Machine learning

Machine learning is one of the most used technologies in this generation. It has varied capabilities that can transform businesses across industries for the better. From being considered as a niche technology, machine learning is now seeing an increased adoption within companies in all sectors.

From a global perspective, brands are leveraging machine learning to accelerate innovation and better customer experience. For example, Nike uses machine learning for personalized product recommendations. In the F&B industry, Dominos maintains its 10 minutes or less pizza delivery time using machine learning technologies. Another widely used example is how automobile giant BMW uses machine learning to analyze data from vehicle subsystems and predicts the performance of vehicle components and recommends when they should be serviced.

In 2021, ML became a priority for tech companies to achieve revenue growth while reducing costs. In 2022, those companies are expected to explore many matured applications of this technology. Disruptive tech organizations have been leading this technology across many areas like process automation, customer experience, and security.

Media And Entertainment Industry

Media giants like Amazon and Netflix have already popularized the data-based content consumption channels in recent times. When the world got initially struck with the global pandemic, the demand for new consumption models grew and left companies to leverage their artificial intelligence and machine learning capabilities to create value for the customers. In this process, ML is going to be crucial for the media and entertainment industry, whether it’s developing better recommendation engines, delivering hyper-targeted services, or presenting the most relevant content in real-time. Predictive modeling will also be key in communicating with the customers on time, anticipating their future demands, and making good investments.

Manufacturing Industry

IoT devices have already flooded this industry and it is only going to increase. Machine learning will be critical to bridging the gaps created by huge amounts of data. It will serve as a building block for the industry along with automation, data connectivity, real-time error detection, supply chain visibility, warehousing efficiency, cost reduction, and asset tracking. Keeping traditional processes aside, ML will facilitate innovation and efficiency in the coming days.

Algorithmic e-commerce

Algorithmic e-commerce or the smart, systemic digitization of business functions often handled manually will usher in widespread adoption and utilization of AI and ML by enterprises in the e-commerce sector. For example, AI-powered natural language generation (NLG) content will produce an algorithmic e-commerce experience, where customers receive bespoke online shopping experiences through customized product and category descriptions that turn a product page into a personalized sales pitch. Ultimately, this booming trend will lead to a market shift that delivers more value to consumers where vendors take a more product-type approach to personalization and customer experience, versus a consulting-product approach, as they’ve done previously.

Healthcare Industry

The coronavirus global pandemic has highlighted the importance of investing in and optimizing healthcare systems. ML is being considered as the most promising technology that enables healthcare providers to generate large volumes of data for insightful clinical decisions. Machine learning also enables huge processes in drug discovery, cutting down the long discovery and development time and reducing overall costs. It can also improve healthcare delivery systems to better the overall quality of healthcare at low costs. In the future, ML is predicted to be a critical part of clinical trials. Including pharmaceuticals and the biotech industry, machine learning will be having a huge impact in all aspects.

New AI And ML Innovations With NLG

Natural language from phonetics, understanding, processing, and generation has seen significant advancements in the last few years. As a result, the combination of AI and ML technology with NLG is rapidly pushing the boundaries of what is possible. Large and small companies already utilize the technology across multiple sectors and industries. Already consumers use applications like Google phone calls and enterprise applications like business process automation based on unstructured data (i.e., text to voice). Facebook has also achieved impressive results in semi-supervised and self-supervised learning techniques utilizing AI and NLP.