Author: Andrew Wilson

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AI with real-time data

Data is the lifeblood of Artificial Intelligence. AI/ML models develop the capability to perform tasks based on the quality and quantity of training data consumed. As emerging technologies, Artificial Intelligence and Machine Learning can ingest large amounts of relevant Big Data and provide incredible accuracy and efficiency with their solutions. For this piece, we’ll be exploring how AI is harnessing the power of real-time data, data that is providing the accuracy, speed, and opportunity to scale that companies and world governments are looking for.


Real-time data is any information received immediately after discovery. Such data could be static or dynamic in nature, and it can be used for navigation and tracking purposes. It can also be used for analysis purposes, and research, after a large sum of such real-time data has been collected.

Real-time data analysis involves analyzing the data immediately on reception. Users draw quick conclusions with the data and publish their findings, or use obtained insights to improve processes.

A popular example of real-time data that is being consumed daily is the coronavirus infection spread/cases across the globe. South Korea flattened its curve by introducing an app that could monitor citizens’ whereabouts and warn them about nearby infection hotspots. China has used active surveillance techniques for increased testing. Computer vision systems have helped the Chinese government identify unhygienic practices specific to this pandemic (such as crowded zones and citizens avoiding facemasks) in real-time and assess the risk such practices bring.

Coming back, customer relationship management (CRM), fraud detection, and credit scoring, among other popular use cases, take advantage of real-time data.


With real-time data available in the petabytes, it shows that there is no shortage of such data’s availability. AI/ML models require volumes of training data to achieve the highest accuracy possible. Real-time data allows these models to learn from the latest information, thus increasing the model’s relevancy with time.

From agriculture to banking, here are some interesting industries wherein AI and real-time data are aiding innovation:


50% of the world’s population depends on agriculture for their survival. For farmers to succeed in this space, they need to monitor their crops and ensure that only the highest quality of produce is being delivered. With the help of AI, farmers now have a higher guarantee of growing uninfected and market-ready fruits and vegetables.

AI and real-time data in agriculture

Farmers can monitor the moisture level of soil using soil and water level sensors. Such sensors can alert farmers about low water levels, and action can be taken accordingly. Microsoft’s agriculture project (FarmBeats) has been able to use AI, Edge, and IoT to reduce water intake by 35% while increasing farm productivity by 45%.

Computer vision systems can detect insects and pests that are threatening to ruin crops. This allows farmers to sprinkle pesticides only on the affected crops, thus saving resources. AI today can also help farmers with weather prediction. Having such information can help farmers to mitigate potential damage to their crops.


The human body has multiple measurable parameters that provide insights. Real-time patient data is a boon for surgeons looking to monitor parameters such as blood loss and oxygen levels. Using such information, medical professionals can determine the necessity for high-level treatments and take a specific action.

AI and real-time data in healthcare

Also, real-time data collected from multiple patients can understand trends and identifying health patterns. Using such insights, doctors can detect health issues at the earliest stages and recommend the appropriate treatment. For example, doctors are able to review mammograms 30 times faster with AI, with accuracy levels of 99%. This allows hospitals to save on resources by eliminating unnecessary biopsies.


Nations can use computer vision for real-time surveillance of sensitive locations, borders, ports, etc. We can train computer vision systems with the relevant training data (that depicts illegal activities and national threats) to understand when to alert authorities. By monitoring such locations, governments will know when to call for military assistance. This will ensure the military will be used only when absolutely necessary thus ensuring reduced national security risks. Attacking moving targets using a combination of computer vision and advanced artillery.

AI and real-time data in defence

Such technology allows militaries to flip the cost curve. For example, if a $1m missile takes down an F-35 jet, it becomes an expensive loss for the air force that lost the jet. But what if the same air force sends a low-cost unmanned vehicle (such as the Octatron Skyseer, which costs $25k-$35k)? If it gets destroyed by a similar missile, the army that launched that missile bears the loss, because it’s more expensive for them to use their missile. Hence, the cost curve gets flipped.

The United States, the world’s most powerful military force, introduced Project Skyborg for developing drones and UAVs (Unmanned Flying Vehicles). China is also working towards weaponizing their AI capabilities with military robotics and drone technology. Recently at the UN, the US, Russia, South Korea, and Israel expressed their desire to explore AI-inspired weaponry, also known as “killer robots” to the opponents of such technologies.


In a fast-paced industry such as banking, real-time data aggregation happens in volumes every second. There are multiple parameters and customer touchpoints that banks can use to their advantage. Real-time banking data will allow Machine Learning algorithms to understand what each customer is like and accordingly create a personalized experience, thus improving customer engagement. For example, if a customer is reviewing his/her personal investments regularly, banks could offer investment advice.

AI and real-time data in banking

Fraud detection is another important application of AI in banking. When a bank’s ML model notices activity that has often ended up in fraud, they can alert authorities to take action. Using AI-backed fraud detection solutions, banks have reported an increase in detecting real fraud by 50%. Such systems help banks build trust with their new customers and retain loyal ones.


I mentioned this earlier under AI’s influence on real-time data in agriculture. Meteorologists construct and operate several models and data sources to track the activity of clouds (shapes, movements, density, etc.). With numerous real-time data points obtained from weather analysis, it’s humanly impossible to cover all aspects of weather effectively. Artificial Intelligence becomes the only viable solution for handling such large data volumes, due to its ability to consume Big Data with ease.

AI and real-time data in weather forecasting

While monitoring cloud sizes and other weather parameters with AI, weather forecasters have the luxury of reporting more accurate information. For example, such AI systems can detect comma-shaped clouds from satellite imagery. Comma-shaped clouds are a strong visual indicator of severe weather events. Also, satellite imagery of the earth’s ozone layer with AI-backed insights can allow lawmakers to create policies surrounding climate change and climate protection strategies.


