darkshapes

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Artists, raison d'être: Can we re-think tech that works for people, & not just corporations? Can we improve the world our children inherit? Deliberate, discuss, develop. This is DARKSHAPES. We build

The End of AI Frontiers – Machine Learning (Theory)

In 2023 after the ChatGPT release, it was clear to me that research needed to be more organized to succeed than was typical within Microsoft Research. The amount of support and focus which other research groups were applying was great enough that even with a clever idea pursued by one person it was likely to arrive late and unconvincing because one person could not move fast enough and the baselines which one person could assemble just were not strong enough to really show that an idea was worth taking flight. Several other folks thought similarly and we organized to create the AI Frontiers sub-organization within MSR.

Dark Cloud Gathers as Bill Comes Due for AI Industry

AI companies are finding they can no longer sustain footing the bill of their heavily vibe coding customers.

The Applied Machine Learning Collective of the Rockies

We're building a hands-on, community-led space where AI/ML practitioners actually grow together. A modern guild where newcomers learn from journeymen, journeymen sharpen their skills alongside experts, and everyone works on real problems that matter.

AI, Apple and Google — Benedict Evans

As machine learning starts working, how does that change Google, Apple and the smartphone interface? What companies get reshaped around machine learning? 

Democratizing AI Companies — The Collective Intelligence Project

As AI reshapes society, the companies behind these technologies face a critical challenge: how to build corporate governance structures that prioritize public benefit over profit maximization;.

Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies

Many powerful computing technologies rely on implicit and explicit data contributions from the public. In this paper, we synthesize emerging research that seeks to better understand and help people action this data leverage. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern.

Microsoft’s AI Red Team Has Already Made the Case for Itself

Since 2018, a dedicated team within Microsoft has attacked machine learning systems to make them safer. But with the public release of new generative AI tools, the field is already evolving.

When everyone has AI and the company still learns nothing

Are people using AI, or is the organization learning from it? What changed because we spent those tokens? And who moves discoveries from individuals to teams to organizational capabilities?

Deconstructing the Machine Learning Value Chain

This essay offers a materialist analysis of machine learning in the form of a critical intervention that deconstructs the value chain of this technological para

Against the dark forest

The complex of ideas I’m going to call the Dark Internet Forest emerges from mostly insidery tech thinking, but from multiple directions.

Cory Doctorow: The people who tell you ‘AI is changing everything’ are lying

It has become impossible to tell managers mesmerised by artificial intelligence that the tools are not, in fact, helpful. So employees just play along with the fiction to keep their jobs, writes our tech columnist

A New Ethical Open Source License for Machine Learning from ml5.js?

I put things here so they are on the internet

Companies Are Being Torn Apart by AI "Workslop," Stanford Research Finds

Not only is AI hampering productivity, but it's also blowing up collaboration and souring employee dynamics.

Humanism without (All) Humans

Microsoft Humanism puts corporate muscle behind an overweening class of bad AI philosophies

Data Scientist: The Sexiest Job of the 21st Century

Back in the 1990s, computer engineer and Wall Street “quant” were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don’t just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School’s Davenport and Greylock’s Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.