Although I’ve always been a big fan of online videos and find many such projects entertaining to watch, I’ve never really seriously considered doing anything on YouTube. That’s despite the fact that I’m fully aware that some people are making a living on this endeavor.

First of all, YouTube has changed dramatically over the years and not for the better. Specifically, the algorithm used for featuring what’s hot on the YouTube homepage has degraded drastically, in a desperate effort to promote “fresh” content creators. In other words, if a producer doesn’t publish videos frequently, they are not promoted much by the algorithm, something that inevitably gives rise to sloppy and cheap content, created merely to satisfy that mindless algorithm. Of course many YouTube fanatics (or YouTubers as they like to call themselves) have their own channels and networks of promoting their stuff, so they get some views regardless. However, the effort it takes to build such a network and the fact that it require constant work to keep it active, makes the whole process inefficient and problematic in many ways.

In addition, YouTube has started to filter its content in an effort to block offensive videos from being made available. It’s not that the company gives a damn about what you view since there is already a plethora of super low quality videos over there, but it wants to avoid lawsuits. So, in a desperate effort to save its ass, YouTube has aggressively started filtering its content through any means necessary. This includes having its own unpaid workers, some dedicated users that have nothing else to do with their time, to do this deed for YouTube. Of course these people are not trained while the guidelines they have been given are vague at best. So, it’s up to their limited discernment to figure out what constitutes a bad video and what doesn’t, so what they flag is oftentimes seemingly random. This way, many legitimate videos have been filtered as inappropriate just because some idiot couldn’t tell what they were about. This resulted to the corresponding producer not receiving any revenue from these videos, despite the amount of work he/she has put into these projects.

Moreover, the revenue YouTubers make from a single video is not that high, unless the video goes viral. What’s worse, the revenue decreases exponentially since just the most recent and most popular videos attract enough viewership. Who cares about something that was published a year ago, right? Well, wrong. If a video is of a certain quality standard, it is bound to be good to watch even after a year or two after its release date. Then again, most YouTubers have given up on quality videos since those take a lot of time and they need to get something online soon, if it is going to be fresh. So, since I don't have a whole crew working for me, if I were to do YouTube videos I'd make a fairly small income from the videos themselves, unless of course I were to have some sponsor. Sponsor ads however are not something the viewer wants to watch, so once you have a sponsor in a video, its quality immediately drops.

Furthermore, as I have a better alternative to YouTube (the Safari platform), it makes no sense whatsoever to settle for a less professional platform. Besides, YouTube is only popular because it's been around the longest and with newer and better platforms entering the scene lately, it's doubtful this trend will continue. As a bonus for not working for YouTube, I don’t have to worry about the Article 13 issue that seems to trouble YouTubers, nor do I have to busk for subscriptions from my viewers. I still get some nasty comments from time to time, but the majority of the feedback I receive is positive.

Finally, there is also the recent fiasco with the YouTube Rewind 2018 video (which broke the record for the number of dislikes in a single video, as well as the record of how quickly a video accumulates dislikes). This may seem insignificant to the YouTube fanatic, whose allegiance to YouTube and Alphabet trumps any rational thoughts on this matter, but the fact is that the company doesn't care about its content creators. Otherwise, it would mention the ones that actually make a contribution to it, instead of veering away from them, in favor of a celebrity and some not so relevant YouTubers. I don't know about you, but I'd rather not make videos at all than publish my videos to a platform like this, which fails to appreciate its contributors.

So, if you are someone thinking of becoming a content creator and make a revenue from all this, there are better ways than YouTube. Perhaps it was a viable option once but right now it’s one of the worst places to publish your stuff. Besides, with Safari and other quality-based platforms out there, figuring out what to do with a quality video is really a  no-brainer.

(Image by lazyprogrammer.me)

PCA has attracted a lot of questions among all of my mentees over the years, so I decided to make a fairly in-depth video on the topic. Unlike other education material on PCA, this one is light on the math, while there is a lot of emphasis on the concepts as well as how they apply to a data scientist's work. You can check out the video on Safari here.

Note that in order to view the video in its entirety you'll need a subscription to the Safari platform. Cheers!

