Podcasts about dikw

In the continuation of the last episode, Todd re-explores his oft-ignored Path to Empathy, the DIKW pyramid, and data Overload before exploring religion's real purpose and offering our pompous atheist friends the back of his rhetorical hand. From that joyful and uplifting summit, he moves on to question how woke parents can sacrifice their own children to a foreign ideological god.    Also: Introducing The Desert of the Why! We know what legislative riders are. What about "truth" riders?  The one thing in news, information, and tech that must remain unspoken. Why climate change hysterics are misguided and a waste of time. How the stupid cliques we form as kids flare up like herpes for the rest of our lives. Todd's new Gatekeeper disconnect. Chasing Che.    Like it? Share it!  https://toddzillax.substack.com https://www.youtube.com/channel/UCjdLR140l--HufeRSAnj91A  

https://www.yourcyberpath.com/80/ In this episode, Kip and Jason, along with special guest Drew Church, take a closer look at the NIST risk management framework to help facilitate selecting the right kind of security for your system and help clarify how to direct resources towards the right controls. Drew Church, RMF expert and global security strategist at Splunk, is here to talk about the different steps of RMF, the importance of preparation work, and understanding the bigger picture of what you want your system to accomplish. They also go through the seven steps of RMF in detail: prepare, categorize, select, implement, assess, authorize, and monitor, highlighting the best procedures and ways of going about completing each step, as RMF is highly structured. They also call attention to soft skills and how invaluable they are throughout your cybersecurity career. Drew and Jason also explain different terms, including STIGS, DIKW pyramid, and POAM, and their importance while developing the RMF. Finally, they go over various tips and tricks to make sure you are ready for your assessment, like knowing what your system is going to be graded on and maybe also testing beforehand, as well as having in mind that the assessors are not going to be experts in your system.  What You'll Learn ●     What is RMF (and what it's not)? ●     Are RMF and CSF the same? ●     What are the seven steps of the RMF? ●     How important is the DIKW pyramid in RMF? ●     What is the secret to success of system assessments against RMF controls? Relevant Websites For This Episode ●     www.YourCyberPath.com ●     www.nist.gov ●   www.splunk.com Other Relevant Episodes ●     Episode 62 - The NIST Cybersecurity Framework ●     Episode 56 - Cybersecurity Careers in the Defense Sector ●    Episode 22 - Impress Us with Your Resume Skills Section

“It's a mistake to think that creating information from data is easy.” - Jose Berengueres   In this episode of Data Chats, Chris Richardson interviews Jose Berengueres. He is an associate professor in design thinking, data visualization and computer science at the United Arab Emirates University. He is also the author of Data Viz and Sketch Thinking   They discuss Why you should think beyond excel for creating graphs and charts The importance of the DIKW pyramid (Data, Information, Knowledge and Wisdom) How culture influences communication, specifically visualization decisions The problems with pie charts and color decisions Recommended Resources Tableau on Twitter Email: jose@uaeu.ac.ae to receive a free PDF copy of the book Data Viz. ** Write DATAVIZ in the subject line of the email   Continue Learning Data Science for Business Leaders This course teaches you how to partner with data professionals to uncover business value, make informed decisions and solve problems. Learn More Business-Driven Data Analysis This course teaches a proven, repeatable approach that you can leverage across data projects and toolsets to deliver timely data analysis with actionable insights. Learn More

Новый выпуск подкаста рубрики "Эра искусственного интеллекта" приурочен началу учебного года. Автор рубрики Светлана Вронская, ведущая Telegram-канала Analytics Now, выступает в роли учительницы, рассказывающей своим ученикам о 5D - пяти понятиях, описывающих разные аспекты работы с большими данными. Это DIKW, DataOps, Data-as-a-Service, Data Fabric и dark data. __________________________________________ Telegram-канал TAdviser: https://t.me/tadviser Telegram-канал Analytics Now: https://t.me/analyticsnow Присылайте свои предложения и комментарии по развитию подкастов TAdviser на почту editor@tadviser.ru

Find David Benjamin online: Twitter: https://twitter.com/complexitydb Linkedin: https://www.linkedin.com/in/davidbenjaminsyntegrity/ Memorable Quotes from the episode: [00:14:17] "Every time you hit something complex, it's new, it's different. There is no playbook, there is no checklist. And really what you have to do is get all the right people involved in sort of sharing what they see. No believe, understand, get them buying into the right way to to move forward. And the last thing I'll say is, you know, the right way to move forward when it's complex is to try things." [00:17:41] "...you start to see that really the key to dealing with complexity is to get people get their fingerprints all over the solution, get their fingerprints all over the strategy. And I can't imagine a situation where doing Data strategy is only complicated because, again, the market, the business you're in, the people who work there, you know, it's going to be different every time. And of course, are you going on and on." Highlights of the show: [00:01:29] Guest Introduction [00:02:53] Where did you grow up and what was it like there? [00:03:31] What did you imagine your future would be like? [00:05:07] What types of things were you doing that then eventually led to to this being the thing that you chose to pursue? [00:06:22] What does it mean to be a systems thinker? [00:12:36] What's your favorite example of a problem that on the surface looks like it fits the description of complicated, but as you start to dig a little bit deeper, it turns out that it's actually complex. [00:14:07] How is the problem solving process different for a complicated versus a complex problem? [00:17:38] The nature of complexity. [00:19:39] How do we best deal with that? How do we best deal with something we've never dealt with before without knowing what's going to work and what's not going to work? [00:24:40] What is DIKW model? [00:32:53] Letting go is a huge part of good leadership. [00:37:57] What are some questions that we can ask ourselves so that we can find a way forward in these types of situations? [00:39:57] Can we inject requisite variety into our world in different ways? [00:48:00] What's the number one book of Stafford Lehr that myself and the audience would really benefit from his thoughts? [00:48:55] What's the difference between constructing a good question and asking a question? [00:51:39] What are rules of queueing as you've discussed in your book? [00:53:18] How do faulty assumptions make us ask bad questions? [00:55:33] Why is it that when we are having these face to face types of interactions and discussions that we're able to create complexity? [00:57:27] Does that have an effect on anything when we're working together? [00:59:31] Is cracking complexity, an art or a science? [01:01:33] How do we create serendipity when we're working on complex problems and maybe when we're working isolated from people? [01:04:34] RANDON ROUND [01:04:49] It's one hundred years in the future. What do you want to be remembered for? [01:08:23] What do most people think within the first few seconds when they meet you for the first time? [01:09:09] Do you think you have to achieve something in order to be worth something? [01:09:59] What are you currently reading? [01:11:39] What song do you have on repeat? [01:12:28] What's the story behind one of your scars? [01:13:02] What is one of the greatest values that guides your life? [01:13:45] If you could have any superpower, what would it be and why?