Real-time data is voluminous, and with each passing year, we are collecting almost double the amount of the previous year. AI/ML models are the only systems out there that can handle such data, and developers are discovering numerous applications for implementation. Real-time data offers solutions for quick decision making and AI models are here to make that process faster.

This article was originally published on TechPluto.

Pricing Datasets

Business is all about making money, and that includes the business of transferring money. Money transfer companies (MTCs) or international remittances companies provide multiple options for customers (individuals and companies) to remit income and other expenses nationally and internationally.

Pricing datasets for foreign exchange companies

MTCs provide online and offline payment solutions. Offline solutions refer to physical stores located across cities internationally. Using these stores, customers can provide physical cash and request their delivery in another currency to a store closest to the recipient. Offline stores are the most traditional options for overseas money transfer. Such stores are generally located in most cities across the world, especially in airports. Online payment solutions include online money transfer options such as bank transfer, credit/debit card, sofort, ACH direct debit, etc. This is an easier and more convenient alternative for customers who are savvy with internet banking options.

So, how do such businesses make their money? 

Foreign exchange companies make their money from the charges levied on each transaction. Transfer fees represent a bulk of a money transfer company’s profits, and charging such fees allows them to increase their bottom line. 

Pricing datasets provide incredible competitive advantage

The transfer fees are determined based on several important money transfer parameters, the primary ones being transfer amount, transfer speed, and transfer rate. 

  • Transfer account – The amount of money transferred in a single transaction
  • Transfer speed – The time taken in seconds, hours, or days, for the money to reach the recipient.
  • Transfer rate – The exchange rate for the selected send and receive currencies

Competitors across the money transfer space function within the same parameters, thus making it tricky to stand out. Some businesses are focusing more on offline stores while some others are directing their efforts into advanced online payment solutions. Irrespective of such business plans, every money transfer company can utilize competitor intelligence to optimize its parameters and increase revenue. 

Competitor Intelligence and its advantages

Investopedia defines competitor intelligence as the ability to gather, analyze, and use information collected on competitors, customers, and other market factors that contribute to a business’s competitive advantage.

In the money transfer space, access to competitor intelligence such as their transfer fees levied for selected send amounts, payment options, and delivery speed can be analyzed to understand where such businesses are profiting. Using such numbers, you can optimize your offerings, thus improving customer satisfaction and increasing your bottom line.

Pricing datasets for money transfer companies

Competitor intelligence in the form of pricing datasets allows you to track your competition, study competitor reaction to pricing changes, and explore untapped markets. It also helps you understand which aspect of your business operations needs to be improved to stay on top of the foreign exchange game!

Uses of competitor intelligence

Some of the popular use cases include:

  • Identifying competitors that offer the best deals to their customers
  • Fees charged by competitors across all currency corridors
  • The total premium charged by each competitor on all currency pairs
  • Time to disbursement vs premium charged

Bridged offers competitor intelligence solutions to money transfer companies looking to conquer the forex market. Click here to access our free pricing datasets, and let’s figure out a solution that gets you to the top!

AI Trends: AI and Finance

April 28, 2020 | Financial Services | No Comments

AI in Finance

One of the most exciting industries that AI has influenced is banking and finance. AI developers have identified multiple use cases for automation and machine learning in financial services and money transfer operations.

The financial services industry is all about making good financial decisions. Be it the bank or the customer, everyone wants to make the best bang for their buck. With the advancement in business intelligence and data science, we now know that the answers to all our questions lie in clean, consolidated, and insight-offering data. For financial institutions to gain an advantage today, they need to leverage the power of data, something that’s often called the lifeblood of Artificial Intelligence and Machine Learning models.

In the past few years, Artificial Intelligence has shown how it can revolutionize virtually every industry. Here’s how AI will continue to influence the finance space:

Personalized Banking

Artificial Intelligence has paved the way for a higher emphasis on customer satisfaction. With user data, banks can understand the type of customer they’re catering to, and suggest personalized services accordingly. 

AI in personalized banking

Banks collect user data from customer transactions, subscriptions, investment plans, etc. Today, banks can acquire even more information using chatbots. Banking chatbots allow customers to voice their concerns and receive immediate attention. Banks can collect such queries from multiple customers for gaining insights. For example, if many customers complain about not being able to perform a certain type of online transaction, banks can decide to make the process simpler by optimizing their webpage/user interface. 

Such chatbots can also perform quick transactions, provide balance sheets, etc.

Personalized banking can also help customers achieve their financial goals. Based on a customer’s financial information, banks can provide personalized advice and an objective route to monetary success. Personalized services such as bill payment reminders, expense planning, etc. can go a long way in increasing customer satisfaction and brand loyalty.

Credit Scoring  

Credit scoring makes lending decisions simpler for banks and money lenders. Such scores are determined using data representing your payment history, current debt, credit length, new credit, and credit types. Each of these data points hold different weights while determining a customer’s credit score.

AI in credit scoring

Today with Artificial Intelligence, the credit scoring system can benefit from higher objectivity. AI models provide a more accurate representation of a customer that wants to borrow money, all at a far lesser cost. AI-backed credit scoring processes can function on a higher level and a more complex set of rules. Using such a credit scoring system, banks and money lenders will have a better understanding of how much money can be made of money lent. With AI-backed credit scoring, banks can distinguish between applicants that perform regular late payments and customers that pay bills on time. Such a credit scoring system can also provide a more accurate score to young applicants who do not have a long history of borrowing money.

Managing Risk

Markets fluctuate and economies rise and fall. These behaviors are influenced by factors such as the sub-prime mortgage crisis (that led to the 2008 financial crisis), and the coronavirus outbreak that is causing the on-going 2020 stock market crash. 

