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The Rocky Road Toward Explainable AI (XAI) For AI Autonomous Cars 



The AI systems doing the piloting of autonomous cars will need to provide explanations to curious passengers about the route being undertaken. (Credit: Getty Images) 

By Lance Eliot, the AI Trends Insider  

Our lives are filled with explanations. You go to see your primary physician due to a sore shoulder. The doctor tells you to rest your arm and avoid any heavy lifting. In addition, a prescription is given. You immediately wonder why you would need to take medication and also are undoubtedly interested in knowing what the medical diagnosis and overall prognosis are. 

So, you ask for an explanation. 

In a sense, you have just opened a bit of Pandora’s box, at least in regard to the nature of the explanation that you might get. For example, the medical doctor could rattle a lengthy and jargon-filled indication of shoulder anatomy and dive deeply into the chemical properties of the medication that has been prescribed. That’s probably not the explanation you were seeking.   

It used to be that physicians did not expect patients to ask for explanations. Whatever was said by the doctor was considered sacrosanct. The very nerve of asking for an explanation was tantamount to questioning the veracity of a revered medical opinion. Some doctors would gruffly tell you to simply do as they have instructed (no questions permitted) or might utter something rather insipid like your shoulder needs help and this is the best course of action. Period, end of story.   

Nowadays, medical doctors are aware of the need for viable explanations. There is specialized “bedside” training that takes place in medical schools. Hospitals have their own in-house courses. Upcoming medical doctors are graded on how they interact with patients. And so on.   

Though that certainly has opened the door toward improved interaction with patients, it does not necessarily completely solve the explanations issue. 

Knowing how to best provide an explanation is both art and science. You need to consider that there is the explainer that will be providing the explanation, and there is a person that will be the recipient of the explanation. 

Explanations come in all shapes and sizes. 

A person seeking an explanation might have in mind that they want a fully elaborated explanation, containing all available bells and whistles. The person giving the explanation might in their mind be thinking that the appropriate explanation is short and sweet. There you have it, an explanation mismatch brewing right before our eyes. 

The explainer might do a crisp explanation and be happily satisfied with their explanation. Meanwhile, the person receiving the explanation is entirely dissatisfied. At this point, the person that received the explanation could potentially grit their teeth and just figure that this is all they are going to get. They might silently walk away and be darned upset, opting to not try and fight city hall, as it were, and merely accede to the minimal explanation proffered. 

Perhaps the person receiving the explanation decides they would like to get a more elaborated version. They might stand their ground and ask for a more in-depth explanation. Now we need to consider what the explainer is going to do. The explainer might believe that the explanation was more than sufficient, and see no need to provide any additional articulation.   

The explainer might be confused about why the initial explanation was not acceptable. Maybe the person receiving the explanation wasn’t listening or had failed to grasp the meaning of the words spoken. At this juncture, the explainer might therefore decide to repeat the same explanation that was just given and do so to ensure that the person receiving the original explanation really understood what was said.   

You can likely anticipate that this is about to spiral out of control. 

The person that is receiving this “elaborate” explanation is bound to notice that it is the same explanation repeated, nearly verbatim. That’s insulting! The person receiving the explanation now believes they are being belittled by the explainer. Either this person will hold their own tongue and give up trying to get an explanation, or try hurtling insults about how absurd an explanation the explanation was.   

It can devolve into a messy affair, that’s for sure.   

There is a delicate dance between the explainer and the providing of an explanation, along with the receiver and the desired nature of an explanation.   

We usually take these differences for granted. You rarely see an explainer ask what kind of explanation someone wants to have. Instead, the explainer launches into whatever semblance of an explanation that they assume the person would find useful. Rushing into providing an explanation can have its benefits, though it can also start an unsightly verbal avalanche that is going to take down both the explainer and the person receiving the explanation.   

Some suggest that the explainer ought to start by inquiring about the type of explanation that the other person is seeking. This might include asking what kind of background the other person hasin the case of a medical diagnosis, whether the other person is familiar with medical terminology and the field of medicine. There might also be a gentle inquiry as to whether the explanation should be done in one fell swoop or possibly divided into bite-sized pieces. Etc.   

