As nanotechnology hits its stride in agriculture, advances in machine learning are being harnessed to make field operations more effective while improving food safety.

Nanoparticles are extremely small, measured in billionths of a metre or nanometres. This is the realm of molecules — it takes about three water molecules to make one nanometre, and DNA is about two nanometres wide. A human hair is more than 80,000 nanometres thick.

Now, agriculture researchers are developing applications for nanoparticle use involving antimicrobials that can protect plants, as well as slow-release insecticides and fertilizers.

Many of these nanoparticles are designed be absorbed into food plants to do their work. This has caused both interest in seeing how they function and concern as to whether they might have effects on other plants or accumulate at harmful levels in food.

Nano-scale materials occur naturally and are part of regular diets. For example, casein micelles, which contain various proteins, fats and minerals, are used to make cheese. Synthetic nano-scale materials are called engineered nanoparticles, or ENPs.

According to Xingmao “Samuel” Ma at Texas A&M University the problem of tracking ENPs is complex.

In an article for the university, Ma explained that silver nanoparticles, widely used for their antimicrobial properties, can have hundreds of different shapes, sizes and coatings. Testing for every kind, in every type of plant, quickly becomes impossible for humans.

But perhaps not for machines.

Ma and his colleagues began with an existing database created from past research on metallic nanoparticles and where they accumulated in plants. They applied two algorithms to analyze data.

• Valued for their antimicrobial properties against a wide range of plant pathogens. They can also enhance plant growth.

Nano alumino-silicates

• Used as efficient pesticides.

Titanium dioxide

• Used as a disinfecting agent for water. It has also been found to enhance plant performance by stimulating certain enzymes and promoting nutrient uptake.

Carbon

• Graphene, graphene oxide, carbon dots, and fullerenes are used to improve seed germination.

Metals and magnets

• Zinc oxide, copper oxide nanoparticles, and various magnetic nanoparticles are used for various ag applications.

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Imagine if funding and donating to an NGO or a cause anywhere in the world was as easy as ordering on Amazon. With today’s technology and integration of information, and a little bit of work filling in the gaps, this is doable.

Philanthropy has seen many efforts through initiatives such as, for example, the Giving Pledge, a commitment by ultra-high-net-worth individuals to donate the majority of their wealth, to reach donors and expand giving. While these efforts are essential, more is needed to reach smaller classes of donor and to help them connect with credible causes that match their interests easily.

With the Covid-19 crisis revealing deep structural and social inequalities across the globe, philanthropy and philanthropic dollars need to be optimised to help mitigate the urgent health, economic, social and fiscal impact of the pandemic. A collective humanitarian response that can make targeted giving easier for large donors while harnessing the power of smaller, informed donors is now more critical than ever.

There is a way to achieve this, through the development of a cloud-based philanthropic platform that utilises artificial intelligence to amalgamate credible data on global needs and global giving into easily accessible and comprehensible information that donors can act upon. Such a platform could allow donors to filter their searches on location, themes, organisation size and charity ratings, as well as funding gaps.

Where policy responses fall short and gaps in the allocation of urgent funding exist, an AI-centred philanthropy data platform could allow donors to respond to economic and social challenges based on reliable, real-time global data and analysis.

AI technology is already used as a tool to advance efficiency and growth in businesses. Companies like Netflix and Amazon use AI recommendations to match offers to individual preferences and purchases and to inform content development, optimising their profits.

Similar technology, albeit for the purpose of global common good could be used to ensure that supply of philanthropic donations meets the demand for support while improving transparency and accountability. AI in the form of a recommendation engine could, for example, match donor criteria from themes to locations to ratings and NGO partners with causes via the philanthropic platform.

A 2018 report on private philanthropy by the Organisation for Economic Co-operation and Development (OECD) found that only 28 per cent of funding benefitted the least developed countries. The data shows that private development financing is largely bypassing the most vulnerable. Moreover, financing often misses the actual needs of local communities, as funding is likely to be allocated without the weigh-in and participation of local charity or NGO staff. By logging onto an online platform, philanthropists and individual donors could see where giving is capped or in excess, enabling them to invest where they care while matching needs on the ground and maximising the impact of their investments.

