Lifeboat Foundation

In 2021, a book titled “The Human-Machine Team: How to Create Synergy Between Human and Artificial Intelligence That Will Revolutionize Our World” was released in English under the pen name “Brigadier General Y.S.” In it, the author — a man who we confirmed to be the current commander of the elite Israeli intelligence unit 8,200 — makes the case for designing a special machine that could rapidly process massive amounts of data to generate thousands of potential “targets” for military strikes in the heat of a war. Such technology, he writes, would resolve what he described as a “human bottleneck for both locating the new targets and decision-making to approve the targets.”

Such a machine, it turns out, actually exists. A new investigation by +972 Magazine and Local Call reveals that the Israeli army has developed an artificial intelligence-based program known as “Lavender,” unveiled here for the first time. According to six Israeli intelligence officers, who have all served in the army during the current war on the Gaza Strip and had first-hand involvement with the use of AI to generate targets for assassination, Lavender has played a central role in the unprecedented bombing of Palestinians, especially during the early stages of the war. In fact, according to the sources, its influence on the military’s operations was such that they essentially treated the outputs of the AI machine “as if it were a human decision.”

Formally, the Lavender system is designed to mark all suspected operatives in the military wings of Hamas and Palestinian Islamic Jihad (PIJ), including low-ranking ones, as potential bombing targets. The sources told +972 and Local Call that, during the first weeks of the war, the army almost completely relied on Lavender, which clocked as many as 37,000 Palestinians as suspected militants — and their homes — for possible air strikes.

NATIONAL HARBOR, Md. — Shield AI in the next year plans to have its Hivemind digital pilot working aboard three additional types of aircraft, bringing the total to nine.

The California-based company has already folded the autonomous flight software into three classes of quadcopters, its own V-Bat drone, the F-16 fighter jet and the Kratos-made MQM-178 Firejet drone.

Up next are two more Kratos products, the XQ-58 and BQM-177, according to Brandon Tseng, the president of Shield AI. The firm has not picked a third candidate.

Smaller LLMs seemingly have faster reactions.

A newly developed AI method can calculate a fundamental problem in quantum chemistry: Schrödinger’s Equation. The technique could calculate the ground state of the Schrödinger equation in quantum chemistry.

Predicting molecules’ chemical and physical properties by relying on their atoms’ arrangement in space is the main goal of quantum chemistry. This can be achieved by solving the Schrödinger equation, but in practice, this is extremely difficult.

Study: Dr ChatGPT tell me what I want to hear: How different prompts impact health answer correctness

As AI becomes increasingly integral to our daily lives, its ability to provide accurate and reliable information, particularly in sensitive areas such as health, is under intense scrutiny. The study conducted by CSIRO and The University of Queensland researchers brings to light the nuanced ways in which the formulation of prompts influences ChatGPT’s responses. In the realm of health information seeking, where the accuracy of the information can have profound implications, the findings of this study are especially pertinent.

Using the Text Retrieval Conference (TREC) Misinformation dataset, the study precisely evaluated ChatGPT’s performance across different prompting conditions. This analysis revealed that ChatGPT could deliver highly accurate health advice, with an effectiveness rate of 80% when provided with questions alone. However, this effectiveness is significantly compromised by biases introduced through the phrasing of questions and the inclusion of additional information in the prompts.

Assisted by quantum physics and machine learning, researchers have developed a transparent window coating that lets in visible light but blocks heat-producing UV and infrared. The coating not only reduces room temperature but also the energy consumption related to cooling, regardless of where the sun is in the sky.

Windows are great. They provide views of the park you live across from or the bird-filled tree outside your office. But, windows can also be not-so-great. Letting in light (and the view) is one thing, but with light comes heat, especially in the hotter months.

On hot days, up to 87% of heat gain in our homes is through windows. UV radiation from sunlight passes easily through glass, heating up the room and increasing the likelihood that you need to turn on the air-con or else forgo any light (and, again, that view) by closing the curtains or lowering the blinds. However, researchers at the University of Notre Dame have developed a window coating that blocks heat-producing UV and infrared light while allowing visible light in, reducing both room temperature and cooling energy consumption.

GPT-4 is already better at changing people’s minds than the average human is, according to new research. The gap widens the more it knows about us – and once it can see us in real time, AI seems likely to become an unprecedented persuasion machine.

We don’t tend to like thinking of ourselves as being particularly easy to manipulate, but history would appear to show that there are few things more powerful than the ability to sway people to align with your view of things. As Yuval Noah Harari points out in Sapiens, his potted history of humankind, “shared fictions” like money, religion, nation states, laws and social norms form the fundamental backbones of human society. The ability to assemble around ideas and co-operate in groups much bigger than our local tribes is one of our most potent advantages over the animal kingdom.

Continue reading “GPT-4 is 82% more persuasive than humans, and AIs can now read emotions” »

Some amount of chatbot hallucination is inevitable. But there are ways to minimize it.

By Lauren Leffer

In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges.

Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image.

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have introduced a new framework that simplifies the multi-step process of traditional diffusion models into a single step, addressing previous limitations. This is done through a type of teacher-student model: teaching a new computer model to mimic the behavior of more complicated, original models that generate images.