This article has explored three promising prompting techniques that have been developed to reduce the occurrence of hallucinations in large language models.
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Cornell researchers have developed a new optimization tool to estimate motion throughout an input video, which has potential applications in video editing and generative AI video creation.
Federica is a Senior Technical SEO specialist at Merkle EMEA, collaborating with clients to support achieving their marketing objectives through agile SEO strategies. She specialises in on-page and technical optimization techniques for websites spanning diverse industries, including finance, public organizations, and telecommunications. In her spare time, Federica enjoys staying up to date with Tech & Automation news as well as going for long walks in nature to recharge after work.
Combinatorial optimization problems (COPs) have applications in many different fields such as logistics, supply chain management, machine learning, material design and drug discovery, among others, for finding the optimal ...
Prompt Engineering - ANALYZE SEO Prompt - Using the ANALYZE SEO directive, we can leverage ChatGPT's capabilities to analyze and provide insights on search engine optimization (SEO) strategies. This technique empowers us to tap into ChatGPT's knowledge and expertise to enhance website visibility and performance in search engine rankings.
Tuning and Optimization Techniques - In this chapter, we will explore tuning and optimization techniques for prompt engineering. Fine-tuning prompts and optimizing interactions with language models are crucial steps to achieve the desired behavior and enhance the performance of AI models like ChatGPT.
Optimizing Prompt-based Models - In this chapter, we will delve into the strategies and techniques to optimize prompt-based models for improved performance and efficiency. Prompt engineering plays a significant role in fine-tuning language models, and by employing optimization methods, prompt engineers can enhance model responsiven
ChatGPT – For SEO - SEO stands for Search Engine Optimization. It's a set of practices and strategies that website owners and marketers use to improve the visibility of a website on search engines like Google, Bing, or Yahoo. It offers a range of tools such as keyword research and analysis, automation and reporting, op
ChatGPT – For Code Writing - ChatGPT can serve as a versatile companion and assist developers in various coding tasks such as generating code snippets, bug fixing, code optimization, rapid prototyping, and translating code between languages. This chapter will guide you, through practical examples in Python using the OpenAI API,
TinyDB - Caching Query - Catching query is an advanced feature of TinyDB with the help of which it caches the query result for performance optimization. In this way, when we run the same query again, TinyDB doesn't need to read the data from the storage. We can pass the cache_size to the table function to optimize the query
This course delves into topics crucial for professional MongoDB use in practice, such as documenting NoSQL schemas, data indexing and optimization, transacti…
Imperia, a Spanish SaaS solution for the management and optimization of supply chain, announced the closure of a €3 million funding round. This round is
The second post in this series on mesh shaders covers best practices for writing mesh and amplification shaders, as well as how to use the AMD Radeon™ Developer Tool Suite to profile and optimize mesh shaders.
Our GDC 2024 presentations this year include work graphs, mesh shaders, AMD FSR 3, GI with AMD FidelityFX Brixelizer, AMD Ryzen optimization, RGD, RDTS, and GPU Reshape!
Artificial Intelligence Optimization (AIO) news, analysis, trends, tactics, and how-to guides from Search Engine Land, the publication SEO & PPC pros rely on.
Tutorial on the commands to list the running processes in Linux to maintain the system health, troubleshoot the errors, system optimization, and security.
Multi-objective optimization is a class of optimization problems with multiple conflicting objectives. We study offline optimization of multi-objective policies from data collected by a previously deployed policy. We propose a pessimistic estimator for policy values that can be easily plugged into…
Second-order optimization methods, such as cubic regularized Newton methods, are known for their rapid convergence rates; nevertheless, they become impractical in high-dimensional problems due to their substantial memory requirements and computational costs. One promising approach is to execute…