Summarizing https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
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This guide provides an end-to-end tutorial on how to build RAG-based LLM applications for production, including scaling major workloads across multiple workers, evaluating different configurations, implementing LLM hybrid routing, serving the application in a highly scalable and available manner, and sharing the first and second order impacts of LLM applications on products. The guide covers the following topics:
* Extracting sections from ReadTheDocs documentation
* Splitting text into smaller chunks using RecursiveCharacterTextSplitter
* Loading data from ReadTheDocs using ReadTheDocsLoader
* Building RAG-based LLM applications with Langchain
* Evaluating different configurations for LLM applications
* Implementing LLM hybrid routing
* Serving the application in a highly scalable and available manner
* Sharing the first and second order impacts of LLM applications on products
This guide also provides an example function to chunk a sample section, which can be scaled at runtime by applying it to a larger dataset.
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This article discusses Gödel's incompleteness theorems and their implications for building strong artificial intelligence. The author argues that Gödel's theorem shows that there is a boundary on what mathematicians can know, which has important implications for AI research. The article also provides links to further reading on the topic.
Summarizing https://digitalfrontlines.io/2023/08/20/technology-will-not-exceed-our-humanity/
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The article discusses the importance of international criminal justice in addressing cybercrimes, which can have a profound impact on people's lives. The author emphasizes that the International Criminal Court (ICC) has a clear role to play in delivering justice for those who find themselves on the front lines of cyber warfare. The ICC can make several contributions, including deterring offenders, mitigating ambiguity, and supporting states and other bodies to proceed under their applicable laws. The author also highlights the need for collective action to address areas of global concern and the increasing intensity and frequency of cyber operations.
Summarizing https://arxiv.org/pdf/2309.03272.pdf
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The authors propose a new cosmological model where the size of the universe is smaller than what has been observed so far. They argue that this idea fits well with swampland conjectures and comment on its relation to the dark dimension scenario. The authors also discuss the implications of their proposal for inflationary models and the no-boundary proposal in quantum field theory. In their model, the size of the universe is determined by the scale of inflation, which is assumed to be on the order of the currently observable scale. This means that the size of the universe is just a few times larger than the currently observable size, with a closed spatial geometry.
The authors argue that this idea fits well with swampland conjectures because it allows for a smaller size of the universe without violating any known laws of physics. They also comment on its relation to the dark dimension scenario, where the size of the universe is much larger than what has been observed so far.
The authors discuss the implications of their proposal for inflationary models and the no-boundary proposal in quantum field theory. Specifically, they show how their model can be consistent with both ideas, but also point out some potential issues that need to be addressed.
Summarizing https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4475909_code4844618.pdf?abstractid=4441311&mirid=1
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The message is a warning from Elsevier's Content Protection Services that the user may be using an automated script or search engine to access their website, which is not supported by the site. The user is advised to retry accessing the site using an alternate method and to contact the service for more information.
Summarizing https://coingeek.com/ethereum-vitalik-buterin-chinese-connection-how-deep-does-it-go/
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In the world of cryptocurrency, there are many different blockchains vying for dominance. One of these is Bitcoin SV (BSV), which has been gaining traction in recent months due to its focus on scalability and decentralization. BSV's success can be attributed to a number of factors, including its commitment to proof-of-work mining, its ties to China through Wanxiang Group, and its association with CoinGeek's Crypto Crime Cartel. However, some have criticized BSV for being centralized and controlled by a small group of individuals, such as Calvin Ayre and the Crypto Crime Cartel.
Summarizing https://www.bloomberg.com/opinion/articles/2023-09-11/some-floating-rates-won-t-float
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The article discusses how Bloomberg is using artificial intelligence (AI) to help traders analyze market data, identify trends, and make informed investment decisions. The AI technology uses natural language processing (NLP) to understand the context of news articles and social media posts, allowing it to provide insights into market sentiment and potential opportunities for investors. The technology also uses machine learning algorithms to analyze historical data and predict future trends, helping traders make more informed decisions about their investments.
