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Rishal Hurbans

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since Sep 22, 2020
I've been obsessed with computers, technology, and crazy ideas since childhood. Through my career I have been involved in the leadership of teams and projects, hands-on software engineering, strategic planning, and the end-to-end design of solutions for various international clients. I have also been responsible for actively growing a culture of pragmatism, learning and skills development within my organisations, community, and industry.
I have a passion for business mechanics and strategy, growing people and teams, design thinking, artificial intelligence, and philosophy. I have founded various digital products to help people and businesses be more productive and focus on what’s important. I also speak at conferences around the globe to make complex concepts more accessible and help people elevate themselves.
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Recent posts by Rishal Hurbans

Thank you for your questions, everyone. I hope all the winners enjoy the read and learn something useful.
Good question. I think it's best answered with these extracts from the book.


For the sake of our sanity, and to stick to the practical applications in this book, we will loosely define AI as a synthetic system that exhibits “intelligent” behavior. Instead of trying to define something as AI or not AI, let’s refer to the AI-likeness of it. Something might exhibit some aspects of intelligence because it helps us solve hard problems and provides value and utility. Usually, AI implementations that simulate vision, hearing, and other natural senses are seen to be AI-like. Solutions that are able to learn autonomously while adapting to new data and environments are also seen to be AI-likeness.

Here are some examples of things that exhibit AI-ness:

A system that succeeds at playing many types of complex games

A cancer tumor detection system

A system that generates artwork based on little input

A self-driving car

The bottom line is that AI is an ambiguous term that means different things to different people, industries, and disciplines. The algorithms in this book have been classified as AI algorithms in the past or present; whether they enable a specific definition of AI or not doesn’t really matter. What matters is that they are useful for solving hard problems.

Hi Randy. Regarding YouTube, Netflix, and similar services, AI algorithms are used for search and recommendations. These usually feed into each other. Notice that when you search for a movie on Netflix that isn't available, it will recommend movies with similar themes and often storylines. Content recommended to you is also powered by this engine. This learning is happening based on data from everyone's interactions. Apart from streaming services, some traditional AI algorithms like genetic algorithms have been employed in linear television advertising - that is pricing ads, and choosing the best time to show the ads based on the audience, content, and sometimes even world news. Finding the most optimal placement of ads can be seen as a search which many algorithms in the book are suitable for.
Hi Elaine. Great question. The book is intentionally completely code language-agnostic. The code examples are pseudocode which should be able to be translated to any modern programming language. With that said, there is a supporting Python code repository that can be used as a reference: https://github.com/rishal-hurbans/Grokking-Artificial-Intelligence-Algorithms
Hi. The approach is to introduce the algorithms through practical examples and visual explanations. Each chapter starts with the overarching intuition of the algorithm using examples everyone can relate to. We then dive into the algorithm as a recipe / sequence of steps with visual walkthroughs, and finally, pseudocode to map the visuals and examples to code. There is also a supporting open source repository: https://github.com/rishal-hurbans/Grokking-Artificial-Intelligence-Algorithms
Hi Carl. Yes, chapter 9 introduces the intuition of gradient descents, why it's useful, and how it fits into calculating weights for artificial neural networks. The specific implementation of Autograd is not explicitly discussed, but there is sample code to teach the fundamental principles of the gradient descent concept.
Hi there. Absolutely. The intention of the book is to teach AI algorithms using visual tutorials, and practical examples. There is some math in some areas, but the logic is explained simply, without any assumption of prior Calculus knowledge.
Hi Randy. It's arguable if the location matching is AI or not, but I wouldn't recommend that you necessarily write this by hand. Given the problem your app is trying to solve, I recommend using a spacial database that provides ways to query geospatial data. One that I've used with success in the past is PostGIS https://postgis.net/. With that said, if you're interested in the logic and inner workings of algorithms that systems like these might leverage, I think Grokking AI Algorithms will be useful.
The target audience in mind was developers who want to learn about AI algorithms through visual and practical material, rather than mathematical proofs. It's worth noting, the book is meant to demystify different approaches to solving problems with "intelligent" algorithms apart from just machine learning approaches - that is: search, genetic algorithms, swarm algorithms, and others.
Happy to be here. I'll try my best to answer any questions.
Hi! I'm Rishal. I'm passionate about making hard concepts easier to understand. I'm looking forward to my book promo for Grokking Artificial Intelligence Algorithms.
3 months ago