“AI” has become a significant trend in the consumer world, while “ML” remains the driving force behind advancements in the business sector.
While Artificial Intelligent and Machine Learning are closely related, they are not interchangeable. Therefore, Artificial Intelligent is the overarching concept. As such, Machine Learning is a specific approach within that broader field focused on machines learning from data.
ML is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to learn and make predictions or decisions based on data.
ML specifically involves the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Examples include recommendation systems, spam filters, predictive analytics, and autonomous vehicles.
ML can be divided into several types, including:
- Reinforcement Learning: The model learns by interacting with an environment to maximize some notion of cumulative reward.
- Supervised Learning: The model is trained on labeled data.
- Unsupervised Learning: The model works on unlabeled data and tries to find hidden patterns.
To demonstrate the current capabilities of Artificial Intelligent, 90% of this page was written using GPT-4.
2. Devices within a network or IoT framework will continuously enhance their performance by collectively sharing data and advancing based on the intelligence gathered.
With the rising popularity of AI, one might wonder if ML is losing its relevance. The answer is “absolutely not!” Hence, ML continues to evolve, enhancing its capabilities through advancements such as Quantum ML, integration with IoT, optimization techniques, and leveraging the ever-expanding potential within AI.
1. Positioning Artificial Intelligent (AI) within your solution is one aspect; understanding the potential of Machine Learning (ML) is another.
