Tag: AI

Data Executive’s Read 2023 | Book suggestions

Staying sharp in the data realm is like juggling flaming laptops – challenging and a tad risky. To keep my executive skills from going the way of the floppy disk, I’ve committed to tackling a whopping 10,000 pages of books annually. Like private brain gym, but with more words and fewer sweaty towels. (Not only) for executive, reading 300+ pages book is a large time investment, so you better pick a worthy one. Therefore, below I( offer list of this year’s best reads in 2023, curated to inspire, educate, and maybe even give you a chuckle. Think of below listed books an potential beacon in maze of staying tuned to data wizardry!

 

Blue Ocean SHIFT

Topic | Innovation, Strategy

If you ever went through some Strategic management training, this name might ring the bell with you. You also might roll your eyes, as Renée Mauborgne and W. Chan Kim published their first introduction to Blue Ocean in 2004, so whooping 20 years ago. But wait I am not that ignorant, there is more to this suggestion.

Blue Ocean strategy (BOS) is one of the major concepts in strategy how to differentiate your business from (blood thirsty, break-the-neck) competition. It is framework that enables you to innovate no matter how good/bad or unique your products or services are. If you have not read this book before, close the gap immediately. I used it several assignments of my career and the methodology always yielded interesting new business strategies.

However, even if you did read the original 2004’s Blue Strategy book, this one is different. Authors of the original concept bring additional insights how to not only design the differentiating strategy, but foremostly also how to implement it. They added and rewritten original scope of BOS based on learning from 20-years of implementing it in industries and public organizations. Hence the updated name reference to “SHIFT” in Title. I honestly think, this is a must read for any middle or top manager.

Link | https://www.amazon.com/Blue-Ocean-Shift-Competing-Confidence/dp/0316314048

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AI 2041

Topic | Sci-Fi Fiction + AI commentary  

Many authors and books try to explain the major shift in ArtificialIntelligence (or AI) in last days. Few writers also dare to predict or speculate about where it might takes us from here further.

️ However, the book from Kai-Fu Lee and @ChenQuifan is very special and different. Kai-Fu is formal Executive from Google, Apple and likes, responsible for implementing AI solutions. When he talks AI methods, he most likely headed implementation of the early pilots of that. Real well of AI knowledge and experience.

He teamed with Sci-Fi author to write unique piece narrated by dozen of stories (all happening around year 2041). In each story/chapter they first introduce the future use of AI in real life, only to finish the chapter with facts and details of how this will be implemented and what is the realistic stage of future AI to expect before 2041.

The book is somewhat thick, but absolutely worth and easy to read, as you can dig through it one story at a time. I think it is especially good gift for somebody who wants to understand the (future) of AI, but does not have technical background to read white papers.

Link | https://www.amazon.com/AI-2041-Ten-Visions-Future/dp/059323829X

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Becoming a Data Head

Topic | Data-driven, Management, Data literacy

Decision to put this book on my reading list was stemming from the curiosity.  The book reviews suggest that this book is good entry-book for executive trying to be data-driven or AI-ready. Being SVP Data & Analytics (and seasoned Data Scientist) myself, hardly the fit for my career phase. But I have seen so many books claim (and fail) to introduce you to Data Science bushes, that I was tempted on how this book will be doing? Yet another flat-falling promise?

No, quite the contrary! This book really walks its talk. Namely walks you as user through different stages of Data analytics and Data Science smoothly. Even the basic concepts are explained in no-nonsense style that does not require any previous knowledge from you, but also does not insult (your intelligence) neither gets you bored, if you are reading things already obvious to you. You can also decide how “far into the woods” do you want to dive and stop reading any time you think this is exactly the level of understanding that is enough for you. Or maybe you look even deeper to understand the principles of what you just read?

I strongly recommend this book for anybody trying to change career into data jobs. I find it also great present for any manager or executive if you want to enlighten them in data.

Link | https://www.amazon.com/Becoming-Data-Head-Understand-Statistics/dp/1119741742/

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COLORWISE

Topic | Data Visualization, Storytelling

As somebody shaping (literally) thousands of visualization year after year, I welcome books describing the rules and good (and bad) practices for creating visualizations. I have few in my library (and suggested them in my previous reading lists), but they often talk more about what kind of graph to chose and how to shape the composition. Many of them take use of color for granted (or touch the issue only from the side).

The ColorWise is book giving “color choice” and “color coding” in graphs and visualization full spot-light.  It explains the background of colors in very non-academic way and surely taking you beyond your previous knowledge about color usage. It also gives clear guidance on how to create your graph color schemes, if you are anchored with some of the brand (must-have) colors. What is more, it goes also deeper into psychology of different color schemes and warns you about cultural or color deficiency pitfalls of your graphs. If you are already pro, you will often nod your head with “Exactly!” on your lips … and you still learn few new aspects to think about. If you are “regular” color user, your color coding skills will take significant boost. I strongly recommend for anybody , who needs to produce dashboards or presentations regularly in their work.

Link | https://www.amazon.com/ColorWise-Storytellers-Guide-Intentional-Color/dp/1492097845

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BUILD

Topic | Strategy, Data, Product management

Many admire TonyFadell for what he achieved. He built iPod for Apple and basically saved Apple from falling. And then humbly he built iPhone on top. And if that would not be enough for you, then he built the brand new company Nest that started the whole SmartHome category of technology and sold it to Google for few billions. So certainly inspiring person enough. But if you are not a tech geek, you probably did not hear his name before or care too much. Nor did I. And I regret so.

His book BUILD is interesting mixture of advice and guidance for people who want to have their life (and career) a bit more in their hands. He narrates the story from the adolescence through earlier years in job up to CEO-part of your life. And yes, maybe you will never (want to) be CEO, but the story is still a good guidance. It might sound fluffy, but whoever you are in business, I am quite sure you can take some benefit from some chapter of this book. Yes, occasionally you have to pardon him Tony’s American optics, but the smell of it is more like fragrance you know, but would not wear yourself, not a sensoric disgust.

‍ I especially admire a chapter on how data plays different role in building individual phases of the product. It gives you clear idea guidance on where data is horse and where it is (still needed but rather) cart. Going through 3 layers of management (Team Lead to SVP) myself, I can confirm that his views of how to perceive your role is very accurate and I was amazed how he can compress the essence into (often just) few pages of the text.

All in all, this book is Masterpiece (uh, I told you that already, right? ). And I strongly suggest you to read it. The earlier the better. Because some of the lessons he gives I had to learn hard way and I only wished he had written that book earlier. Have a great read!

