1. A small bookstore hired you to build its database. You have to create a new Access Database with the following entities and attributes:

Author: AuthorID, ALastName, AFirstName.

Book: BookID, BookTitle, AuthorID, ISBN, Price, Edition, CopyrightYear, Description

Customer: CustomerID, CLastName, CFirstName, CStreet, CCity, CState, CZipCode, CPhone, CEmail,

Order: OrderID, CustomerID, OrderDate

OrderDetail: OrderID, IDOrderDetail, BookID, QuantityBook

Enter 5 records on the entities: Customers, Books, Authors, and Orders: and enter a different number of details for each order.

2. Voice-based artificial intelligent devices are not just jukeboxes with attitude; in fact, they just might be the primary way we interact with all future machines. August 31, 2012, was the date four Amazon engineers filed the patent for Alexa, an artificial intelligence system designed to engage with one of the world’s biggest and most tangled datasets: human speech. The patent had a simple diagram and 11 words. “Please play ‘Let It Be’ by the Beatles.” A small device replies “No problem, John,” and the song begins playing. Millions of Alexa machines were sold when the device debuted in 2014. Home-based Al systems are turning out to be big business as banks, universities, law firms, and so on compete to create simple devices that people can talk to directly. What makes voice-based Al so appealing to consumers is its promise to conform to us, to respond to the way we speak and think-without requiring us to type on a keyboard or screen. That’s also what makes it so technically difficult to build. We aren’t at all orderly when we talk. Instead, we interrupt ourselves. We let thoughts dangle. We use words, nods, and grunts in odd ways, and we assume that we are making sense even when we are not. Thousands of Amazon staffers are working on this challenge, and the company currently lists over 1,100 Alexa job requests on its website. Machine learning techniques reexamined thousands of exchanges in which Alexa stumbled. With Alexa’s usage surging, Amazon now has access to an expansive repository of human-computer speech interactions, giving it an edge in fine­tuning its voice technology. External data adds value, such as a massive database of song lyrics loaded into Alexa in 2016, helping ensure if you ask for the song with “drove my Chevy to the levee,” you will be steered to Don McLean’s “American Pie.” Of course, with all technology, there are bugs and glitches. I am sure you have heard of the frightening Alexa rogue laughter that was happening in people’s homes during the night. Amazon stated Alexa’s random laugh that was creeping customers out was the result of Alexa mistakenly hearing the phrase “Alexa, laugh.” Amazon changed the phrase to “Alexa, can you laugh?” which is less likely to have false positives, and disabled the short utterance “Alexa, laugh.” Amazon also reprogrammed Alexa’s response from simply laughter to “Sure, I can laugh,” followed by laughter. This confirms the theory that Alexa was falsely triggered and not possessed. While it’s promising the company issued a fix, that probably isn’t enough to comfort users who allegedly heard Alexa laughing for no reason in the middle of the night. 1. Define the three primary types of decision-making systems and how machine learning technology could help transform decision-making. 2. Identify how machine learning can transform a traditional business process such as checking out of a grocery store. 3. Explain the relationship between bias and machine learning for Alexa. 4. Argue for or against the following statement: Machine learning systems like Alexa invade user privacy.