The Conference for Machine Learning Innovation

Artificial Intelligence and Machine Learning

Session
Join the ML Revolution!
Register until September 16:
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Join the ML Revolution!
Register until September 16:
✓Save up to $290
✓10% team discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
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Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
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Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Infos
Thursday, November 24 2022
11:15 - 12:00
Room:
Stage 1 - SNG

Artificial intelligence and machine learning are two branches of computer science that are closely related. These two technologies are the most popular when it comes to developing intelligent systems. Despite the fact that these are two related technologies and that they are sometimes used interchangeably, both are distinct terms in different contexts. On a broad level, we can distinguish AI and ML as follows: "AI is a larger concept for creating intelligent machines that can simulate human thinking capability and behaviour, whereas machine learning is an application or subset of AI that allows machines to learn from data without being explicitly programmed."
Artificial intelligence systems do not need to be pre-programmed; instead, they use algorithms that can interact with their own intelligence. It employs machine learning algorithms such as Reinforcement learning and deep learning neural networks. AI is being used in a variety of applications, including Siri, Google, AlphaGo, and AI in chess playing. AI is classified into three types based on its capabilities: weak AI, general AI, and strong AI. We are currently working with weak AI and general AI. Strong AI, which is said to be more intelligent than humans, is the AI of the future. Machine learning allows a computer system to predict or make decisions based on historical data without being explicitly programmed. Machine learning makes extensive use of structured and semi-structured data in order for a machine learning model to produce accurate results or make predictions based on that data.

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Behind the Tracks