Link | https://www.amazon.com/Build-Unorthodox-Guide-Making-Things/dp/B09CF2YB6Z/

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All in on AI

Topic | AI, Growth, Strategy

I have read most of 15 books that @DavenportTom authored and mostly were happy about them. Therefore, when I saw his newest piece ALL IN ON AI, I was full in anticipation.

Author introduces group of businesses that decided to make artificialintelligence the center piece of their business strategy and operation. They really went ALL-IN on it. Book walks you first through how does such a AI-ALL-IN company looks like. What are common denominators, but also industry specific aspects. Quickly you understand how to spot the markers.

But that’s only start of it. In the remainder of the book Davenport (and his co-author) provide examples of how to your existing business into AI-ALL-IN state. They do it cleverly, picking real companies (‘ stories) from different maturity levels and industries. Authors also methodically link the needed AI-markers to the development in the stories, proving that common denominators are actually fitting and well chosen.

Who is this book for?
Well, for anybody who envisions or dreams about taking benefit of progressive technologies in their work. For those wanting to step-up or future-proof their business.
It’s also good gift idea for employees trying to pitch the AI change to top manager(s).

Link: https://www.amazon.com/All-AI-Companies-Artificial-Intelligence/dp/1647824699/

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Good Data

Topic |  Data, Ethics, Search data

Reading Sam Gilbert’s book Good Data is stimulating and entertaining at the same time (you just need to see through authors masked humor). Sam is seasoned data professional, who does not fall into cliche and mental short-cuts oof today’s data speak.

Not always had I agreed to his opinions, but all the questions he raised in the book made me really (re)think what I considered role of data to be in different corners of business and our society. Thus, if you ask “What questions should we have about future of data?” , this book will get you there.

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Just for the answers to those questions, please, think a bit more critically than the author suggests. All in all, quick and fun to read, opening new horizons. Worth few days of reading.

Link | https://www.amazon.com/Good-Data-Optimists-Digital-Future/dp/1787396339

 

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Don’t Make Me Think (Revisited)

Topic | UX, Product management, Web design

Web and App’s became our window of everyday activities, social interaction, shopping and most of of work (certainly so during COVID). In 1990’s and 2000’s institutions and businesses were trying to impress us by physical real estate. But how do us digital institutions treat now?

This book is for everyone, who wants to grasp the basics (yes, it is starting from ground) of how to design digital interface on web or app. Even though this might sound like UX designer guideline (which I was happy user if it was), it is really served in down to earth language and does not require from you any design domain knowledge. (but it leaves you with some after you read through).

It is not long read and I strongly encourage anybody interacting in our with Web and App’s (or have a say in their design) to at least skim through this. No regret move!

Link | https://www.amazon.com/Dont-Make-Think-Revisited-Usability/dp/0321965515

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Extremely ONLINE

Topic | Creators, Social Media

At first glance, the subject of online influencers might not seem like a page-turner. However, a friend’s recommendation led me to Taylor’s exploration of the hidden layers behind social media’s evolution, and I was instantly captivated.

This book isn’t just a timeline of social media from the late 90s; it’s a narrative that weaves through the changing social dynamics influenced by online platforms. It provides an intriguing mix of statistical data and storytelling, revealing how various online communities engage with social media.

The book also offers surprising insights into questions like:

  • What was the first major topic that sparked the blogging revolution?
  • How did the requirement for influencers to disclose sponsorships impact the effectiveness of advertisements?
  • What truly contributes to societal polarization if not social media algorithms?
  • Which other social networks suffered at the hands of Twitter?

️| For those in marketing or content creation, this book is an essential read from start to finish. It’s equally crucial for parents or soon-to-be parents to understand the evolving relationship between kids and social media.

For me the book has a bit special twist, that is likely to work for you as well if you are in your late 30’s or 40’s. It maps the development of internet consumption for our generation, as when blogs hit the internet was exactly the time that our generation started to interact with it.

Link | https://www.amazon.com/Extremely-Online-Untold-Influence-Internet/dp/1982146869

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Machine Learning Design Patterns

Topic | Machine Learning, Data Science

This book feels like the Swiss Army knife for machine learning enthusiasts. It’s the first of its kind as it dives into the wild world of ML design patterns. Forget about dry, technical jargon; this book is like a treasure map, guiding you through 30 quirky, yet ingenious design patterns, each one a secret weapon against those head-scratching ML problems. It’s like finding cheat codes for a video game, but for machine learning!

Imagine a cookbook, but instead of recipes for apple pie, it’s chock-full of solutions for when your AI project decides to go on a coffee break. Whether you’re a seasoned data scientist or just someone who accidentally wandered into the machine learning aisle, this book is your trusty sidekick. It’s the kind of read that makes you think, “Ah, so this is what Google’s brainiacs do for fun!” – solving problems and making ML as approachable as a friendly robot assistant.

Link | https://www.amazon.com/Machine-Learning-Design-Patterns-Preparation/dp/1098115783

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CRUX

Topic | Strategy, Business Analysis

As someone with a background in Strategic Management, I’ve devoured nearly every strategy book available. Through my extensive reading, I’ve discovered two authors who consistently deliver valuable strategic insights: #GaryHammel and #RichardRumelt.

‍♂️ Therefore, to no surprise, Richard Rumelt’s #CRUX stands out as a masterpiece (again). It skillfully guides you in crafting authentic strategies for your business or team and shatters common executive misconceptions, like the necessity of a mission statement, misconstruing international expansion as strategy, or overvaluing shareholder interests. It’s also an excellent resource for learning to spearhead genuine strategic development.

I strongly recommend this book to all executives. Be prepared for a reflective and sometimes uncomfortable journey through your previous strategy endeavors. It’s equally insightful for middle managers, equipping them with the knowledge to challenge and refine the strategies proposed by their higher-ups. Overall, it’s a perfect read to gift yourself or others during a vacation.

Link | https://www.amazon.com/Crux-Richard-Rumelt/dp/1788169514

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The Choice Factory

Topic | Marketing, Psychology, Feature engineering

The Choice Factory” by Richard Shotton is an exceptional read, especially recommended for data analysts focused on human behavior modeling and prediction, as well as marketers seeking to boost their marketing conversions via leverage (or taking tail-wind of) natural human tendencies.

