Therefore, SendPulse will add Italian, French, German, Turkish, Simple Chinese, Indonesian, Korean, Japanese, Arabic, and by the way, Spanish (LatAmerica), Brazilian Portuguese localization. Currently we've used pre-launch product for English and Russian audience. In total we will be have 13 foreign language groups. The language detection are based on geolocation. The modeling technology is based on math method, behavioral analyses to find lookalike audiences (digital twins) to create on behavior models in consuming content, clicks, design, social demographics - the simple comparison will be Facebook Artificial Intelligence for native advertising.

Top Gun was released 30 years ago and it looks as if the Maverick of tomorrow will be made of microchips. Developed by a University of Cincinnati (US) doctoral candidate, an Artificial Intelligence (AI) called ALPHA has consistently beaten other AIs and a retired United States Air Force Colonel in a high-fidelity, air-combat simulator using what's known as a genetic-fuzzy system that relies on off-the-shelf PC processors to do what was thought to be the reserve of supercomputers. Unmanned Combat Aerial Vehicles (UCAVs) have made great strides in recent years, going from items of speculation to the decks of aircraft carriers. But however well they've done in taking off, landing, and carrying out assigned aerial missions, there's still been a big gap between what a human pilot can do and what a combat drone can hope to achieve. Until recently, experienced humans have found it easy to beat UCAVs in simulations after learning their tricks and weaknesses.

The Machine Intelligence Summit showcased opportunities of advancing trends in AI & machine learning with experts in NLP, computer vision, neural networks, object recognition, and explored how it will impact transport, manufacturing, healthcare, retail and more.

Google's DeepMind is embarking on a new research project to help doctors spot the early signs of sight-threatening eye diseases. The company's British-based artificial intelligence division will use machine learning to analyze more than one million anonymous eye scans, creating algorithms that can detect early warning signs that humans might miss. The project is DeepMind's second collaboration with the UK's National Health Service (NHS), but the first to use artificial intelligence. DeepMind is hoping to spot two eye conditions in particular: wet age-related macular degeneration and diabetic retinopathy, the latter being the fastest growing cause of blindness around the wold. "There's so much at stake, particularly with diabetic retinopathy," DeepMind co-founder Mustafa Suleyman told The Guardian.

Magic circle giant Clifford Chance is the latest City outfit to embrace the mysterious world of artificial intelligence (AI), striking a deal with Canadian software provider Kira Systems. According to the Canary Wharf based firm, the intelligent software will help its lawyers quickly analyse contracts, identify potential legal issues, improve speed, and, as a result, increase all round efficiency. Furthermore - according to the software designer - not only can Kira be put to work straight away, requiring very little set up time, she it can actually learn on the job, growing in intelligence through training provided by the firm's lawyers. Our clients are under substantial pressure to reduce legal spend. At the same time, they need more support to manage the increasing risks and complex issues that their ...

Coding will join this list in time, however, where it differs wildly from the afore mentioned examples is it is unlikely to be lovingly preserved for future generations to admire, fiddle with or better still, reactivate. Its essence will not be reified for one specific reason – it can't be touched and humans value tactility. We touch immediately, both inside and outside the womb. Today, we find ourselves at a pivotal moment in our existence and about to experience an exponential period of rapid technological growth the likes of which is quite probably beyond our comprehension and at a base level, will have serious implications for coding. We rather arrogantly think that because we have a good grasp of our own technological advancement so far, we can somehow predict the mass cultural and behavioural shift about to happen as we question our own skills in the world.

Some supervised machine learning techniques include decision trees, regression, Bayesian methods and deep learning (neural networks). Many of these algorithms also have parameters which must be tuned to achieve the best accuracy. Some algorithms have very few parameters to be set, while others, such as neural networks, have quite a few and can require some investigation. We are currently doing some work using neural networks for predicting user behaviour. While they can require a lot of tuning, neural networks are a very powerful tool for making predictions, and with recent advancements (such as GPU-accelerated Tensorflow) they have the ability to build models with data at unprecedented scale.

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.