Data and analysis made available through AI recommendations could also fill gaps in available information needed to inspire potential investors. It could also provide a feedback loop for NGOs on what they can do to improve their standing. In addition, philanthropists and individual donors could use AI recommendations to give directly to causes that they care about, reducing reliance on intermediary organisations to distribute funds. That would allow for more opportunities to directly engage with beneficiaries.

Implementing AI to optimise giving within the philanthropic sector is not a new idea. NGOs and campaigns around the world are already using it to enhance the impact of their activities. Examples include platforms like Philanthropy.ai and organisations like WaterAid, which use AI recommendations to connect donors with beneficiaries and causes around their respective work.

While these efforts demonstrate the benefits of AI for specific philanthropic activities, a cross-sector, collaborative approach is needed to fully harness the benefits of AI beyond individual causes and for the sector as a whole. An AI-centred, cloud-based platform has the potential to address this need by engaging stakeholders invested in philanthropy across sectors in an unprecedented, real-time mapping of the philanthropic landscape.

Such a platform could also expand the pool of available data, and help to ensure that new philanthropic investments build on existing successful interventions by securing the buy-in and input of stakeholders across sectors. Qualitative data made available on the platform could also serve as a valuable resource for campaigns like the Giving Pledge and inter-governmental partners like the OECD, providing them with lessons to direct their interventions and greater visibility to promote their activities.

Finally, the platform itself could allow a virtual space for philanthropists, individual donors, NGOs, intergovernmental organisations and other partners to connect, exchange lessons and explore collaboration and co-funding opportunities to strategically drive investments and direct support where needed.

A joint effort and long-term commitment from a broad range of members of the philanthropic community is needed to develop a platform like this, and to provide the inputs that AI could use. It will require the leadership of a credible international organisation, the collaboration of tech companies and input from local and international NGOs and inter-governmental organisations.

Following the development phase, vetted local chambers of commerce, foundations, local NGOs and bilateral organisations would need to collect data on philanthropic support and feed the data to the AI in an organised way, such as through the use of tags.

Of course, there are drawbacks to using AI technology and challenges that should be mitigated during the development and implementation phases. Nonetheless, with the scale and complexity of humanitarian crises growing and the primary and secondary impacts of the Covid-19 pandemic looming, AI has the potential to empower philanthropists and individual donors while strengthening the world of giving to meet the challenges ahead.

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competitive online gaming market, where most of the casinos have increased their gaming range to include golf, retail, and theatre, players are searching for more than just video slot machines and table games. With such a good number of available entertainment options in casinos, the most valuable players are not only those that spend lots of money on the casino floor. Also valuable are those who spread their money across all the property offerings.

To have a more holistic view of players’ behaviour requires a full picture of the spending behaviours of all players and a careful analysis of player preferences. Therefore, it is no longer enough for casinos to know the high rollers that plagiarize them at the current time. 

They need to go deeper into the player base and identify players whose activities go far beyond the casino floor, and the best way to achieve this successfully is through analytics.

Some of the profits that data science could bring to casinos include helping you identify the following:

How Much is Each Player Worth?

In the casino industry, this is a very important issue. The prediction of a client’s future conduct is not easy. It requires several variables, many of which are not easily accessible to casinos. Income, ethnicity, and reasons for a trip are included in those variables (Convention vs. Vacation). 

Once the player’s value is ascertained, they can then be shared into groups based on other behaviours, which can lead to effective marketing campaigns.

How Much Can You Expect a Player to Lose in the Future?

In order to use data mining for estimating predicted value in the future, companies must know simple measurements, like average daily theoretical loss or average trip theoretical loss, based on historical behaviour, that will produce fairly accurate predictions of future value. 

A model built to anticipate potential gaming trip value could, for instance, be generated on the basis of historical data on theoretical win, credit line, actual win, and time, nights spent, and average bets. This way players can spin slots for money that have the best winning rates. Models can also be built by using categorical variables like predictors, such as sex, ethnicity, age, etc.

Who are The Most Valuable Players?

In addition to the potential worth modelling for players, there are other ways of analyzing the importance of a player to the enterprise. One way of identifying the best player is to try to get the talented players out of the unqualified.

 This can be accomplished by evaluating the percentage of gambling trips in which the player actually loses or wins money. For example, on a total of four trips, did a player lose money each time? Although this may only mean that the gambler plays until he’s out of money or time, it’s also a very easy way to classify the players who don’t win much.

