Where AI is going in 2020

guest · Jan 5, 2020

Top minds in machine learning predict where AI is going in 2020
January 2, 2020
https://venturebeat.com/2020/01/02/top-minds-in-machine-learning-predict-where-ai-is-going-in-2020/

AI is no longer poised to change the world someday; it’s changing the world now. As we begin a new year and decade, VentureBeat turned to some of the keenest minds in AI to revisit progress made in 2019 and look ahead to how machine learning will mature in 2020.

We spoke with PyTorch creator Soumith Chintala, University of California professor Celeste Kidd, Google AI chief Jeff Dean, Nvidia director of machine learning research Anima Anandkumar, and IBM Research director Dario Gil.
Everyone always has predictions for the coming year, but these are people shaping the future today [...]
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guest · Jan 5, 2020

AI creativity will bloom in 2020, all thanks to true web machine learning
TensorFlow.js is great new creative tool in the developer's arsenal
January 2, 2020
https://thenextweb.com/podium/2020/...2020-all-thanks-to-true-web-machine-learning/

Machine learning has been trotted out as a trend to watch for many years now. But there’s good reason to talk about it in the context of 2020. And that’s thanks to developments like TensorFlow.js: an end-to-end open source machine learning library that is capable of, among other features, running pre-trained AI directly in a web browser.

Why the excitement? It means that AI is becoming a more fully integrated part of the web; a seemingly small and geeky detail that could have far reaching consequences.
It could trigger a societal change in perception, where people will expect websites to provide utter relevance for any given moment, but with minimal intervention and instruction.

And now that we finally have the gift of true web machine learning, 2020 could well be the year that developers unleash their AI creativity.
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stapp · Jan 12, 2020

Are we on the cusp of an ‘AI winter’?

The last decade was a big one for artificial intelligence but researchers in the field believe that the industry is about to enter a new phase.
Hype surrounding AI has peaked and troughed over the years as the abilities of the technology get overestimated and then re-evaluated.
The peaks are known as AI summers, and the troughs AI winters.
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https://www.bbc.co.uk/news/technology-51064369

Krusty · May 11, 2020

Australian Air Force trialling AI search and rescue project off Stradbroke Island in Queensland

Artificial Intelligence could soon help save the lives of people lost at sea and speed up rescue operations by learning to recognise life rafts amid waves and whitewash.

Unlike human rescuers, AI does not fatigue or become distracted which means when a search goes on for days, and sometimes weeks, there is a much greater chance of success.

The Australian Air Force and the Australian Maritime College have just completed the first 'proof-of-concept' trials for their new project, off the coast of Stradbroke Island, alongside the Queensland volunteer coastguard.
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https://www.abc.net.au/news/2020-05...intelligence-search-and-rescue-trial/12234000

guest · Aug 2, 2020

AI is struggling to adjust to 2020
August 2, 2020
https://techcrunch.com/2020/08/02/ai-is-struggling-to-adjust-to-2020/

2020 has made every industry reimagine how to move forward in light of COVID-19: civil rights movements, an election year and countless other big news moments.

On a human level, we’ve had to adjust to a new way of living.
The issue with AI training in 2020 is that, all of a sudden, we’ve changed our social and cultural norms.

Computer vision models are struggling to appropriately tag depictions of the new scenes or situations we find ourselves in during the COVID-19 era. Categories have shifted. For example, say there’s an image of a father working at home while his son is playing. AI is still categorizing it as “leisure” or “relaxation.” It is not identifying this as ‘”work” or “office,” despite the fact that working with your kids next to you is the very common reality for many families during this time.
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On a more technical level, we physically have different pixel depictions of our world. At Getty Images, we’ve been training AI to “see.”

Think of it this way — cleaning may now include wiping down surfaces that already visually appear clean. Algorithms have been previously taught that to depict cleaning, there needs to be a mess. Now, this looks very different. Our systems have to be retrained to account for these redefined category parameters.
Another issue for AI right now is that machine learning algorithms are still trying to understand how to identify and categorize faces with masks.
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Identifying the new context we’re asking the system to understand is the first step toward moving visual AI forward.
We all have to monitor for inconsistencies and inaccuracies. Persistence and dedication to retraining computer vision models is how we’ll bring AI into 2020.
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