The reason you want to build an AI system is to solve a problem. You first need to know what you want to solve and the desired outcome of building this AI system. While you are figuring out what problem you want to solve, remember that AI is a platform or tool through which you can solve a problem. AI is not the solution itself. Hence the time that you spend to identify the problem is most crucial to the entire effort. Ensure that you get enough inputs from potential users. This gives you diverse points of view which in turn allows you to leverage on market opportunities that you might have otherwise missed out.
You need data to build an AI system. Data is essentially information that the system will process, understand, and learn to help you solve the problem. Data is of two kinds, structured data and unstructured data.
Structured data adheres to a rigid format of information so that there is consistency when processing the information and analytics. Unstructured data on the other hand is everything else that is not structured data. Information that is recorded is not uniform in pattern. The beauty of AI lies in the fact that computers can analyze unstructured data and access a large amount of information.
Most often, we think that the key to building a great AI system is its algorithms. In truth, the key to great AI is in clean data. It shouldn’t come as a surprise that 80% of a data scientist’s time is spent in cleaning and organizing information before writing an algorithm.
Usually the bigger the firm, the more the data. The more data, the more likely it is that the data is stored in silos. Hence this kind of data is not AI-ready and might not useful unless it cleaned and sorted. Using such data may result in duplication and contradictory results. Hence it is necessary that the data is organized and tagged wherever necessary before feeding the information to the computer.
An algorithm is a step-by-step process or method of solving a problem. This is essential what teaches or trains the AI. There are three general types of AI learning, supervised learning, unsupervised learning and reinforcement learning.
Supervised learning algorithm uses data that is well labeled. Labeled data is essentially data that is already tagged with the correct answer. Think of it as learning that happens under supervision of a teacher.
Unsupervised learning on the other hand refers to the machine learning technique where no supervision of the process is required. It requires that you allow the model/algorithms to work on its own to discover information. There are no labelled data to assist the model. This kind of learnings allows you to run much more complex processes yet can be unpredictable.
There’s a third kind of training – reinforcement learning. It is a type of machine learning technique that trains algorithms using a system of ‘reward’ and ‘punishment’. In reinforcement learning, there is no pre-defined correct answer to learn from. Instead, the algorithm decides what to do to perform a given task in the best way possible. It receives rewards by performing the task correctly and penalties for performing it incorrectly.
Once you have selected the algorithms, you need to train the model by feeding it data. At this point it is important to note that for successful training of the algorithm/model lies in its accuracy. Here’s the thing – there are no set rules or commonly accepted thresholds on accuracy, but you need to establish a minimum threshold within your selection framework.
There are four things you need to train the model:
Now, the training data must contain the correct answers (referred to as target). The algorithm then finds patterns in the training data and maps the input data attributes to the target. The model captures these patterns that the algorithm has found. For example, let’s say you want your AI system to identify spam emails. You first need to provide the algorithm with training data that contains information on emails which you already know are spam. The algorithm using information to output a model that predicts whether a new email is spam or not.
If the predictability of the model is reduced, you need to revisit the all the steps taken to building the model.
It's no secret that there are plenty of programing languages from C++ to Python to R. These programming languages offer a vast and powerful set of tools and extensive machine learning libraries to users. Here are two commonly used programming languages for building AI systems.
It tops the list because of its simplicity. The syntaxes are simple and easy to learn. It takes a short development time. One of the most useful library kits is the NLTK – the Natural Language Tool Kit. Numpy is a library that helps solve scientific computations. scikit-learn is another library that is commonly used for machine learning.
It is one of the most effective languages used for analyzing and manipulating data. Apart from being a general-purpose language, it has plenty of packages such as RODBC and Gmodels that make it easy to implement the machine learning algorithms.
Though there are many services that platforms that offer various services in silos, it is best to go for one that provides all the services in one place. Plug and play platforms also known as MLaS or Machine Learning as a Service is one of the most useful infrastructures that have paved the way for broader reach of ML. These platforms are cloud-based and come with a ton of advanced analytics which can be easily incorporated alongside multiple AL algorithms and programming languages. Another key to the success of MLaS is their ability to allow swift deployment. Some of the popular platforms include
With that, you are more familiar with what goes on behind screens and how an AI system is built. If you are curious to learn about what happens backstage at Brainalyzed, leave us a comment.