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To utilize AI in an organization’s frameworks and administrations, you will require programmers who are capable

Artificial intelligence is at the forefront of everyone’s thoughts — particularly organizations hoping to speed up development past what they’ve recently had the option to accomplish. With

Python

With respect to current innovation, the main motivation behind why Python is generally positioned close to the top is that there are AI-explicit systems that were made for the language. One of the most famous is TensorFlow, which is an open-source library made explicitly for AI and can be utilized for preparing and deduction of profound brain organizations. Other AI-driven systems include: scikit-learn – for preparing AI models. PyTorch – visual and normal language handling. Keras – fills in as a code interface for complex numerical computations. Theano – library for characterizing, improving, and assessing numerical articulations. Python is additionally perhaps the simplest language to learn and utilize.  

Java

It ought to be obvious that Java is a significant language for AI. One justification behind that is the way common the language is in versatile application improvement. What’s more, considering the number of portable applications that exploit AI, it’s an ideal pair. Besides the fact that Java works with can TensorFlow, however, it additionally has different libraries and structures explicitly intended for AI: Profound Java Library – a library worked by Amazon to make profound learning capacities. Kubeflow – makes it conceivable to send and oversee Machine Learning stacks on Kubernetes. OpenNLP – a Machine Learning instrument for handling normal language. Java Machine Learning Library – gives a few Machine Learning calculations. Neuroph – makes it conceivable to plan brain organizations. Java likewise utilizes streamlined investigating, and it’s not difficult to-utilize sentence structure offers graphical information show, and integrates both WORA and Object-Oriented designs.  

C++

C++ is one more language that has been around for a long while, yet at the same time is a real competitor for AI use. One reason for this is the way generally adaptable the language is, which makes it impeccably appropriate for asset concentrated applications. C++ is a low-level language that takes better care of the AI model underway. What’s more, in spite of the fact that C++ probably won’t be the best option for AI engineers, it can’t be overlooked that large numbers of the profound and AI libraries are written in C++.  

Can AI learn programming?

For the most part by programming, it means a method of people entering directions for a PC to complete a calculation. On the off chance that that is the definition, the response is not in light of the fact that they are not people. Yet, PC programs truly do play out the delegate occupation of deciphering or incorporating significant level dialects to machine language. Ponder SQL for instance. It’s an explanatory language so the people just indicate what they believe do not how could make it happen. The information base framework then takes care of this issue by thinking of a calculation to get it done. That is similar to programming, right? Also, could these calculations at any point really gain for a fact? Indeed. Also, they really do as of now. Inquiry enhancers and JIT compilers do this. That is similar to AI figuring out how to program I assume. The pattern is that scripting languages are getting increasingly high level. That implies that the human can program all the more gainfully by passing on a greater amount of the work to the compiler or language mediator. Sometimes we could possibly express something like “Siri, fabricate me an application to let me know where to stop for supper on my cross-country excursion”, and it will banter with us and ask us what highlights we need and so forth. We are far from what I accept. However, when we arrive, we are as yet programming, right at a lot more significant level. As of now, the circumstance is that PC machine language is fixed by the equipment. Significant level dialects permit us to communicate in code through a mediator which simply permits us to assign large numbers of the subtleties to the PC. In any case, coding is not adaptable. Assuming that we program it wrong, it won’t in any casework. It will do everything we said it to do, wrongly, or maybe let us know what we are requesting that it do is incomprehensible and decline to order. For a PC to communicate in our human language it should have the option to address our mistakes or if nothing else propose redresses when we tell things that are off-base or vague. IDEs (Integrated Development Environments) are improving yet they aren’t exactly by then yet. Generally, we converse with them and they simply whine to us. We’ll be at the following stage when they don’t simply gripe at, all things considered, and figure out how to manage our mistakes and equivocalness.  

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Top 10 Game Related Programming Languages To Master In 2023

Game developers must check out these top 10 programming languages in 2023

The

C++

C++ is a high-level programming language used to build the biggest console and Windows games. It offers a lot of scalabilities and can be used for small and large gaming projects alike also it is platform-agnostic, which means you can simply move projects from one OS to another. C++ is surely one of the best

JAVA

Java is one of the best object-oriented

C#

In a game engine like Unity, C# is the programming language that you code in, but C++ is at the engine’s core. C# is one of the best programming languages for Windows and Xbox Games. Pokemon Go and Super Mario Run are two popular gaming projects developed with C#.

