Artificial Intelligence (AI) plays a central role at many solutions being fielded today. It has almost become a word that is thrown at any software system now and is sometimes used interchangeably with machine learning, although those are not always (but could be) the same thing.
In our set of algorithms that look for patterns in chaotic systems, we use a type of A.I. that has a Machine Learning (ML) aspect to it (which we will describe that in detail elsewhere). In this section, we will understand what is actually meant by A.I. and M.L. in a general sense.
Artificial Intelligence in the most general sense has been called whatever is required to get a machine to exhibit intelligence.
That can be many things to many people. Forms of A.I are:
- Driving directions.
- Driving directions that avoid traffic.
- Shopping recommendations.
- Help booking a trip with hotels that people with families like.
- Help you choose a restaurant based on people who have made reservations at similar places to you.
In short, it is meant to be a quick go-to place to make your life easier.
A.I. Isn’t a self-aware device in your phone that is out to kill you – that is the stuff of movies. A.I. is reality is a wide array or spectrum of things including but not limited to:
- Machine Learning
- Machine Learning simply put is a branch of artificial intelligence that uses one or more algorithms to look at data, extract it from some noisy area (e.g., stock market data) and use that information to predict some type of future behavior. This is typically done with some type of statistical model to understand the patterns in that data.
At Basal Smart Solutions pulling signals out of noisy data is our core competency. We have a series of algorithms we use that can attack any dataset. And can use them individually or together to achieve the optimal result. Our primary toolset aimed at the social media world uses all of them in our One For All Social online tool.
- Deep Learning
- Deep learning is more of a subset of machine learning that typically uses algorithms that mimic or gather inspiration from the structure and function of the human brain. This is possibly the most complex version of a machine learning algorithm, but it also holds the most promise in the most complex datasets.
- Neural Networks
- These networks consist of node layers. Typically, an input layer, and one or more layers that aren’t seen, as well as an output layer. This type of network allows the classification and clustering/storage of data to be done very quickly. This type of network is used in many everyday applications, including the Google search algorithm.
- Predictive Systems
- One thing should be reiterated. Predictive algorithms have many variations. Not every predictive analytic will apply to every situation. However, if data is properly managed, and perhaps even normalized, it is possible to use some of the most powerful of these analytics on just about any noisy dataset. This data conversion may sound like magic, but it will be described in an upcoming post how it can be used to prepare a dataset for analysis.
Here at Basal Smart Solutions, and with our One For All Social online tool, we use solutions that sit across every single one of these areas and combine them. We do this to not only determine the patterns in the noisiest of data systems, but to also determine what other datasets are influencing that noisy dataset to trend in the direction it trends.
For every trend, there must be a cause. Identifying those causes permits the most accurate analytic to be achieved.
