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Rishal Hurbans is the Business Solutions Manager at Entelect where he is responsible for business development, strategic planning, ideating, and designing and developing solutions for local and international clients; whilst actively nurturing knowledge, skills, and culture within the company, community, and industry. He has a passion for business mechanics and strategy, growing people and teams, design thinking, artificial intelligence, and philosophy.

Rishal is the author of Grokking Artificial Intelligence Algorithms with Manning Publications, aimed at demystifying AI algorithms for technologists by teaching the approaches through practical problem solving and visual explanations: 

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Rishal Hurbans’ Website: https://rhurbans.com/  http://bit.ly/gaia-book 

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Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:49) – “Grokking Artificial Intelligence Algorithms'' consists of 10 chapters that explore different AI approaches. It's part of Manning Publications, MEAP, which means Manning Early Access Program. And the benefit of that is we get feedback from readers as we release chapters, which allows us to refine and create a better book at the end of the day. Once all the chapters have been released and we get some feedback, the book would be then printed and finalized. 

(03:22) – The term Grok or Grokking is to gain a deep understanding about something, but through intuition and through some sort of feeling about it, demystifying these algorithms that are sometimes underappreciated. Including the modern hyped concepts like machine learning and neural networks, to actually help the reader understand why it works and how it's useful to the day to day.

(05:16) – A lot of Funding has gone into creating this kind of skills and capabilities in different organizations. There's a lot of solutions and proof of concepts that have been bolts that work in theory or work in a ring-fence environment, but perform poorly in production or don't provide the value that was originally envisioned. A lot of effort is going into understanding now, what are the critical aspects to what we're doing with this technology. How do we understand it better? And how do we target it or direct it in a better way as opposed to running a bunch of experiments and see what works.

(07:03) –  It's not a lack of engineering or a lack of know-how in actual execution. In any technology that we've built, especially software, at the end of the day, it comes down to solving a real world problem, whether that's a business problem or, whatever the case might be. Usually it comes down to a business problem that you're solving. 

(07:49) – It's not actually addressing the problems in a meaningful way because we just tried everything. Also, partly it's because people have been trying the hype buzzwords, because they're a good idea. And you feel like if you're not doing it, you're doing something wrong. From a global decision-making perspective, the stakeholders involved there, the different people involved, they need to have a better understanding of what problems the technology is solving, as opposed to just simply using it, to implementing it for the sake of it.

(09:40) – The focus on the different algorithms is driven by a theme or concept I mentioned just a bit earlier. So instead of trying any new technique that you come across, I wanted to highlight the advantages of some of the underappreciated algorithms. The goal was basically to expand a technologist or a developer's mind in terms of what the possibilities are when being faced with a problem. There's no silver bullet and here are the advantages and disadvantages of the different approaches. 

(12:35) – Specifically with search, it's mainly exhaustive, you had to try every possibility to find a good solution, whereas, more modern approaches try to estimate a good solution. A person would have to know what questions to ask. What modern approaches and machine learning and deep learning try to do is learn from examples and learn from previously made decisions to figure out the questions.

(14:07) – Modern algorithms are geared towards different problems that we're trying to solve now, but computing has definitely made it possible for things like artificial neural networks to become more prominent.

(16:11) – Large amount of data that's been collected through connecting the world, the actual value that's hidden within that data and the kind of advancements in computing have allowed us to leverage these algorithms. And as I said, old algorithms that can now do some really powerful and useful things. 

(17:26) – The implant search is also sometimes referred to as adversarial search. It's essentially used for two player games like chess, and the whole concept is centered around an agent predicting the future. So if I'm an agent. And I see a certain state of a chess board. I would make a move and then simulate every move that my opponent could make and score that. Games like Dota and StarCraft, they're using something completely different. So they're leveraging reinforcement learning and deep learning. 

(19:03) – You're not working on a two dimensional space where you're moving pieces a few blocks at a time you're working in a very fluid environment. It's almost simulating reality. Detailing every single piece of information and representing that as a state and then trying to predict every possible future for that state becomes very difficult to do with traditional adversarial search approaches.

(19:52) – They try to let an agent learn from experiencing the game. What a deep mind, open AI and similar organizations have done is basically allowed an agent to play itself many times and figure out what short-term actions and mid-term actions may result in long-term rewards. I'd like people to be more pragmatic because the more pragmatic you are, the more effective you are at solving what's important.

(22:49) – Technology, including data science, including software engineering or mobile development or whatever facet of technology we're working in, I see it as a tool or a vehicle to deliver value or solve a problem. There's a difference between a successful project and a successful solution. There's this deep focus on what tools and libraries and technologies and programming languages, and what are you using as opposed to, why are you using it? What are you trying to achieve with it? And that's not just a problem in data science. It's a general theme, but we're getting better as we go.

(25:57) – A big misunderstanding is the glamour in bolding, something with machine learning or AI algorithms about 60, 70% fried, depending on the surveys, you look at 60 to 70% of  data scientists work is usually understanding, cleaning, preparing, enriching, augmenting that data before it becomes useful. And even after you do all that work, you don't actually know if that data is going to solve your problem or not.

(26:58) – Every solution should contain some sort of data science or AI element to it. And that's not really the case. So unless there is a clear use case, not that fits the use of some sort of either classification or reinforcement learning or optimization algorithms. Unless there's a real use case for that, it shouldn't just be taken into consideration. You should think critically about how you can build a minimum solution that solves the problem in the best way. 

(29:19) – I would have spent a technical perspective and a growth perspective specifically in the area of AI and machine learning, I would have made a bigger effort to figure out why math is useful in these concepts. Do not give up on that and perhaps try and seek material or people or mentors or someone that can explain to you in a more human way, how these mathematical principles work, but more importantly, why they're important.