What Players Come Together?

Another vital consideration in the topic of player worth is household worth. This means the combined worth of a group of players who are likely to make their trips together. The house’s worth can be more difficult to identify, as these players could decide to stay in one room or go to separate rooms. There is also the chance that one player may only come when followed by another player. Additionally, there is a chance that a player could make trips without another player.

Although it is a very tricky thing to identify household worth, it can be of great value by helping to account for revenue that could look like two separate players but can be combined into a single “household.”  Many player management systems feature the functionality to connect accounts so that players that come in groups (i.e., married couples) can be easily spotted. Unfortunately for all casino analysts, players may not be allowed to run multiple accounts due to system limitations or business policies based on gaming regulations and tax.

Nevertheless, data mining could be used to spot groups of players that come in pairs or groups together without linked accounts.

Conclusion

The above and many more are the advantages and potential gains that data science can bring to casinos. However, casinos must first understand how to use data science in their favour.

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Ken Goldberg thinks artificial intelligence will enable surgical robots to achieve their best function — not replacing surgeons but augmenting their work by reducing the monotony of specific subtasks like suturing.

The William S. Floyd Jr. Distinguished Chair in Engineering at UC Berkeley, Goldberg and his research team have continued to advance the field.

The group — which includes postdoctoral researchers Minho Hwang and Jeffrey Ichnowski and PhD student Brijen Thananjeyan — has demonstrated how a deep neural network plus 3D-printed depth-sensing markers can train a da Vinci surgical robot to automatically perform peg transfer slightly faster and more accurately than an experienced surgical resident.

They also published a paper in Science Robotics last November about new AI software that allows robots to learn how to rapidly plan smooth motions that increase speed and reduce wear in factories, warehouses…and operating rooms.

Their work encompasses so much more, too. (Here’s a list of published research.)

Goldberg recently consulted with his research team to provide some insights to Medical Design & Outsourcing and MassDevice about where the surgical robotics space could advance in coming years:

MDO: You’ve spoken before of an emerging generation of surgical robots. Tell us more.

Goldberg: Many of the early patents on surgical-assist robots are expiring, so there are several new companies entering the market. For example, Johnson and Johnson purchased Auris, and Medtronic purchased Mazor in the past 2 years. Many new companies that are emerging in Asia — including China and Korea — are also developing a new generation of surgical-assist robots at lower cost and working to introduce some supervised autonomy.

Rather than replacing human surgeons, an emerging new generation of robots will assist surgeons by performing tedious subtasks such as suturing and debridement to improve consistency, reduce fatigue and open the door to long-distance tele-surgery.  Advances in AI can be applied to data collected from surgical systems such as Intuitive’s da Vinci to learn underlying control policies for subtasks including cutting, suturing, palpation, dissection, retraction and debridement.

MDO: How can artificial intelligence advance robot-assisted surgery? Give us an example.

Goldberg: We were recently able to automate peg transfer, a common training procedure for minimally invasive surgery, with 99.4% accuracy (357/360 trials) [on a da Vinci Research Kit] — even when the robot tools are switched. This task is challenging because it requires high accuracy, and the cables that drive surgical robots’ joints stretch during motion, which can significantly reduce their accuracy.[We’ve learned] to correct errors at key points in the task visually by using demonstrations from a human teleoperator.

We also present an alternate approach to this task in two papers, one published and one preprint. These approaches use fiducial markers and depth-sensing with deep learning to learn a model of how the robot moves as a function of its past motions, which is a complex function of its cabling properties. Using this learned model for control, we achieve 94-100% accuracy on bilateral and unilateral versions of the task, respectively. Using a trajectory optimization procedure, we are able to roughly match or outperform a human surgeon in terms of speed as well.

MDO:  TransEnterix executives see AI as an edge when it comes to competing better with its Senhance system. What do you think of their Intelligent Surgical Unit?

Goldberg: Learning how to position and move the surgical camera without manual adjustment is a very interesting problem.  It is tedious for the surgeon to do this herself, so it would be helpful if a robot could anticipate, for example, as a suture advances beyond the field of view and track the camera to move accordingly.  This is also subtle because erroneous camera motions could be frustrating for surgeons.

