Make no mistake about it: machine learning has been here for more than five years now. Advancements in Big Data technologies and decreasing costs of sophisticated circuitry with massive computing power, well, both forces have enabled speedy growth for machine learning.
In fact, most search engines and social media platforms leverage machine learning to get “smarter” every day. At the core, it’s about intelligent algorithms that make use of massive data and extract information of immense value.
Now, machine learning has been working quietly in the background for some years, and is past the stage of being dismissed as a buzzword.
Potential applications of machine learning are mind boggling! Machine learning is beginning to drive measurable business benefits with its applications, and at its pace of growth and proliferation, will soon have massive value to add to societal aspects as well. Let’s take you through the most attention-worthy events and trends in the machine learning technology space.
Psychological tuning of machine learning algorithms
Machine learning has been in use in quite a few financial management and analyses applications, particularly for fraud detection and credit checking. Recently, speedy advancements in machine learning have reflected in the fact that financial institutions are using machine learning-powered tools in processes such as risk assessments, loan approvals, and asset management. However, there’s an undeniable psychological aspect of financial decision making when humans do it.
Efforts are being made to replicate these human psychological response mechanisms in advanced machine learning applications.
Most noteworthy is the research being done at OpenCog Foundation, wherein the team has already made headway and enabled an artificial intelligence component to make hedge fund trading decisions by responding to market predictions, news, and social media sentiments. This is the beginning of the era where machine learning begins to accommodate the amorphous element of human decision making.
Motivating innovators to unleash end user applications
IBM, Facebook, Google, and Microsoft have been the big boys in machine learning research and application development. The coming couple of years will witness a focus on unleashing smart applications, digital assistance platforms, and gadgets that leverage the power of machine learning. These could result in dramatic success stories, and dismal failures, for these innovators.
Advancement to the next level of machine learning research needs to be funded by state-of-the-art machine learning tools, and this approach will necessitate that innovators reduce the time to market for ML powered applications.
Machine learning trends and the cloud
Democratization of machine learning and artificial intelligence technologies is a trend that will continue for the next five years. Open standards and algorithm economy are critical to the speedy progress of machine learning and artificial intelligence. Prebuilt machine learning algorithms are already being used in business intelligence and advanced analytics solutions.
Google’s TensorFlow is a testimony to the fact that open standards are a core pillar for machine learning’s success. SaaS has already fostered data science as a mainstream enterprise IT activity. Now, with prebuilt machine learning algorithms, enterprises have the potential of implementing self-service and highly customized AI and analytics solutions.
Forking of algorithm economy
The “algorithm economy” deserves deeper discussion here. Businesses are likely to rely on canned ML algorithms to enhance datacentric activities such as CRM, predictive analyses, and business intelligence. This algorithm economy will drive competition between data companies.
Algorithm-driven decision making will also give rise to a parallel economy, where innovators will try to create proprietary machine learning algorithms that could act as differentiators for large competing enterprises.
A transformative force for medicine and health care
We touched upon the possible societal transformation role that machine learning could play in the not too distant future. Health care and medicine stand to advance in multitudes, riding on the prowess of machine learning.
By leveraging the potential of wearable technologies and centralized databases of medical information, machine learning can help in the realization of a “precision health” concept, wherein pointed diagnoses can be made in a fraction of the time it takes today — and without subjecting patients to dozens of medical tests.
Machine learning can mitigate errors in diagnoses and treatment recommendations, and can break down all cultural and lingual barriers that separate patients from the best medical advice.
Demand-supply gap in data science
McKinsey research had predicted that the end of the decade would be marked with an industry-wide realization of a massive data technology void. The demand and supply gap between data science and machine learning expertise is pretty evident, and is on the rise.
Machine learning-focused data science is not yet a part of industry-oriented academic syllabi, and the state of industrial workshops in this realm is also not very healthy. Practical knowledge of data-science applications in conjugation with machine learning is likely to replace the stress of theoretical knowledge.
As the big tech innovators reveal their machine learning apps, gadgets, and platforms, the industry and academia will have to react by implementing mechanisms of data science and machine learning education. This also means that employment opportunities for people who are currently onboard the machine learning train are huge, with coveted roles in the world’s biggest business organizations beckoning.
Going from good to great
It’s true, machine learning has truly revolutionized the way computing works, enabling computers to learn with time, as they keep on getting access to larger and more structured datasets, paving the way for mitigating programming impasses of the past.
