Lifeboat Foundation

Large Language Models (LLMs) have shown great capabilities in various natural language tasks such as text summarization, question answering, generating code, etc., emerging as a powerful solution to many real-world problems. One area where these models struggle, though, is goal-directed conversations where they have to accomplish a goal through conversing, for example, acting as an effective travel agent to provide tailored travel plans. In practice, they generally provide verbose and non-personalized responses.

Models trained with supervised fine-tuning or single-step reinforcement learning (RL) commonly struggle with such tasks as they are not optimized for overall conversational outcomes after multiple interactions. Moreover, another area where they lack is dealing with uncertainty in such conversations. In this paper, the researchers from UC Berkeley have explored a new method to adapt LLMs with RL for goal-directed dialogues. Their contributions include an optimized zero-shot algorithm and a novel system called imagination engine (IE) that generates task-relevant and diverse questions to train downstream agents.

Since the IE cannot produce effective agents by itself, the researchers utilize an LLM to generate possible scenarios. To enhance the effectiveness of an agent in achieving desired outcomes, multi-step reinforcement learning is necessary to determine the optimal strategy. The researchers have made one modification to this approach. Instead of using any on-policy samples, they used offline value-based RL to learn a policy from the synthetic data itself.

New process returns original quantum entanglement after certifying it, removing onerous “trusted source” requirement.

Is reality indistinguishable from information? Is consciousness a self-aware, self-modifying information field? Does information have intrinsic meaning? How does meaningfulness arise? How do sentient and non-sentient entities differ in the way they perceive and process information?…

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Could information be the stuff of which everything is made? Information seems so abstract, not a substance or a thing, so how could it be the building blocks of reality? There are ways and reasons how information can literally be reality, some scientists claim, and their ideas are revolutionary.

Could information be the stuff of which everything is made? Information seems so abstract, not a substance or a thing, so how could it be the building blocks of reality? There are ways and reasons how information can literally be reality, some scientists claim, and their ideas are revolutionary.

Ken, a 36-year-old Uber and Lyft driver in Houston, drives about four to five hours per day — in addition to his full-time analyst job — to supplement his income. Last year, he earned a combined $25,000 driving for Uber and Lyft from about 2,000 trips, according to screenshots of earnings documents viewed by Business Insider.

While he accepts most rides, he said he prioritizes trips that pay at least $0.80 to $1.00 per mile, excluding vehicle expenses — a ride’s base pay and distance are displayed on the app. He also tries to avoid trips that take him too far out of Houston because he worries he won’t be able to find trips for the ride back. He calls these “empty miles.”

“I have seen a 50-mile trip that only $20 was offered,” Ken previously told Business Insider. “I wouldn’t be doing that.” He asked that his last name not be included for fear of professional repercussions.

In another twist on the OpenAI saga that raged over the weekend, Microsoft has swooped in and hired Sam Altman and Greg Brockman.

A study from an international team led by researchers from Nagoya University in Japan and the University of New Hampshire in the United States has revealed the importance of the Earth’s upper atmosphere in determining how large geomagnetic storms develop. Their findings reveal the previously underestimated importance of the Earth’s atmosphere. Understanding the factors that cause geomagnetic storms is important because they can have a direct impact on the Earth’s magnetic field such as causing unwanted currents in the power grid and disrupting radio signals and GPS. This research may help predict the storms that will have the greatest consequences.

Scientists have long known that geomagnetic storms are associated with the activities of the Sun. Hot charged particles make up the Sun’s outer layer, the one visible to us. These particles flow out of the Sun creating the ‘solar wind’, and interact with objects in space, such as the Earth. When the particles reach the magnetic field surrounding our planet, known as the magnetosphere, they interact with it. The interactions between the charged particles and magnetic fields lead to space weather, the conditions in space that can affect the Earth and technological systems such as satellites.

An important part of the magnetosphere is the magnetotail. The magnetotail is the part of the magnetosphere that extends away from the Sun, in the direction of the solar wind flow. Inside the magnetotail is the plasma sheet region, which is full of charged particles (plasma). The plasma sheet is important because it is the source region for the particles that get into the inner magnetosphere, creating the current that causes geomagnetic storms.

From Wi-Fi-connected home security systems to smart toilets, the so-called Internet of Things brings personalization and convenience to devices that help run homes. But with that comes tangled electrical cords or batteries that need to be replaced. Now, researchers reporting in ACS Applied Energy Materials have brought solar panel technology indoors to power smart devices. They show which photovoltaic (PV) systems work best under cool white LEDs, a common type of indoor lighting.

Indoor lighting differs from sunlight. Light bulbs are dimmer than the sun. Sunlight includes ultraviolet, infrared and visible light, whereas indoor lights typically shine light from a narrower region of the spectrum. Scientists have found ways to harness power from sunlight, using PV solar panels, but those panels are not optimized for converting indoor light into electrical energy.

Some next-generation PV materials, including perovskite minerals and organic films, have been tested with indoor light, but it’s not clear which are the most efficient at converting non-natural light into electricity; many of the studies use various types of indoor lights to test PVs made from different materials. So, Uli Würfel and coworkers compared a range of different PV technologies under the same type of indoor lighting.