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  • Mayank Batavia
  • October 09, 2018 01:15:39 PM

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The blog occasionally discusses digital marketing and AI. But it mostly discusses laws that impact the way we use technology.

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How 3d Printing Is Helping Fight Coronavirus Despite All Odds

3D Printing is re-inventing itself by trying to meet the acute shortage of medical devices, objects and even quarantine rooms As we sit down to write how 3D printing is fighting against Coronavirus, the officially confirmed cases of Coronovirus disease worldwide are over to 2.46 MN with over 165,000 deaths. The sheer size of the […] The post How 3d Printing Is Helping Fight Coronavirus Despite All Odds appeared first on Technology services...

3D Printing is re-inventing itself by trying to meet the acute shortage of medical devices, objects and even quarantine rooms

As we sit down to write how 3D printing is fighting against Coronavirus, the officially confirmed cases of Coronovirus disease worldwide are over to 2.46 MN with over 165,000 deaths.

The sheer size of the Covid-19 threat makes it important for everyone to join hands and fight this dreadful pandemic.

Historically, 3D printers have been used mostly for prototyping industrial items. Sometimes, 3D printed objects have also been used for aesthetic purposes, like printing a miniature of your favorite super-hero.

Different countries have seen 3D printing grow differently. Top 3D printing companies in India, for instance, have helped prototyping in a unique manner.

But this time around, 3D printing is doing new stuff: helping fight a never-before pandemic.

Why 3D printing enjoys a unique position

The NewYorker reported, on March 31, that the US had a stock of about one hundred sixty thousand ventilators. Sadly, the news reported, many of them weren’t functioning properly.

3D printing may be capable of manufacturing such life-saving equipment.

The 3-D printing and manufacturing facilities need some retooling to achieve this, but the goal is achievable.

3D printing facilities come in handy at such times because they can accommodate “rapid prototyping”, the report mentioned. Traditional manufacturing, on the other hand, is dependent upon making molds, which is a time-consuming process.

What all medical objects can 3D printing easily print

Printing complex medical equipment is not feasible at the moment. And even if such equipment could be made, questions about their reliability might persist since their testing would take a long time.

But there is so much that a 3D printing company could do.

Here’s a list of the various medical objects 3D printer could print:

  • Face masks
  • Face shields
  • Testing kits
  • Personal Protective Equipment (PPE)
  • Respirator valves
  • Safety eyewear
  • Ventilator parts
  • Door openers (that prevent further spread of infection)
  • Temporary housing (yes!)

What 3D printing companies are doing to fight the Covid-19 pandemic

3D printing companies are offering their machines and software to assist healthcare warriors in anyway they can. This goes a long way in preventing further spread, testing better, improving caregiving conditions and protecting doctors, nurses, medical professionals, patients’ attendants and the general populace.

3D printing companies are manufacturing – actually printing – a wide variety of medical objects, protective equipment, testing kits accessories and more.

We will cover two unusual efforts:

Quarantine rooms

Yes, 3D printing is also making entire quarantine rooms or quarantine booths!

Winsun, a 3D printing firm specializing in architectural work, has printed isolation wards. While 3D printing in the construction sector is not a novelty, using the technology to creatively fight the pandemic is certainly worthy of respect.

Using recyclable material including construction rubble, the company has printed safe, environment-friendly quarantine wards. These wards are equipped with a shower and air-conditioning too. The company claims the quarantine rooms meet the requisite standards.

Face shields

While the product isn’t technically complicated, but the efforts put behind them deserve praise.

Madiha Choksi, a research technologist at the Columbia University Libraries, is behind this task. Upon learning about the shortage of PPE, she first researched various 3D printing options and models available.

Next, she put to use two 3D printers. Making some improvements in the printing files she had, Choksi turned out a few face-shield samples. When they were found fit for use, she took up the challenge to mass produce them.

With the help of volunteers who later joined her, Choksi has already donated 7,500 protective face shields to hospitals in New York city.

Safety measures required

3D printing is helpful in times of pandemics like this, but it isn’t magical.

What’s more, it cannot be exempt from any safety precautions required for healthcare equipment.

We have listed below some of the precautions manufacturers need to take. If you are a 3D printing company, a member of 3D printing community, you sell, service or use 3D open-source printers or simply take up 3D printing projects, it’ll be a good idea for you to bear these points in mind (and also spread the word):

  • If you use plastic to manufacture any safety equipment, remember there is no clear understanding how long the Corona virus can survive on plastic. However, many experts believe that period is around 2-3 days. So, it will be a good idea to store packed items for at least 3-4 days before you begin distributing them.
  • Be sure to store different items in different places. This will minimize the risk of one set contaminating the other, technically called cross-contamination.
  • Handle objects carefully. Use gloves at all times. This is important because otherwise you end up contaminating the very items expected to protect human lives.
  • As they say, just because something can be 3-D printed doesn’t mean it will be as effective as a traditionally manufactured item. It’s critical to study key parameters carefully before commencing 3-D printing.

Challenges ahead

We are seeing technology improving human lives in a number of ways. For example, artificial intelligence is used in drug development. Though not a technology leader in AI, India is using AI for agriculture remarkably well.

So it should be no surprise that people are using 3D printing to fight Covid-19.

Promising as it may sound, the use of 3D printing in fighting Covid-19 is full of risks, challenges and complications.

