Exploring the Role of Big Data in Business Transformation

In today’s digital “domain”, businesses are increasingly turning to big data to gain a competitive edge. Big data offers valuable insights that can be used to make informed decisions, develop more effective strategies, and drive business transformation. As the name suggests, big data refers to the vast amounts of data that businesses collect and analyze. By leveraging advanced analytics tools, such as predictive analytics, machine learning, data fusion and visualization, companies can unlock the potential of this data to gain valuable insights and optimize their operations.

Big data can be used to identify and target potential customers, predict trends and market dynamics, and optimize marketing campaigns, production and inventory management.

By understanding customer behavior and preferences, businesses can develop effective marketing strategies that are tailored to the needs of their target audience. Companies can adjust production levels to meet consumer needs which helps to reduce costs by preventing excess inventory and ensuring right products are available when needed.

Additionally, big data can be used to identify and capitalize on business opportunities, such as developing products and services that are more likely to be successful as well as uncover hidden patterns and trends that may be missed by traditional methods.

In addition to marketing, big data can also be used to inform decision-making. By analyzing data from all areas of the business, firms can gain a better understanding of their operations and identify areas for improvement. This data can be used to develop strategies to reduce costs, streamline processes, and increase efficiency. As businesses become more data-driven, they are better equipped to respond.


Uses of Artificial Intelligence in the Financial Services Industry

Artificial intelligence is reinvigorating the way we work, and one of the biggest industries affected by AI is the financial services industry. AI is making it easier for financial institutions to provide services faster, with fewer errors and improved customer experience.

AI is increasingly being used by banks and credit unions to detect fraud and money laundering, reduce costs, and create innovative products and services. From being used in investment management to customer service, AI-powered applications can help financial institutions and advisors provide better advice to their clients by analyzing customer data and making better decisions about user experience, risk tolerance and investment opportunities. AI can also automate customer service tasks such as answering questions, providing advice, and processing payments.

In the investment management sector, AI-powered applications are being used to improve portfolio performance and reduce trading costs. AI can analyze market data and make recommendations based on historical trends and current conditions. We have seen companies such as, XYZ AND ABC, using predictive analytics to detect fraud even before it occurs.

In addition, AI is being used to automate certain aspects of financial services. AI-powered applications can automate repetitive tasks, such as data entry, data analysis, and report generation. This can reduce costs, improve accuracy, and free up employees to focus on more complex tasks.

AI-powered applications are now being used to create new investment products and services, such as automated trading systems and robot-advisors. In fact, research shows that AI in banking is set to hit $300 billion mark by 2030.

how is Virtual Reality is shaping the world?

Virtual Reality (VR) is a computer-generated simulation of a three-dimensional environment that can be interacted with in a seemingly real way by a person. The person wears a VR headset that tracks their head movements and displays the virtual environment. VR technology has come a long way in recent years, and the level of immersion it offers has greatly improved.

One of the most significant benefits of VR is its ability to create new and unique experiences that were previously not possible.

For instance, VR can be used for educational purposes, allowing students to explore and interact with a virtual environment that teaches them about history, science, or any other subject. In a similar manner, VR can also be used for therapeutic purposes, helping patients with anxiety or phobias confront their fears in a controlled and safe environment.

In the entertainment industry, VR is used to create new and exciting video games that allow players to be fully immersed in the game world. VR games are more engaging and offer a level of immersion that traditional games cannot match. VR can also be used to create virtual tours of museums, art galleries, and other cultural institutions, providing access to these institutions to people who may not have the ability to visit them in person.

Another area where VR is being used is in the field of architecture and interior design. Architects and designers can create virtual models of buildings and spaces, allowing clients to experience and interact with their designs in a fully immersive way. This technology can also be used in the construction industry and manufacturing to visualize and plan projects, reducing the risk of errors and increasing efficiency.  VR is being used in the military and emergency services for training purposes, allowing them to simulate real-world scenarios in a controlled and safe environment.

