You’ve probably heard that Artificial Intelligence (AI) is the next big thing for finance, healthcare, and a number of other industries. This HiTech technology will help shape the future for one reason: when it comes to developing and implementing great features for apps, few things are as powerful as AI.
Table of Content
- How to Get Started with AI for Your App
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AI has the power to automate repetitive tasks, saving time and money, but most importantly, it can help improve an app’s User Experience in a number of ways. In this post we explain how to get started with AI from a business perspective, the basics that you need to know, and how to implement it into your app.
How to Get Started with AI for Your App
Like with any other subject, the first thing you should do when starting with Artificial Intelligence is to establish a clear conceptual framework and a list of important references. This is a constantly evolving field that moves pretty fast. What goes on today might be very different from what happened two years ago, and although this sounds demotivating, it is actually the opposite. With each new step, new opportunities arise, and this is very good but also very challenging for your app if it wants to stay relevant.
For businesses, it is very important to understand where the field is moving and where it will most likely move in the near future. Also, it is important to have strong resilient teams that can easily adapt to new environments. Lastly, having a clear goal of how you will implement AI is essential.
Stay Informed and up to Date
AI’s dynamism makes it a fascinating field of study, but one that nonetheless requires being up to date with the latest advances. When it comes to app development, this becomes particularly true. It is best to implement the latest AI technology in your app. That way, your app’s User Experience will stand out, engaging users and leaving competitors behind. Using inadequate and outdated technologies can bring long term undesired effects like burdensome legacy systems.
Take the example of Natural Language Processing (NLP), a promising field of AI that, in short, tries to make sense of human language. In the past couple of years, advances like transformers and GPT-3 have changed the rules of the game in unexpected ways. These tools are just being explored, but their potential applications look promising, so much that they can make some well-established technologies outdated.
Build Resilient Teams
We can safely expect disruptive inventions to become the norm instead of rarities. As a result, it is important for your organization to be able to easily iterate towards new technologies each time a new breakthrough occurs. This can only be done with the right people.
Having a team of technologists is a great way to get involved with AI. Whether you decide to do this through a third party or in-house personnel, your AI team should be able to address your company’s specific needs. Prepare your team to adapt to new technologies when the time comes, and most importantly, make it easy for them to implement new processes. Sometimes the greatest threat to implement new technologies comes from within companies, not from outside.
Have a Clear Goal
Keep in mind the nature of your company when implementing new technologies. It is common for companies wanting to implement AI into their processes to see themselves as an AI company. This is a common mistake you probably want to avoid.
In practice, an AI company is strictly involved in activities like research and development associated with AI. Most other companies will only be casual adopters of AI; your company will most likely fit in this category. Just like having a website does not make you an internet company, having AI implemented into your app does not make you an AI company.
The Fundamentals of Artificial Intelligence
To stay up to date successfully with this fast-moving field of computer science, it is probably best to start by understanding the theoretical principles and fundamentals behind AI. To do that, we strongly recommend you check out this course by Andrew Ng, one of the world’s leading experts in terms of AI, and founder of deeplearning.ai, one of the most important organizations in terms of spreading Artificial Intelligence literacy.
What Is AI?
There are many misconceptions about what AI is or isn’t. For a start, it is important to understand that there are two general types of AI:
- Artificial Narrow Intelligence: This is often referred to as a one-trick pony because it is designed to perform specific tasks. This is what most AI applications look like.
- Artificial General Intelligence: This is what we usually think of when we think about AI. In this form, the technology can do anything a human can. There is still a long road ahead before we get there.
If you want to implement AI into your business processes, make sure to train your team properly. Also, align your strategy with what you plan to do, both in terms of business and technology. Nonetheless, keep an open mind and give yourself some space to experiment.
AI and Data
Artificial Intelligence, in all of its forms, uses important amounts of data. That’s why one of the most important things you need to do is implement a secure data pipeline and DevOps best practices. By doing so, you will be able to collect, store, manage and analyze data so that it can later be used as an input for your AI systems; a great AI idea without the correct data will work poorly. Managing user data can be tricky, so make sure to always comply with regulations, and most importantly, make your users feel that they can trust you. Respecting their privacy and giving them the proper privacy guarantees is a must.
What Is Required to Build an AI System for Your App
Nowadays, it is possible for all sorts of companies to build world-class digital products. Thanks to cloud computing technologies, any business can access state of the art resources for their apps. This makes it possible to build digital products in a fast way, update them easily, and scale when needed.
These are the most important things you should consider in order to build a powerful AI software system for your company.
- Development Team: More important than specific expertise for your team is having the will to learn. Since this field is constantly evolving, being able to learn and unlearn becomes an essential skill for your developers.
- Technological Tools: Tools like TensorFlow and PyTorch can help you build powerful AI projects based on Machine Learning and Deep Learning. Some tools are open source and have great documentation.
- AI Strategy: Don’t just follow a trend. Set clearly defined goals and address specific issues that can be easily upgraded in time. Take the time to understand how your company can benefit from AI.
- Data Pipeline: Without proper data, your AI will lack power. Make sure to review every aspect of your data, from how it is collected to what it will be used for.
How to Create an AI from Scratch
If you are literally just starting from scratch to build an AI feature for your app, make sure to perform some good old brainstorm sessions. They are a great way to organize your ideas and see what opportunities may lie ahead. Rather than doing a leap of faith into the unknown, involve your team members in ideation workshops that can help sketch out a clear idea of what the company needs.
Onboarding all teams, sharing ideas, concerns, and potential opportunities is a great way to start. Socialize what needs to be achieved and make collaboration possible across teams. Don’t approach this as a siloed project, but rather as something that is strategic for the company.
Most companies do not have the local resources to develop AI tools. There’s no need to worry if this is your case. Luckily, companies like Koombea can help you develop the right HiTech tools and configure your IT infrastructure.
How to Make an AI Robot
There’s recently been a lot of buzz regarding robots and how they can help companies increase their productivity. The main difficulty in doing so is acquiring the robots. They represent an important expense that, even with increased productivity, is hard for many companies to afford.
A great solution to this is hiring them under a Robots as a Service (RaaS) scheme. However, keep in mind that, even under this solution, you will need a development team that can help you implement the robots into your processes.