We divide them in four broad categories:

  • Technical and operational challenges
  • Hygiene standards and medical compliance
  • Commercial and legal compliance

Technical and operational challenges

  • Full-face masks should fit the breathing equipment setup, tubing and wiring of the equipment already in use at hospitals.
  • In a hurry to supply masks, gloves, protective gear or ventilator accessories, 3D printing companies will have to resort to reverse engineering to avoid reinventing the wheel. Reverse engineering may not fully explain the intricacies of some products. As a result, sub-standard items may reach patients and healthcare professionals.

Hygiene standards and medical compliance

  • The pieces you manufacture or print using your 3d printing need to be approved for safety by various bodies.
  • The quality of the material you use for 3D printing should not be a type that easily encourages contamination.
  • Looking at the current scenario, various regulatory bodies are in a difficult situation. If they relax standards, these equipment could bring in unforeseen risks and cause collateral damage. If they do not relax standards, patients who could have otherwise been saved might die for want of such devices and protection equipment.
  • Most 3D printing companies aren’t aware of the various hygiene standards that dictate manufacturing of medical items. As a result, some 3D printed products to fight Covid-19 could actually end up worsening the already grim situation.

Commercial and legal compliance

  • Most devices and accessories for advanced medical equipment like ventilators are covered by patents and intellectual property rights. When you supply 3D printing parts for such equipment, you might be unwittingly breaking the IP rights.
  • Because these 3D printed items aren’t produced by compliant organisations, any malfunctioning caused due to performance or quality issues of the items could become messy from the legal point of view. It be very difficult to fix answerability and responsibilities in such cases.


There’s absolutely no doubt every individual, every industry it working harder than ever to fight the pandemic. Naturally, it’s interesting to watch how the 3D printing industry is fighting Covid-19.

As mentioned earlier, 3D printing has some amazing features and strengths. For instance, there is no match for 3D printing when it comes to rapid prototyping.

3-D printing companies, therefore, are at a special advantage.

That said, however, there are some challenges too. And some of these challenges are too massive to be overcome in a short window that the Coronavirus pandemic offers to fight back.

By all means, use 3-D printed devices, masks or similar protective gear. But also remember, they are a stop-gap arrangement till a vaccine is found.

We’ll wrap with this line from 3D Printing Industry site: Just because something can be 3D printed, doesn’t mean it should be 3D printed.

Feature Image courtesy Photo by Hush Naidoo on Unsplash

The post How 3d Printing Is Helping Fight Coronavirus Despite All Odds appeared first on Technology services news.

Europe’s data strategy and ambitions

The European Commission (EC), on February 19 2020, released a document titled A European Strategy for Data. This document is a road-map for the member countries of the European Union (EU) in the data economy for the next five years. More importantly, the document clearly spells out the ambition of the EU: “(to) become a […] The post Europe’s data strategy and ambitions appeared first on Technology services...

The European Commission (EC), on February 19 2020, released a document titled A European Strategy for Data. This document is a road-map for the member countries of the European Union (EU) in the data economy for the next five years.

More importantly, the document clearly spells out the ambition of the EU:

“(to) become a leading role model for a society empowered by data to make better decisions – in business and the public sector”.

This objective is significant principally for two reasons.

One, data is the raw material for what could be the technology of future, namely artificial intelligence (AI). Many people believe the EU has already lost the war to dominate AI. The winner would either be the USA or China.

Of course, there is a section of experts who believe Europe will win the AI war. Crafting a long-term strategy for data including its storage, processing and usage, could turn out to be a major step in the direction.

The second reason is relatively obvious. Europe’s General Data Protection Regulation (GDPR), the EU law on data protection, which came into effect in May 2018, today serves as a strong reference for all nations that are framing such laws.

In the light of the GDPR, the document in question is only a natural successor. Once you have the regulations in place, now you can “address systemic issues related to platforms and data.”

In this post, we give a brief overview of the document A European Strategy for Data. We cover what exactly is the document, what it aims to cover, what the EC is trying to do, what are the likely opportunities ahead and so on.

And of course, the most important question: what exactly does the EU plan to do down the line.

What the document is about

One of the key objectives behind this is to “create a European analytical framework for measuring data flows”.

More specifically, “The European strategy for data aims at creating a single market for data that will ensure Europe’s global competitiveness and data sovereignty”(Source).

In other words, the European Commissions’s data strategy lays down the early ‘pillars’ to make sure that data is available for the improvement of economy and the society, without sacrificing the individual’s ability to control their personal data.

What all areas can benefit from the correct use of data


Every single field that humans can conceive of can benefit from the right usage of data. Here is a glimpse of what data can do in fields as diverse as you can imagine:

Improving healthcare

The correct application of data storage and usage can lead to optimal resource allocation. This, in turn, results in huge costs savings. For example, better use of data could result in a saving of an estimated US$ 5.5 BN for malaria treatment and prevention alone.

Saving on labor costs

None of us are strangers to having spent long time waiting for flights or trains. Had we been warned in advance, it would have collectively saved countless human-hours. It could prevent clogging at airports and railroad stations because passengers wouldn’t have to wait. Real-time notification of delays can change a lot of things. For example, notification of delayed trains alone can save an estimated 27 million working hours, equivalent to over US $ 816 MN in labor costs.