The difficulty with that kind of pre-game formation is that sometimes the receiver doesn’t want to go through that gauntlet. They just want an explanation (or so they say). Trying to do a preamble is likely to irritate that receiver, and they will feel as though the explanation is being purposely delayed. This could even smack of hiding from the facts or some other nefarious basis for delaying the explanation. 

All told, we expect to get an explanation when we ask for one, and not have to go through a vast checklist beforehand.   

Another twist to all of this entails the interactive dialogue that can occur during explanations.   

The manner of explanations is not necessarily done in a one-breath fashion from start to end. Instead, it is more likely that during the explanation, the receiver will interrupt and ask for clarification or have questions that arise. This is certainly a sensible aspect. If the explanation is going awry, why have it go on and on, wherein instead the receiver can hopefully tailor or reshape the direction and style of the explanation.   

For example, suppose that you are a medical professional and have gone to see a medical doctor about your sore shoulder. Imagine that the doctor doing the diagnosis does not realize that the patient is a fellow medical specialist. In that case, the explanation offered is likely to be aimed at a presumed non-medical knowledge base and proceed in potentially simplistic ways (with respect to medical advice). The person receiving the explanation would undoubtedly interrupt and clarify that they know about medicine and the explanation should be readjusted accordingly.   

You might be tempted to believe that explanations can be rated as being either good or bad. Though you could take such a perspective, the general notion is that explanations and their beauty are in the eye of the beholder. One person’s favored explanation might be a disastrous or terrible one for someone else. That being said, there is still a modicum of a basis for assessing explanations and comparing them to each other. 

We can add a twist on that twist. Suppose you receive an explanation and believe it to be a good one. Later on, you learn something else regarding the matter and realize that the explanation was perhaps incomplete. Worse still, it could be that the explanation was intentionally warped to give you a false impression of a given situation. In short, an explanation can be used to purposely create falsehoods. 

That’s why getting an explanation is replete with problems. We often assume that if we ask for an explanation, and if it seems plausible, this attests that the matter is well-settled and above board. The thing is, an explanation can be distorted, either by design or by happenstance, and lead us into a false sense of veracity or truthfulness at hand. 

Another angle to explanations deals with asking for an explanation versus being given an explanation when it has not been requested. An explainer might give you an explanation outright because they assume you want one, whereas you are satisfied to just continue on. At that point, if you disrupt the explanation, the explainer might be taken aback.   

Why all this talk about explanations? Because of AI.   

The increasing use of Artificial Intelligence (AI) in everyday computer systems is taking us down a path whereby the computer makes choices and we the humans have to live with those decisions. If you apply for a home loan, and an AI-based algorithm turns you down, the odds are that all you’ll know is that you did not get the loan. You won’t have any idea about why you were denied the loan.   

Presumably, had you consulted with a human that was doing the loan granting, you might have been able to ask them to explain why you got turned down.   

Note that this is not always the case, and it could be that the human would not be willing or able to explain the matter. The loan granting person might shrug their shoulders and say they have no idea why you were turned down, or they might tell you that company policy precludes them from giving you an explanation. 

Ergo, I am not suggesting that just because a human is in the loop you will necessarily get an explanation. Plus, as repeatedly emphasized earlier, the explanation might be rather feeble and altogether useless.    

In any case, there is a big hullabaloo these days that AI systems ought to be programmed to provide explanations for whatever they are undertaking.   

This is known as Explainable AI (XAI). 

XAI is growing quickly as an area of keen interest. People using AI systems are going to likely expect and somewhat demand that they get an explanation provided to them. Since the number of AI systems is rapidly growing, there is going to be a huge appetite for having a machine-produced explanation about what the AI has done or is doing.   

The rub is that oftentimes the AI is arcane and not readily amenable to generating an explanation.   

Take as an example the use of Machine Learning (ML) and Deep Learning (DL). These are computational pattern matching algorithms that examine data and try to ferret out mathematical patterns. Sometimes the inner computational aspects are complex and do not lend themselves to being explained in any everyday human-comprehensible and logic-based way. 

