Future Scoping: Artificial Intelligence for One

So this, is my vacuum cleaner. As you can see, I’ve added a bit of flourish to Doraemon. He’s an excellent house companion – getting to work at precisely 7:30 in the morning, and doing the entire apartment and nicely getting back to his spot. Every now and then he does get tangled on some random wires I dropped, but otherwise, he gets the job done and and a darn good job of it too.

I’ve also already set my lamp to turn on at 7:30 am and a Google Home to wake me up at 7:30. And my Fitbit also to buzz at 7:30.

At this point you can guess that I tend to wake up at 7:30am.

You may also be aware of other Bots like good old Siri. Ah yes, well, she’s growing a little old in the tooth now, compared to the new kids on the block like Alexa who can pretty much replace your babysitter in engaging the kids, or Google (aka Google?.. I was trying to think of more human name).

We’re still at an early stage where really, we’re just putting names on various flavours of automation. We set the rules, or the parameters of engagement and the bots (hardware and software), do as they are asked.

Wait is Automation, AI?

If you look at where AI is right now – the ‘truest’ form of machine learning that in 2019 we could still get away with calling AI, is in the cosy arms of large technology companies which have the volume of data enough to ‘teach’ the system and scale the learning to something else.

For example, self driving cars require a tremendous amount of data. In fact, why don’t you go ahead and pat your back here, because you’re also help helping train the ‘brain’ of self driving systems (particularly the one by google)

Most of the automations and what dear marketers call Smart, is just a set of if->then conditions, like if temperature of room is >40C, turn on AC. Works great, no arguments there. We’re now taking this about a million levels (which actually isn’t a lot) to get to the point where we can ask a program to look at a photo and tell us whether its a traffic light or a bus.

So really, I’m not talking about AI today, but of tomorrow. We’re getting there. The ‘there’ is what is most interesting.

AI for One

As AI stands now, we are just entering into an era where the computing power required to do this level of machine learning can now be done on a mobile phone. Remember, the amount of training data itself took Gigabytes, Petabytes, exabytes of storage (imagine at least a closet to a massive shopping mall), and the processing took a couple of horses to an army (yep, just stick with that visual), and now that all can be done on 1 chocolate bar sized device called your smartphone. This will lead to more interesting use cases and business models.

Here are a couple of random ideas:

Privacy AI

In the real world, if you are rich, famous, or crazy enough, you’ll probably have security guards who are hired to keep the masses a good couple of meters away from you at minimum, and also plan and defuse any wider threats. The online world is going to get more and more turbulent, from all the apps that we use, browsing, and new VR/AR experiences that we are exposed to.

Imagine then, a self teaching, always learning PrivacyGuard AI that knows you intimately and actively takes steps to prevent you from being exposed to online threats, or anything that is out of the ordinary for you. Think of it as a more advanced watch dog rather, someone who will growl when they don’t know you but will ease up when seeing positive interaction.

For example, getting spam calls – the AI will listen in, and block those (like email spam filters). Much of the logic for this may be written by humans initially, but as features become more standardised, any new form of communication can be monitored, tracked, learnt and safeguarded. After all, everything is bits, bytes, words and pictures.

Learning AI

This AI takes the form of a virtual teacher that is assigned to you at your first class in school, and that already has information about you from your birth (your Privacy Bot has approved this interaction), and from then on, your Learning Bot knows exactly how you learn, what you like, what you don’t like.

Your persona (fully anonymised, come on), is compared directly with the growth pattern of a child in Norway, Australia, China and how you learn gets real time feedback on how to maximise learning.

If this sounds like shoving data and tests into your Childs mouths, nope – LeanAI knows when to let up. After all, it has the data of a few million other people over a decade of growth.

Citizen AI

Democracy is tough. Like how do we pick 1 person who is the best to lead, and then the hundred or so to help lead the country, etc. Every single bill, law, would have so many nuances, that well, that’s why politics is a full time job right.

