Top 10 Most Asked Artificial Intelligence Questions in 2021
Analytics Insight has selected the 10 most asked artificial intelligence questions in 2021
1. What are the Examples of AI in real life?
Robo-readers for Grading
In the era of technology, where education is rapidly shifting towards online learning, MOOCs have become a new norm of education. Robo-readers are used to grade essay questions and assignments based on certain parameters acquired from huge data sets.
Online Recommendation Systems
Online recommendation systems are used by e-commerce and social media websites to provide a better customer experience.
Navigation and Travel
Google Maps, GPS, and Autopilot on Airplanes are some of the best examples of AI in Navigation and travel.
Machine Learning models process large amounts of banking data and check if there is any suspicious activity or anomalies in the customer transactions. AI applications proved to be more effective than humans in recognizing fraud patterns as they were trained with historical data of millions of transactions.
Human error is responsible for more than 90% of accidents happening on the road every year. Technical failures in a vehicle, roads and other factors have little contribution to fatal accidents. Autonomous vehicles can reduce these fatal accidents by 90%.
2. What are the differences between AI, ML, and DL?
Artificial Intelligence as an umbrella covers everything related to making a machine think and act like a human. Machine Learning and Deep Learning are subsets of AI and are used to achieve the goals of AI. AI consists of the algorithms and techniques that enable a machine to perform the tasks commonly associated with human intelligence. Machine Learning is a subset of Artificial Intelligence and is mainly used to improve computer programs through experience and training on different models. In Machine Learning, where the model tends to surrender to environmental changes, Deep Learning adapts to the changes by updating the models based on constant feedback. It’s facilitated by the Artificial Neural Networks that mimic the cognitive behavior of the human brain.
3. What is the difference between inductive, deductive, and abductive Machine Learning?
Inductive Machine Learning learns from a set of instances to draw the conclusion. Deductive Machine Learning derives the conclusion and then improves it based on the previous decisions and abductive Machine Learning is a Deep Learning technique where conclusions are derived based on various instances.
4. What is perceptron in Machine Learning?
Perceptron is an algorithm that is able to simulate the ability of the human brain to understand and discard; it is used for the supervised classification of the input into one of the several possible non-binary outputs.
5. What are the advantages of neural networks?
Neutral networks require less formal statistical training and have the ability to detect nonlinear relationships between variables. They can detect all possible interactions between predictor variables and the availability of multiple training algorithms.
6. What is TensorFlow?
TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experiential learning architectures and to work on them to produce desired outputs.
7. What is an autoencoder? Name a few applications.
An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are:
- Data denoising
- Dimensionality reduction
- Image reconstruction
- Image colorization
8. Explain the commonly used Artificial Neural Networks.
Feedforward Neural Network is the simplest form of ANN, where the data or the input travels in one direction. Convolutional Neural Network takes input features in a batch. This will help the network to remember the images in parts and can compute the operations. Recurrent Neural Network is mainly used for signal and image processing.
9. How does data overfitting occur and how can it be fixed?
Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data. This causes an algorithm to show low bias but high variance in the outcome. Overfitting can be prevented by using the methods of cross validation and feeding more training data. Using regularization, removal of features, and use of ensemble models can help in fixing the overfitting system.
10. Which algorithm does Facebook use for face verification and how does it work?
Facebook uses DeepFace for face verification. It works on the face verification algorithm, structured by Artificial Intelligence (AI) techniques using neural network models. They detect facial features, align and compare the features, represent the key patterns by using 3D graphs and classify the images based on similarity.