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A native Londoner, I come from a developer background, and now write features and news on artificial intelligence and machine learning. Current regular clients include unite.ai and ITransition.
I began my writing career around 2000, while still working as a developer, contributing articles to a wide variety of newspapers and magazines including The Times (UK), The London Evening Standard, The British Journal of Photography, and many others.
In my several years at Dennis Publishing, I published a continuous stream of magazine articles across many of the company's brands including The Week, Micro Mart, Computer Shopper and the Fortean Times. I also co-founded and helped launch the entertainment website Den Of Geek.
In subsequent years I worked for a variety of publications in London, including The Spectator, and in 2017 decided to write exclusively about machine learning, artificial intelligence, big data, and related topics.
My work is regularly featured on the front page of Hacker News, and over the past year I have developed a strong line in news analysis and thought leadership. I encourage forward-looking companies to engage in this kind of content in order to be distinguished from the increasing avalanche of generic 'evergreen' content. While research and a thorough approach are central obligations to engage readers, increasingly your voice needs a distinct and committed point of view, which I can provide.
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This is my portfolio website, comprised of a single page with hundreds of links to my work.
My latest work for Unite, entirely on AI/ML, with an emphasis on NLP and image synthesis, as well as synthetic data and privacy.
My Muckrack profile, which automatically updates to include my latest work.
A look at the ways that neural rendering could be cheaply obtained from games engines.
An in depth look at the new generation of image synthesis frameworks in 2021.
An examination on the current text-creation capabilities of platforms based on GPT-3.
A feature on predictive analytics tools.
A skeptical take on an apparently terrifying new trend in AI image synthesis.
NEWS: Researchers from Ireland, the UK and the US have warned that the growth in hyperscale AI training datasets threaten to propagate the worst aspects of their internet sources, contending that a recently-released academic dataset features ‘troublesome and explicit images and text pairs of rape, pornography, malign stereotypes, racist and ethnic slurs, and other extremely problematic content’.
An influential 2019 piece on the emerging use of AI in visual effects.
A look at three unique approaches that reconcile conflicting needs into effective and unified frameworks suitable for recommender systems. Two of these systems focus on both collaborative filtering and content based filtering, while the last one is used strictly as a content based solution
NEWS: A new research collaboration between France and the UK casts doubt on growing industry confidence that synthetic data can resolve the privacy, quality and availability issues (among other issues) that threaten progress in the machine learning sector.
NEWS: Researchers from China have developed a method to copyright-protect image datasets used for computer vision training, by effectively ‘watermarking’ the images in the data, and then decrypting the ‘clean’ images via a cloud-based platform for authorized users only.
The unexpected growth of online systems and correspondingly higher traffic levels caused by the advent of COVID-19 has led to an unprecedented increase in malicious network activity. In a climate where face-to-face discussion has been transitioned to VoIP environments, and where an increasing volume of information is forced onto network channels, the available network attack surface has grown notably since the start of 2020.
In this article, we'll look at both approaches, and we'll see that the choice of supervised vs unsupervised machine learning solutions may be decided by more than just the state of the data.
As first-party data gathering becomes the new lodestar for marketers and data brokers, the increased attention on ‘closed’ data-gathering systems risks to drag one of machine learning‘s most fervent research sectors down into controversy and greater regulation.
The challenges that DAIN and similar interpolation packages face in creating AI-based slow-motion.
Dlib is an open source suite of applications and libraries written in C++ under a permissive Boost license. Dlib offers a wide range of functionality across a number of machine learning sectors, including classification and regression, numerical algorithms such as quadratic program solvers, an array of image processing tools, and diverse networking functionality, among many other facets.
This machine learning overview looks at how data-centricity has come to power our society at large, and where machine learning, among other data processing technologies, is positioned in this new reality.
NEWS: A new study from the University of Massachusetts Amherst has pitted English teachers against crowdsourced workers at Amazon Mechanical Turk in assessing the output of Natural Language Generation (NLG) systems, concluding that lax standards and the ‘gaming’ of prized tasks among AMT workers could be hindering the development of the sector.
A speculative look at the post-COVID data science sector.
In this article we'll examine some of the approaches and solutions that various governments are taking to develop meaningful AI legislation, along with the central issues that are driving public and industry pressure for increased regulation and oversight of AI software development.
NEWS: A study from MIT has used machine learning techniques to identify biased phrasing across around 100 of the largest and most influential news outlets in the US and beyond, including 83 of the most influential print news publications.
Apple's Core ML mobile machine learning framework is the user-friendly face of one of the newest sectors to draw tech headlines in recent years — machine learning on smartphone and small-form factor mobile devices.
A look at the core technologies and principles behind the use of machine learning to identify objects in videos.
Writing up the latest AI/ML academic and industry research papers; also some news analysis. My work can be seen at https://www.unite.ai/author/martinanderson/
I worked there from 4/2021 until now
Explanatory and speculative features on the current state of machine learning and artificial intelligence, with an emphasis on business applications.
I worked there from 2/2019 until now
I worked there from 5/2017 until now
Generating, commissioning, editing and sub-editing copy related to current technological developments. Promotion of thestack.com (now Techerati.com) and CloserStill Media. Attendance of national and international conferences and events. Analysis reports, media handling, seeking out new leads for stories and new speakers for our events. Lead generation and SEO work. Referral-based traffic generation.
I worked there from 9/2014 until 5/2017
Production executive and co-digital supervisor.
Company: The Spectator
I worked there from 5/2012 until 9/2014
I managed and promoted the various marketing websites and micro-sites for the IT company Wanstor Ltd in Victoria. The job involved successful SEO work in getting the company in the top 3 results (usually first) for specific keyword phrases, designing, commissioning and overseeing of digital-wallpaper make-over for the HQ, the creation of elaborate Flash and Powerpoint presentations, dealing with numerous suppliers, contractors and general contacts, and ensuring that the profile of the company w
I worked there from 6/2011 until 4/2012
From autumn 2007-2009 I was co-editor of the popular Dennis Publishing entertainment website www.denofgeek.com. Here I wrote hundreds of features, lists and reviews, including the site's biggest-ever feature (at 1m+ unique views).
Company: Dennis Publishing
I worked there from 6/2006 until 6/2011