What sets this book apart is its reliance on proven real-world best practices, presented not as isolated case studies, but as principles backed by comprehensive research. Another key strength of the book also lies in its concise, easily digestible chapters, each ending with practical, actionable advice on how to implement these insights.

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I strongly endorse this book for anyone looking to gain a deeper understanding of human behavior in feature engineering for ML prediction models or for marketing optimization context.

Link | https://www.amazon.com/Choice-Factory-behavioural-biases-influence/dp/085719609X

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The Ruthless elimination of Hurry

Topic | Work-Life balance, Mental health

The Ruthless Elimination of Hurry,” as the title aptly indicates, is more than just a book; it’s a compelling manifesto advocating for a deliberate shift away from the relentless pursuit of speed for its own sake.

In our fast-paced world, where speed is often synonymous with efficiency and success, this book presents a refreshing perspective. It acknowledges that while speed can be beneficial (except when it leads to a speeding ticket!), it shouldn’t be the primary objective. Speed should be a tool, employed judiciously and only when truly necessary. The book emphasizes the importance of intentionality in our actions, encouraging us not to rush mindlessly but to consider the purpose and value of our speed.

Authored by John M. Comer, a U.S. pastor, the book is understandably infused with religious references and teachings, particularly focusing on Jesus and other Christian elements. For some readers, this religious aspect might seem predominant, but the book’s core message transcends religious boundaries. If one can look past the religious overtones, or perhaps even draw insight from them, “The Ruthless Elimination of Hurry” reveals itself as a deeply thought-provoking and intriguing read.

It’s a book that challenges the status quo of our hurried lives. It invites readers to pause, reflect, and reconsider the pace at which we live. The author’s insights offer a unique perspective on how slowing down can lead to a more fulfilled, purpose-driven life. This makes the book an essential read for anyone feeling overwhelmed by the ceaseless rush of modern life and seeking a path to a more balanced, intentional existence.

Link | https://www.amazon.com/Ruthless-Elimination-Hurry-Emotionally-Spiritually/dp/0525653090

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Data Science on AWS

Topic | ML operations, Data Science, Data engineering

Ah, the wild ride of prototyping machine learning models! Many of us have gone through fast prototyping (or toy examples) of the Machine learning clustering or prediction models in notebooks or sand-box environments. It’s like building a Lego castle in your living room – fun, easy, and oh-so-satisfying. But then, you decide to move that castle to the real world, and suddenly, it’s like trying to assemble it in a windstorm. Surprise! Porting your perfect little prototype into the jungle of a live environment is like herding cats while juggling.

Most of today’s implementations are left with no choice but to run in cloud, virtual machines set-up. Requiring additional complexity and care to even deliver the bleak functionalities of the easy, local machine PoC. This book is about how to think of Machine Learning aspects of live solution in advance. To understand what combo of the tools one should expected to be deployed, to run your machine learning train properly on rails. It is must-read text not because you will be ever coding the things and connectors mentioned in material. It is essential rather because you need to understand what everything your teams have to go through to make it all happen for you.

Link | https://www.amazon.com/Data-Science-AWS-End-End/dp/1492079391

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Text As Data

Topics: NLP, Machine Learning

As the title of the book rightly suggests, text has been for long perceived as special “animal”. On the edge of the data analytics, much more obscure than analysis of the relational data by SQL or by Predictive analytics. Text analytics was also managed by dedicated (python) packages and often by NLP-specializing-only staff. If you were not one, you would probably just reach out for (simplified) predefined functions in NLTK (or similar code library).
Those times are over. Text is mainstream. If you were not convinced before ChatGPT burst, now there is no way to disprove it. But Text analytics still finds the audience (and practitioners) left in pre-text era, only having rough idea how to address data that is stored in troves of text.

Therefore, This book comes as a kind of gift. If you admit to be one of those having general (read limited) only understanding of insight extraction from text and how to set-up the text analytics in your team, if you have not been treating text equally heavy as ML or Reinforcement learning, this book helps you to close that gap. It’s well written and always illustrated on telling examples. If you missed to buy the ticket for departing text analytics “train”, this is your fast track to get on it.

Link | https://www.amazon.com/Text-Data-Framework-Learning-Sciences/dp/0691207550

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The Coming Wave

Topic | AI, Philosophy

Hold onto your hats, folks! Mustafa Suleyman’s “The Coming Wave” isn’t just a book; it’s like a roller coaster ride into the future, where your coffee maker might be plotting world domination. Suleiman, the AI whiz-kid and DeepMind co-founder, is dishing out a buffet of mind-boggling predictions. Imagine a world where your vacuum cleaner is judging your music taste and your fridge is gossiping about your late-night snack habits. That’s the kind of AI party Suleiman’s inviting us to.

But wait, there’s a catch. It’s not all about tech wizardry and gadgets having a mind of their own. Suleiman waves a big, bright warning flag about AI’s dark side. Picture a world where AI is like that one overachieving cousin who’s great at everything but sometimes scares the living daylights out of you. He’s like the cool uncle of the tech world, telling us to enjoy the party but maybe hide the fine china just in case.

So, whether you’re a tech-head, a skeptic, or just someone who’s curious if your phone is silently laughing at your TikTok attempts, “The Coming Wave” is your handbook for the AI age. It’s like a survival guide for the digital jungle, complete with a map, a flashlight, and a slightly ominous warning about the creatures lurking in the shadows. Buckle up and get ready for a wild ride into the future, where your toaster might just be the smartest thing in your house!

Link | https://www.amazon.com/The-Coming-Wave/dp/1847927491

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Julia High Performance

Topic | Data engineering, Data Science

No, this is not a mesh of the Shakespeare’s famous love novel and Performance marketing guide. Julia might still be the new kid on the block in the programming world, especially compared to Python, the reigning “lingua franca” of data science. But don’t be fooled – this emerging language packs a punch with its speed and efficiency. “Julia High Performance” by Avik Sengupta and Alan Edelman is like the ultimate guidebook for this speedster of a language.

Think of this book as your go-to manual for making your code run like a sprinter on a caffeine high. It’s like a masterclass in getting the most out of Julia, from understanding its high-speed capabilities to avoiding performance roadblocks. While some readers might wish for a deeper dive into the more intricate examples, the book remains an eye-opener, proving its worth by empowering users to supercharge their projects, leaving Python in the dust. Some users even boasted a tenfold performance boost after switching from Python/NumPy to Julia – think about leaving the comfort zone and head towards a coding glow-up!

This book, admittedly,  is a bit of the Joker card, but if you did not pick anything above and you are reasonably fluent in Python coding, maybe give it a try.