Making farming more productive

A large proportion of world’s farming depends on monsoon. And monsoon, in turn, is but one of the many vagaries nature. Microsoft, for example, has used data of over 30 years to build the MAI (Moisture Adequacy Index) to build AI-based app that lets farmers know the best time to sow seeds. Such use of AI in agriculture can drastically change the way the world carries out farming.

Building organized societies

China has been spearheading the usage of data and building AI systems. The Social Credit System of China has leveraged data not only to track traffic offenders but also reward those follow regulations. Appropriate deployment of such systems can make the common human’s life easier and lead to a safer, more organized society.

Clearly, the data strategy of the European Commission is geared towards remaining an organized society, simply by better leveraging data.

What is the data available on data

Naturally, the entire exercise to build a data ecosystem is based on solid numbers. The EC has a very small but beautiful compilation on the numbers related to data (You can find it here). Following is an overview of the world of data, as mentioned in the study:

  • In 2018, the world produced 33 zetabytes of data. By 2025, this number will grow to 175 zetabytes.
  • The value of data economy in 2018 is pegged at almost US$ 332 BN. In 2025, the figure would be almost three times as big, reaching US$ 914 BN.
  • The data economy of in EU today contributes to 2.4% of the total GDP. By 2025, the same will contribute 5.8% of the GDP, representing a growth of over 140%.
  • The percentage of EU population with basic digital skills will be 65% of the total in 2025. That will be a 20% rise from 2018.
  • The number of data professionals too is expected to double by 2025 from what it was in 2018.

What are the objectives of Europe’s data strategy

The six objectives of A European Strategy for Data are clearly laid down in the early part of the document.

Here’s each of the six objectives behind European Commission’s data strategy:

1. Empowering users

In line with the GDPR, this document ensures data subjects retain control over their data. This will be complemented by nurturing SMBs to build digital skills.

2. Fostering investment

A specific European High Impact Project to process data, develop relevant tools and architecture, and shape governance is envisaged with an investment of over US$2.2 BN.

3. Forming legislation

The document paves way towards forming and adopting relevant legislation.

4. Promoting cloud

This is one of the most significant objectives in the context of countering US and Chinese dominance in cloud technology. The document aims to promote the setting up of data processing facilities and design regulatory framework for cloud.

5. Systematizing usage

The policy document intends to build the framework for opening up data that’s publicly held and let its reuse in a more meaningful, structured manner. It aims to foster development in governance, business and entrepreneurship, and counter environmental and societal challenges. The underlying idea is that data should be freely available to everyone, irrespective of whether it is a multinational or one-person startup.

6. Targeting sectors

This objective looks at creating data spaces that are common to all member nations of the EU. The idea is to serve critical sectors like healthcare and manufacturing.

Why the data strategy matters to Europe so much

The answer is simple, but the solution to the problem is not easy.

Europe is struggling to fight the dominance of the USA and China in AI. Without a strong strategy, a robust infrastructure and aggressive funding, Europe might lose the war forever, probably irrecoverably.

As mentioned above, one of the six objectives behind this document is to promote cloud. Look at the chart below, prepared from the numbers reported by Statista:

You noticed that Europe is nowhere in the top, right?

That is why Europe is in a sort of now-or-never situation when it comes to cloud. If the EU doesn’t really have plan and execute it well, it will sink into technological oblivion, probably never to regain the loss.

Feature image source: Photo by Markus Spiske on Unsplash

The post Europe’s data strategy and ambitions appeared first on Technology services news.

How Artificial Intelligence is helping in drug development

Sumitomo Dainippon Pharma Co., Ltd, Japan and Exscientia Ltd., UK have announced the Phase I clinical trial of DSP-1181, a drug created using Artificial Intelligence (AI). The drug aims to treat obsessive-compulsive disorder (OCD). The trial begins in Japan from March 2020. The company claims it is the first instance of using AI to develop […] The post How Artificial Intelligence is helping in drug development appeared first on Technology services...

Sumitomo Dainippon Pharma Co., Ltd, Japan and Exscientia Ltd., UK have announced the Phase I clinical trial of DSP-1181, a drug created using Artificial Intelligence (AI). The drug aims to treat obsessive-compulsive disorder (OCD). The trial begins in Japan from March 2020.

The company claims it is the first instance of using AI to develop a drug that’s being tested on humans.

Pharmaceutical expertise was contributed by Sumitomo Dainippon Pharma. Exscientia, on the other hand, provided the technology by using its Centaur Chemist AI platform.

In another instance, Deep Genomics, a Canadian company, had announced that it had used AI to fully understand Wilson’s disease. It has also used AI to detect a potential treatment for the same. Wilson’s disease is a rare genetic disorder where excess copper builds up in the patient’s body, often reaching life-threatening levels.

Is computing technology beginning to replace human researchers?

Will these developments set the standards for research in other fields?

Are we on the cusp of a new scientific revolution?

The contribution of modern technology in pharmaceutical industry is all too well known.

In this post, we begin by looking at how Exscientia used AI for drug discovery. We discuss whether AI is indispensable in the pharmaceutical industry.

Next, we look at a few pharma companies using AI. We also ask if AI in drug discovery is over-hyped. Finally, we look at the key challenges for wide-scale AI adoption in pharma companies.

How AI platform is used for drug development

To understand how AI develops drugs, let us understand the standard cycle of development of drugs.

Researchers identify a target protein that’s causing the disease. They study such proteins carefully and for a long time. Otherwise, there’s a big risk of losing huge amount of money on the wrong protein. Also, there’s an added risk that the protein would be related to the disease, but isn’t the one that’s causing the disease.

