Sunday, June 18, 2023

Review of Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

This book review was written by Eugene Kernes   

Book can be found in: 
Genre = Science
Book Club Event = Book List (11/25/2023)

Watch Short Review


“AI is a field that includes a broad set of approaches, with the goal of creating machines with intelligence.  Deep learning is only one such approach.  Deep learning is itself one method among many in the field of machine learning, a subfield of AI in which machines “learn” from data or from their own “experiences.” To better understand these various distinctions, it’s important to understand a philosophical spilt that occurred early in the AI research community: the split between so-called symbolic and subsymbolic AI.” – Melanie Mitchell, Chapter, Page 24

“It’s no secret: deep learning requires big data.  Big in the sense of the million-plus labeled training images in ImageNet.  Where does all this data come from?  The answer is, of course, you – and probably everyone you know.  Modern computer-vision applications are possible only because of the billions of images that internet users have uploaded and (sometimes) tagged with text identifying what is in the image.” – Melanie Mitchell, Chapter 6: A Closer Look at Machines That Learn, Page 93

“The phrase “barrier of meaning” perfectly captures an idea that has permeated this book: humans, in some deep and essential way, understand the situations they encounter, whereas no AI system yet possesses such understand.  While state-of-the-art AI systems have nearly equaled (and in some cases surpassed) humans on certain narrowly defined tasks, these systems all lack a grasp of the rich meanings humans bring to bear in perception, language, and reasoning.  This lack of understand is clearly revealed by the un-humanlike errors these systems can make; by their difficulties with abstracting and transferring what they have learned; by their lack of commonsense knowledge; and by their vulnerability to adversarial attacks.  The barrier of meaning between AI and human-level intelligence still stands today.” – Melanie Mitchell, Chapter 14: On Understanding, Page 212


Is This An Overview?

There are many different approaches to creating Artificial Intelligence (AI), to creating machines with intelligence.  Deep learning, is a subset of machine learning, in which machines learn from data or their own experiences.  Deep learning requires data, much of which is obtained from various free digital sources in which humans tag images with identifying text.  Using user data not only to sell the data to other firms, but also to improve their products.  Machines learn in a supervised learning procedure, in which different weights are applied to process examples.  AI can also learn through trial and error, with randomly chosen weights.  There are limits to AI learning, as machines do not learn on their own, they do not engage in open-ended categories, and they do not actively seek information.    

There is a barrier of meaning for AI.  They do not understand the meaning of the questions asked of them.  Computers do not understand the meaning of situations they encounter.  For a computer, meaning is derived by the way the symbols can be combined, operated on, and correlated.  AI has difficulties with abstract information, and transferring knowledge from one information domain to another.  AI performs well on narrowly defined tasks, in which the situations are similar and are highly expected.  AI has a higher chance of making errors in unexpected situations that occur infrequently.  This is known as the long-tail problem, for the vast range of unexpected situations that AI can encounter.


Do AI Think, See, And Speak?

For some, thinking only occurs in biological entities because biological entities have a conscious.  An awareness of their own actions and feelings.  No machine has a conscious, therefore cannot think.   

Machines have difficulty with object recognition because programs see pixels and cannot easily differentiate between the objects that the pixels can form.  The objects themselves can appear very differently in different images.  Correlations within images does not mean that the computer will properly identify the appropriate object.  Humans are assumed to know what an object is, no matter the image.  But there is much less proof that a computer actually sees and classifies an object appropriately.

AI can read the information that is there, but cannot extrapolate based on information not present.  Does not understand what is left unsaid.  Making it difficult to understand language.


What Is The Future Of AI?

There are many potential futures for AI such as AI going rouge, taking over jobs, and make autonomous decisions that are not understood.  AI can possibly make human creativity and emotions, basically the human spirit, easy to reproduce.

AI can enhance the quality of life, but there are limitations to AI safety.  There is disagreement about how to proceed with AI, either to embrace their capabilities or approach with caution given AI vulnerabilities.   AI should be regulated using experiences from AI practices and government agencies.  Neither alone can be trusted.  There are ethical, political, and technical decisions that need to be made. 



This is not a book about the popular diverse future perspectives on AI potential or what AI would do. This is a book about the methods used to train AI, and the limitations to AI learning. 

Questions to Consider while Reading the Book

•What is the raison d’etre of the book?  For what purpose did the author write the book?  Why do people read this book?
•What are some limitations of the book?
•To whom would you suggest this book?
•What is Artificial Intelligence (AI)? 
•How do machine learn?
•What is deep learning?
•What are the limits to machine learning?
•What is the supervised learning process?
•Can machines learn by trial and error?
•What is the barrier of meaning? 
•Do machines understand what they are tasked with processing?
•What does AI do well?  What does AI do poorly?
•What is the long-tail problem?
•Can machines think?
•Can machines recognize objects in images?
•Can machines read?
•What is the future of AI?
•How should AI be regulated? 
•What is the Turing Test? 
•What is symbolic and subsymbolic AI? 
•What is a network?
•What is Moore’s law? 
•How does test data effect machine learning? 
•How do digital firms use user data? 

Book Details
Publisher:             Picador [Macmillan Publishers Limited]
Edition ISBN:      9780374715236
Pages to read:       249
Publication:          2020
1st Edition:           2019
Format:                 eBook 

Ratings out of 5:
Readability    4
Content          4
Overall          4