zato sam i napisao da će služiti kao nekakva inspiracija - pomoćnik , prilikom kreiranja bilo čega.Slika, tesktova, video klipova itd…
samo 
ma ne, ok je za AI za postavljanje temelja texta pa ga doraditi, ali da ćemo moći ga full automatizirati bez posljedica kao recimo što radi Nikos sa svojim golf blogom sumnjam. To će sve za koji mjesec doći na naplatu. Al ako uspije tko dotad izvući neke novce, super 
2 Likeova
Ja mislim da clanak treba sastavljat iz vise promptova, iz vise pokusaja istog prompta pa onda uporedjivati slicnost. Nije moj da se razumijemo, cak sta vise nije ga ni chatGPT pisao nego dosta starija verzija AI-a.
Ovaj AI clanak pola mojih novinara na Upworku ni blizu ne bi napisalo. Ali znaci nije AI pisao na topic nego na podnaslove.
Is There a Language Better Than Python for Machine Learning? Explore Your Options!
Introduction
Python is a programming language that you might consider if you want to learn machine learning. However, there are other options available, each with its own set of advantages and disadvantages. In this article, we’ll look at some Python alternatives and weigh their advantages and disadvantages.
What is Machine Learning?
Machine learning is a type of artificial intelligence that allows computers to learn from data and make decisions without the use of coding instructions. It entails algorithms that can recognize patterns and use them to provide insights and make predictions. Machine learning algorithms are used in a variety of applications, including natural language processing, computer vision, and robotics.
Python is a popular machine learning language due to its simplicity and flexibility.
Other options are available depending on your project and goals. You might be better off using one of them. C++, Java, R, and Julia are all alternative machine learning languages with advantages and disadvantages for various applications. C++ is a powerful language with high performance and scalability.
Java is ideal for enterprise applications and has a large user base. R is a powerful open-source language that includes a number of statistical and analytical packages.
Julia is ideal for data science, is free and open-source, and has a high performance. Depending on the application, each of the four languages has strengths and weaknesses. One of them may be more appropriate than the others.
Why is Python so popular for machine learning?
Python is widely used in machine learning because of its robustness and ease of use. It has extensive libraries that allow for the rapid development and deployment of machine learning models.
It also has a large user base and a wealth of resources to assist with any potential problems that may arise. Python provides flexibility in terms of development approaches, allowing the user to select from a number of options when creating a model. All of these factors combine to make it an excellent choice for machine learning applications.
Python is simple to learn and can be quickly mastered by developers with no prior experience in machine learning.
This makes it an appealing language for beginners, allowing them to get up and running quickly and with minimal fuss. Users have access to a wide range of different algorithms and techniques that can be used in their applications due to the wide variety of libraries available.
This allows developers to experiment with different approaches until they find the one that works best for their specific use case. Python is an excellent choice for speed and scalability. It is a highly efficient language that can process large amounts of data quickly.
This makes it well-suited for more complex models, such as deep learning, where speed is critical. It is easily scaled and deployed across multiple machines, making it suitable for larger projects. These factors contribute to Python’s popularity in the machine learning space.
Python Alternatives
If you’re looking for a Python alternative for machine learning, you have plenty of options. C++, Java, R, and Julia are all excellent languages to consider. Each has advantages and disadvantages, so it is critical to weigh all of the factors before selecting one.
With C++, you’ll have access to a variety of machine learning libraries and frameworks, as well as the ability to compile code and run it on any platform. Java has a large library of machine-learning algorithms and an easy-to-learn syntax.
R is an open-source language with a large number of packages and libraries for data analysis and visualization. Julia has a low learning curve and is designed to provide simple and fast code execution.
When comparing these languages, it is critical to consider their similarities.
All of these languages have powerful development tools and libraries, a robust support community, and a diverse set of packages to choose from. However, because each language is distinct and has its own set of advantages and disadvantages, it is critical to conduct thorough research before making a decision. Whichever language you choose, it is critical to thoroughly learn the language in order to achieve the best results.
C++
C++ is an excellent choice for machine learning projects. C++, as a statically typed language, provides a lot of power, allowing you to build complex algorithms and data structures faster than you could with Python.