MDO: Overall, what are you most excited about in the robot-assisted surgery space?

Goldberg: I’m hopeful that we can develop systems that can learn to perform specific subtasks such as suturing or debridement faster and more accurately than human surgeons, thus relieving human surgeons of tedium, allowing them to focus on more nuanced aspects of surgery and also to reduce time in the operating room.

This research field is growing rapidly: There are now 30 labs worldwide doing experiments with a research version of the Intuitive surgical-assistant robot, and new hardware and algorithms are being published every month.

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Could giving robots voice help them learn human commands?

Robots have become an integral part of human’s daily lives. They help us in numerous ways, from performing complex tasks to lifting heavy weights and assisting the elderly, playing with kids, and entertaining people at events. They can interact with people in any scenario. However, construing a human language still a challenge for robotic systems. Training them with real-world experiences and knowledge about the world could help robots understand natural language.

People use language to express emotions, direct behavior, ask and answer questions, provide information, and ask for help. Language-based interfaces for robots require minimal user training and expression of a variety of complex tasks.

In a paper, researchers from MIT describes a new way to train machines. They noted that children learn language by observing their environment, listening to the people around them, and understanding what they see and hear. With keeping that in mind, they created a tool called semantic parser that mimics the experience of children learning a language. Parsers are already being used for web searches, natural-language database querying, and voice assistants. The system observes captioned videos and links the words that speakers say with recorded objects and actions.

As parsers are trained in sentences annotated by humans, they could be used to improve natural interaction between humans and robots. According to the paper, a robot equipped with the parser could observe its environment to reinforce its understanding of spoken commands, even when the spoken sentences are not fully grammatical or clear.

Earlier, Analytics Insight reported that how giving voice to robots within healthcare influence human perception. Already, robots are delivering a wide range of healthcare services and opportunities to medical personnel and advancing patient care delivery. In this article, we noted how researchers at the University of Auckland and Singapore University of Technology & Design have been using speech synthesis techniques to create robots that sound more empathetic. As part of their study, researchers tested a hypothesis on how a robot’s voice can impact users’ understanding by conducting a simple experiment using a robot called Healthbot. They used a professional voice artist for the robot’s voice, which was recorded while reading dialogs in two voice variations: a flat monotone and an empathetic voice.

More broadly, teaching a machine to speak and making them able to recognize human voice is a crucial yet effective step as spoken language is the most intuitive form of interaction for humans. In 2018, it was reported that researchers in Japan attempted to bring audition, or power of listening, to robots. Proposed by Tokyo Institute of Technology Professor Kazuhiro Nakadai and Professor Hiroshi G. Okuno of Waseda University in 2000, “Robot Audition” is a research area. For this, they turned their research public and made it open-source software. This essentially helped them generate interest and diversified the research. Their research was officially registered in the IEEE Robotics and Automation Society.

So, when robots and robotics systems are able to learn and recognize the human language, they will have a more emphatic impact on people’s lives.

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Machine-learning techniques, best known for teaching self-driving cars to stop at red lights, may soon help researchers around the world improve their control over the most complicated reaction known to science: nuclear fusion.

Fusion reactions are typically hydrogen atoms heated to form a gaseous cloud called a plasma that releases energy as the particles bang into each other and fuse. Getting these reactions under better control could create huge amounts of environmentally clean energy from nuclear reactors in fusion power plants of the future.

“The connection between machine learning and fusion energy is not obvious,” said Sandia National Laboratories researcher Aidan Thompson, principal investigator for a three-year Department of Energy Office of Science award of $2.2 million annually to make that very connection. “Simply put, we have pioneered machine-learning’s use to improve simulations of the reactor’s wall material as it interacts with plasma. This has been beyond the scope of atomic-scale simulations of the past.”

The expected result should suggest procedural or structural modifications to improve nuclear energy output, he said.

Power of machine learning in modeling nuclear fusion

Machine learning is powerful because it uses mathematical and statistical means to figure out a situation, rather than analyze every piece of data in the desired category. For example, only a small number of dog photos are needed to teach a recognition system the concept of “dogginess”— in other words, “this is a dog”—rather than scanning every dog photo in existence.

Sandia’s machine-learning approach to nuclear fusion is the same, but more complicated.

