The advent of social media has made us a visually driven generation, with both the camera and the internet being available in a single touch. This increasingly image based sector can be a powerful marketing tool if used efficiently. Adweek says that over 3.2 billion images are shared online every day. The visual content has become an imperative part of the messages shared on the web. Using AI and image analytics, this rich source of information can be tapped into, in order to gain consumer insights and make the best decisions for the brand. Artificial intelligence and image recognition are also making it easier for marketers to identify visuals in social media for better metrics and customer service.

Watching out for the brand

AI and image recognition can be used to understand the need for a visual perception of the logos, placement of the products and how products are actually being used in real life. Social media is a massive open database. It has a huge open source framework and large worldwide collaboration. An analytics tool will help in keeping track of the number of times a certain brand has featured in the billions of photos that are posted every day. This can be used as a tool to measure the marketing effectiveness and gauge the ROI. A photograph containing a logo might reveal more about the context in which the products are consumed. These image mentions are often passive and unfiltered but analysts can learn a lot more from them. Insights gleaned from images can help inform decisions surrounding the context in which the product is advertised and how different groups use your product differently. This information will enable the brand to integrate the data-driven visual imagery into brand storytelling.

Gaining consumer insights

As mentioned before, analyzing the data could provide insights into how products are being used, providing a way to track brand displays online hidden within pictures, and allowing the brand to find out when influencers are using their products. Social media has turned into a rich resource that provides consumer and customer insights, by letting the brand analyze nuanced metrics like customer opinion, emotion analysis, audience interests and demographics, which can in turn help understand and better predict consumer behavior.

Brands can identify the target segment discussing relevant topics and the sentiment or perception they associate with the brand or product. As the brand keeps track of their audience, they would easily be able to analyze the change in trends and act upon it. Such data can help the marketers to come up with the right and most effective product strategy.

Improving customer service

Social media has paved a direct and public way of contacting the company or a brand. People can tag the brand both under circumstances of duress or of celebration. Keeping track of the brand mentions, enabled by AI can help the brand to respond to the customer in the most appropriate manner. Research shows that the speed of a response in social customer care is a far stronger cause of customer happiness than even solving the customer’s issues.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


Companies of this age invest heavily on data to stay ahead of the competition. Data analysts and data science experts are hired to analyze the vast expanse of data available, an understanding of which will render them capable of making informed and educated choices, optimizing marketing strategies and putting off projects that have a lower rate of success. Once the initial goal of securing customers is achieved, the next step for a successful company is to keep the customers entranced with their working, thereby building the company on their loyalty. For example, recommendation services that are high quality and geared towards recommending new products to customers has the potential to dramatically improve sales per customer and price point per order. Data collected from the customer base can be analyzed to give the customers what they need. Some potential issues can also be solved in the same manner, before they even materialize. Optimizing product locations is another way to provide customer services in a personalized manner.

An excellent example of how effective use of data can result in success is that of Netflix, which is presently valued over 164$ billion, having overtaken Disney’s place as the world’s most valuable media company.

Some guidelines to optimize the company’s profits by using data include:

  1. Setting clear goals

Laying out well defined goals and expectations of the company in order to make the action plan is important as this will give data scientists the opportunity to adopt the right methodology. Netflix is an online streaming channel, with over 130 million users. Their success rates depend on their customer satisfaction, on whether the subscribers like what they are watching. Here Netflix uses predictive analytics to put forth options which the user will find favorable, as it is based on the data collected from the user’s experience. When the channel succeeds to strike a chord with a viewer, then the credibility increases.

  1. Identifying available data

The available data has to be analyzed brought to the right format, so that it can be used to bring about the goals of the company. With the large number of subscribers come tremendous amounts of data that Netflix can use. During the onset of subscription, the customer has to give information about his/her interest in specific genres. They are also asked to rate the movies which they have already seen. Such information is used by Netflix to help them to discover new movies and T.V shows, something that is integral towards its success. Data is also collected from events like the customer’s searches, the date the show was watched, the device on which it was watched, when the program was paused, when the program is re-watched etc. The vast data collected from such events helps Netflix to understand their subscriber’s choices and preferences. This will in turn be used for the user, providing them with a much more personalized experience.