Because C++ is more efficient, your programs will run faster and use fewer resources than Python counterparts. This makes C++ an excellent choice for optimizing algorithms and data structures for maximum speed and power.
C++ provides access to core system functions, APIs, and libraries, allowing you to interact with the underlying hardware more easily. C++ also has a vibrant developer and user community, so you’ll have plenty of resources and support for your project. If you require assistance, you will be able to obtain it quickly and easily.
You’ll also have access to a plethora of libraries and tools to help you build your project more quickly.
Because C++ is a compiled language, it is easier to debug and optimize your code because the compiler can detect and report errors as well as provide recommendations for improving performance. C++ is an excellent choice for machine learning projects. Its strong typing, efficiency, and access to core system functions make it a powerful, versatile language that can help you build complex algorithms and data structures faster and more efficiently than Python - all while benefiting from robust community support and powerful debugging tools.
Java
Java is an excellent alternative to Python for machine learning. Java is fast and dependable, and it is supported by a large open-source community. It’s an excellent language for software developers who want to easily integrate their ML models into existing programs.
Java also has a plethora of libraries and packages to assist developers in making the most of their machine-learning applications. Java is designed to give the developer more control, making it easier to control and debug models. Java is an excellent choice for those interested in machine learning because of its simple syntax and wide range of features.
R
R is a powerful but relatively simple to learn language that can be used for machine learning. It has a wide range of statistical and visual graphing functions and is ideal for general data exploration and analysis.
R is also an excellent language for prototyping machine learning algorithms, as it includes a wide range of packages ranging from linear and logistic regression to deep learning algorithms. Furthermore, its syntax is simple, making it easier for beginners to grasp.
The language also supports data manipulation and visualization, making it an ideal tool for data scientists. The R community provides users with a variety of resources, tutorials, and online courses. R is a popular choice for those who are new to machine learning.
Julia
Julia is yet another machine learning alternative to Python. It is fast and dependable, with a robust toolkit for data analysis and manipulation.
Julia provides strong support for scientific computing and a wide range of numerical libraries, making it an excellent choice for machine learning projects. Julia is simple to learn, with a friendly syntax and intuitive functions. This makes it an appealing option for those new to machine learning.
Julia has a vibrant community with active support and online resources, making it simple to seek assistance when needed. Overall, Julia is an excellent machine learning language and a viable alternative to Python.
The Benefits and Drawbacks of Using Python Alternatives for Machine Learning
Python is a popular machine learning language, but it is not the only one. C++, Java, R, and Julia are all possible alternatives.
Each has advantages and disadvantages, and understanding them is essential for selecting the best language for your project. C++ is ideal for machine learning because it is fast and efficient.
On the other hand, debugging code errors can be difficult and time-consuming.
Java is a fantastic machine-learning language known for its robustness and scalability. It is slower than C++ and can be difficult for beginners.
R is a fantastic data analysis language that is primarily used for statistical analysis.
It has a large function library, which makes it simple to create complex applications. When it comes to machine learning, it is slower than C++ or Java.
Julia is a relatively new language that is gaining popularity due to its ease of use and speed. It is also open source and has a large library of machine learning packages.
Because the language is still in its early stages, it can be quite buggy, and there isn’t much help and support available. Understanding the pros and cons of each language is critical to making the best decision. Each of these languages has advantages and disadvantages that must be considered before deciding which to use.
C++
C++ is a powerful programming language, making it an excellent choice for machine learning. It has a fast runtime, which means it can process data and create models quickly. Unlike Python, C++ gives users more control over the code and its performance.
C++ can be used to create robust and scalable applications, making it suitable for larger projects. C++, as an object-oriented language, allows users to create and use classes and objects to write more efficient code.
C++ is an excellent language for machine learning because it is powerful, efficient, and simple to use. It is also compatible with modern frameworks and libraries, so you won’t have to worry about compatibility issues.
C++ is also popular among data scientists and machine learning engineers, so finding resources and support for it should be simple. C++ can be used to build robust applications that can be easily scaled up, making it an excellent choice for larger machine learning projects that require scalability and speed.
