Artificial intelligence software is a very general space, with a number of different subcategories, including AI platforms, chatbots, deep learning, and machine learning. Deep learning becomes even more granular with further subcategories, such as NLP, speech recognition, and computer vision (image recognition). Each of these subcategories offers users a very different functionality that are all potentially valuable to businesses moving forward.
For developers trying to build their own intelligent applications on top of another platform, AI platforms are the ideal solution. Like a standard application platform, these tools often provide drag-and-drop functionality with prebuilt algorithms and code frameworks to assist in building the application from scratch. The difference between AI platforms and cloud platforms as a service (PaaS) products is the former provides the ability to add in machine and deep learning libraries and frameworks when constructing the application. AI platforms ultimately give applications an intelligent edge; they are a mix of open-source and proprietary products, meaning they make possible the creation of an intelligent application with little overhead. However, for those without sufficient development knowledge, these platforms may prove to be challenging, even with the inclusion of drag-and-drop functionality for beginners.
Chatbots are one of the more refined areas of AI software and have very specific purposes in the business world: customer experience and automation. These solutions utilize NLP to interact with customers via text and voice conversations. Chatbots are often used as the first line of defense for call center or live chat customer service agents. By using a chatbot to determine the severity of a request or the reason for the interaction, businesses can better direct customers or prospects. These tools can interpret the general theme of requests and ensure that the correct person responds to the inquiry. Additionally, chatbots can be used as virtual assistants or customer support tools, like the new Facebook chatbots feature. The more chatbots interact and speak with users, the more they can learn and adapt their vocabulary and their general intelligence. This is all possible because of the machine and deep learning functionality within the software.
Deep learning algorithms differ from machine learning algorithms specifically because they use artificial neural networks to make their predictions and decisions, and do not necessarily require human training. With artificial neural networks, elaborate algorithms can make decisions in a similar way as the human brain. However, the decisions are made on a smaller scale because replicating the amount of neural connections in the human brain is currently impossible. Deep learning can be broken down into the subcategories of image recognition (computer vision), natural language processing (NLP), and voice recognition. Image recognition algorithms allow applications to learn specific images pixel by pixel; the most common usage of an image recognition algorithm may be Facebook’s ability to recognize the faces of your friends when tagging them in a photo. NLP has the ability to consume human language in its natural form, which allows a machine to easily understand simple commands and speech by the user. NLP is widely used in applications like iPhone’s Siri or Microsoft’s Cortana in Windows products. Each of these subcategories utilize artificial neural networks and rely on the networks’ deep layers of neural connections for an increased level of learning.
The machine learning algorithm category consists of a broad range of libraries and frameworks that can perform a variety of machine learning tasks when correctly implemented. When embedded into software, these predominantly open-source algorithms allow applications to make decisions and predictions based entirely on data. These algorithms learn, often using supervised or reinforcement learning, based on the data sets presented to them for consumption. These styles of machine learning do require some element of human training. There are a number of different machine learning algorithm types, including association rule learning, Bayesian networks, and clustering and decision tree learning, among many others. The ability to connect machine learning algorithms to data sources to use them when building intelligent applications requires a high level of development skill and technical knowledge.