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Artificial Intelligence

AI, ANN and other forms of an artificial Intelligence

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From Scratch to AI Chatbot: Using Python and Gemini API

Level of difficultyEasy
Reading time3 min
Views645

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!

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Total votes 1: ↑1 and ↓0+1
Comments3

VERBAL CALCULATION (VC) IN EVIDENCE-BASED DSS AND NLP

Level of difficultyMedium
Reading time14 min
Views279

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.

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Total votes 3: ↑3 and ↓0+4
Comments0

How to Learn Python FREE in 8-Week: The 80/20 Learning Plan

Level of difficultyEasy
Reading time6 min
Views2.8K

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.

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Total votes 2: ↑2 and ↓0+2
Comments0

Learn How to Use ChatGPT in 2024: 2-Step Guide with Prompt Examples

Level of difficultyEasy
Reading time3 min
Views1.4K

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.

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Master Data Analysis with ChatGPT — How to Analyze Anything (Beginners Guide)

Level of difficultyEasy
Reading time3 min
Views1.7K

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.

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Total votes 2: ↑2 and ↓0+2
Comments4

Mastering ChatGPT

Level of difficultyMedium
Reading time7 min
Views2K

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.

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Total votes 5: ↑5 and ↓0+5
Comments0

AI-powered semantic search using pgvector and embeddings

Level of difficultyMedium
Reading time9 min
Views1.5K

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.

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Total votes 1: ↑1 and ↓0+1
Comments0

Let's kill all frameworks at once

Level of difficultyMedium
Reading time14 min
Views6.2K

The general trend of technology development is characterized by surges and declines. Consider, for instance, the mass movement of human bodies. Initially, horses and wagons were used, which gradually evolved into a distinct industry. Then trains appeared abruptly. Horses were quickly forgotten, and the focus shifted to a new avenue. Steam became an object of study and evolved into a complex science. Diesel and electricity developed concurrently. At a certain point, steam engines became obsolete, and everyone transitioned to diesel and electricity. Similarly, we are now transitioning to electric cars that require significantly fewer fluids.

Technologies evolve and function until new technologies completely replace them. I believe we are entering an era where framework and Electron technologies may be eclipsed by generative AI. Let's examine some examples.

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Comments8

AI for Software Business Analysis

Level of difficultyEasy
Reading time5 min
Views18K

Generative AI is creating waves in the way we work, significantly revolutionizing the software development process. AI tools are appearing in various phases of software development, such as design, development, and testing. However, there aren't many tools specifically focused on software business analysis tasks.

But with a little creative thinking, we can put "one-size-fits-all" applications like ChatGPT to good use. It can definitely speed up execution of many typical tasks and free up analysts to focus on the more challenging, strategic aspects of the job.

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Total votes 1: ↑1 and ↓0+1
Comments1

ChatGPT to Help You Become a 10x Programmer

Level of difficultyEasy
Reading time2 min
Views7.1K

I believe that every programmer has at least once heard about ChatGPT and its marvelous abilities to process, calculate and create huge amounts of data; if not, go check out this Wikipedia article - https://en.wikipedia.org/wiki/ChatGPT.

Can you imagine that some 50 years ago people could not even believe that there may be something artificial surpassing humans in so many areas? Nowadays, we have this marvel at the distance of a few tabs on a phone screen or a keyboard; however, there is still a sadly large number of people who do not fully—if at all— utilize all the perks of ChatGPT in their lines of work. This is mostly related either to people's reluctance to learn new technologies or the fear of losing coding skills they have previously gained—which is not the case with using ChatGPT properly.

In this article I want to give you some of the most useful uses of ChatGPT for your coding work. Remember, there is nothing shameful in using the AI, since this the development and further implementation of it in our day-to-day life is inevitable, so we should start adapting to it as early as we can to take the full advantage of this "magical" technology. Let's get started.

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Comments5

Alpha Go && Alpha Go Zero

Reading time3 min
Views1.6K

Today I would like to discuss the games Chess and Go, the world's champions, algorithms and Al.

In 1997, a computer program developed by IBM Deep Blue defeated the world Chess champion Garry Kasparov. Go remained the last board game in which humans were still better than machines.

Why is that?

Chess is primarily distinguished from Go by the number of variations for each move. Chess, the game is more predictable with more structured rules: we have value for each figure (e.g bishop = 3 pawns, rook = 5 pawns -> rook > bishop), some kind of openings and strategies. Go, in turn, has incredibly simple rules, which creates the complexity of the game for the machine. Go is one of the oldest board games. Until recently, it was assumed that a machine was not capable of playing on an equal footing with a professional player due to the high level of abstraction and the inability to sort through all possible scenarios - exactly as many valid combinations in a game on a standard 19×19 go-ban are 10180 (greater than the number of atoms in the visible universe).

However, almost 20 years later, in 2015, there was a breakthrough. Google's Deep Mind company enhanced AlphaGo, which was the last step for the computer to defeat the world champions in board games. The AlphaGo program defeated the European champion and then, in March 2016 demonstrated a high level of play by defeating Lee Sedol, one of the strongest go players in the world, with a score of 4:1 in favour of the machine. A year later, Google introduced to the world a new version of AlphaGo - AlphaGoZero.

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Total votes 2: ↑2 and ↓0+2
Comments1

An introduction to the world of AI for designers

Level of difficultyEasy
Reading time5 min
Views1.5K

Every day a new neural network appears and every day more opportunities are opened to designers to simplify their workflow. Someone fundamentally refuses to use them, because “there is no life in machinex and technologies”, and someone is only happy to find a way to reduce the amount of work. Personally, I belong to the second type and want to share the most detailed gait on neurons I have acquired lately. 

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Total votes 1: ↑1 and ↓0+1
Comments0

SwiftUI & ChatGPT. The world is changing. Again

Level of difficultyEasy
Reading time4 min
Views3K

Everything that follows from this point forward input prompts, followed by ChatCGP’s responses, complete with sample code in Swift.

> Hey ChatGPT, can you make a SwiftUI registration form with name, address and city fields?

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Total votes 5: ↑4 and ↓1+3
Comments1

How Will Artificial Intelligence Influence Online Sports Betting?

Reading time4 min
Views2.5K

Online sports betting is a vertical of the gambling industry that has witnessed a massive surge in recent years. It is a great source of entertainment and thrill for online punters and bettors. Moreover, it also provides monetary benefits that are enough to entice the average layman. This is one of the most prominent reasons why people are gravitating towards these online sports betting platforms.

Additionally, the growing popularity of these platforms is urging sports betting software development companies to innovate and upgrade their platforms to cater to the growing needs of the user base. This is where Artificial Intelligence comes into the equation, AI has been at the forefront of innovations and development and has been offering users an enhanced user experience across multiple platforms. 

Artificial Intelligence technology has allowed Sports betting platforms to evolve with time and streamline their operations for better efficiency and enhanced productivity. This is why sports betting platforms all over the world are adopting this technology to offer better features and functionality to users and also increase their productivity and revenue. 

In this article, we will highlight how Artificial Intelligence has influenced the sports betting industry. So without further delay, let’s get started. 

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