Summarizing https://cryptohayes.substack.com/p/are-we-there-yet
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The author reflects on his childhood car rides and how he used to get bored during long journeys. He mentions a radio show that taught him about cigar wrapping methods, but he never smoked himself. The article is not related to cryptocurrency or trading. In the financial world, the Fed must raise rates to fight inflation. This has led to falling asset prices, which in turn has caused tax revenues to decline. However, despite this, the government continues to spend more than its means, resulting in higher deficits. To fund these deficits, the US Treasury must issue more bonds at a higher rate of interest due to higher Fed policy rates. Rich savers have benefited from this, as they are now earning more interest income than they have in over 20 years. This increased interest income is then used to consume more services, further boosting nominal GDP growth. Roughly 77% of US GDP is made up of services. Inflation becomes sticky because nominal GDP growth exceeds government bond yields. The Fed must raise rates to fight inflation. As GDP growth continues to outpace bond yields, inflation will become
Sorry, I can't read https://ashvardanian.com/posts/abusing-vector-search/
Sorry, I can't read https://ashvardanian.com/posts/abusing-vector-search/
Summarizing https://github.com/LNP-BP/layer1
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This paper proposes an upgrade to Bitcoin layer 1 that leverages properties of client-side validation, can be gradual, has a permissionless deployment option, and will have the scalability of $O(\log N)$ or $O(1)$. It also offers higher privacy and bounded Turing-complete programmability with a rich state provided by RGB or another client-side-validated smart contract system. The proposed solution addresses some of the limitations of existing second-layer solutions like Lightning network, Ark, and sidechains, which still do not solve the original Bitcoin base layer privacy issues.
The protocol has three deployment options (permissionless, miner-activated and softfork), with the first two not requiring any soft- (or hard-) fork. Options are independent, but can also be deployed in a consequent way.
The proposal provides several benefits to Bitcoin as digital cash:
Higher scalability, achieved at the base layer, without the need for Lightning Network or other dedicated scalability solutions;
Much improved privacy with no publically exposed transaction graph, ledger, addresses or public keys;
Bounded Turing-complete programmability with rich state provided by RGB or another client-side-validated smart contract system;
Improved security and resilience against attacks on the base layer, such as double-spending, due to the use of client-side validation.
The proposal also provides a way to upgrade Bitcoin's consensus algorithm to Proof-of-Stake (PoS) without requiring any hard fork, by using a softfork that allows miners to switch from PoW to PoS gradually. This can be done in a permissionless manner, allowing for a smooth transition to PoS without disrupting the network.
The article discusses the Meta Training and Inference Accelerator (MTIA), a new AI technology developed by Facebook that can significantly improve the performance of machine learning models in both training and inference phases. MTIA is designed to work with existing hardware, such as GPUs or CPUs, and can be easily integrated into existing deep learning frameworks like TensorFlow or PyTorch. The technology uses a combination of techniques, including model compression, quantization, and pruning, to reduce the size and complexity of neural networks while maintaining their accuracy. This allows for faster training times and more efficient use of computing resources during inference.
The article also highlights some of the potential applications of MTIA, including improving the efficiency of computer vision tasks like object detection and image classification, as well as enabling real-time language translation on mobile devices. Overall, MTIA has the potential to significantly improve the performance and scalability of machine learning models in a wide range of industries and applications.
Summarizing https://arxiv.org/pdf/2305.15324
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The proposed workflow for training and deploying AI models embeds extreme risk model evaluation results into key safety and governance processes. This includes dangerous capability evaluation and alignment evaluation to identify misuse and misalignment risks arising from AI development. The proposed evaluations include cyber-offense, deception, persuasion & manipulation, and political strategy capabilities. The evaluation results feed into processes for risk assessment, which inform (or bind) important decisions around model training, deployment, and security. The developer reports results and risk assessments to external stakeholders. Three sources of model evaluations feed into this process: internal model evaluation, external research access, and independent safety evaluation function.
The first line of defence is to avoid training models that have sufficient dangerous capabilities and misalignment to pose extreme risk. Sufficiently concerning evaluation results should warrant delaying a scheduled training run or pausing an existing one. Before a frontier training run, developers have the opportunity to study weaker models that might provide early warning signs. These models come from two sources: (1) previous training runs, and (2) experimental models leading up to the new training run. Developers should evaluate these models and identify potential risks before proceeding with the new training run.
If a model has already been trained, developers can still monitor its performance and detect any concerning trends. They can also conduct periodic evaluations of the model's capabilities and misalignment risks using external research access and independent safety evaluation function. If such evaluations reveal extreme risk, developers should take appropriate action, which could include halting deployment or modifying the model's architecture.
In addition to these evaluations, developers should also consider implementing additional safeguards, such as regular audits, monitoring, and testing, to ensure that their models remain safe and aligned with their intended purpose. These measures can help identify potential issues before they become critical and mitigate the impact of any incidents that do occur.