HTML

In this list of the top 10 programming languages for gaming projects, next is HTML5 a popular platform for creating cross-platform and cross-browser applications and games, according to game developers. It can also be utilized interchangeably with JavaScript. HTML is a simple to learn programming language and does not need extensive programming understanding of algorithms, making it a prominent choice among game designers.

CUDA-C

CUDA-C is one of the top programming languages used by game developers to build desktop games. When it comes to gaming, CUDA-C cores make your game appear more realistic by presenting high-resolution visuals that create a profound 3D impression. You will also examine that your games are more lifelike, with better lighting and colors.

Lua

In the list of the top 10 programming languages for gaming projects, Lua is number six. It is a lightweight, cross-platform scripting language, that is gaining traction in the gaming industry. Because of its easy language syntax, it has become one of the top programming for games. Lua is the major language used by game engines such as Gideros mobile, Corona SDK, and CryEngine. Age of Conan, American Girl, Angry Birds, and Aquaria are among the most popular Lua gaming projects.

Python

This programming language does not require any introduction. Python is one of the most user-friendly and flexible programming languages for game developers. It uses the Pygame framework and enables programmers to rapidly prototype games. Python is earning its share of glory as one of the best video game programming languages for gaming projects.

JAVA SCRIPT

As per GameDev Academy, JavaScript is one of the most well-known cornerstones of web development. As the world rapidly moves towards a web-based economy, online games are getting more common. For creating interactive gaming projects, JavaScript is without a doubt one of the top programming languages.

Swift

Swift is the perfect choice for gaming projects. Developers are intrigued by Swift and want to make use of new features to develop their best games yet. Using SpriteKit, you will learn how to animate sprites and textures. Along the way, you will master physics, animations, and collision effects and how to build the UI aspects of a game.

CSS3

The online gaming industry literally stands upon the game developers who use programming languages in almost every area of the game. They use several programming languages , sometimes more than one at a time. The right language depends on the project, what features you need the language to have, and your experience level as a programmer. According to Statista, by 2023, the online gaming industry will value about Rs 155 billion. The coders need to seamlessly work on the development to get well-animated and flashy video games. Hence, comes the programming skills that one needs to know to ensure that they can develop the right game. Here are the top 10 game-related programming languages that these developers should master in 2023.C++ is a high-level programming language used to build the biggest console and Windows games. It offers a lot of scalabilities and can be used for small and large gaming projects alike also it is platform-agnostic, which means you can simply move projects from one OS to another. C++ is surely one of the best programming languages for gaming projects. The Witcher 3, Dark Souls, Elder Scrolls V: Skyrim, Player Unknown’s Battlegrounds (PUBG), Fortnite, and more are gaming projects created with C++.Java is one of the best object-oriented programming languages for general computer programming that was created in 1995. Java was established to have as few dependencies as possible, particularly in comparison to other programming at the time and even now. Java is one of the top programming languages that enable gaming developers to build games for every platform. It is one of the most popular programming languages for gaming projects in 2023. Mission Impossible III, Minecraft, FIFA 11, Ferrari GT 3: World Track, and more are gaming projects developed with Java.

Artificial Intelligence Vs. Human Intelligence: Top 7 Differences

Introduction

Artificial intelligence has come a long way from the fictional AI character JARVIS to real-life ChatGPT. However, human intelligence is an attribute that supports individuals in learning, comprehending, and coming up with innovative solutions to challenges, versus artificial intelligence, which imitates humans based on provided data. Since AI has become so prevalent today, a new discussion, artificial intelligence vs. human intelligence, has emerged comparing the two rival paradigms.

What is Artificial Intelligence? 

A subfield of data science called artificial intelligence is associated with creating intelligent computers that can carry out various tasks that often call for human intelligence and perception. These sophisticated machines can learn from previous errors and historical data, analyze the surrounding circumstances, and decide on the necessary measures. 

AI is an integrated field that draws on ideas and methods from many other disciplines, including computational science, cognitive sciences, language studies, neuroscience, psychology, and mathematics. 

The machine is capable of self-learning, self-analysis, and self-improvement, and while processing, it requires minimal or almost no human effort. 