3. Adopting the right methodology and being data driven.

With an in-depth understanding of the data at hand, the right tool has to be chosen so as to use the information most effectively. Netflix uses algorithms for predicting the user’s choice based on his previous ratings. It is said that the recommendation system used by Netflix influences 80% of the content the subscribers watch on Netflix. Recommendation systems are simple algorithms which aim to provide the most relevant and accurate items to the user by filtering useful stuff from of a huge pool of information base. Content based systems, recommends item based on a similarity comparison between the content of the items and a user’s profile.

Collaborative Filtering algorithm considers “User Behaviour” for recommending items. Other users’ behavior and preferences over the items are used to recommend items to the new users.

A personalized video ranker orders the entire Netflix collection for each member profile in a personalized way, keeping in mind their interests, habits and choices. Jenny McCabe, Director of Global Media Relations says “We always use our in depth knowledge (aka analytics and data) about what our members love to watch to decide what’s available on Netflix….If you keep watching, we’ll keep adding more of what you love.”

An action plan based on the available data would enable the company to take the right decisions. In 2011 Netflix outbid top television channels like HBO and AMC to earn rights for a U.S version of ‘House of Cards.’ At a cost of $4 million to $6 million an episode, this 2-season series cost over $100 million. Such a big decision was made on the data that they already had.

Using methods like clustering analysis, sets with similar attributes are studied. A lot of users watched the David Fincher directed movie The Social Network from beginning to end. The British version of “House of Cards” has also been well watched. Those who watched the British version “House of Cards” also watched Kevin Spacey films and/or films directed by David Fincher. Such factors gave them the confidence to make the $100 million investment, which turned profitable in the end. Here, using association mining of customer information with similar behavior is targeted to make a decision that satisfies that particular set.

  1. Testing regularly

Data driven models should be constantly checked as demographics have a way of changing gradually. The algorithms that Netflix uses are constantly revised in order to achieve maximum optimization. Bill Franks, Chief Analytics Officer, International Institute for Analytics says that “ I can say that no changes in Netflix products are not tested and validated and we do not just test to test. If we do not believe it will not improve, it will not be tested. We have 300 major tests of products and dozens of variations within”

Data science practices should be implemented wisely. Netflix has successfully shown us that machine learning can been used to convert the user’s cravings into the company’s business goal. Quantitative data is always a good basis on which better and cost effective decisions can be taken. Data can predict whether certain innovations or experimental projects can take off.

While discussing analytics, Netflix co-founder Mitch Lowe says “He [Reed Hastings] taught me how to use analytics to make decisions. I always thought you needed a clear answer before you made a decision and the thing that he taught me was [that] you’ve got to use analytics directionally…and never worry whether they are 100% sure. Just try to get them to point you in the right direction. ”

Intelligent use of data can reap benefits, but it should be done responsibly. Data should be protected from others and used carefully. Data science should be seen as a solution to solving problems as well as a way to greater rewards; it should be given due importance.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


The ever evolving technology powered by artificial intelligence is transforming many industries. The healthcare industry is no exception. Artificial Intelligence (AI) and machine learning technologies are increasingly being depended upon to keep up with the influx of constant new information about health conditions, treatments and medical technology. Today, machine learning algorithms and predictive analytics are being used to reduce drug discovery time, provide virtual assistance to patients and diagnose ailments by processing medical images.

From the past to the present

The Information age has brought with it an influx of technology that aims to make healthcare cheaper. Opposed to earlier where manual labor and doctors were heavily relied upon, artificial intelligence in the present can help scan thousands of images and identify patterns at a fraction of time and machine learning can help improve sensitivity and accuracy over time. Such developments make many procedures in healthcare much cheaper than what it has been in the past.

For example, AI can bring down the cost of a cancer screening test, by reducing the time to perform the operation and by bringing down the doctor’s fee, since highly skilled endoscopists may no longer be required to perform screening tests. Medical VR (virtual reality therapy) has been evidenced to stop the brain from processing pain and reduce pain in hospitalized patients. This, in turns, shortens the length of the patient’s stay in the hospital, which, also lowers the costs of care

Key Applications of AI in Healthcare

Virtual Reality

Although VR indeed set sails to enhance the demanding gamer’s experience, it has also made significant improvements to the lives of people with autism, lazy eye, chronic pain, and other health conditions. Startups like Floreo use virtual reality to help make the delivery of therapy simplified so parents can support their offspring from home. Their product uses mobile VR to instigate social interactions with autistic kids by spurring virtual characters in a scene. It can also be used in a manner that influences the brain to reduce chronic pain. A faster recovery time can be clocked using innovative technologies. Mindmaze is a Swiss app that allows patients to practice how to move their fingers or lift their arms in a fun fashion with the help of VR. Although patients do not carry out the actual movement, their engagement, motivation, and attention is notably improved with audio-visual feedback, which could speed the recovery of traumatized nervous systems.

