"How do you get a girlfriend?" This exchange would be pretty familiar in the more squalid corners of the internet, but it might surprise most readers to find out that the misogynistic response here was written by an A.I. Recently, a YouTuber in the A.I. community posted a video that explains how he trained an A.I. language model called "GPT-4chan" on the /pol/ board of 4chan, a forum filled with hate speech, racism, sexism, anti-Semitism, and any other offensive content one can imagine. The model was made by fine-tuning the open-source language model GPT-J (not to be confused with the more familiar GPT-3 from OpenAI). Having its language trained by the most vitriolic teacher possible, the designer then unleashed the A.I. on the forum, where it engaged with users and made over 30,000 posts (about 15,000 posted in a single day, which was 10 percent of all posts that day). "By taking away the rights of women" was just one example of GPT-4chan's responses to poster's questions.

Everybody has heard about the latest cool thing™, which is DALL·E 2 (henceforth called Dall-e). A few months ago, when the first previews started, it was basically everywhere. Now, a few weeks ago, the floodgates have been opened and lots of people on the waitlist got access - that group included me. I’ve spent a day playing around with it, learned some basics (like the fact that adding “artstation” to the end of your phrase automatically makes the output much better…), and generated a bunch of (even a few nice-looking) images.

Heinz has proved that even computers prefer its ketchup with a marketing stunt that had OpenAI's Dall-E 2 generator create a series of sauce-inspired images. Apparently, when the team fed the software random ketchup-related phrases, the results were overwhelmingly plastered with elements of Heinz' signature branding. We have to say, this AI art is some of the least weird we've seen, even with the ketchup bottle floating in a swimming pool. A perfect follow-up to the campaign that had people draw their own impressions of ketchup (this was hilarious, ketch-up on it right here), the experiment simply proved that Heinz is synonymous with ketchup, whoever (or whatever) you ask. Of course, the iconic logo had a big part to play (it's so good, it should be in our best logos list) – but so did that shade of red, and the bottle shape.

The future looks bright, perhaps thanks to the three Suns? These past few days, we've been tinkering with two AI engines. DALL·E 2 is an artificial intelligence system that can create realistic images and art from a description in natural language. It is part of a larger OpenAI set of models. Midjourney is a similar AI program that creates images from textual descriptions.

Long before getting access to and tinkering around with DALL·E 2 this year, during my previous semester at uni, I worked on a ‘Neural Style Transfer’ project (the link to which can be found here)…

A Midjourney user has posted a series of terrifying images created by the artificially intelligent (AI) software that depicts the last selfie ever taken. The apocalyptic and nightmarish images detail ghoulish beings staring into the lens as the world crumbles all around them. Robot Overloards posted the video series to his TikTok page, where he posts other creepy images that have been generated by the AI machine Midjourney. The selfie images, which were generated by the prompt "selfie at the end of the world," have proven to be incredibly popular with one of the creator's videos receiving over 13 million views and over two million likes. Typical comments include: "I didn't plan on sleeping anyway."

Over the past year, machine learning and artificial intelligence technology have made significant strides. Specialized algorithms, including OpenAI's DALL-E, have demonstrated the ability to generate images based on text prompts with increasing canniness. Natural language processing (NLP) systems have grown closer to approximating human writing and text. And some people even think that an AI has attained sentience. And as Ars' Matt Ford recently pointed out here, artificial intelligence may be artificial, but it's not "intelligence"--and it certainly isn't magic.

ABSTRACT An ideal synthetic population, a key input to activity-based models, mimics the distribution of the individual-and household-level attributes in the actual population. Since the entire population's attributes are generally unavailable, household travel survey (HTS) samples are used for population synthesis. Synthesizing population by directly sampling from HTS ignores the attribute combinations that are unobserved in the HTS samples but exist in the population, called'sampling zeros'. A deep generative model (DGM) can potentially synthesize the sampling zeros but at the expense of generating'structural zeros' (i.e., the infeasible attribute combinations that do not exist in the population). This study proposes a novel method to minimize structural zeros while preserving sampling zeros. Two regularizations are devised to customize the training of the DGM and applied to a generative adversarial network (GAN) and a variational autoencoder (VAE). The adopted metrics for feasibility and diversity of the synthetic population indicate the capability of generating sampling and structural zeros - lower structural zeros and lower sampling zeros indicate the higher feasibility and the lower diversity, respectively. Results show that the proposed regularizations achieve considerable performance improvement in feasibility and diversity of the synthesized population over traditional models. The proposed VAE additionally generated 23.5% of the population ignored by the sample with 79.2% precision (i.e., 20.8% structural zeros rates), while the proposed GAN generated 18.3% of the ignored population with 89.0% precision. The proposed improvement in DGM generates a more feasible and diverse synthetic population, which is critical for the accuracy of an activity-based model. INTRODUCTION Activity-based models (ABMs) simulate and forecast daily activity tours of the population at an urban scale, which comprise multiple hierarchical dimensions of individual-level preferences in continuous time and space - when, where, for how long, in what sequence, and by which travel modes activities are performed (1, 2).

NEW DALL·E 2 commercial usage rights

I recently started an AI-focused educational newsletter, that already has over 125,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Artificial intelligence(AI) research in text-to-image synthesis has gone off the charts in recent months. Models like OpenAI's DALL-E 2, GLIDE or Google's Parti or Imagen have shown the possibilities of emulating creative expression using deep learning.