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alternatives to dall-e 2

It’s difficult to predict the negative impacts of an idea or invention, but certain frameworks can help entrepreneurs see a little bit further.

Thoughts on prompts and search boxes

This online interdisciplinary colloquium will explore avant-garde artistic legacies and their boundary-crossing futures.

A new device allowed a paralyzed patient to "speak," spelling out full sentences in real-time with an error rate of about 8%.

A one-year graduate degree program for Digital Product Design.

Vincent Warmerdam builds a lot of NLP tools (https://github.com/koaning). Many of these tools target the scikit-learn ecosystem and there's a theme of labeling across many of them. A recent focus of his stack of tools is to improve training data. In this video, Vincent and Jay discuss a few of these tools and show how they work together. These tools are discussed in the video: - Human-learn: a toolkit to build human-based scikit-learn components - Doubtlab: a toolkit to help find doubtful labels in data - Embetter: A library that makes it very easy to use embeddings in scikit-learn - Bulk: a library that uses embeddings to leverage bulk labeling The talk includes live demos for each and to show how some simple tricks can go a long way. === Join the Cohere Discord: https://discord.gg/co-mmunity Discussion thread for this episode (feel free to ask questions): https://discord.com/channels/954421988141711382/1042163984817721527 Vincent on Twitter: https://twitter.com/fishnets88 human-learn: Natural Intelligence is still a pretty good idea. https://koaning.github.io/human-learn/index.html https://github.com/koaning/human-learn/ doubtlab: Doubt your data, find bad labels. https://koaning.github.io/doubtlab/ https://github.com/koaning/doubtlab embetter: just a bunch of useful embeddings https://github.com/koaning/embetter bulk: A Simple Bulk Labelling Tool https://github.com/koaning/bulk Calmcode: Code. Simply. Clearly. Calmly. Video tutorials for modern ideas and open source tools. https://calmcode.io/ About The Speaker: Vincent worked as an engineer, consultant, researcher, team lead, and educator in the past. Currently, he works as a Machine Learning Engineer over at Explosion, the company behind spaCy and Prodi.gy. In addition to his work at Explosion, he also maintains many scikit-learn-related plugins as well as a popular learning resource over at calmcode.io. He's also a frequent speaker at conferences where he defends common sense over the hype in ML. === Contents 0:00 Introduction 3:06 Tools for Data Quality 9:18 human-learn: Natural Intelligence is still a pretty good idea. 12:28 human-learn: demo 27:11 doubtlab: Doubt your data, find bad labels. 42:35 embetter: just a bunch of useful embeddings 46:16 embetter: demo 58:10 bulk: A Simple Bulk Labelling Tool 1:00:20 bulk demo: exploring text data 1:10:47 bulk demo: exploring images 1:16:20 Why use the scikit learn API? What are the benefits and limitations? 1:17:22 Programmer productivity tips

Steamship is building a platform to put Language AI in the hands of all developers. Plympton is partnering with Steamship on a fellowship program to explore the intersection of tech & literature. You’ll join a two-month-long fellowship in which we’ll coach you through building new features for Writing Atlas. We’ve planned each feature for a smooth development experience, and the results will be something you can show off in production.

bb15 art space is seeking proposals for its 2023-2024 program around the open-ended theme of friction. Friction is against sameness, the continuum, the immediate. An emerging friction defeats expectations, questions the agreement and is itchy. Frictioning implies resistance, going against a certain motion. bb15 is looking for projects that bring tension, distortion and counter-current into the flow of things.

A presentation of Future Art Ecosystems 3 (FAE3) – Serpentine Arts Technologies’ annual strategic briefing. FAE3 provides new learnings around decentralised technologies.

Design Matters

This year we're looking for projects exploring AI and responsible design.

Research Associate (Fixed Term) in the Leverhulme Centre for the Future of Intelligence at the University of Cambridge.

Stable Diffusion and DALLE 2 are disrupting our media landscape and there’s a need for critical discussion. Intellectual property, automation, deception and ...

Case studies DFF supports strategic litigation to advance digital rights in Europe, by providing financial support for strategic court cases and catalysing collaboration between those working to advance digital rights.Strategic litigation – litigation with broad impact and which can bring about legislative or policy change – has proven to be…

Use your phone’s camera to identify emojis in the real world. Can you find all the emojis before time expires?

Put together 16 crazy AI websites that can help you change your voice, create unique colour palettes, and convert text into animations…

Activists in the internet health movement will gather at MozFest to move the needle in tech, art, social responsibility, ethics, and AI. Will you join us in March 2023?

This Project is an attempt to use deep learning as a tool to question human representation. Human faces and their humanist understanding of the “natural” are modified with facial features of unwanted bodies. Influenced by posthuman visions, the aim is to overcome a binary worldview and embrace hybrid futures and diverse bodies.

The Nooscope Manifested: AI as Instrument of Knowledge Extractivism. By Matteo Pasquinelli and Vladan Joler (2020)

Tens of thousands of women are sent to prison in Egypt every year. Their crime?

Multiplayer Exhibition Space for New Media Art

A digital extension for the Teenage Engineering OP–Z synthesizer translating music into real-time AI generated imagery.

Blog about survival in the world of technology and all this cyberpunk around

Onomatopee

While machine learning—computer programming designed for taxonomic patterning—offers useful insights into racism and racist behavior, a gap is present in the relationship between machine learning and its connection to the racial history of science and the Black lived experience.

Symposium

One Click Jukebox with Autosave - Google Colaboratory