Machine Learning algorithms can learn from historical data and identify patterns. Such systems can locate economic threats at an early stage and create a call for mitigation. This will ensure that the financial mistakes of the past never happen again. AI/ML models can perform high-level market analysis that would take ages for humans to perform. As a result, the machines can alarm us about potential issues the economy could face, thus giving us the time and the data resources to tackle it.

Tackling Fraud

Another important activity in banking is locating financial fraudsters. As mentioned earlier, AI systems that can assign highly representative and accurate credit scores can also detect users known for fraudulent activities. AI-backed fraud detection systems analyze a customer’s transactions and purchasing habits. If AI systems detect unusual activities (such as the sudden withdrawal of an unusually large sum of money/expensive purchases unlike regular activity), they can trigger the required security mechanisms, thus saving the customer from being de-frauded.

Automated Trading

We have witnessed an increase in the number of data-driven investments in the past couple of years. With data, everyone has an objective reason to make a decision, and this includes trading on the stock market.

AI in automated trading

Also known as algorithmic, quantitative, or high-frequency trading, it has allowed for more fact-based reasoning behind investing. AI models view data with ultimate objectivity, and human flaws such as confirmation bias don’t influence AI models. So, when an AI model suggests a certain lucrative investment, you can rest assured that the suggestion is based on exhaustive stock market research. 

Trading floors also benefit from AI-inspired solutions since such systems save time, and we all know that time is money.


While dealing with money, we can’t compromise on accuracy. Artificial Intelligence provides that very solution to the financial services industry. Future predictions for AI in finance show the industry is rapidly changing, and processes are being revolutionized left, right, and center. 

From AI-backed credit scoring to personalized banking, Artificial Intelligence has forced every major banking institution to become a tech company. Interestingly, Goldman Sachs, one of the world’s largest investment banking enterprises, employs more software programmers and tech engineers than Facebook! It’s a growing sign of the future of services in finance, who’s “a” today stands for none other AI.

Business post-COVID19

The coronavirus pandemic has created unforeseen challenges for businesses across industries. It’s safe to conclude that COVID19 has put world business on a standstill. A return to normal entails a vaccine or an anti-viral, and both are at least a year away from being released. So as of today, the only effective strategy we have is social distancing and hygienic practices, such as wearing masks and using hand sanitizers.

Out of 2.1 million positive cases globally, 145,000 patients have died. The only reason these numbers aren’t higher is due to Government-enforced lockdowns. While these lockdowns are reducing infection spread, they’ve had a severe toll on businesses. Mass unemployment has ensued, with the US alone reporting 22 million displaced workers. Companies in the Tourism and Hospitality industries have seen revenues drop down to 0, with no light at the end of the tunnel. Well, not yet at least. 

Kristalina Georgieva, the head of the IMF announced that the recession the global economy is currently in could be worse than the major crash of 2008. Millennial and Gen Z business owners may not remember how bad things got during the last financial crisis. But, entrepreneurs who were operating at the time can testify to the severity of the struggle. Companies today have chosen to re-group and re-strategize to make it through these unprecedented times. 

I’ve compiled the following pointers that my company is keeping in mind to navigate around these uncertain times:

A change in the online landscape

The demand for essential commodities is at an all-time high despite the lockdowns across countries. People are also spending more time than usual on their devices. This is changing the online landscape significantly and allowing for all companies – big or small, to capture the online marketplace. 

Canada and the United States experienced a 56% rise in online orders (between 22nd March and 4th April 2020). Amazon’s sales have increased significantly ever since the lockdown, and their first quarterly figures are poised to be 22% higher than that of last year. 

The internet will become more important than it has ever been.

In Indonesia, ride-hailing companies Gojek and Grab are offering “ready-to-cook” features on their respective apps, allowing consumers to get frozen meal-kits delivered to their homes in order to break their fast. 

Bengaluru’s civic corporation body, the Bruhat Bengaluru Mahanagara Palike, or BBMP has managed to assemble, on Whatsapp,  merchants, hyperlocal logistics companies, and even on-demand service providers, to cater to the public’s needs. 

Money transfer companies are providing secure and user-friendly online platforms for remittances. This is an important step for them to recover from the instant elimination of their offline stores.

Late-night shows in the United States have already begun hosting on social media platforms. Across industries, companies are making the best use of the online medium to deliver to their existing customers, but more importantly, to build new ones. 

Perhaps there’s an opportunity here for your business as well. 

Globalism under threat

While member countries of the European Union are generally in sync with the body’s one Europe view, they acted independently to tackle COVID 19. Due to the outbreak, pre-existing sentiments of populism and xenophobia will amplify. The case for a world without borders will seem unrealistic. Governments will pass stricter legislations surrounding foreign travel and immigration.

Countries will become shy of globalism.

As a result, companies that rely on other nations to complete their supply chains will have to explore alternatives to accomplish the same from their home countries. COVID19 has exhibited how the world’s heavy dependency on China has today stunted their economies and hurt business. Businesses might have to learn how to survive without significant foreign outsourcing.

Low/no contact solutions

Times are hard for all high-touch, close contact businesses. This is especially true for those businesses in the Hospitality, Manufacturing, and Healthcare industries, as they cannot operate online. Since there is a general trust deficit across customers, here is a list of possibilities that could help your business cope:

  • Advanced online payment options will become the norm, while cash transactions will reduce
  • Food and grocery delivery services will become more popular
  • Several healthcare companies have introduced chatbots where patients can key in their symptoms. This can help doctors arrive at a quick diagnosis, in a remote manner
  • Contactless dining options will be introduced in most restaurants – with the food aggregator companies leading the way 
  • Manufacturers could introduce remote-controlled manufacturing processes, thereby reducing the number of workers in close proximity to each other

If low contact solutions prove that they can effectively tackle infection spread, they could then lay the foundation for bringing back the most affected industries – Hospitality and Tourism.  

