AI will enhance REMOTE LEARNING [2021 trends]

Attempts on distance learning have been popping here and there for some years. After all Udemy, Coursera (and vast DYI YouTube flood) have been here for years. Still, few months back, remote learning turned into billion students’ issue over the night. Along with somewhat rude shock (to parents) on how rudimentary (or non-existing) the contemporary education-over-distance in reality is.

This all has been happening with AI training its edu-tech muscle in background, mostly unnoticed. It’s a bit ironic it took pandemic to reveal that the gap between human- and machine-teacher is not that big after all (not to the machine’s blame). With parents seeing teachers to struggle with establishing zoom call first place (or even sending and collecting the hard copy assignments via post), it only takes some courage on edu-tech side to tip the avalanche down the valley. And examples there are …

Chinese online education company named VIPkid has launched project with 700 000 students that received online courses with embedded AI elements (like fun characters “assisting” the actual human teachers in videos). AI’s is always cooking the extra content uniquely (and specifically) to progress of the individual student. More strikingly, VIPkid also did A/B test of some classes taking human-only content and other exposed to enhanced, AI-supported content along the very same teacher. The results speak for themselves: in AI-supported classes the correct answers on final examination went from 50% to 80% and passing the course went from 80-tish to 90-tish percentage points.

Well, it makes all of the sense, after all. Each individual student accumulates “learning gaps” in different topics of the curriculum. Human teacher, however skilled or eager to help, can stop to re-explain only that many concepts per given learning hour. Thus, having the general, common content, supported with individual support on what you specifically didn’t get, must inevitably lead to better overall class result.

There is one important caveat to notice here, though. From the essence of the thing, this AI supported content will primarily come in major world languages (first). This might breed inequality in education standards for communities where none of the most widely used languages is spoken (or even taught). So if you can read between the lines, … , yes English, Chinese or Spanish would be good.

What this means for teachers? That will be follow in one of my 2021 soon-to-come blogs. Stay tuned.

 

 

 

 

Does your insurance cover HITTING A SATELLITE? [2021 trends]

Hey, watch your way! You nearly hit the other vehicle! No, this is not replica from driving school scene. These words will echo more and more often out of the space mission control centers. The sky is getting extremely crowded and you should correct your image of Earth-surrounding space: From vast hollow room to something more like morning commute traffic jam.

Sole Musk’s Space-X satellite program emits more than 12 000 (!) individual satellite units, not even counting all the military, weather and navigation ones. Orbits for possible launches will get scarce, the same way that FM spectrum frequencies were limited for radio stations in 1990s.

Besides the fact that mutual coordination (and interference prevention) becomes more and more complex, this satellite extravaganza will be taking its toll on other, serious aspects of science, too. Professional astronomers will have to live through vast light smog. As most of the launched satellites encompass solar panels to “fuel” the operation and its communication units. And those trying to sent something upwards, will have to count too many trajectories to smuggle through orbiting traffic.

Luckily, companies are not just waiting idle for this issue to be addressed. Some proactively mount light shades on satellites, while still others try to launch into orbits less detrimental for astrophysics observation. But low latency, global internet coverage (being recently the main urge behind the satellite launch programs) will be too much of the temptation. So watch out any time you want to send somebody to Mars. They might get seriously hurt in crash accident.

 

 

 

 

CONSPIRACIES take refuge into ALT-TECH [2021 trends]

The fact that conspiracy theories have been gaining more and more traction is equally obvious as the COVID was main topic of 2020. Actually, those two topics have been reigniting each other heavily (with peek for me being internet discussion suggesting that new COVID vaccine will be administered through 5G towers and they need to be destroyed).

As if consequences of these gibberish non-sense itself were not troubling enough, there is another dangerous trend to spot. The ever-increasing heat of info-war is now potentially to marry other mega trend into explosive and deadly mixture.

