Artificial Intelligence (AI), coupled with Machine Learning (ML) and Natural Language Processing (NLP), is changing the way people approach technology. Whether it is self-driving cars or automated stock trading, smart assistants, or manufacturing robots, every small or big innovation has some AI in it.
AI sifts through a vast amount of data and identifies patterns, making computers and systems trainable to perform pre-defined tasks.
The AI industry is slated to touch the US$ 126 billion mark by 2025. Many IT behemoths, such as IBM, Microsoft, Samsung, Google, and Infosys, invest heavily in AI technology. Together, the companies mentioned above account for the lion’s share in the number of AI patent applications filed by all companies globally.
Artificial Intelligence is the future, and you need AI to scale up your business.
However, before developing an Artificial Intelligence solution, you need to know the cost to develop an AI solution.
The Step-By-Step Guide to Developing Artificial Intelligence Solutions
Building an Artificial Intelligence Solution requires four things – knowledge about the user, an understanding of what you wish to achieve, a clear roadmap, and testing. All these steps come before deployment.
An AI solution does not always have to be complicated and costly. Take the case of Amazon Machine Learning. It allows Data Scientists and Web or Application Developers an uncomplicated platform to build, test, and launch ML models conveniently, at a fraction of the cost they need to do it manually.
It is easy to understand the importance of AI with a simple example. Suppose you spent 24 hours in front of your computer designing a model that delivered approximately 8.9 lac accurate predictions in one month. With AI, you can get the same output at a fraction of the time and money you spent.
The following is a step-by-step guide to designing an Artificial Intelligence Solution:
1. Define the Problem
As with anything new, the first step to designing an AI solution is getting an answer to the question, “What do I wish to achieve?”
You need first to identify the pain-point of the user and the value your solution will provide to the user. At this point, it is good to note that the answer may or may not contain Artificial Intelligence. AI is there to simplify the task or reduce its cost, and the answer will determine AI’s role, purpose, and scope.
Your client seldom cares whether you are using AI or not. Hence, your product or solution must give value for money, with or without AI. Gmail, for instance, has been using AI since their ‘beta’ days. It has a robust spam filtration mechanism, which is perhaps the reason behind its glorious success.
Hence, before designing an AI solution, you need to know the purpose of the product or solution, which will define the role of AI in your project.
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2. Organize the Data
The next step is to collect and segregate the data in small, meaningful chunks. When you do it by yourself, you need lots of resources to execute the task. In contrast, when you integrate AI, the task can be completed much faster.
Data is of two types – structured and unstructured.
Structured data is easier to process and analyze. For example, if you are processing customer records, structured data will follow a sequence, like a name, address, date of birth, sex, marital status, etc.
The unstructured data is random data with no uniformity. Besides text, unstructured data may also contain pictures, words, audio, infographics, etc. Artificial Intelligence sifts through vast unstructured data and finds a pattern, converting it to structured data.
Data scientists usually spend a major part of their time cleaning, check, move, and organize data. AI can simplify the task by automating the process.
3. Choose an Appropriate Algorithm Type
Before building an Artificial Intelligence Solution, you need to choose an algorithm or technique to train your AI model. Algorithms are of three types – Supervised Learning, Un-supervised Learning, and Reinforcement Learning.
In Supervised Learning, the developer trains the computer through examples or inputs. When the computer receives the same input, it automatically produces the output depending on the input it received during the training.
In Unsupervised Learning, you create a model that identifies common patterns and changes the output. You can create a group with a similar pattern and train the model to understand the input and send it to the appropriate group.
In Reinforcement Learning, you train the model through a game-like simulation. The Reinforcement Learning model is considered more stable than the rest.
4. Train and Direct the Algorithm
Now that you have selected the algorithm, you have to input the data needed to train the AI model. You must ensure that the model is accurate and fits your selection framework.
Keep room for retraining the model, as the moment you test or deploy it, it will point out the areas that need rework.
A functional model is predictable. In case your model’s predictability is less, you need to restructure the model and, if required, follow the steps mentioned above all over again.
5. Choose the AI-Language
The language you select for AI depends on many factors. You can choose from a wide range of languages, such as C++, Python, Java, R, and others.
When it comes to convenience and interoperability, R and Python are considered the best languages. Both Python and R are simple to understand, and their machine learning libraries are perfectly suited for coding complex algorithms.
Python contains a powerful library known as the Natural Language Tool Kit or NTLK. It reduces the stress of manual programming, as you can get most of the coding automatically within the library.
6. Select the Right Tools and Framework
Whether you wish to create a live video app with AI or only mine data, you need the right tools and framework to execute the task flawlessly.
Some popular tools and framework for designing Artificial Intelligence solutions are listed below:
- Scikit Learn – It is an MI library that facilitates unsupervised calculations and provides more functionality than NumPy, Python, or SciPy.
- Accord.net – Its pre-defined libraries are suitable for large-scale image and audio processing.
- Sonnet – It is a high-end AI framework that can deal with complicated structures.
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Conclusion
Besides Scikit Learn, Accord.net, and Sonnet, several other tools and frameworks like Amazon Machine Learning, Tensorflow, Theano, MXNet, and CNTK can make designing AI solutions simpler than you think. Test the solution before deployment and keep room for restructuring, depending on user feedback.