THE POINT IS...



More than the problem, our inability to handle that problem disturbs us!

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A dozen things about AI ethics

Emerging Technologies Leadership Disruption Artificial Intelligence The Future of 

Artificial Intelligence is the imitation of human-like intelligence by the non-human abstract agency. Although primarily we assign this term to the computer system only, I think there is merit in seeing it as a broader system that involves humans and machines together. Upon adopting this more expansive concept, you will realize that AI can be a company too! Just think about Google, Facebook, and other companies as a form of AI. Do you think that's plausible?

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The technology works best for you, only if you stay in charge of it!

Emerging Technologies Leadership Disruption Artificial Intelligence 

Last year when Apple launched a credit card, they also debuted in the technology bias domain. A wealthy tech entrepreneur (one of their own) was given ten times more credit limit than his wife on the new Apple Card. It was contrary to the fact that the couple had their assets held in common. When one complained, the answer was, "It is just the algorithm," Steve Wozniak (Apple’s co-founder) said. Apple as well as Goldman Sachs, the bank backing the card, both were unable to explain, why's that! In layperson's term, it was more like, "Computer says so...!" jingle that any company will sing at you when they have declined you for a loan, or something similar. That is THE problem.

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My Manifesto for 2020

Emerging Technologies Leadership Disruption The Future of 

This decade is going to be a significant one for humanity as I see much disruption planted on the way. Last week, the first week of the new year was crowded with wishing messages and memes, gifs, greetings, and some other things. Many people have asked me about my goals for this year and if I have made any resolutions. This year, not! I think resolutions and goals are overrated these days. Typically, most of us would have made resolutions or goals for this year. And, as usual, some will follow through, and some will fall through. What if, instead of making any resolutions, we come up with the purpose or agenda for the year? I reckon that would be easier, wouldn’t it?

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Our mindset can have a significant impact on our lives!

Transformation Leadership Disruption The Future of 

There was once a man who lived by the side of the road and sold hot dogs. He was hard of hearing, so he had no radio. He had trouble with his eyes, so read no newspapers. Moreover, of course, he didn't watch television. However, he sold delicious hot dogs. He put up signs on the highway telling everyone how good they were, he stood on the side of the road and cried out to all that past, "Buy a hot dog, they are the best in town." So people bought his hot dogs, and he increased his meat and bun orders. He bought a bigger stove to take care of all the extra business. He finally got his son to come and help him out with his business.

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The future of writing is the future of thinking! Software intelligence has improved so much so that now your email programs can finish a sentence for you as soon as you start to write it. Smart replies have been around for the past few years and both kinds, lazy and busy, are using them already. What’s more, news and articles are also getting written by AI, and so are the product descriptions. Just the last month one of my contacts published a book that boasted having a chapter written by AI.

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Responsible AI — Everyone Knows Why, but No One Knows How! Future-tech always brings us two things — a promise and the consequences. It’s those consequences that responsible AI is about; if it is not, then it should be. Much of our current daily lives as consumers intertwine with artificial intelligence. There is no doubt that Artificial Intelligence is a powerful technology. However, power comes with responsibility! While many appreciate the ease and convenience of these solutions, AI is still an emerging technology.

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Only AI Supervision Can Expose This Since most of the AI solutions are appearing as black boxes, it is becoming more relevant to supervise them regularly. Not monitoring these systems could mean that things would go wrong without getting noticed. A significant problem if you see issues somewhat later, and would be a huge trouble if you never notice them until severe consequences occur. Practically, we train machine learning systems on a fixed dataset. However, in reality, we almost always subject these systems to the data outside of their training distribution (long tail as some would call it). How can you be sure that AI systems work robustly on the real data?

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When I Missed a Train Station This incident happened several months ago — I was going home after a day-long work in the city and boarded the train from City Town Hall. Then, as usual, got involved with my smartphone, until my wife called to check my arrival time. I looked up to see the nearby station/area to give her an estimate. That’s when I realised I missed my station already and I was five stations ahead. The first thing I did — acknowledged that I was heading in the wrong direction. Next, I quickly estimated how to correct the course, i.e. get down to the next station and catch another train to my destination.

