These are my answers to questions about AI and its business practice, discussed among ~200 of my fellow classmates from IIT Bombay. They are modified slightly to protect privacy, to remove specific references and for better narration. This is the first part of a series of these posts. The second part discusses insights about ‘Why Doesn’t AI Work?’ , the third about ‘AI Hacks That Do Work.’ and the last about ‘Why and How to Get Started with AI.’

At the simplest level data science is just that — a scientific analysis of data. …

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I am often asked to articulate some problems that people can start working on immediately and get some real world industry experience in artificial intelligence. The hard part for these situations is that the problem or a part of it should be meaningful to people with every level of technological sophistication. So here is an attempt to articulate such a problem for Natural Language Processing.

Consider this Wikipedia based data set (40MB), which is built by the SQUAD team at Stanford. Try to solve the following problems for the text contained in these articles. …

Deterministic Behavior of Software has Come into Focus with AI.

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Ada Lovelace spent more than a year thinking about the world’s first program. She published it in 1843. Since then, there have been many definitions of software and many characteristics that industry has generally accepted for good software — it should complete a specific task, it should not be fallible to small errors, it should be debuggable and maintainable, it should have clear metrics that track its success, among others.

So far, no one has bothered to say that in addition to all other such characteristics, a good software should exhibit deterministic behavior. Determinism has evolved as a philosophical concept…

Income inequality is going to be only one part of it.

This is not a political writeup, but an enumeration of the factors that may entrepreneurs, businesses and individuals may find useful as they think about the next decade.

Picture by Clay Banks vis

Joe Biden will take over as the President of United States in 2021 after 80 million people voted for him. This is unprecedented in US history, so are the 74 million votes that his opponent received. A central issue in the divisive election was the K-shaped recovery from pandemic-induced economic hardships in 2020. …

Traditional SaaS Business Models Don’t Work for most AI Startups

AI has been top of mind of the startup world for at least five years. Hundreds of billions have been invested, thousands of entrepreneurs and intrapreneurs — some of the best minds of our generation — have tried their hands with varying degrees of success, and none of the pure AI companies has made it to IPO.

We all have learnt, some the hard way, that artificial intelligence cannot be packaged into a product. The factors that scale a typical venture backed company, say in SaaS, don’t apply to AI.

  1. First, to be useful an AI must train on data…

The world of real estate is often thought to be a slow adopter of technology, relying more on handshakes and glib negotiations. Increasingly, this view is turning out to be a myth. Artificial intelligence is making the world of real estate to be more nimble and more efficient. Consider these five applications below:

Netflix uses AI to suggest what you should watch next. It predicts your tastes and lines up a collection that it thinks you will like. Amazon uses similar technology, called a recommendation engine, to suggest that you may like to buy an ottoman after you bought a…

No data science team is the same, which, by itself is not a problem. The trouble is that there is no real way to judge whether the role you are joining is going to catapult you for success or turn out to be the biggest mistake of your career. A data science job can really surprise you when you join it.

In this article, I have suggested some strategies to avoid these surprises, but let’s set the context and the extremes first.

The biggest misconception out there is that there is one, homogenous, data science role. In truth, four completely…

One of the key tasks of a product manager is to keep track of multiple workstreams related to their product or feature. A project manager, a division manager and many other business managers find themselves in similar situations from time to time.

A common tool to track progress of all these workstreams is to use status indicators like the traffic signals. Green means everything is good. Yellow typically means that things are on the borderline and Red means things are alarmingly behind. Different teams and even different individuals have different thresholds about when to change the color.

In my opinion…

While there are so many new advances in Natural Language Processing (NLP) making headlines these days, in truth artificial intelligence is far from the point it can carry rich human conversations. There are many challenges, but a big one is the ability to use lies effectively. Consider these three scenarios:

When humans communicate using language the literal meaning of the text is often different from the intended meaning. If someone says “Praful is not the brightest bulb,” you understand that I am not an electric appliance and that the speaker does not hold my intellectual prowess in high regard. …

If AI were to wipe out humans in the future, the following would have to be true.

Free Will is a deeply philosophical concept that no Medium post can really do justice to. To discuss whether humans have Free Will takes tomes. For AI, though, we can pose a simple question — Will the artificially intelligent machines of the future make their own conscious decisions, or whether they will be slaves to their algorithms and training data?

If AI were to wipe out humans, it would have to decide on its own accord to do so, and it would have…

Praful Krishna

AI, Product, Strategy, Digital Transformation

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