Now consider the scenario, that we can train a person, a replica of our beliefs, values, along with life experiences and lessons that we’ve picked up. This Citizen AI would then go through all the bills and let the government system know what your opinion would be. A poll may submit feedback directly, while a formal referendum could require human confirmation.

When elections come up, your CitizenAI would give you the final say with an informed feedback. This could reduce a hot topic from a binary Stay/Leave based on emotion to a multivariate analysis that tells you that ‘you’ would be 60% happy with a Yes.

Who watches the Watchers?

The thing with AI is that at the heart of it, it is data and logic. And though at the moment we struggle with the advanced logic of a self generated machine learning system, we can estimate at least based on the training data of what the generated logic should do.

As systems get more advanced, much like interviewing your security guard before you hire him, we will want to have more transparency around how these bots works. Perhaps with standardised tests (which well, can also be gamed, ahem Dieselgate).

Perhaps then, another AI that we can set the parameters to test, and that system would test the tool AI’s that we want to check before using.

God help us when the AI’s unite.


If you liked reading this or you have ideas, or best of all – disagree with anything you’ve read, then please do drop a comment!


Futurescoping #3: Bots vs Bots

We are now on the verge of AI – through its various forms, being built around us to enhance our lives. Take for example, Siri, the first popular voice assistant, now which works seamlessly in your home (once of course you have bought the entire apple gear). We now have Amazon Alexa (in the echo hub form since 2014), and Google Home – now all competing for your attention.

This article isn’t a review of these, or a comparison of their features, but rather, what happens when we have our AI bots, which aid us, starting to interact directly with one another. As a qualifier note, I am going to liberally use AI where the system uses more complex systems than Rule based ones, at least using neural nets (systems which learn rather than following only what they were programmed). And let’s just call them Bots, for ease of conversation.

Take for example, this video, based on an original viral in Nov 2016, of 3 AI assistants, forming an infinite loop based on the output of one device (prerecorded “Hey Siri, Ok Google, or Hey Alexa”), being the calling inputs for the respective other device. This loop, which uninterrupted, would possibly continue until the devices physically wear out (minuscule imperfections in the silicon manufacturing) or you know, the universe ends.

OK Nice, but how does this affect me?

You may not have realized it, but if you are a user of a smart phone (iPhone or Android, no need to argue over which is better) – you are already an AI User. The searches you make on Google Search are designed to be the most optimal results for you – based on all your previous searches and what you ended up clicking through. Google Now, now known as Google Assistant,  is now integrated into Google Home, and is designed to know when you are leaving home, inform you of the time it takes to get to work or to an airport if you had booked a flight, and possibly more intrusively, what to type next when you are responding back to someone. These are fairly basic features mind you, but we are starting to see Smart tools in the form of scheduling bots (hello,  and the rise of Self Driving Cars, and more.

The Dark Side?

So what happens when, for the lack of better terms, we let the bots loose? We have few instances to base our vision on this, but one came to mind: In 2010, the US stock market fell steeply by 10%, which was a rapid decent, for really, no apparently reason.

Image result for new york times may 6th flash in the market


What happened (in as simple terms as I can put it), is that various high frequency trading algorithms did what they were built to do, and completed trades which would have taken hours/days by humans within seconds/minutes and brought down the stock market. The interesting part of this was the net result of multiple algorithms reacting off one another, a bit like Siri, Alexa, and Google bouncing off one another.

