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As Artificial Intelligence gains more and more momentum, what are your biggest concerns with AI in BPM and in the workplace in general?
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Humans have always been able to adapt to revolutions. But that took years. My biggest concern is that the AI revolution is so fast that maybe the workforce doesn't have enough time to convert and adapt to the new landscape. Workers that use lot of "human touch" (nursery, teachers, etc) are not in danger. But there are several positions in immediate danger.

For example, call centers can replace part of their workforce with AI based agents for common questions. Maybe not (right now) for specialized assistance, but definitely to easier cases like "re check the hotel reservation".

And of course, in the BPM field, this situation is exactly the same, being RPA another driver for the AI revolution.
CEO at Flokzu Cloud BPM Suite
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  1. more than a month ago
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AI was invented to solve problems humans intelligence can't solve (or can't solve within a realistic framework of time, resources and utility) - these are highly non-linear and extremely complicated problems.

My biggest concern is that we will apply AI indiscriminately to solving problems that are within our reach to solve, depriving us of the skills and the perspectives that will help us grow. Without such human skills and perspectives to audit the results of the AI solutions, these solutions may increasingly depart from human understanding. The consequences of that are bleak.
CEO, Co-founder, Profluo
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  1. more than a month ago
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My biggest concern is that it creates a bigger gap between rich and poor; between the ones that adapt and make money (or less cost) out of it and the ones that are overwhelmed by all of this.

Luckily I also see positive things like applying AI for better health care, more efficient growing of food, energy saving. All cool, but my biggest concern is the social effect it will have on the short term.
Sharing my adventures in Process World via Procesje.nl
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Sadly, Emiel , with respect to "social effect", the attitude of many "hired guns" in large corporations seems to be ' . . . . .. let them eat cake".

Here is a post from my Facebook re Wells Fargo in the news today.

QUOTE
Not yet another "discovery" !
Henry Wells and William Fargo must be spinning in their graves.
My grandmother would have said "Well, I never. . . "
Should be interesting to see if any of this "extra" revenue is left over to pay fines after all of the "exit bonuses" are paid out.
ENDQUOTE

http://money.cnn.com/2017/08/31/investing/wells-fargo-fake-accounts/index.html
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Weaponization and "going postal."
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In the workplace?? ?
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I think that there are a multitude of rolling challenges that are not being addressed. In order not to repeat myself, I leave here a pointer about it called the man and the machine.
References
  1. https://ultrabpm.wordpress.com/2017/09/21/the-man-and-the-machine-manifesto/
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  1. more than a month ago
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1. People will try, again, solve all their business problems with AI only instead of using together available and new technologies.

2. Lack of the obvious synergy between process models and AI – availability of both machine-executable models and data allows a) improving models, b) finding root causes of some problems and c) anticipating the process execution.

Thanks,
AS
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  1. more than a month ago
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  3. # 6
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The important thing re AI is to keep stupid people away from it.

John Cleese summed it up nicely
https://www.youtube.com/watch?v=wvVPdyYeaQU
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  1. more than a month ago
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Failure to have the necessary transparency in exactly what the AI does and the logic driving it. Interesting program on TV on why planes crash....yes some caused by computerised automation of actions with pilots failing to understand....As ever AI being over hyped even attracting global leaders making uninformed claims....nothing new there! People will always drive actions and knowledge needs to be priority in build and use of AI; my fear is ignorance will result in big "crashes".....
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  1. more than a month ago
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  3. # 8
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Along an imaginary bell curve from first AI steps and learning curves, over to a peak of max. added value from AI at the workplace, ending in a dystopian Skynet substitution of the human element, I believe that we are pretty much still at the very beginning of things.
Many so-called AI application are more often than not "just" programs that extend existing features closer to the end users. So, at the heart of many current front facing AI applications, one usually finds complex, dynamic yet limited "if-then-else" algorithms, rather than applications that are based on - and integrated to - advanced, self-learning and cognitive platforms such as IBM Watson.
For the latter however, I for one am eagerly looking forward to the benefits these advancements will provide - especially to the possibility of designing and implementing complex solutions, entirely formulated in natural languages and expressions. I hope that this will enable more people than ever before to express and to instrumentalize creative solutions to complex real world challenges.
NSI Soluciones - ABPMP PTY
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  1. more than a month ago
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  3. # 9
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A couple of challenges in near-term.

