Paul R. Daugherty

I grabbed this book from my Little Free Library, thinking it might help me think through how to further automate and modernize my workplace. H & M provides a very broad framework for thinking through artificial intelligence and the changes it is ushering in. It may not help you think through your specific challenges, but its optimism, that AI should be viewed as a groundbreaking opportunity that will enhance and enable humans, allowing them to perform more creative and human work with superhuman powers, rather than putting them out of jobs, is reassuring. H & M reads like many of the freebies given away at industry conferences: clear, concise, and molded around a theoretical framework, in this case MELDS (mindset, experimentation, leadership, data, and skills) and the "missing middle." It is filled with case studies and examples, none of which gets more than a page or two, and some figures, many of which add very little to your understanding. They conducted a massive survey for their research but I only learned this in the last chapter, because the book is highly qualitative and seems not to refer to the survey all that much. Like many of these conference give-aways, the book is a bit of SWAG that may be worth the read. If for no other reason than to allay the fear that the machines are taking over the world. Now that this review is written I am ready to put the book back in my Little Free Library for the next reader.… (más)

A comprehensive survey of Artificial Intelligence (AI) and robotics in businesses around the world is presented. Basic principles are drawn from those observations including a mapping of functions and processes best done by machines and those best done by humans. In a departure from more traditional thinking about AI where the application is to augment or replace human participation, the authors discuss the growing trend toward a symbiotic or collaborative relationship between man and machine. The book breaks new ground in that respect.
The usefulness of the book is to prick the interest of business leaders to employ AI algorithms by citing examples of economic successes in many applications. While doing so, Daugherty and Wilson persistently caution against the potential violation of ethical and regulatory limits. Privacy of data gleaned from unwitting sources to build data bases is one example. They also attempt to counter the assertion that applying AI will have an adverse economic impact by reducing workforce requirements by defining functions newly created. Their argument is somewhat hollow in that specific skill sets are softly defined and do not track the skill sets of workers apt to be displaced.
While applying AI is covered, the inner workings—algorithms and data sets—are not; those details are proprietary and not germane—the target reader is executive management and not designer or programmer. That does not preclude these observations and questions:
1. Machine functions are digital—computers work with ones and zeroes. Human brains are analog devices. That is a fundamental difference and may constitute an AI limiting factor unless
a. Computers based on biological neural circuitry are developed or
b. Pseudo analog algorithms using digital data are developed. (It is probable that this has been accomplished.)
2. Digital data suggests that AI logic is restricted to deductive with only limited utilization of inductive logic. Does that define the border between what can best be done by either machine or human? Metrics are in the domain of deductive logic and probability and intuition in the inductive domain. Sensory stimulation is more deductive and perceptiveness is more inductive. The ability to assess stress in the voice of complaint callers to help desks might seem to be perception but actually be an integration of sensor measurement of pitch, tremor, rate of speech, word choice or some other quantifiable information.
3. It seems likely that a successful AI application depends on an existing data set which implies that truly creative results are beyond the scope of AI. Data sets must exist to provide determinants, metrics or models against which differences or similarities can be assessed. Countering this concern is the fact that over the course of history few if any at all advances in technology have been creative—all have been the emulation of natural processes or adaptations of more rudimentary devices or processes.
4. Although this book is restricted to business application, application to other fields seems possible and could go far in the advancement and acceptability of AI. An example that comes to mind is the criminal justice system. Can AI replace or augment juries? Incorrect decisions—acquittal of a guilty defendant or conviction of an innocent defendant could be reduced if, for example, crime scenes are modeled, suspect behavior measured, trial presentations tailored, etc. AI techniques to assess witness veracity might be an extension of the algorithms used to assess stress in help desk callers mentioned above. But the administration of justice goes beyond determining culpability so humans must have the ability to intercede.
5. The growing acceptance and usage of AI is apt to go beyond developing software algorithms and apps. As mentioned above, computers or processors based on principles other than digital technology might be developed—voltages to define ones and zeroes are crude determinants—blending analog and digital approaches is enticing. The development of quantum computer technology is underway and promises to be a game changer. Development of new sensors is another possibility—wireless encephalography, remote DNA analyses, et al.

The best measure of a book’s worth is its ability to provoke the reader into delving deeper into the subject matter. This book accomplishes that.… (más)