In conclusion, Virtual Reality technology has enormous potential for a wide range of applications, from education to emergency services, therapy, and beyond. As the technology continues to evolve, it will become even more accessible and offer even greater levels of immersion and interactivity.

Applications of Artificial Intelligence in the Automotive Industry

AI in Automotive Industry

A century ago, the notion of machines being able to comprehend and perform intricate calculations and come up with effective solutions to pressing concerns was more of a science fiction than a foreseeable reality.

Artificial Intelligence has revolutionized various industries, and the automotive industry is no exception. From infusing AI into the production process and supply chain to inspections and quality control to increase efficiency of businesses, and using AI to make improvisations in the passenger and driving experience for customers.

“I think we see the merging of several worlds, the tech industry, the internet and the automotive industry. These two worlds merging is like a smart phone on wheels, or you can say it’s a car that has many of the capabilities of smart phones and computers and so on.” said Dieter Zetche, the former head of Mercedes-Benz and the current chairman of TUI AG Group.

One of the major applications of AI in the automotive industry is in the development of autonomous vehicles. AI algorithms can process vast amounts of data and provide decision-making abilities to vehicles, allowing them to operate without human intervention. This has opened up new opportunities for the automotive industry, including the development of self-driving taxis and delivery vehicles, which are expected to become more widespread in the future.

Predictive maintenance via AI algorithms helps analyze large amounts of data from vehicle sensors and predict when a component is likely to fail. This allows manufacturers to schedule maintenance and repairs before a breakdown occurs, reducing the likelihood of vehicle downtime and improving customer satisfaction.

AI is also used in the design and development of vehicles. AI algorithms can analyze data on consumer preferences, driving patterns, and road conditions to optimize vehicle design and functionality. This can lead to the development of safer and more efficient vehicles, with features tailored to meet the needs of customers.

In conclusion, AI is playing an increasingly important role in the automotive industry. Its use has improved production processes, increased efficiency, and enhanced the driving experience for individuals. As AI technology continues to evolve, it is expected to play an even greater role in shaping the future of the automotive industry.

Explaining the why of algorithmic conclusions

Explaining the why of algorithmic conclusions
Explaining the why of algorithmic conclusions

There was a deathly silence in that vast hall of a thousand-plus data mining and artificial intelligence practitioners in New York City recently when the speaker made the following statement: ‘If you reject a consumer loan application and the consumer asks why her loan was rejected, you will get into regulatory trouble if you say, ‘I don’t know, the algorithm did it’.’

Considering that the conclusions that algorithms make, be it in loan approvals or in deciding which stock a hedge fund should buy or sell, or in health care, are treated as sacred truths, this sudden demand for ‘explanations’ sounded like a thunderbolt from the sky.

In viewing this emerging debate, it is important to go back to the foundational years of statistics, the methods of which lie at the heart of today’s Machine Learning and Artificial Intelligence and Karl Pearson, in the Britain of the 1880s.

His ‘correlation coefficient’, which is the first baby step that anyone who studies statistics takes even at the school level. It, for example, helps you determine whether glucose level in humans increases with age.

Given the level of glucose in the blood of individuals of, say, a dozen people of different ages, it helps us calculate whether age and glucose level are ‘correlated’.

Pearson, in the England of the 1880s, went on to define a great many other things, which serve as the foundation of the science of statistics and its contemporary version, Machine Learning and Artificial Intelligence, such as principal component analysis, the chi-squared test and the histogram.

These statistical tools that we revere so much even today were essentially used to put a scientific basis to propagate ‘eugenics’, the belief that human beings come from different races and that there are ‘inferior’ races and ‘superior’ races and that no amount of training or education could improve a person of an ‘inferior’ race.

From those early days, as the 20th century progressed, statistics was enlisted for many other causes, including the computation of the gross domestic product, that one number which is nowadays used to conclude how well or badly a country and its government are doing.

By the 1950s statisticians were sitting at the highest policy making circles and helped created five-year plans.

