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Dynamic random-access memory is a type of random-access semiconductor memory that stores each bit of data in a memory cell, usually consisting of a tiny capacitor and a transistor, both typically based on metal–oxide–semiconductor (MOS) technology. While most DRAM memory cell designs use a capacitor and transistor, some only use two transistors. In the designs where a capacitor is used, the capacitor can either be charged or discharged; these two states are taken to represent the two values of a bit, conventionally called 0 and 1. The electric charge on the capacitors gradually leaks away; without intervention the data on the capacitor would soon be lost. To prevent this, DRAM requires an external memory refresh circuit which periodically rewrites the data in the capacitors, restoring them to their original charge. This refresh process is the defining characteristic of dynamic random-access memory, in contrast to static random-access memory (SRAM) which does not require data to be refreshed. Unlike flash memory, DRAM is volatile memory, since it loses its data quickly when power is removed. However, DRAM does exhibit limited data remanence.

Non-volatile random-access memory (NVRAM) is random-access memory that retains data without applied power. This is in contrast to dynamic random-access memory (DRAM) and static random-access memory (SRAM), which both maintain data only for as long as power is applied, or forms of sequential-access memory such as magnetic tape, which cannot be randomly accessed but which retains data indefinitely without electric power.

Want to learn about Initial Coin Offerings and why many cryptocurrency projects launch their own? What is an ICO? Find out on Binance Academy.

Initial Exchange Offering (IEO) | Definition: The fundraising will be conducted on a well-known exchange’s fundraising platform, such as Binance Launchpad

TL;DR: In this project, we collected and cataloged AI alignment research literature and analyzed the resulting dataset in an unbiased way to identify major research directions. We found that the fiel…

Today, we’re releasing our LLaMA (Large Language Model Meta AI) foundational model with a gated release. LLaMA is more efficient and competitive with previously published models of a similar size on existing benchmarks.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Qualcomm AI Research deploys a popular 1B+ parameter foundation model on an edge device through full-stack AI optimization.

A Penrose tiling is an example of an aperiodic tiling. Here, a tiling is a covering of the plane by non-overlapping polygons or other shapes, and aperiodic means that shifting any tiling with these shapes by any finite distance, without rotation, cannot produce the same tiling. However, despite their lack of translational symmetry, Penrose tilings may have both reflection symmetry and fivefold rotational symmetry. Penrose tilings are named after mathematician and physicist Roger Penrose, who investigated them in the 1970s.

A tessellation or tiling is the covering of a surface, often a plane, using one or more geometric shapes, called tiles, with no overlaps and no gaps. In mathematics, tessellation can be generalized to higher dimensions and a variety of geometries.

Mesa-Optimization is the situation that occurs when a learned model (such as a neural network) is itself an optimizer. In this situation, a base optimizer creates a second optimizer, called a mesa-optimizer. The primary reference work for this concept is Hubinger et al.'s "Risks from Learned Optimization in Advanced Machine Learning Systems". Example: Natural selection is an optimization process that optimizes for reproductive fitness. Natural selection produced humans, who are themselves optimizers. Humans are therefore mesa-optimizers of natural selection. In the context of AI alignment, the concern is that a base optimizer (e.g., a gradient descent process) may produce a learned model that is itself an optimizer, and that has unexpected and undesirable properties. Even if the gradient descent process is in some sense "trying" to do exactly what human developers want, the resultant mesa-optimizer will not typically be trying to do the exact same thing.[1]   HISTORY Previously work under this concept was called Inner Optimizer or Optimization Daemons. Wei Dai brings up a similar idea in an SL4 thread.[2] The optimization daemons article on Arbital was published probably in 2016.[1] Jessica Taylor wrote two posts about daemons while at MIRI: * "Are daemons a problem for ideal agents?" (2017-02-11) * "Maximally efficient agents will probably have an anti-daemon immune system" (2017-02-23)   SEE ALSO * Inner Alignment * Complexity of value * Thou Art Godshatter EXTERNAL LINKS Video by Robert Miles Some posts that reference optimization daemons: * "Cause prioritization for downside-focused value systems": "Alternatively, perhaps goal preservation becomes more difficult the more capable AI systems become, in which case the future might be controlled by unstable goal functions taking turns over the steering wheel" * "Techniques for optimizing worst-case performance": "The difficulty of optimizing worst-case performance is one of the most likely re

In ethical philosophy, consequentialism is a class of normative, teleological ethical theories that holds that the consequences of one's conduct are the ultimate basis for judgment about the rightness or wrongness of that conduct. Thus, from a consequentialist standpoint, a morally right act is one that will produce a good outcome. Consequentialism, along with eudaimonism, falls under the broader category of teleological ethics, a group of views which claim that the moral value of any act consists in its tendency to produce things of intrinsic value. Consequentialists hold in general that an act is right if and only if the act will produce, will probably produce, or is intended to produce, a greater balance of good over evil than any available alternative. Different consequentialist theories differ in how they define moral goods, with chief candidates including pleasure, the absence of pain, the satisfaction of one's preferences, and broader notions of the "general good".

A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.

Domain-driven design (DDD) is a major software design approach,[1] focusing on modeling software to match a domain according to input from that domain's experts.[2]

Saadi Shīrāzī, better known by his pen name Saadi, also known as Sadi of Shiraz, was a Persian poet and prose writer of the medieval period. He is recognized for the quality of his writings and for the depth of his social and moral thoughts.

What is a SAFE? SAFE stands for Simple Agreement for Future Equity. The SAFE was developed in California and popularized by leading Silicon Valley

The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is a zero-player game, meaning that its evolution is determined by its initial state, requiring no further input. One interacts with the Game of Life by creating an initial configuration and observing how it evolves. It is Turing complete and can simulate a universal constructor or any other Turing machine.

A cellular automaton is a discrete model of computation studied in automata theory. Cellular automata are also called cellular spaces, tessellation automata, homogeneous structures, cellular structures, tessellation structures, and iterative arrays. Cellular automata have found application in various areas, including physics, theoretical biology and microstructure modeling.

John von Neumann’s Cellular Automata Cellular automata (CA) are mathematical models used to simulate complex systems or processes. In several fields, including biology, physics, and chemistry, CA are employed to analyze phenomena such as the growth of plants, DNA evolution, and embryogenesis. In the 1940s

Memory safety is a property of some programming languages that prevents programmers from introducing certain types of bugs related to how memory is used. Since memory safety bugs are often security issues, memory safe languages are more secure than languages that are not memory safe. Memory safe languages include Rust, Go, C#, Java, Swift, Python, and JavaScript. Languages that are not memory safe include C, C++, and assembly. Types of Memory Safety Bugs To begin understanding memory safety bugs, we'll consider the example of an application that maintains to do lists for many users.