... 4. You passed! Say you are building an AI system to help make diagnoses from X-ray scans. You deserve a pat on the back for finishing this course. 6. 10. Answer- Artificial narrow intelligence (ANI) Q.2 What do you call the commonly used AI technology for learning input (A) to output (B) mappings? Share to Twitter Share to Facebook Share to Pinterest. Differences between Machine Learning and Data Science. AI technology can be biased. This preview shows page 1 - 3 out of 5 pages. Problems with the data. Explainability is usually achieved through building a chatbot to talk to the user to explain its outputs. Andrew Ng’s latest course (AI for everyone) gives you the keys to understanding this digital transformation of our society and the tools to apply it in your activities. Question 1. It is therefore necessary to make aware of leaders and decision-makers by a non-technical approach to AI, as did Omar bin Sultan Al Olama, Minister of State for Artificial Intelligence of the United Arab Emirates, with the University of Oxford through a one-year program that trained 94 government officials (fontes in French and in English). Using an adversarial attack on the AI system to change its outputs to be less biased. AI for Everyone : Week 1 Quiz and Answers. Indeed, the AI ​​team can guide the project team on the nature, quantity and quality of the data to be acquired to improve the performance of the AI model (eg, recording every minute and not only every 10 minutes, images in similar quantity in different categories, etc.). (Select all that apply), 7. - Ritik2703/Coursera---Programming-for-Everybody-Getting-Started-with-Python- The content of this MOOC is free and here are the key elements of the week 1. Credit: all images in this article come from the MOOC Andrew Ng, AI for everyone. As having a website is not enough to become an Internet business, it is not enough to use one or more DL models to become an AI company. 9. It … What are the current limitations of AI technology? Which of these are good practices for addressing bias in AI? View Homework Help - Week 4 Assignment.pdf from ELECTRONIC 106 at Dawood University of Engineering & Technology, Karachi. 3/30/2019 AI For Everyone - Home | Coursera For Everyone - Home _ Coursera.html 1/6 Week 2 Quiz Quiz, 10 questions 10/10 points (100%) Congratulations! AI For Everyone--QUIZ Answer. Next Item 1. AI for Everyone : Week 4 Quiz and Answers. All Completed! Posted on July 12, 2020 July 12, 2020 by admin. (Select all that apply)AI technology can discriminate. In fact, thanks to training from a dataset, a DL model creates a representation of the world that it can then apply to any new data A similar to those of the dataset to predict B. I.e., given the input A, to ask a human to provide B. ;) Today we have a lot of data (up to Big Data) and the computational ability to analyze it (GPU). Score 100%. Your email address will not be published. The Andrew NG MOOC “AI for Everyone (Week 1)” provides the keys to the analysis. What are the current limitations of AI technology? Email This BlogThis! What we know how to develop today are AIs, each specialized in one task. Your email address will not be published. (Select all that apply)AI technology can discriminate Click Here To View Answers.1. Dawood University of Engineering & Technology, Karachi, Dawood University of Engineering & Technology, Karachi • ELECTRONIC 106, Dawood University of Engineering & Technology, Karachi • MGT MISC, Copyright © 2020. Q.1 Which of these terms best describes the type of AI used in today’s email spam filters, speech recognition, and other specific applications? ;) 4. Why now? Most AI systems are highly explainable, meaning that it’s easy for a doctor to figure out why an AI system gave a particular diagnosis. AI For Everyone Week 4 Solved Quiz (Coursera) Click to Download AI for Everyone Week 4 Coursera Solved Quiz. Many organizations already use Data Science to analyze their data in order to extract the main characteristics (ex: importance of this or that parameter on the value of the target, general trends, etc.). AIs, not an AI. Congratulations! (Select all that apply). All Completed! Data (dataset) for training an AI model is divided into 2 groups: structured data and unstructured data. Using AI to synthesize a fake video of a politician saying something they never actually said. It can thus be summarized that an AI model is very often a model that learns to give an output B (prediction) from an input A (given). Errors can be incorrect labels, false or missing values ​​as well as data of a different nature. You will see examples of what today’s AI can – and cannot – do. Originally inspired by neural networks of the brain, DL models are composed of several layers of computational units (artificial neurons), each capable of detecting more and more complicated characteristics of a training dataset.


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