For sure, the world is too complex–you can’t program it. We have to have self-learning systems. More so, when we create 2.5 quintillion bytes of data, every day — so much that 90 per cent of the data in the world today has been created in the last two years alone
In 1854, when telegram was started on an experimental basis, in India, no one anticipated it to be a huge success as a ‘messenger of good and bad news’ for generations of Indian. It celebrated vital moments of life, of pain and pleasure. Technology has changed a lot, since, then.
Yet, when the 163-year-old telegram service ended on July 16, 2013, it came as a shock to many. The Twitter of the pre-digital era, once the fastest means of communication for millions of people, the humble telegram was laid to rest marking the end of an era.
But, even while it rested in peace, the ‘message it left behind’ was loud and clear. The world is in a flux, technology life cycles are getting even shorter, and to succeed and thrive in the new normal we constantly need to reinvent ourselves, and embrace change.
Yes, for those who heard it right…For sure, the world is too complex–you can’t program it. We have to have self-learning systems. More so, when we create 2.5 quintillion bytes of data, every day — so much that 90 per cent of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few.
How can we best respond to this shift? How can we take advantage of the opportunity to innovate, differentiate and grow? And how can we do all this cost efficiently, leveraging and optimizing the newest information technologies as part of their overall physical operations?
Step on to the age of cognitive systems. Cognitive systems will help us think and make better decisions. They are capable of learning from their interactions with data and humans, essentially continuously reprogramming themselves.
Traditional computers are designed to calculate rapidly. Cognitive systems are built to analyse information and draw insights from it. Traditional computers are organized around microprocessors. With cognitive systems, it’s about the big data and drawing insights from it through analytics.
We need cognitive systems because recent developments in business, society and technology require new capabilities. The emergence of social networking, sensor networks and huge storehouses of business information create a seeming overabundance of information. Systems are being asked to find patterns and draw conclusions, often in near real time, from massive information and in situations where precise answers are hard to find.
For instance, Watson, the Jeopardy-playing supercomputer, is part of a new “third era of technology” where computers learn. Using advances in natural language processing and analytics, the Watson technology can process information similar to the way people think, representing a significant shift in the ability for organizations to quickly analyze, understand and respond to vast amounts of Big Data.
The ability to use Watson to answer complex questions posed in natural language with speed, accuracy and confidence has enormous potential to improve decision making across a variety of industries from health care, to retail, telecommunications and financial services.
Consider healthcare. In less than a year, Memorial Sloan-Kettering, one of the world's oldest and largest private cancer centres, has immersed Watson in the complexities of cancer and the explosion of genetic research which has set the stage for changing care practices for many cancer patients with highly specialized treatments based on their personal genetic tumour type.
Starting with 1,500 lung cancer cases, Memorial Sloan-Kettering clinicians and analysts are training Watson to extract and interpret physician notes, lab results and clinical research, while sharing its profound expertise and experiences in treating hundreds of thousands of patients with cancer.
So far, Watson has ingested more than 600,000 pieces of medical evidence, two million pages of text from 42 medical journals and clinical trials in the area of oncology research. Watson has the power to sift through 1.5 million patient records representing decades of cancer treatment history, such as medical records and patient outcomes, and provide to physicians evidence based treatment options all in a matter of seconds.
Such innovations represent a breakthrough in how medical professionals can apply advances in analytics and natural language processing to “Big Data,” combined with the clinical knowledge base, including genomic data, in order to create evidence based decision support systems.
These Watson-based systems are designed to assist doctors, researchers, medical centres, and insurance carriers, and ultimately enhance the quality and speed of care. Oncologists located anywhere can remotely access detailed treatment options based on updated research that will help them decide how best to care for an individual patient.
Indeed, the applications are immense. While programmable systems will be around for years to come, the centre of innovation is beginning to move to cognitive systems. We’re on the leading edge of a technology transformation that promises to utterly transform business and society once again.
Are we ready to seize the opportunity?
The writer is vice-president & Managing Partner, Global Business Services,
IBM India & South Asia.