While buzzwords like Generative AI (GenAI), Large Language Models (LLMs), and chatbots dominate the conversation, they are just one branch of a much larger tree: Machine Learning (ML).
What Machine Learning really is
At its core, Machine Learning is simply the ability of a system to learn from data - without being explicitly programmed for every single rule or condition. Instead of writing a list of instructions, we provide the machine with examples, data, and feedback so that it can "learn" how to make predictions or decisions on its own.
This makes ML incredibly versatile and powerful. And importantly, it's not just about text or images.
Real-world applications of Machine Learning
Machine Learning has quietly become the backbone of many technologies we rely on every day, often without even noticing it. Here are just a few examples:
• Fraud detection - Banks and payment providers use ML models to detect unusual patterns in financial transactions.
• Predictive analytics - Businesses forecast demand, customer behavior, and market trends using ML-driven prediction engines.
• Recommendation engines - Netflix, YouTube, and Spotify suggest content based on what the user likes, thanks to ML.
• Process automation - Repetitive tasks, from document sorting to IT monitoring, can be automated with intelligent ML models.
• Anomaly detection - In cybersecurity and network monitoring, ML is used to spot unusual traffic patterns that could signal an attack.
These examples show how ML is woven into both consumer experiences and enterprise-grade solutions.
The role of Deep Learning
Within Machine Learning, there's a powerful subset called Deep Learning (DL). Unlike traditional ML methods, Deep Learning uses layered neural networks that mimic (at a very simplified level) how the human brain processes information.
This allows deep learning systems to handle much more complex patterns, often in environments with huge volumes of data.
Some of the most recognizable applications of Deep Learning include:
• Voice recognition (like Siri, Alexa, or Google Assistant)
• Image classification (like facial recognition or medical imaging)
• Autonomous vehicles (self-driving cars interpreting their surroundings)
• Natural Language Processing (NLP) (chatbots, real-time translation, sentiment analysis)
Without Deep Learning, many of the "wow" moments in AI would not be possible.
GenAI: Just the tip of the iceberg
So yes - Generative AI is exciting. The ability of LLMs to generate text, code, and even images feels groundbreaking, and it is. But it is just the surface.
The real power of AI comes from understanding the full landscape of Machine Learning: from traditional algorithms that quietly make predictions behind the scenes, to advanced Deep Learning systems that enable groundbreaking applications.
When we only focus on buzzwords, we miss the broader picture. And it's the broader picture that reveals where AI can truly drive value across industries - from cybersecurity, to healthcare, to financial services, and beyond.
When we hear the word AI, the mind immediately goes to chatbots, image and video generation, LLMs in general - because they are loud trends.
Our software, InSight leverages ML to achieve precise thresholds for Internet traffic. When something goes out of the threshold, the software sends and alarm and a message: The problem you are having is most likely this...
This way, we free the time of the IT teams from constant manual monitoring, enabling proactive analysis instead of putting out fires.
Furthermore, we provide the direction for troubleshooting, preventing or significantly lowering downtime if it even comes to it.
But we do not let AI take action. We strongly believe that humans need to be the one to call the shots, in order to minimize errors.
The combination of ML and human expertise is the best weapon against cyber threats and networks instability.