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@滔滔 发表于 2018-7-18 08:48
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充实每一天 发表于 2018-7-18 05:08
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2018-07-18

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1. 今天阅读到的有价值的全文内容链接:
Continue reading: “Artificial Intelligence — Opportunities Ahead”
https://www.pmi.org/learning/tra ... rojectified-podcast

2. 今天阅读到的有价值的内容段落摘录:
Michael Chui
The first thing is, why are we looking at this at all? I think one of the things that we have observed as we've, you know, looked at the world - and I'm a technologist by training, a computer scientist, cognitive scientist - is some perhaps surprising advances in the technologies themselves around Artificial Intelligence and robotics. You know, as a former Artificial Intelligence researcher I would have thought just a few years ago that the task of creating a self-driving car would be years away, just a very difficult set of engineering challenges, etcetera. And yet here where I live in San Francisco, nowadays if I drive on the streets, you know, it's more likely than not you'll run across one of these self-driving cars in test mode. And so there's that, there's a set of things that have happened in the cognitive realm as well, you know, not only beating the world champion in chess two decades ago but now the world champion in Go, which is a game that's many times more complex and difficult than chess; but then the ability for machines to read lips better than deaf people do. And so again, to a certain extent the technology is surprising us. And then when you think about what the implications for these technologies are in the world of work, and the degree to which things that we thought only humans might be able to do effectively, now the machines are doing things as well or even better. You know, there's a natural series of questions about what does this mean for work? What does this mean for employment? You know, are we going to have mass unemployment going forward because the robots and AI will take over everyone's job? And so that was really the motivation for the research. So what did we find and how did we think about it? I think one of the things that we looked at doing as we looked at jobs, you know while others have studied jobs themselves, we actually thought that if you look at any individual occupation that's the wrong level of granularity to examine this phenomenon. Because everyone in their job, everyone in their occupation does multiple different activities, each of which has a different propensity for machines to automate. And so rather than looking only at 900 or so jobs that the US Bureau of Labor Statistics catalogs, we looked at all of the constituent activities, over 2000 of them in total. And for each of those we scored them against 18 different capabilities which potentially could be automated. So everything from cognitive tasks, recognizing known patterns or developing novel patterns, doing logical problem-solving; some physical things such as fine motor skills, gross motor skills, navigating the physical world; linguistic things, understanding when someone's talking, being able to process it, being able to respond in a natural language such as English; and even some social and emotional capabilities, the ability to read or recognize human emotion in another person, process that and then respond in an appropriate way. So against 18 of these different capabilities we tried to understand what level is necessary in order to accomplish all of the activities that we pay people to do in the economy. And then at the same time we looked at to what extent could technology actually accomplish different capabilities of those 18 different capabilities, and just compared. And you ask what are some of the surprising findings? Again, to a certain extent they're not surprising in retrospect but at least at a topline, if we just look at technical potential, what we found was about half of all the activities we pay people to do in the global workforce could be automated by adapting currently demonstrated technologies; not even requiring some sort of breakthrough in technology by some of these Artificial Intelligence and robotics researchers. Literally by adapting currently demonstrated technologies, half of the things we pay people to do in the global economy could be automated. Now that's a huge number. The other thing that we tried to understand was, well look if that's theoretically possible from a technology standpoint, you know, how long and how fast might that actually happen? And this is where we tried to understand, look what are the things that have to happen in order for technology to actually be adopted? And number one, we have to solve a bunch of technology problems. So while I said theoretically half of the activities could be automated by adapting currently demonstrated technologies, that adaptation mostly hasn't been done. So the technologists actually have to spend money and time integrating those technologies together and adapting them for individual activities. That takes some time. In many cases, you know, millions of dollars and years to do. So, you know, in order to get things to actually become automated and adopted, the technology problems have to be solved. You need to have a positive business case, so you need to compare the price of the technology versus the cost of human labor, and then also net in all of the other potential benefits of automation such as reduced variability, increase through-put, decreased errors, greater safety. And then even when you have a positive business case, even when that all nets out positively, there's a natural adoption curve, and we've looked at dozens of different technologies and across all those technologies, between the time of their commercial adoption and their eventual plateau in adoption, the time of commercial availability and the eventual plateau in adoption, takes in the neighborhood of eight to 28 years. And so when you net all of those things together, the point at which half of today's activities might be automated, the middle of all of our scenarios is 2055. And so that's quite some time. Now we modeled a scenario that's 20 years later and 20 years earlier, so again we're humble enough to know we can't predict the future and there's lots of variability here. But again, what we think is that things will happen somewhat slow in macro - that is, it takes a long time for half of the world's activities to be automated - but faster in micro in the sense that if you're the individual who is affected by these technologies, or if you're a company that has to compete on the basis of these technologies, this might happen quite quickly for you. If you think about business, the hard work is usually not the development of the technology itself or even the acquisition and procurement; it's all of the project management, to be honest, the change management that has to happen.
I think there are two broad categories of potential implications of the adoption of these technologies for people in the project management space. Number one, I think it will change the activities that a project manager does in terms of just the way that they execute their job, and then it will change the way that the projects that they manage also will get done. So let me just comment on each of those in turn. In terms of the way that projects get done, and as you mention there's a huge variety in terms of the actual projects that are being done across sectors and functions, etcetera, but there are three broad categories of activities which we found to have the highest susceptibility to technical automation. Number one is a little bit unsurprising, they are physical activities in predictable environments. You know, think about an assembly line in a factory for instance, that's a classic example. But there are a number of different other sorts of physical activities and predictable environments which I think will be increasingly automated. You know, this includes agriculture for instance, you know, what happens within a barn. So there's still a lot of opportunity there even though we sort of understand how this might work within a factory. But I think there are two other categories of types of activities which again have high susceptibility of automation and we'll find more and more of the projects that project managers are managing increasingly they'll need to manage not only the people but the machines that do these things, and they are number one: collecting information, and number two: processing information.  
                          