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Why Framing the Correct Hypothesis Is Critical for AI? Artificial Intelligence, being such a powerful technology, inherently carries a high risk. It is a source of significant new threats that we must manage. This risk is not concentrated in one specific section of AI; instead, it is spread over all the stages and components of the AI system. It includes AI solution design methodology, the AI solution itself, its development and deployment and then ongoing use. Preventing each of risk means we will have to evaluate each component in its own right and seek avenues of prevention. Even before we discuss prevention, identification is necessary.

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5 mistakes to avoid when training your AI system Meticulous teaching is the fundamental requirement for having an excellent performance consistently. However, there are a few caustic mistakes to which traditional (and contemporary) data analytics, or statistical methods are susceptible. The collective calling for such issues is often known as garbage in garbage out. Here are five mistakes that you should avoid when training your AI system.

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The cardinal sequence to follow when testing your AI Any testing, whether it is traditional software testing or AI testing, should start with the premise — there is a problem (or a bug) until proven otherwise by way of tests. A typical software testing sequence is quite logical and almost immutable due to its limited nature. However, in the case of AI testing, change in sequence is possible, and this change has a full potential to make or break the system performance. If you do not test AI in proper sequence, it can increase the risk exposure of the business significantly.

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7 things a coach must do when working with startup founders When I started my entrepreneurial journey, I did not have a coach or mentor, and so I had to walk a very long and dwindled path. Although there was no formal coach for me for a very long time, I always had people around me telling me what I should do and what I shouldn’t. Every time I listened to someone, I had to navigate through the complicated maze of thoughts that advice generated. I know how painful that can be, and this is one of the many reasons I started supporting startups in the role of coach and mentor. Every founder does not have to repeat the same mistake to learn valuable lessons.

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What do you see in this picture? There is a good reason why machines and humans are different. One day I asked my 3-year-old, "What do you see in this picture?" She said, "A mooo!" (= cow). It wasn’t the answer I expected! I persisted, "What else do you see?" She is very observant, "A tree!". Okay, that’s not wrong. I asked again, "What else?" She, "Look, another tree!". Wow! I didn’t think of that. "And what else?" "Chocolates!". I had no idea where this came from! I, "Where do you see chocolates?" (a digression from original question). She pointed at the things hanging by the tree. "These are coconuts, not chocolates", I said. She wouldn’t agree. Argh — kids!

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13 principles to help you maintain sanity in business While working with over 85+ businesses of various sizes in the last 20 years, I discovered a characteristic pattern in the companies that were the best and the rest. I have tried to model this into an explainable and understandable form here as The Sanity Model. As I depict in this model, a business must walk the fine line to remain in the safe zone. While the technology-first approach is a definite way to upset the customers, a right balance is necessary to sustain the customer-first approach. Businesses often get carried away by the smart technology at the disposal and assume that we can use it to solve every problem around us. Emerging technologies are new hammers, avoid treating all the issues as nails.

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Why Is Plug-n-Play Promise of IoT Not Helping? Internet of Things has been there for quite some time and is now stabilising in the primary market. It is a good thing, because of all the noise during hype-cycle, there was too much to digest. Now that the tide has subsided, we see meaningful deployments of IoT solutions, and they are on the rise. However, I can say that things are far from settled, especially from commercial and industrial roll-outs perspective. Projects are still taking longer than usual, although there is no standard definition of what is normal!

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The Data About You May Not Be Your Data! You may often think your data on social networks is private, secure, and is owned by you. However, there is more to this, and it starts with a critical question — How do you define "your data"? Technology is becoming pervasive day by day. With the proliferation of social networks & free services, our data is at stake, big time! Data being private and secure is entirely feasible, and we should be concerned about it only so much. However, ownership, on the other hand, is a grey area. Moreover, mostly, it is not working in users’ favour. However, before we could delve into the question of ownership, it is essential to understand and define the term — your data.

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Here is a better way to fix AI risks AI is often presented as a panacea that can somehow resolve everything. As it stands today, the outlook is still highly debatable. AI, however, has now become a real-world application technology, and it is becoming part of the modern life fabric. However, the future of AI highly depends on the trust factor. If AI is to drive the business and overall social success, it cannot hide in a black box. To have confidence in the outcomes, obtain user-trust, and ultimately capitalise on the opportunities, it may be necessary to open up the black box. Then again, it will not always be possible to do it for various reasons, and hence, a pre-emptive approach is necessary.

