Very interesting approach by Google’s researchers to the “cocktail party problem”. The team trained a CNN to determine which person is speaking in a video with multiple overlapping sounds, and to amplify that speech while reducing other noise. Applications include better automated subtitles, and improved hearing aids. https://research.googleblog.com/2018/04/looking-to-listen-audio-visual-speech.html
Posted by Inbar Mosseri and Oran Lang, Software Engineers, Google Research People are remarkably good at focusing their attention on a par…
Stripe's advances in AI, based on hundreds of billions of data points, have been able to reduce fraud by 25% without materially affecting non-fraud acceptance rates.
I’m loath to use the term, but Stripe is a revolutionary product. It allows pretty much anyone to accept card payments just by adding a few lines of code to their site, without having to deal with…
A fascinating set of guidelines for making complex work which we typically say "has" to be done face to face (like designing) effective when the team works remotely. I particularly like the emphasis on using a spectrum of tools to support "stepping up" the "bandwidth" of a conversation from asynchronous text (e.g. email, slack) to synchronous, real-time, visual methods (e.g. video chat). Others, such as drop in sessions for constructive feedback on work items, look equally useful, and i look forward to testing some of these out with the team.
More and more companies are seeing the benefits of remote work for productivity in the workplace. As Director of Design at Zapier, I frequently get asked the question of how the design process works…
Choosing an effective loss function is a critical part of training ML models. This thought provoking article reminds us to be critical in the choice of this function, especially as in many models the reward function itself is unclear – does a recommendation system (e.g. promoting new articles, or songs) simply create an echo chamber, or does it broadly converge on the mean? Which of these should score higher? If we penalise the system when users don't click on articles which violate their confirmation bias – are we acting ethically?
Musings on systems, information, learning, and optimization.
While at first Intel appears to be catching up in the race to develop chips optimised for AI, looking deeper reveals a broader, longer term strategy to develop open code allowing any competing or complimentary framework (Tendorflow, Caffe, MXNet, etc.) to run at optimum efficiency on their hardware. Back to those chips (and the dodgy performance charts) – the authors of this article point out that you get better single chip performance from hardware 3 generations old compared to the newest silicone – although the real measure isn't single chip, but how to scale out complex models across "farms" of devices, as the big boys (e.g. Facebook) do.
Intel has been making some interesting moves in the community space recently, including free licenses for its compiler suite for educators and open source contributors can now be had, as can rotating…
A practical example of the importance of appearance: “appearing tall … is linked to increased social status across cultures, which researchers hypothesize has an evolutionary origin: If you were a taller caveman, you were probably better at taking down megafauna.”
Sitting tall gives him the confidence boost he needs.
Pouring resources in to Alexa, AWS and experiments like Amazon Go, Amazon invested nearly $23bn on R&D last year, nearly 1/3 of the total spend of the top 5 (next come Alphabet, Intel, Microsoft, and Apple).
Tech companies claimed the top five spots again this year.
IBM's 5 properties of effective AI?
1 Managed (durable infrastructure, effective data pipelines, data and model governance)
2 Resilient (automatic alerts when model drift is excessive)
3 Performant (runs in reasonable time on cost effective infrastructure)
4 Measurable (model accuracy, data volume, value released)
5 Continuous (evaluate and retrain models as needed)
A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. So why the long face? Be…
As the cost of AI drops, things which aren't currently thought to be solvable through prediction will suddenly be viable – and this will primarily be complimented with human judgement. Computers predict better than people can, but then these predictions will be "handed off" to a human to use judgement to determine the response (such as whether or how to act, or to ignore). Ultimately, the authors recommend that companies develop a "thesis" outlining what you plan to "predict" (e.g. what is "best"), the time until AI becomes so embedded that investments without it are not viable, recognising that progress towards that point will be exponential.
Rotman School of Management professor Ajay Agrawal explains how AI changes the cost of prediction and what this means for business.
The demise of the retail store may have been (greatly) exaggerated. Yes, many big box stores are disappearing, being unable to compete on cost or selection with online vendors, many companies are turning to technology to survive the change by inviting themselves directly in to customers' homes, or optimising their supply chain and product ranges using AI and small, local, relevant locations. Others are making the retail store the place you go to try out a physical product which you then buy online, creating "showroom destinations" for customers. One thing's clear – this battle is not yet lost.
We discuss the technologies and trends, from supply chain software to in-store AR technology, that are helping today’s brick-and-mortar retailers stay competitive as e-commerce continues to grow.