Blurred lines between formal and online education

COVID19 has thrust several educational institutions to shift online. With many reputed institutions making a quick transition, it is more or less established that education does not require a physical classroom or a campus for that matter. But are they able to continue to engage their students? That remains a looming question. 

The idea of formal education will be under threat in a post COVID19 world.

Post COVID19, any business in the education industry will have to think beyond the traditional notions of learning. Professionals are opting for online courses to further excel in their careers. This signals a massive opportunity for traditional schools and universities to reinvent learning. Learning that meets the needs of a new economy. 

Accordingly, recruitment criteria may change, by focusing more on provable skills and less on expensive degrees. 

Tech companies, in particular, will find this highly applicable.

Plan for today, prepare for tomorrow

Although the coronavirus pandemic has fundamentally changed the business landscape, tomorrow’s environment will be different, but no less rich in possibilities for those who are prepared. 

It’s pertinent for businesses to understand that everything we know ceases to operate in the same manner. COVID19 has shown that it’s time to steer the world business according to a “new normal” and build based on that premise.

AI in manufacturing can help bring the economy back on its feet

During the coronavirus outbreak, practicing social distancing and implementing lockdowns are the only viable solution to mitigate the virus’s spread. But, due to this, many industries are suffering, and world economies are facing trying times.

Reports suggest that the pandemic could cause the global economy to shrink by 1% in 2020. The statistic makes sense because the coronavirus has disrupted global supply chains and halted international trade activities. Industries such as hospitality and tourism have taken the biggest hit since world tourism is practically impossible during a pandemic. Large-scale events that cater to large gatherings (such as music festivals and movie screenings) have been postponed indefinitely. And, unless we develop a vaccine or achieve herd immunity, there are no signs of such events returning anytime soon. 

So, while the present reality isn’t showing many signs of economic promise, we don’t have any other choice but to put on our problem-solving hats and tackle this challenge. Emerging technologies such as AI can provide a much-needed boost to the economy. There are many challenges to confront due to the coronavirus pandemic. The one I’d like to focus on is the hurdles in manufacturing.

Problems faced by manufacturing

Manufacturing by nature doesn’t provide the luxury of working remotely, unlike industries such as Information Technology. And, the production of articles on a large scale using machinery cannot stop due to a pandemic. Most manufacturing setups are in developing countries, and halting operations will displace many workers and challenge their livelihoods. It will also create worldwide shortages which can cause further crises.

AI can get the manufacturing economy back on track

It’s clear that out of all industries affected, operations within manufacturing needs to return to normalcy as soon as possible. This can take place only if we can provide a hygienic and safe working environment that doesn’t put workers at risk of infection. Creating low contact processes and automating various factory floors could be strong first steps to get manufacturing up and running, and here’s where AI could provide saving grace, thus contributing to the economy:

AI in manufacturing

During times like these, AI can help manufacturing units by offering higher quality control, higher hygiene levels for workers, and transparency across teams with better communication. Also, AI-inspired robots can allow workers to maintain safe distances from each other, thus lowering the odds of contracting the virus. AI tools can give manufacturing the advantage it needs during this health crisis, and here’s how it will help get the economy back on track:

Contact-less machine control

Ideally, manufacturing floors will want to reduce the number of times workers come in contact with objects and shopfloor equipment pieces. AI-inspired gesture identifiers could take advantage of the human voice or human gestures. This is handy for switching on motors, initiating assembly line processes, and so on. For example, in a glass factory, workers could increase a furnace’s temperature by simply voicing a command or performing a hand gesture. Factories can train workers to use such new generation techniques and equipment. This will increase their skills and also reduce their chances of getting infected.

AI offers contact-less solutions for controlling machines

To develop such AI systems, we will need a variety of training data. Data that can teach systems to understand human communication and human languages. Such data will then be converted into system signals that instruct various mechanical processes. If it’s a machine that recognizes speech commands, the training data will comprise a large volume of audio files. Files that represent instructions, commands, and orders. And, if it’s meant to recognize hand gestures, it would use multiple annotated images of various hand signs that indicate a certain instruction or command. 

If implemented, this could allow workers to avoid touching surfaces and thus encourage a safe working environment.

Intelligent Automation

AI can automate many processes in factory assembly lines. Processes that presently use many workers at once. Automated processes will help factories stop workers from crowding in a single space. 

For example, factories need to review and screen their products before shipment. The reviewing and screening process is generally a manual one, in which multiple workers physically examine the same product. This increases the number of contact points, thus increasing the probability of infection spread. 

Automation will bring manufacturing back to normalcy

With the help of Artificial Intelligence, such review processes can be automated. AI developers can train computer vision systems to identify faulty products and packaging issues, after which they can alert concerned officers. Computer vision systems can also monitor the manufacturing floor. This will be especially useful for observing chemical reactions, heating/cooling operations, cutting/joining processes, and so on.

Leveraging data to handle supply/demand

Due to the coronavirus outbreak, manufacturing units cannot depend on historical figures to predict supply and demand. Demand for manufactured products has dwindled across industries. And, due to the world’s supply chain taking a hit, businesses can’t manufacture products with pre-pandemic efficiency.

AI can use manufacturing data to predict supply and demand

Here’s where businesses can leverage the power of data. Artificial Intelligence models can learn from present trends in supply and demand, to suggest manufacturing solutions. For example, an AI model can scan databases representing the demand for a certain product and determine how much of that product a factory should manufacture. Such models can also study the amount of raw material generated across the world. This helps determine whether a factory will be able to meet present-day demands.

Using such data, businesses can zero-in on target markets and ensure that their inventory allows them to cater to such markets. 


