Data Science and Machine Learning – What the Future Lies Ahead
In the last few years, data science and machine learning have been taking the tech landscape by storm. As a massive amount of data is being generated almost every single day by organizations as well as the common people through numerous data points, almost every industry is striving to gain actionable insights from that data to be able to make strategic business decisions. Data science can be considered as a comprehensive amalgamation of data analysis, inference, algorithm computation etc that helps to solve critical business problems. In this post, we’re going to discuss the major trends in the fields of data science and machine learning that are expected to continue in the near future.
AI will become more prevalent
While AI has been around for decades, greater processing speeds together with access to massive amounts of rich data, it’s all set to be integrated into our everyday lives in a more widespread manner. From natural language generation to image and voice recognition to predictive analysis and more – AI-enabled applications can be found almost everywhere. But AI is still in its nascent stage and the near future will see more advanced applications of AI. Automated machine learning should become more common and be able to transform the entire field. Applications will start to rely on AI increasingly to improve the overall experience. So, we can expect to see an increase in the number of intelligent apps across industries.
More power to IoT platforms
We’ve already been experiencing some IoT platforms for some time now. IoT essentially refers to a huge network of objects, each of which comes with a unique IP address and the ability to connect to the internet. While being connected, these objects can communicate with each other. The data captured from present IoT devices like smart meters, sensors etc will be utilized to make smarter decisions with the help of predictive analysis. For instance, predict electricity usage from smart meters to help authorities plan distribution in a better and cost-effective manner.
Growth of cloud-based intelligence
Algorithms can help organizations obtain critical insights about their present and future business operations, but this proposition is usually expensive with no assurance of a bottom-line increase. Organizations often have to deal with having to capture data, hire highly-paid data science professionals and train them to handle changing databases. Now, with the emergence of more cloud-based intelligent options, there’ll be no longer be the need of managing robust infrastructure as cloud system can develop new models as the scale of operations increase while delivering more accurate results as well. More open-source machine learning frameworks are coming to the field, gaining pre-trained platforms which can recommend products, tag images, perform natural language processing tasks etc.
Personalization will become more personal
Organizations are already making waves in reaping the advantages of recommendation engines which help them reach the target market more accurately. Cortana and Siri are two classic examples of virtual assistants that have served numerous smartphone users by learning the search patterns constantly to recommend personalized results. High-end technologies behind these implementations are relentlessly running algorithms which track the personal preferences and online behavior to understand every user uniquely. As part of the upcoming trends in machine learning, personalization of search results with the help of AI-powered analytics is going to help the businesses in a smarter and advanced manner. This is more likely to impact the operational performance of natural language search, real-time targeting, predictive merchandising, conversational commerce etc.
Growth of deep reinforcement learning
In recent few years, DRL or deep reinforcement learning has proven to be one of the more promising subfields of machine learning, apart from being more amusing. AlphaGo, for example, a socially aware robot came with an approach which matched human-level performance in multiplayer games. While many of the recent applications of deep reinforcement learning may not be that much entertaining, they’re expanding the reach of their approaches in some quite interesting manners. In last year, for example, research was going on DRL for real-time advertising, news recommendations etc, which are only a small fraction of the upcoming possible applications.
Growth of more specialized hardware
Traditional CPUs have had limitations when it comes to running machine learning systems. GPUs or Graphics Processing Units come with an advantage in working with these algorithms. These days, AI experts are using FPGAs (field-programmable gate arrays) for machine learning. In the near future, FPGAs may even outperform GPUs.
One consistent thing across all these trends is with the advancement of technology, more businesses will embrace data science and machine learning revolution to make the maximum use of big data. The competition in terms of using advanced tools and technologies will become more tightened, and the teams with the strongest strategies will tend to gain a major competitive advantage across the business domain.