Link | https://www.amazon.com/Julia-High-Performance-Avik-Sengupta/dp/178829811X

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How ChatGPT really works in SIMPLE WORDS (and pictures)

Many of us have probably already played with new-kid-on the-block of the Artificial intelligence space, ChatGPT from OpenAI. Providing prompt of any question and getting no-gibberish, solid answer, very often factually even precise is fascinating experience. But after few awe moments of getting answer to your “question of the questions” you maybe wondered how does the Chat GPT actually really work?

If you are top-notch Data scientist you could probably go into documentation (and related white-papers) and can simulate (or even write own) transformer to see what is going under hood. However, besides those few privileged, usual person is probably deprived of this, ehm, joy. 😊 Therefore, let me walk you through the mechanics of ChatGPT in robust, but still human-speak explanation in next few paragraphs (and schemas). Disclaimer = I compiled this overview based on publicly available documentation for the 3.0 version of the GPT. The newer versions (like 4.0 ) work with same principles but have different size of neuron nets, look-up dictionaries and context vectors, so if you are super-interested into how the most recent version works, please extend your research beyond this article)

 

6 main steps

Even though our interaction with ChatGPT looks seamless, for every query to it, there are 6 steps going on (in real time). Media label the ChatGPT in single phrase as “artificial intelligence”, but it is worth mentioning that of these 6 steps, only 2 and half are actually real artificial intelligence components. Significant part of the ChatGPT run is actually relatively simple math of manipulating vectors and matrices. And that makes the details of the ChatGPT even more fascinating, even for the “lame” audience.

 

It’s start with compressing world into 2048 numbers

The first step of the ChatGPT work is that it reads through the whole query that you provided and scans for what are you actually asking. It analyzes the words used and their mutual relations ships and encodes the context (not yet the query itself, just the topic) of the question. You might be amazed by fact that ChatGPT converts whole world and possible questions you ask into combination of 2048 topics (represented by decimal numbers). In a very simplifying statement you can say that ChatGPT compresses the Internet world into 2048-dimensional cube.

 

Context first, then come tokens

As outlined in previous paragraph, in process of answering our prompt the ChatGPT first takes some (milliseconds) time to under the context of the query before actually parsing through the query itself. So after it decides, who area(s) of “reality” you are interested in, than it meticulously inspects your entire question. And it literally does so piece by piece, as it decomposes the given question into tokens. Token is in English usually a stemmed word (base) with ignoring the stop-words or other meaning non-bearing parts of the text. In other languages token can be obtained differently, but as rule of the thumb:  number of tokens <= number of words in the question.

For every token the GPT engine makes a look-up into predefined dictionary of roughly 50K words.  Using hashed tables (to make the search super fast), it retrieves a vector (again 2048 elements long one) for each token. This way each word of the query is linked to topic dimensions. As the system does not know in advance how many words will your request have, there needs to mechanism to accommodate for any (allowed) length of the query. To be flexible with this, chatGPT forms a extremely long vector (2048 * number of tokens), in which the sub-vectors coming from dictionary lookup for each token is arrange one after another into sequence. Therefore 100 words long query might have even up to 204 800 vector elements. even larger 500 words request might have more than 1 mil of the letters. This vector is than processed, but first we need to do one more important change.

Where to look (or How to swim in this ocean of data)

As we learned 500 words long request to ChatGPT might arrive at more than 1 mil numbers encoding this request. That is a real ocean of the data. If you as human received such a long prompt for answer, I guess you would struggle even with where to focus the attention first place. But no worries here, so would the GPT if it was not for the Attention mechanism. This AI technique researched only in last 10 years (papers from 2014 and 2017) is the real break0through behind GPT and is also the reason why language models were able to achieve the major step-up in “intelligence” of communication.

The way that Attention mechanism works, it calculates (still through linear algebra matrices) pair of two (relatively short vectors) for each of the token. These vectors are labeled as KEY and VALUE. They are representation of what is really important (and why) in the text. This way the engine does not force neural network to put equal weight ( = focus) on all million input numbers, but select which subsection of the query vector are crucial for answering the question. When then combined into transformed SUM of the elements, it provides the recipe for how to “cook” the answer to question. what might sound like (yet another) complication, is actually key simplifier and energy saver. While past approached to language moles assumed “memory” holding equally important each word of the query text (or assigning same, gradual loss of attention into previous words). That was prohibitive expensive and hence limited the development of better models. Therefore,  jumping over the attention hurdle unlocked the training potential of AI models.

 

Finally AI part

It might be counter-intuitive for many, but first 3 steps of the GPT have actually nothing to do with Artificial Intelligence. It is only step 4, where the real AI magic can be spotted. Essence of the 4th step is the Transformer core. It is a deep neural network, with 96 layers of the neurons, a bit more than 3000 neurons in each of the layers. The transformer part can be actually named also the Brain of the GPT. Because it is exactly the transformer layers that store the coefficients trained from running large amounts of texts through neural network. Each testing text used for training of the AI, leaves potentially trace in the massive amount of the synopses between the GPT “neurons” in form of the weight assigned to given connections.

As unimaginable the net of hundreds thousands (or millions) neurons are to us humans, so is the actual result of the Transformer part of GPT is probability distribution. No, not a sequence of words or tokens, not a programmed answer generating set of rules, just probability distribution.

 

Word by word, bit by bit …

Finally in step 5 of the Chat GPT we are ready to generate the textual form of the answer. GPT does that by taking the probability distribution (from step 1) and running the decoder part of the Transformer. This decoder takes distribution and finds the most probable word to start the answer with. Then it takes the probability distribution again and tries to generate second word of the answer, and third, then forth and so on, until the distribution of probabilities calls special End-of-request token. Interestingly enough, the generation does not prescribe how many words will the answer have, neither it defines some kind of satisfaction score (on how much you answered the query already with so-far generated sequence of words). Though ChatGPT does not hallucinate the answer or bets on single horse only.  During the process of the creation of the answer there are (secretly) at least 4 different versions (generated using beam search algorithm). Application finally chooses one that it deems most satisfactory for the probability distribution.

