Artificial Intelligence Software?
Best Artificial intelligence Software allows machines to learn from experience, intercept new inputs, and perform human-like tasks. Technologies such as machine learning (ML) and natural language processing (NLP) are used to train computers to perform specific tasks by processing large volumes of data. AI is gaining momentum as it can add intelligence to the existing products. Best Artificial Intelligence Software offer fast response, tailored approach, highly accurate information, and fewer redundancies—all of which important to achieve high efficiency in an organization. Moreover, AI combines data from multiple resources and uses it as a knowledge store, which results in accurate predictions about consumer needs.
Best Artificial Intelligence Software
Best Artificial Intelligence Software is a computer program that impersonates human behaviour by learning several data patterns and insights. It is being used by technology firms to design and develop intelligent applications from ground with the help of Machine learning and deep learning capabilities. By extension, the goal of systems is to develop systems capable of tackling complex problems in ways similar to human logic and reasoning. With applications in autonomous cars, video games, speech recognition, and computer-based vision, technologies such as computer vision, deep learning, and natural language processing are witnessing significant growth in the artificial intelligence market. The best Artificial Intelligence Software Market is expected to grow from $10.1 billion in 2018 to $126.0 billion by the end of 2025.
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Artificial Intelligence Software mainly focuses on the development of intelligence machines, which can think and work almost like humans. For example, speech recognition, problem-solving, learning and planning. Since its inception, Artificial Intelligence software has expanded its reach across end-user verticals. The major applications of Artificial Intelligence software include patient data & risk analysis, inpatient care & hospital management, and healthcare imaging & diagnostics in healthcare; predictive maintenance & machinery inspection, quality control, and material movement in manufacturing; HMI in automotive; precision farming and drone analytics in agriculture; product recommendation and planning, payment services management, and supply chain management and demand planning in retail; antivirus and identity and access management in security; and virtual assistant in HR.
This section describes the mapping of top 25 players in the best Artificial Intelligence Software market. These players are ranked on the basis of 2 major factors - business strategy excellence and strength of product portfolio. The detailed rating is given on the basis of product offerings and business strategies of 25 companies that belong to the ecosystem of the best Artificial Intelligence Software market.
Business strategy excellence describes how players are increasing their presence in the market. The factors considered in the “business strategy excellence” section are rated on the basis of the performance of the company in the best Artificial Intelligence Software market. Factors such as geographic footprint, effectiveness of growth strategy, and technology innovation have been considered while rating the business strategy excellence section of the company.
The strength of product portfolio describes players’ approach toward launching artificial intelligence software based product offerings and innovations. The factors in “strength of product portfolio” section are rated on the on the basis of the Artificial Intelligence software, hardware and services offered by each player in the best Artificial Intelligence Software market. Factors such as focus on product innovation; breadth and depth of product offering, and product feature and functionality have been considered while rating the strength of product portfolio section of the company.
Visionary leaders in the best Artificial Intelligence Software market generally receive high scores for most evaluation criteria. They have a strong service portfolio, a robust market presence, and effective business strategy. Visionary leaders are the leading market players in terms of new developments such as product launches, innovative technologies, and adoption of growth strategies. These players also have a broad portfolio of products that cater to most regions worldwide. Visionary leaders primarily focus on acquiring the leading market position through their strong financial capabilities and well-established brand equity.
Dynamic differentiators in the best Artificial Intelligence Software market are established vendors with very strong business strategies and a decent product portfolio. They generally focus on a specific type of technology related to the product. There are many companies in the best Artificial Intelligence Software market who largely depend on their competitive R&D. These players have established business channels and required resources, but offer less competitive products compared to visionary leaders.
Innovators in the best Artificial Intelligence Market demonstrate substantial product innovations compared to their competitors. They have a highly focused product portfolio. However, they do not have very strong growth strategies for their overall business. New players can make steady profits even if there is a high demand for innovative best Artificial Intelligence software products. These players hold a smaller share in the market but have innovative products portfolio and services in the best Artificial Intelligence Software market.
Emerging companies in the best Artificial Intelligence Market have niche service offerings and are starting to gain their positions in the market. They have a good business strategy which helps them compete with other vendors in the best Artificial Intelligence Sofware market. They can be new or old entrants in the market and may require some more time before gaining significant traction in the market.