You may or may not have come across the term Alt-tech. Sparing you with too much of the details, Alternative-Tech scene (or Alt-tech) is movement that tries to create alternatives to digital Goliaths of Google and Facebook type. The underlying motivation behind building alternatives to digital mega services is to “bring back freedom to internet” (read as addressing whatever injustice the big players had done to internet life in their rise to dominance). Free speech and anit-censorship is most often cited propeller of the movement. The argument goes that you can’t really access if Google search is returning the right thing if there is no real alternative to, ehm do you hear me saying, “googling” first place.  One of the typical “comparing banners” is attached here:

[Disclaimer: Author if this blog does not subscribe to Alt-tech movement, this article is just to point out its mere existence]

The dangerous part of the story is that Alt-tech can serve as refuge for the conspiracy and fake news scene. While it is – by design – less (or not at all) regulated, with everybody pretending rom mind his/her own business. It is also less prone on protecting the identity of its users (= we might not even know who is really behind that content). And that feels exactly as environment suited for conspiracy movements. No matter that some of the pro-Trumpian, anti-vaccination, 5G-abolition or COVID-denial groups started to self-organize in this media. With less oversight these messages mushroom faster than in (to some extent) fact checking main steam. As a result, alt-tech is both likely to get poisoned by conspiracies and give enough oxygen for their social media fires. Admittedly, a bit scary dot at the end of our 2021 trends overview.

 

 

4 TYPES of BOSSES WHO DO NOT UNDERSTAND analytics

When I wrote a blog about the Analyst Loneliness Syndrome a few weeks ago, I knew I wasn’t talking about isolated cases. However, the magnitude of readers’ responses have completely knocked me out. It is bitter-sweet mixed feeling of sadness and joy that you nailed something right, but you feel sorry that so many people are suffering from this syndrome. So I decided to talk to some of those who contacted me about that blog and to write extension of original post. This time about one of the three core factors of analytical loneliness: the management side of things.

The classic HR maxim says: “Employees don’t leave companies, they runaway from their superiors.” Although I do not quite agree with this generalization, I have to admit that in 4 out of 5 cases when I changed my job, it was true (this is my greeting, Rasto, you are the exception). One can leave boss behind for a variety of reasons, but in most cases it’s a combination of some of the following “evergreen’s“: He can’t appreciate my work; He does not understand the area and therefore I get mostly nonsense tasks; He does not believe me and hints me so; No inspiration or development from him, I only rot; His/her moral standards and deeds are in deep contrast with my beliefs.

Certainly, managerial superficiality and incompetence can affect you in almost every industry, but I would like to specialize in a typical example of this ailment in Data Analytics and Data Science. The traditional managerial characters have gained some additional spicy ingredients in this industry. After all, judge for yourself, here are 4 TYPES of MANAGERS that don’t understand analytics:

1] Don’t drag me into details

BOSS_type_1Managerial Profile: It’s incredible, but even today there are still many companies where data analysts is “stuck” under the Head of Business or Marketing. It is often a tragic consequence of widely spread belief that data can  significantly influence company’s revenue. As a result, analysts are moved under the Head of business or marketing to make this influence happen . However, these are usually managers who have a mathematics aversion, developed already at their (primary or secondary) school. Anything more complicated than percentages leave them restless. Simple numbers like sum and average (of course, they only mean arithmetic average) ok, but everything else is already far too complicated. Any statistics beyond the correlation are just “academic curls” (or crap). Their phobia from numbers and more sophisticated analyses comes from the fact that they have never understood this area, are not in control of it and thus are afraid of it. They don’t believe in the power of calculations nor AI, they solve everything intuitively and on the basis of proven approaches (read as: it worked once in the past). He prefers human speech as communication, simplifies every schema or spreadsheet into 2-3 sentences. However complicated the analysis is, at the end everything has to end up in Excel, which can be filtered by columns, and must not be more than 50 lines. When you try to “dive into” the results of your work, (s)he will tell you “Let’s do not get too technical” (just tell me the essence)

Implications for your work: If you work for such a manager, you will be probably having a very frustrating working life. Since this kind of managers never did any analytical work, (s)he doesn’t know what IS real and what’s NOT. Neither in terms of procedures and results, but especially not in terms of time you need. So get ready to receive ridiculous tasks in gallows deadlines (what could take so long, right?). He has intuitive expectations about every assignment he gives you. If you fail to match it with real data, a tough week is waiting for you. No matter how thoroughly you prepare your analysis output, (s)he will take one or two of the most obvious (= most primitive) conclusions, thereby gradually discouraging you from coming with more sophisticated procedures first place. Sooner or later, there will also come attempts to censor “illogical” analysis outcomes. If it is necessary to present to “seniors” conclusions, (s)he will let you do it (while throw half of the slides out of deck as useless). Because if the top management did not like it by chance, (s)he will drown you with “it not making sense even to him/her”, but it just happen to be the calculation result. In the area of expert or personal development, you are down to pure fate of Robinson Cruse.

