The productivity paradox

Recognising AI as a "general-purpose machine", rather than a distinct and immediately implementable tool or technique, can help explain why the anticipated gains are not yet being seen. With the introduction of other general-purpose machines, like the electric motor, the computer, the steam engine, it took decades for companies and industries to identify how they needed to change – simply automating an existing business process is unlikely to give massive benefits. Rethinking how an organisation achieves outcomes independent of the existing process or tooling is – but it'll take many more years to materialize.
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Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

Novel new approaches to the use of AI – highlighting its use as an augmentation to human judgement, not a substitute – are welcome, but still fall far short of addressing the elephant in the room – most machine learning or AI today is still pure statistical inference, often without even implicit acknowledgement of the path of causation (grass grows when the sun shines – but which causes which?). Until this problem question is addressed, even the new approaches outlined in this article will just be fitting data to a curve.
A branch of A.I. called deep learning has transformed computer performance in tasks like vision and speech. But meaning, reasoning and common sense remain elusive.

A freshly funded battery startup aims to ease the cobalt crunch

It's an often overlooked problem, but although electric vehicles produce zero emissions at point of use, and can be powered entirely with emission free fuel (e.g. renewable power sources), they still contain a lot of rare materials, often mined or produced under somewhat questionable ethical standards, so it's great to see how the world is starting to respond.
Conamix, a little-known startup based in Ithaca, New York, has raised several million dollars to accelerate its development of cobalt-free materials for lithium-ion batteries, the latest sign…

New AI method increases the power of artificial neural networks

Although i was aware of the benefits of sparsely connected neural networks, this paper outlines an additional, slightly counter-intuitive property – scale-freeness through a method they call "Sparse Evolutionary Training). Starting from a sparse network, the model randomly adds new connections and drops weaker ones, "evolving" in to a more model which is more complex to define, but ultimately simpler, with fewer "hubs" (scale-freeness), and this less densely connected end state is quicker to stabilise (as it requires fewer calculations) and to maintain and train than a traditional statically connected network.
An international team of scientists from Eindhoven University of Technology, University of Texas at Austin, and University of Derby, has developed a revolutionary method that quadratically…

The Weird World of Thieves in Sweden

As Sweden turns to an increasingly cashless society, criminals are having to find other ways to line their pockets. Last year a high profile string of robberies of moving trucks of Apple merchandise ( were finally bought to an end by a police sting, but now criminals have started catching and selling endangered species – some can go for around $120,000 per animal.
As the country ditches cash, some criminals turn to stealing owls.

Uber to invest €20 million in building flying taxis in France

Uber plans to launch a flying car service ("Uber Elevate"), predicting it'll be ready around 2025 – progress is being further accelerated by a $20M investment in a dedicated Research and Development site in Paris, Uber's first technology centre outside the US, focusing on research in to airspace management and overcoming the legal and practical issues this brings up (do you really want hundreds of flying cars zooming over your home?)
Ride hailing app Uber wants to build flying taxis in France and announced on Thursday it would invest €20 million to develop the project.

The paradox of automation and self driving cars

It’s becoming increasingly apparent that Level 2 “self driving” cars are quite simply dangerous. The most recent incident (involving a Tesla model S which crashed in to a parked police car) has highlighted that partially automating the complex task of “driving” is potentially worse than not automating it at all ( But the “paradox of automation” is not a new phenomena – economist Tim Harford has previously written about this problem (, and Alphabet has chosen to skip Level 2 automation entirely, heading straight for vehicles which need no human intervention at all (“level 4” and beyond)

33 Industries Other Than Auto That Driverless Cars Could Turn Upside Down

The impact of self-driving cars will be felt far and wide. Aside from the obvious (insurance industry, petrol stations, professional drivers, crash repair centres), CB Insights points out that seemingly disconnected industries – like fast food, real estate, media and healthcare – are also set to be jolted from their comfort zones. Not all of these are negative – if you could watch movies while being shuttled around that's a boon for those who tell internet access and streaming services. Others are more subtle – how the price of real estate will be affected is as yet unclear (and how will that impact public transport?)
Fast food, real estate, military operations, even home improvement — many large industries will have to shift their strategies in the wake of driverless cars.

How a Pioneer of Machine Learning Became One of Its Sharpest Critics

A future of truly intelligent machines requires causal reasoning, not simply "nontrivial curve fitting" (the probabilistic association of cause and effect), argues Judea Pearl. Development of true reasoning – why a given action has a certain outcome, not just that they're correlated – would allow machines to "ask counterfactual questions" – in effect, to predict how a change creates a likely outcome that has never been seen before – and potentially even develop agency and free will. He puts lack of progress in this area down to a missing "calculus for asymmetrical relations" (knowing that the sun causes the grass to grow and not vice versa).
Judea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can’t compute cause and effect.