detect features, handle translation variance, and learn through backpropagation has propelled it to the forefront of cybersecurity technology. As cyber threats grow more complex and diverse, Deep Learning continues to outperform traditional Machine Learning in a wide range of security applications, pushing the boundaries of what's possible in threat detection and prevention.

Drawbacks

Road to Deep Learning for any use case is not simple , lets look at the complications and the trade offs

• High computational cost: Training deep learning models requires substantial computational resources especially for Training , including powerful GPUs and extensive memory, which can be expensive and time-consuming
• Data dependency: Deep learning algorithms require large amounts of high-quality labeled data for effective training. Gathering and labeling vast datasets can be time-consuming, expensive, and sometimes impractical
• Limited interpretability  and transparency: Many deep learning models function as "black boxes," making it difficult to understand their internal decision-making processes.