Rise in the usage of Big Data
Big data and AI are becoming part of business organizations driven by the availability of data from sources such as the Internet of Things (IoT). Nowadays, companies are striving to analyze the consumer data obtained from CRM systems, product reviews, and media comments to understand their customers at a granular level, making their marketing more targeted and effective. Digitization in the manufacturing industry has increased the capability to access, analyze, and manage vast volumes of data while rapidly developing the information architecture in the factory. Data plays a critical role in the successful operation of a manufacturing plant. A strongly coupled digitized system in the manufacturing industry improves the overall quality and reduces the cost by improving defect tracking and forecasting abilities. By analyzing the data, best Artificial Intelligent Software systems can draw conclusions on the machine’s condition and detect irregularities to provide predictive maintenance.
In the healthcare industry, big data comprises information generated from clickstream analytics, and web and social media interactions; readings from medical devices such as sensors, ECGs, X-rays, and pulse oximeters; healthcare claims and other billing records; and EMRs, prescriptions, and biometric data, among other sources. Using complex computer algorithms, AI platforms can analyze unstructured data and allow physicians to scrutinize large volumes of datasets in a short span of time and derive an optimal course of action.
Exponential Rise in the Adoption of Cloud based Services
Exponential growth in cloud services is offering huge space for AI vendors to grow and expand geographically. Cloud computing is proving to be beneficial for businesses of all sizes. Many solution providers are deploying the solution on demand for their customers, and this is shifting the trend toward cloud-based IT framework. Implementation and hardware costs for AI technologies are high, and every retailer does not have dedicated IT resources and robust infrastructure. High implementation cost is one of the major factors for businesses accessing AI solutions through cloud-based deployments.
Cloud-based solutions reduce the upfront costs and eliminate concerns regarding the maintenance of servers. Thus, cloud-based AI solutions are useful as they improve scalability and are cost-effective for small and medium-sized businesses (SMBs) and organizations that find on-premises solutions expensive. The increasing adoption of cloud-based technology and IoT is a great opportunity for the best Artificial Intelligent Software solution providers, which would eliminate concerns related to expenditure and installation expenses.
Growing Demand for Simulated Assistants
Intelligent virtual assistants provide proactive recommendations for product substitutes; handle high-volume, low-value communications; enable customers to effectively answer questions about their accounts; provide technical support, and fill out applications or forms. These activities are anticipated to augment the customer relationship value graph for enterprises drastically, fueling the growth of the virtual assistant market. Furthermore, virtual assistants can provide an emotional association intended specifically for a customer’s decision path. Also, virtual assistants can build meaningful online involvements at a portion of the cost of human support while also augmenting revenue through enhanced product education and selling strategies, which drives customer engagement and satisfaction.
Virtual assistants are now also being adopted in sales for generating leads, assisting in sales reports, scheduling calls, conducting research on prospective customers, and following up with sales teams to remind them to enter info into the customer relationship management (CRM) system. The growing adoption of intelligent virtual assistants for various applications in marketing and sales is expected to drive the best Artificial Intelligent Software market.
Artificial Intelligence Software - By Hardware
The best Artificial Intelligence Software market for hardware has been segmented into processor, memory, and network. There is an increasing competition among established companies and start-ups in the market, leading to product launches and developments, including both hardware development and software platforms to run machine learning algorithms and programs. The AI market for hardware is expected to grow at a high CAGR during the forecast period. This can be attributed to the increasing need for hardware platforms with high computing power to run various AI software. The presence of major companies that contribute to the AI sector in North America has made the region a major market for hardware related to AI.
The best Artificial Intelligence Software market for hardware has been segmented on the basis of processors into GPUs, MPUs, FPGAs, and application-specific integrated circuits (ASICs). GPUs were made famous by NVIDIA (US), and tensor processing units (TPUs) were launched by Alphabet (US) in early 2016. Intel Corporation is a leading provider of CPUs, and Xilinx Inc. (US) is a major provider of FPGAs.
