We are living in an era of always on devices. Very few amongst us will remember the last time we turned off our cell phone! But what we do not realize is that we are not only “live” on the digital highway all the time, we are also creating a lot of data on it through our, posts, comments, text messages etc. Can you imagine that a just over 25 years ago, you were doing none of these things and therefore not leaving behind any sort of digital foot print. Now in a matter of seconds, digitally you are all over the place.
Think about it, so many people, but each of us leaving behind digital footprints similar to the others but unique in our own way. We at RTAP believe that this opens up a huge opportunity in the area of exploring the human mind and behavior through machine learning technology.
Dream Come True….
They say marketing is all about having a piece of your consumer’s mind. Therefore, if one is able to find out people having similar needs it will open up unimaginable marketing opportunities. For example: similar product preferences, healthcare needs etc. All this can be attempted now, move effectively with machine learning technology.
Getting Wiser: Machine Learning….
We at RTAP believe that, the availability of data in real time from a large number of sources gives us the ability to build cognitive based systems. Since RTAP is designed on the principles of machine learning, it helps you become smarter as times goes by. It allows you to bunch people, products and preferences in a more predictable manner thereby helping enterprises to offer products (and services) as per needs and desires of the consumer.
Future based on Past: Supervised Learning….
Supervised learning algorithms make predictions based on a set of examples. For instance, historical data on car colors can be used to guess future color preferences before they come out of assembly lines. Each example used for training is labeled with the value of interest—in this case the car color. A supervised learning algorithm looks for patterns in those value labels. It can use any information that might be relevant—demographics, buyers age, the type/model of the car, competitor data—and each algorithm looks for different types of patterns. After the algorithm has found the best pattern it can, it uses that pattern to make predictions for unlabeled testing data—next color choices for cars under production.
This is a common and practical example of machine learning. With RTAP’s machine learning platform we can help you look at several specific types of supervised learning like: classification, regression, and outliers. These topics are discussed briefly below:
Classification: Here we use data to predict a type. There are different types of classification algorithms like binomial, polynomial etc. depending upon the number of possible choices. Based on this we can further carry our cohort analysis to bunch up outcomes.
Regression: When a value is being predicted, as with prices of goods and services, supervised learning is called regression.
Outliers: Sometimes the goal is to identify things that are amiss or not fitting into a pattern or expected result. In credit card fraud detection, for example, a large purchase from an unknown location can trigger an alert of something being wrong. The most common way of doing this is to find out a known buying pattern for a particular credit card customer and if things look absolutely different from that pattern trigger an alert.
Tidy up to Find Things: Unsupervised Learning ….
In unsupervised learning, data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its pattern. This can mean grouping it into clusters or finding different ways of looking at complex data so that it appears simpler or more organized. RTAP for Life, which is our solution to analyze large amounts of unstructured genetic data to find disease pathways looks at many complex sets of algorithms, so simply arrive at an acceptable data structure or pattern for biologist to determine the presence or absence of a particular disease. Similar algorithms are also available in RTAP for Work to look at enterprise data on a number of use cases.
Experience Counts: Reinforcement Learning….
In reinforcement learning, the algorithm gets to choose an action in response to each data point. The learning algorithm also receives a feedback, indicating how good the decision was. Based on this, the algorithm modifies its strategy in order to achieve the highest reward. Reinforcement learning is common in robotics and Internet of Things applications. At RTAP, we are building on such cognitive algorithms to help businesses get smarter over a period of time and continuously learn from their hits and misses.
Take Away for Now:
We started with the being “ON” all the time and the data we are generating on a continuous basis. We then explored ways of finding your digital footprint and the importance it bears to manufacturers and service providers. We then looked at the enormity of this problem and figured out a way to make our computers (machines) do all the computational work for us to learn from this. We also talked a little bit about what we are doing at RTAP using Machine Learning Technology. The topic of cognitive sciences or Artificial Intelligence is nothing new, it has been discussed and applied in various ways over several generations. So what has really changed for this generation is the availability of large amounts of data in real-time and the ability of our modern day computers to process it. If I have to sum it up in a few words, what really has changed is you and the power of silicon.