Computer Vision and Robotics

Medical imaging is the biggest and most established area of computer vision and is used by computer-aided diagnostics for personalized therapy planning, care assistance and for better decision-making.

Robotic surgery has been making waves in the industry and is being hailed for being ‘minimally intrusive’, thereby allowing the patients to heal faster from smaller incisions. They also analyze data to guide the surgeon. One popular example is the da Vinci Surgical System features a magnified 3D high-definition vision system and tiny wristed instruments that bend and rotate far greater than the human hand. As a result, da Vinci enables the surgeon to operate with enhanced vision, precision and control.

Among other robots, the HeartLander is a miniature mobile robot that can enter the chest through an incision below the sternum. It reduces the damage required to access the heart and allows the use of a single device for performing stable and localized sensing, mapping, and treatment over the entire surface of the heart.

Virtual Assistants

Virtual nurses are high on demand as they offer many benefits including round the clock availability and quick answers. They offer regular communication between the patients and the care providers. Care Angel’s voice powered virtual nurse assistant provides wellness checks through AI. Another digital nurse is Molly, created by the startup Sensely, which monitors a patient’s condition and follows up with treatments, between doctor visits.

Administrative Automation

AI can help in compiling, managing and analyzing medical records and other data. Automated Administrative tasks can thus save money and time. Robots collect, store, re-format, and trace data to provide faster, more consistent access. Mundane tasks analyzing tests, X-Rays, CT scans can me made faster and more accurate. The data collected and stored can accessed consistently. Technology such as voice-to-text transcriptions could help order tests, prescribe medications and write chart notes. IBM’s cloud based intelligence Watson, mines big data and helps physicians provide a personalized and more efficient treatment experience. It is also among the pioneers of the field.

Doubts still remain

Even though there have been breakthroughs in the healthcare industry with regards to the applications of artificial intelligence, people still harbor fears of mismanaged care due to a mechanical error. The lack of human insight and data privacy issues are other concerns that the industry has to deal with. While technology can support the highly trained medical professionals, the chances of it taking over the industry completely remains very low.

The future

In a few years, the market for AI-powered healthcare technologies will exceed 6 billion dollars. A demand for electronic, data driven and virtual-based care is the driving force, especially because they offer more convenient, accessible and affordable care. Patients look forward to gaining greater insight into their own health and finding a more appropriate level of care for their needs.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


Everybody who owns a smart phone has already experienced artificial intelligence in their lives. Technology strives to make everything faster and better.

Security becomes an integral part of such a fast-paced life, where humans are distracted by the macro and tend to overlook the micro things around them. According to the U.S Department of Justice, on an average, almost 4 million burglaries take place every year. Artificial Intelligence powered Home security systems rise to the occasion, offering a better and cheaper solution than what was previously offered in the industry.

Things of the Past

Traditional home security systems were laborious and manual in nature. It required  monitoring by people, taking shifts to keep guard 24 hours a day, thereby making it expensive. Even in the owner’s absence, the guards called up the emergency services if someone broke in.

A few years after the traditional systems were deemed insecure, home surveillance systems used simple sensors and alarms to detect intruders. Such systems did not require constant human supervision. If something went wrong, an alarm would go off and the security professionals would be contacted. But in most cases such alarms were false, which resulted in a major waste of time, money and resources.

The Smart Solution

Today, incorporating artificial intelligence into security systems gives it the ability to recognize the difference between an intruder and visitor. Through the advances of technology such as computer vision and deep learning, security systems have the potential to track the visitors and guests. Conventional security systems on the other hand would have sounded an alarm when they detected movement.

Police stations usually report that most of the alarms turn out to be false. Giving the owner the power to disable the alarm when there is no threat present saves time and money but will bypass that if the owner doesn’t respond soon enough, keeping one out of harm’s way.

Lighthouse is an example of a leading smart home camera, enabled with advanced computer vision and AI technology. The brand claims that it creates a 3D model of the room to understand what it sees, using technology from self-driving cars. Modern security systems also let the cameras be connected to the owner’s phone, so that they can view the security footage live or whenever required.

