Artificial Intelligent

Definition 

For common understanding, we can say that artificial intelligence is a mixture of two things artificial means something develops by human begins and intelligence means the capability to understand events and act accordingly.   

So AI is the most emerging field of computer science plays a vital role in complex decision-making. Hence it is the basis for computer learning. Human understanding, perception, communication, and decisions making ability enhancement is the purpose of AI. These capabilities directly develop and establish a positive impact on faster reaction.

Examples 

1. Smart cars and drones: some time ago, it was a dream to use automated cars. Now it has become possible. A company name Tesla convert this dream into reality. We can see semi-automated cars on the road. Along with such fully functional cars, we have smart drones as well. Further development is in progress by the world’s largest companies like Amazon on drone delivery programs. It all becomes possible due to AI.

2. Music and media streaming 

3. Navigation and travel techniques 

4. Smart home devices

5. Smartphone 

Subfields of Artificial Intelligence 

Machine learning and deep learning fall under the subfields of artificial intelligence.

1. Machine Learning

Machine learning is a subfield of artificial intelligence. It uses data and algorithms to reproduce ways in which humans learn. It increases accuracy and efficiency.

· The learning process of machine learning is carryout by human interference this is because the process makes machine learning human dependent. 

· To determine the difference between data inputs, a set of features are concluded by human experts.

· As from the name, it is clear that machine learning means that a machine that learns from input data set.

· It deals with training a machine from data to accomplish any task with a high-performance rate.

· System learn new things from input data

Examples 

1. Image recognition is done through machine learning.

2. Machine learning also support prediction and extraction processes.

3. It plays a vital role in medical diagnosis.

2.Deep learning  

Deep learning is a type of machine learning. It deals with algorithms while understanding the function and structure of the brain. For training unstructured data, deep learning plays a vital role.

Deep learning has considered being very power full because it has many features.

The time required for feature engineering is minimum than machine learning. 

Sometimes its algorithms can be overkill because of less complex problems, so it is clear that deep learning is effective with a large amount of data.  

Another limitation of this technique is that it requires expensive resources. Processing units must have high-speed as well as GPU are also needed to train the data.

Examples 

There are some examples of deep learning 

  • Facial recognition 
  • Smart shopping method
  • Virtual assistance 

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