Next, the research process tries to find a compound or a molecule that would influence the protein. In order to influence the disease-causing protein correctly, the compound should be able to alter the protein. Due to this alteration, the protein will no longer be able to continue contributing to the disease.

During this process, inefficient compounds are tossed aside and only safe, efficient compounds are taken further.

So what is the role of AI in drug discovery and development?

Because there are hundreds and thousands of molecules out there, human researchers cannot manually test each of these molecules.

Yet, without testing each of them, there’d be no way of knowing which molecule would be the most appropriate to fight a certain disease.

So this is what AI platforms do. First, experts will feed in them parameters. They rummage through all the molecules. Each of these molecules is compared against the parameters.

Because it’s an intelligent system, the AI platform will keep learning and thereby identify one or more compounds that it finds most equipped to fight the disease.

How data is fed into AI for drug development

Today, research, feedback, reports, patient records and a whole lot of other things add massive amount of data on each disease. It is becoming close to impossible for humans to process or utilize all that data. Artificial Intelligence systems, on the other hand, are perfectly equipped to sift through all the data and make meaningful interpretations out of that.

There many, many channels of feeding data to the AI system for drug discovery and development.

One source of the data is, obviously, patients suffering from that data. This data is collected from patients at different stages of the disease.

But there’s more.

Data is also collected from people without the disease. Deep-learning programs run both the types of data and learn more about proteins whose presence makes a difference between a healthy patient and an ill one.

The machine learning abilities of the system strives to find and establish connections between proteins and diseases.

The importance of AI in drug development

As mentioned earlier, the huge amount of data that we produce isn’t easy to handle for humans. Here are some reasons why AI is becoming more important to the pharmaceutical industry:

  • Costs: The cost of bringing drugs to market has roughly doubled in the decade 2003 to 2013. Also, the returns on research have dropped from 10% to less than 2%. AI, with its accuracy, has the promise of improving this.
  • Speed: The lab-to-market time has increased to 12 years. If AI can really deliver as some people hope today, regulatory agencies could be more trusting. That means AI-developed drugs could be given a pass over animal testing models and move straight to patients.
  • Innovation: It might sound like a bit of exaggeration, but drugs for simple diseases have all been discovered. The ones that haven’t found any cure are the ones that are complex. Drugs for such diseases are difficult too, and AI with its deep-learning mode might turn out to be the right solution.
  • Bias: Human researchers, no matter how hard they try, might often be limited by their personal preferences and biases. As a result, they may chase compounds and proteins based on their bias and hunches. Such an approach costs huge amount of money. AI can be free from such prejudice, making the process more cost-effective and less error-prone.

Which pharma companies are using AI to develop drugs

Here are the major companies that are using AI to develop drugs:

  • Genetech is looking for cancer treatment with the help of the AI system of GNS Healthcare.
  • Sanofi is working with Exscientia on metabolic-disease therapies.
  • Atomwise is trying to find new treatment routes for drugs that are already found and in use. It is interesting to note that the technology used here is the one that’s used in facial recognition.
  • Deep Genomics has already announced that it has understood Wilson’s disease, with the aid of Artificial Intelligence.
  • Lantern Pharma is using AI to sift through the records of failed drug trials in order to apply corrections.
  • Pfizer is using IBM Watson. The objective is find cancer drugs, or more specifically immuno-oncology drugs.

What can AI do in future for the pharmaceutical industry

Despite all the claims by various experts (both in pharma and AI sector), there are many who think much of this is over-hyped.

Nevertheless, AI holds a lot of promise. Here are some of the expectations on what AI can do for diagnosis, drug development and treatment in future:

  • Deep learning could create and make meaning out of a large pool of annonymized data collected from all over the world.
  • Artificial Intelligence may aid in early detection of dangerous diseases like cancer.
  • AI would be able to find individual compounds that can act on just the right proteins, without impacting or disturbing the rest.
  • AI would ultimately be competent enough to knock off a few years from the drug-development cycle. In other words, drugs would hit markets and benefit patients earlier.
  • Sophisticated and specially trained AI systems may be able to provide patient prognosis.
  • Once regulators and researchers can trust AI enough, we may see many drugs permitted to pass over animal testing and directly moving to human trials.

Challenges for AI in drug development

While the future expectations mentioned above sound exciting, there are a sizeable number of challenges that AI will have to battle before machine learning, deep learning and artificial intelligence can significantly contribute to drug development.

Here are the top 5 challenges AI faces in the pharma sector:

Challenge 1: Absence of clear regulations. To be fair, this isn’t a case of regulators going slow. The fact is there aren’t enough precedents – at least, not yet – for regulators to form appropriate and encouraging laws. And let’s not forget the goals of regulations and innovations are often contradictory. The first is always slow in accepting in change while the second is in a hurry to usher change.

Challenge 2: Poor quality of data. In spite of tall claims, the fact remains that bad data will only produce bad results. While we do have a lot of data today, we do know a lot of is bad. By bad data, we mean unreliable, inconsistent or simply poorly structured data. In all these cases, AI will likely come up with poor solutions.

Challenge 3: Quantity of data. Do we really have a lot of data? Not always. As a matter of fact, there are a huge number of diseases where data is conspicuous by its near-absence. Because only rich data can produce results – at least as of now – we need AI systems that can make sense out of small data. In contrast, social credit systems in China had humungous data to learn from.

