An electron gun is an electrical component in some vacuum tubes that produces a narrow, collimated electron beam that has a precise kinetic energy.

An electron microscope is a microscope that uses a beam of accelerated electrons as a source of illumination. As the wavelength of an electron can be up to 100,000 times shorter than that of visible light photons, electron microscopes have a higher resolving power than light microscopes and can reveal the structure of smaller objects. A scanning transmission electron microscope has achieved better than 50 pm resolution in annular dark-field imaging mode and magnifications of up to about 10,000,000× whereas most light microscopes are limited by diffraction to about 200 nm resolution and useful magnifications below 2000×.

An air gap, air wall, air gapping[1] or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network.[2] It means a computer or network has no network interface controllers connected to other networks,[3][4] with a physical or conceptual air gap, analogous to the air gap used in plumbing to maintain water quality.

Standards and recommendations for transitioning organizations to quantum-secure cryptographic protocols are outlined, including a discussion of transition timelines and the leading strategies to protect systems against quantum attacks.

8 years of cost reduction in 5 weeks: how Stanford's Alpaca model changes everything, including the economics of OpenAI and GPT 4. The breakthrough, using self-instruct, has big implications for Apple's secret large language model, Baidu's ErnieBot, Amazon's attempts and even governmental efforts, like the newly announced BritGPT. I will go through how Stanford put the model together, why it costs so little, and demonstrate in action versus Chatgpt and GPT 4. And what are the implications of short-circuiting human annotation like this? With analysis of a tweet by Eliezer Yudkowsky, I delve into the workings of the model and the questions it rises. Web Demo: https://alpaca-ai0.ngrok.io/ Alpaca: https://crfm.stanford.edu/2023/03/13/alpaca.html Ark Forecast: https://research.ark-invest.com/hubfs/1_Download_Files_ARK-Invest/Big_Ideas/ARK%20Invest_013123_Presentation_Big%20Ideas%202023_Final.pdf Eliezer Tweet: https://twitter.com/ESYudkowsky/status/1635577836525469697 https://twitter.com/ESYudkowsky/status/1635667349792780288 Self-Instruct: https://arxiv.org/pdf/2212.10560.pdf InstructGPT: https://openai.com/research/instruction-following OpenAI Terms: https://openai.com/policies/terms-of-use MMLU Test: https://arxiv.org/pdf/2009.03300.pdf Apple LLM: https://www.nytimes.com/2023/03/15/technology/siri-alexa-google-assistant-artificial-intelligence.html GPT 4 API: https://openai.com/pricing Llama Models: https://arxiv.org/pdf/2302.13971.pdf BritGPT: https://www.theguardian.com/technology/2023/mar/15/uk-to-invest-900m-in-supercomputer-in-bid-to-build-own-britgpt Amazon: https://www.businessinsider.com/amazons-ceo-andy-jassy-on-chat-cpt-ai-2023-2?r=US&IR=T AlexaTM: https://arxiv.org/pdf/2208.01448.pdf Baidu Ernie: https://www.nytimes.com/2023/03/16/world/asia/china-baidu-chatgpt-ernie.html PaLM API: https://developers.googleblog.com/2023/03/announcing-palm-api-and-makersuite.html https://www.patreon.com/AIExplained

In the setting of profound ocular blindness, numerous lines of evidence demonstrate the existence of dramatic anatomical and functional changes within the brain. However, previous studies based on a variety of distinct measures have often provided inconsistent findings. To help reconcile this issue, we used a multimodal magnetic resonance (MR)-based imaging approach to provide complementary structural and functional information regarding this neuroplastic reorganization. This included gray matter structural morphometry, high angular resolution diffusion imaging (HARDI) of white matter connectivity and integrity, and resting state functional connectivity MRI (rsfcMRI) analysis. When comparing the brains of early blind individuals to sighted controls, we found evidence of co-occurring decreases in cortical volume and cortical thickness within visual processing areas of the occipital and temporal cortices respectively. Increases in cortical volume in the early blind were evident within regions of parietal cortex. Investigating white matter connections using HARDI revealed patterns of increased and decreased connectivity when comparing both groups. In the blind, increased white matter connectivity (indexed by increased fiber number) was predominantly left-lateralized, including between frontal and temporal areas implicated with language processing. Decreases in structural connectivity were evident involving frontal and somatosensory regions as well as between occipital and cingulate cortices. Differences in white matter integrity (as indexed by quantitative anisotropy, or QA) were also in general agreement with observed pattern changes in the number of white matter fibers. Analysis of resting state sequences showed evidence of both increased and decreased functional connectivity in the blind compared to sighted controls. Specifically, increased connectivity was evident between temporal and inferior frontal areas. Decreases in functional connectivity were observed between occipital and frontal and somatosensory-motor areas and between temporal (mainly fusiform and parahippocampus) and parietal, frontal, and other temporal areas. Correlations in white matter connectivity and functional connectivity observed between early blind and sighted controls showed an overall high degree of association. However, comparing the relative changes in white matter and functional connectivity between early blind and sighted controls did not show a significant correlation. In summary, these findings provide complimentary evidence, as well as highlight potential contradictions, regarding the nature of regional and large scale neuroplastic reorganization resulting from early onset blindness.

This article describes the four types of relationships and why only one of them sets you up for long term success as well as the reputational cue ball.

In differential calculus and differential geometry, an inflection point, point of inflection, flex, or inflection is a point on a smooth plane curve at which the curvature changes sign. In particular, in the case of the graph of a function, it is a point where the function changes from being concave to convex, or vice versa.

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.

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.