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I Think the AI Bias Is Ill-Defined, Here Is Why The ethical framework for any technology as such has been one of my favourite and close to the heart topics. A few weeks ago, I was writing in response to the consultation on Australia’s Ethics Framework for Artificial Intelligence, which was invited by the Department of Industry, Innovation & Science. One of the critical section was obviously around bias, which was linked in defining fairness principle for AI. The draft paper proposed fairness as one of the core principles for AI explained it somewhat like this: The development or use of the AI system must not result in unfair discrimination against individuals, communities or groups.

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7 Myths of IoT — Busted! The Internet of Things has been mythicized and has sometimes stoked irrational fears and irrational enthusiasm, for it to create a positive impact on us, we need to focus more on the facts. Internet of Things (IoT) is one of those hot topics, which means many things to many people. However, a few myths are floating around. IoT has been mythicized and has sometimes stoked irrational fears and irrational enthusiasm. Some of the unfortunate myths are also contributing to false economics, and in the long run, they would affect everyone. Internet of Things has a great potential to enable improvements in so many facets of life; the list is endless. However, for it to create a positive impact on us, we need to focus more on the facts.

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Handling Concept Drift at the Edge of the Sensor Network Consider a scenario where you are using an electric motor in your workshop. You have installed a few sensors on the outer body of this motor, and these sensors continuously send you the data over Wi-Fi. Then you have an elaborate setup on the cloud where your intelligent application program analyses those parameters and determines your motor’s health status. This scenario, of course, takes into account the trained pattern of motor vibration and current consumption by the motor during the learning phase. Moreover, so long nothing changes; this pattern recognition works like a charm. That is what a good machine learning would entail.

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Why Should You Build Your Own IoT Platform? Let us begin with this recent news about a nasty surprise of subscribed cloud compute platform…While revealing some uncomfortable truths about the cloud computing industry, the writer says, "While it’s often billed as being cheaper than maintaining your servers in your own data centre, that’s generally only true at small scales. At larger scales, like that of Lyft, the costs can quickly add up to be significant." As the writer continues to warn, "Take Lyft’s cloud situation as a reality check on cloud computing — both its promise for smaller companies, as well as the potential for it to be more expensive than many might think."

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Using MQTT Broker? You Must Know This Simply having 10 connected devices could mean that the MQTT broker should be capable of handling 100 messages simultaneously. There are several options for developing a message broker with full duplex functionalities and various supporting features. Some of these options are the use of a raw TCP socket, a raw UDP socket, AMQP, and CoAP. Most of these alternatives have more limitations and complications than benefits, especially when compared to MQTT. This is where MQTT becomes the most appropriate, efficient, and convenient choice, especially when building our own Internet of Things platform. Remember that all of these protocols can coexist, and we could deploy on the same cloud instance, if necessary.

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What Should a Good IoT Platform Have? Always be mindful of the functions and features that are must-have or nice to have. For every product, some functions and features are must-have or are nice to have. When we distinguish between the two, the selection becomes relatively simple. Building your own IoT platform makes much more sense. For any middleware platform to be worthy of being part of the Internet of Things, it is imperative that it has the following functionalities and capabilities.

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What comes first — cart or horse? Well, it depends! Fact is, avoiding the temptation to love your solution more than the customer’s problem pays a great deal eventually. For once, I did not want to ramble about "cart before the horse" issue that exists widely in the technology-based solution industry today. It is too painful to keep saying it without seeing a change in the uptake of the morale of the idiom. However, wait, isn’t the IoT and AI-based solutions are much of a torch-bearers of this phenomenon? Indeed, they are! Even Google wants to be AI-first company rather than being customer first, and others follow.

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Losing jobs to an AI is not the real problem Not having anything better to do when there is extra time at hand makes people uncomfortable. A few years ago, I was working with a banking sector client, and our goal was to save roughly 1.5 hrs per day per sales team member. That is whopping 7.5 hrs or one working day per week, and with the team size circa 75 members, it was a significant monetary benefit. Who wouldn’t have wanted that type of productivity increase? However, we still faced significant and constant resistance. Until the day when it dawned upon us, and we figured out the potential reason behind it.