What should you do about it: I hate to be an evil prophet, but if you are serious about your analyst career, run away from there. In fact, this kind of manager is unrealistic to improve, because he considers more complicated analytics to be a necessary evil that suits him only when it confirms his intuitive hypotheses. Otherwise it is unnecessary “trying to look smart” that has no support in (his/her) reality. The only alternative to fleeing would be to attempt a coup d’état (whistle-blow him to a higher level of control and they might exchange him). But honestly, this kind of managers have a stiffer root and they have more “merits” than you have of convincing arguments. So sooner or later you just leave (with great relief).

2] Scared rabbit

BOSS_type_2Managerial Profile: This type of manager stems from the first type and often represents a generational shift or personality development from “Don’t-drag-me-into- details” type. What remains the same, (s)he never did an analytical work himself, so things are not understood. The “move forward”, however, is that they do not reject a more sophisticated analysis because it has come upon them that they cannot do without it anymore. To this “improvement” he was pushed most likely by the CEO / shareholder attitude or the fact that he noticed all the competitors around already using analytics, so we must have it, too. However, as (s)he does not understand things him/herself, he is only trying to follow very elementary steps, often mimicked from professional conferences or buzzwords (anybody Big Data?). (S)he is stiff whenever you enter her/his office, because (s)he knows that the debate with you will revolve around an important subject (s)he doesn’t control. Nevertheless, in order to survive (s)he must feed to levels about him/her (who forced the analytics first place) the illusion that (s)he is not only interested in analytics but also orientates well in it.

Implications for your work: The consequences are similar to situation when you have to get out of a dark room filled with things. (S)he only progresses slowly through the familiar outlines, (s)he first gropes everything thoroughly to make sure we don’t bump into something hard.As a result you will only get elementary assignments, everything will have to be tested in a small pilot (= no effect anyone could notice). Concept that to you train a model first on 1,000 people and then scale to 100,000 doesn’t make sense, does not ring a bell with her/him. Therefore, most projects will die after the pilot. He’ll never fight for better software or a more powerful computing engine, “let’s try first with what we have. When we do, then we can ask for more money.” (S)he’s too soft, because (s)he can’t steer you by essence (since (s)he doesn’t understand it) and so (s)he will try to do it in a moderate way. You won’t get strong decisions or quality feedback from them. Do not expect a vision where to follow, you often will be firefighters of issues that fell from top (and which (s)he cannot conceptualize and prioritize). Since (s)he is uncertain in your area, he will explain everything from Adam (sometimes repeatedly, as it has been overwritten by other issue in his/her head). Most probably he will never let you present the results of your work, so that it is not revealed to leadership that he does not understand even half of what you do.

What should you do about it: If you do not mind (or even prefer) that this kind of managers isolate you from contact with the top management, you can survive in this setting quite comfortably. However, you will have to educate your direct superior continuously (sometimes repeatedly on the same topics). Do not expect any career growth or expert development, at most you will be left with the space to self-tune. As a intermediary station, this is not a completely unbearable. But primitive and repetitive tasks and professional stagnation will catch you up sooner or later. If you have lived with such a manager for more than 3 years, look around where your peers have moved. Your train might be running away.

3] When we in ’95 did this …

Boss_type_3Managerial Profile: It is a manager who once worked as an analyst. Of course, when data analysis meant OLAP and mainly SQL data reporting. (S)he didn’t get too wild with predictive models, Monte Carlo simulations, or neural networks. So (s)he did not realize that data analytics is done completely differently today. In addition, his/her abilities are more of a memory-optimism that is often transformed into “When we tried this way in ’95, it worked”. In a sense, this type of manager is more dangerous than the first 2 named types. If some tries to convince you of something that is true, it is always worse when (s)he thinks being right rather than being not sure about the issue. In addition, this type of manager wants to be involved in every detail because he remembers that it was exciting to reveal new connections (maybe (s)he is nostalgic about it even). In fact, (s)he does not realize that “is no longer playing the same league as the young ones”.

Implications for your work: Perhaps the biggest risk of this type of managers is micromanagement. By living in belief that they understand the area and by having nostalgic memories of the times when they did something real with the data, they will seize every opportunity to “engage in the project.” This can sometimes go so as far as to “volunteer to help” and take parts of the project on their shoulders. (what is to be avoided by far, if for nothing else at least to meet the project deadline). Speaking of those deadlines, the second major risk of working with such a manager is unrealistically optimistic time-frames. After all, when we did it in the 95s, it took just … The biggest risk in the long run is that it will slow down (or “torpedo” by expert “arguments”) your introduction of the modern trends (to keep up with you). Maybe (s)he won’t even do it consciously, but if you take two steps back, after a few years you’ll find that you are more or less spinning in a vicious circle.