3.今天阅读的自我思考点评感想                          

(1)We should understand what level is necessary in order to accomplish all of the activities that we pay people to do in the economy, at the same time we should look at to what extent could technology actually accomplish different capabilities

(2)If that's theoretically possible from a technology standpoint, how long and how fast might that actually happen? And this is where we tried to understand, look what are the things that have to happen inorder for technology to actually be adopted?

(3)Understand the process to be a successful AI technology practice:

#1,we have to solve a bunch of technology problems. The technologists have to spend money and time integrating those technologies togetherand adapting them for individual activities.

#2:You need to have a positive business case, so you need to compare the price of the technology versus the cost of human labor, and then alsonet in all of the other potential benefits of automation such as reduced variability, increase through-put, decreased errors, greater safety.

#3:Even when you have a positive business case, even when that all nets out positively, there's a natural adoption curve, the time of commercial availability and the eventual plateau in adoption takes inthe neighborhood of 8 to 28 years.

#4:When you net all of those things together, the point at which half oftoday's activities might be automated, the middle of all of our scenarios is 2055.

(4)It takes a long time for half of the world's activities to beautomated - but faster in micro in the sense that if you're the individual who is affected by these technologies, or if you're acompany that has to compete on the basis of these technologies, this might happen quite quickly for you.

(5)The hard work in business is usually not the development of the technology itself or even the acquisition and procurement; it's all of the project management-------the change management that has tohappen.

(6)Two broad categories of potential implications of the adoption ofthese technologies for people in the project management space:

#1:it will change the activities that a project manager does in terms ofjust the way that they execute their job, and then it will change theway that the projects that they manage also will get done. We'regoing to start to see those things happen in steroids you can get the machines to do "the boring stuff" and perhaps the less interesting stuff, then people are freed up to do the things that are perhaps more engaging, more interesting.

#2:How to use these technologies to improve performance. Don't just look at an individual activity and say, okay we can automate thatactivity; rather completely re-examining the entire process and how can you re-think or transform that process by using these technologies is really in practice what happens. The way that we view these machines changes over time based on familiarity, based on understanding the risks and just experience.

(7)What a PMP need to do?

#1:A project management professional needs to be able to understandtechnologies, understand the art of the possible, and try to stay atleast a breast if not ahead of what these technologies can do;because they can both affect and improve the work that anyprofessional does as well as the projects thatthey're trying to manage.

#2:some of our other research has led us to the conclusion that theeffective use of data and analytics is changing the game. Being ableto become more familiar with statistics, experimental design and these other sorts of disciplines where rather than just businessjudgment and experience, being able to apply numerical quantitative statistical analyses to decision-making will be very, very important.

#3:Focus on some of the skills, some of the capabilities which will bemost difficult for the machines to do - whether it's motivatingothers, whether it's emotional intelligence, whether it's being creative - and then being flexible and agile. Technologies are goingto affect all of us and we're going to change what we do day to day. How can we be resilient, how can we learn how to learn? All of those sorts of soft skills, mindset, capabilities which are in those realms are going to be increasingly important going forward and to be ableto cultivate practice. In certain cases, you can learn over time,which will also stand people in good stead as the technologies continue to wash over the work that we all do. The thing that will bemost valuable and engaging is how do you motivate people, how do you lead a group, how do you get people to do the things that need to be done?

昨天阅读2小时, 累计阅读21小时.
Content: judgment in managerial decision making
Author: Max H. Bazerman & Don A. Moore
Abstract: chapter 1&2 introduction and overconfidence
I would like to draft a mind map for this introduction and overconfidence.
Learning: I am thinking a question, which one is better for a chief executive, optimistic & over-confident Or pessimistic & under-confident. Maybe it is related to the corporate life cycle which are baby phrase, growth phase, maturity phrase, revival phrase, decline phrase.