Tag Archives: machine learning

GEN Summit: AI’s breakthrough year in publishing

This week’s GEN Summit marked a breakthrough moment for artificial intelligence (AI) in the media industry. The topic dominated the agenda of the first two days of the conference, from Facebook’s Antoine Bordes opening keynote to voice AI, bots, monetisation and verification – and it dominated my timeline too.

At times it felt like being at a conference in the 1980s discussing how ‘computers’ could be used in the newsroom, or listening to people talking about the use of mobile phones for journalism in the noughties — in other words, it feels very much like early days. But important days nonetheless.

Ludovic Blecher‘s slide on the AI-related projects that received Google Digital News Initiative funding illustrated the problem best, with proposals counted in categories as specific as ‘personalisation’ and as vague as ‘hyperlocal’.

Digging deeper, then, here are some of the most concrete points I took away from Lisbon — and what journalists and publishers can take from those.

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What changed in 2017 — and what we can expect in 2018 (maybe)

Because he sends me an email every December, Nic Newmanhas a tag all of his own on this blog. So as this year’s email lands in my inbox here’s my annual reply around what I’ve noticed in the last 12 months — along with some inevitably doomed predictions of what might happen in the next year…

Surprising in 2017: horizontal storytelling and Facebook disappointments

The rapid spread of horizontal storytelling (‘tap to advance’) struck me particularly this year. 2017 saw it become the default for new launches, from Facebook’s new ‘Messenger Day‘ feature and Medium’s Series, to Instagram‘s Carousel feature and WhatsApp‘s Status feature, while the BBC news app’s videos of the day feature used the same approach too. Continue reading

Data journalism’s AI opportunity: the 3 different types of machine learning & how they have already been used

I understand that you want me to explain how Ava works (from Ex Machina)

This week I’m rounding off the first semester of classes on the new MA in Data Journalism with a session on artificial intelligence (AI) and machine learning. Machine learning is a subset of AI — and an area which holds enormous potential for journalism, both as a tool and as a subject for journalistic scrutiny.

So I thought I would share part of the class here, showing some examples of how the 3 types of machine learning — supervised, unsupervised, and reinforcement — have already been used for journalistic purposes, and using those to explain what those are along the way. Continue reading

The machine that learns how to stop whistleblowers

INSIDER THREAT John connects via VPN Administrator performs ssh (root) to a file share - finance department John executes remote desktop to a system (administrator) - PCI zone John elevates his privileges root copies the document to another file share - Corporate zone root accesses a sensitive document from the file share root uses a set of Twitter handles to chop and copy the data outside the enterprise USER ACTIVITY

An example of whistleblower behaviour taken from Harry McLaren’s slides

Workplace surveillance is nothing new, but this slide from Harry McLaren’s talk on Machine Learning for Threat Detection illustrates particularly well the challenges facing journalists wishing to protect whistleblowers.

McLaren is talking about malicious threats, and the way that machine learning can be used to identify suspicious patterns of behaviour. But the example given above is equally useful in illustrating the way that similar behaviour might be used to identify an employee intending to whistleblow on illegal, unethical or dangerous behaviour by his or her organisation. Continue reading