What should you do about it: For some people, such a job can be comfortable and they let themselves to be fooled that it might have turned out much worse off (see the first and second type of manager). If you are at the end of a career or are among those who prefer traditional to innovative, just enjoy a comfortable life there. However, if most of your working life is still ahead of you, you need to foster space for professional growth. And the pace should at least match the market growth to avoid becoming “unnecessary junk in the labor market”. Therefore, I recommend that you sit down with such a manager and ask for autonomy: part of your working time (e.g. 1 day a week) to test new trends (which (s)he does not hints you to). If the manager does not agree, (s)he is probably well on his/her way to transform into Type 1], and so should your answer to it be in the spirit of advice for that type (see above).

4] Jules Verne

Jules_VerneManagerial Profile: To avoid wrong impression that a manager is a problem only when (s)he knows less about the issue than you do, there is also the opposite case. I personally hate the principle, when the best surgeon is nominated to be the hospital director, with the argument that others appreciate and respect him. Regrettably, even in analytics, the most skillful (or the most powerful) analyst becomes a team leader or department manager. It happens so often because the levels of control over it are some of the first three types, and so they need someone to cover the technical side of things. Jules Verne is a type of manager who once was Data Scientist or at least a sophisticated data miner. After (s)he stops officially being responsible for direct performance, and is charged with task to manage other analysts, often one of the following things usually happens: 1) Becomes lazy and realized that (s)he no longer wants to return to writing queries or code (resulting in a gradual loss of touch for analyst’s work) or 2) will finally take the chance to do those cool types of analysis that the nobility did not allow him to do before. Often both of these transform into a non-critical acceptance of “hype news” in the industry. After all, he also wants to brag on the beer with other data managers what cool things we are in our company. As the consequence the journey becomes a goal. Trying this-or-that is more important than making something easier to really work. While (s)he is no more responsible for time spent on the individual steps, rather (s)he already determines the strategy for future.

Implications for your work: The assignments become increasingly confusing, because “Try to plug in a neuron net and let’s see what it brings.” Of course, half-successes go into drawer immediately to free up the runway for yet another new approaches to try. The result is a frequent change of priority and a gradual absence of a sense of real effect. The absence of value added gets noticed soon also by the “those up”, as will the time pass working in the Jules Verne’s team also means an increased risk that some organizational change will wipe out the entire team from Earth’s surface (read org chart) without any warning. At the same time, this kind of managers push their people into the position of generalists rather than specialists, which must not necessarily suit everyone well. Projects’ track record might look impressive in CV, but when you gonna by interviewed by someone who really did that (and not just tried as your team did), you will badly grill on your own barbecue stick.

What should you do about it: If you are JUNIOR in this area, it is paradoxically more advantageous for you to stay for a few years. Getting a broad (and shallow) outlook at the beginning of a career is not necessarily a bad choice. However, do not take too high a mortgage so that you do not bleed when your team suddenly ceases to exist one nice morning. If you are a SENIOR, confront the manager with the flicker that (s)he shows. Give him/her a feedback that you want to finalize the projects and that one new idea a week a probably enough. If he doesn’t understand or laugh at you, go to his supervisor to describe the situation and say either HIM/HER (or YOU). Both answers will be the right choice for you. If you are the first to do this, you probably save the rest of the team, but you will not regret the possible departure (possibly with handsome severance pay to get rid of you quickly).

Have you stumbled across one of these 4 types in the workplace? Have you ever experienced yet another type of dysfunctional Data Manager? Share your impressions at info@mocnedata.sk. I keep my fingers crossed for you to avoid those types of people. And if you happen to meet them, try to follow the advice from this blog. Bon voyage!

