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The Future of Machine Learning: Trends, Observations, and Forecasts

By   /  March 10, 2016  /  No Comments

pg_ml_030716The fundamental assumption in Machine Learning is that analytical solutions can be built by studying past data models. Machine Learning supports that kind of data analysis that learns from previous data models, trends, patterns, and builds automated, algorithmic systems based on that study. This article takes a realistic look at where that data technology is headed into the future.

As Machine Learning relies solely on pre-built algorithms for making data-driven analysis and predictions, it claims to replace data analytics and prediction tasks carried out by humans. In Machine Learning, the algorithms have the capability to study and learn from past data, and then simulate the human decision-making process by using predictive analysis and decision trees.

The Two Drivers of Machine Learning Solutions: Raw Data and Data Models

On one side of Machine Learning are the raw data and on the other side the data models. Machine Learning enables data-driven decision systems to continuously learn from new data and adapt itself to deliver “reliable and repeatable” results. The newer technologies like Big Data and the Internet of Things have given a new leash to the traditional Machine Learning practices.

Some well-known applications of Machine Learning are as follows:

  • Customer feedback for businesses on Twitter
  • Online recommenders in e-commerce sites such as Amazon or Netflix?
  • The self-driving Google Car
  • Fraud detection systems

Emerging Trends and Forecasts for Machine Learning

The variety of applications that Machine Learning supports includes search engines, image recognition, speech analysis, filtering tools, and robotics. The author of the article,
Where Machine Learning Is Headed, predicts that in the coming year, the global community will witness a tremendous growth of smart apps, digital assistants, and main-stream use of Artificial Intelligence. Machine Learning will proliferate the mobile market and enter the territories of drones and self-driving cars. Gartner’s Ian Bertram predicts that more domain-specific and Machine Learning-enabled technologies will emerge this year. Democratization of AI/machine learning will continue, according to Mark Koh. The demand for making algorithms more easily available will push vendors to offer many new Machine Learning tools. Though such canned products will be available in the market, the skills required to fine tune existing algorithms, tweak the data, and develop an advanced model will remain in demand.

Gartner’s Hype Cycle for Emerging Technologies

According to Gartner a number of new “embryonic technologies” will continue to increase in terms of their market maturity:

  • People-Literate Technology or PLTs: They can covert voice or text messages into retainable intelligence will dominate personal communication and by 2020, about 40 percent people will use PLTs as the primarily mode of technological interaction.
  • The Brain-Computer Interface: Claims to provide certain brain patterns to the computer for controlling a device or a program will also become popular.
  • Bioacoustics: These technologies are front-runners in the world of digital humanism that connects humans with digital businesses and workplaces. Apart from connected homes, smart robots, and self-driving cars, bioacoustics may also become important.


Some Important Observations about Machine Learning

The following observations on Machine Learning demonstrate many of its current uses and where various industry contributors believe it is going:

  • InfoWorld claims that though all Machine Learning technologies share the common goal of learning from past data to deliver improved results, the techniques to achieve that goal vary widely, from very simple techniques like linear or statistical regression to very complex ones like neural nets. Nowadays, the term “Machine Learning” is easily used as a marketing buzzword to differentiate competing products in the market.
  • According to Gartner’s 2015 Hype Cycle Report of Emerging technologies (also discussed above), Machine Learning only recently surfaced on Gartner’s chart, but has managed to surpass the expectations of its followers. In fact, Machine Learning has displaced and assumed the importance of Big Data. Big Data technologies have matured into mainstream business practices, and so no longer feature on the Hype Cycle.
  • Machine Learning is a new tool for better forecasting. In businesses, forecasting demand is increasingly becoming an insurmountable challenge, frequently leading to erroneous results and The trends in the demand data fluctuate so much, and the inherent causes behind those fluctuations are so complex that understanding demand variability is beyond the scope of most business leaders and managers. Moreover, manual factors intensify the human bias in demand planning activities. Now Machine Learning seems to offer a solution for demand forecasting. With the inherent capability to learn from current data, Machine Learning can help to overcome challenges facing businesses in their demand variations.
  • Wall Street is increasingly gearing up for newer technologies that will gradually control the fixed-income trading, block chains, and predictive analytics. McKinsey thinks that this trend will result in reduced manpower in the front and back offices. Global investment banks embracing automated trading will have a chance to increase their profits by at least 30 percent. A Wall Street insider feels the investment banking and trading businesses need more Data Scientists and Big Data Experts and fewer sales people and operators for continued success. This article also predicts that soon banks will embrace digital trading activities by partnering with tech startups.
  • In the recent Economic Forum at Davos, the data expert Andrew McAfee revealed that while the Industrial Revolution freed humans from manual labor, the Digital Revolution probably reduced the supremacy of the human brain. In Germany, an algorithm for reading street signs achieved a recognition rate of 99.4 percent, while that same rate for humans is only5 percent.   According to Kaggle, the current Machine Learning algorithms are performing better than humans in domains, which were primarily dominated by humans. Google, Amazon, and Netflix are some of the large brands that are increasingly relying on Machine Learning rather than domain expertise to run their business operations.


Machine Learning is Full of Contradictions

Thomas Frey happens to be a futurist at The Da Vinci Institute. He has predicted these three inevitable consequences of a machine driven age. These disadvantages could have long-term effects across the business spectrum:

  1. The increasingly automated lifestyle presented by Machine Learning systems is gradually eroding the human strength to fight odds and overcome obstacles. If life becomes that simple and trouble free, the entire human race can become quite vulnerable to threats and sudden turn of fortunes!
  2. Machine-driven solutions will tend to have a “canned” look and feel, thus devaluing originality and reducing the chances of “innovative” solutions.
  3. Too much machine dependence can dissolve human interdependencies – the core of human civilization. Is that something desirable?

A Final Note

Amidst the controversies and contradictions surrounding Machine Learning, here are some assured trends for the future:

  • The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce.
  • Most businesses will tap into algorithmic models for their operational and customer-facing functions.
  • More proprietary, Machine Learning algorithms will act as a major differentiator in business.

So is Machine learning is beneficial or harmful in the long run? It certainly has many advantages for the Data Management and the business world, but what about other consequences? Only time will tell.

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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