Numerous industries have felt an enormous shift due to machine learning, and large tech giants continue to vie for top data science talent.
Manufacturing, for example, saw the implementation of smart factories, where machines can essentially talk to each other and predictive analytics can forecast any potential problems. Retailers harnessed the power of machine learning to provide customers with more accurate recommendations and more accurately predicted buying patterns. And the automotive industry began heavily investing in machine learning to predict accidents and take one step closer to the self-driving car.
In 2019, all of these industries will continue to take strides in advancing their use of machine learning algorithms. But as businesses invest more and more into this technology, four more industries will experience a transformation in 2019.
The stock market
Using publicly available earning documents, one research group applied machine learning technology to the stock market. They used Microsoft’s Azure Machine Learning Workbench to create the model which combined stock price data before and after market announcements. The group had to look at stocks in individual industries because the jargon used in each is wildly different. After sorting stocks into ‘low performing,’ ‘middle performing,’ and ‘high performing,’ the team’s best result was a 62% chance of predicting a low performing stock.
Seeing as 62% is higher than a chance bet (50-50), this has profound implications for the future of machine learning in the stock market. The research group used Microsoft’s tool, which is open to anyone and designed for projects such as this. In 2019, data scientists can refine this model for more accurate forecasting which will change the game for stock market investments.
There was no shortage of security scares in 2018. Cambridge Analytica, in particular, made businesses and individuals concerned about online privacy. Now, companies are using machine learning to prevent breaches. Security companies deploy machine learning algorithms to detect anomalies in networks. They train data to learn from itself, improving over time.
Machine learning-based malware scanning is something that businesses will want to use in 2019. Instead of searching for signatures for specific families of malware, the scanning tool detects characteristics of malware, making it harder for attackers to infiltrate a system. Since hackers are becoming more sophisticated, the software to detect them must advance as well.
Many companies are using deep learning techniques which allow algorithms to adjust and self-regulate themselves, continuously evolving and learning. While many businesses, including Google, heavily invested in machine learning techniques to counter hackers, next year the technology will likely evolve and become best practice for keeping data safe.
Telecommunication companies have massive amounts of data, but until recently, many of these companies did not have the tools to do anything or extract insights from this data. Machine learning is helping telecom companies build new revenue models, address consumer needs better, and reduce operational costs. Telecom businesses are using machine learning to improve internal process such as optimising mobile tower operations. What will be interesting to see evolve in 2019, however, is the use of machine learning to deliver better customer experience.
Chatbots and virtual assistants are not new, but machine learning has made considerable strides in language understanding and speech recognition. This technology can help give customers solutions for common connectivity issues or deploy a technician when the problem needs more attention. The savings are enormous for telecom companies and resolutions will be much faster for customers.
Food and beverage
Many businesses in the food and beverage industry are using machine learning to make sure items are stored and maintained properly, as not to spoil. A lot of this takes place in the manufacturing stage, and machine learning gives companies greater insight and a guarantee that the items are handled properly and therefore safe for buyers to consume.
In the coming year, machine learning use in cold chain monitoring will grow, as well as machine learning in inventory management. These algorithms can help retailers maximise profit by eliminating loss when products expire. Combined with smart sensors, machine learning techniques can help keep store shelves stocked but not with too much product that will create losses.
Global machine learning growing
The global machine learning market is set to grow from $1.41 billion in 2017 to $8.81 billion by 2022, representing a compound annual growth rate of 44.1 %. Many industries will continue to transform with this technology, but these four industries, in particular, will see a revolution this coming year. As businesses continue to invest in machine learning, the advancements in the field will mean significant ROI and competitiveness in the market.