Berlin Meetup: Cool Feature engineering [my slides]

AI_in_ACTION

 

 

 

 

 

 

 

 

 

 

Dear fellows, 

on Wednesday 20th Feb 2019 I have been invited to speak at AI IN ACTION Meetup organized by ALLDUS in Berlin. The topic was one of my favorite issues, namely Feature engineering. This time we looked at the issue from the of How To Do Cool Feature Engineering In Python. If you had the chance to be in the Meetup crowd and failed to note down some figure, or if you are interested to read about what were the ideas discussed even thought you were not there, here you can find attached the presentation slides from that MeetUp.

slides >>> FILIP_VITEK_TeamViewer_Feature_Selection_IN_PYTHON

If you have any question of different opinion on some of the debated issues, do not hesitate to drop me a few lines on info@mocnedata.sk ;

Social Media have too much of Diesel

This is not an ecology section of TheMightyData.com portal. Neither is this blog about if Facebook or LinkedIn drive electric cars or classical combustion engines. The issue is more serious here, our privacy and its handling are at stake. Well, just make your own call on this:

Indisputably the biggest scandal of the automotive industry was the Diesel Gate. In this well protracted case car producers modified the software in the car in a way that it runs more efficiently (read with lower engine output) but only in situation when car computer realized it is docked to emission measurement device. As a result the congestion smog emitted by car was much lower during the test compared to values from real drive out there on the roads. While most of the countries only realize emission tests in technical review sites (enabled with those dockers), diesel cars appeared to be more ecological as they truly are.

Important twist of this scandal was fact that Automotive (by car software manipulation) created environment where they were the only guards to themselves. Being your own judge does not necessarily mean you will cheat. But if you add strong competitive fight and cheaper market entrants the odds of the misbehaving are rising. Therefore, often the only factor swerving from fair customer treatment to fraud is the management integrity. In retrospect we know that automotive managers failed to hold the principles railing.

If the manipulated cars were passing one emission test after the other, you might wonder: How, on Earth, could they have been caught? The punchline of the story is actually very interesting and carries over the lessons learned for social media that are mentioned in blog headline. The misbehaving of the car producers was detected by NGO which is testing the car economy in real life usage. Their emission readings were for Diesel cars order of magnitude off the  laboratory (docker) measurements, while regular gasoline engines’results were quite close to official numbers. That rose suspicion or experts and tipped the avalanche of revelation of these fraudulent practices. But how does this relate to social media?

Recent social media businesses are just before similar period that car manufacturers were. When it comes to Fake news control of hoaxes eradication, they are both the messenger of the news and the quality (and user impact) watchdog. It is only subject to their inner moral integrity how well they gonna police the standards. What is more, they are in similar position when it comes to our private information and it (commercial) usage. Even though their business mode is built on monetizing information of its own users, there is next to none regulation setting the limits or reason or punishing greed of those Goliath’s. Cambridge Analytics issue was the the poster-child example of what we are talking about. If you think that GDPR has brought some justice to the topic, look at how pathetic the improvement of some companies are. When it comes to XAI (Explainable AI, depicted here) there is not even an general guideline proposed.

Therefore, the answer to this treacherous mode might be similar as was in Diesel Gate. After the emission full outbreak, honest automotive companies desperately called for independent agencies measuring the real emitted levels of gases in real day-to-day car driving to run the public test of actual emissions. They realized that their business (and brands) are user trust dependent. Car, after all, is The tool that we all put our lives and lives of our families into chance. Thus, line of though such as “If they had cheated about such banal thing  as emissions, what else from security features could they have lied about?” is dangerous rope to balance on. Unfair ecology treatment (let’s face it most of us is ignorant about) might easily turn into customer mistreating Wolkswagen (or other brand). So the prevention of repeating of the fraud, has been guarded by free third parties.

The problem of social media is that they still are in Diesel phase.  They did bot understand/admit the value of third party auditing. Mark Zuckerberg (and other social media executives) are talking us down with statements that best prevention is to hand over the fake news fight to internally developed detection routines. They do so also in midst of several blunt failures about conspiration theories or scandals of mistreatment of privacy data of own users. Social media companies simply don’t realize that they risk scenario that hit the automotive industry. They also neglect fact that if Facebook (or social network) users feel like they are being lied or bullied about own privacy data,  users will trigger massive exodus from that platform. If you count on human laziness to prevail and people swallowing their concerns, you better know that 1] for forthcoming generation the Facebook is already not a first social media choice; 2] Similar piece of mind about high (mental) switching cost was held by mobile operators, banks or utilities and they have to struggle to keep their user-bases.

Social media still have too much of the Diesel. If you want to benefit out of this, don’t go selling electric cars to Zuckerberg, but rather design for them algorithms to evaluate their work with user data. Or launch a new social media network that is going to have transparent and accessible audit of user data handling built directly into core functions from very beginning. Because social media will also go through their Diesel Gate, And probably pretty soon …