Writing with wisdom is what will make your writing endure. We often say that transformation is better than information, which is true. But a simple transformation may not be enough. You need to transform into the wisest version of yourself, extracting the essence of the world you see around you and turning it into a capacity to make good judgments. In this episode, I discuss the DIKW pyramid framework and how it can be used to write with wisdom. Join The Word Leader Facebook Community to learn more about writing, connect with like-minded fellows, and get support in your writing journey.

Writing with wisdom is what will make your writing endure. We often say that transformation is better than information, which is true. But a simple transformation may not be enough. You need to transform into the wisest version of yourself, extracting the essence of the world you see around you and turning it into a capacity to make good judgments. In this episode, I discuss the DIKW pyramid framework and how it can be used to write with wisdom. Join The Word Leader Facebook Community to learn more about writing, connect with like-minded fellows, and get support in your writing journey.

Are we creating the democratic utopia many naively envisioned at the Internet's birth? Or, are we descending into hell on Earth unleashed by informational anarchy and the freed, and anonymous, human beast? A prelude to my deep dive into Ellul's Propaganda, this episode begins with a description of the DIKW pyramid then moves on to how Plato's Illusion of Knowledge fable applies to the digital Matrix dominated by Social Media, Google, and ostentatious sophists posing as "influencers." Also: a discussion ofDr. Elias Aboujaoude's stellar book Virtually You and a one-sided examination of the God-Devil duality lurking inside each of us. Like it? Share it!   The Social Dilemma: watch It! https://www.netflix.com/title/81254224Dr. Eli: http://www.eliasaboujaoude.comHis book: https://www.amazon.com/Virtually-You-Dangerous-Powers-Personality/dp/0393340546 For the masochist: www.escapingthecave.com      

