If you've been closely following the progress of Open AI, the company run by Sam Altman whose neural nets can now write original text and create original pictures with astonishing ease and speed, you might just skip this piece. If, on the other hand, you've only been vaguely paying attention to the company's progress and the increasing traction that other so-called "generative" AI companies are suddenly gaining and want to better understand why, you might benefit from this interview with James Currier, a five-time founder and now venture investor who cofounded the firm NFX five years ago with several of his serial founder friends. Currier falls into the camp of people following the progress closely -- so closely that NFX has made numerous related investments in "generative tech" as he describes it, and it's garnering more of the team's attention every month. In fact, Currier doesn't think the buzz about this new wrinkle on AI isn't hype so much as a realization that the broader startup world is suddenly facing a very big opportunity for the first time in a long time. "Every 14 years," says Currier, "we get one of these Cambrian explosions. We had one around the internet in '94.

Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug, drug-target, and target-target interactions. However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes. In this paper, we present the effectiveness of variational graph autoencoders (VGAE) in modeling latent node representations on multimodal networks. Our approach can produce flexible latent spaces for each node type of the multimodal graph; the embeddings are used later for predicting links among node pairs under different edge types. To further enhance the models' performance, we suggest a new method that concatenates Morgan fingerprints, which capture the molecular structures of each drug, with their latent embeddings before preceding them to the decoding stage for link prediction. Our proposed model shows competitive results on two multimodal networks: (1) a multi-graph consisting of drug and protein nodes, and (2) a multi-graph consisting of drug and cell line nodes. Our source code is publicly available at https://github.com/HySonLab/drug-interactions.

A coordinate system is a foundation for every quantitative science, engineering, and medicine. Classical physics and statistics are based on the Cartesian coordinate system. The classical probability and hypothesis testing theory can only be applied to Euclidean data. However, modern data in the real world are from natural language processing, mathematical formulas, social networks, transportation and sensor networks, computer visions, automations, and biomedical measurements. The Euclidean assumption is not appropriate for non Euclidean data. This perspective addresses the urgent need to overcome those fundamental limitations and encourages extensions of classical probability theory and hypothesis testing , diffusion models and stochastic differential equations from Euclidean space to non Euclidean space. Artificial intelligence such as natural language processing, computer vision, graphical neural networks, manifold regression and inference theory, manifold learning, graph neural networks, compositional diffusion models for automatically compositional generations of concepts and demystifying machine learning systems, has been rapidly developed. Differential manifold theory is the mathematic foundations of deep learning and data science as well. We urgently need to shift the paradigm for data analysis from the classical Euclidean data analysis to both Euclidean and non Euclidean data analysis and develop more and more innovative methods for describing, estimating and inferring non Euclidean geometries of modern real datasets. A general framework for integrated analysis of both Euclidean and non Euclidean data, composite AI, decision intelligence and edge AI provide powerful innovative ideas and strategies for fundamentally advancing AI. We are expected to marry statistics with AI, develop a unified theory of modern statistics and drive next generation of AI and data science.

According to The Information, OpenAI received an implied valuation of nearly $20 billion in a stock sale that took place last year. The participants in the stock sale reportedly included Sequoia Capital, Tiger Global Management, Bedrock Capital and Andreessen Horowitz.

The company Mubert is now venturing into a generative AI system that creates music based on text input. It is still in its infancy. Founded in 2017, U.S. startup Mubert specializes in generative AI for royalty-free music. Mubert's text-to-music app is a first attempt at generative AI that generates music from text input. A demo version at Huggingface allows users to input the prompt, from which the system then pulls individual keywords and matches them to the internal tagging of recorded sound clips, assembling a piece up to 100 seconds long.

AI has been rapidly finding industrial applications, such as the use of large language models to automate enterprise IT. Those applications may make the question of actual intelligence moot. The British mathematician Alan Turing wrote in 1950, "I propose to consider the question, 'Can machines think?'" His inquiry framed the discussion for decades of artificial intelligence research. For a couple of generations of scientists contemplating AI, the question of whether "true" or "human" intelligence could be achieved was always an important part of the work.

AI image generators, which create fantastical sights at the intersection of dreams and reality, bubble up on every corner of the web. Their entertainment value is demonstrated by an ever-expanding treasure trove of whimsical and random images serving as indirect portals to the brains of human designers. A simple text prompt yields a nearly instantaneous image, satisfying our primitive brains, which are hardwired for instant gratification. Although seemingly nascent, the field of AI-generated art can be traced back as far as the 1960s with early attempts using symbolic rule-based approaches to make technical images. Yilun Du, a PhD student in the Department of Electrical Engineering and Computer Science and affiliate of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), recently developed a new method that makes models like DALL-E 2 more creative and have better scene understanding. Here, Du describes how these models work, whether this technical infrastructure can be applied to other domains, and how we draw the line between AI and human creativity.

There is a trending new AI tool on the block, but instead of creating images, this AI tool analyzes them and spits out crude roasts of anyone they depict. Days are gone netizens leveraged DALL·E 2 and racist image-spewing DALL·E mini to generate silly art for Twitter shit posting. The new AI tool, known as the CLIP Interrogator and created by a generative artist who goes by the handle Pharma psychotic, is technically an artificial intelligence powered tool to discover "what a good prompt might be to generate new AI art like an existing one." In reality, CLIP Interrogator tends to spit out descriptions of people that can be mundane, puzzling, staggeringly insensitive, and, at times, admittedly a bit hilarious. The new AI tool told one user she looked "tired and drunk," for instance, and accused another user of having a "deformed face."

I think we can all agree that 2022 is the year of the AI generative art boom. If you've been keeping tabs on social media, you've likely seen some artificial intelligence-inspired memes floating around on your newsfeed. Some of the most widely shared images were developed by DALL-E Mini, now known as Craiyon, a publicly accessible image generation tool that debuted this year. Now, three additional applications, Midjourney, DALL-E 2, and Stable Diffusion have beta versions that are available to play with today! At a distance, it looks like the four tools operate with the same premise -- enter a text-based prompt and receive a series of relevant pictures through advanced machine learning that combs through and learns from millions of images on the internet.

Amidst the Russian invasion of Ukraine, the risk of nuclear war is now larger than it has been since the end of the Cold War. The spectre of nuclear annihilation, once thought a thing of the past, has returned. While technology can avert some forms of annihilation, for example by diverting major asteroid strikes, these naturally occurring risks are likely small, evidenced by our long history free from them. The same cannot be said for those caused or exacerbated by technology. Nuclear war, climate change, engineered bioweapons, and even pandemics: these risks are unfortunately all too familiar.