Open Source AI Platforms – Meaning, Uses And Top 5 Of The Best Open Source AI Platforms For You

Open Source AI Platforms – Meaning, Uses And Top 5 Of The Best Open Source AI Platforms For You

Given the recent global events, it is evident that artificial intelligence has rapidly progressed, resulting in the emergence of numerous chatbots and trends.

This development has significantly impacted the software and programming industries, including robotics. As AI continues to evolve, it has introduced innovative methods of integrating software and programming into daily operations. With Open source AI this can be made possible.

Open-source AI platforms are helping humanity move toward a futuristic world faster than most anticipated.

OpenAI, Microsoft, and Google have had an outrageous month in the artificial intelligence (AI) space, and this field keeps accelerating.

What Is Open-Source AI?

To explain this term, it’s necessary to comprehend some related concepts.

Open source is a phrase used in software development which allows any programmer to collaborate on software development.

The objective is to create robust and efficient programs in a shorter time frame. This approach enables the incorporation of innovative ideas from multiple programmers to fuel technological progress. It’s similar to a group project in which humanity benefits from the team’s collective effort.

Artificial Intelligence is a field of computer science that focuses on creating programs and algorithms. These algorithms are designed to solve problems or answer queries and help machines operate more human-like.

There are several subfields of this science, including:

  • Natural language processing (NLP), which focuses on developing natural interactions between humans and computers. Specialized software helps machines process human language, create understandable words, and interact with humans through language.
  • Machine learning (ML), which prioritizes a machine’s ability to analyze information and use it to make recommendations or decisions based on the data sets it has provided.
  • Computer vision is all about creating machines that can understand and interpret visual information.
  • Robotics that can physically perform tasks without human micromanagement, including interaction with humans.

Currently, companies use image-based verification methods to confirm our human identity, where we select images with a shared feature, like cars or volcanoes. However, machines cannot still extrapolate information from these images.

Open source AI refers to software developers working together on AI projects that are accessible to the public, aiming to enhance the integration of computing and humanity.

To connect open-source AI with marketing, we can add the keyword Industry 4.0. Industry 4.0 implies that advanced computing and AI have ushered in a new age of human efficiency, highlighting the importance of utilizing open-source AI in marketing strategies.

The first industrial revolution was about creating machines to work via steam or water power.

  • The second industrial revolution was when we converted machines to electric power and embraced mass production. Products were built by human assembly lines, assisted by electric conveyor belts that brought the work to their hands.
  • The third industrial revolution was when we plugged computers into the machinery to boost efficiency and automation. Car factories now have machines programmed to quickly and precisely build cars without human assembly lines.
  • And now the fourth industrial revolution — dubbed Industry 4.0 — is about how the industry is changing now that humanity and computing are so closely interconnected. Business doesn’t just happen in boardrooms and on factory floors anymore. We carry it in our pockets.

Nowadays, we have the convenience of buying and reinvesting stocks from the comfort of our homes with just a simple click. With the help of voice-activated computers, we can even order groceries, schedule appointments on our digital calendars, and enjoy some jokes without lifting a finger.

This new interconnected technological era has paved the way for open-source AI to play a pivotal role in marketing strategies.

How Can Marketers Use AI?

AI provides various features to marketers willing to explore, ranging from minor aids to managing entire campaigns on their behalf. We will present some examples of how AI can be utilized to give you an idea of its potential.

1. Automated Social Posts

One of the ways to utilize intelligent technology in marketing, even in small ways, is to utilize software that can schedule and post pre-loaded social media content for you.

You can set the frequency of the posts, ranging from multiple times a day to once a month or more, and then load up all of your prepared content. The software will automate the process for you according to your custom schedule.

2. Content Creation

The field of AI is currently experiencing growth in its application to content creation, which is a common challenge for marketers. They face the difficulty of generating ideas that will resonate with their target audience and producing content quickly to improve conversion rates.

By utilizing AI-generated content, companies can save time and money on hiring writers and instead focus on ensuring the quality of the content produced by their experienced writers.

3. Personalized Emails And Data Capture

Many of us have likely received follow-up emails that remind us of items left in our online shopping carts or inform us of price drops on items in our wish lists. It would be impossible for humans to write personalized emails for the billions of daily online transactions, but AI can do it.

By using algorithms to analyze user data and interactions with a brand, AI can create customized email content and schedule and send it without any human intervention.

For instance, by simply saying, “Send a thank you note to Savannah,” an algorithm can retrieve Savannah’s email address from your contact list, generate a thank you email, and insert Savannah’s name into it.