The coronavirus outbreak has revealed how Artificial Intelligence, Machine Learning, and Computer Vision are a blessing in disguise. Using such emerging technology, businesses can figure out novel methods to sail through these new and economically terrifying circumstances. Just like in manufacturing, we can develop AI systems for various industrial processes to ensure minimized contact without compromising on efficiency. With technology such as AI, we might just be able to maneuver through the pandemic successfully and bring the economy back on its feet.

Mistakes to avoid while training AI models

Artificial Intelligence involves the pursuit of human-ness in technology. Like teaching a child, AI development involves two things. The first being providing the study material (training data) and second being the learning method (Machine Learning, Deep Learning, etc.). 

For an ML model to perform well, it requires extensive training with a variety of training data. ML models consuming large amounts of training data allows them to understand diverse examples. And, a comprehensive training process increases the model’s odds of understanding and acting on the data at hand. 

The common problem faced by most developers is a misapplication of what was mentioned above. Simple strategy based problems have quick fixes, but those can seem distant or non-existent during the thick of the development phase. Here are some of the common mistakes developers make while training AI models, along with tips to avoid them:

Poor training data development

Training data is the juice that keeps AI/ML models functioning. Bad quality training data leads to bad quality results. It’s as simple as that. Bad quality is a broad term here, so allow me to break it down:

Lack of training data

ML models need multiple examples of a situation to understand how to tackle it. When there is a lack of training data, your model will not be able to identify real-world examples effectively. Analogous to how we learn, an AI model can function as required only if there has been a large number of examples to learn from (in this case, a large amount of training data). 

Unclean data

Having a large volume of training data is worth nothing if it’s quality is below par. Training data that’s riddled with errors will only confuse your ML model, which will render it unusable. Think about it, you can’t expect a student to learn if the reading material is filled with mistakes. 

Common examples of unclean data include inaccurately annotated images and videos, irrelevant data points, faulty conversational datasets (generally poor grammar and tonal issues).

Narrow data

To add the element of human experience to your AI/ML model, developers need to train it to understand specific rare scenarios and edge cases. Many AI developers falter here. They build algorithmically sound models, but they don’t train it to perform well when encountered with uncommon scenarios. For example, if an autonomous vehicle isn’t trained to tackle rare situations (such as protestors on the street, kids randomly running, etc.), the end result could be fatal.

The straightforward but tedious solution to solving this is exploring all scenarios your model might encounter, and feed datasets that represent all possible circumstances.

AI/ML model development snags

Even if the training data is sound, the AI/ML model at hand needs to be powerful enough to not only consume that data but reproduce usable results. Here are some common mistakes:

Machine Learning where it isn’t necessary

Yes, in many scenarios, companies decide to implement machine learning even when it doesn’t serve the purpose or serves it inadequately. In many situations, procedural logic does the job, so determine the need for ML implementation accordingly.

Performance analysis

Even if an ML model can perform the right processes with the data fed to it, there might be issues beyond training data and AI/ML algorithms that can restrict the model from functioning effectively. Consider this performance-related issue: if the model exhibits a lag while producing results, that might not help in certain use cases. Taking the example of an autonomous vehicle, if it takes even as long as a second to identify a pedestrian in the middle of a street, the vehicle might still end up causing an accident. Factors surrounding performance influence real-life consequences, so it’s important to identify such issues.

Mixing up correlation and causation

It’s easy to allow your ML model to function based on correlating certain data points consumed to determine a cause. Consider this conflation of correlation and causation: “The faster windmills rotate, the more wind is observed. Hence wind is caused by rotation.”

While that statement’s fault might seem obvious to us, it might be fair logic to an AI/ML model’s mind. In most cases, acting based on correlation may not have significant adverse consequences. But, it displays an inaccuracy in the model’s algorithm. Ideally, correlation and causation shouldn’t be misunderstood, even by an AI/ML model.


Training an AI model is no simple feat. It involves a comprehensive understanding of the human mind and a serious attempt to replicate it. We’re making great strides in the science of Artificial Intelligence, but we still have ways to go. We can traverse those ways faster if we identify and eliminate key mistakes that make our model’s performance suffer. And we can do that only if we understand the common mistakes that we need to avoid while training and developing our AI models.

AI Trends: AI and Medicine

April 21, 2020 | Artificial Intelligence | No Comments

AI and Medicine

Artificial Intelligence is here to improve our lives, by not just making things more efficient, but also increasing our lifespan. Companies across industries are experiencing the advantages that come with AI innovation, especially the healthcare space. 

Throughout human history, we’ve been able to understand the parameters that determine health better, and we’ve developed accompanying technology. With vaccines in the late 1700s, anesthesia and medical imaging in the 1800s, to organ transplant and immunology in the 1900s, healthcare innovation has been on an upward slope. In the 21st century, AI in medicine is showing us how that slope can rise further. Here are some popular trends in AI and medicine:

Virtual Doctors

Chatbots have experienced a rise in popularity across industry operations, and healthcare has an important use case for them. In a time such as the coronavirus outbreak, there aren’t enough medical professionals available to attend to all patients. In the USA, the ratio of physicians to the country’s population is around 277 to 100,000. The World Health Organization recommends a 1:1000 ratio, but 44% of its member nations don’t meet this criterion.

Conversational AI in medicine

Here’s where medical chatbots can reduce that gap. Chatbots offer 24/7 accessibility and instant responses. For patients facing unusual symptoms, they can address their health issues from the comfort of their homes with a chatbot. Conversational AI in medicine can be trained with conversational datasets that represent diagnoses for various diseases. Accordingly, the chatbot can recommend direct solutions for simpler cases or schedule a doctor’s appointment for the more serious ones. 

Treating Patients With Alzheimer

Many of our populations’ senior citizens end up suffering from Alzheimer’s disease. It’s the first step towards severe cognitive decline and it can have a toll on the affected individual and his/her loved ones. While it has always been hard to predict the chances of cognitive decline due to Alzheimer’s, today, AI can reliably perform the same prediction. 

