Fake AI apps disguised as ChatGPT and DALL·E clones are flooding app stores with sophisticated spyware that steals personal data and monitors users.

The Posha and the Thermomix TM7 are the closest things to a home robot chef that mere mortals can afford. The catch is that you're the prep cook. The holiday is still almost a week away, and I'm sick of Thanksgiving. I've already made four rounds of mashed potatoes, three of mac and cheese, and three turkeys (with more still waiting in my fridge) as part of testing smart probes to help smoke turkeys outside and preparing seven-course holiday meal kits for friends and family. I was eager to finally outsource some of the cooking by testing two very different robo-chef devices, the Thermomix TM7 and the Posha kitchen robot . Both promise to plan my meals and also do most of the cooking, which sounds pretty good to me. The Thermomix descends from a German device launched in 1968--a time when the best-known robot chef was cartoon Rosie on --that was essentially a blender with a heater. It's since caught on big in countries from Italy to Portugal to Australia, and over the years it's added multi-tier steaming, baking, proofing, a touchscreen, an encyclopedic recipe app, and a whole lot of smart features.

In this paper, we aim to answer three key open questions: (i) whether and how brain networks are cross-correlated at different time scales with long-range dependencies ("long-memory" process,

As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We address this problem in the context of multiple hypotheses testing, where for each hypothesis, we observe a p-value along with a set of features specific to that hypothesis. For example, in genetic association studies, each hypothesis tests the correlation between a variant and the trait.

Neural Information Processing Systems http://nips.cc/

Furthermore, as the interpolation is learned and state-dependent, our method can deal with heterogeneous observability.

This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG). Specifically, we describe a factor-graph-based model with customized factor-functions defined based on domain knowledge, to infer pathologic brain activity with the goal of identifying seizure-generating brain regions in epilepsy patients. We utilize an inference technique based on the graph-cut algorithm to exactly solve graph inference in polynomial time. We validate the model by using clinically collected intracranial EEG data from 29 epilepsy patients to show that the model correctly identifies seizure-generating brain regions. Our results indicate that our model outperforms two conventional approaches used for seizure-onset localization (5-7% better AUC: 0.72, 0.67, 0.65) and that the proposed inference technique provides 3-10% gain in AUC ( 0.72, 0.62, 0.69) compared to sampling-based alternatives.

Newfoundland dogs are still practicing the same lifesaving skills they would have used in the 19th century. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a dark and stormy night and you've suddenly found yourself swept off of your wooden vessel into the wild Atlantic Ocean. It's 1893, so your woolen clothes are pulling you down to Davy Jones' locker. What kind of dog would want to rescue you?

Neural Information Processing Systems http://nips.cc/

One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge.