It is all around us. Artificial Intelligence, Machine Learning, Robotics have begun making in-roads in our daily lives. What exactly is Machine Learning? As the term suggests, Machine learning is an application of artificial intelligence (AI) that provide systems, the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

In this 21^st century, it’s shaping and simplifying every human’s lives and decisions they take. New trend technology is getting smarter and smarter-accelerating human innovation. In numerous ways technology is impacting human’s life. It's generally true that machine learning is useful because it automates processes and saves time, so humans can focus their time and energy on more complex decision making.  For example, using chatbots for addressing customer queries and concerns automates the entire solving and decision making process.

Here we bring you exciting areas that Machine Learning is helping save time and money:

1. Healthcare – One of the most visible ways machine learning is changing the global landscape can be seen in the healthcare industry. Instead of using doctors to examine every patient who come to a hospital, there’s now a form of AI that can establish a diagnosis of a patient based on the answers to a set of pre-drafted questions. Statistics by Juniper Research states there has been a 12 percent increase in patients who can be diagnosed by artificial intelligence. This is estimated to rise up to 75% by 2020. Over time and with proper programming, ML will not be able to monitor and diagnose physical illness, but also mental health.

2. E-Commerce Sales and Support – Machine Learning has exploded in the E-Commerce space in the past decade. The most visible form of AI / ML are the product suggestions that pop-up on your mobile / tablet based on the browsing history. Today many e-commerce companies are using AI / ML for lead generation and for addressing customer queries and companies. Chatbots is an example of the adaptation of Machine Learning in this space

3. Financial Services – Using Machine Learning, financial companies are able to identify key insights in financial data as well as prevent any occurrences of financial fraud. This is also used to look for opportunities in investments and trade. Usage of cyber surveillance helps in identifying those individuals or institutions which are prone to financial risk, and take timely action to prevent fraud.

4. Government - Government agencies like utilities and public safety have a specific need for Machine Learning as they have multiple data sources, which should be mined for identifying useful patterns and insights. For example sensor data can be analyzed to identify ways to minimize costs and increase efficiency. Furthermore, ML can also be used to minimize identity thefts and detect fraud

5. Smart Home and Security – Home automation has transformed our lives for the better, adding convenience and comfort to our lives. For the best home security, homeowners have started using ML - integrated alarm systems and investigation cameras. Smart home security comes with many other features including alerting when kids get home from school and tracking pets around the complex.  These cutting-edge systems can even call for emergency services; thus, strengthening the home’s security. AI-integrated alarm systems and cameras use machine learning and facial recognition to build a list of frequent guests to home to detect unwelcome guests instantly.

6. Education – Machine Learning has assumed prominence in the education space with the growth of on-line learning. Based on the information / content that you want to learn, ML throws up suggestions and reference materials to help you learn more and better.

The role of CIO’s in Machine Learning:

Chief Information Officers (CIO) are the epicentre for successfully implementing ML in organizations. The success of Machine Learning stems from a clear understanding of the needs of the organization and what it intends to do with this technology. No longer can CIOs decide “if” they should use ML, but instead where and how to apply it. It is here the role of CIOs get amplified and they become central to successfully implementing Machine Learning. First and foremost, CIO’s should be helping the organization to make better use of their data and continually improve decisioning based on AI learning from past outcomes. CIOs should ensure that the powerful tools being used are accessible by more than just a few analytic gurus in the organization.

Having said this, CIOs should:

• CIOs need to create skunk teams to experiment with the various ML approaches and choose from a variety of alternatives such as TensorFlow vs PyTorch vs Apache Spark etc
• CIOs need to create tie-ups with AWS or Azure or GCP to utilize the ML capabilities of these cloud providers and tie-in these capabilities with their enterprise Cloud
• Being aware of local data and privacy laws is much needed for the CIOs of today. ML needs tons of data to create self-learning models. This brings up the question of privacy. The moot question here is “how much is needed without compromising on compliance an safety of data”.
• CIO’s play a key role in sensitising the organizations about the benefits of ML and its impact to the top-line and bottom-line of an organization.
• Last, but not the least, Machine Learning is assumed to eliminate people from jobs. But that is quite not true. It is here the role of CIO becomes important. Machine Learning cannot perform on its own unless data is fed into the system. And who will feed data into the systems? HUMANS, right? And vast amounts of data needs to be fed into the systems. Definitely, people are the ones who are going to make it happen.

What we are seeing today in the sphere of Machine Learning is just the tip of the iceberg. Machine Learning continues to shape most major industries for the better by improving data accuracy and data gathering. With time, we can expect ML to play a more prominent role in our daily lives.