In a quest to understand the role of artificial intelligence (AI) in advertising, The Drum, in partnership with Teads, has unveiled a new documentary, The Automation of Creativity, shot in Tokyo, London and Amsterdam. The 16-minute film explores how artificial intelligence is beginning to impact the creativity of advertising and the role of human creatives. To date, artificial intelligence (AI) machines have been able to write poetry, drive cars and there is even talk of a machine possibly winning a Pulitzer one day. Turning the focus on the ad industry, The Automation of Creativity film stars the world's first artificial intelligence creative director, AI-CD ß, launched by McCann Erickson Japan. AI-CD ß is set a brief by Mondelez in the film and presents its creative idea back to the client.

My last post focused on one key distinction between machine learning (ML) and econometrics (E): non-causal ML prediction vs. causal E prediction. I promised later to highlight another, even more important, distinction. I'll get there in the next post. But first let me note a key similarity. ML vs. E in terms of non-causal vs. causal prediction is really only comparing ML to "half" of E (the causal part).

Early in this decade interest in machine learning started to take off. Possibilities of machine learning seem endless. Today machine learning is truly & well underway for mass adoption. Anthony Goldbloom in his 2016 TED talk outlines how automation & machine learning will shape work of the future. Machines are getting very good at all high frequency routine tasks.

A few years ago I added an implementation of the max-margin object-detection algorithm (MMOD) to dlib. This tool has since become quite popular as it frees the user from tedious tasks like hard negative mining. You simply label things in images and it learns to detect them. It also produces high quality detectors from relatively small amounts of training data. For instance, one of dlib's example programs shows MMOD learning a serviceable face detector from only 4 images.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. My lecturer is a full-time Applied Math and CS professor at the Technical University of Denmark, in which his research areas are logic and artificial, focusing primarily on the use of logic to model human-like planning, reasoning and problem solving.

All the different programming languages out there seem to be a better fit for machine learning tasks than Ruby, right? Python has scikit-learn, Java has Weka, and there's Shogun for machine learning in C, just to name a few. On the other hand, Ruby has an excellent reputation for fast prototyping. In this tutorial, we will build a system that can automatically categorize BBC sports articles for you. Oh, and we'll do it in Ruby,OK?

Since January 2015 Christian Bubenheim has been Senior Vice President Marketing & Product of AutoScout24. Before that he was part of the management team of Amazon Deutschland GmbH and responsible for the business unit „Consumables" including health & beauty as well as foods. From 2003 to 2008 he was General Manager for the worldwide end consumer business and the product marketing of Thales Magellan, a worldwide leading manufacturer of GPS devices. AutoScout24 is all about empowerment. We put people center stage. Our mission is to empower and support users in successfully buying and selling cars.

Big data is in many ways still a wild frontier, requiring wily smarts and road-tested persistence on the part of those hoping to find insight in all the petabytes. On Tuesday, IBM announced a new platform it hopes will make things easier. Dubbed Project DataWorks, the new cloud-based platform is the first to integrate all types of data and bring AI to the table for analytics, IBM said. Project DataWorks is available on IBM's Bluemix cloud platform and aims to foster collaboration among the many types of people who need to work with data. Tapping technologies including Apache Spark, IBM Watson Analytics and the IBM Data Science Experience launched in June, the new offering is designed to give users self-service access to data and models while ensuring governance and rapid-iteration capabilities.

While scientists and sci-fi writers alike have been speculating and experimenting with AI for decades, the emergence of the current spectrum of artificially intelligent machines and software has perhaps been driven by one factor more than any other – the overwhelming abundance of data that organisations of all types have to deal with today. The sheer weight of data available to businesses and other entities is simply beyond the scope of human operators to sort, analyse and utilise. But with the advent of artificial intelligence and machine learning, we now have the tools available to not only deal with this data, but to properly utilise it to deliver business insights and efficiencies. This is as true in human resources as in any other sphere of business. Recruitment, in particular, is an area in which many HR professionals feel there is considerable scope for improvement.

Major technology firms are racing to infuse smartphones and other internet-linked devices with software smarts that help them think like people. The effort is seen as an evolution in computing that allows users to interact with machines in natural conversation style, telling devices to tend to tasks such as ordering goods, checking traffic, making restaurant reservations or searching for information. The artificial intelligence (AI) component in these programs aims to make create a world in which everyone can have a virtual aide that gets to know them better with each interaction. Google is making a high-profile push into AI, with the internet titan's chief referring to it as a force for change as powerful as powerful as smartphones. Google Assistant software is being built into new Pixel handsets -- aiming to outdo Apple's Siri -- enabling users to organize and use information on the devices and in the cloud -- to check emails, stay up to date on calendar appointments, news or ask for traffic and weather data.