Machine Learning and Artificial Intelligence Explained

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The new buzzwords today are artificial intelligence and machine learning. The terms machine learning and artificial intelligence are many times wrongly used interchangeably.

AI is best defined as the broad science that is used to mimic human activities. Machine learning is a subset of AI that trains the machine to learn through data and pattern recognition. In simple terms, AI is the science of computers emulating humans; machine learning is how machines learn from data.

Artificial Intelligence Explained

Artificial intelligence is the simulation of human intelligence; artificial intelligence is a science about learning to emulate or mimic human beings.

Artificial intelligence’s core is about having a machine’s intelligence be as close as possible to the natural intelligence displayed by all animals, including humans.

AI or Artificial Intelligence learning takes place by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition, and machine version.

Artificial intelligence requires a foundation of specialized hardware and software to write and train machine learning algorithms. In other words, AI and machine learning work together.

Machine Learning Explained

Machine learning is part of AI or Artificial Intelligence which is data analysis. It is the automatic analytical model of AI or Artificial Intelligence. Machine learning is based upon computing technologies; as technology improves and continues to improve, so will machine learning.

Machine learning is based upon pattern recognition and data. The machine will use those patterns and data to learn from. With machine learning, computers can learn to perform specific tasks without being programmed; they learn from the data and see the patterns they are given.

The key with machine learning is that the program is given some data, and once they have the algorithms for that data, they can use what they have learned to solve a new set of problems. This is the key to machine learning. It is not just about humans punching in an algorithm and then the machine mimicking it. With machine learning, the device can analyze previous data and make new assumptions.

In machine learning, the computer is given a whole bunch of data; then the computer will take that data and analyze that data and the patterns; from that data and patterns, the computer will give you some new assumptions and information. The machine can now predict what you will need by the data and patterns they have seen.

As this technology gets better and machine learning is more accurate, the data or what the machine has learned will become more powerful. The key to machine learning is the machine will adapt and learn from its data and computations independently.

It is predicted that in the future, machine learning’s data will be highly accurate. Soon the machines will be able to read data and patterns with more accuracy than ever before.

A great example of machine learning is Google’s self-driving car. The technology to run that car is both Artificial Intelligence and machine learning.

The google car is outfitted so that the car itself is processing the data as the data comes in; with that data, the google car can then predict with accuracy what the other vehicles on the road are going to do. The google car is like a vast machine collecting this data, analyzing patterns, and then using what they have learned for the self-driving car to drive itself.

For the Google car to work, it needs granular data – large volumes of data the vehicle can analyze – and very diverse data to see the patterns and make accurate predictions.

Machined Learning and Artifical Intelligence Explained

For artificial intelligence to work, it needs to have machine learning. In general terms, artificial intelligence systems work by adjusting large amounts of label training data, analyzing the data for correlations and patterns, and using these patterns to make predictions.

In AI programming, the focus is on three cognitive skills: learning, reasoning, and self-correction.

Today artificial intelligence technology can calculate and make logical assumptions from their data and patterns; AI can have perceptual intelligence to see, listen, feel, and know the situation.

But artificial intelligence does have some limits. One is cognitive intelligence which is about understanding and independent thinking and decision-making as humans do.

As artificial intelligence becomes better, including the machine learning aspects of artificial intelligence, machines will eventually mimic human behavior even more than they do today.

Where To Find Artifical Intelligence Explained

Today the places where you can find the most AI scholars globally are in the United States. The United States has the most significant amount of AI scholars in the world. In second place is in China.

But if you look at the interest among countries in artificial intelligence or the research interest in artificial intelligence, China outweighs the United States. That is because China is very interested in all aspects of artificial intelligence and is actively looking at research in artificial intelligence. It also means that many of the essential research labs may one day be in China.

We feel that AI and machine learning will continue to become an essential part of our world. We will begin to see more things as self-driving cars and medical equipment where the machine may read the x-ray and tell you precisely what is wrong.

AI will be used in the supply chain where the AI and machine learning will work together to predict the needs of the supply chain before anyone else knows.

Artificial intelligence and machine learning technology are here to stay. It is a technology that will continue to become important. Machine learning will continue to be refined, analyze more data and patterns than before, and predict accuracy.

If you are interested in seeing how Mondoro can help you with your business – we would love to become a valued member of your supply chain.

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How Do Information Systems Support The Supply Chain?

Anyone in the supply chain knows and understands that information systems are critical to the supply chain. Information systems provide the basis for which the supply chain can make the correct decisions.

Information systems and the information we can obtain are critical in any supply chain. That is because for correct decisions to be made, we must have and use accurate information.

You can discover more by reading How Do Information Systems Support The Supply Chain? by clicking here.

What Is The Difference Between Supply Chain Management and Logistics?

Supply chain management is about the collaboration and partnerships to get the goods from raw material to the end consumer; it is about the partnerships and alliances within this process. Logistics is one part of supply chain management; logistics involves moving goods from one place to another.

To learn more about loading a dry shipping container, you can read our blog on What Is The Difference Between Supply Chain Management and Logistics? by clicking here.

Anita Hummel
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