Lifeboat Foundation

If it were up to Larry Ellison, the exorbitantly rich cofounder of software outfit Oracle, all of us will soon be smiling for the camera — constantly. Not for a cheery photograph, but to appease our super-invasive, if not totally omnipresent, algorithmic overseers.

As Business Insider reports, the tech centibillionaire glibly predicts that the wonders of AI will bring about a new paradigm of supercharged surveillance, guaranteeing that the proles — excuse us, “citizens” — all behave and stay in line.

“We’re going to have supervision,” Ellison said this week at an Oracle financial analysts meeting, per BI. “Every police officer is going to be supervised at all times, and if there’s a problem, AI will report that problem and report it to the appropriate person.”

The hybrid camera system eliminates blind spots and significantly reduces delays in obstacle detection.

Combining event cameras with artificial intelligence, researchers have developed a system that detects obstacles 100 times faster.

1,428 likes, — thesessionca on October 13, 2024: Repost 🎥: @marionawfal.

Comment “Tesla” to get ONLINE MONEY HACKS

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In recent years, artificial intelligence (AI) and deep learning models have advanced rapidly, becoming easily accessible. This has enabled people, even those without specialized expertise, to perform various tasks with AI. Among these models, generative adversarial networks (GANs) stand out for their outstanding performance in generating new data instances with the same characteristics as the training data, making them particularly effective for generating images, music, and text.

GANs consist of two neural networks, namely, a generator that creates new data distributions starting from random noise, and a discriminator which checks whether the generated data distribution is “real” (matching the training data) or “fake.” As training progresses, the generator improves at generating realistic distributions, and the discriminator at identifying the generated data as fake.

GANs use a loss function to measure differences between the fake and real distributions. However, this approach can cause issues like gradient vanishing and unstable learning, directly impacting stability and efficiency. Despite considerable progress in improving GANs, including structural modifications and loss function adjustments, challenges such as gradient vanishing and mode collapse, where the generator produces a limited variety, continue to limit their applicability.

They really jumped the shark with this one.

Nets wont do it, nets wont cut it, and to me, nets say: we dont know and we sorta give up. We need One System to be able to engage All Types and All Classes of drones, w/ EMF — RF jammers, Microwaves, Lasers, Projectiles, and Missiles. All acting simultaneously, to engage a So Called Drone Swarm.

U.S. Air Force officials at Langley Air Force Base in Virginia are looking at installing anti-drone nets to help protect F-22 Raptor stealth fighters on the flightline. This comes nearly a year after the base was subjected to waves of still-mysterious drone incursions, which The War Zone was first to report. It also underscores the U.S. military’s continued lag when it comes to responding to the very real threats posed by uncrewed aerial systems, at home and aboard, and particular hurdles to doing so domestically.

Langley’s 633rd Contracting Squadron put out a notice on October 4 asking for information about potential counter-drone netting that could be installed around up to 42 existing open-ended sunshade-type shelters at the base. Langley, now technically part of Joint Base Langley-Eustis, is one of a select few bases to host F-22s and is a key component of the Air Force’s posture to defend the U.S. homeland.

Continue reading “Protective Nets To Shield F-22s Eyed For Airbase Swarmed By Mystery Drones” »

Quantum memory lets a quantum computer perform a task not necessarily with fewer steps, but with less data. Could this in itself be a way to prove quantum advantage?

The new papers show that quantum memory lets a quantum computer perform a task not necessarily with fewer steps, but with less data. As a result, researchers believe this in itself could be a way to prove quantum advantage. “It allows us to, in the more near term, already achieve that kind of quantum advantage,” said Hsin-Yuan Huang, a physicist at Google Quantum AI.

But researchers are excited about the practical benefits too, as the new results make it easier for researchers to understand complex quantum systems.

Continue reading “‘Quantum Memory’ Proves Exponentially Powerful” »

Predibase announces the Predibase Inference Engine, their new infrastructure offering designed to be the best platform for serving fine-tuned small language models (SLMs). The Predibase Inference Engine dramatically improves SLM deployments by making them faster, easily scalable, and more cost-effective for enterprises grappling with the complexities of productionizing AI. Built on Predibase’s innovations–Turbo LoRA and LoRA eXchange (LoRAX)–the Predibase Inference Engine is designed from the ground up to offer a best-in-class experience for serving fine-tuned SLMs.

The need for such an innovation is clear. As AI becomes more entrenched in the fabric of enterprise operations, the challenges associated with deploying and scaling SLMs have grown increasingly daunting. Homegrown infrastructure is often ill-equipped to handle the dynamic demands of high-volume AI workloads, leading to inflated costs, diminished performance, and operational bottlenecks. The Predibase Inference Engine addresses these challenges head-on, offering a tailor-made solution for enterprise AI deployments.

Join Predibase webinar on October 29th to learn more about the Predibase Inference Engine!

Seems interesting. But B.D. has fallen behind now, imo.

A new joint research agreement between Boston Dynamics and the Toyota Research Institute combines leading teams in robotics and AI.

Continue reading “Boston Dynamics & TRI Announce Partnership” »