Originally published at www.navigate-ai.com

AI-driven automation will soon be everywhere in some shape or form. It’s a reasonable time to ask whether AI automation will be “good” if one looks beyond the narrow objective of short-term cost reduction and considers overall employment levels and wages.

In the March Navigate-AI newsletter we reviewed the McKinsey and KPMG surveys that found that Covid has quickened the pace of corporate adoption of AI. Some further datapoints have come through:

May 25th, 2021

On April 21st the European Commission released its “ Proposal for a Regulation laying down harmonised rules on artificial intelligence “. Its reception has, inevitably, been mixed. Like it or not, the regulation represents the first attempt globally to introduce a legislative framework for AI and will set a precedent for any similar developments elsewhere. Below I share my thoughts on whether such regulation is necessary and assess its suitability, its proportionality and also the degree to whether it is likely to be future-proof. …

Originally published at https://www.theaigroupie.com.

Looking back on the US 2020 election deepfakes seem to have played a negligible role. The impact of any AI generated misinformation is being dwarfed by our own innate competency for misinformational mischief.

In the near-term we are much more likely to be deceived by deeptweaks than deepfakes — the automated generation of low quality text, the dialling up or down of image attributes (a person’s age, emotion, gender, etc.), or the insertion of short passages of dialogue or new gestures into pre-existing video content. …

Originally published at https://www.theaigroupie.com.

Now in its third biannual year of publication, Stanford University’s AI Index report has become just that: a means by which to meaningfully index and track progress in AI on a range of dimensions: technical performance, economic impact, ethics, etc. It runs to 222 pages. To save you the trouble of reading it here are my top five takeaways.

1. Unprecedented: the only word to describe the rate of progress in AI performance

Material lifts in the performance of core techniques — raising them to commercially viable levels — are happening over the timescales of a few years (and in some instances months). It’s beginning to feel as if the…

Facebook’s AI team this month announced a breakthrough in computer vision which has gone largely unnoticed. Not only ironic but also remarkable given that it marks a significant change in the economics — and consequently the dynamics — of new entry into the world of AI.

Jonathan Borba on Unsplash

Up until now most machine learning models in production use a technique called supervised learning which needs labelled training data, and typically lots of it. If you want to predict which incoming customer service emails correspond to which needs so that you can route them automatically to the right team, you need training data…

Kizel at iStock

All the hullabaloo about the privacy issues surrounding face recognition technology leaves me cold. That’s not to say I’m neutral about it. On the contrary I’m viscerally distressed at the possibility that we may lose forever an aspect of our lives that we currently take for granted: our anonymity. But this flurry of regulatory interest in face recognition is a bit like Michael Douglas in Disclosure (anyone remember that?)… we’re not solving the real problem.

AI is going to present us with a firehose of ethical challenges over a compressed timescale the likes of which we have never before encountered…

Every two years since 1994 the very best of the world’s computational biologists gather at the Critical Assessment of protein Structure Prediction (CASP) competition to predict the structure of a handful of proteins based solely on their amino acid sequence. After nearly twenty five years of effort, its organisers must have begun to feel a little like the fairytale princess waiting in vain for her prince to come. Each successive gathering was marked by slow progress painstakingly won as CASP woo-ers flamboyantly unveiled ever more creative tactics and yet failed to win the princess’ hand. Then in 2018, at CASP13…


People lose a lot of sleep over the definition of AI — of what it is and what it isn’t. Below is one view. You may very well disagree. I’m not overly bothered. I’ve written this in part so that by the end of reading it you won’t be either.

Rommel Davila at Unsplash

The figure below portrays one way to think about this. Deep learning is a subset of machine learning which is itself a subset of AI. Data science often makes use of machine learning techniques, but is usually quite distinct from AI in terms of its objectives and its outputs.

Right now there are a few other macro-preoccupations that deserve our attention. Nonetheless, whilst we’re busy dealing with these, AI advancement proceeds apace. Here’s six reasons why each of us should give a fig.

Roman Ivaschenko at Dreamstime

Originally published at theaigroupie.com

  1. AI will permeate and enhance more and more of our daily lives

AI already plays a central behind the scenes role in our lives. I’m not about to rattle off some futuristic day-in-the-life. The future is already here.

When you wake-up, you may find yourself instructing your AI-powered smart speaker to adjust the heating before you attend to a few chores. You order some goods online. They’re available for 24 hour delivery because an AI algorithm has anticipated local demand and has had them warehoused in a local distribution centre. You order some groceries. They’re delivered…

Tariq Khatri

Perspectives on the strategic, socio-economic and existential impacts and implications of AI. For practitioners and non-practitioners alike. navigate-ai.com

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