“It is not a trivial problem to physically observe what is going on within a reactor’s walls as these structures are internally bombarded with hydrogen, helium, deuterium and tritium as parts of a super-heated plasma,” said Thompson.

He described components of the circling plasma striking and altering the composition of the retaining walls and heavy atoms dislodging from the struck walls and altering the plasma. Reactions take place in nanoseconds at temperatures as hot as the sun. Trying to modify components using trial and error to improve outcomes is extraordinarily laborious.

Machine-learning algorithms, on the other hand, use computer-generated data without direct measurements from experiments and can yield information that eventually could be used to make plasma interactions with containment wall material less damaging and thus improve the overall energy output of fusion reactors.

“There is no other way of getting this information,” said Thompson.

Small number of atoms predict the energy of many

Thompson’s team expects that by using large datasets of quantum-mechanics calculations under extreme conditions as training data, they can build a machine-learning model that predicts the energy of any configuration of atoms.

This model, called a machine-learning interatomic potential, or MLIAP, can be inserted into huge classical molecular dynamics codes such as Sandia’s award-winning LAMMPS, or Large-scale Atomic/Molecular Massively Parallel Simulator, software. In this way, by interrogating only a relatively small number of atoms, they can extend the accuracy of quantum mechanics to the scale of millions of atoms needed to simulate the behavior of fusion energy materials.

“So why is what we are doing machine learning and not just bookkeeping lots of data?” asks Thompson rhetorically. “The short answer is, we generate equations from an infinite set of possible variables to build models that are grounded in physics but contain hundreds or thousands of parameters that keep us within range of our target.”

One catch is that the accuracy of the MLIAP model depends on the overlap between the training data and the actual atomic environments encountered by the application, said Thompson.

These environments may be various, requiring new training data and alteration of the machine-learning model. Recognizing and adjusting for overlaps is part of the work of the next few years.

“Our model at first will be used to interpret small experiments,” Thompson said. “Conversely, that experimental data will be used to validate our model, which can then be used to make predictions about what is happening in a full-scale fusion reactor.”

The target for giving fusion researchers access to the Sandia machine-learning models to build better fusion reactors is approximately three years, said Thompson.

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September 11, 2020 – A machine learning model was able to detect different clusters of gut bacteria that could potentially identify individuals with existing cardiovascular disease (CVD), according to a study published in the journal Hypertension.

Recent studies have found a link between gut microbiota, the microorganisms in human digestive tracts, and CVD, the leading cause of mortality worldwide. Gut microbiota is highly variable between individuals, and differences in gut microbial compositions between people with and without CVD have been reported.

“Based on our previous research linking gut microbiota to CVD in animal models, we designed this study to test whether it is possible to screen for CVD in humans using artificial intelligence screening of stool samples,” said Bina Joe, PhD, FAHA, the study director, Distinguished University Professor and Chairwoman of the department of physiology and pharmacology at the University of Toledo in Toledo, Ohio.

“Gut microbiota has a profound effect on cardiovascular function, and this could be a potential new strategy for evaluation of cardiovascular health.”

Researchers used data from the American Gut Project, an open platform for microbiome research based in the US. The team leveraged innovative machine learning methods to analyze microbial composition of nearly 1,000 stool samples. Approximately half of the samples were from people with CVD.

Researchers found that the model was able to identify different clusters of gut bacteria that could potentially help identify individuals with and without CVD. The results show the potential for machine learning and artificial intelligence to improve CVD screening.

“Despite the fact that gut microbiomes are highly variable among individuals, we were surprised by the promising level of accuracy obtained from these preliminary results, which indicate fecal microbiota composition could potentially serve as a convenient diagnostic screening method for CVD,” Joe said.

“It is conceivable that one day, maybe without even assessing detailed cardiovascular function, clinicians could analyze the gut microbiome of patients’ stool samples with an artificial machine learning method to screen patients for heart and vascular diseases.”

Researchers have previously used machine learning to improve cardiovascular disease treatment and detection. A study published in Cardiovascular Research showed that machine learning tools could predict patients’ long-term risk of heart attacks and cardiac deaths better than standard methods used by cardiologists.

In that study, researchers found that subjects’ predicted machine learning scores aligned accurately with the actual distribution of observed events.