Using a combination of biometric data and cognitive tests, AI models can determine a patient’s risk levels. Also, AI models are learning how to convert brain signals into machine-readable text. Such technology can also improve Alzheimer’s research and therapy.

Vaccine Identification

The coronavirus pandemic has created the wild chase for a vaccine, for its the solution to ending the world’s present lockdown. Organizations across industries are exploring ways in which they can contribute. This has led to exciting players entering the vaccine game. Tech giants such as Microsoft and Google are developing AI solutions for vaccine development. 

AI in vaccine development

When it comes to viral infections, their vaccines are produced by combining the original virus with another virus that can weaken the former. This combination will ensure that the virus to be tackled cannot reproduce or multiply well, thus being rendered ineffective. 

But, vaccine development is easier said than done. Pathogens have highly complex methods for dodging mainstream medication. Pathologists have to go through multiple protein structures and medicinal ingredients to zero in on the one that will cripple the virus or bacteria at hand.

Here’s where AI comes in. Artificial Intelligence models can read through thousands of research papers within the same time it takes a researcher to go through about 10. Also, an AI model can recommend vaccine ingredients that exhibit higher chances of success. Training such a model involves making it understand how protein folding and virus-to-virus interaction takes place. With the appropriate training datasets, AI models will be ready to help medical researchers and pathologists locate the right vaccine. Since clinical trials always are the rate-limiting step, AI models provide the advantage of saving time in other areas of vaccine development.

Managing Medical Records

Data is everywhere, and medical records have plenty of that. These records contain patients’ medical history, diseases, surgeries, etc. They also include treatment methodologies used for each scenario. So now, we’ve got a database of patients’ health issues with its accompanying solution. Such data can be converted into machine-readable text for AI models to consume. Once these models receive a list of a patient’s symptoms, using the data consumed, they can perform simple diagnoses and suggest treatment methods and medication. This can help doctors save time and be on top of their game even when presented with rare cases.


Popular wearables such as Fitbit Flex, Samsung Gear 2, and Apple Watch have revolutionized the way we communicate with our mobile devices. It has also shown us how it can aid our fitness regimes, with counting our steps and measuring our heart rates. But, wearable tech has potential beyond the above-mentioned activities. Advanced wearables can today detect quivering or irregular heartbeats, symptoms that are a sign of possible blood clots, stroke, heart failure, and other complications concerning the heart. Such information can be sent directly to a medical professional for immediate attention.

Cancer Treatment

AI in cancer therapy has allowed for numerous solutions to tackling this fatal disease. Computer vision systems can be trained to detect cancerous cells and understand what these cells look like at different stages. Adopting computer vision into cancer diagnosis results brings higher accuracy into the picture. This helping doctors and medical professionals provide better treatment options and extend more lives.


Surgical procedures require high-level precision, and it takes a skilled hand to perform them successfully. AI-inspired surgical equipment could help guide a surgeon performing a complex procedure such as open-heart surgery, cesarean section, cataract, etc. 

AI in surgery

Using a combination of visual datasets and movement-based training, surgical AI equipment can locate parts of the body that need attention, sometimes better than a surgeon can. When developed successfully, such technology can revolutionize surgical procedures by increasing the number of successes and increasing patients’ trust.

AI-inspired genomic medicine

The National Human Genome Research Institute describes genomic medicine as a medical discipline that involves using genomic information about an individual for clinical care. Already popular in oncology and infectious diseases, this emerging field of medicine has arrived during the same era as Artificial Intelligence. By consuming large volumes of datasets containing genomic information, AI models can learn how to detect patterns and provide first draft health reports. Doctors can review such reports and use them for their medical practice.


While the avenues for AI implementation in healthcare are practically endless, AI’s ability to provide accurate results becomes the defining factor. Healthcare involves life or death situations, and any room for error could result in undesirable consequences.

Human error is inevitable and it’s something even the world’s best medical professionals can be victims of. Here’s where AI can relieve them of some pressure. AI models in medicine can learn from well-built training data sets, identify trends, and provide recommendations for medical practitioners.

Especially during this coronavirus outbreak, we need to provide doctors with the best technology available for them to perform their tasks with higher precision. AI trends provide for good news at this point, and hopefully, it’s forward sloping curve flattens out the coronavirus’.

AI and COVID 19

As we experience the coronavirus pandemic, we have an influential role to play for mitigating the virus’s spread. At an individual level, social distancing goes a long way in blocking potential routes for infection spread. Added to that, if you wash your hands regularly and wear a mask whenever you’re in public, you’re doing your part to keep the coronavirus at bay!

COVID 19 - the coronavirus

Businesses also have a vital role to play here, and they’re exploring avenues to help fight this battle. Essential services are operating around the clock to ensure their inventories are full. Governments have also roped in private firms to help with medical resources. For example, the Trump Administration issued an order under the Defense Production Act to make General Motors manufacture surgical masks at scale. 

Along with these measures, companies that have a working knowledge of Artificial Intelligence are identifying coronavirus-relevant use cases and developing intelligent models. As an emerging technology, industries are slowly adopting AI. With the hopes of mitigating the spread or finding a cure to COVID-19, here’s how AI can be used for tackling it:

Vaccine development

Historically, vaccine development has been an effective strategy for tackling contagious diseases. This is why pathologists are researching numerous relevant antigens and immunogens for tackling COVID 19. The AI strategy for vaccine identification is two-fold: 

  • Suggesting vaccine components by understanding viral protein structures
  • Scouring through medical research papers at a pace faster than medical professionals

Pathologists are researching nucleic acid vaccines (one of the three types of vaccines, others being whole-pathogen vaccines and subunit vaccines) as these are the ones relevant to crippling the coronavirus. AI helps pathologists understand a variety of molecule structures, and their ability to fold the coronavirus (the action that will render the virus ineffective).

