What’s artificial intelligence?

Ai and machine learning is anything done by a program or a machine that if a human did the same activity, we would say the human did not need to apply intelligence to accomplish the task.You don’t stop to reason for any of those things.

“Minsky and McCarthy, both considered founders of AI, say that artificial intelligence is anything done by a program or a machine that if a human did the same activity.”

Artificial Intelligence and Robotics , Introduction to Artificial Intelligence

Intelligence is the ability to do the things that don’t require reasoning. Things like understanding and generating language, sequencing your leg muscles as you walk across the floor, or enjoying a symphony.

how to interpret things in your visual field, language, and all other sensory input. And you can do the right thing without reasoning. You can easily prepare all of your breakfast without any reasoning. 🙂

Doing things that “require thought” or reasoning, like playing chess or solving integrals are things that computers can already do.

This misunderstanding about what intelligence really is has cost us 60 years and a million man-years of banging our head against the wall.

Deep learning is the currently most popular expression of an alternative path to a “better kind of AI”. Artificial Intuition is a special branch of Deep Learning tailored at understanding text.

The easiest way to know whether you are dealing with classical (futile) or modern AI is whether the system requires you to supply any models of the world (MOTW).

Top 5 AI technologies in machine learning

  1. Microsoft Cognitive Toolkit
  2. Caffe
  3. Torch
  4. Accord.NET
  5. Scikit-learn

1. Microsoft Cognitive Toolkit

Microsoft says that the open source framework is capable of “training deep learning algorithms to function like the human brain.”

Some of the vital features of the Microsoft Cognitive Toolkit include highly optimized components capable of handling data from Python, C++, or BrainScript, ability to provide efficient resource usage, ease of integration with Microsoft Azure, and interoperation with NumPy.

2. Caffe

Initially released in 2017, Caffe (Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on expressiveness, speed, and modularity. The open source framework is written in C++ and comes with a Python interface.

3. Torch

Initially released in 2002, Torch is a machine learning library that offers a wide array of algorithms for deep learning. The open source framework provides you with optimized flexibility and speed when handling machine learning projects—without causing unnecessary complexities in the process.

4. Accord.NET

The open source framework is suitable for production-grade scientific computing. With its extensive range of libraries, you can build various applications in artificial neural networks, statistical data processing, image processing, and many others.

5. Scikit-learn

Initially released in 2007, scikit-learn is an open source library developed for machine learning. This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction.

Better to start from a Model of the Mind (MOTM) or a Model of Learning. A well done MOTM can then learn anything it needs to know to solve problems in any domain.

.In other words, all intelligences, natural or artificial, have to spend most of their time answering the question “what matters”.

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