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Companies that don’t use AI will soon be obsolete.

From making faster, better decisions to automating rote work to enabling robots to respond to emotions, AI and machine learning are already reshaping business and society. What should you and your company be doing today to ensure that you’re poised for success and keeping up with your competitors in the age of AI?

Artificial Intelligence: The Insights You Need from Harvard Business Review brings you today’s most essential thinking on AI and explains how to launch the right initiatives at your compan

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Book ID Asin: 1633697894
Book Title: Artificial Intelligence: The Insights You Need from Harvard Business Review (HBR Insights Series)
Book Author: Harvard Business ReviewThomas H. DavenportErik BrynjolfssonAndrew McAfeeH. James Wilson
Book Format and Price:
Book Format Name: Kindle
Book Format Price: $17.50
Book Format Name: Audiobook
Book Format Price: $0.00
Book Format Name: Hardcover
Book Format Price: $40.24
Book Format Name: Paperback
Book Format Price: $15.68
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Book Format Price: $27.29
Book Price: $15.68
Book Category: Books, Computers & Technology, Computer Science and unknown
Book Rating: 136 ratings

Name: Loyd Eskildson
Rating: 5.0 out of 5 stars
Title: InvaluableBackgroundandGettingStartedMaterials
Date: Reviewed in the United States on February 26, 2020
Review: The most important general-purpose technology of our era is AI, especially machine learning. AI is poised to transform every industry, just as electricity did a century ago. It is improving rapidly — vision systems such as used in self-driving cars formerly erred when identifying a pedestrian as often as 1/30 frames, now less than 1/30 million, while error rates for recognizing images from a large ImageNet database with several million photographs fell from > 30% in 2010 to about 4% in 2016. The speed of improvement has accelerated rapidly in recent years, given the adoption of ‘deep’ neural nets. A second type of major improvement has been in cognition and problem-solving — having now beaten the finest human poker, chess, Jeopardy, and Go players. Google’s DeepMind team used ML to improve cooling efficiency by over 15%, even after optimized by human experts. Voice recognition is now nearly equal human performance, even in noisy environments. Security guards will soon nearly disappear.Prior to deep learning and neural networks, machine learning improved as training data grew, but only up to a point. Deep learning has been used with 36 million examples and more.Computer speeds have also improved — JPMorgan Chase introduced a system for reviewing commercial loan contracts that used to take humans 360,000 hours, now done in a few seconds.It’s comparatively straightforward to label a body of data and use it to train a supervised learning. — thus supervised ML systems are more common, for now.Reinforcement learning is embedded in systems that master Atari video games and board games like Go; also to develop stock-market trading systems and select MSN headlines.Udacity, Coursera and fast.ai can get smart, motivated students to be able to create industrial-grade ML deployments. The required talent and hardware can also be bought or rented. If ‘success’ is defined as ‘significantly improving performance, sufficient data to do so is often surprisingly easy to obtain. WorkFusion works with companies to bring higher levels of automation to complicated back-office processes. Once it is confident enough in its classifications, it takes over the process.An example of task-and-occupation redesign is the use of machine vision systems to identify potential cancer cells. Process redesign involves the reinvention of the workflow and layout of Amazon fulfillment centers after the introduction of robots and optimization algorithms based on machine learning. Similarly, business models need to be rethought to take advantage of ML systems that can intelligently recommend music or movies in a personalized way.Machine learning systems hardly ever replace the entire job process/business model. Most often they complement human activities that can make their work more valuable. Eg. the chat room sales support system at Udacity didn’t try to build a bot that could take over all conversations — it simply advised humans how to improve performance. (Other systems, however, have taken most of these tasks over.)Polanyi’s Paradox: Both ML systems and humans know more than they can explain. Problems can occur if the data fed ML is biased or lacks certain combinations. Regardless, the appropriate benchmark is not perfection but the best available alternative. The advantage of machine-based systems is that they can be improved over time and are consistent.ML systems like those at Affectiva are already at or beyond human-level performance in discerning a person’s emotional state on the basis of tone of voice or facial expression. Other systems can infer when even the best poker players are bluffing — even in the very complex Heads-Up No-Limit Texas Hold’em.ML is the most important general-purpose technology of our era. The time of tectonic change in the business world has begun. However, it’s not access to the new technologies themselves or the best technologists that separates winners from losers. It’s innovators open-minded enough to see past the status quo and envision very different approaches. Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t.Facebook a few years ago had just a few individuals in its ML group and needed days to run an experiment. The focus was on better targeting users with ads. Now several hundred employees run thousands of experiments/day. It’s focus, however, hasn’t been to simply get the best talent and write the best algorithms — instead, its been creating an AI workshop in which anyone can use AI to achieve a goal. Both deeply knowledgeable programmers and complete newbies can take advantage of Facebook’s AI workshop wares. Many companies err by focusing efforts around building the best algorithms, or hiring developers who claim to have the best algorithms. Real algorithmic breakthroughs, however, are only about 2–3/year, at best. Thus, focusing there takes lots of effort to make marginal gains. Instead, you should focus on finding people most obsessed with getting any algorithm to do a job. Focus on giving your existing algorithm more and better data, experimenting faster. Only after about three years did Candela think about upgrading some of his algorithms. (Joaquin Candela, Facebook’s Applied MachineLearning group)Rather than try to provide all AI needs for Facebook staff, Candela’s group provided them with simple tools with which they could do it themselves. (Saving communication time/effort, clear accountability.) Lumos, for example, is computer vision AI and capable of combing through photos to learn what they contain. And sometimes old-fashioned rules-based searching can achieve 80% of a solution.Many companies are waiting for AI to mature and AI expertise to become more available — then become ‘fast followers.’ However, the foundations of current AI were established decades ago. Second, development takes time — Sloan Kettering Cancer Center has been working with IBM to use Watson for treating some forms of cancer for over six years — and the system still isn’t ready for use. Finally new AI systems typically require new roles/skills for those using them → training time. Monitoring for potential fraud must also occur.Scaling, however, can be rapid. Thus, by the time a late adopter has completed preparations, earlier adopters will have already taken considerable market share and be able to operate at lower costs with better performance. Late adopters may never catch up.McKinsey contends AI will create $13 trillion of GDP growth by 2030. Andrew Ng advises starting small — choosing 2–3 company-level pilot AI projects. Their success will help convince stakeholders to invest further in AI capabilities. Choose projects than can be completed within 6 o- 12 months and have a high chance of success. (Probably should avoid areas where failure could be disastrous.)Example: if your firm is a medical device producer, building an AI-assisted recruiting platform will not likely impress key decision-makers and runs the risk of being outperformed by another such project having a much larger user base. Consider working with external partners to quickly bring in AI expertise. (Eventually you’ll want your own in-house AI team; however, waiting to build such before executing may be too slow vs. AI’s rise.)A rich source of AI project ideas lies in automating tasks humans are now doing via supervised learning. (AI is better at automating tasks than jobs; also much faster and simpler.) First clearly state the desired timeline, outcome, and a reasonable budget. Small project are better. Be certain to credit any associated business team members for their help.