With increasing technological advancements, large hardware devices are expected to be replaced by smaller, efficient, and powerful neuromorphic chip-based systems. GPUs and FPGAs are widely used to implement machine learning algorithms. In terms of throughput, GPUs are almost 100 times faster than FPGAs, whereas in terms of power efficiency, FPGAs are 50 times better than GPUs. GPUs are extensively used to accelerate computational workloads, and their capabilities are growing faster than those of x86 CPUs. These processors execute multiple computing threads. Several companies are deploying GPUs for all sorts of computer vision algorithms as they provide better computational capabilities than CPUs. Owing to these factors, GPUs account for the largest share of the best Artificial Intelligence Software market among hardware components.
An MPU contains all, or most of, the CPU functions and is the “engine” that goes into motion when the computer is on. A microprocessor is specially designed to perform arithmetic and logic operations that use small number-holding areas called registers. Typical microprocessor operations include adding, subtracting, comparing 2 numbers, and fetching numbers. These operations are the result of a set of instructions that are part of the microprocessor design.
A GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output on a display device. It is nothing but a programmable logic chip specialized to handle graphics applications and display functions that render high-quality images, animations, and videos. GPUs on stand-alone cards utilize their own memory, whereas GPUs in chipsets share the main memory with CPUs. In a traditional setup of GPUs, DRAM chips are placed side by side and connected to the GPU via long copper traces on a PCB. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles. The parallel structure of modern GPUs makes them more efficient than general-purpose CPUs for algorithms where the processing of large blocks of data has to be done in parallel.
An FPGA is an integrated circuit that can be configured by a customer or designer even after it is manufactured. Hence, it is called field programmable. FPGAs are programmed using hardware description languages, such as VHSIC hardware description language (VHDL) or Verilog. FPGAs offer advantages such as rapid prototyping, short time to market, ability to reprogram in the field for debugging, and long product life cycle. FPGAs contain individual programmable logic blocks known as configurable logic blocks (CLBs). These logic blocks are interconnected in such a manner that the user can configure the computing system multiple times. FPGAs contain large resources of logic gates and RAM for complex digital computation.
Other processors include ASIC, DSP, IPU, and VPU. An ASIC is a microchip custom manufactured for specific design tasks for special applications. Compared with other programmable logic integrated circuits, ASIC offers high speeds as it is dedicated to performing one particular application only. ASIC offers a user- specific customized solution. Digital signal processor (DSP) is a specialized microprocessor optimized for the operational needs of digital signal processing. DSP is mainly used to measure, filter, or compress continuous real-world analog signals. Visual processing unit (VPU) is a class of processors intended to accelerate machine vision tasks. Image processing unit (IPU) is specially designed to support video and graphics processing functions, and interface with video and image sensors and displays.
AI technologies such as machine learning, computer vision, and predictive analytics require a large volume of data to train, test, and validate neural network algorithms, which may present storage challenges for data administration. A few start-up companies are exploring high-bandwidth parallel file systems to increase both throughput and efficiency.
Hardware used in network systems mostly comprises Ethernet adaptors and interconnects. NVIDIA’s DGX-1 uses Dual 10GB Ethernet adaptor. The company also provides a high-speed interconnect—NVIDIA NVLink. It is a high-bandwidth, energy-efficient interconnect that enables ultrafast communication between a CPU and a GPU, and between GPUs. NVIDIA’s NVLink offers 5–10 times faster data transfer than conventional PCIe Gen3 Interconnect. Along with NVIDIA, Intel (US) and Mellanox Technologies (Israel) are the key providers of network interconnect adapters for general as well as AI applications.
Artificial Intelligence Software - By Software
Best Artificial Intelligence software is nothing but software that is capable of behaving intelligently. Creating intelligent software involves simulating a number of capabilities, which include reasoning, learning, problem-solving, perception, and knowledge representation.
In recent years, most developments have been witnessed in AI software and related software development kits. The software integrated into computer systems carries out complex operations. It synthesizes the data received from the hardware systems and processes it in an AI system to generate an intelligent response. Today, artificial intelligence software is used in various applications, such as smartphone assistants, ATMs that read checks, voice and image recognition software on social network, and software that serves up ads on many websites.
The best Artificial Intelligence Software market based on software offerings is further classified into: AI Platforms, Application Program Interface (API), and Machine Learning Framework.