“In this prospective trial, machine learning demonstrated high performance in risk assessment for myocardial infarction and cardiac death in asymptomatic subjects,” the researchers stated.  

“By objectively combining clinical data and quantitative CT measures, machine learning provided significantly superior risk prediction compared with the coronary artery calcium score. These promising results suggest that machine learning has a potential for clinical implementation to improve risk assessment.”

In a separate study published in 2018, a team from Stanford University were able to create a personal health management tool that combined EHR data and machine learning algorithms to accurately diagnose the heart condition known as abdominal aortic aneurysm (AAA).

Researchers used the hierarchical estimate from agnostic learning (HEAL) health management tool to analyze patients’ genome sequences and EHRs to identify individuals with the cardiovascular disease.

“For each individual, HEAL accurately predicted his/her AAA risk using personal genome and EHR data. On the other hand, for the same individual with newly adopted lifestyles resulting in physiological changes (e.g., from a high cholesterol to a low cholesterol diet), HEAL can immediately update his/her AAA risk upon corresponding changes conditioned on the person’s genome baseline,” Stanford researchers said.

“This allows us to further investigate the interplay between personal genomes and lifestyles underlying disease predisposition.”

As machine learning and AI become more widely used in healthcare, these tools could make the transition from research to clinical care for improved patient health and outcomes. 

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Ophthalmology could be the next specialty to look into utilizing new deep learning technology to screen and diagnose patients with ocular disorders.

A team, led by Nihaal Mehta, MD, New England Eye Center, Tufts Medical Center, determined whether a model-to-data deep learning approach without needing to transfer any data can be applied in ophthalmology.

In the single-center cross-sectional study, the investigators examined patients with active exudative age-related macular degeneration (AMD) who underwent optical coherence tomography (OCT) at the New England Eye Center between August 2018 and February 2019.

The investigators sought main outcomes of the training of the deep learning model, using a model-to-data approach, and recognizing intraretinal fluid on OCT B-scans.

The model-to-data approach was taken by freezing the model parameters from a prior study where a deep learning model was trained to segment IRF on Heidelberg Spectralis OCT B-scans.

The model parameters, retraining code, data preprocessing, and code for evaluation were packaged from the University of Washington and transferred using GitHub.

The model was training with a learning curve Dice coefficient greater than 80% using 400 OCT B-scans from 128 patients, 69 of which were female. The mean age of the patient population was 77.5 years old.

The scan protocol consisted of 512 A-scans per B-scan and 128 B-scans per volume, while the spectral-domain OCT system has an 840 nm central wavelength, as well as 68 000 A-scans per second, an A-scan depth of 2.0 mm, an axial resolution of 5 μm, and a transverse resolution of 15 μm.

The investigators compared the model with manual human grading of IRF pockets and found no statistically significant difference in Dice coefficients or intersection over union scores (P > 0.05).

“A model-to-data approach to deep learning was demonstrated for the first time, to our knowledge, in ophthalmology,” the authors wrote. “Using this approach, the performance of the deep learning model was trained and showed no statistically significant difference in quantifying the intraretinal fluid pockets in OCT compared with human manual grading. Such a paradigm has the potential to more easily facilitate large-scale and multicenter deep learning studies.”

While more deep learning tools are being used in virtually every medical specialty, there remains concerns regarding data privacy, security, and sharing. However, by using a model-to-data approach, the model itself can be transferred rather than the data, circumventing many of the existing challenges.

This technique has been tried in other specialties, but has not yet been attempted in ophthalmology. However, this technology could be transformative in the space due to ophthalmology’s dependence on outpatient ancillary testing.

Machine learning and deep learning have already been applied in ophthalmology in a variety of contexts and to a range of clinical conditions, ranging from diabetic retinopathy, age-related macular degeneration,9 and glaucomato, Stargardt disease, and post–small incision lenticule extraction surgical outcomes.

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Amid the Covid-19 pandemic, there’s been a heavier reliance on technology, including the internet of things to facilitate communications amid social distancing and lockdown measures. However, it’s also positively impacting the way medical services are delivered with more advanced technology.

“The Internet of Things (IoT) is revolutionizing the way we live and work,” wrote Alyssa Rapp in a Med Tech Intelligence article. “From tweeting refrigerators to driverless cars, a vast array of physical devices are now connected to the Web.”

