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Category: futurism – Page 31
Bio-Rad invites you to our 2nd India Cell Gene Therapy Symposium 2024 After many decades of effort, the future of cell and gene therapies (CGT) is incredibly promising. A flurry of recent successes has led to the approval of several life changing treatments for patients and many more therapies are in development. CGT seek to correct the root cause of an illness at the molecular level. These game changing medicines are reshaping how we address previously uncurable illnesses — transforming people’s lives.
VIEW VIDEOS Thank you for joining us. All sessions were recorded and will be available on the Globe Events YouTube channel. Visit Globe.com/events for future events Thank you for joining our first annual Future of Medicine, streaming live from Boston on November 13.
One day our Sun will die, and consume our world in fire, but is it possible to refuel our Sun so that it might live trillions and trillions of year to come?
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Credits:
Refueling Our Sun.
Science \& Futurism with Isaac Arthur.
Episode 367, November 3, 2022
Produced \& Narrated by Isaac Arthur.
Written by:
Forces imposed by laser light can manipulate the shape of a membrane’s vibrational modes.
There’s a mineral so rare that only one specimen of it has ever been found in the entire world.
It’s called kyawthuite (cha-too-ite), a tiny, tawny-hued grain weighing just a third of a gram (1.61 carats). On first glance, you might mistaken it for amber or topaz; but the unassuming mineral speck has value beyond measure.
The stone itself was purchased in 2010 at a market in Chaung-gyi in Myanmar by gemologist Kyaw Thu, who thought the raw gem was a mineral called scheelite. After he faceted the stone, though, he realized that he was looking at something unusual.
Researchers from the University of Maryland and Adobe Introduce DynaSaur: The LLM Agent that Grows Smarter by Writing its Own Functions
Posted in futurism, robotics/AI | Leave a Comment on Researchers from the University of Maryland and Adobe Introduce DynaSaur: The LLM Agent that Grows Smarter by Writing its Own Functions
Traditional large language model (LLM) agent systems face significant challenges when deployed in real-world scenarios due to their limited flexibility and adaptability. Existing LLM agents typically select actions from a predefined set of possibilities at each decision point, a strategy that works well in closed environments with narrowly scoped tasks but falls short in more complex and dynamic settings. This static approach not only restricts the agent’s capabilities but also requires considerable human effort to anticipate and implement every potential action beforehand, which becomes impractical for complex or evolving environments. Consequently, these agents are unable to adapt effectively to new, unforeseen tasks or solve long-horizon problems, highlighting the need for more robust, self-evolving capabilities in LLM agents.
Researchers from the University of Maryland and Adobe introduce DynaSaur: an LLM agent framework that enables the dynamic creation and composition of actions online. Unlike traditional systems that rely on a fixed set of predefined actions, DynaSaur allows agents to generate, execute, and refine new Python functions in real-time whenever existing functions prove insufficient. The agent maintains a growing library of reusable functions, enhancing its ability to respond to diverse scenarios. This dynamic ability to create, execute, and store new tools makes AI agents more adaptable to real-world challenges.
The technical backbone of DynaSaur revolves around the use of Python functions as representations of actions. Each action is modeled as a Python snippet, which the agent generates, executes, and assesses in its environment. If existing functions do not suffice, the agent dynamically creates new ones and adds them to its library for future reuse. This system leverages Python’s generality and composability, allowing for a flexible approach to action representation. Furthermore, a retrieval mechanism allows the agent to fetch relevant actions from its accumulated library using embedding-based similarity search, addressing context length limitations and improving efficiency.
Protein PKMζ is a key molecular switch for long-term memory. Targeting PKMζ activity may enable new treatments for memory-related disorders.