As 2019 comes to an end, we think about the changes we are going to make in the next year. Technology like AI has been a major element of change in the lives of humans. So, it’s only fair to think where 2020 will take our technological advancements?
There is no doubt that we are standing on the verge of another industrial revolution and Artificial Intelligence is the one fueling it this time. We have seen some breakthroughs in the areas of AI, Machine Learning, and Deep Learning in 2019. These technologies have made it possible for computers themselves to interpret and analyze information in a very sophisticated way. Machines can now perform complex tasks such as facial recognition and Big Data Analytics due to AI advances.
The people that are the developers and leaders of AI always have some new predictions to share with us on the Tech topic. We have gathered for you some quotes by the tech Savvy that give insight into where AI is destined to reach in the upcoming year.
CEO, Nauto, Dr. Stefan heck talks about the impact and usage of AI in autonomous vehicles:
“Vehicle AI is going to be designed to break the law: Autonomous Vehicles should not and cannot be designed to drive the speed limit (roads were designed with speeding factored in)—it will have to learn to go over the limit and match speeds of other drivers to be implemented wide-scale and keep other drivers safe. The same idea applies to car crashes—cars will have to learn to break the law/driving rules if it means saving more lives. In 2020, we’re going to see a greater call for and debate around designing algorithms to act illegally.”
“Edge processing with AI creates a better IoT experience. IoT device makers know the benefits of edge-based processing, but until now, many of the challenges in terms of cost, performance, and security have made it impractical for implementing in consumer products and systems. The shift toward more use of edge processing in conjunction with cloud connectivity has begun in earnest and will continue to evolve in 2020. From a consumer perspective, this trend will result in an IoT experience that’s faster, more reliable and more private.”
“As opposed to being a cause of job insecurity in 2020, artificial intelligence will prove to be a crucial tool for improving careers. Through AI, employers will be able to provide enhanced opportunities for their employees and facilitate the diversity of experience they crave. Thanks to AI, employees will be able to expand and enhance their skill sets and ensure they stay relevant in a rapidly evolving market. Accountability, particularly concerning explaining results and bias prevention, will continue to go hand-in-hand with AI’s development.”
Data Analysis and AI are the best tools for marketing for SMEs. Georges Bory, Managing Director, ActiveViam tells just how it’s going to get even better in 2020:
“Retailers will increasingly turn to AI as their main source of marketing insights as opposed to surveys and studies. Many retailers, however, will still struggle to turn those insights derived from AI into actionable business rules for their pricing, supply chain and merchandising. AI is becoming more and more mainstream, but not all companies yet realize that using AI technology for data analysis is only the first step into a larger process of true transformation.”
As we talk about AI in specific fields, Ashish Thusoo, Co-Founder and CEO, Qubole, is optimistic about the globalization and widespread use of AI:
“Every company will become an AI company, modernizing its workforce to support innovation. As enterprises learn how to effectively and efficiently deploy AI-based projects in cloud environments, they’ll turn their attention toward hiring data analysts and scientists with expertise in machine learning and deep learning.”
In the age of revolution, it is necessary to use technology for real-world problems. VP Global Marketing, SIOS Technology Frank Jablonski has some great insights to share:
“Machine learning and artificial intelligence will deliver cost savings through greater cloud efficiencies. Achieving this will require the environment or application to understand when it needs more resources and then automatically scaling up those resources to meet the increased demand. Conversely, the technology will need to understand when specific resources are no longer needed and safely turn them off to minimize costs.”
Talking about real-world problems, Jeroen Tas, Philips’ Chief Innovation & Strategy Officer is especially quotable:
“AI’s main impact in 2020 will be transforming healthcare workflows to the benefit of patients and healthcare professionals alike, while at the same time reducing costs. Its ability to acquire data in real-time from multiple hospital information flows – electronic health records, emergency department admissions, equipment utilization, staffing levels, etc. – and to interpret and analyze it in meaningful ways will enable a wide range of efficiency and care enhancing capabilities.”
“Neural network architectures will continue to grow in size and depth and produce more accurate results and become better at mimicking human performance on tasks that involve data analysis. At the same time, methods for improving the efficiency of neural networks will also improve, and we will see more real-time and power-efficient networks running on small devices.”
Anand Janefalkar, Founder and CEO, UJET is hopeful that AI is not just for customers but also for the workers:
“In 2020, AI will dramatically improve the employee experience (EX). The ability to automatically and instantly collect data from across multiple channels, analyze it and provide actionable insight will enable support agents to more quickly, easily and accurately address customer inquiries and come to highly satisfactory issue resolution.”
After all, is said and done, AI for AI is the goal. VP of IBM Research AI, Sriram Raghavan predicts where that’s headed:
“In 2020, expect to see significant innovations in the area of what IBM calls ‘ AI for AI’: using AI to help automate the steps and processes involved in the life cycle of creating, deploying, managing, and operating AI models to help scale AI more widely into the enterprise. Also, we will begin to see more examples of the use of neurosymbolic AI which combines statistical data-driven approaches with powerful knowledge representation and reasoning techniques to yield more explainable & robust AI that can learn from fewer data.”