The program can either send it immediately or read it to you, allowing you to make changes before sending it.

4. Ad Targeting And Pay-Per-Click Campaigns

If you use Google or Facebook for advertising, tools like AdWords provide comprehensive information and precise details to help you measure the effectiveness of your advertising campaigns. These tools also enable you to bid for pay-per-click (PPC) advertising, which allows you to allocate your advertising budget effectively.

Through AI analysis, you can identify the audience interacting with your ads and then redirect your advertising budget towards groups that may not have been considered through traditional market research. This could result in discovering more leads and conversions than expected, which can be a pleasant surprise.

Ethical Considerations Before Using Open-Source AI

Nothing new comes easy. Even the highest level of technology development has important human elements that must be addressed sooner rather than later.

1. Biases

Algorithms and datasets are created by humans with their own biases, whether they know them or not.

These biases can impact the output of artificial intelligence programs, particularly if the output is designed to change based on human behaviour in virtual environments.

One way to address this issue is to develop programs that steer clear of stereotypes and misinformation, especially when searching for bias-charged words.

Reducing bias in programming is currently a major focus in the field of AI, and solutions and regulations are still being developed to address this problem.

2. Incorrect Or Incomplete Information 

The fact that something is found on the internet or detected by AI does not necessarily imply that it is accurate or truthful. Similarly, something being widely accepted or popular does not guarantee its correctness.

Furthermore, even if the information you have is true, it does not necessarily mean that you completely understand the situation, regardless of how extensively you search for the truth using a search engine.

Open Source AI Platforms And Tools 

We have created a collection of open-source AI tools to present you with top choices to consider as you explore this area.

You can use this list to determine whether adopting micro AI could enhance your ROI or if bigger open-source AI projects are more appropriate for achieving your company’s objectives.

1. TensorFlow

TensorFlow is a comprehensive framework that facilitates collaboration among programmers to develop innovative projects and leverage existing models created by other experts.

It is a powerful AI platform that provides training videos to assist you in getting started. success.

What we like: This platform supports several programming languages, including Swift, Python, and JavaScript — the most common programming language used on Earth.

Price: Free.

2. PyTorch

Like TensorFlow, PyTorch is a complete platform designed to help transform concepts into effective applications. It is a comprehensive framework created to support various aspects of open-source AI project development, providing vast libraries and datasets to utilize.

PyTorch is user-friendly for developers who are already familiar with coding in Python. The platform’s object-oriented approach assists in bundling up usable code chunks that perform a single task.

These reliable “objects” can be integrated into more complex sequences to accomplish more complicated tasks, promoting collaboration among programmers.

Pro tip: Programmers fluent in Python flourish here, but it also has a C++ interface for those who don’t code with Python.

Price: Free.

3. Keras

Keras is an API that is geared towards human use, enabling the sharing of the front end of deep learning models quickly and straightforwardly.

This interface allows for the easy exportation of models from Keras, which can then be utilized across various platforms such as browsers, iOS, and Android. The Python libraries offered by Keras tend to concentrate on artificial neural networks..

Best for: Programmers who prefer a more streamlined user interface while working with the newest versions of TensorFlow, simplifying interaction with the software as it’s being built.

Price: Free.

4. OpenAI

OpenAI has been making headlines due to its revolutionary impact on natural language processing (NLP) AI programs. One of its noteworthy models, Codex, enables the conversion of natural language into code in a specific programming language of your choosing.

Additionally, similar to other open-source AI projects, OpenAI’s models are available for user customisation. OpenAI is not only advancing the capabilities of AI assistants like Alexa and Siri but also taking the next step in Industry 4.0.

This AI can generate its own natural language responses by synthesizing information instead of merely directing users to a website and reading the answers aloud. This is truly remarkable progress, and you have the opportunity to collaborate with it!

Price: Free $18 credit to experiment for three months, then prices are token-based and depend on what you use as you go.

5. OpenCV

OpenCV is a popular open-source AI platform that specializes in computer vision. While TensorFlow can be considered a general AI undergraduate degree, OpenCV has a master’s in AI vision.

Its library was written in C, which makes it highly adaptable and can be used on various platforms, from PowerPC Macs to robotic dogs.

OpenCV boasts a new C++ interface, and developers have created wrappers for other languages, such as Java and Python, to facilitate cross-language development.

Best for: Developing AI specifically for computer vision applications.

Price: Free, including for commercial use.

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