Welcome to another special edition of „Mediocrity and Madness“! Usually this Podcast is dedicated to the ever-widening gap between talk and reality in our big organizations, most notably in our global corporates. Well, I might have to admit that in some cases the undertone is a tiny bit angry and another bit tongue-in-cheek. The title might indicate that. Today’s episode is not like this. Well, it is but in a different way. Upon reflection, it still addresses a mighty chasm between talk and reality but the reason for this chasm appears more forgivable to me than those many dysfunctions we appear to have accepted against better judgement. Today’s podcast is about artificial intelligence and our struggles to put it to use in businesses. This podcast is to some measure inspired by what I learned in and around two programs of Allianz, “IT Literacy for top executives” and “AI for the business”, which I had the privilege and the pleasure to help developing and facilitating. I am tempted to begin this episode with the same claim I used in the last (German) one: With artificial intelligence it is like with teenage sex. Everybody talks about it, but nobody really knows how it works. Everybody thinks that everyone else does it. Thus, everybody claims he does it. And again, Dan Ariely gets all the credits for coining that phrase with “Big Data” instead of “artificial intelligence” which is actually a bit related anyway. Or not. As we will see later. To begin with, the big question is: What is “artificial intelligence” after all? The straightforward way to answering that question is to first define what intelligence is in general and then apply the notion that “artificial” is just when the same is done by machines. Yet here begins the problem. There simply is no proper definition of intelligence. Some might say, intelligence is what discerns man from animal but that’s not very helpful, too. Where’s the boarder. When I was a boy, I read that a commonplace definition was that humans use tools while animals don’t. Besides the question whether that little detail would be one that made us truly proud of our human intelligence, multiple examples of animals using tools have been found since. To make a long story short, there is no proper and general definition of intelligence. Thus, we end up with some self-referentiality: “It’s intelligent if it behaves like a human”. In a way, that’s quite a dissatisfying definition, most of all because it leaves no room for types of intelligences that behave – or “are” – significantly non-human. “Black swan” is greeting. But we’re detouring into philosophy. Back to our problem at hand: What is artificial intelligence after all? Well, if it’s intelligent, if it behaves like a human, then the logical answer to this question is: “artificial intelligence is when a computer/machine behaves like a human”. For practical purposes this is something we can work with. Yet even then another question looms: How do we evaluate whether it behaves like a human? Being used to some self-referentiality already, the answer is quite straight forward: “It behaves like a human if other humans can’t tell the difference from human behavior.” This is actually the essence of what is called the “Turing test”, devised by the famous British mathematician Alan Turing who next to basically inventing what we today call computer sciences helped solving the Enigma encryption during World War II. Turing’s biography is as inspiring as it is tragic and I wouldn’t mind if you stopped listening to this humble podcast and explored Turing in a bit more depth, for example by watching “The imitation game” starring Benedict Cumberbatch. If you decide to stay with me instead of Cumberbatch, that’s where we finally are: “Artificial intelligence is when a machine/robot behaves in a way that humans can’t discern that behavior from human behavior.” As you might imagine, the respective tests have to be designed properly so that biases are avoided. And, of course, also the questions or problems designed to ascertain human or less human behavior have to be designed carefully. These are subjects of more advanced versions of the Turing test but in the end, the ultimate condition remains the same: A machine is regarded intelligent if it behaves like a human. (Deliberately) stupid? It has taken us some time to establish this somewhat flawed, extremely human-centric but workable definition of machine intelligence. It poses some questions and it helps answering some others. One question that is discussed around the Turing test is indeed whether would-be artificial intelligences should deliberately put a few mistakes into their behavior even despite better knowledge, just in order to appear more human. I think that question comes more from would-be philosophers than it is a serious one to consider. Yet, you could argue that if taking the Turing test seriously, in order to convince a human of being a fellow human the occasional mistake is appropriate. After all, “to err is human”. Again, the question appears a bit stupid to me. Would you really argue that it is intelligent only if it occasionally errs? The other side of that coin though is quite relevant. In many discussions about machine intelligence, the implicit or explicit requirement appears to be: If it’s done by a machine, it needs to be 100%. I reason that’s because when dealing with computer algorithms, like calculating for example the trajectory of a moon rocket, we’re used to zero errors; given that the programming is right, that there are no strange glitches in the hardware and that the input data isn’t faulty as such. Writing that, a puzzling thought enters my mind: We trustin machine perfection and expect human imperfection. Not a good outlook in regard to human supremacy. Sorry, I’m on another detour. Time to get back to the question of intelligence. If we define intelligence as behavior being indiscernible from human one, why then do we wonder if machine intelligence doesn’t yield 100% perfect results. Well, for the really complex problems it would actually be impossible to define what “100% perfect” even is, neither ex ante nor ex post but let’s stick to the simpler problems for now: pattern recognition, predictive analysis, autonomous driving … . Intelligent beings make mistakes. Even those whose intelligence is focused onto a specific task. Human radiologists identify some spots on their pictures falsely as positive signs of cancer whilst they overlook others that actually would be malicious. So do machines trained to the same purpose. Competition I am rather sure that the kind listener’s intuitive reaction at this point is: “Who cares? – If the machine makes less errors than her human counterpart, let her take the lead!” And of course, this is the only logical conclusion. Yet quite often, here’s one major barrier to embracing artificial intelligence. Our reaction to machines threatening to become better than us but not totally perfect is poking for the outliers and inflating them until the use of machine intelligence feels somewhat disconcerting. Well, they are competitors after all, aren’t they? The radiologist case is especially illuminating. In fact, the problem is that amongst human radiologists there is a huge, huge spread in competency. Whilst a few radiologists are just brilliant in analyzing their pictures, others are comparatively poor. The gap not only results from experience or attitude, there are also significant differences from county to country for example. Thus, even if the machine would not beat the very best of radiologists, it would be a huge step ahead and saving many, many lives if one could just provide a better average across the board;  – which is what commonly available machines geared to the task do. Guess what your average radiologist thinks about that. – Ah, and don’t mind, if the machine would not yet be better than her best human colleagues, it is but a matter of weeks or months or maybe a year or two until she is as we will see in a minute. You still don’t believe that this impedes the adaption of artificial intelligence? – Look this example that made it into the feuilletons not long ago. Autonomous driving. Suppose you’re sitting in a car that is driven autonomously by some kind of artificial intelligence. All of a sudden, another car – probably driven by a human intelligence – comes towards you on the rather narrow street you’re driven through. Within microseconds, your car recognizes its choices: divert to the right and kill a group of kids playing there, divert to the left and kill some adults in their sixties one of which it recognizes as an important advisor to an even more important politician or keep the track and kill both, the occupants of the oncoming car … and unfortunately you yourself. The dilemma has been stylized to a kind of fundamental question by some would-be philosophers with the underlying notion of “if we can’t solve that dilemma rationally, we might better give up the whole idea of autonomous driving for good.” Well, I am exaggerating again but there is some truth in that. Now, as the dilemma is inextricable as such: bye, bye autonomous driving! Of course, the real answer is all but philosophical. Actually, it doesn’t matter what choice the intelligence driving our car makes. It might actually just throw a dice in its random access memory. We have thousands of traffic victims every year anyway. Humankind has decided to live with that sad fact as the advantages of mobility outweigh these bereavements. We have invented motor liability insurance exactly for that reason. Thus, the only and very pragmatic question has to be: Do the advantages of autonomous driving outweigh some sad accidents? – And fortunately, probability is that autonomous driving will massively reduce the number of traffic accidents so the question is actually a very simple one to deal with. Except probably for motor insurance companies … and some would-be philosophers. Irreversible Here’s another intriguing thing with artificial intelligence: irreversibility. As soon as machine intelligence has become better than man in a specific area, the competition is won forever by the machines. Or lost for humankind. Simple: as soon as your artificial radiologist beats her human colleague, the latter one will never catch up again. On the contrary. The machine will improve further, in some cases very fast. Man might improve a little, over time but by far not at the same speed as his silicon colleague … or competitor … or potential replacement. In some cases, the world splits into two parallel ones: the machine world and the human world. This is what happened in 1997 with the game of Chess when Deep Blue beat the then world champion Gary Kasparow. Deep Blue wasn’t even an intelligence. It was just a brute force with input from some chess savvy programmers but then humans have lost the game to the machines, forever. In today’s chess tournaments not the best players on earth compete but the best human players. They might use computers to improve their game but none of them would stand the slightest chance against a halfway decent artificial chess intelligence … or even a brute force algorithm. The loss of chess for humankind is a rather ancient story compared to the game of Go. Go being multitudes more complex than chess resisted the machines about twenty years more. Brute force doesn’t work for Go and thus it took until 2016 until AlphaGo, an artificial intelligence designed to play Go by Google’s DeepMind finally conquered that stronghold of humanity. That year, AlphaGo defeated Lee Sedol, one of the best players in the world. A few months later, the program also defeated Ke Jie, the then top-ranking player in the world. Most impressive though it is that again only a few months later DeepMind published another version of its Go-genius: AlphaGo Zero. Whilst AlphaGo had been trained with huge numbers of Go matches played by human players, AlphaGo Zero had to be taught only the rules of the game and developed its skills purely by playing against versions of itself. After three days, this version beat her predecessor that had won against Lee Sedol 100:0. And again only three months later, another version was deployed. AlphaZero learnt the games of Chess and Go and Shogi, another highly complex strategy game, in only a few hours and defeated all previous versions in a sweep. By then, man was out of the picture for what can be considered an eternity by measures of AI development cycles. AlphaZero not only plays a better Go – or Chess – than any human does, it develops totally new strategies and tactics to play the game, it plays moves never considered reasonable before by its carbon-based predecessors. It has transcended its creators in the game and never again will humanity regain that domain. This, you see, is the nature of artificial intelligence: as soon as it has gained superiority in a certain domain, this domain is forever lost for humankind. If anything, another technology will surpass its predecessor. We and our human brains won’t. We might comfort ourselves that it’s only rather mundane tasks that we cede to machines of specialized intelligence, that it’s a long way still towards a more universal artificial intelligence and that after all, we’re the creators of these intelligences … . But the games of Chess and Go are actually not quite so mundane and the development is somewhat exponential. Finally, a look into ancient mythology is all but comforting. Take Greece as an example: the progenitor of gods, Uranos, was emasculated by his offspring, the Titans and these again were defeated and punished by their offspring, the Olympians, who then ruled the world, most notably Zeus, Uranos’ grandson. Well, Greek mythology is probably not what the kind listener expects from a podcast about artificial intelligence. Hence, back to business. AI is not necessarily BIG Data Here’s a not so uncommon misconception: AI or advanced analytics is always Big Data or – more exactly: Big Data is a necessary prerequisite for advanced analytics. We could make use of the AlphaZero example again. There could hardly be less data necessary. Just a few rules of the game and off we go! “Wait”, some will argue, “our business problems aren’t like this. What we want is predictive analysis and that’s Big Data for sure!”