Modev Blog

Imran Haider

Imran is technology researcher and writer at Modev. He has a background in Product Marketing, Business Analysis and IT Product Management. Now a days if he is not writing, he is normally reading or listening to audio books on AI, Machine Learning, Blockchain and Robotics.

Recent Posts

Books, Blogs and Videos—curated resource list on AI, ML and DL

by Imran Haider

There is no shortage of online resources if you want to learn more about AI, Machine Learning etc. The world is buzzing with ever new pieces of information on these emerging technologies. Which at times can be overwhelming. Partly because of this reason we started Modev blog. The aim is to create content that explains these complex and often times confusing topics in a way everyone can understand. Especially the developer community because their time is much better spent on building things. Rather than trying to make sense of industry trends.

Types of Machine Learning Algorithms

by Imran Haider

In any AI system, the hardest things to learn are obvious tasks, such as walking. Walking is an overlooked task because it's so natural to us--in fact, we struggle to even articulate what makes us walk the way we do.

State of Voice Platforms

by Imran Haider

Use of the word “platform” in the title is rather cautious. Because there is only one true platform in Voice and that's Amazon Alexa. While Google and Apple are in the game, you can't call their offerings platforms as yet. They have products and technologies in place but there ain't much for developers to do. Let's roll back a little bit and take a holistic view at the state of the industry. VoiceLabs recently released their 2017 VoiceReport. The report is worth a read in entirety. The picture below, however, is particularly important in understanding the different parts of a Voice stack.

What is Modev Exo Summit and why you should attend it?

by Imran Haider

Pick any large scale software product and you will find plenty of content on the tech side of it. But almost none on how people working on it actually work. For example, how they handle Pager or Crisis Management? This knowledge remains in the boardrooms and never gets out. While there are instances where it's hard to share that knowledge because of confidentiality. But more often than not the lessons learned are generic and shareable.

How Deep Learning works?

by Imran Haider

Unlike Artificial Intelligence (AI) or Machine Learning (ML), Deep Learning (DL) is a relatively new term circa 2010. However the underlying technology is not new. Deep Learning is based on Artificial Neural Networks (ANN). Artificial Neural Networks are as old as AI itself. The original idea behind AI was to make computers as smart as human brain. And that's what ANN tries to do by mimicking the brain. The recent hype around DL, especially since 2010, is because the idea seems close to its actual fruition. This dream like scenario is made possible by two recent technological breakthroughs i.e. GPUs and Big Data.

Coding in the Age of Machine Learning

by Imran Haider

Industrial Revolution automated labor work. Information Revolution automated the mental work. Machine Learning Revolution will automate the automation itself. —Pedro Domingos

Machine Learning, Deep Learning and the Hype

by Imran Haider

Artificial Intelligence is an umbrella term and covers everything that makes computers more intelligent. Think of it as electricity. Just like electricity fueled the industrial revolution, AI will fuel the information revolution. Soon we will have a computer inside everything. And hence everything will be prone to some level of AI embedded in it. Machine Learning is one way to make that happen. Another way would be to hand program everything and let evolution take place. That process is tedious and almost impossible. There is a limit to how much code humans can write. Machine Learning (ML) enables computers to write code for themselves.

Design in the age of AI

by Imran Haider

Design is often a misnomer. The kind of cool word that you want to use so people know you are doing important work. Even traditional roles are, sometimes, renamed so the term somehow shows up in them. The word, or field for that matter, was rather obscure before Apple started using it in product intro's. And since then everyone seems to have something to do with it. Which makes talking about the subject rather tricky. So, for the purpose of this article we will divide Design into two categories.

Artificial Intelligence, Where We Are

by Imran Haider

“As soon as it works, no one calls it AI anymore.” —John McCarthy

John McCarthy coined the term Artificial Intelligence in 1956. And he rightly complained about it. The idea of computers getting more powerful than humans is not new. It has been with us since inception of computers itself. Yet, every time computers start to do what one generation considered sci-fi, the next generation takes it everything but AI.