Artificial intelligence (AI) has gradually been making its way into business software and will continue to for the foreseeable future. These intelligent applications have incorporated machine and deep learning algorithms into their everyday functionality to better automate tasks for the user. Automating these processes saves the user time and energy, makes their job simpler, and allows employees to work more efficiently and productively. While there are some that believe AI is out to replace their jobs, they will be pleasantly surprised that, in most cases, this is a false assumption. Instead, the application of AI will simply make their jobs easier. For the developers interested in building their own intelligent applications, the AI platforms, machine learning algorithms, and deep learning libraries and frameworks used to create such functionality are found in the following subcategories:
Artificial intelligence (AI) is becoming a staple of all business software, whether users are aware of it or not. Often, AI and machine learning capabilities are embedded inside applications and provide users with functionality such as automation or predictive capabilities. These intelligent applications make the processes and tasks conducted by businesses and employees simpler and easier with the help of AI, but it is important to differentiate between tools that are AI-enabled, and those that help develop intelligent applications.
AI software is the latter. It provides developers with tools to build intelligent applications, whether that be adding machine learning or speech recognition to a solution, or creating an entirely new application from scratch with the help of an AI platform. These developer tools are often algorithms, libraries, or frameworks of code, or developer kits that can help users create machine and deep learning functionality for software. The use of AI in software will eventually become nothing more than a norm: a feature that is not considered revolutionary, but one that is considered necessary. The software world is striving to reach that norm with the use of AI developer tools.
Those who believe that the widespread use of AI in business will be the downfall for human employees are mistaken. Instead, AI software will help improve the employee experience and offer streamlined, automated ways for workers to complete manual, mundane tasks. It will help companies work smarter and make more intelligent decisions. AI software provides software engineers with the tools to building these solutions that will help benefit employees in all areas of business.
The reason someone would use AI software is to build an intelligent application from scratch or add machine or deep learning to a pre-existing software application. AI software allows users to implement general machine learning, or more specific deep learning capabilities, such as natural language processing, computer vision, and speech recognition. While this is the primary, and somewhat obvious, reason, there are many motivations behind this rationale, with the following being some of the most common themes:
Automation of mundane tasks – Business may implement machine learning to help automate tedious actions that employees are required to do in their day-to-day. By utilizing AI for these tasks, companies can free up time for employees to concentrate on more important, human-necessary aspects of their jobs. AI software does not offer a way to automate humans out of their jobs, but instead offers a supplemental tool to help improve their performance at work.
Predictive capabilities – Predictive functionality is similar to automation in the sense that it performs a task or provides an outcome that the solutions assumes is correct instead of a human needing to do it manually. This can be as simple as expense management solutions adding an expense to a report on its own. How would a software know to do this? Because it uses AI and machine learning to understand that the user puts the same charge on their report every month. So instead of the employee needing to add it every month, it predicts what will be on the report and automates it for them. This type of predictive capability can be added to applications with AI software.
Intelligent decision-making — While you might think that predictive solutions make intelligent decisions, this aspect of AI helps human beings make intelligent decisions instead of the software doing it for them. Machine learning can help take the guesswork out of making critical business decisions by providing analytical proof and predicted outcomes. This functionality not only helps take human error out of decision-making, but can help arm users with the information necessary to defending the decisions that they make.
Personalization — By using machine learning algorithms, software developers can create a high level of personalization, improving their software products for all users by offering unique experiences. Creating applications that recognize users and their interactions allows for powerful recommendation systems, similar to those used by Amazon to help personalize consumer shopping, or the film recommendation capabilities of Netflix.
Creating conversational interfaces – Given the popularity of consumer conversational AI offerings, such as Amazon’s Alexa, Apple’s Siri, and Google Home, the use of conversational interfaces is broaching into the B2B world. For software companies trying to innovate and keep up with these advancements, AI software is the place to start. Implementing speech recognition into a software can allow users to interact with the application in a streamlined, unique manner.
While many employees probably interact with intelligent applications, the users of AI software are primarily software engineers who use the tools to build those intelligent applications. It takes a high knowledge of machine learning and software development to fully utilize AI software. There is a significant gap in the need for machine learning developers and the number of qualified candidates. Many larger companies offer high wages to those who can build machine learning algorithms or have the knowledge to train deep learning models. Despite the demand and lucrative salaries, there is still a general shortage of those who can take advantage of AI software.
Similarly, there is a shortage in data scientists, another position that may use AI software. Data scientists are not necessarily developing intelligent applications, but instead using machine learning models to extract actionable insights from data. Most frequently, these employees would take advantage of predictive analytics or natural language processing, among other AI software features, to pull valuable business insights from company data. Many businesses will also pay a large salary for data scientists as their value becomes increasingly higher due to the amount of data companies are consuming.
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.
AI platforms – 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 – 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 – 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.
Machine 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.
AI has been consistently one of the biggest tech trends over the past half decade, and as the marketing saturation for AI continues, the buzzwords can become overwhelming. However, within AI software, G2 Crowd has determined a few trends within the trend: embedded AI and machine learning as a service (MLaaS).
Embedded AI — Machine and deep learning functionality is becoming embedded in nearly all types of software, whether the user is aware of it or not. The use of embedded AI inside software like CRM, marketing automation, and analytics solutions is allowing users to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Embedded AI will only pick up in the coming years in a similar fashion to the way cloud deployment and mobile capabilities have over the past decade or so. Eventually, it will be so commonplace that vendors will not need to highlight the fact that their product benefits from machine learning; it will just be assumed and expected.
Machine learning as a service — The software world has moved to a much more granular, microservices structure in recent years, particularly for development operations needs. Additionally, the boom of public cloud infrastructure services has allowed large companies such as Google, Amazon, and Microsoft to offer development and infrastructure services to other businesses with a pay-as-you-use model. AI software is no different, as those same companies are offering machine learning as a service (MLaaS) to other companies. Developers can easily take advantage of these prebuilt algorithms and solutions by feeding them their own data to gain insights from. It saves smaller businesses time, resources, and money by not needing to hire skilled machine learning developers, but instead use systems built by other enterprise companies. MLaaS will only grow further as businesses continue to rely on these microservices and the need for AI increases.
Many potential users assume that AI software is capable of everything right out of the box, but nearly always this is not the case. AI software requires a great deal of data to learn what you want it to learn. Users will often need to train machine learning algorithms, using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application. (A computer vision model cannot determine whether an image is a cat or a dog unless it has learned what a cat looks like and what a dog looks like.) There is also a shortage of people who understand how to build these algorithms and train them to perform the actions they need. The common user cannot simply fire up AI software and have it solve all their problems. Instead, it takes a great deal of software development and machine learning knowledge. However, as the need for these professionals increases, so will the number of qualified candidates and the capabilities of the applications they are building.
Deep learning algorithms differ from machine learning algorithms specifically because they use artificial neural networks