. I personally and vehemently believe this is a misconception. I actually assume, it is a misconception with a purpose but before sinking deeper into speculation, let’s look at an example, a real business problem. I have spent quite some years in the insurance business. Hence please apologize for me using an insurance example. It is very simple. The idea is using artificial intelligence for calculating insurance premiums, specifically motor insurance third party liability (TPL). Usually, this is a mandatory insurance. The risk it covers is that you in your capacity of driving a car – or parking it – damage an object that belongs to someone else or that you injure someone else. Usually, your insurance premium should reflect the risk you want to cover. Thus, in the case of TPL the essential question from an actuary’s point of view is the following one: Is the person under inspection a good driver or a not so good one? “Good” in the insurer’s sense: less prone to cause an accident and if so, one that usually doesn’t come with a big damage. There are zillions of ways to approach that problem. The best would probably be to get an individual psychological profile of the respective person, add a decently detailed analysis of her driving patterns (where, when, …) and calculate the premium based on that analysis, maybe using some sort of artificial intelligence in order to cope with the complex set of data. The traditional way is comparatively simplistic and indirect. We use a mere handful of data, some of them related to the car like type and registration code, some personal data like age or homeownership and some about driving patterns, mostly yearly mileage and calculate a premium out of these few by some rather simple statistical analysis. If we were looking for more Big Data-ish solutions we could consider basing our calculation on social media timelines. Young males posting photos that show them Friday and Saturday nights in distant clubs with fancy drinks in their hands should emerge with way higher premiums than their geeky contemporaries who spend their weekends in front of some computers using their cars only to drive to the next fast food restaurant or once a week to the comic book shop. The shades in between might be subtle and an artificial intelligence might come up with some rather delicate distinctions. And you might not even need a whole timeline. Just one picture might suffice. The forms of our faces, our haircut, the glasses we fancy, the jewelry we wear, the way we twinkle our noses … might well be very good indicators of our driving behavior. Definitely a job for an artificial intelligence. I’m sure, you can imagine other avenues. Some are truly Big Data, others are rather small in terms of data … and fancy learning machines. The point is, these very different approaches may well yield very similar results ie, a few data related to your car might reveal quite as much about the question at hand as an analysis of your Instagram story. The fundamental reason is that data as such are worthless. Valuable is only what we extract from that data. This is the so-called DIKW hierarchy. Data, Information, Knowledge, Wisdom. The true challenge is extracting wisdom from data. And the rule is not: more data – more wisdom. On the contrary. Too much data might in fact clutter the way to wisdom. And in any case, very different data might represent the same information, knowledge or wisdom. As what concerns our example, I have first of all to admit that I have nor analytical proof – or wisdom – about specifics I am going to discuss but I feel confident that the examples illustrate the point. Here we go. The type of car – put into in the right correlation with a few other data -- might already contain most of the knowledge you could gain from a full-blown psychological analysis or a comprehensive inspection of a person’s social media profile. Data representing a 19 year old male, living in a certain area of town, owning a used but rather high powered car, driving a certain mileage per year might very well contain the same information with respect to our question about “good” driving as all the pictures we find in his Facebook timeline. And the other way around. The same holds true for the information we might get out of a single static photo. Yet the Facebook timeline or the photo are welling over with information that is irrelevant for our specific problem. Or irrelevant at all. And it is utterly difficult to get a) the necessary data in a proper breadth and quality at all and b) to distill relevant information, knowledge and wisdom from this cornucopia of data.  Again: more data does not necessarily mean more wisdom! It might. But one kind of data might – no: will – contain the same information as other kinds. Even the absence of data might contain information or knowledge. Assume for instance, you have someone explicitly denying her consent to using her data for marketing purposes. That might mean she is anxious about her data privacy which in turn might indicate that she is also concerned about other burning social and environmental issues which then might indicate she doesn’t use her car a lot and if so in a rather responsible way … . You get the point. Most probably that whole chain of reasoning won’t work having that single piece of data in isolation but put into the context of other data there might actually be wisdom. Actually, looking at the whole picture, this might not even be a chain of reasoning but more a description of the certain state of things that denies decomposition into human logic. Which leads us to another issue with artificial intelligence. The unboxing problem Artificial intelligences, very much like their human contemporaries, can’t always be understood easily. That is, the logic, the chain of reasoning, the parameters that causally determine certain outcomes, decisions or predictions are in many cases less than transparent. At the same time, we humans demand from artificial intelligence what we can’t deliver for our own reasoning: this very transparency. Quite like us demanding 100% machine perfection, some control-instinct of ours claims: If it’s not transparent to us (humans), it isn’t worth much. Hence, a line of research in the field of artificial intelligence has developed: “Unboxing the AI”. Except for some specific cases yet, the outlook for this discipline isn’t too bright. The reason is the very way artificial intelligence works. Made in the image of the human brain, artificial intelligences consist of so-called “neural networks”. A neural network is more or less a – layered – mesh of nodes. The strength of the connections between these nodes determines how the input to the network determines the output. Training the AI means varying the strengths of these connections in a way that the network finally translates the input into a desired output in a decent manner. There are different topologies for these networks, tailored to certain classes of problems but the thing as such is rather universal. Hence AI projects can be rather simple by IT standards: define the right target function, collect proper training data, plug that data to your neural network, train it … . It takes but a couple of weeks and voila, you have an artificial intelligence thatyou can throw on new data for solving your problem. In short, what we can call “intelligence” is the state of strengths of all the connections in your network. The number of these connections can be huge and the nature of the neural network is actually agnostic to the problem you want it to solve. “Unboxing” would thus mean to backwardly extract specific criteria from such a huge and agnostic network. In our radiologist case for example, we would have to find something like “serrated fringes” or “solid core” in nothing but this set of connection strengths in our network. Have fun! Well, you might approach the problem differently by simply probing your AI in order to learn that and how it actually reacts to serrated fringes. But that approach has its limits, too. If you don’t know what to look for or if the results are determined not by a single criterion but by the entirety of some data, looking for specifics becomes utterly difficult. Think of AlphaZero again. It develops strategies and moves that have been unknown to man before. Can we really claim we must understand the logic behind, neglecting the fact that Go as such has been quite resistant to straightforward tactics and logic patterns for the centuries humans have played it. The question is: why “unboxing” after all? – Have you ever asked for unboxing a fellow human’s brain? OK, being able to do that for your adolescent kids’ brains would be a real blessing! But normally we don’t unbox brains. Why are we attracted by one person and not by another? Is it the colour of her eyes, her laughter lines, her voice, her choice of words …? Why do we find one person trustworthy and another one not? Is it the way she stands, her dress, her sincerity, her sense of humour? How do we solve a mathematical problem? Or a business one? When and how do the pieces fall into place? Where does the crucial idea emerge from? Even when we strive to rationalize our decision making, there always remain components we cannot properly “unbox”. If the problem at hand is complex – and thus relevant – enough. We “factor in” strategic considerations, assumptions about the future, others’ expectations … . Parts of our reasoning are shaped by our personal experiences, our individual preferences, like our risk-appetite, values, aspirations, … . Unbox this! Humankind has learnt to cope with the impossibility of “unboxing” brains or lives. We probe others and if we’re happy with the results, we start trusting. We cede responsibilities and continue probing. We cede more responsibilities … and sometimes we are surpassed by the very persons we promoted. Ah, I am entering philosophical grounds again. Apologies! To make it short. I admit, there are some cases in which you might need full transparency, complete “unboxing”. And in case you don’t get it, abolish the idea of using AI for the problem you had in mind. But there are more cases in which the desire for unboxing is just another pretense for not chartering new territory. If it’s intelligent if it behaves like a human why do we ask for so much more from the machines than we would ask from man? Again, I am drifting off into questions of dangerously fundamental nature. Let’s assume for once that we have overcome all our concerns, prejudices and excuses, that despite all of them, we have a business problem we full-heartedly want to throw artificial intelligence at. Then comes the biggest challenge of all. The biggest challenge of all: how to operationalize it Pretty much like in our discussion at the beginning of this post, on the face of it, it looks simple: unplug the human intelligence occupied with the work at hand and plug in the artificial one. If it is significant – quite some AI projects are still more in the toy category – this comes along with all the challenges we are used to in what we call change management. Automating tasks comes with adapting to new processes, jobs becoming redundant, layoffs, re-training and rallying the remaining workforce behind the new ways of working. Yet changes related to artificial intelligence might have a very different quality. They are about “intelligence” after all, aren’t they? They are not about replacing repetitive, sometimes strenuous or boring work like welding metal or consolidating accounting records, they dig to the heart of our pride. Plus, the results are by default neither perfect nor “unboxable”. That makes it very hard to actually operationalize artificial intelligence. Here’s an example. It is more than fifteen years old, taking place at a time when a terabyte was an still an incredible amount of storage, when data was still desired to be stored in warehouses and not floating around in lakes or oceans and when true machine learning was still a purely academic discipline. In short: the good old times. This gives us the privilege to strip the example bare of complexity and buzz. At that time, I was together with a few others responsible for developing Business Intelligence solutions in the area of insurance sales. We had our dispositive data stored in the proverbial warehouse, some smart actuaries had applied multivariate statistics to that data and hurrah, we got propensities to buy and rescind for our customers. Even with the simple means we had by then, these propensities were quite accurate. As an ex-post analysis showed, they hit the mark at 80% applying the relevant metrics. Cutting the ranking at rather ambitious levels, we pushed the information to our agents: customers who with a likelihood of more than 80% were to close a new contract or to cancel one … or both. The latter one sounds a bit odd, but a deeper look showed that these were indeed customers who were intensely looking for a new insurance without a strong loyalty. – If we won them, they would stay with us and loyalty would improve, if a competitor won them, they would gradually transfer their portfolio to him. You would think that would be a treasure trove for any salesforce in the world, wouldn’t you? Far from it! Most agents either ignored the information or – worse – they discredited it. To the latter purpose, they used anecdotal evidence: “My mother in law was on the list”, they broadcast, “she would never cancel her contract”. Well, some analysis showed that she was on the list for a reason but how would you fight a good story with the intricacies of multivariate statistics? Actually, the mother-in-law issue was more of a proxy for a deeper concern. Client relationship is supposed to be the core competency of any salesforce. And now, there comes some algorithm or artificial intelligence that claims to understand at least a (major) part of that core competency as good as that very salesforce … . Definitely a reason to fight back, isn’t it? Besides this, agents did not use the information because they regarded it not too helpful. Many of the customers on the high-propensity-to-buy-list were their “good” customers anyway, those with who they were in regular contact already. They were likely indeed to make another buy but agents reasoned they would have contacted them anyway. So, don’t bother with that list. Regarding the list of customers on the verge of rescinding, the problem was a different one. Agents had only very little (monetary) incentive to prevent these from doing so. There was a recurring commission but asked whether to invest valuable time into just keeping a customer or going for new business, most were inclined to choose the latter option. I could continue on end with stories around that work, but I’d like to share only one more tidbit here before entering a brief review of what went wrong: What was the reaction of management higher up the food-chain when all these facts trickled in? Well, they questioned the quality of the analysis and demanded to include more – today we would say “bigger” – data in order to improve that quality, like buying sociodemographic data which was the fad at that time. Well, that might have increased the quality from 80% to 80+% but remember the discussion we had around redundancy of data. The type of car you drive or the sum covered by your home insurance might say much more than sociodemographic data based on the area you live in. … Not to speak of that eternal management talk that 80% would be good enough. What went wrong? First, the purpose of the action wasn’t thought through well enough from the start. We more or less just choose the easiest way. Certainly, the purpose couldn’t have been to provide agents with a list of leads they already knew were their best customers. From a business perspective the group of “second best customers” might have been much more attractive. Approaching that group and closing new contracts there would have not only created new business but also broadened the base of loyal customers and thus paved the way for longer term success. The price would of course have been that these customers would have been more difficult to win over than the “already good” ones so that agents would have needed an incentive to invest effort into this group. Admittedly going for the second-best group would have come with more difficulties. We might have faced for example many more mother-in-law anecdotes. Second, there was no mechanism in place to foster the use of the information. Whether the agents worked on the leads or not didn’t matter so why should they? Worse even with the churn-list. From a long-term business perspective, it makes all the sense in the world to prevent customer churn as winning new customers is way more expensive. It also makes perfect sense to try making your second-best customers more loyal but from a short-term salesman’s or -woman’s perspective boiling the soup of already good customers makes more short-term sense. Thus, in order to operationalize AI target systems might need a thorough overhaul. If you are serious, that is. The same holds true if you would for example want to establish machine assisted sentiment analysis in your customer care center. Third, there was no good understanding of data and data analytics neither on the supposed-to-be users’ side nor on the management side. This led to the “usual” reflexes on both sides: resistance on the one side and an overly simplified call for “better” on the other one. Whatever “better” was supposed to mean. Of course, neither the example nor the conclusions are exhaustive, but I hope they help illustrate the point: more often than not it is not the analytics part of artificial intelligence that is the tricky one. It is tricky indeed but there are smart and experienced people around to deal with that type of tricky business. More often than not, the truly tricky part is to put AI into operations, to ask the right questions in the first place, to integrate the amazing opportunities in a consistent way into your organization, processes and systems, to manage a change that is more fundamental than simple automation and to resist the reflex that bigger is always better!   So much for today from “Mediocrity and Madness”, the podcast that usually deals with the ever-growing gap between corporate rhetoric and action. I dearly thank all the people who provided inspiration and input to these musings especially in and around the programs I mentioned in the intro, most notably Gemma Garriga, Marcela Schrank Fialova, Christiane Konzelmann, Stephanie Schneider, Arnaud Michelet and the revered Prof. Jürgen Schmidhuber! Thank You for listening … and I hope to have you back soon!  

