What is artificial intelligence, what is meant by machine learning, and what is the relationship between them?

 What is artificial intelligence, what is meant by machine learning, and what is the relationship between them?

Is there a difference between artificial intelligence and machine learning? What is the? Which is better? How does one help the other? Can we compare? Artificial intelligence and machine learning are closely related to each other, but what is the connection between them?
What is artificial intelligence? its applications? its types? its categories? Do we deal with it in our lives now? What universities are recommended to study this field?
Artificial intelligence and machine learning Don't worry, we will answer all your questions through this article.

What is Artificial Intelligence?

It is the intelligence that a computer system possesses by using logic and mathematics to simulate the ability of the human brain to think.
Through this intelligence, the machine can simulate human cognitive functions such as learning or problem solving.

The definition of artificial intelligence AI:

Can machines think? A simple world-changing question posed by Alan Turing, artificial intelligence is the science that seeks to answer this question with a “yes”.
Undoubtedly, this goal is controversial as there is still no unified, globally agreed upon definition of artificial intelligence, but we can define it as an interdisciplinary science of computer science that attempts to simulate human intelligence in machines.

What is machine learning?

It is an artificial intelligence application that helps a computer learn without direct human intervention. Thus, machine learning uses the mathematical models of the data, so the computer system can continue to learn and improve on its own, according to the experience it has.
Thus, we find that a smart computer uses artificial intelligence to think like a human being, and even to perform its tasks as well, and machine learning is the way the system develops its intelligence.

How do artificial intelligence and machine learning work together?

Artificial intelligence and machine learning interact, but how do they work together? Here are the basic principles being worked on:
  1. Building an AI system using machine learning and other technologies.
  2. While machine learning models are created by studying patterns in the data.
  3. Then the data scientists improve the machine learning models based on the patterns of the data.
  4. The process is repeated and improved until the accuracy of the models is high enough for the tasks to be performed.

Artificial intelligence and machine learning capabilities:

Artificial intelligence and machine learning create huge prospects for companies to analyze their customers, whether to improve their products or marketing, in addition to generating profits through its following capabilities:

1. Predictive analytics:

This ability helps companies analyze customer trends by discovering the relationships between data and the models hidden within them, so they analyze customer behavior and build expectations and strategies on this basis.

2. Recommendation Engines:

Through recommendation algorithms, recommendation systems are built that suggest products that suit users' desires, not randomly.

3. Speech recognition and natural language understanding:

Speech recognition enables a computer system to not only identify words in spoken language, but also to understand natural languages, that is, to recognize meaning.

4. Image and video processing:

These capabilities allow it to recognize faces, objects, or movements in images or videos, and perform functions such as visual search.

5. Sentiment analysis:

The computer system uses sentiment analysis to identify and categorize positive, neutral or negative situations that are expressed in the text and thus a deep study of the reactions.

Advantages of artificial intelligence and machine learning:

The relationship between AI and machine learning offers powerful advantages to companies in nearly every industry – with new capabilities constantly emerging.
Here are some of the features companies have already tested:

1. More data entry sources:

Artificial intelligence and machine learning enable companies to discover valuable insights into a wider range of data sources, whether they are structured (databases) or unstructured (images, voice, text) .

2. Make Better and Faster Decisions:

Using AI to reduce human error, a combination that leads to better decisions increases the efficiency of decisions because they are based on better data.

3. Increasing operational efficiency:

Thanks to artificial intelligence and machine learning, companies become more efficient by automating processes, reducing costs and freeing up time and resources for other priorities.

Artificial intelligence and machine learning applications:

Companies in many industries are creating applications based on the correlation between artificial intelligence and machine learning.
These are just some of the ways AI and machine learning are helping companies transform their processes and products:

1. The field of commerce and retail:

Retailers use artificial intelligence and machine learning to improve their inventory, build recommendation engines, and improve customer experience through visual search.

