Four Weekly Tech Newsletter – Feb 14

A round up of the latest tech stories, curated for you weekly, by Four.

Lead articles from February 14

daniel oberg 41Wuv1xsmGM unsplash

Needed: people to put the intelligence in AI

Is the digital workforce ready to take over? Well, not quite. AI may be capable of assuming many tasks, but it will be some time, if ever, that it could replace jobs on a widespread basis. It simply has too many limitations.Instead, we need to acquaint a generation of workers with technologies to take on the more mundane, repetitive portions of their jobs, and in turn elevate their decision-making roles within enterprises. That’s the word from Steve Shwartz, AI author, researcher and investor…

hunter harritt Ype9sdOPdYc unsplash 1

AI progress depends on us using less data not more

In the data science community, we’re witnessing the beginnings of an infodemic — where more data becomes a liability rather than an asset. We’re continuously moving towards ever more data-hungry and more computationally expensive state-of-the-art AI models. And that is going to result in some detrimental and perhaps counter-intuitive side-effects. To avoid serious downsides, the data science community has to start working with some self-imposed constraints.

piotr makowski 27LH 0jXKYI unsplash

A new artificial intelligence makes mistakes on purpose

It took about 50 years for computers to eviscerate humans in the venerable game of chess. A standard smartphone can now play the kind of moves that make a grandmaster’s head spin. But one artificial intelligence program is taking a few steps backward, to appreciate how average humans play—blunders and all. The AI chess program, known as Maia, uses the kind of cutting-edge AI behind the best superhuman chess-playing programs. But instead of learning how to destroy an opponent on the board, Maia focuses on predicting human moves, including the mistakes they make.

efe kurnaz RnCPiXixooY unsplash

The future of work: it's coming sooner than you think

Prior the pandemic, you could ask a dozen people what “the future of work” meant and get 13 different answers. Some insisted it was about distributing discrete responsibilities among two-pizza teams, while others preached about robots eliminating jobs and the need for universal basic income as compensation. Then COVID-19 pressed the fast-forward button, and we learned about the immediate, practical future of work in a hurry. The most obvious lesson – you don’t need to be at the office to get stuff done – was already understood in tech, just never proven at scale.

naja bertolt jensen IUBc0cxN7Lc unsplash

Artificial Intelligence software detects ocean plastics from the air

As millions of tons of plastic wash into the ocean everyday, scientists have their work cut out for them in trying to keep tabs on it, but they may soon have a useful new tool at the their disposal. Researchers at the University of Barcelona have developed an algorithm that can detect and quantify marine litter through aerial imagery, something they hope can work with drones to autonomously scan the seas and assess the damage. Taking stock of our plastic pollution problem is a tall order, with so much of it entering the ocean each day and being broken down into smaller fragments…

pietro jeng n6B49lTx7NM unsplash 1

How machine learning is being used in software delivery

On any given day, new code is being written, old code is refactored, third-party libraries are added and removed, external APIs are integrated, and plenty more. Software delivery is no longer an explicit stage at the end of development, but instead, it is a continual procedure within the daily development process, with deployments occurring daily or even hourly. ML processes are now more than ever being applied to software deployment to optimize processes so that software companies can continue to develop and deploy efficiently.