 

Last (nail) polish

As humans, we might consider the job done by step 5 already, so what on Earth is the sixth step needed for? Well anybody thinking so, forgets that human person talking formulates the grammatically correct (or at least most of us) sequence. But AI needs a bit of the help here. The answer generated by Decoder still needs to undergo several checks. This step is also place where filtering or suppressing of the undesirable requests is applied. There are several layers on top of the generated raw text from previous stage. This is also (presumably) place where translation from language to language happens (e.g. you enter you question in English, but you ask GPT to answer in Spanish).  The final result of the query answer has been delivered, user can read through. And ask next question 🙂

The flow of the questions in the same conversation thread can actually lead to updating or tweaking the context parameters (Step 1) of given conversation. The answering context thus gets more and more precise. Strikingly, the Open AI’s GPT models actually store each of the conversation, so if you need to refer back to some past replica of conversation, GPT will still hold the original questions and answers of that talk branch. Your answer (and questions) remain thus historized and in full recall any time in future.  Fascinating, given the number of users and queries that they file.

 

Steps Summarized 

The above described steps of the GPT answer building have been neatly summarized into following slide, providing additional details and also indicating the transformations made in individual steps to enable the total answer flow. So if you want to internalize the flow or simply repeat the key training architecture/principles, please read through the following summary:

 

Few side notes to realize …

Though the actual mission of this blog post is to walk the reader through the (details of) process of generating the answer to the query prompt for GPT, there are few notable side facts stemming from the way that GPT is internally organized. So if you want to collect few “fun fact” morsels that make you more entertaining dinner buddy for your next get-away with friends (or for Sunday family lunch), here is few more interesting facts to be aware of (in GPT realm):

And bit of zoom-out view

Besides the fascination with HOW actually ChatGPT works, I often receive also questions about it’s future or speed of the past progress. I summarized the most common questions (I received) into below show-cased 1-pager. So if your curiosity is still on high level, feel free to charge yourself with these FAQs:

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Did ChatGPT pass Data Science technical interview?

On last day of November 2022, bit in the shadow of the Cyber week craze, there has been released by OpenAI team for free testing the new ChatGPT. It is aimed to be an chat-bot using strong GPT 3.5 natural language model, capable of not only casual conversation but also able to answer real (even tough) expert questions, or write creatively texts, poetry or even whole stories.

As the features (and performance) of the model are pretty awesome step-up to what we have seen so-far, its launch immediately rolled the snowball of testing it in plethora of the domains. The craze seems to be actually so intense, that it is believed to be the first digital tool/service to reach 1 million of new users within 5 days of its official release. (To be fair, I think it is the first recorded one only, I am quite sure that in countries like India or China it is not unheard of gaining 1 mil users fast for something really catchy 😊)

But back to core story. The ChatGPT use-case, that was bringing the most havoc on LinkedIn and many blogs and news portals, is fact that can produce real snippets of code based on very simple specification of what the code should do. You can go really like “Show me code to predict survival rate on Titanic” and it returns in snap the Python code to fetch the data, create predictive model and run it, all in gleaming, well commented Python coding language. Or so it looks.

In effort to create my own opinion, I tried (and collected others’) attempts on coding inquiries to investigate the real quality of the code. I made a short summary of this early investigation in this this LinkedIn post. Tl;DR = it was not flaw-less code; if you try to run it, you will still often stumble upon errors, BUT … For somebody not having a clue how to attack the problem, it might be more than an inspiration.

 

Few days later, my dear friend (and former colleague) Nikhil Kumar Jha came with the idea to ask the ChatGPT one of the technical interview questions he remembered from the time I was hiring him into my team. He passed me the question and answer in message. And I have to say, the answer was pretty solid. That made my mind twisting. So, we quickly agreed to take the whole battery of the test that I use for technical interview for Data scientist and submit the ChatGPT “candidate” through the whole interview hassle. Rest of this blog tries to summarize how did the robot do and what are the implications of that. But before we get there: What do you think: Has the ChatGPT passed the technical round to be hired?

Technical interview to pass

Before jumping into (obviously most) juicy answer to question at the end of previous paragraph, let me give you a bit of the context about my interview as such. The market of the Data Scientist and Machine Learning engineers is full of “aspirational Data Scientists” ( = euphemism for pretenders). They rely on the fact that it is difficult to technically screen the candidate into details. Also the creativity of the hiring managers to design very own interview questions is relatively low, so if you keep on going to interview after interview, over several tens of rounds you can be lucky to brute force some o them (simply by piggybacking on the answers from failed past interviews).

To fight this, I have several sets of uniquely designed questions, that I rotate through (and secret follow-up questions ready for those answering the basic questions surprisingly fast). In general, the technical round needs to separate for me the average from great and yet genius from great. Thus, it is pretty challenging in its entirety. Candidate can earn 0 -100 points and the highest score I had in my history was 96 points. (And that only happened once; single digit number of candidates getting over 90 points from more than 300 people subjected to it). The average lady or gentleman would end up in 40 – 50 points range, the weak ones don’t make it through 35 points mark even. I don’t have a hard cut-off point, but as a rule of a thumb, I don’t hire candidate below 70 points. (And I hope to get to 85+ mark with candidates to be given offer). So now is the time to big revelation…

Did the ChatGPT get hired?

Let me unbox the most interesting piece here first and then support it with a bit of the details. So, dear real human candidates, the ChatGPT did not get hired. BUT it scored 61 points. Therefore, if  OpenAI keeps on improving it version by version, it might get over the minimal threshold (soon). Even in tested November 2022 version, it would beat majority of the candidates applying for Senior data science position. Yes, you read right, it would beat them!

That is pretty eye-opening and just confirms what I have been trying to suggest for 2-3 years back already: The junior coding (and Data Science) positions are really endangered. The level of the coding skills needed for entry positions are, indeed, already within the realm of Generative AI (like ChatGPT is). So, if you plan to enter the Data Science or Software engineering career, you better aim for higher sophistication. The lower level chairs might not be for the humans any more (in next years to come).

What did robot get right and what stood the test?

Besides the (somewhat shallow) concern on passing the interview as such, more interesting for me was: On what kind of questions it can and cannot provide correct answers? In general, the bot was doing fine in broader technical questions (e.g. asking about different methods, picking among alternative algorithms or data transformation questions).

It was also doing more than fine in actual coding questions, certainly to the point that I would be willing to close one-eye on technical proficiency. Because also in real life interviews, it is not about being nitty-gritty with syntax, as long as the candidate provides right methods, sound coding patterns and gears them together. The bot was also good at answering straight forward expert question on “How to” and “Why so” for particular areas of Data Science or Engineering.

Where does the robot still fall short?