Lately it’s hard to find someone who is a legit data scientist and yet doesn’t talk about Stats as if it’s a new religion or something. Don’t get me wrong; I find Stats a very useful tool in data analytics, especially data science. However, there are other, usually most suitable options out there to have in one’s data science toolbox.

First of all, Statistics is the state-of-the-art approach to data modeling, if you live in the mid 20th century. In our time, Stats, particularly frequentist Stats, is greatly outdated and many of the assumptions it makes about the data don’t make any sense. Also, transforming the data so that it fits the assumptions many Stats models make, is a time-consuming process which may or may not be worth the trouble. Of course if you know nothing else, or have trust issues with novel modeling options, then Stats may be the best option for you. In this case, however, it is best to brand yourself as a Stats professional instead of a data scientist, since the latter implies that you do more than just Stats.

In addition, pretty much all of the metrics used in Stats can be improved heavily by negating the normality assumption. The more data I come across, the more certain I am that this assumption may make sense in some cases, but in the majority of cases it doesn’t hold. So, using metrics that have this assumption embedded in them doesn’t really help anyone. What’s more, all this framework inevitably shapes one’s mindset and so if you get used to the unreasonable assumptions Stats usually makes about the data, you may not be able to think of the data in a different way.

Moreover, with the advent of A.I., especially the A.I. that’s directly applicable to data science, the data transformation and modeling options available to data scientists have increased dramatically. So, relying on Stats is more of a preference rather than a necessity. Besides, it’s extremely unlikely that a Stats model will be able to outperform an A.I. one when the latter is well configured.

Finally, there are other new data analytics methods waiting to be discovered and used in data science. Heuristics have made a comeback and are more and more popular in data science research, especially when it comes to complex datasets. So, sticking to Stats when there is a plethora of possibilities out there that can tackle a problem more effectively is just depressing.

Having said all that, Stats is a useful subject to learn, as it can aid one’s learning of the data science craft. Much like learning basic Mechanics can be useful if you want to being a Physics professional, learning Stats can be quite useful. Sticking to it and thinking of it as gospel, however, is not. That’s why after learning about it, it’s best to seek to expand your understanding of data analytics through delving into other frameworks, such as Machine Learning, A.I. based systems, and heuristics. Stats is just one of the tools available in the data scientist's toolbox...

These days I did something I’d been putting off for a while now, as if it didn’t work out, it would mean that I’d have to throw away my computer, so to speak. I didn’t exactly meddle with any of the computer’s hardware but came as close to it as I could, without physically changing the machine. Namely, I tweaked the boot software and configured a new OS that I’m now using. “What’s wrong with the old OS?” you ask. Well, I’d tweaked it way too much in the past, so it was now quite unstable. Yet, even at this pitiful state, it was better than some other OSes I’ve had over the years, so it’s hard to complain about it.

Whatever the case, getting down to the nitty gritty of a computer isn’t easy and there is a surprising lack of people out there able or willing to help out. Also, the forums although generally useful, don’t always have the exact issue you are looking to solve, so you basically need to rely on your own skills. Fortunately, I did a thorough back-up of all my data beforehand, so nothing could get lost. Also, I was quite meticulous with the whole process and had a back-up plan in place. A lot of shell scripting was involved and although I'm not super confident about this type of interaction with a computer, it's not as daunting as it seems either. Of course, if you do it more, like professionals in the field, it may even seem the best way to interface with a computer. I'm not there yet though, but I have a deeper appreciation of the merits of this approach to interfacing than I did before.

This whole thing is akin to the engineering approach to things, where failure is always taken into account since things break more often than people think. Thinking that everything is going to be fine, just because it worked fine in someone’s presentation or tutorial is naive and doesn’t really spell out professionalism. That’s why having the right mindset about all this stuff is essential. Algorithms, equations, and coding libraries can only get you so far. After that, you are on your own and you need more than just a solid understanding of the theory but also the ability to deal with the adverse circumstances that will probably present themselves sooner rather than later.

Now, in you work as a data scientist or an A.I. professional you’ll probably have no need to do low level work on a computer (unless you are setting up a new pipeline), but if such a challenge presents itself, you are better off facing it. And who knows, maybe you’ll do more than just upgrade your computer through this whole process, since chances are that you’ll also be upgrading yourself.