It is utilized in practically every business, including the media industry, the healthcare industry, graphics and animation, and more, to help technologies replicate human action based on their behavior. 

What is Human Intelligence? 

Human intelligence refers to a person’s intellectual capacity, which enables them to reason, understand a variety of expressions, comprehend challenging concepts, solve mathematical problems, adapt to changing circumstances, use knowledge to control their environment and communicate with others.

Human intelligence and behavior have their roots in a person’s distinctive admixture of genetics, childhood development, and experience with diverse events and surroundings. Furthermore, it entirely relies on the individual’s ability to use their newly acquired knowledge to transform their surroundings. 

Artificial Intelligence vs. Human Intelligence

Here is an elaborated difference between human intelligence and artificial intelligence: 

ParameterHuman IntelligenceArtificial Intelligence Origin Humans are born with the capacity to reason, think, assess, and perform other cognitive chúng tôi is an innovation created by human insights; Norbert Wiener is associated with helping to progress the field by theorizing about the mechanisms of criticism.Learning CapabilitiesHuman intelligence can pick up new information via observations, experience, and educating oneself and put it into novel scenarios.Using statistical models and algorithms, AI can learn from enormous amounts of data. They cannot build a uniquely human analytical style; they can only learn through data and regular training.Creativity Using innovative thinking and creativity, human intelligence can generate fresh concepts, literature, music, and chúng tôi can create novel approaches using existing trends and data but lacks inherent innovation and originality.Decision MakingHuman decisions can be subject to subjective factors not based solely on chúng tôi interprets according to completely collected data, which makes it strongly objective in decision-making.Nature Human intelligence is analogous. Artificial intelligence uses digital machines.Energy Use The human brain uses around 25 watts of energy.Modern-day computers use around 2 watts of energy.Social Skills The capacity to comprehend abstract concepts, the degree of self-awareness, and sensitivity to the sentiments of others distinguish humans from other social animals. Artificial intelligence is still developing the capability to read and recognize relevant interpersonal and passionate signals.

What AI Cannot Do without – The “Human” Factor

People show their emotions and have the ability to interpret the facial expressions and moods of others, but artificially intelligent machines are not trained to do that. Although AI-enabled machines can mimic human speech, they lack human touch since they cannot express empathy and other emotions.

AI is based on codes that restrict it from finding creative answers to new challenges. They operate as intended, which limits their capacity to understand the context and devise sophisticated solutions.

While AI can learn extremely fast, it lacks logical thinking and is, therefore, unable to reason and challenge the facts to the same extent humans can.

In-demand Machine Learning Skills

There are certain machine learning skills that are currently in demand for the artificial intelligence industry: 

Programming languages such as Python, C++, R, etc. 

Applied mathematics 

Natural language processing 

Data Science 

Communication and data visualization skills

Statistics and probability  

Artificial Intelligence vs. Human Intelligence: What Future Holds?

Digital existence is enhancing human abilities while challenging long-standing human activity. Code-driven technologies have reached more than half of the world’s population regarding ambient data and connection, providing previously unthinkable potential and significant risks. 

When the discussion of AI vs. humans comes into action, we look at a bright coexisting future. The next generation will be raised in an era where human beings and humanoids coexist, with humanoids functioning to assist humans.

Also Read: This is How Experts Predict the Future of AI

Best Machine Learning and AI Courses Online

Here are the top 5 online courses that can help individuals intrigued about artificial intelligence progress in the field:

Getting Started with Decision Trees

Checkout the course here!

Machine Learning Certification for Beginners

This free certification course is the perfect start for the machine learning journey. The course comprises basics of ML, the introduction of Python to data science, using tools like NumPy, sci-kit- learn and more, real-life, hands-on projects, concepts of feature engineering and more. It is a short course that requires only 8-10 hours per week.

Checkout the course here!

Loan Prediction Practice Problem Using Python

A short and interesting free course designed for people who want to learn how to implement machine learning and data science in their real-life monotonous problems. The course majorly focuses on the use of classification. It includes a practical problem that will be solved using classification and other approaches that can be implemented in machine learning.

Checkout the course here!