One of the surprising shortcomings was for example when prompted on how to solve the missing data problem in the data set. It provided the usual identification of it (like “n/a’, NULL, …), but it failed to answer what shall be done about it, how to replace the missing values. It also failed to answer some detailed questions (like difference between clustered and non-clustered index in SQL), funny enough it returned the same definition for both, even though prompted explicitly for their difference.

Second interesting failure was trying to swerve the discussion on most recent breakthroughs in Data Science areas. ChatGPT was just beating around the bush, not really revealing anything sensible (or citing trends from decade ago). I later realized that these GPT models still take months to train and validate, so the training data of GPT is seemingly limited to 2021 state-of affairs. (You can try to ask it why Her Majesty Queen died this year or what Nobel prize was awarded for in 2022 in Physics 😉 ).

To calm the enthusiasts, the ChatGPT also (deservedly and soothingly) failed in more complicated questions that need abstract thinking. In one of my interview questions, you need to collect the hints given in text to frame certain understanding and then use this to pivot into another level of aggregation within that domain. Hence to succeed, you need to grasp the essence of the question and then re-use the answer for second thought again. Here the robot obviously got only to the level 1 and failed to answer the second part of the question. But to be honest, that is exactly what most of the weak human candidates do when failing on this question. Thus, in a sense it is indeed at par with less skilled humans again.

How good was ChatGPT in the coding, really?

I specifically was interested in the coding questions, which form the core of technical screening for Data Science role. The tasks that candidate has to go through in our interview is mix of “show me how would you do” and “specific challenge/exercise to complete”. It also tests both usual numerical Data Science tasks as well as more NLP-ish exercises.

The bot was doing really great on “show me how would you do …” questions. It produced code that (based on descriptors) scores often close to full point score. However, it was struggling quite on specific tasks. In other words, it can do “theoretical principles”, it fails to cater for specific cases. But again, were failing, the solutions ChatGPT produced were the usual wrong solutions that the weak candidates come with. Interestingly, it was never a gibberish, pointless nonsense. It was code really running and doing something (even well commented for), just failing to do the task. Why am I saying so? The scary part about it that in all aspects the answers ChatGPT was providing, even when it was providing wrong one, were looking humanly wrong answers. If there was a Turing test for passing the interview, it would not give me suspicion that non-human is going through this interview. Yes, maybe sometimes just weaker candidate (as happens in real life so often as well), but perfectly credible human interview effort.

Conclusions of this experiment

As already mentioned, the first concern is that ChatGPT can already do as good as an average candidate on interview for Senior Data Scientist (and thus would be able to pass many Junior Data Scientist interviews fully). Thus, if you are in the industry of Data analysis (or you even plan to enter it), this experiment suggests that you better climb to the upper lads of the sophistication. As the low-level coding will be flooded by GPT-like tools soon. You can choose to ignore this omen on your own peril.

For me personally, there is also second conclusion from this experiment, namely pointing out which areas of our interview set need to be rebuilt. Because the performance of the ChatGPT in coding exercises (in version from November 2022) was well correlated with performance of human (even if less skilled) candidates. Therefore, areas in which robot could ace the interview question cleanly, signal that they are probably well described somewhere “out in wild internet” (as it had to be trained on something similar). I am not worried that candidate would be able to GPT it (yes we might replace “google it” with “GPT it” soon) live in interview. But the mere fact that GPT had enough training material to learn the answer flawlessly signals, that one can study that type of questions well in advance. And that’s the enough of concern to revisit tasks.

Hence, I went back to redrafting the interview test battery. And, of course, I will use “ChatGPT candidate” as guineapig of new version when completed. So that our interview test can stand its ground even in era of Generative AI getting mighty. Stay tuned, I might share more on the development here.

 

Older articles on AI topic:

AI tries to capture YET another human sense

Want to learn AI? Break shopping-window in Finland

REMOTE LEARNING now on AI steroids

5+1 interesting AI videos

 

 

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KNIHY, ktoré ma NAJVIAC POSUNULI v 2020

Ľudia, čo ma poznajú dlhšie, vedia, že na označenie knihomoľ sa nemôžem príliš urážať. Čítam naozaj rád (a pomerne veľa). Avšak len tí najbližší vedia, že si dávam každoročne predsavzatie prečítať za rok viac ako 10 000 strán kníh (nad rámec iných zdrojov ako časopisy, blogy či novinové články.) Za posledných 11 rokov sa mi nepodarilo tento záväzok splniť iba raz. Tohto roku sa mi darí podozrivo dobre (musím si zaklopať), zhltol som už 32 kníh. Aj keď čitateľský výsledok zachraňovala najmä letná dovolenka. Počas prvej vlny Korony totiž séria COVID článkov (s viac ako 30 000 slov) tu na Mocnedata blogu pohltila celú moju energiu. A tak knihy boli ten prepotrebný recharge.

Knihy, ktoré si kupujem, starostlivo vyberám. Vďaka tomu tie príjemné prekvapenia výrazne predbiehajú sklamania. Kníh však stále vychádza (na svete) veľa a tak nájsť tie skutočné poklady naozaj vyžaduje určité úsilie. Rozhodol som sa preto podeliť s Vami o to najlepšie, čo postretlo mňa zatiaľ v roku 2020. (Na oddych čítam aj knihy, ktoré sa vecne do okruhu MocneData tém nehodia, zhrniem tu však iba tie, ktoré predpokladám, že by mohli byť inšpiratívne aj pre čitateľov MocneData.sk portálu):

Competing In The Age of AI

Zameranie: Dáta, Dátová Analytika, Biznis

Kníh o algoritmoch Umelej inteligencie a ich aplikovaní nájdete neúrekom. Ako to už býva, ako náhle je nejaká téma super populárna, mnoho autorov sa chce zviesť na vlne. To je dôvod, že väčšinu AI kníh, ktoré dnes dostanem do rúk, po preštudovaní obsahu (a začítaní sa do pár kapitol) sklamane vrátim naspäť do police kníhkupectva. Šliapnuť vedľa nie je aktuálne vôbec ťažké. Táto kniha ma však, naopak, úplne pohltila. Doručí totiž presne to, čo sľubuje jej nadpis. Systematický návod, ako zaviesť AI do akejkoľvek firmy či organizácie. Vysvetľuje princípy, ktorých sa držať, ponúka checklisty aspektov, na ktoré netreba zabudnúť. Nenájdete tu žiadne floskule alebo helikoptérové rady. Aj dielčie kroky sú jasne štrukturované a hneď na začiatku naozaj uvidíte, kde sú hlavnné miesta pre zasadenie AI riešení vo vašej konkrétnej firme. Text je navyše tak písaný, že sa nebudete vedieť dočkať dalšej kapitoly, aby ste pochopili, čo má byť tým ďalším kúskom mozaiky. Na základe inšpirácií z tejto knihy som napísal pre top manažment našej firmy plán AI inovácií na ďalšie roky.