Last week on the podcast I discussed the love of learning. But, as always, learning is one thing and practical application is something completely separate.  With that in mind, this week's podcast is devoted to some practical examples on how to use learning to improve. True learning for whatever it is that we want to improve comes down to our ability to ask ourselves questions. To learn in the moment, we need to have a level of awareness and challenge ourselves with the difficult questions that are so easy to ignore.  For those not listening to the podcast, here are the three things I highlight:  First - Preparation.  I use running as the example on the podcast. But in order to get started, you have to prepare and that includes the “why.” If you want to start running - ask yourself the “why” question. Is it because of self image and you want to get in shape? Is it because you want to lose weight? Is it because you want to impress another person? All of these questions will impact your ability to take running (or anything other habit) on. You may find that running isn't the right answer, after all. Second - Collect Data. Businesses use data to make big decisions - why aren't you?  Data has to go through iterations. From data you have to garner information, which you have to turn into knowledge, which you then have to move to wisdom. It's called the DIKW pyramid. For example, knowledge is knowing a tomato is a fruit, wisdom is knowing not to put it in a fruit salad.  If you want to start a running regimen - track everything. Each day, write down your distance and time that you ran. Put some notes on what was good or not.  There are two types of data here - both have value. Objective data (time/distance) and subjective (feelings). Both of these provide value as to why you are or are not moving towards your goal. This is true for running, writing a book, launching a business. If you did not do what you wanted to today - ask yourself why. You may realize you don't want to do it. Third - Learn in the Moment Use the data to make decisions in the moment. If you always end your run at a certain spot, maybe challenge yourself to go a different distance. If a distance isn't challenging enough, maybe run for a certain amount of time. Each of these will change up the perspective and help keep it interesting.  If you find every Tuesday you go slower than the other days, maybe something is impacting that. If you find that you are only going twice a week, but want to go five, you can ask yourself the question of what's impacting your ability to get it done.  We all have these amazing ideas and lists of things we want to accomplish. But if you aren't focused on it daily, or weekly, then it will always just be an idea.  I hope the examples on the podcast were helpful! Enjoy the week!

