Lifeboat Foundation

One month into living under Russian occupation in northern Ukraine, Marina cycled cautiously through her village. She was five doors from her elderly parents’ blue garden gate when three soldiers ordered her to stop. Grabbing her hair, they dragged Marina into a neighbour’s empty house.

“They forced me to strip naked,” the 47-year-old said, picking at the skin around her fingernails. “I asked them not to touch me, but they said: ‘Your Ukrainian soldiers are killing us’.”

Continue reading “Gang-rape and genital electrocution: How Russia’s war crimes in Ukraine could go un-prosecuted” »

The explicit modeling of the input modality is typically required for deep learning inference. For instance, by encoding picture patches into vectors, Vision Transformers (ViTs) directly model the 2D spatial organization of images. Similarly, calculating spectral characteristics (like MFCCs) to transmit into a network is frequently involved in audio inference. A user must first decode a file into a modality-specific representation (such as an RGB tensor or MFCCs) before making an inference on a file that is saved on a disc (such as a JPEG image file or an MP3 audio file), as shown in Figure 1a. There are two real downsides to decoding inputs into a modality-specific representation.

It first involves manually creating an input representation and a model stem for each input modality. Recent projects like PerceiverIO and UnifiedIO have demonstrated the versatility of Transformer backbones. These techniques still need modality-specific input preprocessing, though. For instance, before sending picture files into the network, PerceiverIO decodes them into tensors. Other input modalities are transformed into various forms by PerceiverIO. They postulate that executing inference directly on file bytes makes it feasible to eliminate all modality-specific input preprocessing. The exposure of the material being analyzed is the second disadvantage of decoding inputs into a modality-specific representation.

Think of a smart home gadget that uses RGB photos to conduct inference. The user’s privacy may be jeopardized if an enemy gains access to this model input. They contend that deduction can instead be carried out on inputs that protect privacy. They make notice that numerous input modalities share the ability to be saved as file bytes to solve these shortcomings. As a result, they feed file bytes into their model at inference time (Figure 1b) without doing any decoding. Given their capability to handle a range of modalities and variable-length inputs, they adopt a modified Transformer architecture for their model.

The Los Angeles Affordable Housing Challenge, the 16th installment of Buildner’s affordable housing competition series, welcomes architects and design enthusiasts from around the globe to submit inventive solutions to tackle Los Angeles’ housing crisis. As the city grapples with skyrocketing rents, gentrification, and expensive starter homes, affordable housing for lower-income households has become increasingly scarce.

This competition seeks to generate imaginative and pragmatic solutions to address the diverse housing needs of Los Angeles residents, including families, single professionals, and couples. Participants are encouraged to think beyond conventional housing models and explore innovative designs that offer flexibility, affordability, sustainability, and a sense of community.

Architect Samira Rathod built a house in Gujarat that stays naturally cool even in peak summer. Here is how:

Different people tend to have unique needs and preferences—particularly when it comes to cleaning or tidying up. Home robots, especially robots designed to help humans with house chores, should ideally be able to complete tasks in ways that account for these individual preferences.

Researchers at Princeton University and Stanford University recently set out to personalize the assistance offered by home robots using large language models (LLMs), a class of artificial intelligence models that are becoming increasingly popular after the release of ChatGPT. Their approach, presented in a paper pre-published on arXiv, was initially tested on a mobile robot called TidyBot engineered to tidy up indoor environments.

“For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios,” Jimmy Wu, Rika Antonova and their colleagues wrote in their paper. “In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away.”

Tobi Alaji grew up as an artist who loved designing and creating new things. Today, she has merged her love for art and skill in software development to build the tech empire, ‘TECHTEE’ which she runs. TechTee is a digital agency and software house that creates an all-around digital experience by combining the best software with the most intriguing customer-centered designs.

TechTee is one of the world’s first Black-owned and woman-founded digital agency according to nftnow. The company boasts of collaborations with famous companies like Deutsche Bank, La Perla, NBC, M&S, and Apple, among others.

In an interview with Business Leader, she shared that she never wanted to own a business growing up because she never understood what it entailed. However, when she was 17, Tobi taught herself Java from a software development book she picked in the Shepherds Bush Library. Since then, she continued to teach herself and practice coding.

Yup that’s right a Tesla Affordable Home.

Known for turning a sofa in the Boca Chica SpaceX office into his bed, Elon Musk, The World’s Richest Man, took it to another level when he announced Tesla’s $10,000 sustainable unboxable moveable home. If you are remotely familiar with the Tesla CEO, you wouldn’t be surprised that he is building a sustainable home. So, how did Elon go from overhauling the tech space to completely disrupting the real estate industry?Well, it all started with this tweet from 2020. At the height of the pandemic, Elon Musk made this insane announcement.

Human influences have the potential to reduce the effectivity of communication in bees, adding further stress to struggling colonies, according to new analysis.

Scientists at the University of Bristol studying honeybees, bumblebees and stingless bees found that variations in communication strategies are explained by differences in the habitats that bees inhabit and differences in the social lifestyle such colony size and nesting habits.

The findings, published today in PNAS, reveal that anthropogenic changes, such as habitat conversion, climate change and the use of agrochemicals, are altering the world bees occupy, and it is becoming increasingly clearer that this affects communication both directly and indirectly; for example, by affecting food source availability, social interactions among nestmates and their cognitive functions.

😗😁 Very interesting findings around the olmec statues.

In a March 31 tweet confirming its recovery, Ebrard referred to the massive stone carving as “the Olmec piece most sought after by Mexico. … It’s about to return to its home, from where it should never have been stolen.”

Mexican officials found out earlier this year that the Manhattan District Attorney’s Antiquity Trafficking Unit had recovered the piece, which is roughly five feet wide, six feet tall and carved out of a slab of stone weighing nearly one ton.

Continue reading “Long-Lost ‘Earth Monster’ Olmec Head Found in Denver, Now Destined for Mexico” »