COVID 19 vaccine development

Korea-based Deargen created a deep-learning model that can employ simplified chemical sequences that can predict a molecule’s chances of disabling the coronavirus. Google’s DeepMind is using Artificial Intelligence models to study properties of the novel virus, which includes understanding the protein structures associated with SARS-CoV-2. 

Chatbots for diagnoses 

Chatbots help companies save time and resources. As the coronavirus spreads, the world has understood that hospitals are overloaded. And, most countries don’t have the right medical infrastructure to handle numerous patients.

AI in diagnosis using chatbots

Patients can use medical chatbots to perform quick diagnoses. after which it can predict the likelihood of a patient being infected. This ensures that only patients who have a high probability of testing positive end up visiting a hospital. Medical chatbots reduce the load on medical professionals and hospital resources, thus saving lives truly at risk.

Monitoring infection spread

With location data, we can identify infection hotspots across geographies. This can be used to predict the odds of infection spread based on earlier instances. Users report whether they’re positive or negative on an app. And based on that user data, the app can determine which parts of a neighborhood need to be avoided. It can also notify users who have visited hotspots and request them to get tested. Such data will be immensely valuable to governments imposing lockdowns, and it will provide them with the data needed for creating a path to normalcy.

COVID 19 infection spread

South Korea used an app that can locate infected citizens and their local travel history. During a mandatory two-week self-quarantine, the app helped the South Korean government trace infection spread by identifying cases wherein people were at a two meters distance from each other. Using this app helped South Korea flatten the curve, thus exhibiting how this battle is one that can be won. 

Israel has created a survey reading AI that takes advantage of user-reported data for tackling the COVID 19 spread. Using this, populations can be warned about risky locations, and users at a higher risk of contamination can avoid infection. 


The world is now on red alert and everyone is battling this pandemic together. Social distancing has proven to be the single most effective tool we have at our disposal. People have been asked (in many cases forced by law) to wear masks and maintain physical distance while in public. For the ones already infected, hospitals are doing their best to ensure the right resources such as hospital beds and ventilators are available.

While all this is good, we still have the problem of a shortage of medical professionals, nurses, and hospital resources. Also, we don’t fully understand this virus and a vaccine isn’t exactly close to being ready for usage. Here’s where we’ve got Artificial Intelligence at our disposal. 

AI can speed up human processes and allow us to quickly reach our process’ finish line. By using AI for vaccine creation, pathologists can locate and develop a suitable vaccine faster for tackling COVID 19. Chatbots can reduce the load on doctors and ensure multiple patients are tested. And, a combination of location and user data can help the public and governments understand what the situation surrounding infection spread looks like.

Coupled with human intelligence, AI has the potential to get us out of this pandemic sooner rather than later. The answer lies in the efficiency of AI implementation.

Difference between Data Science and Big Data Analytics

What is Data Science?

Data science is a scientific methodology, to obtain actionable insights from large unprocessed data sets and structured data. It focuses on uncovering things that we do not know. It is a source of innovative solutions for our problems.

It uses a variety of models and means of extracting and processing information. It analyses data on the concept of mathematics and statistics with the help of automated tools. Cleanse data, find data connections, analyse, and predict potential trends. Manipulate, identify disconnected data points, and explore the probabilities and combinations.

It encourages us to try distinct ways to analyze information. Capture data, program it and solve specific problems with data science. It provides a new perspective towards data, enhances usability to provide insights. Data science can support accurate business decisions and tackle big data.

Data scientists use programming languages like SQL, Python, Java, R, and Scala for multiple analytical functions. They write algorithms, build statistical and predictive models to analyze data.

What is Big Data Analytics?

What is Big Data Analytics?

Big Data effectively processes enormous data, extensive information, and complex data that traditional applications cannot attempt. Big data consists of a variety of structured and unstructured data. Introduce cost-effective and latest forms of information to enable enhanced business insights. It can highlight market trends, customer preferences, customer behavior, and buying patterns

Data analytics can help in the organization’s goals by measuring the current and past events and plans form future events. It performs statistical analysis to create a meaningful presentation of data by connecting patterns to strategize business. It eases immediate improvements, problem-solving, and respond to specific concern area. Data analysts require knowledge of Pig, Hive, R, SQL, and Python.

Data Analytics needs well-defined data sets to address particular problematic areas of business. For better results, the data analysts need to have technical expertise and knowledge of mathematics and statistics. Data mining, database management, data analysis, and skills to convey the quantitative results achieved from data.

Data Analysis has important role in Data Science; it performs a variety of tasks such as collecting and organizing data. It assists in presenting the data in charts, graphs, comparative tables, and build relational databases for organizations.

Data analysis and data analytics sound similar, data analysis includes everything a data analyst practices compiling and analyzing data. Whereas data analytics is a subsection of data analysis, it uses technical tools and data analysis techniques to achieve business objectives.

What is the Difference between Data Science and Big Data Analytics?

Data Science is an integral part of Artificial Intelligence, Machine Learning, Search Engine Engineering, and Corporate Analytics. Big Data Analytics is widely used to find actionable items in fields such as healthcare, gaming, and travel industries.

With a greater scope of data, science helps in data mining for varied and unique fields. Big Data analysis mainly focuses on processing large data. Simplification of the differentiation, data science provides thought for questions you should ask and big data analytics helps in discovering answers to questions.

Data science lays a strong foundation by initiating a focus on future trends, improves observations of data movements, and provides potential insights. Big data analytics provides the path for practical application of actionable insights.

Data analytics examines large data sets and data scientists create algorithms, work on creating new models for prediction.

Are there any Similarities between Data Science and Big Data Analytics?

Are there any similarities between Data Science and Big Data analytics?