Java
Java is an excellent choice for machine learning projects because it is quick, portable, and dependable. Java is a popular programming language among professional developers, and it is easy to find experienced Java developers in most markets. The language also includes a large library of open-source tools, making it very easy to develop machine learning projects quickly.
Java is extremely secure, making it an excellent choice for projects requiring a high level of security.
Java is the best Python alternative because it is easy to learn, fast to develop, and secure. When it comes to machine learning, Java has a plethora of user-friendly libraries. There’s something for every type of machine learning project, from deep learning to natural language processing libraries.
The language is well-suited to large-scale projects, making it easier to build complex machine-learning systems. Java also has some of the best support for distributed computing, making it easier to scale machine-learning applications across multiple servers.
Java is an excellent language for machine learning projects. It’s fast, secure, and has excellent libraries for complex projects.
With the vast array of developer resources available, it’s simple to find experienced developers to assist with your project. If you want an easy-to-learn alternative to Python that also provides excellent support for machine learning projects, Java is your best bet.
R
R is an excellent language for machine learning, especially if you have a background in statistics. It is well-suited for data exploration and manipulation due to its wide range of data types and powerful built-in functions. R also supports a wide range of models, including deep learning and neural networks.
The syntax is simple to learn, and the language is highly extensible, allowing you to access a variety of custom libraries, functions, and packages. It also has excellent data visualization support, with a plethora of libraries for creating interactive charts and graphs.
The disadvantage is that R can be slow and inefficient when compared to other languages. It also has a steep learning curve and can be difficult to debug.
As a result, it is usually not recommended for large projects or complex machine-learning tasks.
If you’re new to data science and machine learning, R is an excellent choice. It’s an approachable and powerful language with a large library of packages and functions that provides a lot of flexibility. If you’re thinking about using R for machine learning, make sure to read up on the language and experiment with coding.
It’s also a good idea to use a virtual environment to test out different libraries, functions, and packages before incorporating them into your project. With a little practice and patience, you’ll soon be able to harness the power of R for your machine-learning tasks.
Julia
If you’re looking for a machine learning language, Julia is worth looking into. It is a relatively new language that is both powerful and user-friendly. It has an easy-to-learn syntax and features like multiple dispatch for writing fast and efficient code. It also has extensive support for data science and machine learning libraries. Julia also makes it simple to integrate with other languages, allowing you to reuse existing code and libraries from languages such as Python and R.
Julia also performs admirably when it comes to running machine learning algorithms. It is designed to be extremely efficient, making it an excellent choice for large-scale applications. Julia’s extensive libraries also make machine-learning algorithms simple to implement. Julia’s GPU support allows you to take advantage of powerful hardware for even faster execution times. Julia is an excellent choice for machine learning. It’s simple to use and has a wide range of features and libraries. With so much to offer, it’s no surprise that Julia is becoming more popular as a machine learning language.
Similarities Between These Alternatives
There are a few common threads among Python alternatives for machine learning. They are all Turing-complete programming languages that can be used to write complex algorithms. They all have powerful libraries for manipulating data and executing machine-learning tasks.
You can also use multiple languages within the same project and work on different parts of the same algorithm - this is especially useful if you’re fluent in more than one language. Each language has advantages and disadvantages. While C++ is known for its speed and ability to write tighter code, Java is well-known for its simple syntax and extensive libraries.
R and Julia are both useful for data-intensive tasks, but Python stands out for its intuitive syntax and strong community support. It is critical to select the language that best fits your project and your skills.
Closing Thoughts
Choosing a language for Machine Learning is an important decision because it has a significant impact on the development process. When deciding between Python and another language, it is critical to weigh the advantages and disadvantages of each.
C++ is a good choice for its speed and flexibility, Java for its robustness, R for its statistical capabilities, and Julia for its ease of use. All of these languages have some things in common, such as being object-oriented, having a large tool library, and supporting cross-platform development. The best language for Machine Learning is determined by the application and the user’s skill set.
Python is a great choice because of its large package library and simple syntax, but many developers find it slow. If you want a language that is faster or has more specific capabilities, some of the alternatives discussed may be worth considering.
To da. Kako kažu, neće ai vam uzeti posao, nego ljudi koji koriste ai.
