AI, ANN and other forms of an artificial Intelligence
Hey there! I'm excited to dive into an exciting topic today — how AI can help us create more effective prompts. Did you know that AI-generated prompts can be 30% more effective than those crafted by humans? Let's explore how we can harness this power to simplify prompt engineering and make our interactions with AI more efficient.
I'm excited to tell you about Meta's Llama 3.1, a powerful AI language model you can use for free. In this article, I'll show you how to install and run Llama 3.1 on your computer. This means you can use it without the internet and save money on AI costs.
Hey there! It's exciting to see how AI changes how we work and create stuff. I've been trying out many new AI tools recently, and I want to share my favorite picks for June 2024. These tools are amazing and could help you whether you're making content, running a business, or just curious about AI.
In this article, I want to showcase AI tools for creating summaries from YouTube videos. These AI tools can quickly summarize a video's content so you don't have to watch the entire thing. I'll demonstrate how to use these AIs to rapidly extract the main points from videos.
Do you want to know how to build an AI image analyzer? Then read this article till the end! I'm going to show you how to build AI analyzer tools really simply, so you almost don't have to have any prior knowledge. I will take you step by step, and we will use Project IDX and the Gemini API. This means you don't have to set up anything; everything we will do is on the cloud. If you're ready, then let's get started!
Recently, there has been a lot of talk about the success of artificial intelligence (AI), although this usually means another achievement in the field of generative neural networks.
And few people, speaking about AI, try to explain what they themselves understand by the term “artificial intelligence.” After all, it’s one thing to write about “AI problems,” and quite another to endow an ordinary computer algorithm with at least the rudiments of intelligence.
After all, the etymology of the established phrase “artificial intelligence” is not unambiguous and can take on different meanings depending on what meaning the author is trying to put into it.
In this article, I want to introduce you to something truly amazing—Perplexity AI. This AI tool has incredible potential, and I believe it can be a game-changer for many of you. I'll walk you through how to use Perplexity AI and highlight some of its great features.
In this article, we are going to do something really cool: we will build a chatbot using Python and the Gemini API. This will be a web-based assistant and could be the beginning of your own AI project. It's beginner-friendly, and I will guide you through it step-by-step. By the end, you'll have your own AI assistant!
In this article, I’m going to discuss something really important. If you’re a data analyst or you want to learn data analysis, please watch this video till the end because it’s really important.
Few days ago, Mira Murati, Chief Technology Officer at OpenAI, introduced the company's latest GPT model - GPT-4o. A new flagship model set to change how people interact with AI.
S.B. Pshenichnikov
The article outlines a new mathematical apparatus for verbal calculations in NLP (natural language processing). Words are embedded not in a real vector space, but in an algebra of extremely sparse matrix units. Calculations become evidence-based and transparent. The example shows forks in calculations that go unnoticed when using traditional approaches, and the result may be unexpected.
The use of IT in Natural Language Processing (NLP) requires standardization of texts, for example, tokenization or lemmatization.
After this, you can try to use mathematics, since it is the highest form of standardization and turns the objects under study into ideal ones, for example, data tables into matrices of elements. Only in the language of matrices can one search for general patterns in data (numbers and texts).
If text is turned into numbers, then in NLP these are first natural numbers for numbering words, which are then embedded into real vectors is irreversible ed in a real vector space.
Perhaps we should not rush to do this but come up with a new type of numbers that is more suitable for NLP than numbers for studying physical phenomena. These are matrix hyperbinary numbers. Hyperbinary numbers are one of the types of hypercomplex numbers.
Hyperbinary numbers have their own arithmetic, and if you get used to it, it will seem more familiar and simpler than Pythagorean arithmetic.
In Decision Support Systems (DSS), the texts are value judgments and a numbered verbal rating scale. Next (as in NLP), the numbers are turned into vectors of real numbers and used as sets of weighted arithmetic average coefficients.
Mechanism Attention
Analysis of the principles of work in seq2seq tasks.
Mastering the lesson materials will allow you to study a more advanced technology: mechanism transformers
I know it can be hard to learn a new programming language. In this article, I want to share my plan with you. It's a way to learn Python in eight weeks using videos, articles, and practice exercises. Exercises are very important because I think the best way to learn is by doing them.
I've created this learning plan for people who don't have much free time. You only need about 30-50 minutes a day and consistency. In my plan, I use the 80/20 principle, which will help you learn the most important things first and improve the rest through practice.
For those who read this article to the end, I have prepared a learning tracking sheet to help you track your progress.
In this article, I will tell you all you need to know about ChatGPT, show you how to use it, and teach you the right way to ask your questions.
To learn the basics, you don't need to spend your money and time watching hour-long tutorials. You can grasp the essentials in just 1-3 minutes and then enhance your skills through practice.
Today we’re diving into an exciting feature within ChatGPT that has the potential to enhance your productivity by 10, 20, 30, or even 40%. If you’re keen on learning how to leverage this feature to your advantage, make sure to read this article until the end. This feature stands out because it allows you to analyze almost anything by uploading your data and posing various questions to ChatGPT. Whether it's business data, your resume, or any other information you wish to explore, ChatGPT is here to deliver answers based on your specific dataset.
I want you to know all the latest information, which is why in today’s article, I’ll talk about the mind-blowing AI that was released a few days ago! If you want to know more details, please read this article till the end!
In today's rapidly advancing technological landscape, natural language processing and comprehension have become essential components of everyday life. Leading the charge in this arena is OpenAI's ChatGPT API, renowned for its exceptional ability to understand and interact with human language. Imagine elevating ChatGPT's functionality to new heights, enabling it to carry out specific tasks based on commands given in natural language. This article aims to shed light on the potential of incorporating function calling into the ChatGPT API, thereby enhancing its utility. I will illustrate through practical examples how such extensions can unlock a myriad of opportunities and applications.
In the age of information, the ability to accurately and quickly retrieve data relevant to a user's query is paramount. Traditional search methodologies, which rely on keyword matching, often fall short when it comes to understanding the context and nuances of user queries. Semantic search, which seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms, has emerged as a solution to these limitations. However, implementing semantic search can be complex, involving advanced algorithms and understanding of natural language processing (NLP).
Existing solutions such as Elasticsearch and Solr have been at the forefront of tackling these challenges, providing platforms that support more nuanced search capabilities. These tools use a combination of inverted indices and text analysis techniques to improve search outcomes. Yet, the advent of machine learning and vector search technologies opens up new avenues for enhancing semantic search, with solutions like OpenAI's Embeddings API and the pgvector extension for PostgreSQL leading the charge.
Many of us have heard stories where one was able to complete days worth of work in minutes using AI, even being outside of one's area of expertise. Indeed, often LLM's do (almost) miracles, but today I had a different experience:
