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Do Businesses Need an API-driven Strategy?

An Application Programming Interface defines interactions between multiple software intermediaries. APIs define the types of calls or requests you can make.

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Do Businesses Need an API-driven Strategy

An Application Programming Interface defines interactions between multiple software intermediaries. APIs define the types of calls or requests you can make, how you make them, the data formats you use, and the conventions that you follow.

In recent years, businesses have begun to incorporate APIs into their business operations and develop APIs for third-party use. As APIs become essential tools for all companies, you will undoubtedly ask yourself whether you need an API-driven strategy for your business. So, here is an excellent overview of how enterprises are integrating and using APIs today.

1. Enabling APIs for Mobile Devices

With more people worldwide accessing content from mobile devices than home computers, businesses have realized that their websites and applications must be optimized for mobile devices. However, mobile apps are continually evolving.

So, your business’s mobile applications must be available for multiple operating systems and tools, such as Windows, iOS, and even next-generation devices like smart televisions.

That places high stress on the development and maintenance process and how data reaches applications and sent back. Securing your data channel is vital, and that is where APIs can help.

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Companies are increasingly designing APIs for consistent data experiences, and layering application builds on top. Developers may need to collaborate to ensure API lifecycles include all necessary elements, from the planning and design to the development and deployment. Find out more about API collaboration on RapidAPI’s site.

2. Accelerating Your Reach for Transactions and Content

For e-commerce sites, APIs make the transaction process much more comfortable with a more extensive set of interfaces than those used by an official website. APIs act as an accelerator for enabling purchase transactions and content distribution from a broad range of sources.

Companies like Walgreens use APIs for things like submitting photographs for printing and filling out prescriptions. With an API-driven strategy for specific elements of your company’s services, you can create more efficient operations and gain higher customer satisfaction.

3. Developing New Business Models

APIs can also be products in their own right. If your company decides to go down that route, you can enable and deliver a valuable service to partners and third-party developers for another excellent revenue stream.

If you are successful in this API-driven strategy, you can create a core infrastructure for users and develop healthy and loyal following partners and third-party developers. So, consider using APIs to power new business models.

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4. Driving the Innovation of Internal Operations

If businesses want to survive in the long run, they need to innovate continually. APIs enable you to create new internal customer-facing systems.

For providing packaged, accessible interfaces to all of the systems across your organization, APIs are essential because they help your business reduce friction in developing new cross-organizational systems.

Start integrating API-driven strategies into your business operations, and you are sure to see an increase in productivity and revenue over time.

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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

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Will AI render Software Developers Obsolete

Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

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