Skip to main content

The debates on ethics will flare up

 The debates on ethics will flare up


As the AI industry makes significant progress in performing various tasks and actions in the everyday life, questions are raised regarding ethics, responsibilities, and human engagement. Who will be to blame if an artificial intelligence unit performs an illegal act? Do AI bots need any regulations? Will they be able to take over all the human jobs?
The first two questions assume that one day a bot will be legally recognized as a person and could take responsibility or be punished for their actions. Although this perspective is still years away, the debates around ethics are heating up already. Considering different possibilities, scientists are trying to find a compromise regarding the bots’ rights and responsibilities.
However, the possibility that robots will take all the workplaces is actually close to zero. Of course, the AI industry is developing extremely fast, but it is still pretty much in its infancy. 2018 promises to take the hype around this question down. Once we dive deeper into this subject, understand how to interact with the AI, and get used to it, the myth about robots taking over will surely be dispelled.

Image result for debates on ethics

Popular posts from this blog

Weekly challenge 3 data analyst google professional certificate

1 . Question 1 The manage stage of the data life cycle is when a business decides what kind of data it needs, how the data will be handled, and who will be responsible for it. 1 / 1  point True False Correct During planning, a business decides what kind of data it needs, how it will be managed throughout its life cycle, who will be responsible for it, and the optimal outcomes. 2 . Question 2 A data analyst is working at a small tech startup. They’ve just completed an analysis project, which involved private company information about a new product launch. In order to keep the information safe, the analyst uses secure data-erasure software for the digital files and a shredder for the paper files. Which stage of the data life cycle does this describe? 1 / 1  point Archive Plan Manage Destroy Correct This describes the destroy phase, during which data analysts use secure data-erasure software and shred paper files to protect private information. 3 . Question 3 In the analyze phase of the d

Prepare Data for Exploration: Weekly challenge 4

Prepare Data for Exploration: Weekly challenge 4 1 . Question 1 A data analytics team labels its files to indicate their content, creation date, and version number. The team is using what data organization tool? 1 / 1  point File-naming verifications File-naming references File-naming conventions File-naming attributes Correct 2 . Question 2 Your boss assigns you a new multi-phase project and you create a naming convention for all of your files. With this project lasting years and incorporating multiple analysts it’s crucial that you create data explaining how your naming conventions are structured. What is this data called? 0 / 1  point Descriptive data Named convention Metadata Labeled data Incorrect Please review the video on naming conventions . 3 . Question 3 A grocery store is collecting inventory data from their produce section. What is an appropriate naming convention for this file? 0 / 1  point Todays_Produce Produce_Inventory_2022-09-15_V01 Todays Produce 2022-15-09 Inventory

Prepare Data for Exploration : weekly challenge 1

Prepare Data for Exploration : weekly challenge 1 #coursera #exploration #weekly #challenge 1 #cybersecurity #coursera #quiz #solution #network Are you prepared to increase your data exploration abilities? The goal of Coursera's Week 1 challenge, "Prepare Data for Exploration," is to provide you the skills and resources you need to turn unprocessed data into insightful information. With the knowledge you'll gain from this course, you can ensure that your data is organised, clean, and ready for analysis. Data preparation is one of the most important processes in any data analysis effort. Inaccurate results and flawed conclusions might emerge from poorly prepared data. You may prepare your data for exploration with Coursera's Weekly Challenge 1. You'll discover industry best practises and insider advice. #answers #questions #flashcard 1 . Question 1 What is the most likely reason that a data analyst would use historical data instead of gathering new data? 1 / 1