Millions of developers are using Artificial Intelligence (AI) or Machine Learning (ML) in their projects, says Evans Data Corp.
Evans’ latest Global Development and Demographics Study, released in January 2018, says that 29% of developers worldwide, or 6,452,000 in all, are currently using some form of AI or ML. What’s more, says the study, an additional 5.8 million expect to use AI or ML within the next six months.
ML is actually a subset of AI. To quote expertsystem.com,
In practice, artificial intelligence – also simply defined as AI – has come to represent the broad category of methodologies that teach a computer to perform tasks as an “intelligent” person would. This includes, among others, neural networks or the “networks of hardware and software that approximate the web of neurons in the human brain” (Wired); machine learning, which is a technique for teaching machines to learn; and deep learning, which helps machines learn to go deeper into data to recognize patterns, etc. Within AI, machine learning includes algorithms that are developed to tell a computer how to respond to something by example.
The same site defines ML as,
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
A related and popular AI-derived technology, by the way, is Deep Learning (DL), which uses simulated neural networks to attempt to mimic the way a human brain learns and reacts. To quote from Rahul Sharma on Techgenix,
Deep learning is a subset of machine learning. The core of deep learning is associated with neural networks, which are programmatic simulations of the kind of decision making that takes place inside the human brain. However, unlike the human brain, where any neuron can establish a connection with some other proximate neuron, neural networks have discrete connections, layers, and data propagation directions.
Just like machine learning, deep learning is also dependent on the availability of massive volumes of data for the technology to “train” itself. For instance, a deep learning system meant to identify objects from images will need to run millions of test cases to be able to build the “intelligence” that lets it fuse together several kinds of analysis together, to actually identify the object from an image.
Why So Many Developers? Why Now?
You can find AI, ML and DL everywhere, it seems. There are highly visible projects, like self-driving cars, or the speech recognition software inside Amazon’s Alexa smart speakers. That’s merely the tip of the iceberg. These technologies are embedded into the Internet of Things, into smart analytics and predictive analytics, into systems management, into security scanners, into Facebook, into medical devices.
A modern but highly visible application of AI/ML are chatbots – software that can communicate with humans via textual interfaces. Some companies use chatbots on their websites or on social media channels (like Twitter) to talk to customers and provide basic customer services. Others use the tech within a company, such as in human-resources applications that let employees make requests (like scheduling vacation) by simply texting the HR chatbot.
AI is also paying off in financial technology, where it can help service providers (like banks or payment-card transaction clearinghouses) more accurately review transactions to see if they are fraudulent, and improve overall efficiency. According to John Rampton, writing for the Huffington Post, AI investment by financial tech companies was more than $23 billion in 2016. The benefits of AI, he writes, include:
- Increasing Security
- Reducing Processing Times
- Reducing Duplicate Expenses and Human Error
- Increasing Levels of Automation
- Empowering Smaller Companies
Rampton also explains that AI can offer game-changing insights:
One of the most valuable benefits AI brings to organizations of all kinds is data. The future of Fintech is largely reliant on gathering data and staying ahead of the competition, and AI can make that happen. With AI, you can process a huge volume of data which will, in turn, offer you some game-changing insights. These insights can be used to create reports that not only increase productivity and revenue, but also help with complex decision-making processes.
What’s happening in fintech with AI is nothing short of revolutionary – and that’s true of other industries as well. Instead of asking why so many developers, 29%, are focusing on AI, we should ask, “Why so few?”