Challenge 4: Lack of trust. How many patients might be willing to trust drugs developed by AI? Because the mechanism is far from being in place, the acceptance of such drugs will take time.

Challenge 5: Misdirected research. There is always a good chance that AI will expend all its energy only to come up with drugs that are already discovered. While this challenge can be handled with relative ease as compared to the other challenges, a company always runs the risk of losing a lot of money this way.

Concluding remarks

Just like any other nascent technology, a lot of mystery, fascination and distrust surround the use of artificial intelligence in development of medicines.

Yes, we are beginning to see the first signs of momentous tremendous changes AI might bring in. However, the AI technology itself isn’t advanced enough to fully understand and independently design less complicated machines. In that situation, the skepticism of critics is understandable.

The fact remains that we will need more information, more studies, more endorsements before we can fully accept AI as a reliable drug development tool. Till then, we will have to continue double-checking everything.


1. Mayo Clinic

2. Scientific American

3. Nature

4. Wired

The post How Artificial Intelligence is helping in drug development appeared first on Technology services news.

How modern technology in pharmaceutical industry will make an impact

When you hear technology in pharmaceutical industry, what’s your first thoughts? Probably chemicals, labs and large machines. That’s no longer the case. Some time back, Amazon and two other companies started off a healthcare startup Haven. Technology is strongly helping the pharma industry. Read more about the 5 ways pharmaceutical industry is using technology: 1. […] The post How modern technology in pharmaceutical industry will make an impact appeared first on Technology...

When you hear technology in pharmaceutical industry, what’s your first thoughts? Probably chemicals, labs and large machines.

That’s no longer the case. Some time back, Amazon and two other companies started off a healthcare startup Haven. Technology is strongly helping the pharma industry. Read more about the 5 ways pharmaceutical industry is using technology:

1. Helping patients build therapy adherence

Have you ever had to undergo physiotherapy? Till the time you go to the physiotherapy center, you continue doing the prescribed exercises. The moment the schedule at the physiotherapy center ends, you probably stop exercising by yourself.

That’s because a number of reasons, but here are the two principal ones. One, there’s no third-person watching you anymore. And two, there’s no longer any accountability – if you don’t continue the exercise or therapy yourself, no one’s going to ask you.

Pharmaceutical industry can design gamified platforms that will ensure adherence to therapy, as Samina Vaziralli recently mentioned in an interview with Mint.

This is how the platform can likely work. First, it has the therapy details mapped out for you. Next, it records how faithfully you’ve carried out the therapy on each day. Based on that, it predicts your fitness level in future.

Next, the platform can show you what you’re missing out by skipping therapy. Further, it can create a leaderboard of everyone connected (anonymously) to the platform and show you where you stand. Being ranked publicly can be a great motivator.

2. Providing guidance in emergencies and odd hours

It’s 2:30 am in the morning and suddenly your four-month old begins crying uncontrollably. It has not soiled the diaper, it was fed at the right time, it’s not running a fever… what could possibly be wrong?

As a young mother – especially if you’re a first-time mother – your lack of experience in handling such emergencies could raise your anxiety levels.

Someone with a great of deal of experience can be very helpful at such times, right?

Think of a platform, a digital solution that provides you with some immediate counseling and guidance. Remember, the digital solution isn’t a substitute for a doctor – at least, not yet – but think of it as a stand-in. The platform could quickly walk you through the symptoms and do one of the two things:

a. It shows you some of the likely causes of your baby’s discomfort. Maybe it’s a temporary colic, maybe she has a rash, maybe…  The next stage could be how to tackle that, assuming that it’s a small matter that doesn’t need any medical attention

b. It understands your baby needs medical attention. In that situation, it will show you the available doctors and hospitals – probably it’s integrated with a cab service too, if you need one. The platform can also help you fix up an emergency appoint and alert the hospital or the doctor in charge, so that by the time you reach there, some primary arrangements have already been made.

3. Building communities and engaging better

Sometimes pharma companies don’t need to build products, design tools or invent new drugs to help patients. They can provide support and assistance by building online communities.

Sanofi, the US pharma company, has built a Facebook page that’s entirely dedicated to people suffering from diabetes.

So what does the page do?

A lot actually. To begin with, the page promotes awareness of diabetes (often called the silent killer). It shares tips and advices on preventing, managing, and fighting the disease. It shares diet plans that are low on sugar and high on protein and nutrients.

Because people also share their experiences on keep major risks at bay using a wide variety of combinations that may include exercise, proper diet, medications, and other stuff, the online community remains thriving.

4. Involving patients in product design and research

Medical science is not only about drugs and injections. It is about restoring the health and well-being of people. And that includes helping people back to their normal lives as soon as possible after they’ve met an accident.

Consider a special prosthetic limb being designed by a pharma company for an amputee. How would the company know the beneficiary finds the solution perfect?

And the best way would be to collect feedback and that’s what Artificial Limb Centre, Pune (India) did. They conducted a patient satisfaction survey for all the 200 patients who were provided prosthetics in the hospital.

The results were surprising. One would imagine the maximum dissatisfaction would be expressed over factors like comfort in wearing or problem in putting steps.

It turned out that a little over half (52%) expressed dissatisfaction over “Perspiration while wearing” as the single biggest factor in wearing prosthetics!

Such feedback and understanding goes a long way in product design because they democratize healthcare and caregiving.

