Top AI, Machine Learning and Analytics Trends for 2019 and beyond

• Updated on: 2019-02-23
• Read original article here

The technology landscape has changed drastically over the past few years, with many new concepts, approaches and tools. Artificial Intelligence is no longer hype, it is here to stay. The convergence of artificial intelligence, machine learning, deep learning, data science, big data, blockchain, robotics, cloud and IoT are transforming the way we live and work. Several trends began to take off to become reality in 2019 (some are already reality) and in the coming years:

The main use of artificial intelligence applications in business is automating the process of decision making. However, as artificial intelligence becomes more sophisticated, concern around the fear of artificial intelligence systems as black boxes grows. There are several concerns encompassing various themes as fairness, bias, security and others.

Explainable AI is an artificial intelligence whose actions can be explained and understood by people. There are many reasons to invest in explainable AI: regulations, ethical use of data, transparency, compliance requirements and risk. When it comes to the ethical use of data, the explanation of model outputs will drive successful adoption, revealing if sensitive data is causing similar exclusions and avoiding negative ethical outputs, and providing a provable way to show how decisions are ethical. In compliance, explainable AI can provide an auditable record, including all parameters associated with the prediction, enabling the business to meet compliance requirements whenever necessary.

The challenge of explainable AI is to produce more explainable models while maintaining a high level of prediction accuracy, enabling users to understand, trust, and manage their artificial intelligence applications.

Making machines analyze words and understand conversations is not a simple process. There are many nuances and aspects of a language that even humans struggle to understand. Natural Processing and its subsets (Natural Language Understanding, Natural Language Generation and Natural Language Interaction) combines Artificial Intelligence and computational linguistics to allow algorithms to analyze what a user speaks and writes, and perform tasks as Automated Text Writing and Automated Speech. The use of deep learning, in conjunction with natural language, will increasingly enable us to provide document classification and sentiment classification with higher accuracy levels than the current one. With the development of NLP, we can expect that human-machine interaction will improve significantly.

A trend that should greatly change the solutions based on Machine Learning is AutoML. It has the potential to allow developers to have the opportunity to develop and evolve machine learning models, being able to handle complex scenarios without going through the typical Machine Learning model training process. When dealing with an AutoML platform, business analysts will be able to focus on the business issues.

AI will increasingly need specialized processors to training and run machine learning and deep learning models with large data sets. The model often needs additional hardware to perform complex mathematical computations quickly. These chips will be optimized to execute tasks such as speech recognition, object detection, computer vision and natural language processing.

The convergence of Robotic Process Automation (RPA) and AI

The convergence of RPA and AI is allowing organizations to automate manual and repeatable processes and become more efficient. The bots will use more and more natural language processing, intelligent character recognition and optical character recognition to improve the processes and addressing strategic priorities through RPA.

The combination of AI and IoT can work wonders for any industry or business. Devices will become more powerful enabling local data processing. It will provide more agility for business, reducing cloud dependencies and data transfers volumes. There will be an exponential increase in smart devices in a large number of areas, including manufacturing, healthcare, transportation and automotive solutions.

The goal of organizations today is to turn raw data into insights, but it’s not easy to implement a data-driven culture across an organization. Although companies know that they need to leverage their data, a huge number of companies are still in the initial stages of analytics adoption. This happens because to transform raw data into insights, companies need to complete several steps: collect data from multiple data sources, pre-process data, clean and transform the data, analysis, modeling, validation - and after all those steps, the companies can then enable data-driven decisions. Augmented analytics can help companies with these steps.

Augmented analytics is a new paradigm that enables automated data insight using machine learning and natural language processing. Augmented analytics will be essential for delivering unbiased decisions, and will transform how business users interact, consume and act on insights. With augmented analytics, solutions will produce more accurate business forecasts and improve user adoption, enabling better decisions.

AI will be increasingly applied with robotics. The manufacturing and factories will accelerate the adoption of AI. Over the last years, the industry has invested in data technology and sensors, and those that are in a more advanced stage are prepared to apply AI in the factories. The application of AI for self-driving cars and other autonomous devices is also growing fast. Autonomous vehicles are being fitted with communication systems, cameras and sensors to enable the vehicle to generate a huge amounts of data. Those data, when applied with AI, enables the vehicle make decisions just like human drivers do.

Dark data is data which is acquired through many ways in an organization but not used in any manner to derive insights or for decision making. In the organizations, there is still a huge amount of unused dark data. The main reason dark data are not used is because it is generally unstructured. With the evolution of technologies as AI and big data, and also privacy and regulations issues, is causing companies to think how to work better the dark data. Analytics efforts will focus on clarifing strategic, operational and customer insights hidden in dark data sources.

The basic goal of blockchain is to keep all records updated, all of their execution and all authentication, while AI will help make decisions, evaluate and help simplify the independent interaction. Blockchain and AI will ensure uninterrupted integration into the future with some features such as: security, large-scale data handling, decentralized control and data sharing, and open data market. With blockchain and AI technology put together in one place, you can better control how your data can be used for the data set you own. For many organizations where data privacy is important, this technology will be useful. The combination of these two technologies promises to deliver greater benefits in the near future.

Augmented Reality (AR) and Virtual Reality (VR) developers will more and more build clever and cognitive features into their solutions. These technologies are improving quicky and finding use cases in construction, medicine, manufacturing, education, aerospace and architecture. Machine learning algorithms with Computer vision can enable these features to become increasingly smarts and sophisticated.