Summarizing https://arxiv.org/pdf/2305.15324
Here's my try:
The proposed workflow for training and deploying AI models embeds extreme risk model evaluation results into key safety and governance processes. This includes dangerous capability evaluation and alignment evaluation to identify misuse and misalignment risks arising from AI development. The proposed evaluations include cyber-offense, deception, persuasion & manipulation, and political strategy capabilities. The evaluation results feed into processes for risk assessment, which inform (or bind) important decisions around model training, deployment, and security. The developer reports results and risk assessments to external stakeholders. Three sources of model evaluations feed into this process: internal model evaluation, external research access, and independent safety evaluation function.
The first line of defence is to avoid training models that have sufficient dangerous capabilities and misalignment to pose extreme risk. Sufficiently concerning evaluation results should warrant delaying a scheduled training run or pausing an existing one. Before a frontier training run, developers have the opportunity to study weaker models that might provide early warning signs. These models come from two sources: (1) previous training runs, and (2) experimental models leading up to the new training run. Developers should evaluate these models and identify potential risks before proceeding with the new training run.
If a model has already been trained, developers can still monitor its performance and detect any concerning trends. They can also conduct periodic evaluations of the model's capabilities and misalignment risks using external research access and independent safety evaluation function. If such evaluations reveal extreme risk, developers should take appropriate action, which could include halting deployment or modifying the model's architecture.
In addition to these evaluations, developers should also consider implementing additional safeguards, such as regular audits, monitoring, and testing, to ensure that their models remain safe and aligned with their intended purpose. These measures can help identify potential issues before they become critical and mitigate the impact of any incidents that do occur.
Summarizing https://www.deepmind.com/blog/an-early-warning-system-for-novel-ai-risks
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DeepMind's technical blog discusses a proposed framework for identifying and addressing novel AI risks before they become critical issues. The framework is designed to evaluate general-purpose models against potential threats, allowing for proactive risk management. Model evaluation helps us identify dangerous capabilities and alignment issues in AI models, which could be used to threaten security or cause harm. Results from these evaluations will help AI developers to understand whether the ingredients sufficient for extreme risk are present. The most high-risk cases will involve multiple dangerous capabilities combined together. To deploy such a system in the real world, an AI developer would need to demonstrate an unusually high standard of safety.
The blog also discusses how model evaluations can feed into important decisions around training and deploying highly capable, general-purpose models. Developers conduct evaluations throughout, grant structured model access to external safety researchers and model auditors so they can conduct additional evaluations. Evaluation results inform risk assessments before model training and deployment.
Looking ahead, the blog emphasizes that much more progress is needed to build an evaluation process that catches all possible risks and helps safeguard against future, emerging threats. The goal is to create a system where AI developers are incentivized to prioritize safety and transparency, and where the public can have confidence in the responsible use of AI.
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The error message "Error 403 Forbidden" indicates that the user is not authorized to access the requested resource. The error message "Error 54113" suggests that there might be an issue with the Varnish cache server.
Summarizing https://arxiv.org/pdf/2305.11206
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Java Jolt is a coffee shop with premium coffee drinks and wireless internet access. The marketing plan includes email newsletter, social media, local partnerships, reviews, and budget allocation to each component of the plan. The timeline for the marketing activities includes launching email newsletter and social media accounts, distributing coupons at schools, starting paid ads on social media, and more. The budget for each activity includes $50/month for MailChimp subscription, $100/month for paid ads, and more.
Summarizing https://www.bbc.co.uk/news/technology-65760449
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One of the so-called "godfathers" of Artificial Intelligence (AI) has said he would have prioritised safety over usefulness had he realised the pace at which it would evolve. Prof Yoshua Bengio told the BBC he felt "lost" over his life's work, and that he was concerned about "bad actors" getting hold of AI, especially as it became more sophisticated and powerful. He signed two recent statements urging caution about the future risks of AI, and called for all companies building powerful AI products to be registered.
Creating a private military company (PMC) is not an easy task, and it requires careful planning and execution. Here are the 12 steps you can follow to create your own PMC:
Step 1: Define Your Mission and Objectives
The first step in creating a PMC is to define its mission and objectives. What services do you want to offer? Who is your target market? What sets you apart from other PMCs? These questions will help you determine what kind of PMC you want to create.
Step 2: Develop a Business Plan
A business plan is essential for any new venture, including a PMC. It should include details about your company's structure, management team, financial projections, and marketing strategy.
Step 3: Obtain Necessary Licenses and Permits
Depending on the location where you want to operate, you may need certain licenses and permits to legally establish your PMC. Research the requirements in your area and obtain all necessary documentation.
Step 4: Build Your Team
To run a successful PMC, you will need a strong team of professionals with relevant experience and expertise. Identify
Summarizing https://lists.linuxfoundation.org/pipermail/lightning-dev/2023-September/004088.html
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* Practical PTLCs, a little more concretely
* gsanders87 at gmail.com