2. Artificial intelligence and machine learning in medicine and healthcare:

Health organizations are putting artificial intelligence and machine learning to use in applications such as image processing to improve cancer detection or predictive analytics for genome research.

3. Banking and Finance:

Artificial intelligence and machine learning are valuable tools for purposes such as detecting fraud, predicting risks, or providing more proactive financial advice.

4. Sales and Marketing:

Sales and marketing teams use artificial intelligence and machine learning for personalized offers, campaign optimization, sales forecasting, sentiment analysis, and customer inflation forecasting.

5. Cyber ​​Security:

Artificial intelligence and machine learning help organizations protect themselves and their customers by detecting anomalies.

6 . Applying Artificial Intelligence and Machine Learning to Customer Service:

Businesses in a wide range of industries use chatbots and knowledge searches to answer questions, measure customer intent, and provide virtual assistance.

7. Means of transportation:

Artificial intelligence and machine learning are valuable in transportation applications, helping companies improve the efficiency of their roads and use predictive analytics for purposes such as traffic forecasting.

8. Manufacturing:

Manufacturing companies are using artificial intelligence and machine learning for predictive maintenance or to make their operations more efficient than ever before.

Types of artificial intelligence:

Artificial intelligence has 4 main types:

1. Reactive Machines:

    Interactive machines do not need to use more than simple principles, but rather the simplest principles of artificial intelligence, and they interact with the world in the present moment without relying on any prior information or data, making them have limited specialized functions.

    An example of an interactive machine is the famous IBM-designed deep blue chess player, who defeats world-class player Gary as he recognizes the location of the pieces, knows the rules, and makes the most reasonable move at the time without considering the opponent's moves or preconceived moves.

2. Artificial intelligence with limited memory: 

   This type of artificial intelligence has the ability to use previous data and predictions to balance the following decisions, that is, it has memory and therefore has broader capabilities than an interactive machine. Here, artificial intelligence and machine learning interact through several steps, namely:

  • Define training data.
  • Then create a machine learning model and apply it for prediction and it must be appropriate, where three main models of machine learning are used in intelligence with limited memory.

  • Machine learning models for limited memory:

  1. Reinforcement learning: Makes predictions based on repetitions and errors.
  2. Long-Term Memory (LSTM): It uses past data in order to predict future outcomes and uses recent information as well as information from the past in order to reach conclusions.
  3. Evolutionary Generative Adversarial Networks: Uses past experience as well as the role of evolutionary mutation and chance to find the best path. 

  • Repeat these steps.

3. Theory of Mind:

This level of artificial intelligence, which depends on understanding and taking into account the feelings and emotions of humans, animals and even other machines in order to make decisions, has not yet been reached.
The same ability to infer human mental states is a prerequisite for integrating artificial intelligence (AI) into human society.

4. Self-Awareness:

Which means that the machine has awareness on the human level and an understanding and awareness of its existence and the existence of others and their emotions as well, and thus it is able to understand the needs of others based on their method, not just the data, and this type depends on the researchers’ understanding of consciousness and then simulating it and how it is passed to machines.

Global examples of artificial intelligence:

Artificial intelligence belongs to one of the following two main categories:

Narrow AI:

It is the intelligence that lives with us and interacts with it permanently, and it is a successful achievement of artificial intelligence, where weak or narrow artificial intelligence works to perform a certain task well, but it is limited and machines work in it within many restrictions that make it simpler than human intelligence.

narrow artificial intelligence

 Here are some examples of narrow AI:
  • Google search
  • Image recognition
  • Siri, Alexa, and personal assistants
  • self-driving cars
  • Netflix Recommendations

Artificial General Intelligence (AGI):

artificial general intelligence
Artificial general intelligence or "strong AI" is simply the artificial intelligence that movies like robots in the science fiction series Westworld present .
The bottom line is that the greatest thing an AI researcher can achieve is to build a machine with a human level of intelligence, but this path is not easy at all.