With the general disenchantment with economic planning in the late 1980s and fervour about ‘free markets’ and ‘competition’, statistics and jobs for statisticians took a back seat, but in our contemporary era, young men and women with felicity in statistics get the highest starting salaries after graduation and with the advent of Machine Learning and Artificial Intelligence (the contemporary high-sounding words for statistics) this trend has accentuated many-fold.

At the core of these ultra-fashionable disciplines lies the work of Pearson and his contemporaries of the 1890s: Correlations, regressions and so on.

But, just as these late 19th century tools return to prominence, so have the questions about ‘explainability’, spelling out the reasons for a conclusion about, say, why a loan application was rejected, something which goes beyond saying that ‘the algorithm says so’.

Mridul Mishra of Fidelity Investments, at the same conference, offered some suggestions about what ‘explainability’ could be — what makes a ‘good’ explanation for the conclusions of an algorithm. First, he said, try ‘contrastiveness’, if an input into the algorithm changes.

For example, if the percentage of one’s salary that a loan applicant saves every month increases by, say, 10 per cent, does the algorithm spit out a ‘loan approved’ conclusion.

If yes, the ‘explanation’ for the loan rejection by the algorithm is that the loan applicant is not saving enough.

He spelt out several other ways of providing a ‘good’ explanation. (For the record: Counterfactual explanations, Bin-based explanations, Shapely Value explanations and so on). In other words, creating ‘explanations’ for the output of an algorithm is itself rising to an industry status!

While one cannot quarrel with the desire for ‘good’ explanations, I can anticipate some interesting debates in the immediate future. For example, if an IIM applicant’s CAT exam score is 85th percentile and he is rejected, what answer can we give him if he asks for an ‘explanation’ — that there is statistical evidence that the higher a person’s CAT exam score, the better he will be as a manager when he graduates from an IIM? (Having studied this issue, I can safely tell you that no such correlation exists).

Similar questions may get raised about all the various ‘weeding out’ exam scores that we use in our country for promoting kids in schools, admitting them to colleges and so on.

What will be the ‘good’ explanations? That high school exam score is correlated with the literacy level and a minimum income level of parents?

If that turns out to be true, are high-school exam scores merely a measure of the social origin of a kid?

Such debates are, at present, confined to the esoteric world of tech conferences, but I can see interesting debates (and battles) ahead, once our courts start backing the demand for ‘good explanations’ for conclusions made by algorithms.

Facebook likely to launch ‘Portal’ video chat device using AI

Facebook is reportedly set to announce its own video chat device called Portal next week, taking on Amazon’s smart home devices.

Portal will take on Amazon’s Echo Show and will be available in two variants, Cheddar reported on Friday.A larger variant could be priced at around $400, while a smaller variant could go for around $300.

However, it will have integration with Amazon’s Alexa voice assistant and let users play music, watch videos, see cooking recipes, and get news briefs.

Amazon Echo Show and Echo Spot devices add video to the audio experience via voice assistant Alexa. While Echo home devices are microphone-equipped speakers, the Amazon Echo Show and Spot also include a display.

The report said that Facebook had originally planned to announce Portal at its annual F8 developer conference in May “But the company’s scandals, including the Cambridge Analytica data breach and the bombshell revelation that Russia used the platform to interfere with the 2016 elections, led executives to shelve the announcement at the last minute”

The social media giant was yet to comment on this development.

Portal will also reportedly feature a privacy shutter that can cover the device’s wide-angle video camera. It will apparently use Artificial Intelligence (AI) to recognise people in the frame and follow them as they move throughout a room.

How Is AI Used In Healthcare – 5 Powerful Real-World Examples That Show The Latest Advances

When it comes to our health, especially in matters of life and death, the promise of artificial intelligence (AI) to improve outcomes is very intriguing. While there is still much to overcome to achieve AI-dependent health care, most notably data privacy concerns and fears of mismanaged care due to machine error and lack of human oversight, there is sufficient potential that governments, tech companies, and healthcare providers are willing to invest and test out AI-powered tools and solutions. Here are five of the AI advances in healthcare that appear to have the most potential.