This means that the AI is not intrinsically set up for providing explanations. In that case, there are usually attempts to add on an XAI component. This XAI either probes into the AI and tries to ferret out what took place, or it sits aside from the AI and has been preprogrammed to provide explanations based on what is assumed has occurred within the mathematically enigmatic mechanisms. 

Some assert that you ought to build the XAI into the core of whatever AI is being devised. Thus, rather than bolting onto the AI some afterthought about producing explanations, the design of the AI from the ground-up should encompass a proclivity to produce explanations.   

Amidst all of that technological pondering, there are the other aspects of what constitutes an explanation. If you revisit my earlier comments about how explanations tend to work, and the variability depending upon the explainer and the person receiving the explanation, you can readily see how difficult it might be to programmatically produce explanations.   

The cheapest way to go involves merely having pre-canned explanations. A loan granting system might have been set up with five explanations for why a loan was denied. Upon your getting turned down for the loan, you get shown one of those five explanations. There is no interaction. There is no particular semblance that the explanation is fitting or suitable to you in particular. 

Those are the pittance explanations. 

A more robust and respectable XAI capability would consist of generating explanations on the fly, in real-time, and do so based on the particular situation at hand. In addition, the XAI would try to ascertain what flavor or style of explanation would be suitable for the person receiving the explanation. 


And this explainer feature ought to allow for fluent interaction with the person getting the explanation. The receiver should be able to interrupt the explanation, getting the explainer or XAI to shift to other aspects or reshape the explanation based on what the person indicates.   

Of course, those are the same types of considerations that human explainers should also take into account. This brings up the fact that doing excellent XAI is harder than it might seem. In a manner of speaking, you are likely to need to use AI within the XAI in order to be able to simulate or mimic what a human explainer is supposed to be able to do (though, as we know, not all humans are adept at giving explanations).   

Shifting gears, you might be wondering what areas or applications could especially make use of XAI.   

One such field of endeavor entails Autonomous Vehicles (AVs). We are gradually going to have autonomous forms of mobility, striving toward a mobility-for-all mantra. There will be self-driving cars, self-driving trucks, self-driving motorcycles, self-driving submersibles, self-driving drones, self-driving planes, and the rest.   

You might at first thought be puzzled as to why AVs might need XAI. We can use self-driving cars to showcase how XAI is going to be a vital element for AVs.   

The question is this: In what way will Explainable AI (XAI) be important to the advent of AVs and as showcased via the emergence of self-driving cars? 

Let’s clarify what I mean by self-driving cars, and then we can jump further into the XAI AV discussion.   

For my framework about AI autonomous cars, see the link here:   

Why this is a moonshot effort, see my explanation here:   

For more about the levels as a type of Richter scale, see my discussion here:   

For the argument about bifurcating the levels, see my explanation here: 

Understanding The Levels Of Self-Driving Cars 

As a clarification, true self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.   

These driverless vehicles are considered Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and nor how long it will take to get there. 

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some contend). 

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different from driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll see in a moment, the points next made are generally applicable).  

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car. 

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3. 

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here:   

To be wary of fake news about self-driving cars, see my tips here: 

The ethical implications of AI driving systems are significant, see my indication here: 

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: 

Self-Driving Cars And XAI   

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. All occupants will be passengers; the AI is doing the driving. 

One aspect to immediately discuss entails the fact that the AI involved in today’s AI driving systems is not sentient. In other words, the AI is altogether a collective of computer-based programming and algorithms, and most assuredly not able to reason in the same manner that humans can.   

Why this added emphasis about the AI not being sentient? 

Because I want to underscore that when discussing the role of the AI driving system, I am not ascribing human qualities to the AI. Please be aware that there is an ongoing and dangerous tendency these days to anthropomorphize AI. In essence, people are assigning human-like sentience to today’s AI, despite the undeniable and inarguable fact that no such AI exists as yet.   

Now that we’ve laid the stage appropriately, time to dive into the myriad of aspects that come to play on this topic about XAI.   

