Q.A. on Artificial Intelligence
Q. What do you mean by Intelligence?
A. Intelligence is the ability to acquire and apply knowledge and skills to deal with the situations. Click here to know more >>
Q. Who purposed the theory of multiple intelligence?
A. A Psychologist Howard Gardener
Q. Explain the types of Intelligence with suitable examples.
A. There are nine types of intelligence based on skills as
1. Intrapersonal Intelligence
2. Spatial Intelligence
3. Naturalistic Intelligence
4. Musical Intelligence
5. Logical-Mathematical Intelligence
6. Existential Intelligence
7. Interpersonal Intelligence
8. Bodily-Kinesthetic Intelligence
9. Linguistic Intelligence Click here to know more >>
Q. Define the term Decision Making.
A. Decision making is the process of identifying and picking a final choice from an available set of choices, after carefully assessing the available options. Click here to know more >>
Q. What do you mean by Artificial Intelligence (AI)?
A. An Artificial Intelligence is a technology by which we can develop the intelligent machines that behaves like humans. It means AI machines can learn from their surround, think on it and act just like humans. Click here to know more >>
Q. Explain the advantages and disadvantages of AI.
A. Click here to know >>
Q. Explain different applications of AI.
A. Click here to know >>
Q. Give a few examples around you which are not AI.
A. As we know, any machine that has been trained with data and can make decisions/predictions on its own can be termed as AI machines. Therefore, machines like automatic washing machines or Remote control devices like AC, fans, Smart TV, toys cars etc. are not to be as AI enabled machine as theses devices works on pre-defined set of instructions. Click here to know more >>
Q. Explain the domains of AI with suitable examples?
A. The three domains of AI are
1. Data Science (eg. weather prediction, salary prediction etc.)
2. Computer Vision (CV) (eg. facial recognition, image searchin shopping webs like Amazon, QR codes, Google lens, Snapchat filters, expression detection)
3. Natural Language Processing (NLP) (eg. Digital assistants like Alexa, Google Assitant, Siri, cortona, etc. and Language Translators like Google Translator, Microsoft Translator etc.) Click here to know more >>
Q. Give examples of AI application that use nearly all domains of AI.
A. Humanoid robot like Sophia (first humanoid robot), Manav (India's first humanoid robot) etc.
Q. How NLP (Natural Language Processing) is different from NLU (Natural Language Understanding)?
A. Natural Language Processing (NLP) breaks down the language into small and understable chunks that are possible for machines to understand. It focuses on processing the text in a literal sense, like what was said.
Whereas, Natural Language Understanding (NLU) is a subfield of Natural Language Processing. It focuses on extracting the meaning or hidden intent of the sentence.It helps to analyzes the data to determine its actual meaning.
Click here to know more >>
Q. What is a virtual Assistant? Give examples.
A. A Virtual Assistant is an AI-enabled application program that understand natural language voice commands and complete the task for users. Some examples are Cortana (Microsoft), Google Assistant (Google), Siri (Apple), Alexa (Amazon) etc.
Q. What is a Chatbot? Give examples.
A. A Chatbot is an AI-enabled software which can simulate a real life conversation (either written or spoken) between the user and the digital device. Some examples are Watson (IBM), Meena (Google), BlenderBot (Facebook), Replika Chatbot etc.
Q. Explain the difference between Script-bot and Smart-bot.
A.
| Script-bot | Smart-bot |
|---|---|
| A scripted chatbot doesn’t carry the use of Artificial Intelligence. | Smart bots are built on Natural Language Processing and Machine Learning. |
| Script bots are easy to make<. | Smart bots are comparatively difficult to make. |
| Script bot functioning is very limited as they are less powerful. | Smart-bots are flexible and powerful. |
| Limited functionality | Wide functionality |
Q. Explain the follwing terms with suitable examples.
1. Machine Learning
2. Deep Learning
A. 1. Machine Learning allows the computers to learn from experiences by its own, use statistical methods to improve the performance and predict the output without being explicitly programmed. eg. Email spam filtering, product recommendations, online fraud detection etc.
2. Deep Learning focuses on Artificial Neural Network (ANN) by which machines are trained on its own by examing the algorithms and rules. eg. self-driving cars, language translation, natural language processing etc. Click here to know more >>
Q. Explain the key differences between Machine Learning and Deep Learning.
A. Click here to know more >>
| Parameter | Machine Learning | Deep Learning |
|---|---|---|
| Data Dependency | Machine learning can work with a smaller amount of data. | Deep Learning algorithms highly depend on a large amount of data for good performance. |
| Hardware Dependencies | Since machine learning models do not need much amount of data, so they can work on low-end machines. | The deep learning model needs a huge amount of data to work efficiently, so they need GPU's and hence the high-end machine. |
| Feature Engineering | Machine learning models need a step of feature extraction by the expert, and then it proceeds further. | Deep learning does not need to develop the feature extractor for each problem as it tries to learn high-level features from the data on its own. |
| Type of data | Machine learning models mostly require data in a structured form. | Deep Learning models can work with structured and unstructured data both as they rely on the layers of the Artificial neural network. |
| Suitable for | Machine learning models are suitable for solving simple or bit-complex problems. | Deep learning models are suitable for solving complex problems. |
Q. How does machines become intelligent?
A. Machines become intelligent by the training data fed by the developers. This data helps to trained the machines itself and makes them intelligent to achieve the desired results.
Q. What do you mean by AI Ethics? Also, explain the possible ethical issues of AI.
A. AI Ethics refers to the basic moral principles for the development of AI systems that must uses the good code of conduct and produces the results accordingly.
Some possible ethical issues of AI are -
-> Bias and Fairness
-> Accountability
-> Transparency
-> Safety
-> Human AI Interaction
-> Cyber Security and Malicious use
-> Trust, Privacy and Control
-> Automation and Impact over jobs
-> Human Right vs Use of AI Click here to know more >>
Q. Explain the term data privacy.
A. Data Privacy ensures that the data shared by customers/users is only used for its intended purpose. It is also known as Information Privacy. Click here to know more >>
Q. Why AI related apps required a lot of data?
A. AI related apps required a lot of data as a training data to trained itself for providing a friendly and effective user experiences.
Q. Name the possible areas where the use of AI can lead to unemployment.
A. The possible areas where the use of AI can lead to unemployment are
-> In hospitals, robots can be used for taking care of patients.
-> Self-driven vehicles will replace the human driver.
Q. Explain the term AI Bias.
A. AI Bias means the unjust result produced through AI based programs and algorithms because of prejudiced assumptions made during the algorithm development process or prejudices in the training data. Click here to know more >>
Q. Explain the term AI Access.
A. AI access means making AI-enabled machines accessible or available to everyone. Click here to know more >>
Q. List some examples of Human Rights violations of AI.
A. (i) Collecting personal data of users withour their knowledge.
(ii) AI bias
Q. What do you mean by deepfake?
A. Deepfake is a technology that can generate fake digital photos, sound recordings and videos which look just as original as possible.