September 28 & 29, 2020 | Virtual
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The potential power of PM-focused machine learning could be accelerated as a quantum computer could feasibly process much bigger data sets much faster. And I'm sure there will be a number of early adopter projects for tech PMs to run. Outside of that, I'm not envisioning any direct consequences.
Quantum Computation and Artificial Intelligence in the Project Manager Replacement Process
When will quantum computers be ubiquitous enough to be used on mundane activities such as project management? Right now, we are at the `70s of quantum computers: expensive hardware, time share access, few programmers.
When the term AI is used in the context of project management, we are referring to the field of Machine Learning which like all current “exploitations of AI” is “Weak AI”. Machine Learning is using evolved advanced algorithms that analyze data using models and patterns that come to a result through structured logic – it is NOT “Strong AI”, stated another way, we are not talking about the mimicking of human intelligence which is what would be required to “replace a PM”.
Machine Learning at the best is a complementary tool to the project manager, so let all fear and hyperbole be dismissed on this subject. As it relates to Quantum Computing --
now that will be exciting when it becomes stable.
And to George's comments: We should all be looking forward to this!
But, the technology has to be proven and stable, there has to be a use case that companies will buy-in to and it has to have a return on money. A long road to get there! Let's see what evolves.
Quantum computers are in the very begining of their development, are still unstable, and very susceptible to varying sound, temperature and very expensive. According to experts It may still be decades before quantum computers are ready to solve problems that today's classical computers aren't fast or efficient enough to solve, but the emerging "probabilistic computer" could bridge the gap between classical and quantum computing.I believe more that an intermediate solution will emerge within a decade or so.
There is a tendency to mix AI with machine learning and they are different things. Machine learning uses programming through a thing called “neural networks.” This is where Machine Learning “learns” through training algorithms and determining the probable outcome of a situation. The process requires a human to program the information into the ML with data, hours of training and testing and fixing issues in the outcomes.Ex: Medical Diagnosis, Software engineering, Search engine optimization. AI can create outcomes on its own and things that only a human could do. ML is a part of what helps AI by taking the data that it has been learned and then the AI takes that information along with past experiences and changes behavior accordingly. ex: understanding a natural language.
Part of the issue here is the “hyperbole of words” that gets used to describe AI – which has largely become a marketing magnet term to garner attention and funds. For instance, you used the phrase “AI… ex. Understanding a natural language,” which implies awareness and consciousness due to the root meaning of “understand”; which I believe we all recognize does not exist.
The term “AI” has lost its meaning and is being used across a large spectrum of hard-won software engineering fields (e.g. ML) for the desired purpose of marketing momentum. I get the reasoning, but due to the constant storm of hyperbolic articles, many professionals get caught up in the excitement and believe a major transformation is happening, for instance, to our field (e.g. replacement of PM’s).
I’m trying to be a voice of reason on this topic, but recognize my voice is in the extreme minority as the horse has already left the barn – to use an idiom. So, I recognize the difference between these disciplines and only ask that we, as project professionals attempt to moderate ourselves on this topic.
Food for thought.
I do not disagree with you, and people like us who work in the area this distinction is natural, was more to reinforce the fact that for the community it are different disciplines that can complement each other. I am sure that there are many experts on the subject in the community, in the end words are only words as long as the communication is in tune we can understand each others.
I very much appreciate your insights on this subject and in general in the community – keep up the good work! I understood your posting in its context and the words were fine for me, but I wanted to represent the issue I’m seeing (not from you) but in general with the “buzz” around AI-related subject matter.
Not everyone in the community has a technical background and fewer yet have ML, NLP knowledge. However, everyone picks up on the “alarm going off that the project profession is changing and changing quickly due to AI” conversation – that is the conversation I believe should be tempered.
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