The rise of personal digital devices, the Internet of Things and faster machine processing have all given rise to the proliferation of AI in the business world. As big data becomes easier to get, the organization of that data becomes much more important. It can be next to impossible to administrate data using traditional human resources; artificial intelligence has the ability to automate the more rote aspects of data administration.
Connected devices including home appliances, autonomous cars, watches, home security systems, lidars, high res images and even data from Instagram, Facebook and Twitter all contribute to the exponential growth in data. Forward thinking companies are taking advantages of deep neural networks that function from these large data sets. These companies do not need an unlimited supply of data – just enough to give the initial training to the system. This “deep learning” is the true strength of AI – the system will eventually train itself on your behalf.
Larger Scale Means Deeper Learning.
Deep learning may affect your organization in many ways. For best results, you should decide on the type of neural network algorithm that your application development process will employ.
- The recurrent neural network (RNN) – The RNN can be defined as an app that relies on a time factor. Some examples include speech recognition and machine translation.
- The standard neural network (SNN) – The SNN works on a more standard input/output process – you put in certain data that must come from a source, and you receive an answer based on a calculation of those factors. For instance, home price calculators based on walkability, school zone and zip code would use an SNN.
- The convolutional neural network (CNN) – Google employs a CNN in its image recognition technology. In general, a CNN allows you to match a specific unit in an array, no matter how big the data set may be.
- The custom neural network – If you combine the best aspects of the CNN and the SNN, you get a custom neural network. This type of network allows you to capture and analyze data from many sources at once. Autonomous cars, robotics and heavy industrial engineering products all utilize custom neural networks.
How To Use AI In Your Business.
Once you have decided on the type of neural network you will employ, you must understand how to apply it. Here are some of the most common ways that business owners are using AI in different marketplaces.
- Verifying import/export data – Properly vetting importers and exporters before allowing them to use a platform is very important. You may employ an SNN to verify that businesspeople have the appropriate scale of historical transaction value and total credit to do business on a platform. You may also be able to match importers and exporters to clients by scale with this information as well.
- Recommending products – Some companies use a buyer’s previous history of purchases to recommend new products to the customer. YouTube is perhaps the most famous of these systems, giving new suggestions for videos based on what a user has watched previously.
- Supply matching – Putting expected customer delivery dates, delivery methods, order quantity, average order quantity, contract terms and country of origin in as inputs allows an SNN to match suppliers to prospects most efficiently.
- Customer service – Consumers expect an intuitive, personalized interface for digital retail, and AI can give a company the ability to deliver this. A customized network can provide a unique experience for each customer with data that includes previous reviews, interactions, browsing history and orders to create a profile.
- Trade financing – Some companies wisely offer credit for purchases, creating another stream of income outside of simply selling products. If a customer needs to borrow money to complete a sale, then the business needs to know how much of a risk that customer may be (and possibly set an interest rate accordingly). Deep learning algorithms can be built from inputs that include country of origin, customer feedback, orders shipped, the trade amount and of course credit history.
The rather mundane processes above that would take a human hours to perform can quickly be done through AI. As you gain more data, the system gets better as well. Look into the many ways that AI can help your business if you want to stay on the cutting edge.
