Link to the tool: https://clipdrop.co/stable-diffusion-reimagine

Summary:

• An MIT study provides evidence that AI language models may be capable of learning meaning, rather than just being "stochastic parrots".
• The team trained a model using the Karel programming language and showed that it was capable of semantically representing the current and future states of a program.
• The results of the study challenge the widely held view that language models merely represent superficial statistical patterns and syntax.

Fascinating new paper from Jin and Rinard at MIT that shows models might develop semantic understanding despite being trained on text: "We present evidence that language models can learn meaning despite being trained only to perform next token prediction on text, specifically a corpus of programs."

Our vision at IBM is to scale quantum systems to a size where they’ll be capable of solving the world’s most challenging problems. To get there, we’ve set our sights on a key milestone: deploying a quantum-centric supercomputer powered by 100,000 qubits by 2033.

Read more: https://research.ibm.com/blog/100k-qubit-supercomputer

The new wave of AI systems, ChatGPT and its more powerful successors, exhibit extraordinary capabilities across a broad swath of domains. In light of this, we discuss whether artificial INTELLIGENCE has arrived.

Paper available here: https://arxiv.org/abs/2303.12712 Video recorded at MIT on March 22nd, 2023

Google puts it foot on the accelerator, casting aside safety concerns to not only release a GPT 4 -competitive model, PaLM 2, but also announce that they are already training Gemini, a GPT 5 competitor [likely on TPU v5 chips]. This is truly a major day in AI history, and I try to cover it all.

I'll show the benchmarks in which PaLM (which now powers Bard) beats GPT 4, and detail how they use SmartGPT-like techniques to boost performance. Crazily enough, PaLM 2 beats even Google Translate, due in large part to the text it was trained on. We'll talk coding in Bard, translation, MMLU, Big Bench, and much more.

Paper: https://arxiv.org/abs/2306.07052

Abstract:

In this work, we empirically show that updating pretrained LMs (350M, 1.3B, 2.7B) with just a few steps of Gradient Ascent Post-training (GAP) on random, unlabeled text corpora enhances its zero-shot generalization capabilities across diverse NLP tasks. Specifically, we show that GAP can allow LMs to become comparable to 2-3x times larger LMs across 12 different NLP tasks. We also show that applying GAP on out-of-distribution corpora leads to the most reliable performance improvements. Our findings indicate that GAP can be a promising method for improving the generalization capability of LMs without any task-specific fine-tuning.

Pretty cool research. I'm wondering if this method could be applied in a more effective way, e.g. by introducing gradient ascent throughout the full training process (I'd be curious to see how different ratios of descent:ascent during training would affect convergence/generalization abilities). Will also be neat to see this applied to larger models.

Edit for Lemmies: You can read paper here: https://www.researchgate.net/publication/371505904_Gradient_Ascent_Post-training_Enhances_Language_Model_Generalization

In the project "Seeing the World through Your Eyes," researchers at the University of Maryland, College Park, show that the reflections of the human eye can be used to reconstruct 3D scenes. This, they say, is an "underappreciated source of information about what the world around us looks like".

Summary:

• Researchers at the University of Maryland have developed a NeRF-based method to reconstruct 3D scenes from reflections in the human eye. They believe this is an underappreciated source of information about the world around us.

• The method uses the uniform geometry of the cornea in healthy adults to estimate the position and orientation of the eye. An important aspect of the work is the development of a corneal position optimization technique that helps improve the robustness of the method.

• Tests have been performed with both synthetic eye images and real photographs, but only under laboratory conditions. Despite certain challenges, such as inaccuracies in the localization of the cornea and the low resolution of the images, the method is considered promising.

Source: https://the-decoder.com/better-watch-what-youre-looking-at-ai-can-reconstruct-it-in-3d/

Paper & more: https://world-from-eyes.github.io/

The UN report highlights the threat to information security from deepfakes created with the help of AI. Despite the potential of neural networks in solving global problems, the UN has expressed concerns about their use in generating fake images and videos, especially in conflict situations.

The UN is calling on all stakeholders to use AI responsibly, insisting that action be taken to ensure that the technology is used safely and ethically, in line with international human rights. Digital platform owners are also encouraged to invest in content moderation systems and transparent reporting. The UN Secretary General expressed his hope for the joint efforts of the community to solve this problem at the upcoming summit in 2024.