1. AI-assisted robotic surgery

With an estimated value of $40 billion to healthcare, robots can analyze data from pre-op medical records to guide a surgeon’s instrument during surgery, which can lead to a 21% reduction in a patient’s hospital stay. Robot-assisted surgery is considered “minimally invasive” so patients won’t need to heal from large incisions. Via artificial intelligence, robots can use data from past operations to inform new surgical techniques. The positive results are indeed promising. One study that involved 379 orthopedic patients found that AI-assisted robotic procedure resulted in five times fewer complications compared to surgeons operating alone. A robot was used on an eye surgery for the first time, and the most advanced surgical robot, the Da Vinci allows doctors to perform complex procedures with greater control than conventional approaches. Heart surgeons are assisted Heartlander, a miniature robot, that enters a small incision on the chest to perform mapping and therapy over the surface of the heart.

2. Virtual nursing assistants

From interacting with patients to directing patients to the most effective care setting, virtual nursing assistants could save the healthcare industry $20 billion annually. Since virtual nurses are available 24/7, they can answer questions, monitor patients and provide quick answers. Most applications of virtual nursing assistants today allow for more regular communication between patients and care providers between office visits to prevent hospital readmission or unnecessary hospital visits. Care Angel’s virtual nurse assistant can even provide wellness checks through voice and AI.

3. Aid clinical judgment or diagnosis

Admittedly, using AI to diagnose patients is undoubtedly in its infancy, but there have been some exciting use cases. A Stanford University study tested an AI algorithm to detect skin cancers against dermatologists, and it performed at the level of the humans. A Danish AI software company tested its deep-learning program by having a computer eavesdrop while human dispatchers took emergency calls. The algorithm analyzed what a person says, the tone of voice and background noise and detected cardiac arrests with a 93% success rate compared to 73% for humans. Baidu Research recently announced that the results of early tests on its deep learning algorithm indicate that it can outperform humans when identifying breast cancer metastasis. Prime minister Theresa May announced an AI revolution would help the National Health Service (NHS), the UK’s healthcare system, predict those in an early stage of cancer to ultimately prevent thousands of cancer-related deaths by 2033. The algorithms will examine medical records, habits and genetic information pooled from health charities, the NHS and AI.

4. Workflow and administrative tasks

Another way AI can impact healthcare is to automate administrative tasks. It is expected that this could result in $18 billion in savings for the healthcare industry as machines can help doctors, nurses and other providers save time on tasks. Technology such as voice-to-text transcriptions could help order tests, prescribe medications and write chart notes. One example of using AI to support admin tasks is a partnership between the Cleveland Clinic and IBM that uses IBM’s Watson to mine big data and help physicians provide a personalized and more efficient treatment experience. One way Watson supports physicians is being able to analyze thousands of medical papers using natural language processing to inform treatment plans.

5. Image analysis

Currently, image analysis is very time consuming for human providers, but an MIT-led research team developed a machine-learning algorithm that can analyze 3D scans up to 1,000 times faster than what is possible today. This near real-time assessment can provide critical input for surgeons who are operating. It is also hoped that AI can help to improve the next generation of radiology tools that don’t rely on tissue samples. Additionally, AI image analysis could support remote areas that don’t have easy access to health care providers and even make telemedicine more effective as patients can use their camera phones to send in pics of rashes, cuts or bruises to determine what care is necessary.

In the very complex world of healthcare, AI tools can support human providers to provide faster service, diagnose issues and analyze data to identify trends or genetic information that would predispose someone to a particular disease. When saving minutes can mean saving lives, AI and machine learning can be transformative not only for healthcare but for every single patient.

Source : https://www.forbes.com/sites/bernardmarr/2018/07/27/how-is-ai-used-in-healthcare-5-powerful-real-world-examples-that-show-the-latest-advances/#1eca000b5dfb

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What is AI and ML

What is Data Science?

Let’s break the term into its composite parts – data and science. Science works fundamentally through the formulation of hypotheses – educated guesses that seek to explain how something works – and then finding enough reasonable evidence through observations in the real world to either prove the hypothesis right, or falsify it. Data, on the other hand, refers simply to numbers and statistics which we gather for the sake of analysis.