“Nowhere is the impact felt as acutely as in the healthcare sector,” Rapp wrote. “This year, IoT deployments will grow faster in the health arena than in any other industry. In fact, according to McKinsey, the so-called ‘Internet of Medical Things’ (IoMT) will have a global economic impact of $1.6 trillion by 2025.”

From robots to artificial intelligence, a number of disruptive technologies are entering the fray to combat Covid-19. That also goes for applying internet technology to medicine.

“Web-enabled medical technology has the potential to be truly transformative,” Rapp added. “To understand why, you only need to consider some of the potential applications. Imagine, for instance, a ‘smart pill’ that, once swallowed, could collect diagnostic information from inside your body, and wirelessly transmit its findings to your doctor. Or how about a wearable ECG that inconspicuously monitors your heart 24/7 and pings an early warning to your doctor days or weeks in advance of a cardiac event?”

Here are a pair of funds to consider:

• Global X Internet of Things ETF (SNSR): seeks to provide investment results that correspond generally to the price and yield performance, before fees and expenses, of the Indxx Global Internet of Things Thematic Index. The fund invests at least 80% of its total assets in the securities of the underlying index. The underlying index is designed to provide exposure to exchange-listed companies in developed markets that facilitate the Internet of Things industry, including companies involved in wearable technology, home automation, connected automotive technology, sensors, networking infrastructure/software, smart metering, and energy control devices.

• Global X Funds – Telemedicine and Digital Health ETF (EDOC): The Global X Telemedicine & Digital Health ETF (EDOC) seeks to invest in companies positioned to benefit from further advances in the field of telemedicine and digital health. This includes companies involved in Telemedicine, Health Care Analytics, Connected Health Care Devices, and Administrative Digitization. The Global X Telemedicine & Digital Health ETF (EDOC) seeks to provide investment results that correspond generally to the price and yield performance, before fees and expenses, of the Solactive Telemedicine & Digital Health Index.

Additionally, for more market trends, visit ETF Trends.

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The huge reserves of Big Data and Analytics could make enterprises earn unlimited Revenues.

Data is valuable, so much that the fastest-growing companies are adopting data monetization imbibing them a vital component of their strategy. Every modern enterprise is a data-driven company. Data is everywhere in the enterprise, harnessed through strategic partners, supply chains, operations, customers, and competitors, what is critical is the insights derived from it that substantially increase that value.

How do enterprises value their data? That’s one most demanded question in the C-suite asks as the volume of big data grows exponentially. What is interesting to realize, that there is no finite answer. Data like air is free flowing, no wonder that data monetization focuses on increasing the economic value of data.

Leveraging from Data Monetization

There are two primary paths to data monetization. The first is internal focusing on leveraging data to improve an enterprise’s takeaways which include operations, productivity, and products and services

The second path is external that encapsulates creating new revenue streams by making data available to customers and partners.

Gaining the monetary rewards from data, or the ‘Golden Rules of Data Exchange’ include-

1. Understanding the Role and Data value in Business
Smart data utilization also helps in managing risk and provides assurance that the business is compliant with laws and regulations. But it can only serve this purpose effectively if you know where your data resides, how relevant it is and how valuable it could be.

2. Getting Data in Order
Before thinking about monetizing data, companies need to discover what kind of data they hold about their partners, customers, products, assets or transactions and what publicly available data can be called on to increase the value of their proprietary data.

3. Embed data monetization into Business Strategy

Executives should evaluate their key business goals and strategic initiatives through the lens of how data can support them. Once you understand the quality of data and have tied it to business strategy then you can put the right structures in place to monetize it.

4. The potential for data to deliver value is Enormous

Sometimes, though, it’s hard for companies to imagine quite what the opportunities could be because they are so used to pursuing growth through established strategies and revenue streams. That’s why all companies should be open to learning from other businesses and partnering in ways that make sense from a data point of view.

Communicate data’s value to Foster Growth

Monetizing data is still a relatively new experience for many organizations, and even when successful initiatives are in place they aren’t always known to the business as a whole. As data becomes more and more important, companies will need both to communicate and educate internal and external stakeholders so they fully grasp the value data can deliver.

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