Here is where my analogy goes – as we build systems, smart AI based Bot systems – we need to be aware of the consequences of when we have those systems interact with one another. Each Bot was designed to do what it does best – complete against humans. But what happened was a now classic Bot vs Bot scenario. And this is only set to launch now. We will now have competing Apple, Amazon and Google bots, each customized to their user base. Below are some examples of Bots vs Bots that I can see right now, and where they are likely to be heading:


Image: NY Times /Jason Raish


  • Social Media Posting Bots vs. Social Media Reading Bots:
    • On one side, we have Posting Bots, like PostBot 3, which optimizes the hashtags and the timing of your posts to optimize reach, popularity, and other typical social media metrics. As the system learns from the past, it can start customizing the posts content and timing to optimize on these.
    • On the other side, we have now maturing systems that read social media sentiments, and accordingly buy/sell stocks.  They’ve already been doing quite well in the market, as the system learns from the past, it will surely get better.
    • As the race between the two bots evolves, we may be likely to see Posters which can influence their own stock price, as they know the impact they will have on Readers.

Image: Volvo

  • Self Driving Car Bots vs Traffic Systems Bots
    • Here’s likely where we can expect to see cooperation between the Bots.
    • Self Driving Bots are designed to take physical vehicles around from point A to point B, typically in the path that will take the shortest time possible. This is presently done using route mapping systems such as Waze, or Google Maps, and the now emerging field of using multiple sensors to navigate the car in real world. At the moment these systems rely on what they see around them, with overall mapping insights.
    • Traffic Systems Bots, have a very compatible objective of ensuring that all the vehicles travel from their Point As to Point B’s as soon as possible. Using a range of sensors to track the present traffic loads, and by having Bots run all the traffic lights, we can see some remarkable achievements, such as the recent pilots in Pittsburg. In the works since 2012, the lights now adapt to the traffic at various intersections and pass on the knowledge to all other traffic lights
    • The true win will be when the Cars start directly taking to the Traffic Systems, informing them of their individual Point A’s and Point B’s, so that the traffic lights are optimized to reduce everyone’s time. This would radically reduce traffic, reduce pollution (though hopefully we will see net nonpolluting electric cars by then).
      • We may eventually see tiered pricing models for this – pay more to reach faster, though the definition of slow is already on par with our fast now.


Image: Chatbot Magazine

  • Customer Service Chatbots vs Customer Representative Chatbots
    • One of the biggest costs for end customer is Customer Service. As a company, I might wish for customer to please read the FAQs, and call only if you have a real issue. However, there is nothing like chatting with someone who can respond to your specific questions. Here is where Chatbots come in. By providing a chat window, where you can type in a question and the Chatbot responds exactly to your circumstance, its a win-win for both the company and the customer. Various research groups have already estimated billions of dollars in savings:


Chatbots Salary Savings

    • On the flip side, from the customer’s perspective, we have Bots which can act as our representatives to cut through the chatter. For example, Trim is an Chrome Extension that is specifically designed to navigate through Comcast’s customer service chat bot, and NextCaller is designed to navigate through the initial information gathering session so that you don’t need to waste time before getting to the matter.
    • Pretty soon we are going to see smart customer service apps, and smart customer bots, optimized for each others requests (e.g. you just state your intent to your Bot, and your Bot ‘negotiate’s with the customer service Bot).


What does this mean as a Next Gen Company?

Your organisation may be building a next generation digital company, which usually means some form of connectivity to your suppliers, customers, and 3rd party organizations which enhance the experience for your suppliers, customers, and even regulatory authorities.

These connections (in the form of perhaps MicroServices, or APIs), which were (and are) being built for other applications/systems which function more or less as the streams of inputs and outputs of your company’s data and business functions. Most of these services are designed for inputs/outputs on manual requests, or at best, periodic requests from apps. But when we mix Bots into play, suddenly you need to think about the impact of having super intelligent systems with the ability to make micro decisions that have radical impacts your business outcomes.

  • Start thinking and engaging with other ancillary services around your company and how you can automate those processes
  • Once you have a systematic approach for exchanging information, consider building Bots that start learning and optimizing.
  • Build the services to scale (Bots can consume data and streams faster than you can conceive it)
  • Build services to be able to control an avalanche (Bots can get hungry, and do more than you ever thought of)


Note: Views expressed are my own. Looking forward to your comments on this.