What is AI?

“In principle, you can describe every learning aspect or any other form of intelligence to an extent a machine can be made to simulate it. An attempt will be made to make machines use language, form abstractions and concepts, solve problems now reserved for humans, and improve themselves.” - John McCarthy

The attempt highlighted above is through Artificial Intelligence, which is a machine built to solve problems that are usually solved by humans using our natural intelligence.

As a machine, AI improves itself when it learns to solve a problem, becoming more efficient and faster than humans at solving that particular problem.

Branches of AI

There are a few major branches of AI:

1. Machine learning (ML): Machine learning is a process where a machine can learn and solve problems without being programmed. Machine learning uses computer algorithmsMachine learning algorithms to build a model with sample datatraining data to make predictions or decisions without being explicitly programmed.

In the real world, you can apply machine learning in credit card fraud detection. Payment systems learn patterns and techniques that fraudsters use to commit fraud.

2. Robotics: Robotics is an intersection of computer science with engineeringmechanical engineering, electrical engineering, etc.. Robotics defines the design, construction, and operation of robots to help and assist humans. AI researchers use machine learning to set interaction at social levels when developing robots.

3. Expert systems: An expert system in AI refers to a machinecomputer system that imitates a human expert’s decision-making intelligence.

   A real-world example is a medical diagnosis system designed to deduce the cause of disease from data recieved from previously observed medical operations on humans.

4. Natural language processing (NLP): NLP is the study of how machines analyze natural languages and produce meaningful information about the text.

   You can use NLP to determine the corresponding text of an image that represents printed text in the real world.

5. Neural network: A neural network is a series of computer algorithms that recognize underlying relationships in a set of data through a process that imitates the way the human brain operates. You can use neural networks in businesses that deal with many data, e.g., finance operations, forecasting, market research, etc.

6. Fuzzy logic: Fuzzy logic deals with uncertain information. You can use fuzzy logic to modify and analyze uncertain information to determine the degree to which the information hypothesisproposed explanation made based on limited evidence is true.

   You can apply fuzzy logic in facial pattern recognition, control of subway systems, knowledge-based systems, etc.

Challenges

As with any computer science concept, there are always challenges. Below are a few problems facing AI:

• Computing power: Some machine learning algorithms are power-hungryrequire supercomputers computing power. The recent availability of cloud computing has made things easier for developers to build ML models, but at a high cost.

• Data scarcity: You can get data anywhere all over the internet, but valuable data is scarce. Sample data that can efficiently train AI models is also scarce.

  In the struggle to get valuable data, big tech companies are currently facing charges regarding the unethical use of generated user data.

• Limited knowledge: Software engineering and computer science are relatively new fields. AI as a concept is still at its early stages in advancement; so, knowledge of AI is limited to technology enthusiasts, college students, and researchers.

Conclusion

In this Answer, you learned about AI and its branches, and you saw a few challenges currently facing AI technology.

To learn more about AI, check out the following useful resources on our platform:

• Deep learning

• What is machine learning

• What is Speech-to-Text (STT)?

• What is fuzzy logic?