By combining these two, we get data science. What exactly does it mean? Data science is an umbrella term for all techniques and methods that we use to analyze massive amounts of data with the purpose of extracting knowledge from them.

Example of Data Science

Source : Acadgild

Let’s say you are crazy about Cricket, which I am sure you areJ, and there is an ongoing series between India and Australia. India loses the first two matches, much to your disappointment, and you are eager to know what will happen in the next game. You go online to check the results of past encounters between the two nations and notice a trend – every time India has lost two games in a row against Australia, they have come back strongly in the third. You are convinced India will win the next game, and predict the outcome. To everyone’s surprise, your prediction turns out right. Congratulations, you’re a data scientist!

The numbers and statistics that a data scientist observes in the real world may not be so simple, and he/she might even require software to recognize the underlying trends. Nonetheless, the basic idea is the same. Due to its efficiency in predicting outcomes, data science is useful in developing artificial intelligence – what we will explore next.

Artificial Intelligence:

Artificial intelligence (AI) is a very broad term. Chiefly, it is an attempt to make computers think like human beings. Any technique, code or algorithm that enables machines to develop, mimic, and demonstrate human cognitive abilities or behaviors falls under this category. An artificial intelligence system can be as simple as a software that plays chess, or more complex like a car without a driver. No matter how complex the system, artificial intelligence is only in its nascent stages. We are in what many call “the era of weak AI”. In the future we might enter the era of strong AI, when computers and machines can replicate anything and everything humans do.

Source : Business Insider

The saying, “be careful what you wish for” is extremely apt when discussing artificial intelligence. The emergence of AI has thrown up new challenges almost as a by-product of the advanced technology. While it’s good that a car which doesn’t need a driver can take you to your destination with little or no effort on your part, it would be terrible if it crashed along the way. Similarly, it’s good that robots can help with work in industries. However, it won’t be so good, if they declared war against humans. I digress. These are discussions for another time, and I want to move on to the topic of machine learning in the next section.

Machine Learning:

Machine learning is one of the hottest technologies right now. It refers to, as the name suggests, a computer’s ability to learn from a set of data, and adapt itself without being programmed to do so. It’s one kind of artificial intelligence that builds algorithms which can analyze input data to predict an output that falls within an acceptable range.

Supervised Learning
Machine learning algorithms can be of different kinds. The most common of these are “supervised algorithms”. These algorithms make observations using “labels”, which are nothing but predictable outcomes.

Source : Acadgild

You collect a bunch of questions along with their answers. Using these, you train your model to create a question-answer software. This software responds with an appropriate answer every time you ask it a question. Cortana and other digital assistants, which are essentially speech automated systems in mobile phones or laptops, work this way. They train themselves to work with your inputs and then deliver amazing results according to their training.

Unsupervised Learning

“Unsupervised algorithms” are of a different kind. Unlike its counterpart (supervised algorithms), these algorithms do not have a sample data set to guide their learning or help them predict outcomes. Clustering algorithms are good examples of unsupervised machine learning.

Let us suppose you’re in a new environment – it’s your first day in college, and you don’t know anyone. You don’t know the people, the culture, where your classes are, nothing. It takes time for you to recognize and classify things you encounter in your college life. You identify the friends in your group, your competition either in class or other activities, the good food items in the canteen, etc. You learn about these things without knowing what to expect and gather information in a haphazard manner. Your method of learning is unsupervised.

Similarly, when NASA discovers new objects in space from time to time, it is a difficult task to classify them. If the object is very different from known objects such as meteoroids or asteroids, then it requires more time and effort to study the object and gather information pertaining to it. With time and more information on the characteristics of the object, it NASA can classify it – either group it with existing objects or classify it as something new altogether. In this scenario, NASA uses an unsupervised learning technique, much like the way unsupervised algorithms function, without predictable outcomes.

