Artificial Intelligence Software

What is Artificial Intelligence Software?

Artificial intelligence (AI) is becoming increasingly common in business software, and is often embedded within applications to provide users with functionality such as automation or predictive capabilities. However, it’s important to differentiate between AI-enabled tools and AI software, which provides developers with tools to build intelligent applications.

AI software includes algorithms, libraries, and frameworks of code, or developer kits, which can help users add machine and deep learning functionality to software, or even create entirely new applications from scratch. While AI may become a standard feature in software, AI developer tools will continue to play a crucial role in building intelligent applications.

Despite concerns that AI will replace human employees, it is more likely to improve the employee experience by streamlining manual tasks and enabling more intelligent decision-making. AI software empowers software engineers to build these solutions and benefit employees across all areas of business.

Why Use Artificial Intelligence Software?

AI software is becoming more prevalent in the business world, as it offers a range of benefits for creating and improving software applications. The primary reason for using AI software is to build intelligent applications from scratch or add machine or deep learning capabilities to pre-existing software. Here are some common reasons why businesses use AI software:

Automation of mundane tasks – AI software can help automate repetitive and tedious tasks, allowing employees to focus on more important aspects of their job. It does not replace human workers, but rather supplements their work and makes their job easier.

Predictive capabilities – AI software can enable predictive functionality, automating tasks based on patterns and data analysis, improving efficiency and reducing errors.

Intelligent decision-making – Machine learning algorithms can help humans make more informed and intelligent decisions, providing analytical proof and predicted outcomes.

Personalization – By using machine learning algorithms, software developers can create personalized experiences for users, improving software products for all users.

Creating conversational interfaces – AI software can be used to implement speech recognition and conversational interfaces, allowing users to interact with the application in a streamlined and unique manner.

Who Uses Artificial Intelligence Software?

The primary users of AI software are software engineers who utilize the tools to create intelligent applications. However, employees who use those intelligent applications, like predictive analytics or natural language processing, to extract actionable insights from data may also use AI software. Data scientists are responsible for utilizing machine learning models to gain insights from data. While data scientists do not necessarily develop intelligent applications, their work is essential for companies to draw insights from the data they collect. The demand for machine learning developers and data scientists is high, but the number of qualified candidates is low. Companies are willing to offer high wages for those who have the knowledge and skills to work with AI software, as the value of AI continues to increase.

What are the different Kinds of Artificial Intelligence Software?

Artificial intelligence software is a vast field with several subcategories that offer various functionalities, all potentially valuable to businesses. These subcategories include AI platforms, chatbots, deep learning, and machine learning, with deep learning further divided into NLP, speech recognition, and computer vision.

AI Platforms:

For developers who want to build intelligent applications on top of another platform, AI platforms are the ideal solution. These tools provide drag-and-drop functionality with prebuilt algorithms and code frameworks, making it easier to build an application from scratch. AI platforms enable developers to add machine and deep learning libraries and frameworks when constructing the application, giving it an intelligent edge. AI platforms are a mix of open-source and proprietary products, making it possible to create an intelligent application with minimal overhead. However, for those without sufficient development knowledge, these platforms may be challenging to use, even with drag-and-drop functionality.

Chatbots:

Chatbots have specific purposes in the business world: customer experience and automation. These solutions use 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. Chatbots 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. The more chatbots interact and speak with users, the more they can learn and adapt their vocabulary and intelligence, all thanks to the machine and deep learning functionality within the software.

Deep Learning:

Deep learning algorithms use artificial neural networks to make their predictions and decisions, differentiating them from machine learning algorithms that often require human training. With artificial neural networks, 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 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, allowing 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 utilizes artificial neural networks and relies on the network’s 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 various machine learning tasks when correctly implemented. 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 many different machine learning algorithm types, including association rule learning, Bayesian networks, and clustering and decision tree learning, among others. Connecting machine learning algorithms to data sources to use them when building intelligent applications requires a high level of development skill and technical knowledge.

What are the latest trends in Artificial Intelligence Software?

Artificial intelligence has been a dominant tech trend for the past five years. However, within AI software, there are two trends that have emerged and are gaining momentum: embedded AI and machine learning as a service (MLaaS).

Embedded AI – Machine and deep learning capabilities are being embedded in almost all types of software, even if the user is not aware of it. The incorporation of embedded AI within software solutions such as analytics, CRM, and marketing automation is enabling users to streamline processes, automate tasks, and gain a competitive edge through predictive capabilities. In the coming years, the use of embedded AI will become even more prevalent, much like how cloud deployment and mobile capabilities have gained mainstream acceptance over the past decade. In the future, vendors may not need to highlight the fact that their product utilizes machine learning; it will become a standard expectation.

Machine learning as a service – The software industry has shifted towards a more granular, microservices structure, particularly for development operations. The rise of public cloud infrastructure services has enabled companies such as Amazon, Google, and Microsoft to provide development and infrastructure services to other businesses on a pay-as-you-use model. Similarly, these companies are offering machine learning as a service (MLaaS) to other businesses, allowing developers to leverage prebuilt algorithms and solutions to gain insights from their own data. This approach saves smaller businesses time, resources, and money by avoiding the need to hire skilled machine learning developers and instead using systems created by enterprise companies. The trend of MLaaS is expected to grow as businesses continue to rely on microservices, and the demand for AI continues to increase.

What are the potential challenges with Artificial Intelligence  Software?

Artificial Intelligence (AI) software is often considered a magical solution that can instantly solve complex problems. However, this assumption is far from the truth. AI software requires vast amounts of data to learn and make informed decisions. In many cases, users need to train machine learning algorithms using techniques such as reinforcement, supervised, and unsupervised learning to create an intelligent application. For instance, a speech recognition system cannot identify a speaker’s voice without prior training on voice patterns.

One of the major challenges of AI software is the scarcity of skilled professionals with expertise in developing these algorithms and training them to perform specific actions. It is not sufficient to just switch on the software and expect it to solve all problems without prior development and machine learning knowledge. Building AI software requires extensive software development and a deep understanding of machine learning concepts.

Despite the current shortage of AI professionals, the number of qualified candidates is expected to increase as the need for these professionals continues to grow. As a result, the capabilities of the applications they build will continue to expand, leading to more sophisticated and intelligent systems. However, it is crucial to acknowledge the challenges and limitations of AI software before investing time and resources in it.