Connecting them all

AI security systems can also be integrated with the smart appliances at home. Amazon and other big companies are investing a lot of money into this. Digital Assistants like Amazon Echo, among others can work with the security systems, allowing them control over the cameras and electronic locks. They can even learn how to recognize the owner’s voices. Canary home security and intelligence is another leading brand which comes with a 1080p HD camera, 90 decibel siren and a built-in climate monitor. It is integrable with the Google Assistant and Amazon’s Alexa.

The Smart and personal assistants build upon the user inputs, using AI and “learn” to work well with the needs of the user. Machine and deep learning systems begin to track the habits of the user.

AI powered home security systems can thus understand when an event seems unusual, after learning from experience and tighten security levels accordingly. This is helpful especially for families who have frequent visitors or travel often, as the home security system reduces the number of false alarms. More modern AI based lock systems use machine learning to keep track of who is using their key to get into your home and when, thereby keeping the owner updated.

Future is in safe hands

Using AI to power home security combats many problems which were prevalent in the past. It also offers many benefits, letting the owners live more comfortably and securely. The systems which use Machine Learning, learn from experience, that is, the more often these systems are used, the more accurate their abilities become.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.


Almost everybody has experienced artificial intelligence of one level or the other by using everyday things around them. The next big thing everybody is looking forward to is revolution in the automated mobility industry. In 2016, Apple Chief Executive Tim Cook described the challenge of building autonomous vehicles as “the mother of all” AI projects.

While big players like Google, Uber, and Tesla are competing with other each and other prominent companies, investing billions to come up with a commercially successful fleet of driverless cars, AI experts believe that it may take many a year before self-driven vehicles can successfully conquer the unpredictability of traffic.

AI plays the main role, as always

An autonomous car can be defined as a vehicle capable of navigating itself without human help, using various sensors to perceive the surrounding environment accurately.  They can make use of a variety of techniques including radar, laser light, GPS, odometry, and computer vision.

Complex algorithms, cameras and LIDAR sensors are made use of to create a digital world that orients the self-driven car on the road and helps identify fellow cyclists, vehicles and pedestrians. It is extremely difficult to design and produce such systems. They must be programmed to cope with an almost limitless number of variables found on roads. The autonomous vehicle industry therefore looks to machine-learning as the basis for autonomous systems. That is because huge amounts of computing power are required to interpret all of the data harvested from a range of sensors and then enact the correct procedures for constantly changing road conditions and traffic situations.

Deep learning and computer vision systems can be ‘trained’ to drive and develop decision-making processes like a human. Humans naturally learn by example and this is exactly what computers are taught to do as well, ‘think like humans’.

What is deep learning? – Deep learning is a method that uses layered machine-learning algorithms to extract structured information from massive data sets. It is a key technology behind driver-less cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. Each self-driven car is programmed to capture data for map generation, deep learning and driving tasks while they move along the traffic.

Autonomous vehicle industry developments

Google launched its self-driving car project in 2009, being one of the first to invest in this stream. Sensing that autonomous vehicle technology can open up a huge market and disrupting the current one, other tech-giants like Intel, IBM and Apple as well as cab hailing companies- Uber and Lyft and car makers have joined the race.

Alphabet’s Waymo, the self-driving technology development company was launched in December 2016. Waymo has been testing its vehicles Arizona for a little more than a year now. Places like California, Michigan, Paris, London, Singapore, Beijing among others regularly witness test-drives by self-driven cars.

The ground reality

While test-drives have become common in these places, the people have not yet adjusted to it. Research conducted by British luxury car maker Land Rover shows that 63% of people mistrust the concept of driverless cars. They are programmed to drive conservatively. While under the right conditions, it can eliminate aspects of human error and unpredictability like speeding, texting, drunken driving, when they move along with human drivers, the same unpredictability can confuse the autonomous cars. This could lead to accidents as well as a general mistrust over the technology. In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman named Elaine Herzberg in Tempe, Arizona. It is clear from regular reporting of accidents that happen during test-drives that autonomous car technology has a long way to go. Even after succeeding to avoid accidents, self-driven cars will have to face more than a decade long transition period, where humans have to accept this technology as well as give up driving.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

This content is not for distribution. Any use of the content without intimation to its owner will be considered as violation.

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