Part -2
Various Approaches to AI
Different approaches to Ai :
1. Artificial intelligence (AI), as a broad field , encompasses many different approaches ranging from top-down knowledge representation to bottom-up machine learning.
2. There are three relation concept that have been frequently used in recent years : AI, machine learning and deep learning.
3. In general, AI is broadest concept, machine learning is a sub field in AI, and deep learning is a special type of machine learning.
4. Figure illustrates the relations among these three concepts.
5. While the broad field of AI includes many approaches, its popularity is largely due to outstanding performances of machine learning and deep learning.
A. Machine learning
i. Machine learning is an application of Artificial Intelligence (AI) that provide system the ability to automatically learn and improve from experience without being explicitly programmed. i.
ii. Machine learning focuses on a development of computer programs that can access data.
iii. The primary aim is to allow the computer to learn automatically without human intervention or assistance and adjust actions accordingly.
iv. Machine learning enables analysis of massive quantities of data.
B. Deep learning
i. Deep learning is the subfield of artificial intelligence that focus on creating large neural network models that are capable of making accurate data-driven decisions
ii. Deep learning is used where the data is complex and has large datasets.
iii. Facebook uses deep learning to analyze text in online conversations.
iv. Google and Microsoft all use deep learning for image search and machine translation..
Advantages and disadvantages of machine learning
Advantages of machine learning are :
1. Easily identifies trends and patterns:-
a. Machine learning can review large volume of data and Discover specific Trend and pattern that would not be Apparent to humans.
B. For an E-commerce website like :- Flipkart its serves to understand the browsing behavior and purchase histories of its users to help cater to the right products, deals and reminders relevant to them.
C. It uses the results to reveal relevant and advertisement to them.
2. No human intervention needed (automation) :
Machine learning does not require physical force i.e., no human intervention is needed.
3. Continuous improvement :-
a. ML algorithm gain experience; they keep improving in accuracy and efficiency.
b. As the amount of data keeps growing, algorithms learn to make accurate predictions faster.
4. Handling multi - dimensional and multi - variety data :
a. Machine learning algorithms are good at handling data that are multi - dimensional and multi - variety, and they can do this in dynamic or uncertain environments.
Disadvantages of machine learning are :-
1. Data acquisition :
a. Machine learning requires massive data sets to train on, and this should be inclusive/ unbased and of good quality.
2. Time and resources :
a. ML nets enough time to let the algorithms learn and develop enough to fulfill their purpose will a considerable amount of accuracy and relevancy.
b. It also needs massive resource to function.
3. Interpretation of results :
a. To accurately interpret results generated by the algorithms. We must carefully choose the algorithms for our purpose.
4. High error - susceptibility :
a. Machine learning is autonomous but highly susceptible to errors.
b. It takes time to recognize the source of the issue, and even longer to correct it.
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