  1. Making AI transition from just being Next Best Action to independently taking actions
  2. Somehow cleansing the data used in decision making from various human biases
--
Adeel Javed
Intelligent Automation Specialist (BPM, RPA, Rules & Integrations).
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  1. more than a month ago
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  3. # 10
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A good starting position IMO for AI in the area of workflow/workload management might be to prevent things from happening as opposed to trying to automate processing.

As we move away from monolithic apps to more general apps that reach out to IoT devices/systems and accept input from these, the use of AI at pre-processors becomes promising (is the data right for moving forward with this process step? is the source of this data a source we have worked with previously and found to be reputable and reliable? Is the source coming from a location that is expected? is the timing of the source data inflow as expected?)

Every system has boundary conditions - each system has presumably been tested to give the "right": results within its boundaries. A pre-processor can test whether processing can / should / should not be engaged.

@Adeel raises a good point - ". . . .cleansing the data used in decision making".

My suggestion is use AI to detect whether data is OK as is, whether the data needs cleansing (allow the system to only propose cleaning for anything major by referring the proposed action to a human for go/no-go), or whether the data is NOT OK (issue a hard stop).
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In terms of monitoring processes automatically as prescribed would be a good move. Not sure I would call AI see it more of Intelligent Processes which can automatically compare data to ensure within boundaries and present with actions...maybe even automatically implement with necessary notifications to those responsible? The right architecture with rules should make this readily facilitated now?
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  3. # 11
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Erosion of control in AI.

While AI presence explosively grows in business and daily routine, little to none of its regular adopters really understand the technology or take a moment to think about its implications and consequences.

Lion's share of AI grounds on various forms of neural networks or, at least, uses them as an essential component. Mathematically, neural network is a sort of optimization algorithm. Very popular, although not universal, is the usage of conjugate gradients generally known in theory of neural networks as backpropagation.

Strictly speaking, any optimization algorithm requires a rigorous proof of convergence. On a poorly structured data-set optimization may converge to a local minimum far from global optimum or never converge at all by falling into infinite loop of false guesses. As a result, on arbitrary input data AI results will be dubious or simply irrelevant. The rule of thumb in mathematical modeling: you should exactly know the solution of the problem before you try to simulate it on computer. Alas, nearly all practically important business problems do not allow for any rigorous estimates of relevance in their AI representation. Consequently, ever increasing amount of business applications becomes dominated by ad-hoc AI constructs with questionable benefits, advantages, relevance and, ultimately, safety for consumers and business as a whole.

Internally, neural network is encoded by a number of layers composed of nodes with distinct weights determined during network training. Practically, on business level nobody ever cares about real interpretation and sense of these internal structures, which stay hidden on deepest system level while defining essential behavior of all AI systems. As a result, the whole AI world is rapidly becoming an explosively growing but absolutely not controlled black box, which aggressively occupies increasing range of modern business areas. Chaotic spread of AI treats stability and even existence of modern business.

This negative trend with black box AI solutions is drastically different from evolution of science and, especially, mathematics over several previous centuries. While classical mathematics largely shaped modern view of the world, recent arbitrary growth of AI effectively destroys our essential knowledge and cognitive abilities.

BPM and metadata management play indispensable role in opposing these negative AI trends. BPM becomes an essential asset in explicit structuring and control of AI dominated digital business. BPM crystallizes human friendly, understandable view of the technology, which, otherwise, dissipates in AI advances.

https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Colored_neural_network.svg/399px-Colored_neural_network.svg.png
An example of artificial neural network.

https://upload.wikimedia.org/wikipedia/commons/6/6d/Error_surface_of_a_linear_neuron_with_two_input_weights.png
Error surface of a linear neuron with two input weights.
References
  1. https://en.wikipedia.org/wiki/Artificial_neural_network
  2. https://en.wikipedia.org/wiki/Backpropagation
  3. https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
Comment
  1. John Morris
  2. 1 year ago
  3. #4592
TWEET: #Risk of #AI, #math-based: #BlackBox -> Loss of #Tacit -> #Chaos - http://bit.ly/2yaOhrm - @BPMdotcom @PSchooff @BGZinchenko #BPM #Process
Thank you, @John. Concise and evident logical sequence missed by so many AI adepts.
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