Name: Amazon Customer
Rating: 4.0 out of 5 stars
Title: ExcellentoverviewofAIapplicationinbusiness
Date: Reviewed in the United States on September 19, 2020
Review: As a consultant in AI application in business, I have searched for and read many of books and articles on this topic. But I was always disappointed by the lack of two types of information:1. Detailed examples, especially for AI application in the service industry.2. Well explained mental models of how AI could be applied in business.This book does an excellent job on criteria 2. And a very good job on criteria 1.

Name: Amazon Customer
Rating: 5.0 out of 5 stars
Title: Goodbriefoverview
Date: Reviewed in the United States on December 7, 2020
Review: covered the essentials?

Name: Cliente Amazon
Rating: 5.0 out of 5 stars
Title: Niceprint
Date: Reviewed in the United States on March 7, 2020
Review: Excellent quality

Name: Robert Morris
Rating: 5.0 out of 5 stars
Title: HowandwhycompaniesthatdontuseAIwillsoonbecomeobsolete
Date: Reviewed in the United States on September 18, 2019
Review: This is one of the first volumes in a new series published by Harvard Business Review Press. Each offers dozens of cutting-edge insights within a business field of greatest importance now when the global marketplace today is more volatile, more uncertain, more complex, and more ambiguous than at any prior time that I can recall. In this instance, the field is artificial intelligence.Each volume anthologizes HBR articles that are among the most relevant and of greatest value. There are twelve in this volume that focus on AI, following a superb Introduction by Thomas Davenport. In terms of value, if all seventeen were purchased separately as reprints, the total cost would be about $110. Amazon now sells a copy of this volume for only $19.95. That’s quite a bargain.Davenport nails it: “Even as the significance of AI becomes irrefutable, it remains misunderstood. “Executives view AI as a key disruptive technology, employees fear it as a job destroyer, consultants pitch it as a cure-all, and the media hype and deride it endlessly.”This book will help you tune out all this noise and understand AI’s implications for you and your business. No matter your industry, level, or size of the company, this collection of some of HBR’s best recent articles on AI will show you where the technology is going.”Here are the key points he discusses in his overview (Pages xii-xv), material that brilliantly creates a context — a frame of reference — for the seventeen articles that follow:o AI [begin italics] is [end italics] booming in business — at least in certain segments of it.o A variety of different AI technologies are in use.o AI is being applied for various business purposes.o Many large companies are creating infrastructures and processes to managed AI.o Companies are finding success by focusing their AI efforts in certain areas of the organization.o AI hasn’t transformed business — yet.o AI’s overall impact on employment isn’t certain, but jobs will clearly change.o Implementing AI raises ethical questions.For me, one of the greatest benefits of this new series is that each volume illustrates what Wayne Gretsky meant when asked to explain why he played hockey better than almost all of his opponents: “They knew where the puck was. I knew [begin italics] where it was going to be [end italics].”The information, insights, and counsel in this volume — and in the other series volumes I have read thus far — will help business leaders to see where their competitive market will be in months and even years to come.Here are two points of my own. First, there are no AI issues. Rather, ultimately, there are only [begin italics] business [end italics] issues. Also, it is very important to think in terms of enterprise architecture as your organization’s primary strategy when embracing and then acting upon the insights it needs to prosper.

Name: S. E.
Rating: 5.0 out of 5 stars
Title: RegardlessyourAIknowledgethisbookwillhelpyoutograspitsimpactonbusiness
Date: Reviewed in the United States on August 18, 2021
Review: Excellent set of articles both for a significant level of detail and also a big picture understanding of AI and general issues impacting AI.

Name: Annarella
Rating: 5.0 out of 5 stars
Title: Itshighlyrecommended
Date: Reviewed in the United States on September 18, 2019
Review: An excellent collection of essay really useful for people who want to understand what AI is and the impacts.It’s highly recommended!Many thanks to the publisher and Netgalley for this ARC, all opinions are mine.

Name: Nathan Y Jang
Rating: 5.0 out of 5 stars
Title: 1
Date: Reviewed in the United States on September 29, 2020
Review: 1

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