AI platforms offer users a tool kit to create intelligent applications. Data is combined with decision-making algorithms to enable developers to create a business solution. Most platforms, provide pre-built algorithms and simplistic workflows. These algorithms have the functionality to add natural language processing, image recognition, voice recognition, and predictive analytics technologies. The platforms are used to develop learning algorithms and intelligent applications. These platforms can be either machine learning platforms or application program interfaces (APIs).
Application Program Interface (API)
An API provides a platform for a set of routines and tools for building software applications. It also specifies how different software can interact with one another. Additionally, APIs are used when programming graphical user interface (GUI) components. A good API makes it easier to develop a program by providing all the building blocks.
Machine Learning Framework
Machine learning frameworks account for a major share of AI platforms. In January 2016, Microsoft (US) open-sourced its AI framework Brain CNTK to develop new AI software using Microsoft’s code. The company has used this platform to develop speech recognition technology for Skype Translate and Cortana digital assistant.
Artificial Intelligence Software - By Deployment
In recent years, most developments have been witnessed in the area of AI solutions and related software development kits. Best Artificial Intelligence Software solutions are created using nonprocedural languages such as LISP and PROLOG. These languages allow systems to learn and modify responses according to the environment. Requirements to install software include a fast CPU, high-capacity RAM, large storage capacity, a graphics card, and a few specialized input and output devices, depending on the application.
Data-sensitive enterprises prefer on-premises advanced NLP and ML tools to be used in AI solutions. Another important factor is the real-time availability of data for extracting insights; hence, despite speedy developments of cloud-based solutions, ML solutions are expected to remain on premises in some regions. However, the increasing dominance of cloud and the rising cost of support and maintenance of on-premises solutions are likely to drive the adoption of hosted solutions. The on-premise deployment of AI solutions offers significant improvements in terms of data validation, standardization, and robust matching as it is done manually. Leading vendors such as IBM and Google continue to invest in the on-premise deployment mode, along with developing new cloud business models for both NLP and ML tools.
The cloud deployment mode provides multiple benefits, such as reduced operational costs, hassle-free deployment, and high scalability. Cloud deployment for NLP and ML tools in AI is expected to grow with increasing awareness about the benefits of cloud-based solutions. In the best Artificial Intelligence Software market, solution providers are focusing on the development of robust cloud-based solutions for their clients, as many organizations have migrated to either private or public cloud. Moreover, cloud provides additional flexibility for business operations and real-time deployment ease to companies that are implementing real-time analytics. The cloud-based deployment of AI solutions has made it easy for users to apply predictive capabilities to the entire organization. Further, it helps in maintaining a competitive edge by eliminating the administrative roadblocks of the supporting infrastructure and enables organizations to focus on improving their competencies.
Best Artificial Intelligence Software - By services
AI is a complex technology requiring the implementation of sophisticated algorithms. AI-based systems require deployment and integration, and support and maintenance services. Most companies that manufacture and develop AI-based systems and related software provide both online and offline support, depending on the application. Several companies provide installation, training, and support pertaining to AI-based systems, along with online assistance and post-maintenance of software and required services.
Deployment and Integration
Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. Hence, deployment and integration is a key service required for configuring AI systems.
Installation services allow the software to be integrated with the analytics side to enable data retrieval and generate desired result through computation. The use of computer systems for AI further increases the amount of work involved in installation.
Support and Maintenance
Support and maintenance services are provided after installation and training to prevent issues related to operations. Services such as inspection, repair, planned maintenance, and replacement help maintain acceptable standards of systems. Restoring equipment to its operating condition consumes maximum effort.
How is Machine Learning further classified in the best Artificial Intelligence Software market?
Best Artificial intelligence Software is concerned with the use of systems in tasks that are normally considered to require knowledge, perception, reasoning, learning, understanding, and other similar cognitive abilities. It constitutes various technologies that play a vital role in developing its ecosystem. In this study, the AI market has been segmented on the basis of technologies into machine learning (ML), natural language processing (NLP), context-aware computing, and computer vision. These technologies are being implemented to develop various AI products and services for the best Artificial Intelligence Software market.