So, what did I learn from this whole experience? First of all, I now have a deeper appreciation to all those people who do the low-level work in a data science pipeline. It may appear straight-forward from a high-level perspective, but when you get down to it, it isn't simple at all, even if you enjoy working on a CLI. Also, I learned that just because something isn't common enough to be on a forum or a blog article, it doesn't mean it's not important or worth doing. The OS upgrade I did helped realize how vast the spectrum of possibilities is when it comes to OSes and how deviating from the most popular approaches to it is probably the best way to go (or at least the most fox-like way!). Finally, I learned that when you've assembled something yourself, even if it's a fairly straight-forward OS, it makes you appreciate it more. Most things nowadays come preassembled and we don't have to do anything to get them to work, but those things that require our own energy to come to life, be it an OS or a custom data science model, these are the things we tend to remember the most since they change us inside...

About 2 years ago I created a video on the Julia language and how it applies to data science. Although I was still learning the ropes of video creation, I had a lot of useful things to say about the language since I had just published a book on it, a book that is still quite popular among the Julia learners as well as those getting into data science through programming. Now, after version 1.0 has come out, I decided to revisit this topic and provide an update about how Julia factors in the whole data science matter, as well as how it contributes to A.I. applications among other relevant topics. In this video, I explore all these points and without getting too technical, I showcase an updated view of how Julia is still a relevant tool when it comes to data science projects. Enjoy!

Note that you'll need a subscription in order to be able to view this video on the Safari platform. However, once you have paid for it (either for a month or a year), you'll have access to all the content published in there, including all my other videos and books. Cheers!

As you may have heard, article 13 of the European copyright legislation is seen as a major issue for content creators of all sorts, sharing their creations online. Specifically, it can basically block the viewing of various videos (and other creative content) in various EU countries (including the UK). This is because this articles for some reason sees the viewing of this content in certain countries a violation of the content creators’ rights and in an effort to protect creativity, it limits where this content is made available.

I’m not going to argue here about the futility of such a legislation or why such laws don’t make any sense in a world where content creators strive for increased exposure, while it’s extremely unlikely for someone to own all the elements of their videos. Also, personal branding is something the lawyers that drafted this legislation probably don’t quite understand, something reflected in how this law is formulated. Whatever the case, this law is focused on various social media platforms, such as YouTube and Instagram and does not affect SafariBooksOnline.

So, if you are like me and publish your content in respectable platforms where there is a quality control and no issue with European legislation, you are fine. I can’t say that I like this situation with some bizarre law prohibiting the viewing of videos in various countries, but I’m not going to lose sleep over it since this is but the tip of the iceberg of injustices these “free” video platforms offer. Besides, there are various platforms where someone can publish creative content, especially when it comes to educational topics, so opting for the easy way of YouTube is just not the most professional approach. After all, the focus on such a platform is on the quantity and on some ever-changing algorithm for promoting this content, something that doesn’t benefit the content creator to start with.

So, if you have some ideas for educational videos, Safari is a great place to publish them and doesn’t get any headaches from the European Parliament or any other authority that claims to understand how creative content works. As a bonus, you get to collect royalties from your videos, regardless of when they were published or if they are on some “hot” topic, while click-bait is not that common in the video titles. This respect towards the viewers of the videos is reciprocated through a handsome payment from their part, instead of having to put up with annoying ads and overcrowded web pages. So perhaps going with YouTube is not as glamorous as it may seem, with or without Article 13.

For all of you celebrating Thanksgiving, happy Thanksgiving! I don't need to tell you about the importance of being grateful about the good stuff we've good going on in our lives. However, I can do need to tell you is that I'm grateful for all of you visiting my blog and checking out the content I'm developing for Data Science, Artificial Intelligence, Cyber-security, and even Programming (to a lesser extent), in an effort to clarify certain matters and inspire you to learn things in a more in-depth and more enjoyable way. Also, grateful for being in this fascinating field and watching it evolve into something useful and self-sufficient. What are you grateful for?

Enjoy this great family holiday with your loved ones, and if you have some time, check out some of my videos / books!

So, after attending this truly eye-opening conference in Amsterdam last month, I felt obliged to share at least some of the stuff (most relevant to data science) I got from it with other people, through a reliable content sharing platform. So, I wrote an article about this topic on beBee and then created a video which is now available on Safari.