Last week on the podcast I discussed the love of learning. But, as always, learning is one thing and practical application is something completely separate.  With that in mind, this week’s podcast is devoted to some practical examples on how to use learning to improve. True learning for whatever it is that we want to improve comes down to our ability to ask ourselves questions. To learn in the moment, we need to have a level of awareness and challenge ourselves with the difficult questions that are so easy to ignore.  For those not listening to the podcast, here are the three things I highlight:  First - Preparation.  I use running as the example on the podcast. But in order to get started, you have to prepare and that includes the “why.” If you want to start running - ask yourself the “why” question. Is it because of self image and you want to get in shape? Is it because you want to lose weight? Is it because you want to impress another person? All of these questions will impact your ability to take running (or anything other habit) on. You may find that running isn’t the right answer, after all. Second - Collect Data. Businesses use data to make big decisions - why aren’t you?  Data has to go through iterations. From data you have to garner information, which you have to turn into knowledge, which you then have to move to wisdom. It’s called the DIKW pyramid. For example, knowledge is knowing a tomato is a fruit, wisdom is knowing not to put it in a fruit salad.  If you want to start a running regimen - track everything. Each day, write down your distance and time that you ran. Put some notes on what was good or not.  There are two types of data here - both have value. Objective data (time/distance) and subjective (feelings). Both of these provide value as to why you are or are not moving towards your goal. This is true for running, writing a book, launching a business. If you did not do what you wanted to today - ask yourself why. You may realize you don’t want to do it. Third - Learn in the Moment Use the data to make decisions in the moment. If you always end your run at a certain spot, maybe challenge yourself to go a different distance. If a distance isn’t challenging enough, maybe run for a certain amount of time. Each of these will change up the perspective and help keep it interesting.  If you find every Tuesday you go slower than the other days, maybe something is impacting that. If you find that you are only going twice a week, but want to go five, you can ask yourself the question of what’s impacting your ability to get it done.  We all have these amazing ideas and lists of things we want to accomplish. But if you aren’t focused on it daily, or weekly, then it will always just be an idea.  I hope the examples on the podcast were helpful! Enjoy the week!