I hope you’re beginning to realize that artificial intelligence and machine learning are not quite the same. AI is the science that seeks to help machines mimic human cognition and behavior, while machine learning refers to those algorithms that make machines think for themselves. Simply put, machine learning enables artificial intelligence. That is to say, a clever program that can replicate human behavior is AI. But if the machine does not automatically learn this behavior, it is not machine learning.

Deep Learning

Deep learning is only a subset of machine learning. It is one of the most popular forms of machine learning algorithms that anyone can come across. They use artificial neural networks (ANNs). Artificial neural networks are a family of models similar to biological neural networks in humans. These networks are capable of processing large volumes of data. Neurons (different nodes in the model) are inter-connected and can communicate with each other. Final outputs depend on the weight of different neurons. Essentially, it’s an algorithm that can receive and calculate large volumes of input data, and still churn out meaningful output. In deep learning, the artificial neural network is made up of neurons in different layers. These layers lie one over the other. They are separate and multiple instead of one.

What separates deep learning from other forms of algorithms is its ability to automatically extract features from input data. Unlike feature engineering, it does not require any manual support. It is similar to image recognition processes that do not require particular features in the image for interpretation. The processes up information on the go, and use it to deliver their output. Let us consider an example to understand this.

Source : Acadgild

Say you are playing a game along with four of your friends. Your friends are the different layers of the artificial neural network – all standing in a row one behind the other. There is a moderator to enforce the rules of the game. A picture is shown to the first person. His objective is to extract as much information as he can, and pass it on along the row. The game shall go on until the last person can describe the image accurately.While playing, all members standing in queue will have to alter the information, little at a time, to improve the eventual description. Deep learning works in a similar fashion. It refines the information it collects over time to deliver finer results.

Source : Acadgild

Evolution of AI

The picture above clearly depicts the relationship between artificial intelligence, machine learning, and deep learning. It’s apparent that artificial intelligence is the broadest term. Both machine learning and deep learning are subsets of it. In fact, deep learning is also a subset of machine learning. AI is the broadest term out of the three. It also happens to be the oldest. It originated in the 1950’s when the “Turing Test” became popular. A lot of time and energy was devoted in those days to try and create an algorithm that could mimic human beings. The first real success in this endeavour was achieved when machine learning was introduced in the 70’s.

Machine learning was path-breaking at the time, but it had its limitations. Human curiosity knows no bounds, and the will to create a more robust algorithm persevered. Now, the goal became to overcome the limitations of machine learning.

Fast-forward to 2010 when Google launched its ‘Brain Project’, and the perception of artificial intelligence – of what it meant and could achieve – was changed forever.

Source : Blogs.nvidia

How Does Data Science Relate to AI, ML & DL?

As mentioned before, data science is an inter-disciplinary field that requires skills and concepts used in disciplines such as statistics, machine learning, visualization, etc. According to Josh wills, a data scientist “is a person who is better at statistics than any software engineer and better at software engineering than any statistician”. He/she is the jack of all trades manipulating and playing with data. How is the data scientist and his field of study related to artificial intelligence, machine learning, and deep learning?

Source : Acadgild

To answer simply, data science is a fairly general term for processes and methods that analyze and manipulate data. It enables artificial intelligence to find meaning and appropriate information from large volumes of data with greater speed and efficiency. Data science makes it possible for us to use data to make key decisions not just in business, but also increasingly in science, technology, and even politics.


Artificial intelligence is a computer program that is capable of being smart. It can mimic human thinking or behavior. Machine learning falls under artificial intelligence. That is to say, all machine learning is artificial intelligence, but not all artificial intelligence is machine learning. For example, a simple scheduling system may be artificial intelligence, but not necessarily a machine that can learn. Then we have deep learning, which is, again, a subset of machine learning. Both – machine learning and deep learning – fall under the broad category of artificial intelligence. Lastly, data science is a very general term for processes that extract knowledge or insights from data in its various forms. Although it has no direct relation to artificial intelligence, machine learning or deep learning, it can be useful to each of the three.

Artificial Intelligence (AI)

AI (pronounced AYE-EYE) or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.