Link: https://www.amazon.com/dp/1633697622/

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Astrophysics for People in a Hurry

Zameranie : Vesmír

Ak sa hrá futbalový zápas, tak v hľadisku je každý elitným trénerom a futbalovým expertom. A keď sa začne disktuovať o vesmíre, každý sa kasá vedomosťami z čias, keď Pluto ešte bolo (mylne) považované za planétu. Darmo, rychlokurz geniality sa v astrofyzike robí naozaj ťažko. NeiL de Grasse Tyson sa však k tomu priblížil tak blízko, ako to ide. Ako už samotný podtitul naznačuje, rozhodol sa totiž napísať knihu o vesmíre pre ľudí, ktorí nemajú čas (si skôr trpezlivosť) prelúskať sa buchlami, vzorcami či záplavou vedeckých článkov. A urobil to naozaj bravúrne. Kniha je písaná tak, že neodradí od dočítania ani úplneho laika (ktorého fyzika mátala už v škole.) Navyše, posunie vašu mieru poznania vesmíru o toľko ďalej, že nepohoríte ani na prvom rande s astrofyzičkou/-fyzikom. Naozaj jedna z tých kníh, čo stojí za hriech. Vyšla dokonca aj v slovenčine, tak prikladám link na obe jazykové mutácie

Link [EN]: https://www.amazon.com/dp/0393609391

Link [SK]: https://www.martinus.sk/?uItem=275877

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Dark Data

Zameranie: Dáta, Dátová Analytika

Nie, toto nie je kniha o cyberzločine, či mapovaní Tmavej enmergie či Tmavej hmoty. Nie je to ani kniha o čiernej mágií. Aj keď vlastne možno …

Dátová analytika je neraz tak trochu aj mágiou. David J. Hand však komunite dátových analytikov (a s dátami pracujúcich ľudí) urobil veľkú službu. Systematicky totiž zhrnul 15 rôznych dôvodov, pre ktoré nemáme dáta kompletné či dostupné pre analýzu. (Čoho dôsledkom je legendárny GIGO efekt.) Tá podstatnejšia časť posolstva, ktorú sa aj ja snažím často vysvetliť (hlavne) začínajúcim dátovým analytikom, je: Za koľko z tých 15 dôvodov si môžeme ako dátoví analytici sami? Knihu možno poňať aj ako kuchárku toho, čo by ste v dátovej analýze nemali opomenúť a čoho sa naopak vystríhať. Čo si vážim na autorovi najviac, je fakt, že na každý z 15 možných dôvodov (,kde dátová práca “zakopáva”) autor ponúka aj jasné návrhy riešenia (či prevencie). Jednovetová recenzia tejto knihy by znela: Povinné čítanie pre tých, čo za živia prácou s dátami, inšpirácia pre kohokoľvek, kto nechce podľahnúť (neodbornej, či účelovej) manipulácii s faktami.

Link: https://www.amazon.com/dp/069118237X/

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Enjoying It, Candy Crush and Capitalism

Zameranie : Životný štýl, Filozofia

Neviem, ako sa to podarilo, ale tohto roku som mal šťastnú ruku na prekvapivo veľa zaujímavých filozofických kníh. (Možno starnem.) Aby bolo hneď na úvod jasné, filozofické traktáty znášam podobne dobre ako väčšina populácie. Teda tak po 5 stranu. Potom zväčša pregúlim očami a  kniha sa prepadá v čítacom poradovníku. Alfie Brown ma však svojou knihou upútal. Nielen tým, že ju spravil znesiteľne krátku, ale najmä tým, že si vybral tému, nad ktorou som už aj ja premýšľal. Je súčasná Netlix vlna a Hranie hier na mobile len zabíjanie času? (nad ktorým my, knihomoli, môžeme pohrdovačne predniesť svoje “Pchá!”) Alebo ide o legitímne a zmysluplné trávenie voľného času, ktoré zostáva generačne nepochopené? Argumentačne a filozoficky podložená rozprava o tejto téme ma nielen vtiahla do čítania tak, že som si nevšimol, ako ďaleko som za 5tou stranou. Objasnila mi aj postoje niektorých ľudí z môjho okolia. Možno mi budete po prečítaní nadávať, ale fakt by som vám to odporúčil si prečítať. Autor navyše napísal aj ďalšie podobné dielo na trochu inú tému, ktoré je na mojom reading liste ešte do konca roka. Tak prípajam link aj k tomu druhému dielu.

Link: https://www.amazon.com/dp/1785351559

Link na ďalšie dielo autora: https://www.amazon.com/dp/1509518037

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Cesta Na Ropnú Plošinu

Zameranie: Motivácia, Životný štýl

Ak by ste poznali Andreja, asi by ste nepochybovali, že jeho kniha na tento zoznam patrí. Ale keďže ho zrejme nepoznáte, skúsim vám priblížiť, prečo jeho kniha naozaj stojí za prečítanie.

Žijete ako chlapec na sídlisku na strednom Slovensku. Dopočuli ste sa, že v takom Nórsku by sa dali zarobiť skvelé peniaze. Tak si vygooglite nejakých Slovákov v Nórsku na Facebooku, “rozbijete prasiatko” a kúpite si letenku do Nórska. Vystúpite z lietadla a Nórsky sen sa môže začať …

… až na to, že váš Slovenský mobil nemá roaming, neviete po nórsky, nemáte vysokú školu, nikto vám neche dať prácu, prepleskli vás škandinávske ceny, nemáte nikoho blízkeho, vaše úspory sa okamžite rozplynuli a začínate mať povážlivé zdravotné problémy.

Nie, to nie je scénar B-čkového dobrodružného románu. To je skutočný príbeh Andreja Tichého. Ktorý sa, snáď mi to priateľu prepáčiš, dosť naivne vybral do Nórska. Hoci samotný príbeh (z ktorého nechcem vyzradiť najpikantnejšie scény) by bol hodný filmového námetu, táto kniha má  oveľa silnejší odkaz. Ukazuje na to, ako naozaj chcieť niečo dosiahnuť. Ako sa nezlomiť a na čo všetko sa pripraviť. Ale hlavne ako nakoniec uspieť a splniť si (v podstate nereálny) sen. A ako pochopiť, že to je len prvý stupienok v dlhom kariernom a rodinnom živote.