Similarity, the interconnectivity of Data Science and Big Data Analytics brings wonderful results to benefit organizations. Their dependency can affect the overall quality of action strategy and consequences based on those actions. Companies never apply both Data Science and Big Data Analytics together in every situation yet are useful for different purposes. It can help companies in the technological change they are about to have. Both can help companies to understand the data better.

The relationship between them can have a positive impact on the company.

  • In 2019, the big data market likely to grow by 20% and the big data analytics market headed towards the target of $103 billion by 2023.
  • Worldwide the companies in various sectors using big data technology are telecommunications 94.5%, insurance 83%, advertising 77%, financial services 70%, healthcare 63%, and technology 57.5%.
  • Nearly, 81% of data scientists analyze data of non-IT industries.
  • About 90% of enterprise analytics stated that data and analytics are key elements of initiatives taken by their organization towards digital transformation.
  • Data-driven organizations have 23 times more chances of customer acquisition, and 6 times more likely to retain the customer.
  • Businesses are motivated to get more insights, proves the 30% per year growth in insight-driven organizations.
  • By 2020, we can expect 2.7 million job listings for data science and data analytics.

Applications and Benefits of Data Science and Big Data Analytics:

Tremendous benefits of data science are noticeable with the number of industries involved in technological developments. Data science is a driving force for business improvement and expansion.

  • Agriculture: Surprisingly data science bias-free thus can benefit even sectors that were not data-driven. It is a reliable source for suggestive actions for water frequency or quantity manure required, soil suitable crops, the precise amount of seeds needed, etc. Big data analytics can be of great assistance to farmers in yield prediction, crop failure symptoms due to weather changes, food safety, and spoilage prevention and much more. Companies can rest assured of crop quality, precautions taken by farmers during harvesting and packaging, and on delivery possibilities.
  • Aviation & Travel: Data science can help in reducing operating costs, maximizing bookings, and improving profits. Technology can help flyers in taking decisions of routes, connecting flights, and seats before booking. This is the service industry, for better performance in various areas; companies adopt data science. Big data analytics can enhance customer experience through information shared by the company. Users can find travel discounts, delays, customized packages, open tickets, and personalized air and other travel recommendations, etc. Companies can get statistical and predictive analysis about the selective area such as profits against a particular marketing campaign. Social media activities and its positive impact or rates of conversion are some of the insights that can help in cost reduction.
  • Customer Acquisition: The complete process is of high importance and creates high value for businesses. Data Science can help identify business opportunities, amend marketing strategies, and design marketing campaigns. Redefining strategies, redesigning campaigns and re-targeting audiences is possible with data science. Big data analytics highlights the pain and profit points for business. Identify the best possible method for customer acquisition and improve on the basis of data analysis. Return on Investments, profitability, and other important business ratios presented by big data analytics in the simplest form. Big Data of the telecommunications industry can help in getting new subscribers, retaining existing customers, approaching current subscribers to serve based on their priorities, frequency of recharge, package preferences, and use of internet packs, etc.
  • Education: Implementing data science in this sector can help in the student admission process; take calculated decisions, check enrollment rates, dropouts from institutes, etc. Big data analysis can compare the current and past year’s student data, issues in process or course wise predictions of student performance, etc. Colleges and educational institutes can perform various analyses using the data and plan the changes required. Big data analytics can evaluate students for admissions in other courses based on their eligibility, preferences, or inclination.
  • Healthcare: Data science collects data from various applications, wearable gear, and patient data by monitoring constantly. It helps in preventing potential health problems. The pharma research and new medicine coding are eased with data science. It can predict illness, frequent hospitalizations. Hospitals can use it for new cases, to diagnose patients accurately, and take quick decisions and save lives. Big data analytics can help in cost reduction on treatments, treat maximum patients, improve medical services and the estimations needed to serve better with exciting machines.
  • Internet Search: The search engines use data science to write effective algorithms to deliver the accurate results of search queries in milliseconds. Big data analytics can recommend users on their search, product, or services, or show preference based results. Search engines have frequent visitors, their view history, specific requirements, and many preferences. The speedy suggestions can save time and increases the chances of someone clicking the links. Even digital advertisements have strong data science algorithms and they are effective than traditional methods of advertising. The user experience and profitability of companies improve with the help of big data analysis.
  • Financial Services: Banking, insurance, and financial institutes have to deal with huge data and the complexity, data science efficiently deals with. Big data analytics allows us to focus on relevant data from the loads of massive data that influence customer analytics. It helps in operational issues identification, fraud prevention and improved recommendations for customers.

Now with the scope of data science and big data analytics, we can find why customers are loyal or why they leave you. Find what works in your favor and against you. Know more about customer expectations and if you can meet them. Find more of such indications are available at varied data points that lie on websites, e-commerce sites, mobile apps, and social media interactions.

Data Science and Big Data Analytics consider facts thus it empowers us to plan, face competition and perform better. We can proactively respond to requests and anticipate the needs of our customers. Deliver relevant products with no anticipations but data-supported predictions. Link innovation in product and service with a set of customer expectations and new demands that generate with time and technology.

Services can be personalized and respond in real-time for faster service. Optimize and improve operational efficiency and productivity by using various techniques for analytics for continuous change and growth. Risk mitigation and fraud prevention provides added security.

Data Science increases abilities to understand the customers and their decision-making patterns. Big Data analysis helps in anticipating the potential that lies in the future based on current data and its predictions.


Modern businesses generate huge data and taking actions based on valuable insights is extremely unavoidable in order to remain in the competition. By 2021, organizations using big data analysis will be in a position to take a share of $1.8 trillion than the ones less informed. We can look into the data relevancy, using before its stale, reduce the customer experience gaps and deliver in real-time if we are committed to using interweave technology with business. Being a data-driven organization is an intelligent choice.