Machine learning is undoubtedly the biggest topic of discussion in the digital world right now. The things we had only imagined in science fiction movies are now happening in real life and we are very close to achieving a state where machine learning becomes an integral part of everyday life. Newer uses of machine learning and AI are coming into the picture and businesses are making huge investments in research and development to make machine learning work for them.
According to Forbes, "machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted." In this article, we shall discuss some of the top machine learning trends for 2018 and peek into the future of machine learning.
Gartner describes the intermingling of data, intelligence, and learning on the cloud as an "intelligent digital mesh". That is the current state of ML and AI technologies. Critical factors like improvement in data aggregation, data automation, deeper analytics, cloud platforms, and more open source growth have made the field of machine learning a fast-growing one. All reports point to only one conclusion - future is bright for machine learning. According to International Data Corporation (IDC), "the spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021." Deloitte Global predicts that "The number of pilot projects and implementations of ML will double in 2018 as compared to 2017, and they will double again by 2020."
In near future, we will see that the applications of machine learning will not only be restricted to large corporations with deep pockets but will also be accessible to smaller enterprises as well as individuals. Due to the rise in machine learning application programming interface, combined with the sophistication of the cloud, the technology is swiftly becoming accessible to all sizes of businesses.
Out of all the latest trends on machine learning, significant growth in the field of neural networks and deep learning is having the biggest impact in the future of machine learning. Neural networks organize the data and all the elements in a special layered structure so as to emulate the functioning of the human brain. In neural networks, each layer takes inputs and insights from the previous layers, thereby enabling deep learning. This forms the core of most machine learning applications. Market leaders like Google, Amazon, Tesla, Apple, and Microsoft are all diving deep into creating extensive neural networks. In 2016, Google's DeepMind based AlphaGo beat world champion Lee Sedol in a match of Go. Go is a complex game that requires strategy, intuition, and experience. In 2017, AlphaGo Zero taught itself from scratch and achieved mastery to beat the Go playing knowledge of the entire world put together. It proved to be a major win for machine learning and artificial intelligence applications.
Data sciences is one of the fields which is hugely popular with businesses, as they can leverage their existing data and start using machine learning to identify new data patterns, conduct deeper analytics, leverage predictive analytics to investigate the probable future. They are also moving a step ahead with prescriptive analytics to get ready-to-use business decisions backed by data and insights. Forrester report suggests that the Predictive Analytics & Machine Learning (PAML) market will grow at a 21% CAGR through 2021 as more businesses are asking for such solutions. And according to another report by JP Morgan, "Data Science platforms will outperform the broader BI and analytics market; and the Data Science platforms that support machine learning will grow at a 13% CAGR through 2021." The future of ML is quite evident in all the enterprise ML predictions of 2018.
Common consumers are mostly concerned about the applications of machine learning and AI that they can see in real life. And two areas that have seen unprecedented progress are self-driving cars and autonomous drones. The top automobile manufacturers and tech companies are trying to build their own versions of the above and in 2017 we saw some drastic improvements and promising product trials. We can confidently predict that future of machine learning is about these two technologies becoming a part of everyday life and would become highly accessible to individuals. Robotics is also making huge waves in the world of healthcare. Robots that can do highly complex surgeries by studying the data from past videos and data sets are coming into limelight. With time, machine learning will make these systems even better. The field of artificial prosthetics is also getting redefined. Prosthesis that can sense touch, pressure, temperature, light to generate feedback will become a big game-changer.
We have all tried out some of the natural language processing tools and we have also witnessed how fast they are getting better at what they do. With the help of NLP and machine learning, customer conversations are becoming more accurate and focused. In 2018, we would see greater adoption and implementation of NLP-based customer service and marketing solutions. Natural language processing, along with facial recognition is already being used by financial institutions to detect and reduce financial frauds. With the help of Natural Language Processing and machine learning, it is becoming easier and faster to translate in real-time. Instant language translations are becoming highly accurate and are shattering geographical boundaries.
All the data about the customers and prospects is getting collected and fed into machine learning tools to automatically analyze precise customer behavior and generate real-time recommendations and incentives for purchase. Machine learning marketing trends in 2018 clearly indicate that marketing automation will break new ground with the use of ML and AI tools. Automated content creation, micro targeting, real time promotions and pricing, customer connect at various touch-points. Personalized product recommendations have long been some of the expectations of marketers and now they have become a reality; only to transform into even more powerful solutions. And the biggest example is world-famous ecommerce giant - Amazon. Amazon extensively use machine learning to provide appropriate product recommendations, predict customer behavior, detect fraud, manage inventory, and so on. Another case study is that of Netflix. Netflix realized that users usually take up to 90 seconds to search for a movie before they give up. Netflix used machine learning algorithm that constantly churns out personalized recommendations to its 100 million subscribers all over the world. By providing the right recommendations based on customer preferences, Netflix has significantly reduced the number of cancelled subscriptions.
We all have used Siri or Google's Assistant on our phones for quite some time now. What used to be full of glitches is now becoming increasingly accurate and will soon achieve a greater level of precision using machine learning and AI technologies. Personal assistants are getting better at understanding user preferences, behaviors, routines, likes and dislikes, facial expressions, body temperature, heart rate, surroundings, and much more. They can smartly predict what you might need based on a complex analysis of the environmental factors, past behavior patterns, and can even execute digital tasks on your behalf. The strides in machine learning in 2018 will make these personal assistants even smarter and more capable in the future.
Outsource2india, a 19-year-old ISO-certified outsourcing company is always at the forefront of the newest technological developments and strives to achieve excellence in providing top software development services that can benefit small, medium and large businesses across several industry segments across the globe. Although machine learning is a growing field and every day new and innovative use cases are coming up, we can help you leverage it in a cost-effective way and customize it for your specific business needs. We can identify areas where your business can benefit from machine learning and which tools can be implemented to get better insights and generate profitable business outcomes.
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