Objects generated by 3D-GAN from vectors, without a reference image/object. We show, for the last two objects in each row, the nearest neighbor retrieved from the training set. We see that the generated objects are similar, but not identical, to examples in the training set. For comparison, we show objects generated by the previous state-of-the-art [Wu et al., 2015] (results supplied by the authors). We also show objects generated by autoencoders trained on a single object category, with latent vectors sampled from empirical distribution. See paper Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling.

Radical Reads: Generative AI and the Evolution of Ideas

An idea, of course, is the domain of the human mind. Breakthrough advances in generative AI technology, however, are changing the notion of what it means to substantiate an idea, offering new mechanisms for creation and iteration that were – until just recently – entirely bound by our biological capacity…

Read More »
A photographic rendering of a simulated middle-aged white woman against a black background, seen through a refractive glass grid and overlaid with a distorted diagram of a neural network.

Radical Reads: Of God and Machines

All technology is, in a sense, sorcery. A stone-chiseled ax is superhuman. No arithmetical genius can compete with a pocket calculator. Even the biggest music fan you know probably can’t beat Shazam…

Read More »
Hebbia Radical Ventures

Radical Reads: Unlocking Unstructured Data

Any company that can unlock the trove of information that lives in unstructured data, and generate real insights on demand, stands to be a category-defining business. Hebbia was founded by George Sivulka, a Stanford Ph.D. dropout with a research track record across many fields…

Read More »

Radical Reads: AI Enters the Mainstream

Is adopting AI translating into performance results? According to McKinsey, leaders reporting AI adoption have demonstrated stronger financial performance with a 2.1x increase in revenue and 2.5x total return to shareholders…

Read More »

Radical Reads: Predicting credit markets with AI

Founded by Professor Kay Giesecke, the Director of the Advanced Financial Technology Laboratory and leader of the Mathematical and Computational Finance program at Stanford, Infima’s team is comprised of leaders at the intersection of deep learning technologies and capital markets…

Read More »

© 2023 Radical Ventures Investments Inc.