Nie, nechajte sa oklamať šibalským názvom knihy. Toto nie je cestopis, ani návod, ako sa dostať na ropnú plošinu. Toto je energetická vzpruha, ako dosiahnuť svoje sny. Navyše vzpruha, pri ktorej sa zasmejte aj si zahíkate.

Link: https://www.martinus.sk/?uItem=292194

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Never Split The Difference

Zameranie : Vyjednávanie, Biznis stratégia

Iste ste sa už ocitli v situácii, kde išlo o veľa. Hádka s partnerom, požiadavka o zvýšenie platu, obchodné rokovanie alebo dieťa, ktoré sa nevie vspratať do kože. A keď si spätne prehrávate tú situáciu, hlava sa nestačí čudovať, čo to ústa hovoria. Kde hľadať radu, aby sme si to nabudúce (prinajmenšom) sami nekazili?

Hľadať rady o vyjednávaní možno u rôznych profesií. Niektorí vám odporúčajú hrať neústupných tvrďasov. Iní vás nasmerujú k tomu “aby ste dohodu s oponentom smerovali niekam doprostred rozpätia”. Čo si však o optimálne stratégií vzjednávania myslí Policajný vyjednávač, ktorý rieši rukojemnické drámy a únosy? Má ponúknuť protistrane zabitie polovice rukojemníkov výmenou za to, že sa útočník vzdá?

Chriss Voss je absolútna svetová špička vo vyjednávaní s ozbrojenými útočníkmi a teroristami. A ako policajný vyjednávač vždy musí hrať na to, že on vyhrá všetko a terorista nedostane skoro nič. Preto je zaujímavý jeho pohľad na to, ako viesť vyjednávania tak, aby na vašej strane zostal celý jackpot. Ak vám to príde odpudivé (až nefér) pre bežný život, pozeráte sa na to rovnako ako ja, kým som knihu neotvoril. Verte mi však, kniha nie je návodom, ako druhú stranu ošklbať. Je to súbor rád (popísaných na konkrétnych prípadoch), ktoré vám umožnia ísť aj za 50:50 dohody. A pritom neuraziť ani nepodraziť oponenta. Popísané postupy sú však rovnako užitočnou obranou proti zdatným súperom, ak aj sami nechcete aktívne zatlačiť. Kúpa tejto knihy sa vám násobne vráti. Už na najbližšom hodnotiacom pohovore alebo hyisterickej scéne vašej ratolesti.

Link: https://www.amazon.com/dp/1847941494

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DIEŤA 44

Zameranie: Ruská história, Detektívka

Ako som písal v záhlaví tohto blogu, knihy krásnej literatúry do odporúčaní na tomto blogu zvyčajne nedávam. Pri tejto knihe však veľmi rád urobím veľkú výnimku.

Kniha Tom Roba Smitha ma fascinovala tým, že veľmi dlho (vyše sto strán) som nevedel odhanúť. aký žáner vlastne čítam. Chvíľu som mal pocit, že čítam historický román, či literatútu faktu o sociálnej situácii v povojnom Rusku. Alebo detektívku? Ak vám toto moje zmätenie príde nepochopiteľné, tak vedzte, že autor tak pútavo mieša tieto tri línie knihy, že je vám to vlastne úplne jedno. Chcete ďalšiu a ďalšiu stránku, nech je to ktorekoľvek z nich. Inými slovami kniha tak verne prepája opis spoločenskej situácie so životom postáv, až … až zrazu zistíte, dopekla, veď ono je to detektívka. Vrah sa začína nápadne podobať na niekoľko postáv. Začínate mať istotu, kto to asi je. Ale spoločenské zriadenie ho vlastne nechce vypátrať, tak ako bude spravodlivo potrestaný? Dieťa 44 (mimochodom prvý diel trilógie) je skvelá a pútavá kniha na jesený večer aj k opaľovaciemu krému na lehátko. Proste si to užite.

Link: https://www.martinus.sk/?uItem=50893

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Seeing Around Corners

Zameranie : Manažment, Biznis stratégia

Jadrom mnohých problémov  biznisu je, že sa na aktuálne dilemy nazerá metódami spred 20 a viac rokov. Rozhodnutia totiž robia ľudia, ktorí študovali v čase, keď dané témy boli horúcimi novinkami. Ako však dovidieť za horizont? Ako správne prečítať, čo bude IN v najbližších rokoch? Ale hlavne, ako inovovať skôr pomocou “calculated bets” ako cez “plug and pray” projekty.

Hoci Rita McGrath patrí (aj v mojej rozsiahlej knižnici) medzi neznámych autorov, rýchlo si získala moju priazeň tým, ako pragmaticky pomenúva slabé miesta biznisu a skutočné záplaty na tieto diery. Nebojí sa ísť proti mainstream prúdu, takže sa dozviete, že trhový podiel je zastaralá metrika, že skutočné inovatívne firmy sú tie, ktoré na to nepotrebujú zamestnancov alebo že Netflix dlho tápal v tom ako prejsť na predplatné. Nehľadá ikony ani hrdinov (ako je tak bežné pre Amerických biznis autorov). Naopak, servíruje dobre štruktúrované rady, ako systematicky inovovať, ale aj čo sú často opomínané úlohý lídrov v tomto procese, či ako nepodľahnúť tlakom okolia.

Je to hutné čítanie a pripravte sa na to, že budete odbiehať od knižky k poznámkovému bloku či klávesnici si zapísať podnetné nápady. Podctivé čítanie tejto knihy teda zaberie viac času, ako by jej  (inak štandardná) hrúbka naznačovala. Ak však vediete nejaký team alebo (spolu)zodpovedáte za stratégiu či smerovanie nejakej firmy, moje odporúčanie si s chuťou užijete.

Link: https://www.amazon.com/dp/0358022339/

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Ak ste členom komunity MocneData už dlhšie (mimochodom stať sa ním môžete bezplatne tu), tak viete, že svoje čitateľské odporúčania som dával aj po iné roky. Tu sú niektoré z nich:

Predsavzatia na 2020? Skúste prečítať niektorú z týchto TOP KNÍH

Čo čítajú Marketéri – knihy 2016

Skvelé knihy 2017

4 Výborne knihy o dátach 2018

Čo čítajú iní – Milan Schnorrer

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