Support Vector Machine (SVM) in Python and R

If an individual wants to learn about what is SVM? How to use SVM in machine learning? Applications of SVM and more, this free course will answer many other questions. The course design includes the basics of SVM and an understanding of how to implement SVM in Python and R. 

Checkout the course here!

Evaluation Metrics for Machine Learning Models

Evaluation metrics form the core of various ML models. This course will perfectly guide you on how to use evaluation metrics in machine learning, the ways to enhance your models and several other concepts that would help you build interesting models. The course also elaborates on the types of evaluation metrics and evaluation using classification and other methods.

Checkout the course here!

Conclusion  Frequently Asked Questions

Related

Artificial Intelligence: Perception Vs. Reality

In this webinar, we discussed:

1) Where is the market in terms of real adoption of ML and AI?

2) The data issues executives need to solve – or at least understand – before considering ML. Advice for getting started?

3) Challenges: What are the unstated assumptions that cause projects to get tripped up?

4) How does miscommunication contribute to AI project stumbles?

5) Ethics and AI: How should executives be thinking about this?

Ya Xue, VP of Data Science, Infinia ML

James Kotecki, VP of Marketing and Communications, Infinia ML

Top Quotes:

Xue: Oh, I think actually companies are using it. There are some well known example like Tesla self-driving and Google, Google search, Apple’s Siri and all the other application, famous applications. Even just talk about those less famous examples like our company, Infinia, we’ve been in business for almost three years. We’ve done over, I would say, over 30 different projects, different application area and different companies. So we’ve seen a lot of actually companies are using it, using AI as a powerful tool to reduce their cost, to improve business efficiency, and then creating real business value.

Kotecki: But there’s also some nuance to this, in that what does it mean to do AI? And how do people define what it means to actually do it? There’s some truth to that 4% metric as well, whatever… It’s your different numbers but it’s always this very low, this paltry number of people that are actually doing AI and getting real business value from it. I think there’s probably a lot more people who are dabbling, who say they’re doing AI, who throw the label of AI on it, because as a marketer, I can say this from a marketing perspective, it’s better to say that you’re doing something sexy in AI than may be something else. And it’s just a term of the moment.

Kotecki: And also people that are doing a lot of things in AI and haven’t figured out how to operationalize it, how to productionize it, how to deploy it in a widespread way that’s actually getting day-to-day value. It’s not just creating a cool algorithm, which we can certainly do. It’s getting all the way to people in your organization actually getting use from it, which involves a lot of things involving AI, and then a lot of steps after that that involve a lot of change management that’s not as easy as writing an algorithm. So it’s a long process that we’re in the middle of now, a long transformation.

Top Quotes:

Xue: Oh, yes. There are many gaps, actually. So first one is the illusion about AI. So some people have the misunderstanding, like the AI is just a miracle. If you download some off-the-shelf software and you threw the data in, you can get the results you want. So the part that people don’t get is AI is pretty much like any other kind of research and development. It takes time and effort to happen. So that’s one thing, it’s set the right expectation, that’s very important. And another thing executives don’t understand is the consequence of AI success. Yes, it’s a success, and you can make a prediction at 99% accuracy. However, when you deploy it, it’s gonna change your work flow under your business process. Are you ready for it? That’s a bigger question. It has happened multiple times. We develop something, finally you could actually look at, when it’s time to put it into production, oops, we may not be able to do it.

Kotecki: I think there’s a related factor here, which is, I’ve gotten a sense from several past clients, and I think there’s just a sense in the zeitgeist out there that executives would not say this publicly, but a lot of the reason that they might wanna use AI or ML is to replace people, right? Reduce headcount. And they go into projects thinking that that’s what they’re gonna do. Oftentimes, we have seen, just in our narrow corner of the universe, the executives who think that are often then shown that they still need those people to do what Ya is talking about, to do the kind of reviews or they need to reassign those people to higher level projects.

Top Quotes:

Xue: I think the first executive have to recognize is there’s a potential risk because it happens. Bias introduced either in design or in the data, mostly from the data, I would say. Data like machine learning algorithm models need to be trained with large amount of data. Then if the data is under-representing or over-representing a certain group, say, it could be a gender group, it could be a racial group, then the machine learning algorithm will learn that, and they embed that information into the model then will produce biased results. So it’s a well-known problem and the machine learning community is working very hard trying to address this issue.