Note that this is a bit high-level as a video, with emphasis on managerial and senior-level data science practices, rather than hands-on aspects of the craft. However, every data scientist can benefit from this knowledge, especially when dealing with sensitive data. Also, Safari content requires a subscription in order to be accessible to its full length.

In an interview I recently watched, Elon Musk put forward the case of a utility (objective) function for a hypothetical advanced A.I. (basically an AGI) and how special attention must be given to such a task to avoid undesirable results. So, he suggested we use some utility function some person had recommended (probably an A.I. expert), namely that of maximizing “freedom of action for everyone,” something that’s quite reasonable and perhaps even profound if you think about it. However, if you think more about it, it becomes evident that it’s a terrible, terrible idea!

First of all, I mean no disrespect to Elon Musk. I think many of the things he’s created are great, even if some of his ideas are somewhat extreme. So even if he is not a role model of mine, I admire him as a tech entrepreneur and find that he has a lot to offer to the world through his businesses and his ideas for a better world. Except of course his idea for a utility function; that would be catastrophic, though I’m sure that in his mind it’s a brilliant solution to the utility function problem.

For starters, freedom is a very abstract concept even if it’s made more specific by the term “of action” to clarify it. How do you measure freedom of action? How would an A.I. understand this concept, especially if it never gets to experience it? Then, would maximum freedom be a good thing necessarily? Isn’t that a form of anarchy in a way? These are things that need to be addressed before asking an A.I. engineer to implement such a function for this hypothetical A.I. So, unless we figure this out, we cannot be sure that this A.I. will be benign, even if its creators have the best intentions in the world for it.

For example, an A.I. that makes use of this utility function may accelerate the depletion of natural resources of this planet (and any other planet it has access to), in order to ensure that everyone, even some random criminal on the streets or an inmate in a high security prison, has as much freedom of action as possible. Do you see where I’m going with this? Perhaps I’d better stop here before this whole post turns into some dystopian scenario or something.

The utility function problem is a difficult one and in all fairness Elon Musk is not someone knowledgeable enough in A.I. to be able to provide a bullet-proof solution to it. He may know a lot about the topic but I doubt he’s ever created an A.I. system from scratch. And unless you are close to the metal about these things, any ideas you have about how things should be regarding the high-level aspects of such complex systems is just an opinion on the matter, not a serious candidate for a solution to the problem at hand. The latter would be something that has legs and right now it seems that Mr. Musk’s suggestion is floating in the clouds just like many futurists when they talk about A.I. Perhaps that’s why many people don’t take Elon Musk’s warnings about A.I. very seriously, although I believe that’s one of the things he’s got right.

Despite the inevitable risks such an endeavor has, I’ll venture to make a suggestion of my own for a utility function, namely one that evolves over time. In other words, I propose a narrow A.I. whose sole purpose is to optimize the utility function of the AGI, perhaps in a Reinforcement Learning fashion, based on the feedback it receives from other people, while it starts with a utility function that’s as risk-free as possible (based on some simulations we run before we deploy it to the AGI). Some core heuristics may be in place to ensure a large enough diversity of signals that this A.I. will take into account, coordinating the various objectives / values that the AGI will have to uphold. Besides, it would be naive to assume that a human being, no matter how knowledgeable, can be in a position to come up with a utility function that can apply to some creature more intelligent than all the people in the world, forever.

If our own evolution has taught as anything is that there are no absolutes in nature and that we evolve to become better and adjust our values according to the circumstances we face and the challenges we wish to overcome. Why should an AGI be any different, considering that it’s created in our own image?

Although the debate between Frequentist and Bayesian statisticians sometimes takes a more comical turn (XKCD strip), it is still important for a data scientist to know a few things about Bayesian Stats. Of course, purists of the craft will argue that Frequentist Stats will suffice but if you want to stand out of the crowd, it would definitely help going beyond the beaten path, when it comes to data analytics know-how.

This video I made recently highlights the key elements of Bayesian Stats, focusing on the concepts that although fairly straight-forward, may be obscure to the newcomer. Also, without disregarding the invaluable contribution of Frequentist Stats to data science, this video explores how the two differ and how Bayesian Stats has a lot in common with other, more modern, data analytics frameworks. Check it out when you get the chance!

Note that a subscription to the Safari platform is necessary in order to view the video in its entirety.