AI Predictions for 2020: By Pinna Pierre and Kirk Borne


Mar. 1, 2020

“I anticipate that we will see some shake-up in the AI startup space in 2020….these products need to move beyond “AI as a cool tool” to demonstrating value.”

Pinna Pierre: “Four 2020 Trendy Techs: 5G Edge Computing Ethics in AI & Deep Learning. No doubt : 2020 will be the year of 5G! 5G networks will start to be set up & coupled with Edge Computing, by drastically reducing latency, they will allow real time analysis & thus it will revolutionize many sectors, such as healthcare, industry, sustainability, smart cities or VR/ AR. AI Ethics will be more and more important in 2020. Machine Learning Interpretability will be taken more and more seriously, especially by the biggest companies in order for people to regain trust in AI. Privacy will also become more and more important with the development of facial recognition greatly helped by 5G. Facial recognition & 5G will also often face increasingly strict regulations. Machine learning will focus on less data & more on data quality to better explain algorithms decisions & reduce bias. For deep learning, reinforcement learning could be very trendy. “

Kirk Borne: “I anticipate that we will see some shake-up in the AI startup space in 2020, as the proliferation of platforms both rises and falls. The variety will rise, with specialized features and user experiences, while some of the duplicated players will be forced to differentiate their products or face declining sales. The heavy duplication in “citizen data scientist” and automated machine learning “AutoML” platforms is heading for a shake-up in the market. The path to success is clear, as customers of these products need to move beyond “AI as a cool tool”, to demonstrating value – market relevance and competitive advantage will be what customers will look for. Startups can win by adding unique market-specific features and capabilities to their AI tools, including components that address bias, model drift, algorithmic transparency, and operational scalability AIOps.”


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