Kotecki: It’s certainly something to be concerned about. Even if you were completely unethical as an executive yourself, you should be concerned about the headline risks. And any time that you see in the headline of a major mainstream publication, the term bias and the term AI, it’s going to be problematic for whatever company is being highlighted in that article. Even though I think it’s also important to remember that when you hear the term bias, it actually doesn’t necessarily have a negative connotation in and of itself in a data science context. Because Ya was hinting at this, you want to maybe bias an algorithm in favor of candidates that are smart, for example, if you’re looking at a job screening application. So it’s not that we necessarily wanna not have any bias, but we wanna understand it and we wanna get rid of bias that is untoward.

Top 10 Tools To Subsidize Programming Languages In 2023

Programming languages have become an integral part of all business processes 

Development tools come in a variety of forms, including compilers, linkers, assemblers, debuggers, GUI designer, and performance analysis tools. Choosing the right tool is essential since it will be responsible for the overall productivity of the organisation and will be helpful in maintaining the project’s workflow. An aspiring tech professional should be well aware of the different software and

programming

tools that will carry forward the work of the entire organisation. It might seem overwhelming at first since there are so many programming languages, protocols, platforms, and software technologies that have emerged in the recent years. But there are definitely some tools and platforms that are more well-known and have more popularity in the industry than the rest. In this article, we have listed such top tools that can be used to subsidize

programming languages

in 2023.

RAD Studio

RAD Studio is a powerful IDE for building native apps on Windows, iOS, macOS, and Linux. It enables the users to design beautiful and immersive UIs with less coding effort. It provides a single code base for all platforms, can connect with over 20 databases natively with FireDAC’s high-speed direct access, and so many other features.

Embold

Embold is a software analytics platform that analyses source code and uncovers issues that impact stability, robustness, security, and maintainability. With the help of Embold plugins, the users can pick up code and vulnerabilities in the procedures even before making those mistakes. The tool also facilitates deeper and faster checks than standard code editors in over 10 different languages. Its unique anti-pattern detection prevents the compounding of unmaintainable codes. 

Cloud9 IDE

Cloud9 IDE is an online integrated software development environment. It is one of the best software design tools that support many

programming languages

like C, C++, PHP, Ruby, Perl, Python, and several others. The platform clones the entire development environment, with a built-in terminal for command-line wizards. 

Notepad++ 

Notepad++ is a replacement that supports different types of languages. It is written in C++ and uses Win32 and STL that ensure smaller program size and higher execution speed. The users can work on different documents simultaneously, credits to its multitasking feature. The editor makes it easy to inspect files at all stages of embedded software projects, from HEX to C++ source. 

IntelliJ IDEA

Written in Java, IntelliJ IDEA can integrate hundreds of features and tweaks that can make programming easier. Smart code completion for a variety of languages, support for the microservices framework, and built-in developer tools, such as version control and terminal can make this tool incredibly different from the rest. Ranging from frontend JavaScript applications to backend Java, IntelliJ IDEA has proved itself to be one of the most versatile IDEs out there.

Linx

AWS Cloud9

AWS Cloud9 allows the users to write, run, and debug source code with just a web browser. They do not need to install files or configure the development machine to start new projects. Instead, it is already packed with all the important tools required for some of the most popular

programming languages

GeneXus

GeneXus provides a unique platform that captures the needs of the users and generates applications for present and future technologies, with the need to learn a new one. The

program

allows pragmatic developers to evolve quickly and respond to market and technological changes in an agile way. GeneXus is an automatic software generation platform that supports the largest number of databases in the industry. 

DbSchema

DbSchema is a visual database designer and manager for any SQL, NoSQL, or cloud database. The tool enables the users to visually design and interacts with the database schema, design the schema in a team and deploy it on multiple databases, generate HTML5 diagram documentation, visually explore the data, build queries, and so much more.

