Managing extreme AI risks amid rapid progress.

Preparation requires technical research and development, as well as adaptive, proactive governance.

Berkeley Open Extended Reality Recordings 2023 (BOXRR-23): 4.7 Million Motion Capture Recordings from 105,000 XR Users.

Extended reality (XR) devices such as the Meta Quest and Apple Vision Pro have seen a recent surge in attention, with motion tracking "telemetry" data lying at the core of nearly all XR and metaverse experiences. Researchers are just beginning to understand the implications of this data for security, privacy, usability, and more, but currently lack large-scale human motion datasets to study. The BOXRR-23 dataset contains 4,717,215 motion capture recordings, voluntarily submitted by 105,852 XR device users from over 50 countries. BOXRR-23 is over 200 times larger than the largest existing motion capture research dataset and uses a new, highly efficient and purpose-built XR Open Recording (XROR) file format.

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses.

As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance. The absence of trustworthy human supervision over the data collection process exposes organizations to security vulnerabilities; training data can be manipulated to control and degrade the downstream behaviors of learned models. The goal of this work is to systematically categorize and discuss a wide range of dataset vulnerabilities and exploits, approaches for defending against these threats, and an array of open problems in this space.

Novel 2,4-diaminopyrimidines bearing tetrahydronaphthalenyl moiety against anaplastic lymphoma kinase (ALK): Synthesis, in vitro, ex vivo, and in vivo efficacy studies.

A series of novel 2,4-diaminopyrimidines bearing tetrahydronaphthalenyl moiety were synthesized and evaluated for their anti-anaplastic lymphoma kinase (ALK) activities using enzymatic and cell-based assays. Among the compounds synthesized, compound 17b showed promising pharmacological results in in vitro, ex vivo, and pharmacokinetic studies. An in vivo efficacy study with compound 17b demonstrated highly potent inhibitory activity in H3122 tumor xenograft model mice. A series of kinase assays showed that compound 17b inhibited various kinases including FAK, ACK1, FGFR, RSK1, IGF-1R, among others, thus demonstrating its potential for synergistic anti-tumor activity and development as a multi-targeted non-small cell lung cancer (NSCLC) therapy.

Synthesis and evaluation of novel 2,4-diaminopyrimidines bearing bicyclic aminobenzazepines for anaplastic lymphoma kinase (ALK) inhibitor.

A series of novel 2,4-diaminopyrimidine compounds bearing bicyclic aminobenzazepine were synthesized and evaluated for their anti-ALK activities. The activities of these compounds were confirmed in both enzyme- and cell-based ALK assays. Amongst compounds synthesized, KRCA-0445 showed very promising results in pharmacokinetic study and in vivo efficacy study with H3122 xenograft mouse model.

Evolution of patterns on Conus shells.

The pigmentation patterns of shells in the genus Conus can be generated by a neural-network model of the mantle. We fit model parameters to the shell pigmentation patterns of 19 living Conus species for which a well resolved phylogeny is available. We infer the evolutionary history of these parameters and use these results to infer the pigmentation patterns of ancestral species. The methods we use allow us to characterize the evolutionary history of a neural network, an organ that cannot be preserved in the fossil record. These results are also notable because the inferred patterns of ancestral species sometimes lie outside the range of patterns of their living descendants, and illustrate how development imposes constraints on the evolution of complex phenotypes.