“There can be no liberty for a community which lacks the means by which to detect lies.”  “Where all think alike, no one thinks very much.” -Lippmann. At 35:25: The difference between clever & smart and more on the DIKW pyramid and what happens when citizens ignore corrupt data.  The first 35-minutes:  - Justin Amash and the criticism from the left. - Mueller's fumble and the Democrat's ensuing demand that he star in their 2020 reality show/campaign ad. - To Impeach or not Impeach: just do it or STFU! - Democrats are politically impotent to stop anything Trump does. How does that "protest vote" look now?     More: www.escapingthecave.com Subscribe: iTunes and Google Play     

Technology enables, and social media perpetuates, informational anarchy and prioritizes the cheap appearance of knowledge over fostering knowledge itself.  Also, how the Internet changes cognition and the abandonment of expertise to the holy opinion: "my ignorance is equal to  your knowledge." Also:  -The DIKW pyramid and drowning in data. -The Id on Parade, the natural state of man, and Leviathan.   -What happens when reasonable voices abandon the public square to mindless mobs of roving avatars? -When avatars attack -A classic full-frontal Toddzilla rant salvaged from The Lost Episode about Twitter, and more.     Elias Aboujaode's Virtually You:  https://www.amazon.com/Virtually-You-Dangerous-Powers-Personality/dp/0393340546   Nicholas Carr's The Shallows:  https://www.amazon.com/Shallows-What-Internet-Doing-Brains/dp/0393339750   Neil Postman's Amusing Ourselves To Death:  https://www.amazon.com/Amusing-Ourselves-Death-Discourse-Business/dp/014303653X/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1558613196&sr=1-1     Need more Toddzilla?1) Seek help.2) Click the links.3) Gorge on your way to therapy.More episodes/subscribe:My travel archives: www.toddzillaX.comAlso visit: www.christophermedia.netSubscribe: iTunes and Google Play

