ChatGPT AI and beyond: exploring the best AI APIs

Aditi Nair

• 7 min read
ChatGPT AI and beyond: exploring the best AI APIs

Table of Contents:

“Hey Siri, what is today’s weather like?”

You ask, and your iPhone responds within a millisecond.

Sophisticated AI models are the brains behind that suave female voice. They get trained to generate responses to a vast number (and variety) of prompts.

AI is everywhere.

Just last week, we saw Sanctuary AI's humanoid robot. Read more here.

They are driving cars, researching gene sequences, writing novels, creating photorealistic images, and even answering absurd questions.

Last year, OpenAI, a San Francisco-based startup, after years of research, unveiled its AI chatbot, ChatGPT, for public use.

The AI brain behind this chatbot was GPT3.

It was stupidly powerful, generating results for the most complex inputs with remarkable ease.

Within months, similar AI tools flooded the internet.

They each have specific superpowers and idiosyncrasies.

Today, several AI capabilities come bundled with popular products that we use every day.

Digital voice assistants and customer care chatbots are the most common ones.

But AI has penetrated our lives more than we think.

From predictive searches and smart email replies to streaming platform suggestions and social media feeds - AI models are working day and night to make our lives easier.

But how do these AI tools work?

More importantly, how does an AI tool leverage algorithms developed by someone else?

The answer - APIs.

What are APIs?

API is an acronym for Application Programming Interface.

The application can be any piece of software. The interface is a contracted list of accepted behavior between two apps.

Thus, an API, in its simplest, is a software middleman through which two applications can talk to each other.

Developers take advantage of these APIs to integrate functionalities of external applications in their software.

The best analogy for an API, summarized in a tweet:

For example, the weather app on your phone talks to the data stored in the weather bureau’s software system.

It does so via APIs to show you real-time weather information for your location.

Similarly, an AI API is the software intermediary that allows software developers to build AI and machine-learning capabilities into their software, without building highly-complex AI models themselves.

Why should I use an API?

With the advent of powerful computing, operations involving large sets of data have become much easier.

Today’s scalable architecture relies on its distributed nature to avoid a single point of failure.

This means that your application may run in one data center, access data from another data center, and display results to users in another continent.

APIs make this interconnection happen seamlessly.

Apps talk to each other and exchange data to create a cohesive ecosystem.

APIs also prioritize information security. This serves a broader purpose beyond accessing data for data-driven decision-making.

For instance, companies such as Uber have added Google Maps to their applications without developing a web mapping platform of their own.

They have done this simply by utilizing the Google Maps API.

Okay, but how do I use one?

Using AI APIs is similar to any other API.

Most organizations like OpenAI provide detailed documentation on how to integrate their AI capabilities into other software or apps.

Using those instructions, developers can build AI functions in an existing application. Or they can develop a new app running on the AI model that the API got built on.

The complexity of APIs, including AI APIs like OpenAI API or Whisper API, make it difficult for non-technology professionals to deploy these interfaces.

What are the benefits of using AI APIs?

AI APIs inherit the same benefits as their non-AI partners, including:

  • Efficiency: AI APIs come with pre-trained AI models. Developers save time and resources by leveraging these APIs since they no longer need to build complex AI and ML models of their own.
  • Scalability: APIs are infinitely scalable. It means they can increase or decrease their capabilities with the number of requests they handle. By integrating APIs with business processes, you create a highly scalable workflow.
  • Accessibility: AI APIs make building AI functionalities into third-party applications easier. As a developer, you do not need to take a deep dive into the world of artificial intelligence or machine learning algorithms. Instead, knowing how to integrate AI APIs should be sufficient to build intelligent functions for your application.

Where are AI APIs used?

There are numerous examples of AI APIs utilized across various industries. Here are a few examples:

Companies like Amazon, Apple, and Google use AI APIs for their natural language processing tasks. Voice assistants like Alexa and Siri rely on these APIs heavily.

Image recognition software like Google Lens uses sophisticated AI APIs.

Another use of this at work is facial recognition technology used in most smartphones today like FaceID and Face Unlock.

Streaming platforms and social media sites use AI APIs for their recommendations.

Speech recognition software is being used across a lot of chatbots and smartphones.

Predictive analysis APIs, that use artificial intelligence, drive the advertisements you see on popular websites and applications.

Here’s the list of most-used AI APIs in the world today:

Google Cloud AI Platform

When it comes to AI APIs, Google is still the undisputed market leader.

Google’s Cloud-based AI Platform has a range of APIs suited for different tasks - from image, video, and text analysis to natural language processing (NLP).

With Google now releasing Bard, a generative AI model, the APIs are sure to get a functionality boost.

Google Cloud AI Platform can get leveraged by the developers to build applications that benefit from its hyper-scalability and power.

OpenAI API

OpenAI has exploded into the AI scene since last year.

It currently offers the fastest adopted open-source AI model, GPT4.

Duolingo, Khan Academy, Stripe, and many more have already utilized OpenAI’s API and harnessed the potential of GPT4.

The range of APIs offered is highly flexible, scalable, and efficient.

Amazon Rekognition

Amazon currently leads the cloud service offering worldwide with AWS.

Rekognition is their AI API offering that builds on the immense power of AWS to provide easy-to-use interfaces with AWS’ plethora of AI and ML tools.

Capabilities offered include image and video analysis, object and scene detection, content moderation, text recognition, and custom labeling.

IBM Watson

IBM, one of the early adopters of AI and machine learning, has a suite of AI services under the Watson moniker for different tasks.

With Watson APIs, developers can take advantage of Watson’s AI models and capabilities.

From natural language processing and image/video analysis to machine learning and data labeling - to build sophisticated applications.

Microsoft Azure Cognitive Services

Microsoft Azure Cognitive Services is a collection of pre-built AI services.

Developers can use them to add intelligent features to their applications.

It includes APIs for tasks such as image and video analysis, text analysis, and natural language processing. It provides fast and accurate results.

TensorFlow

Another open-source entrant in this list, TensorFlow is a machine learning framework developed by Google.

You can use its AI APIs to train your AI model or use any of its pre-existing, pre-trained AI models for your custom use case.

Be it ML-heavy tasks like data analysis and labeling, NLP, or even text, video, and image analysis - TensorFlow APIs let you deploy apps in the cloud, on local servers, or even on mobile devices.

TensorFlow is already getting used by many organizations to automate and optimize workflows. These include GE Healthcare, Twitter, and Carousell.

Conclusion

Global funding for AI has reached 69 billion USD by 2022.

This means two things.

First, expect to see more AI tools break into existence.

Second, more businesses will integrate AI into their core processes.

For both use cases, AI APIs will arguably play the most pivotal role.

These APIs help developers leverage the full potential of an AI model into their programs.

More research in AI is only going to introduce more AI models - and more AI APIs.

It is crucial to carefully consider your specific requirements when selecting an AI API.

This includes factors such as the type of AI functionality needed, the complexity of your application, and your budget.

Additionally, it is essential to choose a user-friendly API that produces accurate results.

A robust and helpful community must support it.

Want ChatGPT or Bard to power your customer service chatbot?

With AI APIs, the AI world is at your fingertips.