First, be aware that many of the existing self-driving car tryouts have very little if any semblance of XAI in them. The initial belief was that people would get into a self-driving car, provide their destination, and be silently whisked to that locale. There would be no need for interaction with the AI driving system. There would be no need for an explanation or XAI capability.   

We can revisit that assumption by considering what happens when you use ridesharing and have a human driver at the wheel.   

There are certainly instances wherein you get into an Uber or Lyft vehicle and there is stony silence for the entirety of the trip. You’ve likely already provided the destination via the ride-request app. The person driving is intently doing the driving and ostensibly going to that destination. No need to chat. You can play video games on your smartphone and act as though there isn’t another human in the vehicle.   

That’s perfectly fine.   

Imagine though that during the driving journey, all of a sudden, the driver decides to go a route that you find unexpected or unusual. You might ask the driver why there is a change in the otherwise normal path to the destination. They would hopefully prompt an explanation from the human driver. 

It could be that the human driver gives you no explanation or provides a flimsy explanation. Humans do that. In theory, a properly done XAI will provide an on-target explanation, though this can be challenging. Maybe the human driver tells you that there is construction taking place on the main highway, and to avoid a lengthy delay, an alternative course is being undertaken. 

You might be satisfied with that explanation. On the other hand, perhaps you live in the area and are curious about the nature of the construction taking place. Thus, you ask the driver for further details about the construction. In a sense, you are interacting with an explainer and seeking additional nuances or facets about the explanation that was being provided. 

Okay, put on your self-driving car thinking-cap and consider what a passenger might want from an XAI. A self-driving car is taking you to your home. The normal path that would be used is unexpectedly diverted from the AI driving system. You are likely to want to ask the AI why the driving journey is altering from your expected traversal. Many of the existing tryouts of self-driving cars would not have any direct means of having the AI explain this matter, and instead, you would need to connect with a remote agent of the fleet operator that oversees the self-driving cars. 

In essence, rather than building the XAI, the matter is shunted over to a remote human to explain what is going on. This is something that won’t be especially scalable. In other words, once there are hundreds of thousands of self-driving cars on our roadways, the idea of having the riders always needing to contact a remote agent for the simplest of questions is going to be a huge labor cost and a logistics nightmare.   

There ought to be a frontline XAI that exists with the AI driving system.   

Assume that a Natural Language Processing (NLP) interface is coupled with the AI driving system, akin to the likes of Alexa or Siri. The passenger interacts with the NLP and can discuss common actions such as asking to change the destination midstream, or asking to swing through a fast-food eatery drive-thru, and so on.   

In addition, the passenger can ask for explanations.   

Suppose the AI driving system has to suddenly hit the brakes. The rider in the self-driving car might have been watching an especially fascinating cat video and not be aware of the roadway circumstances. After getting bounced around due to the harsh braking action, the passenger might anxiously ask why the AI driving system made such a sudden and abrasive driving action. 

You would want the AI to immediately provide such an explanation. If the only possible way to get an explanation involved seeking a remote agent, envision what that might be like. There you are, inside the self-driving car, and it has just taken radical action, but you have no idea why it did so. You have to press a button or somehow activate a call to a remote agent. This might take a few moments to engage.   

Once the remote agent is available (assuming that one is readily available), they might begin the dialogue with a usual canned speech, such as welcome to the greatest of all self-driving cars. You, meanwhile, have been sitting inside this self-driving car, which is still merrily driving along, and yet you have no clue why it out-of-the-blue hit the brakes. 

The point here is that by the time you engage in a discussion with the human remote operator, a lot of time and driving aspects could have occurred. During that delay, you are puzzled, concerned, and worried about what the AI driving system might crazily do next.   

If there was an XAI, perhaps you would have been able to ask the XAI what just happened. The XAI might instantly explain that there was a dog on the sidewalk that was running toward the self-driving car and appeared to be getting within striking distance. The AI driving system opted to do a fast braking action. The dog got the idea and safely scampered away.   

A timely explanation, and one that then gives the passenger solace and relief, allowing them to settle back into their seat and watch more of those videos about frisky kittens and adorable puppies.   