Zend Studio

State Of Artificial Intelligence In India

In June 2023, India’s national think-tank, the NITI Aayog, released a discussion paper on the transformative potential of Artificial Intelligence (AI) in India. This paper said the country could add US$1 trillion to its economy through integrating AI. Since then, some of the biggest moves made by the government to act on this prediction is the formation of a task force on Artificial Intelligence for India’s Economic Transformation by the Commerce and Industry Department of the Government of India in 2023, and the Union Cabinet in December 2023. These bodies approved an INR3,660 crore national mission on cyber-physical system technologies that involves extensive use of AI, machine learning, deep learning, big data analytics, quantum computing, quantum communication, quantum encryption, data science and predictive analytics. But, what has been the progress in the nation since these ambitious missions were undertaken by the government? According to an analysis by research agency Itihaasa, the progress has been appreciable. When the agency used the number of ‘citable documents’, or the number of research publications in peer-reviewed journals, in the field of AI between 2013 and 2023 as a metric, India ranked third in terms of high quality research publications in Artificial Intelligence. However, when parsed by another metric (citations, or the number of times an article is referred), India ranked only fifth behind the UK, Canada, the US and China which suggests that India must shift its focus to improving the quality of its research output in AI. The report also revealed that the Indian Institutes of Technology and the Indian Institutes of Information Technology were among the primary research centres for AI. Currently, most of the traction in India is in the form of AI pilot projects from the government in agriculture and healthcare, and the emergence of AI startups in cities like Bangalore and Hyderabad. Though these are indications of grassroots level AI adoption, the pace of innovation isn’t comparable to the USA or China today. Some challenges that the progress of AI in India faces are limited availability of manpower and of good quality and clean data, as there is no institutional mechanism to maintain high quality data. A report published by PwC in 2023 revealed another imminent challenge-that even with all the potential benefits of AI, which are envisaged to aid humans, people still have concerns regarding data privacy and are apprehensive to share data for a better experience. A vast majority of participants agree that they have major concerns regarding data privacy to the point that it is near unanimous (93%) and that they are hesitant to even share medical results knowing that it could help provide some personalised knowledge about their health, so data protection still remains a hazy domain hindering the growth of AI.

In June 2023, India’s national think-tank, the NITI Aayog, released a discussion paper on the transformative potential of Artificial Intelligence (AI) in India. This paper said the country could add US$1 trillion to its economy through integrating AI. Since then, some of the biggest moves made by the government to act on this prediction is the formation of a task force on Artificial Intelligence for India’s Economic Transformation by the Commerce and Industry Department of the Government of India in 2023, and the Union Cabinet in December 2023. These bodies approved an INR3,660 crore national mission on cyber-physical system technologies that involves extensive use of AI, machine learning, deep learning, big data analytics, quantum computing, quantum communication, quantum encryption, data science and predictive analytics. But, what has been the progress in the nation since these ambitious missions were undertaken by the government? According to an analysis by research agency Itihaasa, the progress has been appreciable. When the agency used the number of ‘citable documents’, or the number of research publications in peer-reviewed journals, in the field of AI between 2013 and 2023 as a metric, India ranked third in terms of high quality research publications in Artificial Intelligence. However, when parsed by another metric (citations, or the number of times an article is referred), India ranked only fifth behind the UK, Canada, the US and China which suggests that India must shift its focus to improving the quality of its research output in AI. The report also revealed that the Indian Institutes of Technology and the Indian Institutes of Information Technology were among the primary research centres for AI. Currently, most of the traction in India is in the form of AI pilot projects from the government in agriculture and healthcare, and the emergence of AI startups in cities like Bangalore and Hyderabad. Though these are indications of grassroots level AI adoption, the pace of innovation isn’t comparable to the USA or China today. Some challenges that the progress of AI in India faces are limited availability of manpower and of good quality and clean data, as there is no institutional mechanism to maintain high quality data. A report published by PwC in 2023 revealed another imminent challenge-that even with all the potential benefits of AI, which are envisaged to aid humans, people still have concerns regarding data privacy and are apprehensive to share data for a better experience. A vast majority of participants agree that they have major concerns regarding data privacy to the point that it is near unanimous (93%) and that they are hesitant to even share medical results knowing that it could help provide some personalised knowledge about their health, so data protection still remains a hazy domain hindering the growth of AI. Another cultural challenge that India faces is the fact that the cost of failure is much higher here than in the West. While failing in an attempt at big innovation and grand goals might be seen as brave in Silicon Valley, failure often implies a loss of face in India and this has historically meant a lack of room for experimentation. All these challenges tell us that even with government funding and industry participation, India is just at the starting point of what seems to be a promising long road.

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