So what is Machine Learning!

Machine Learning (ML) is everywhere today! If you are using any of the apps or websites for Amazon, Google, Uber, Netflix, Waze, Facebook in your daily busy life, they are all internally supported by machine learning algorithms. Most of the top companies in every sector from Aviation to Oil and Gas, Banking to Retail, Ecommerce to Transportation have products and projects using machine learning tools and technologies. Machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

Wikipedia defines machine learning as “a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions.” As per IBM – Machine learning is the science of how computers make sense of data using algorithms and analytic models. As per SAS – Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

As per Stanford, Machine learning is the science of getting computers to act without being explicitly programmed. It enables cognitive systems to learn, reason and engage with us in a more natural and personalized way. The most powerful form of machine learning being used today, called “deep learning”, builds a complex mathematical structure called a neural network based on vast quantities of data. Designed to be analogous to how a human brain works, neural networks themselves were first described in the 1930s. But it’s only in the last three or four years that computers have become powerful enough to use them effectively. If deep learning will be as big as the internet, it’s time for everyone to start looking closely at it.

Machine Learning (ML) is a form of AI that facilitates a computer’s ability to learn and essentially teach itself to evolve as it becomes exposed to new and ever-changing data. The main components of ML software are statistical analysis and predictive analysis. These algorithms can spot patterns and find hidden insights based on observed data from previous computations without being programmed on where to look. They learn from every experience and interaction.

Some of the ML techniques are Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN). Artificial Neural Networks is where the field of Deep Learning had its genesis from.

Internet of Things (IOT) will produce billions and billions of data points from billions of connected devices by 2020. Machine learning can help companies take the billions of data points they have and boil them down to what’s really meaningful. The future realization of IoT’s promise is dependent on machine learning to find the patterns, correlations and anomalies that have the potential of enabling improvements in almost every facet of our daily lives.

As per HBR, machine learning has tremendous potential to transform companies. Executives who want to get the most out of their companies’ data should understand what it is, what it can do, and what to watch out for when using it. It’s time to let the machines point out where the opportunities truly are! In our next blog, we will take a journey into the various use cases and implementation of ML in different industry.

Share with me your use cases of ML that you want to get included in my next blog!


Cognitive Internet of Things?

Internet of Things (IoT) represents the extension and evolution of the Internet, which has great potential and prospects for modern intelligent service and applications. However the current IoT is still based on traditional static architectures and models by our deep investigation. It lacks enough intelligence and cannot comply with the increasing application performance requirements. By integrating cognition into IoT, we present a new concept of Cognitive Internet of Things (CIoT) and its corresponding intelligent architecture.

Most of the current offerings from several point solution vendors for Internet of Things (IoT) focusses on how to connect devices to see, hear, smell the physical world around and report the observations. However, I would argue that only connectivity and reporting is not enough but capability to learn, think and understand both physical, social and contextual data and apply intelligence is the key. This requirement drives us to develop a new model called “Cognitive” Internet of Things. What is Cognitive? It is more appropriate to refer to “cognition” as an “integrative field” rather than a “discipline” since the study on “cognition” integrates many fields that are rooted in neuroscience, cognitive science, computer science, mathematics, physics, and engineering, etc.

Cognitive computing is one of the most exciting developments in software technology in the past few years. Conceptually, cognitive computing focuses on enabling software models that simulate the human thought process. More specifically, cognitive computing enables capabilities that simulate functions of the human brain such as voice, speech, and vision analysis. From this perspective, cognitive computing is becoming an essential element to enable the next wave of data intelligence for mobile and IoT solutions. Text, vision, and speech are common sources of data used by mobile and IoT solutions.

As per IEEE, Cognitive Internet of Things is a new network paradigm, where (physical/virtual) things or objects are interconnected and behave as agents, with minimum human intervention, the things interact with each other following a context-aware perception-action cycle, use the methodology of understanding-by-building to learn from both the physical environment and social networks, store the learned semantic and/or knowledge in kinds of databases, and adapt themselves to changes or uncertainties via resource-efficient decision-making mechanisms, with two primary objectives in mind:

  • bridging the physical world (with objects, resources, etc) and the social world (with human demand, social behavior, etc), together with themselves to form an intelligent physical-cyber-social (iPCS) system;
  • enabling smart resource allocation, automatic network operation, and intelligent service provisioning

The development of IoT depends on dynamic technical innovations in a number of fields, from wireless sensors to nanotechnology. Without comprehensive cognitive capability, IoT is just like an awkward stegosaurus: all muscle, no brains. To fulfill its potential and deal with growing challenges, we must take the cognitive capability into consideration and empower IoT with high-level intelligence.

The Big Data Institute – Top Ten 2016 Predictions

 customer experience predictions

Here are our predictions for 2016 that The Big Data Institute sees shaping your businesses:

1. Customer Digital Experience will take the center stage for companies competing to win mindshare and share of wallet. Majority of the customer transactions will be initiated through mobile platform – smart phones, tablets, phablets, wearable devices, etc

2. Analytics will become secret weapon for many companies with Big Data and Internet of Things projects on rapid rise. Data Management and Advanced Analytics will become more sophisticated but more business user friendly.

3. Privacy and Security will continue to be top priority for companies as consumers behavior and new laws will drive corporates towards compliance requirements.

4. Consumers will start monetizing their own data using various mediums.

5. Leadership ranks and roles will be transformed with primary corporate roles as – Chief Data Officer, Chief Analytics Officer and Chief Intelligence Officer becoming dominant roles driving IT and Business.

6. Artificial Intelligence, Robotics, Cognitive Computing will be at the top of Hype Curve as companies start exploring and piloting AI, cognitive solutions while Big Data and IOT move towards plateau.

7. M&A on the rise with several acquisitions in IOT, Digital/Mobile and Security solutions.

8. Cloud will become the new standard and will become first choice in many cases for platform.

9. Businesses making IT purchase decisions (solution, software, and project) will be on the rise. In some companies, IT may get decentralized to address the speed and agility requirements.

10. Industry will move more towards pre-packaged/prebuilt solutions include point solutions building towards transformation project instead of large in-house builds.

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