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.

Caveats?
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. ( )

Excellent overview of the major topics in AI, circa 2019. I like her writing style. It's easy to read, and she provides useful examples that illustrate key concepts. I found it enlightening, as a former expert myself, that many of the same issues and questions remain as in my heyday, albeit in somewhat different forms.

The perpetual 20+ year horizon to general AI remains firmly in place, IMHO, and is likely to stay there. I can't say it myself nearly as well as pioneer, Rodney Brooks, as quoted at the end of the book: "When AI got started, the clear inspiration was human level performance and human level intelligence... The fact that we do not have anything close to succeeding at those aspirations says not that researchers have not worked hard or have not been brilliant. It says that it is a very hard goal." ( )

Really great book. The author (an AI researcher) explains how many of today's state of the art AI systems work. These include image recognition, game playing (like AlphaGo) and NLP systems like Google translate. The explanations for how those systems work are really good, with just the right amount of technical detail. We also get to see what the weak points of these systems are, and how far away we are from creating any true intelligence.
I have written quite a long review/summary of it on my blog: https://henrikwarne.com/2020/05/19/artificial-intelligence-a-guide-for-thinking-... ( )