120 AI Predictions For 2019

Me: “Alexa, tell me what will happen in 2019.”

Amazon AI: “Do you want to open ‘this day in history'?"

Me: “Alexa, give me a prediction for 2019.”

Amazon AI: “The crystal ball is clouded, I can’t tell.”

My conversation with Amazon’s “smart speaker” or “intelligent voice assistant” just about sums up the present state of “artificial intelligence” (AI) at home, the office, and the factory: Try a few times and sooner or later you will probably get the correct action the human intelligence behind it programmed it to perform.

 

 

What will be the state of AI in 2019?

The following list features 120 senior executives involved with AI, all peering into their not-so-clouded crystal ball, and promising less hype and more practical, precise, and narrow AI.

“Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with banks at different parts of the world, I see a big uptake in the number of customers that will rely on AI to ‘drive’ their finances and take automated actions to help them reach their financial goals. To deliver effective Self-Driving Finance, financial institutions will require specialized forms of AI for each of their customer segments such as retail, small business, and wealth—moving away from more generic forms of AI towards domain-specific solutions that embed subject matter knowledge and expertise”—David Sosna, Co-founder and CEO, Personetics

2019 will be the year of specialized AI systems built by organizations based on their own data. Given the realization that organizations sometimes have only limited amounts of data, but also require specialized data, organizations will come to realize that they need tools to easily create quality AI data internally. This quality over quantity approach will require organizations to take stock of the data they have and ask themselves key questions: is this data representative of what I’m looking for, and does it match my goal? Will the production data match this training data? Did I strike a balance between repeatability of images and variation? Is my dataset diverse? Taking new approaches to data strategy will be make-or-break for overcoming the challenges of AI’s data problem, to develop AI that works in the real world”—Max Versace, PhD, CEO and co-founder, Neurala

AI will enable greater process discovery.  Process discovery is like a sensor embedded in the application that learns all of the user journeys, using AI to predict the optimal path for interacting with a system. Similar to using a GPS such as Waze when you're driving to unlock optimal routes depending on the time of day, AI will unlock how each employee can best use a system, providing a range of possibilities based on what the individual needs to do”—Rephael Sweary, Co-founder and President, WalkMe

Read more : https://www.forbes.com/sites/gilpress/2018/12/09/120-ai-predictions-for-2019/#4d71e008688c