For more details about ODDs, see my indication at this link here: 

On the topic of off-road self-driving cars, here’s my details elicitation: 

I’ve urged that there must be a Chief Safety Officer at self-driving car makers, here’s the scoop: 

Expect that lawsuits are going to gradually become a significant part of the self-driving car industry, see my explanatory details here:   


There are lots and lots of situations that can arise when riding in a car and for which you might desire an explanation. The car is suddenly brought to a halt. The car takes a curve rather strongly. The car veers into an adjacent lane without a comfortable margin of error. The car takes a road that you weren’t expecting to be on. Seemingly endless possibilities exist.   

In that case, if indeed XAI is notably handy for self-driving cars, you might be wondering why it isn’t especially in place already.   

Well, admittedly, for those AI developers under intense pressures to devise AI that can drive a car from point A to point B, doing so safely, the aspect of providing machine-generated explanations is pretty low on their priority list. They would fervently argue that it is a so-called edge or corner case. It can be gotten to when the sunshine of having achieved sufficiently self-driving cars has been achieved. 

Humans that are riding in AVs of all kinds are going to want to have explanations. A cost-effective and immediately available means of providing explanations entails the embodiment of XAI into the AI systems that are doing the autonomous piloting.   

One supposes that if you are inside a self-driving car and it is urgently doing some acrobatic driving maneuver, you might be hesitant to ask what is going on, in the same manner, that you might worry that you would be distracting a human driver that was doing something wild at the wheel.   

Presumably, a well-devised XAI won’t be taxing on the AI driving system, and thus you are free to engage in a lengthy dialogue with the XAI. In fact, the likeliest question that self-driving cars are going to get is how does the AI driving system function. The XAI ought to be readied to cope with that kind of question. 

The one thing we probably should not expect XAI to handle will be those questions that are afield of the driving chore. For example, asking the XAI to explain the meaning of life is something that could be argued as out-of-bounds and above the pay grade of the AI.   

At least until the day that AI does become sentient, then you can certainly ask away. 

Copyright 2021 Dr. Lance Eliot 

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Listen: How to teach ethics to AI models



Removing bias from artificial intelligence (AI) models may seem as simple as removing demographic information from the data, but it may be more valuable instead to inform the model about the demographics, then weight them to offset bias. That’s one possibility studies have supported, Stephen Thomas tells Bank Automation News in this episode of “The […]

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Artificial Intelligence

Machine Learning vs Deep Learning vs Artificial Intelligence| Know in-depth Difference



ML vs Dl Vs AI | Know in-depth Difference – Analytics Vidhya

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Additive Manufacturing; Perhaps, the Biggest Tectonic Shift Enabling to Industry 4.0



3D printing or additive manufacturing is bringing monumental changes to a number of important industries. Companies have begun printing everything from fully functioning cars to Michelin-stared dinners. Medical research institutes have even experimented with printing functioning body parts.

Recently, additive manufacturing showcased its practical versatility. It is becoming a powerful tool in tackling some of the unprecedented challenges posed by the COVID-19. Helping to supply health workers with personal protective equipment and patients with ventilators.

Advanced manufacturing tools like 3D printing—also known as additive manufacturing—are essential for the types of performance-sensitive applications where this optimization is compelling.

3D printing is more cost-competitive at lower production volumes. Because you do not need to reach an economy of scale to offset setup costs. Therefore, it facilitates the mass customization that generative design makes possible. As the cost of 3D printing continues to decrease and the variety of materials increase, 3D printing is becoming practical for small and mid-volume parts for more and more applications.

The Electric Vehicle and the Automotive Industry holds a prominent position in manufacturing with the latest technologies and Innovations. Automotive electrical and electronic systems are becoming more complex. Making the task of designing today’s cars much more difficult. Infotainment, comfort and convenience features, and even safety- and mission-critical systems such as steering and throttle control are accomplished through electrically powered computers, actuators, and sensors.

Additive Manufacturing with the help of Generative design takes system definitions and requirements as input and generates architectural proposals for the logic, software, hardware, and networks of the E/E systems using rules-based automation. These rules capture the knowledge and experience of the veteran engineers to guide younger engineers throughout the design. Capturing this IP helps companies to develop both vehicle architectures and new generations of engineers as they learn and implement existing company knowledge.