5. Putting data analytics to use

Of the 7 billion plus humans on the planet right now, almost all of them have fallen ill sometime or the other. They have battled the entire range of diseases ever known to humankind – from common cold and headache to cancer and AIDS. China is showing the way by using a lot of data to build its massive social credit system powered by AI.

In the process of their falling ill and treatment, countless pieces of information (read data) were collected. However, only a small percentage of that may have been diligently recorded and an even small proportion is put to use for research.

Wouldn’t it be wonderful if our highly advanced computers, capable of number crunching like no human can dream of, could make sense out of it all? Wouldn’t it be great if AI, machine learning, and big data in the pharmaceutical industry could become more widespread?

Zeynep Erden, a Professor of Strategy and Innovation Management at a B-school in Zurich, Switzerland, says, “Thanks to sophisticated analysis techniques firms will be able to improve their understanding of disease pathways, to plan clinical trials more efficiently and to take timely decisions to stop projects with little chance of success.”

Pharma companies are beginning to turn digital in the sense of engaging better with everyone involved in the healthcare sector – from suppliers and patients to doctors and caregivers. Feeding that learning back into their system gives them an opportunity to not only serve the healthcare sector better but also to stay ahead of competition.


The pharmaceutical industry hasn’t been late in adopting technology, but the pace isn’t what it is in other industries. The use of technology in the pharmaceutical industry throws up as many challenges as opportunities.

On the one hand, the pharma industry is building systems to help patients adhere to therapy schedules, provide emergency help, build communities, involve patients in product design and put machine learning to use.

On the other, the industry is under pressure for better standards, better quality checks and better accountability from industry watchdogs and alert patients alike.

In the middle of all this, optimal use of advanced technology in the pharma sector appears to be their best bet.

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9 ways AI is changing the world of sports

The use of artificial intelligence in sports isn’t a major surprise, given the advancement in technology. Rise in computing power, availability of massive amount of data and an increased willingness of stakeholders to leverage such tools are the three principal reasons why the role of artificial intelligence in sports has gained a lot more importance […] The post 9 ways AI is changing the world of sports appeared first on Technology services...

The use of artificial intelligence in sports isn’t a major surprise, given the advancement in technology. Rise in computing power, availability of massive amount of data and an increased willingness of stakeholders to leverage such tools are the three principal reasons why the role of artificial intelligence in sports has gained a lot more importance recently.

Someone who thinks the application of artificial intelligence in sports is limited to improving the performance of players is in for a pleasant shock. That’s because there are many more avenues where AI is used.

Here we list 9 applications that AI has found in the world of sports.

1. Scouting for Talent and Player Recruitment


A lot data were earlier were either not noticeable, recordable, or not easy to make sense of. Using data science in sports allows teams, coaches and selectors carry out a deeper analysis of complex metrics of potential teammates or players.

Besides, there were areas within players’ performance that were not possible to study, owing to the natural limitations of humans perceptual abilities. Wearables are making such data and recording possible.

With machine learning platform for sports analytics, it’s easier to identify how each player is unique and what unique set of skills they bring. Further, with the help of big data, we are coming closer to building reliable, sophisticated models that intelligently calculate the probability of talent a player can bring.

2. Analyzing Workout and Training Effectiveness

Artificial intelligence is fast building up expertise in building a correlation between quantitative, measurable variables (e.g. runs, goals, timing) and qualitative factors (e.g. concentration, ability to strategize, teamwork).

AI can help put together teams where players best complement each other. To do that, it must first see how and whether the workouts and training programs have been effective in preparing the players.

For deeper analysis, PIQ and Everlast developed what is perhaps the first AI-powered wearable for combat sports like boxing or martial arts. Using machine learning platform, the device captures and analyses small variations in the actions of the boxer. This helps the system understand how successful the training and workouts have been in bringing about a positive change in the boxer.

This information is channeled through a mobile phone app into a leaderboard. This leaderboard displays the performances of all other boxers and points how where individual boxers stand vis-à-vis others.

Such apps can also offer tips on nutrition, fitness guides and personalized training programs that help sportspersons better achieve their goals.

3. Designing Coaching and Performance Improvement Programs

Artificial intelligence is already being deployed in education in China in a big way. So it shouldn’t be surprising to see AI helping sports coaches.

One good way to understand how AI is transforming sports is by studying the role of AI in helping coaching, especially in competitive sports.

Artificial intelligence removes or drastically reduces some of the weak spots in traditional coaching. For instance, professional coaches need to spend years sharpening their skills, and yet there is always some probability of committing an error or overlooking something.

Computing technology can resolve this problem by providing accurate analysis and speed up the process of consistently providing correct analysis. For instance, artificial intelligence could carefully study and analyze the bowling action of a bowler and suggest a training plan that could help the bowler achieve better results.

Constant monitoring of health and fitness parameters, using devices, could prevent injuries. It could spot things like repetitive stress injury before it temporarily or permanently stops a player from playing. Thus, it can end up being an invaluable assistant or perhaps a substitute to the team’s medical professional.

4. Creating Better Fan Experience

If there’s one thing organizers and broadcasters are keen to do in sports today, it’s pleasing fans and creating a superior experience for fans. It appears that artificial intelligence in sports management can do this pretty well.

It can begin with the way fans buy tickets, or rather ‘smart ticketing’. That means fans get a variety of options of buying tickets with variable seating options across the different games they attend.