The difference between artificial intelligence and machine learning:

Most people tend to use the terms artificial intelligence and machine learning as two sides of the same coin without defining the difference.
Very simply, machine learning is a branch of artificial intelligence with broad topics, and this does not mean that they are the same, here are the main differences between them:
  1. Machine learning and deep learning algorithms are used to make a computer system smart enough to not wait for commands and code from a human, which needs massive amounts of data to train a machine learning model so that it gives the correct results and makes predictions based only on that data.
  2. The goal of artificial intelligence is to create a computer system that simulates human intelligence that solves complex problems, while the goal of machine learning is to make machines learn from the data received by them to provide results, predictions and solve questions accurately.
  3. Artificial intelligence is a very broad field that makes the system intelligent to perform complex tasks while machine learning trains machines for specific tasks through the data presented to it.

Best AI and Machine Learning Universities in the World:

Here is a list of the best artificial intelligence universities in the world, whether for undergraduate or postgraduate studies, to meet your ambition to study artificial intelligence:

1. Massachusetts Institute of Technology in the USA:

MIT has a special department of Brain and Cognitive Sciences (BCS) that aims to engineer the human mind by studying it at all levels, whether it is molecules, synapses, algorithms of human cognition and behavior.
The department has post-baccalaureate, undergraduate, graduate, post-doctoral and even summer programs.
  • The first emotional AI was developed in the MIT AI Lab which is dedicated to artificial intelligence and machine learning research.

2. Carnegie Mellon University CMU USA:

CMU was responsible for creating the first autonomous vehicle using neural networks in 1989, with state-of-the-art research facilities and financial resources to create technologies that will change the world for the better, and aim to make the planet safer and healthier using artificial intelligence.
CMU has doctoral, masters and university programs that you can try depending on your situation and they also have separate research departments for human-computer interaction, machine learning and robotics.

3. Stanford University, USA :

The Stanford AI Lab offers one-on-one courses in AI and Machine Learning, Natural Language Processing with Deep Learning, and they have weekly groups discussing papers on everything within AI and Machine Learning technology.
Stanford University has a master's degree in Artificial Intelligence and also offers an AI Graduate Certificate.

4. University of California, USA:

UCB has launched a Center for Human-Compatible Artificial Intelligence. It is one of the oldest institutions of higher education in California.
UCB has the renowned Berkeley Lab for Artificial Intelligence Research - the BAIR Lab, which brings together researchers across the fields of computer vision, NLP, robotics and machine learning, connecting AI with other scientific disciplines and the humanities.
UCB offers Undergrad course in artificial intelligence, machine learning, processing, robotic interaction, deep learning, and even neural computation. Other postgraduate courses related to artificial intelligence and machine learning.
Not only that, but it runs large to small projects within the framework of AI research, and current projects include robotic 3D modeling of building interiors, children’s answering software, etc.

5. Nanyang Technological University - Singapore:

It is an independent research university in Singapore, where it was announced to transform NTU into a smart campus with technology-backed solutions for better learning and even better living experiences by the university’s president in 2018
. A balance between computer science and mathematics.

6. Harvard University - USA:

Harvard University's John A. Paulson School of Engineering and Applied Science (SEAS) and the Institute for Applied Computational Science have a range of graduate programs focused on artificial intelligence in computer science.
An unconventional approach to research is in keeping with the interdisciplinary nature of modern research, and Harvard does not have traditional academic departments.
A cockroach-inspired robot has recently been built at HAMR - the Harvard Ambulatory Microrobot - a versatile robot that operates at high speeds, jumps, climbs, rotates sharply, carries loads and even falls from great distances without injury.

7. University of Edinburgh, UK:

It offers a very wide range of courses. They aim to provide you with practical knowledge in designing and building intelligent systems so that you can apply your skills in a variety of professional settings.
The best find in Edinburgh was the 5-year full-time MSc (MInf) program which focuses on giving a solid foundation with advanced studies in Computer Science, Artificial Intelligence, Machine Learning and Cognitive Science as well as Linguistics, Neuroscience, Psychology and Biology.




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