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al to nije samo AI tekst, to je modifikovan tekst, kao sto je GT prevod automated content, al ak ti na njega izmjenist 20% onda vise nije…
A ovo plain AI mislim da je google relativno lagano prepoznat do nekih 95%, a vjerovatno i vise
Da, ali opet AI stvara text na sebi specifičan način pa je uglavnom prepoznatljiv. Neovisno jel se radi o pet rečenica ili o 500.
Naravno možeš ti sad ići prepravljat text pa probati zaobići detekciju ali onda opet moraš puno vremena utrošiti.
Recimo sad sam probao ubaciti zadnji dio ovog tvog gore teksta u Free AI Detector - AI Content Detection for GPT-3 + ChatGPT i kaže mi da je obviously AI. Al recimo za cijeli text kaže da je 100% human
Ali alati za detektiranje će se još usavršavati.
Biti će to konstantna igra lovice između GPT-a i detektora izgleda.
Evo tu raznih alata za detekciju AI-a pa možete isprobati svoje textove: Percent Human - A list of online tools for detecting lower-quality AI content
sljedeća stepenica u razvitku AI će biti da ga zaglupiš da piše više kao čoek da ga ne bi detektovao gugl.
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To je sadasnji AI. Za 5 godina ce i to biti upitno. Prije ili kasnije ta igra macke i misa ce postati nemoguca, pogotovu onog trenutka kad detekcija AI-a bude znacila da penalizovanjem AI clanka penalizujes i dobar procenat ljudskih clanaka pa ces ih morati kvariti.
CNET se vec bacio na posao 
cool, sad samo čekamo AI bota na WMforumu 
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Kako bi drugačije uopće moglo biti.
Kada se govori o industrijskoj revoluciji i da su strojevi mahom uzeli radna mjesta ljudima … onda se svakako sporazumjeva da se strojevi nisu morali samosvjesno odmetnut od čovjeka da bi konkurirali za radna mjesta.
…svejedno ćemo reći da su strojevi uzeli radna mjesta. (A ne ljudi koji koriste strojeve)
Tako i AI … sve što će nam učiniti, učiniti će nam putem našeg (kolektivnog) odabira.
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Tko je jamio, jamio je…
Treba čekati da Indijci odu na spavanje.
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Ne diraj mi Escobara… 
zasto bi gugl penalizirao AI tekst… guglu je bitno da je korisnik zadovoljan…i da se stranica uklapa u njihov biznis s oglasima… ko ce pisati je nebitno…ne treba zaboraviti da su 90% gugl biznisa mali lokalni korisnici…
Zbog ovoga što je dio njihovog terms & conditionsa:
" Spammy automatically-generated content
Spammy automatically generated (or “auto-generated”) content is content that’s been generated programmatically without producing anything original or adding sufficient value; instead, it’s been generated for the primary purpose of manipulating search rankings and not helping users. Examples of spammy auto-generated content include:
- Text that makes no sense to the reader but contains search keywords
- Text translated by an automated tool without human review or curation before publishing
- Text generated through automated processes without regard for quality or user experience
- Text generated using automated synonymizing, paraphrasing, or obfuscation techniques
- Text generated from scraping feeds or search results
- Stitching or combining content from different web pages without adding sufficient value
If you’re hosting such content on your site, you can use these methods to exclude them from Search."
nije spammy ako ti doda vrijednost… sve te tocke ovaj chatgpt uspjesno rijesava…to je vrijedilo u drugo vrijeme kada je tek web2 pocinjao… radio sam za lionbridge ko rater gugla… i tam su rangirali sajtove po korisnosti za korisnike…spam je samo bio tehnicka manipulaicija koju je danas skoro nemoguce izvesti…
Ovo kaže sam Google:
naravno da kaze… isto ko sto kaze da se nesmije kupovati linkove pa opet svi to rade… kako niko ne dovodi u pitanje generiranje slika od midjournija i dall-e… gugl je odlucival sto je spam do sada… sada se igra mjenja…mislim da je kod generiranja clanaka samo stvar u malo detaljnijim postavkama… ima vec sad 10 stilova pisanja…sve sto ljudski autor moze originalno napravit je gramaticku gresku…kako je gugl iskoristio milione webmastera tako su sada iskoristili njega…
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