Fans could choose to sit with different people during different times – for instance they could sit with their family during some games and with friends or business associates during other games.

The San Francisco Deltas, a new soccer team, is believed to be trying to leverage AI to improve fan engagement.

Next, artificial intelligence could place more choices in the hands of viewers. It could build algorithms that allow build match highlights in forms and lengths that viewers want.

Besides that, the logistics at the game venue could significantly change fans’ experience. Better parking and availability of quality food and merchandise could help fans get a thrilling experience at the game venue. At events like a marathon where there’s a large number of participants, logistics powered by AI can impress both participants and the fans.

AI is creating sports merchandize that’s not just dependent upon the trend but also based on the fans’ preferences emotions and reactions.

Finally, chatbots that could respond to fan queries with basic statistics could also make a sizeable difference.

5. Optimizing Advertising Opportunities

Brands could soon be getting better advertising opportunities, based on the top moments of the game as identified by artificial intelligence.

The automated learning algorithm monitors players’ actions, spectators; emotions and expressions, and commentators’ language to understand which moments of the game are the most exciting or thrilling. IBM has tested Watson to do this in Wimbledon 2017.

Based on this, advertisers would be recommended time slots where their ads, if displayed, could earn optimum engagement.

Apart from offering better value to advertisers, machine learning will help sales people point out parts of the game they can sell better to prospective advertisers.

6. Maximizing Broadcasting and Streaming

One of the key ways sports broadcasting companies try to remain favorites of viewers is by providing quality coverage. That includes great photography, high-quality relay, excellent commentary, interesting graphics based on statistics and language that viewers prefer most.

From the looks of it, artificial intelligence is about to make a huge impact on all of this. To begin with, AI can help choose the most appropriate camera angles both during the match and also while choose replays or re-runs. Any sports fan will tell you how important angle is in photography of almost any sport.

Next, AI can provide accurate and timely statistics to commentators so that they may provide a better real-time commentary. Also, the system can be tuned to allow subtitles in different languages in case of live events, based on the viewers’ choice and location. Beyond this, broadcasters can use to identify correct opportunities to display ads. A combination of all this will provide a better viewing experience.

7. Leveraging Automated Journalism

Automated journalism powered by AI is likely the next big thing in sports broadcasting. The core idea is simple but the applications are powerful: let machine learning evolve into a technology that can prepare readable reports on sports events.

AI is being used to build videos that better capture the highlights of the day’s match. It curates the most exciting moments of the event and compiles it into a video. When done manually, this task took considerable number of man hour, but with the use of AI the time required for the same task can be reduced considerably. This will help media houses cut their time to market for every video.

Wordsmith, a solution built by Automated Insights (Ai), is capable of processing data of the sporting events to quickly produce summaries and stories of the major event of the day. It is capable of understanding style, language norms and grammar rules while crafting the story.

What’s more, there has been research into getting AI deliver cricket commentaries that are 90% accurate.

8.  Managing Safety in Games

One answer to how AI is improving sports is the way it helps better manage parking, reduces congestion and improves the overall experience safer by cutting the chances of mishaps due to poor or bungled parking arrangements.

There’s more too: in sporting events like car racing, safety is a much bigger issue than in sports like, say, swimming or volleyball. Here, AI can use deep learning to develop and improve self-driving cars. These cars will be tested for safety before human drivers use them.

9. Making Refereeing More Accurate

One of the earliest uses of technology in sports is in aiding referee decisions.

For example, in lawn tennis, high-speed cameras have been used to make “in or out” decisions. In cricket, the Hawk-Eye technology has been used in assisting umpires whether the batter is lbw.

Using technology makes the sports event fair and more rule-abiding. It brings in more objectivity in decisions of the referees or umpires. In games like cricket where the batter often gets the benefit of doubt in cases where it’s not possible to judge accurately, the use of technology and AI will make it a more level-playing field.

The system will be able to learn quickly over time, using data to classify position, shots and player stance or positions. As machine learning gets better, rule-infringement will become a great deal accurate and consistent.


It is not easy to trace the introduction of artificial intelligence in sports, but what is sure it is here to stay. It might have started as something of novelty value but quickly became something that gave a competitive advantage to players and teams.

Going forward, it will become a standard fixture, sooner than we think.

Additional reference: CIO

The post 9 ways AI is changing the world of sports appeared first on Technology services news.

Artificial intelligence in Indian agriculture

To some, innovation in farming in India has already arrived in form of Artificial Intelligence (AI). To others, AI applications in the agricultural industry in India appear superfluous and primitive, and likely carry limited potential at best. In India, modern technology in agriculture, often written as Agtech or Agritech, represents hope. That’s because traditional farming […] The post Artificial intelligence in Indian agriculture appeared first on Technology services...

To some, innovation in farming in India has already arrived in form of Artificial Intelligence (AI). To others, AI applications in the agricultural industry in India appear superfluous and primitive, and likely carry limited potential at best.

In India, modern technology in agriculture, often written as Agtech or Agritech, represents hope. That’s because traditional farming practices often fall sadly short when facing challenges like changing climate conditions and global warming.

Technology giants as well as startups are trying to combat several issues by building farming, irrigation and weather technology solutions. For example, Microsoft precision agriculture attempts to “democratizing AI for farmers around the world”. Startups are finding ways so that farmers can receive various inputs and suggestions over feature phones – even a smartphone isn’t required.

