Write a news-style post for a Telegram channel, tailored for a Ukrainian audience. Format the text in Markdown and use appropriate emojis to make it engaging and lively. Answer in Ukrainian. If the text does not contain information about Python innovation, return only the word 'False'. The post should not include any promotional content. Add hashtags at the end.At the end, add a link to the original source (https://www.infoworld.com/article/3567094/why-python-is-the-language-of-choice-for-ai.html).

Text to generate the news post:
Why Python is the language of choice for AI | InfoWorld                                                                                                                                                                                                       							Topics						   Spotlight: AI in EnterpriseVideosNewslettersResources                             AboutAbout usAdvertiseContact UsFoundry CareersNewslettersReprintsPoliciesTerms of ServicePrivacy PolicyCookie PolicyCopyright NoticeMember PreferencesAbout AdChoicesE-commerce Affiliate RelationshipsYour California Privacy RightsOur NetworkCIOComputerworldCSONetwork WorldMoreBlogsBrandPostsEventsVideosEnterprise Buyer’s Guides           Close         AnalyticsArtificial IntelligenceGenerative AICareersCloud ComputingData ManagementDatabasesEmerging TechnologyTechnology IndustrySecurity Software DevelopmentMicrosoft .NETDevelopment ToolsDevopsOpen SourceProgramming LanguagesJavaJavaScriptPythonEnterprise Buyer’s Guides              Back      Close                    Back      Close              										Popular Topics									   Artificial IntelligenceCloud ComputingData ManagementSoftware Development                        Search         Topics        Spotlight: AI in EnterpriseVideosNewslettersResourcesAboutPoliciesOur NetworkMore               Back      Topics      AnalyticsArtificial IntelligenceGenerative AICareersCloud ComputingData ManagementDatabasesEmerging TechnologyTechnology IndustrySecuritySoftware DevelopmentMicrosoft .NETDevelopment ToolsDevopsOpen SourceProgramming LanguagesJavaJavaScriptPythonEnterprise Buyer’s Guides              					Back				       AboutAbout usAdvertiseContact UsFoundry CareersNewslettersReprints           					Back				       PoliciesTerms of ServicePrivacy PolicyCookie PolicyCopyright NoticeMember PreferencesAbout AdChoicesE-commerce Affiliate RelationshipsYour California Privacy Rights           					Back				       Our NetworkCIOComputerworldCSONetwork World           					Back				       MoreBlogsBrandPostsEventsVideosEnterprise Buyer’s Guides        HomeBlogsGenerative AI InsightsWhy Python is the language of choice for AI       						by Jeff Hollan					                                      Why Python is the language of choice for AI feature Oct 22, 20248 mins Artificial IntelligenceDeep LearningGenerative AI                                                                                                               		Python developers are uniquely positioned to succeed in the AI era, with a little help from upskilling. 	       Credit: Zamrznuti tonovi / Shutterstock         The widespread adoption of AI is creating a paradigm shift in the software engineering world. Python has quickly become the programming language of choice for AI development due to its usability, mature ecosystem, and ability to meet the data-driven needs of AI and machine learning (ML) workflows. As AI expands to new industries and use cases, and Python’s functionality evolves, the demand for developers versed in the language will balloon. Python developers who invest in their AI and ML knowledge will be well-positioned to thrive in the era of AI.  Python is the most popular programming language, according to the TIOBE Programming Community Index. Python took its first lead over the other languages in 2021 and continued to explode in popularity as the growth of other languages largely remained stagnant. Meanwhile, nearly 30% of the searches for programming language tutorials on Google were for Python, nearly double the percentage for Java, which is ranked second, according to the PYPL Index, which is based on data from Google Trends. It’s no wonder that the popularity of Python has extended to AI workflows too. Why Python leads for AI development There are a number of factors that make Python ideal for AI development including its ease of use, its rich and growing ecosystem of AI libraries and toolkits, and the libraries and tools available to improve its execution speed and scalability.                          Usability and ecosystem Python is easy to learn and a simple language to write, which makes it accessible to people without programming experience. It doesn’t require developers to write complex boilerplate code, and it can be written iteratively. Libraries in the many AI development toolkits available for Python are typically lightweight and don’t require building or training AI models. Instead, Python developers can use specialized tools from vendors to accelerate AI app development using available models.     The ecosystem around Python is massive. There is a rich set of libraries and frameworks that are specifically designed for AI and ML, including TensorFlow, PyTorch, Keras, Scikit-learn, and Pandas. Those tools provide pre-built functions and structures that enable rapid development and prototyping. In addition, packages and libraries like NumPy and Pandas make data manipulation and analysis straightforward and are great for working with large data sets. Many Python tools for AI and ML are open source, fostering both collaboration and innovation.  User base and use cases As AI development evolves, Python is opening the doors for more people and more use cases. Today, Python can be used for exploratory or even low-code solutions. The majority of AI applications that will be built in the future won’t require the level of customization and power that PyTorch and TensorFlow do. And future AI apps will use a different set of libraries, such as LangChain or LlamaIndex for building applications that use large language models (LLMs).                          Meanwhile, new packages for Python are being added all the time and will expand the horizons beyond just AI to include more common use cases, like building advanced websites. There isn’t a task that developers do with Python today that won’t be impacted by AI in some form.  