Artificial Intelligence: The Insights You Need from Harvard Business Review (HBR Insights Series) Kindle Edition

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.

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.

covered the essentials?

1

Excellent quality

I’ve been working with data for the majority of my career. I started out with Executive Reporting, moved on to Business Intelligence, which then became Business Analytics, and added in Performance Management and Predictive capabilities. Eventually this led to Artificial Intelligence, Machine Learning, Data Science, and more.Artificial Intelligence is much like other technologies in that it is not a question of whether your organization will adopt AI, but rather when and how to tackle that first project. As with most new technologies, that first project is the most daunting of all. I have seen many companies dive in head first and quickly try to keep their heads above water. It’s not difficult, but it’s a matter of identifying the right use cases and the right place to start within your company. Getting started is key and if you haven’t already started, you may already be behind the curve compared to your peers.The articles in this book are a good compilation that can help companies get past that first bump in the road and begin their AI journey in earnest. This would be a great read for any team that is just starting out to help set the stage for their conversations and set the wheels in motion. It would also be a good read for any team that may have stumbled on their first foray and need to get things back on track quickly.As always HBR has solid articles and advice. A pleasure to read these in a combined offering.

Wonderful book.You start it and don't get to know that it finishes. Perfect sequence, industry relevant and interesting examples. Has set a very high bar.A must read and must for collection on the topic.

This book reminds me the uncertainty and the curiosity for the future technology.So. Great!Thank You!

good book