China, a country that faces nearly the same challenges as India does, is growing at a swift pace when it comes to AI. It is deploying AI to build the now well-known social credit system of China. Some China’s top AI companies are world leaders. And to coordinate everything, China has built a long-term action plan for AI.

All this makes it important to understand where India stands in AI. This article covers details of applications of artificial intelligence in agriculture and the future of AI in agriculture in India.

Context for agriculture automation in India

Consider the following:

  • The agriculture and the allied sector contributes less than 16% to its US$ 3 trillion economy today. (Source).
  • Agriculture directly employs 41.1% of India’s working age population (Source).
  • Nearly 50% of India’s land is tilled (Source: Agriculture Census, 2015-16)

So here’s the paradox: To create one-sixth of the total Indian economy, it takes nearly half of India’s land, and that too while employing 2 of every 5 Indians.

This underlines the fact that Indian agriculture is nowhere close to being productive. While the government’s initiatives like the Green Revolution have certainly made the nation self-sufficient in food grains, there is a long, long way to go for agriculture to catch up with other industries.

No prizes for guessing that the answer lies in automation in agriculture, deploying advanced agriculture technology like robotics, AI and Machine Learning (ML).

How AI could benefit agriculture

Agritech has found a worthy partner in AI. In India, the role of artificial intelligence in agriculture can be much bigger than in any other field. That’s because it can reduce costs, improve quality, increase productivity and optimally use resources.

Here are a few use cases that showcase the use of AI in agriculture in India and how self-evolving systems can take agriculture to the next level in India:

1. Soil Analysis and Monitoring

Objective: Monitor soil health and identify specific needs of the soil in general and also in particular reference to the crop targeted

Who is doing it: CropIn, Bengaluru.

Details: The company reports the experiments it conducted in farms that had a collective size of 5,200 acres. The company helped in remote sensing and weather advisory. Based on data collected and analyzed, it offered tips on scheduling and monitoring at various stage of farming. It also offered farmers training on how to maintain and monitor crop health and offered alerts on impending pests or diseases.

2. Image Analysis

Objective: Recognize faces, flora and fauna and other objects and tag them in images

Who is doing it: Intello Labs, Bengaluru

Details: Intello claims it leverages deep learning algorithms for a variety of activities. A click of a photograph can identify the crop’s health. That will tell the farmer what, if anything, needs to be done. The technology can also identify the quality of the harvested product using the photograph. As one of the leading agricultural robotics companies in India, Intello says it can also offer alerts on pest infestation.

3. Predictive analysis

Objective: Suggest the right times to sow the seeds without expensive investment on the part of farmers

Who is doing it: Microsoft India

Details: The AI-based app for sowing that Microsoft tested relied on the climate data collected over 30 years (1986-2015). An important metric MAI (Moisture Adequacy Index) that estimates the amount of rainfall and moisture required for a good crop was calculated. Then it estimated the best times to sow crops. At the right time, farmers were sent automated voice messages and text messages. Farmers just needed to have a basic mobile phone (a feature phone). Farmers did not require have any scanners or any other expensive instruments – that’s the power of new-age agtech.

4. Supply chain management

Objective: Deliver a data-driven online marketplace for agriculture that offers better prices to farmers as well as buyers

Who is doing it: Gobasco, Lucknow

Details: The advantage that Gobasco brings, according to the company, is the AI-optimized automated pipelines for the agriculture produce supply chain. The company further claims they have designed the supply chain keeping mind the India. Apart from the supply chain, the technology tool is supposed to help farmers in automated grading and sorting, leading to smoother transactions for domestically and internationally.

5. Crop cycle expertise

Objective: Help farmers build better crop cycles

Who is doing it: Gramophone, Indore

Details:  A problem that AI-powered chatbots in agriculture need to solve is figuring out the correct crop cycle. Gramophone claims to have invested two years in ground work. Their technology platform leverages ML as well as AI for predictions that range from pest infestation to pricing. Based on the data of temperature, humidity and entomology, the company seeks to provide “personalized farm management solutions” that would guide farmers for the optimal cropping cycle.

6. Farm produce aggregation

Objective: Bridge supply demand gap of agricultural produce

Who is doing it: Jivabhumi, Bengaluru

Details: The scope of AI in agriculture in India can be understood from the way the technology can provide an efficient platform for buyers and sellers of agricultural produce. According to Jivabhumi, their tool will bridge the gap between farmers looking to find markets and consumers looking for affordable agricultural produce. The agtech solution, using blockchain, is capable of tracing the food back to the farm, something that can ensure buyers of the safety of the food they buy.

7. Farmer Advisory

Objective: Provide comprehensive advice and tips along with required service

Who is doing it: Agrostar, Pune

Details: Agrostar claims to be using a combination of knowledge of agronomy, advanced technology and lots of data to offer advisory services to Indian farmer. Currently operating in the three states of Gujarat, Maharashtra and Rajasthan, Agrostar offers service that begins by helping the farmer identify what their crop needs at the moment and ends at doorstep delivery of whatever is needed. This is perhaps how digital farming in India can lead to a more efficient agriculture industry.

Video on how agriculture in India can benefit from AI

Here’s a short video showing how artificial intelligence can help agriculture in India

How AI can help Indian agriculture



1. Financial Express

2. Emerj

3. Bhajan Foundation

4. Respective company websites

The post Artificial intelligence in Indian agriculture appeared first on Technology services news.

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