You may have heard the terms before but do you know what they mean? In fact, they are becoming a new form of governance that will affect all of our lives. We're joined by past guest and friend John Danaher, who has been working in this space for a while, to talk about the Algocracy and Transhumanism Project. The Project now includes a podcast, so we also talk a bit of podcasting with John. Algorithms are even now defining the kind of life that is available to each and every one of us.

You are right that this is a question of forecastability. There have been a few articles on forecastability in the IIF's practitioner-oriented journal Foresight. The problem is that forecastability is already hard to assess in "simple" cases. Suppose you have a time series like this but don't speak German: How would you model the large peak in April, and how would you include this information in any forecasts? Unless you knew that this time series is the sales of eggs in a Swiss supermarket chain, which peaks right before western calendar Easter, you would not have a chance.

'We have arrived,' the personal assistant announced as the automated share car pulled up outside Turing & Partners, one of London's best-known AI-powered law firms. Elon Turing looked up from his notes that were projected before him and got out at the curb on Gresham Street, not far from the Bank of England. It was very warm, as it always was these days, though there was a heaviness in the air that promised a thunder storm. Elon nodded to the car, which zipped away into the pollution free morning and disappeared into a sea of auto-taxis, self-driving buses and other share cars. He took a deep breath and marched up to the front door of the building. The door scanned his face and opened, greeting him politely as he walked into the cooled lobby. At the reception desk was Albert.

It's 2016, and businesses big and small, far and wide have finally stopped using the term Big Data. The consensus seems to be converging on the idea that data alone doesn't solve problems. You still need to understand, analyze, and test test test data using hypotheses to prove intuitions and make solid decisions. Things that should be happening regardless the size of your data. But instead of developing creative uses for the data that we have, we're all now looking to'cognitive computing' and'artificial intelligence' to save us.

One million anonymised eye scans from Moorfields Eye Hospital will be used to train an artificial intelligence (AI) system from Google. Machine learning algorithms will scour the images for signs of diseases such as macular degeneration and diabetes-related sight loss. Moorfields is teaming up with Google's AI division DeepMind during the scheme. Previously, DeepMind faced criticism over a little-known data sharing agreement with three London hospitals. An agreement to share patient data from the Royal Free, Barnet and Chase Farm hospitals over the past five years and continuing until 2017 was revealed by the New Scientist in May.

We're all familiar with the story – sophisticated cyber-attacks are increasing in number and complexity, adding to the workload of already beleaguered security professionals, and helping fuel a surge in cybersecurity job postings. But these positions are incredibly tough to fill because of the insufficient supply of experienced professionals and lack of new talent. In fact, a Stanford University study shows the cybersecurity skilled personnel gap stands at 200,000 unfilled jobs. Between the challenge of choosing the right tool amongst a myriad and the challenge of finding skilled security personnel, you have to wonder: how can companies defend themselves in this increasingly complex threat landscape? Machine learning is an excellent place from which to start.

Google DeepMind has announced its second collaboration with the NHS, working with Moorfields Eye Hospital in east London to build a machine learning system which will eventually be able to recognise sight-threatening conditions from just a digital scan of the eye. The collaboration is the second between the NHS and DeepMind, which is the artificial intelligence research arm of Google, but Deepmind's co-founder, Mustafa Suleyman, says this is the first time the company is embarking purely on medical research. An earlier, ongoing, collaboration, with the Royal Free hospital in north London, is focused on direct patient care, using a smartphone app called Streams to monitor kidney function of patients. The Moorfields collaboration is also the first time DeepMind has used machine learning in a healthcare project. At the heart of the research is the sharing of a million anonymous eye scans, which the DeepMind researchers will use to train an algorithm to better spot the early signs of eye conditions such as wet age-related macular degeneration and diabetic retinopathy.

A serious problem in the Turing test for computer intelligence is exposed in a study published in the Journal of Experimental and Theoretical Artificial Intelligence. If a machine were to'take the Fifth Amendment' – that is, exercise the right to remain silent throughout the test – it could, potentially, pass the test and thus be regarded as a thinking entity, authors Kevin Warwick and Huma Shah of Coventry University argue. However, if this is the case, any silent entity could pass the test, even if it were clearly incapable of thought. The test, devised in 1950 by pioneering computer scientist Alan Turing, assesses a machine's ability to exhibit intelligent behaviour indistinguishable from that of a human. Also known as the'imitation game', it requires a human judge to converse with two hidden entities, a human and a machine, and then determine which is which.

Famous eye hospital Moorfields has agreed to give GoogleHow AI is fuelling the car industry. Read more ... »'s DeepMindHow AI is fuelling the car industry. Read more ... » access to one million anonymous eye scans as a part of a machineHow AI is fuelling the car industry. Read more ... » learningHow AI is fuelling the car industry. Read more ... » studyMicrosoft scans photos to guess what your feelings are.

Once in a while I use AirBnB. There are a couple of features that I (intuitively) use to judge if an apartment is save to book; ratings, images of the flat and the user avatar. Apparently, these avatars play an important part in the overall service and usage of AirBnB. A recent study finds that "Attractive Airbnb hosts are more likely to get bookings, even with bad reviews". With the easy availability of image recognition services, even the everyday researcher can do a small analysis.