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The only human quality AI would never have I was discussing the challenges in realising general AI (AGI) with some of my acquaintances when I recalled a story from my school textbook somewhere around 1993–94. My English language book had this story named "Anukul", written by one of the talented Indian polymaths, Satyajit Ray. He wrote the story in the late 1960s. While it demonstrates fore-sightedness of some of the talented people of the past century, you may also find this story to be borderline spooky if not outright disturbing. The story is about a humanoid-robot, an example of a general AI manifested in the form of a personal assistant. Another film-maker recently made a short film based on this story. The story also hints about what may happen when AI starts questioning our ways of the world.

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Why simplicity will be the key to emerging future If you cannot explain it in simple terms, you do not understand it well enough! A lot of times when I quickly describe something, people ignore me. Some say that over-simplification could be the issue. And for several others, it appears that I am missing something or keeping some information out. Maybe yes! I am distilling that information because it may not be relevant or less relevant. Or perhaps it was there for diverting you from thinking clearly, so I am omitting it. Maybe that information was nothing but a distraction. Or could it be that I am genuinely trying to convey the message instead of blocking it from being understood?

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The key to a great story is not who, what, or when, but why! Whether it is for theatrical reasons or practical ones, purpose or motive has always been the driving factor of the story. Knowing the Why makes things crystal-clear in trying times. It acts as a guiding star when we are in a problem. As far as the business context is concerned, not having an answer to this critical question poses a fundamental problem in almost all the change management initiatives. Additionally, when an organisation is trying to revamp, go to the next level of excellence or advancement, not knowing this (why — the purpose) becomes a roadblock.

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Value or Fluff — What Customers See! Value always implies an explicit trade-off between what you give and what you get. And it applies in almost any context. Although I spoke about this topic in a few forums several years ago, my recent experience made me revisit it because this (phenomena) keeps happening now and then. I firmly believe that how customers and suppliers perceive each other mostly and directly impacts the results. If we want to create a real value for everyone, we must shed our perceptions and see-through customer lenses to be more productive.

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How to Navigate Through The AI Junk Poorly designed AI is nothing short of a banana! These days we are so gung-ho about new technologies (mainly AI) that we have almost stopped demanding for right quality solutions. We are so fond of this newness that we are ignoring flaws new technologies are carrying over. This approach, in turn, is encouraging subpar solutions day by day, and we are sitting on a pile of junk, which is growing faster than ever. The disturbing part of that is — those who are making subpar solutions, still, make us believe that machines will be wiser soon, when and how I wonder! More importantly, why should we tolerate poorly designed solutions until then?

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Why Google, Facebook, and LinkedIn Should Start Paying Us A few weeks ago, there was a minor leak, in one of the bathrooms in our apartment. So, we called the plumber. When the plumber visited, we granted him access to come through the lobby and enter our studio. Now, since the affected bathroom was in the innermost part of our apartment, he had to walk through the living room, kitchen, study, and other bedrooms. For the work purpose, he had to go back and forth a few times, and finally, he fixed the leak. I am sure he would have had a glimpse of what is around, every time he passed by. Now that he has gone, we will not grant him access again, unless it is required. But…what happens to the information (or say the intel) he might have gathered throughout this single transaction?

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When working with emerging technologies — Sanity is the key! Our adamant belief that there is a bright technological future just around the corner is the issue! Several years ago, in my early days of career, when I was working with a home appliance manufacturing company, an exciting incident happened. My team was used to report to a Senior Manager, who was quite steadfast in pushing certain things, especially when he had made up his mind about something. One day he asked us to prepare a report, where critical items should be highlighted in italics and colored fonts, and then we were required to print it and give it to him. The only (big) problem was, we were using a program called "WordStar," and all we had was a dot-matrix printer!

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Will You Kill One to Save Many? I am sure, each one of us in a decision-making role would have faced this at least once. When it comes to a decision that would affect a larger group of people — things are neither easy nor straightforward. If you have to make a quick decision, what is your obvious, frequent choice? Would you go for the popular one or the right one? Let us render one hypothetical example and have some insight into our way of decision-making. A group of children is playing near two railway tracks, one that is still in use while the other is abandoned. Only one child is playing on the abandoned track while the rest are on the operational track…Imagine!

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7 Commandments of Decision Making Good decision making comes from complete clarity and rightful confidence. For this, one must step back from current work portfolio and take a look at the bigger picture regularly. The number one barrier that is holding most people back from achieving their business’ (or even their own) full potential is their inability to make decisions. Here are the seven commandments that would help in nurturing good decision-making ability.

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