Performance aids Python can be extended with libraries like Cython to nearly meet the performance of the C language, and just-in-time compilers like PyPy can significantly improve code execution speed. Critical performance components can be written in C or C++ and wrapped in Python, combining performance with Python’s ease of use. Python makes it easy to transition from a prototype to a production-ready solution, especially with tools designed to scale Python applications, such as Dask or Ray. What AI projects require from Python developers  While Python’s usability makes it easy for even relatively unskilled developers to learn the language, there are specific skills that developers will need to focus on for the AI industry of the future. Developers will need to write code that can quickly and efficiently process large data sets through AI. Understanding concepts like parallel programming, throttling, and load balancing will be necessary. Python developers have the foundational knowledge to succeed at these tasks, but they need to build upon their skill sets to effectively pivot to AI projects, and set themselves apart in a crowded job market.                         One area where there may be a skills gap for Python developers is working with AI agents, which is the next wave of AI innovation. With agentic AI, software agents are designed to work autonomously toward an established goal rather than merely provide information in reaction to a prompt. Developers will need to understand how to write programs that can follow this sophisticated orchestration or sequence of steps.  AI is taking on a more active role in the development process itself too. It’s working much like a copilot in doing the legwork of looking up code samples and writing the software, and freeing up developers so they can focus on code review and higher-level strategic work. There’s an art to getting AI to generate reliable and safe code. It’s an important skill set to develop and it will be critical for developers of the future. How to kickstart your AI learning journey My advice? Developers should always be learning how to use new technologies and supplement their skill sets, but the fast pace of AI innovation brings even more urgency. I’m a strong believer in continuous learning, and I believe the responsibility to learn and grow lies with the individual rather than the company they work for. In today’s world, there are a plethora of free, extremely valuable learning resources at everyone’s fingertips; accessibility and cost are not valid excuses to opt out of upskilling. If developers can begin to chip away at their AI learning goals now — even if only for 15 minutes per day — they will reap the rewards down the line.                         Many companies offer professional development stipends and opportunities for employees, and even the general public, like Google, Snowflake University, and MongoDB University. Coursera and Udemy offer certifications and courses that are both free and fee-based. YouTube offers a lot of tutorials, including one from freeCodeCamp.org, and Codecademy offers a free course on its website. Major universities also offer free Python classes for the public. These resources are everywhere. Nothing beats hands-on training, though. If you can weave AI tasks with Python into your tool set at work and learn on the job, that will benefit you and your company. For those who don’t have that option, I recommend rolling up your sleeves and getting started on Python projects on your own. About a year ago, I spent some time over a few weekends using Python to build a personal AI-based widget to help me with my workout training and nutrition suggestions. This is just one example of how you can take the initiative to learn AI skills in a hands-on, engaging way. I encourage all of the folks I manage and meet with to do the same. The synergies between Python and AI are strong, and they are expected to become stronger as AI gets integrated into additional applications and industries. Python’s simplicity and versatility make it an ideal choice for developers looking to harness the power of AI. As AI technologies evolve and become more prevalent, Python developers have a chance to take the initiative to learn about them and remain relevant and adaptable in a rapidly changing landscape.                         Jeff Hollan is head of apps and developer tools at Snowflake. — Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>.                                           Related content      analysis  And the #1 Python IDE is . . . PyCharm, VS Code, and five other popular Python IDEs duke it out. Which one do you think takes home the prize?   				By Serdar Yegulalp				   Nov 15, 2024  2 mins     								Python							  								Programming Languages							  								Software Development							       news  JetBrains IDEs ease debugging for Kubernetes apps Version 2024.3 updates to IntelliJ, PyCharm, WebStorm, and other JetBrains IDEs streamline remote debugging of Kubernetes microservices and much more.    				By Paul Krill				   Nov 14, 2024  3 mins     								Integrated Development Environments							  								Java							  								Python							       analysis  Python is the most popular language on GitHub Python was in the spotlight all last month, with a new release and a couple of big wins. Here are our picks for the best news and tutorials for Python developers in October.   				By Serdar Yegulalp				   Nov 01, 2024  2 mins     								Python							  								Programming Languages							  								Software Development							       feature  Python threading and subprocesses explained Python lets you parallelize workloads using threads, subprocesses, or both. Here's what you need to know about Python's thread and process pools and Python threads after Python 3.13.   				By Serdar Yegulalp				   Oct 30, 2024  9 mins     								Concurrency							  								Python							  								Programming Languages							       Resources   Videos                      Show me morePopularArticlesVideos   feature  Designing the APIs that accidentally power businesses By Jean Yang Nov 18, 20246 mins APIsSoftware Development     news  Spin 3.0 supports polyglot development using Wasm components By Paul Krill Nov 18, 20242 mins MicroservicesServerless ComputingDevelopment Libraries and Frameworks     feature  14 great preprocessors for developers who love to code By Peter Wayner Nov 18, 202410 mins Development ToolsSoftware Development     video  Building Python wheels to distribute your programs  Nov 15, 20246 mins PythonSoftware Development     video  Creating a pip install-able Python package  Nov 14, 20243 mins PythonSoftware Development     video  How to get better web requests in Python with httpx  Oct 07, 20244 mins Python              About            About us   Advertise   Contact Us   Foundry Careers   Reprints   Newsletters   BrandPosts           Policies            Terms of Service   Privacy Policy   Cookie Policy   Copyright Notice   Member Preferences   About AdChoices   E-commerce Affiliate Relationships   Your California Privacy Rights   Privacy Settings           Our Network            CIO   Computerworld   CSO   Network World                 LinkedIn          X          Facebook             Copyright © 2024 IDG Communications, Inc.