It also offers the ability to consolidate parts, so a single complex geometry created by a generative algorithm and 3D printed can often replace assemblies of dozens of separate parts.


Additive Manufacturing

Always on the cutting edge, the aerospace industry was an early adopter of additive manufacturing. Aerospace companies must meet some of the manufacturing industry’s most stringent standards, and parts and components need to be made of the highest-performance materials. Using AM, engineers can design complex, high-strength parts while also reducing the weight of aerospace components by printing more efficient geometries and eliminating significant amounts of unnecessary material. This allows for lower fuel consumption, reduced CO2 emissions, and reduced costs (plus, better airfares).

Consumer Products

Additive manufacturing comes with a plethora of advantages for manufacturing on-demand supplements for consumer-based products. For marketing teams taking a product from concept to completion, often the biggest amount of time is spent on design. To be sure the product is just right, a great deal of time is spent on creating prototypes to prove concepts to stakeholders and ultimately deliver a consumer-pleasing product.

By embracing AM, marketing teams can develop iterations of their product much quicker, and then rapidly pivot to adjust the design as needed. As additive manufacturing continues to advance with build volume and speed, more consumer products may be produced through AM technologies for quick and efficient mass production demands.


It is not far-fetched to think of living in a house or walking over a bridge that is completely made out of a 3D printer anymore. Soon, this may become reality. In fact, it already is in the Netherlands where the world’s first 3D-printed pedestrian bridge was unveiled in 2018. The structure was created by additive robotics layering molten steel and measures nearly 40 feet across.

Additive manufacturing in the construction market is expanding, ushering in a new era for the industry. According to a study by Transparency Market Research, 3D printing in the infrastructure secretary is predicted to expand at a CAGR of 33 per cent by 2027. Since printed materials can be precisely applied layer by layer when building infrastructure or constructing buildings, AM reduces material waste and allows construction to be as cost-efficient as possible.

Additionally, 3D printing technology allows for complex design structures. Since the materials are printed precisely – reducing the likelihood of infrastructure accidents from construction mistakes and poor design.

While 3D printing is not being employed in everyday bridge building, there is plenty of future potentials as the industry expands. And, with a recent report finding that over a third of all U.S. bridges need major repair work or should be replaced, there may be a rapid expansion of the industry in the near future.

Medical & Pharmaceutical

Additive manufacturing has revolutionized the medical industry, turning what was once science fiction into a new reality. The technology is delivering breakthroughs to doctors, patients, and research institutions. From durable prosthetics and true-to-life anatomical models to surgical grade components, the incredible plethora of objects that have already been successfully printed in the medical field gives a glimpse into the potential that this technology holds for healthcare in the near future.

For example, additive manufacturing allows for 3D printed dental appliances and custom-made devices, such as dentures, crowns, and even Invisalign, to be constructed from a variety of substrates and prints customized to each individual. Currently, the 3D printing market for digital dentistry is valued at $2.5 billion — and is expected to only keep growing. Additionally, additive manufacturing allows for devices such as hearing aids that can be mass-produced made for a better fit to ensure the highest level of comfort for the user.

One of the latest ongoing projects is the use of additive technology by researchers to print human embryonic stem cells. By doing so, these stem cells can then be used to create tissue for testing drugs or growing replacement organs, to print skin that could replace skin that’s been burned or damaged, and to print cancer cells to study and test out new drugs on them. Surgeon Anthony Atala, Director of the Wake Forest Institute for Regenerative Medicine, has even been working on printing organs, believing that printing an organ may soon replace transplanting an organ.

Hence, Additive Manufacturing is leading the industries and manufacturers into an era where the traditional ways of manufacturing would be disrupted, leaving faster, greater, and precise solutions for the manufacturing of future industries and products.

Mayank Vashisht | ELE Times | Technology Journalist

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Frequent run-ins with Indian government complicates tech giants’ plans



Elevate your enterprise data technology and strategy at Transform 2021.

