Lifeboat Foundation

MIT CSAIL researchers have developed a generative AI system, LucidSim, to train robots in virtual environments for real-world navigation. Using ChatGPT and physics simulators, robots learn to traverse complex terrains. This method outperforms traditional training, suggesting a new direction for robotic training.

A team of roboticists and engineers at MIT CSAIL, Institute for AI and Fundamental Interactions, has developed a generative AI approach to teaching robots how to traverse terrain and move around objects in the real world.

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Waymo has expanded its robotaxi service to the general public in Los Angeles, allowing anyone with the Waymo One app to request a ride. This marks a significant step in autonomous vehicle technology, as Waymo continues to lead the industry with over 50,000 weekly passengers and a strong safety record.

Waymo on Tuesday opened its robotaxi service to anyone who wants a ride around Los Angeles, marking another milestone in the evolution of self-driving car technology since the company began as a secret project at Google 15 years ago.

The expansion comes eight months after Waymo began offering rides in Los Angeles to a limited group of passengers chosen from a waiting list that had ballooned to more than 300,000 people. Now, anyone with the Waymo One smartphone app will be able to request a ride around an 80-square-mile (129-square-kilometer) territory spanning the second largest U.S. city.

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Researchers at Penn Engineering have developed PanoRadar, a system that uses radio waves and AI to provide robots with detailed 3D environmental views, even in challenging conditions like smoke and fog. This innovation offers a cost-effective alternative to LiDAR, enhancing robotic navigation and perception capabilities.

In the race to develop robust perception systems for robots, one persistent challenge has been operating in bad weather and harsh conditions. For example, traditional, light-based vision sensors such as cameras or LiDAR (Light Detection And Ranging) fail in heavy smoke and fog.

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Groundbreaking research presented at UEG Week 2024 reveals a promising new treatment strategy for type 2 diabetes (T2D) that could significantly reduce or even eliminate the need for insulin therapy.

This innovative approach, which combines a novel procedure known as ReCET (Re-Cellularization via Electroporation Therapy) with semaglutide, resulted in the elimination of insulin therapy for 86% of patients.

Globally, T2D affects 422 million people, with obesity recognized as a significant risk factor. While insulin therapy is commonly used to manage blood sugar levels in T2D patients, it can result in side effects such as weight gain and further complicate diabetes management. A need therefore exists for alternative treatment strategies.

While labeled a “robot wolf” by its designers, this platform presents itself as a powerful tactical tool likely aimed at military or security applications, where its design and capabilities stand to offer significant operational value.

The robot’s four-legged design is an immediate indicator of its…

At China’s Zhuhai Air Show, a new robotic quadruped known as robot-wolf stole the spotlight demonstrating its capability to respond to real-time voice commands.

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Principal investigator Eric Pierce pointed out that the trial shows that gene therapy for hereditary vision loss is a worthy pursuit for future research. He believes the early research is promising.

“It’s a big deal to hear how thrilled they were to finally be able to see food on their plates,” said Pierce. “These were individuals who couldn’t read a single line on an eye chart. They had no treatment options, which is an unfortunate reality for most people with inherited retinal disorders.”

The goal is to inject CRISPR so that it reaches the retina to restore the ability to produce genes and proteins.

Blocking enzyme SMYD5 inhibits tumour growth and primes lab models for combination therapy, offering hope for more effective treatments.

Microbial systems have been synthetically engineered to deploy therapeutic payloads in vivo.

To enable effective cancer vaccination, we developed an engineered bacterial system in probiotic Escherichia coli Nissle 1917 (EcN) to enhance expression, delivery and immune-targeting of arrays of tumour exonic mutation-derived epitopes highly expressed by tumour cells and predicted to bind major histocompatibility complex (MHC) class I and II (Fig. 1a). This system incorporates several key design elements that enhance therapeutic use: optimization of synthetic neoantigen construct form with removal of cryptic plasmids and deletion of Lon and OmpT proteases to increase neoantigen accumulation, increased susceptibility to phagocytosis for enhanced uptake by antigen-presenting cells (APCs) and presentation of MHC class II-restricted antigens, expression of listeriolysin O (LLO) to induce cytosolic entry for presentation of recombinant encoded neoantigens by MHC class I molecules and T helper 1 cell (T_H1)-type immunity and improved safety for systemic administration due to reduced survival in the blood and biofilm formation.

To assemble a repertoire of neoantigens, we conducted exome and transcriptome sequencing of subcutaneous CT26 tumours. Neoantigens were predicted from highly expressed tumour-specific mutations using established methods^14,15, with selection criteria inclusive of putative neoantigens across a spectrum of MHC affinity^16,17. Given the importance of both MHC class I and MHC class II binding epitopes in antitumour immunity^15,18,19, we integrated a measure of wild-type-to-mutant MHC affinity ratio—termed agretopicity^17,20—for both epitope types derived from a given mutation, to help estimate the ability of adaptive immunity to recognize a neoantigen. Predicted neoantigens were selected from the set of tumour-specific mutations satisfying all criteria, notably encompassing numerous recovered, previously validated CT26 neoantigens¹⁵ (Extended Data Fig. 1a).

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Researchers developed MassiveFold, an enhanced AlphaFold version optimized for parallel processing, which accelerates protein structure predictions from months to hours while increasing structural diversity.