.png?width=259&name=WSAI%20Amsterdam%20Orange%20no%20dates%202000x300%20(1).png)
.png?width=263&name=IM_Mothership_assets_LOGO_MINT%20(2).png){kind=logo}
In the midst of the recent Covid-19 pandemic and the changes in the global business environment that followed, executives have faced many disruptive forces, including changing attitudes and expectations among their customers, tougher global competition, and redundancy or erosion of existing business models.
Topics: AI, Data, Machine Learning, Business
Jana Novohradska, AI Consultant to Deputy Prime Minister`s Office of Slovakia & Gender Equality Rapporteur CAHAI Council of Europe, is one of the panelists on our 'Governing AI' panel at World Summit AI Americas in Montreal. We caught up with her ahead of the summit.
Topics: AI, Data, Machine Learning, Interview, Startups
He may only be 13, but Artash Nath has already been working at the interface of space, robotics and artificial intelligence for the last few years, presenting his projects at multiple international conferences. He is interested in deep space exploration and has been applying machine learning models to space challenges, including predicting risk index of an asteroid collision with Earth and detecting atmospheres of exoplanets using simulated data from space telescopes. His ongoing project is on creating curious and intelligent machines that learn by being placed in an immersive environment. They make efforts to understand cause and effect and are not hesitant to ask “why”.
Topics: AI, Data, Machine Learning, Interview, Startups
Over the past 25 years, computers have slowly encroached into the domain of human intellect, the machines first victory was in 1997 when Kasparov lost to Deep Blue at Chess1. Since then A.I. have continued to expand the games they can beat us at, from Go to Poker, and Shogi to Starcraft2. These achievements of machine learning, whilst shockingly impressive, are in stark contrast to the failures of A.I. in the real world.
Topics: AI, AI Platforms, Data, Machine Learning
If AI is to gain people’s trust, organisations should ensure they are able to account for the decisions that AI makes, and explain them to the people affected. Making AI interpretable can foster trust, enable control and make the results produced by machine learning more actionable. How can AI-based decisions therefore be explained?
Topics: AI, Data, Machine Learning, Responsible AI
Artificial intelligence and machine learning are currently all the rage. Every organization is trying to jump on this bandwagon and cash in on their data reserves. At ThoughtWorks, we’d agree that this tech has huge potential — but as with all things, realizing value depends on understanding how best to use it.
Topics: Data, Data Science, Machine Learning
While many organizations are investing in data and design capabilities, only those that tightly weave these disciplines together will unlock their full benefits.
Often in business, the biggest breakthroughs come when organizations connect different, and seemingly disparate, ideas and approaches. Diversity of perspective has been shown time and again to foster fresh thinking to solve tough problems. Such is the promise for organizations that can effectively bring together data and design.
Topics: Data
Produced by