(Reuters) — Another spat between India’s government and U.S. big tech has exacerbated disillusion among firms which have spent billions to build hubs in their largest growth market, to the extent some are rethinking expansion plans, people close to the matter said.

The government on Saturday said Twitter had not indicated compliance with new rules aimed at making social media firms more accountable to legal requests, and therefore risked losing liability exemptions for content posted on its platform.

Twitter joins compatriots, Facebook, and Facebook-owned WhatsApp in long being at loggerheads with the administration of Prime Minister Narendra Modi over data privacy bills and policies some executives have called protectionist, but tension has escalated in recent weeks.

Police visited Twitter last month to notify it of a probe into the tagging of a political tweet as “manipulated media”, and in February interrogated an Amazon official about the potentially adverse social impact of a political drama. Meanwhile, WhatsApp is challenging the government in court over rules it said would force it to access encrypted data.

“The fear is there,” said a senior tech industry executive in India. “It weighs both strategically and operationally.”

There are no indications the increasing run-ins have led to the delay or cancellation of planned investment.

Still, three senior executives familiar with the thinking of major U.S. tech firms said perceptions of India being an alternative, more accessible growth market to China are changing, and that longstanding plans for India’s role in their operations are being reviewed.

“There always used to be these discussions to make India a hub, but that is being thought through now,” said one of the executives, who works at a U.S. tech firm. “This feeling is across the board.”

Four other executives and advisors also expressed concern about rising tension. All declined to be identified due to the sensitivity of the matter and because discussions were private.

Twitter, Amazon, Facebook, WhatsApp and India’s Ministry of Electronics and Information Technology did not respond to requests for comment.


The government has argued that its rules are needed to stem the spread of misinformation that can spark violence – such as in 2017 when kidnapping rumours shared on message apps including WhatsApp led to lynching. It also said the rules are necessary to hold large technology companies accountable for practices that hurt domestic businesses or compromise customer privacy.

India is a massive market for U.S. tech giants. It is the biggest market for both Facebook and WhatsApp by user numbers, showed data from Statista, and third for Twitter. Amazon has committed as much as $6.5 billion to invest in the country.

To attract small businesses through WhatsApp, Facebook last year invested $5.7 billion in Reliance Industries‘s media and telecommunications arm, Jio Platforms.

Alphabet’s Google also pumped $4.5 billion into Jio last year from a newly created $10 billion fund earmarked for investment in India over five to seven years.


The government has tried to balance attracting high-tech investment with nationalist policies aimed at protecting local businesses and, critics say, advancing its political agenda.

A border confrontation with China prompted it to effectively ban Chinese social media apps, including TikTok and WeChat.

The government has also forced foreign firms to store data locally against fierce lobbying, and its promotion of a domestic payment card network prompted Mastercard to complain to the U.S. government about the use of nationalism.

In 2019, compliance issues with new regulations saw Amazon remove thousands of products from its e-commerce platform. The e-tailer is separately facing scrutiny by the Competition Commission of India for its retailing practices.

Twitter publicly refused to comply with some government demands to remove content, a stance which some industry executives said may have aggravated its current situation.

WhatsApp has gone to court rather than comply with a new law requiring social media firms to trace the origin of dangerous or criminal posts on their platforms. The message app operator said it cannot comply without breaking encryption, while observers said yielding could prompt similar demands in other countries.

At the same time, WhatsApp has faced regulatory delays that have limited its payment service to just 4% of its 500 million customers. Nevertheless, it is pressing ahead with hiring for a service it has called a “globally significant” opportunity.

Government officials have shown little patience for objections. IT minister Ravi Shankar Prasad said any robust democracy must have accountability mechanisms, such as the ability to identify the originator of messages.

“A private company sitting in America should refrain from lecturing us on democracy when you are denying your users the right to effective redressal forum,” Prasad said in an interview with the Hindu newspaper published on Sunday.

Still, continued antagonism could imperil Modi’s ambition of making India a go-to investment destination.

“It’s a question of what you would develop in a three-to-five-year horizon,” said another executive familiar with the thinking of U.S. firms. “Do you do that in India or do you do that in another country. That’s where the conversation is.”


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