This episode discusses our understanding of the DIKW model (which is Data, Information, Knowledge, and Wisdom), and how we can apply this model for our teams

Today on the show we're talking about INTPs and using the DIWK model for personal growth. DIKW stands for Data, Information, Knowledge and Wisdom as a way to navigate how we as humans understand information. Since INTPs are so informationally driven, this model is incredibly powerful for breaking a simple way to starting thinking about your personal growth from this perspective. Join me as we break down emotions and data on this episode of DOPEamine! http://dopeamine.teachable.com http://www.bit.ly/AboutCnote --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app --- Send in a voice message: https://anchor.fm/dopeamine/message

In this episode, Frank and Andy talk to two guests, Ronald Schmelzer and Kathleen Walch, co-founders of  AI Today podcast (https://www.cognilytica.com/category/podcasts/) . Links (http://thedatadrivenbook.com) Sponsor: Audible.com (http://thedatadrivenbook.com) – Get a free audio book when you sign up for a free trial! Notable Quotes Cognilytica (https://www.cognilytica.com/) is amazing!([04:00]) All chatbots are dumb – for now. ([09:00]) Machine Learning vs. Machine Reasoning ([11:30]) The DIKUW Pyramid (https://en.wikipedia.org/wiki/DIKW_pyramid) ([11:55]) More about Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_Graph) … ([14:00]) More about Common Sense (https://en.wikipedia.org/wiki/Commonsense_knowledge_(artificial_intelligence)) … ([15:00]) On generalization ([16:05]) ML and the Elephant in the Room (https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/) ([16:22]) Movie reference: Guardians of the Galaxy (https://www.imdb.com/title/tt2015381/) ([17:00]) How did the AI Today (https://www.cognilytica.com/category/podcasts/) podcast get started? ([18:00]) AI Today podcast with Dragos Margineantu, AI Chief Technologist at Boeing (https://www.cognilytica.com/2018/05/23/ai-today-podcast-38-interview-with-dragos-margineantu-boeing/) ([19:44]) Is AI retro? ([22:50]) Movie Reference: Short Circuit (https://www.imdb.com/title/tt0091949/) ([23:30]) Did you find data or did data find you? (0[25:00]) Tech Breakfast DC (https://www.meetup.com/TechBreakfast/events/226841343/) ([28:30]) AOL (https://www.aol.com/) plug ([31:25]) What’s your favorite part of your current gig? ([32:00]) More about pseudo-AI (https://www.cognilytica.com/2018/07/17/does-fake-it-till-you-make-it-work-in-ai/) … ([33:45]) Shout-out to Brent Ozar (https://brentozar.com) (just not by name) ([38:00]) When I’m not working, I enjoy ___? ([39:45]) I think the coolest thing in technology is ___? ([41:12]) Bubble programming language (https://bubble.is/) ([42:15]) I look forward to the day when I can use technology to ___. ([45:00]) “Don’t overshare…” ([46:30]) The loneliest people (https://www.ajc.com/news/national/study-says-most-americans-feel-lonely-young-adults-are-the-loneliest/pIRVGfKilaPLGS3CtwG4WM/) ([47:00]) Warning: Do not watch movies while driving. ([48:30]) Also, eating tacos while driving is difficult. ([49:00]) “Lefties are alright…” – Kathleen ([49:30]) Ron may be a pool shark. ([51:30]) Forbes (https://www.forbes.com/sites/cognitiveworld/people/kathleenwalch/#44cf2cba6ee5) . ([53:00]) Ron’s book recommendation: Hackers: Heroes of the Computer Revolution (https://smile.amazon.com/dp/B003PDMKIY) ([56:00]) Kathleen’s book recommendation: My Not-So-Perfect Life (https://smile.amazon.com/dp/B01GYPY88Y) ([57:00]) Kathleen’s other book recommendation: The Glass Castle (https://smile.amazon.com/Glass-Castle-Memoir-Jeannette-Walls-ebook/dp/B000OVLKMM/) ([57:40]) You can Sandy River (https://www.sandyriveroutdooradventures.com/) in Farmville ([1:00:00])

In this episode Joel and Antonia talk about the DIKW model (data, information, knowledge, wisdom) and how to apply it to today's media landscape of alternative facts and fake news.   http://www.personalityhacker.com/