Machine learning enables systems to learn without being explicitly programmed. This technology can reliably and quickly scan, parse, and react to anomalies. Machine learning enables systems to automatically improve their performance with experience. ML aims to develop a computer program/algorithm that can access data and use it to train itself with no human intervention. ML is expected to account for the largest market share throughout the forecast period. This is attributed to the enormous availability of data, also called big data, and increasing adoption of ML by enterprises and federal agencies to gain useful insights. The common applications of machine learning include advertising, computational finance, predictive maintenance in manufacturing, fraud detection, e-mail spam filtering, text processing, network security threat detection, search recommendations, and video analysis. ML is being adopted by businesses dealing with large volumes of data, where the time previously dedicated for poring over charts and spreadsheets is now being used to seek intelligent ways to automate data analysis.
Deep learning is a type of machine learning technology based on multiple algorithms to create relationship among data. Deep learning uses artificial neural networks to learn multiple levels of data, including texts, images, and sounds. Its algorithms help identify patterns from a set of unstructured data. The growing application of deep learning algorithms is driving the growth of the best Artificial Intelligence Software market. Deep learning technology is used in applications such as voice recognition, fraud detection, voice search, recommendation engines, sentiment analysis, image recognition, and motion detection.
Deep learning enables a machine to build a hierarchical representation. For instance, the forefront layer might look for simple edges, the next one for the collection of edges forming shapes such as rectangles or circles, and the third might identify body parts such as the nose and eyes. After a few considerable layers to identify the required data, the neural network puts these features together, resulting in a machine capable of recognizing faces.
Supervised learning refers to algorithms that are provided with a set of labelled training data, with the task of learning what differentiates the labels. For example, modern image recognition algorithms, such as Google Image Search, can accurately distinguish tens of thousands of objects, and modern facial recognition algorithms exceed the performance of human beings. By learning what makes each category unique, the algorithms can then be presented with new, unlabelled data. In supervised learning, it is critical to choose a representative training dataset.
Supervised learning is segmented into classification and regression. Classification consists of algorithms such as support vector machines, discriminant analysis, naive Bayes, and nearest neighbour algorithm. Regression can be further segmented into linear regression, support vector regression (SVR), Gaussian process regression (GPR), ensemble methods, decision trees, and neural networks.
Unsupervised learning refers to an algorithm provided with unlabelled training data, with the task of inferring the categories all by itself. Sometimes labelled data is rare, or the task of labelling is itself hard, or labels might not exist. For example, while there is enormous amount of data to examine, attempting to label data would be extremely time-intensive, and it would be hard for a human to determine what label to assign. Given how good machines are at finding patterns in large datasets, it is often much easier to simply have the machine separate data into groups for users. Unsupervised learning consists of clustering methods, which further consist of algorithms such as K-means, K-medoids, fuzzy C-means, hierarchical, Gaussian mixture, neural networks, and hidden Markov model. Vectra Networks (US) uses unsupervised machine learning models to learn about a specific customer environment, with no direct oversight by a data scientist. Cortica (Israel) uses unsupervised learning in its autonomous AI platform and provides intelligence that enables autonomous vehicles, smart cities, and autonomous drones.
Reinforcement learning is a goal-oriented learning based on interaction with the environment. It allows machines and software agents to automatically determine the ideal behaviour within a specific context to maximize their performance. SparkCognition (US) offers DeepArmor, which is powered by advanced natural language processing (NLP) and an ensemble of machine learning algorithms with reinforcement learning. It can compare the DNA of new threats with that of millions of threats on which it has been trained.
Other machine learning technologies include semi-supervised learning, a combination of supervised and unsupervised learning, and federated learning. Semi-supervised learning deals with the problem of how, if possible, to take advantage of a huge volume of unclassified data to perform classification in situations when, typically, the labelled data is scarce. Typically, a small amount of labelled data and a large amount of unlabelled data are used for training a semi-supervised learning model. Semi-supervised learning is deployed in cases where acquired data requires skilled professionals to train and learn from it. Federated learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data distributed over a large number of clients each with unreliable and relatively slow network connections.
What is Natural Language Processing?
NLP is a type of automatic speech recognition and is among the fastest-growing AI technologies. NLP is the ability of a computer program to understand human speech. NLP is used for the real-time translation and development of systems that can interact through dialogues. Natural language processing applications allow users to communicate with a computer in natural language.
NLP consists of text analytics, speech analytics, input voice response (IVR), pattern and image recognition, auto-coding, classification and categorization, and optical character recognition (OCR). In NLP, a computer uses a statistical model that converts natural language into programming language. This is also called speech-to-text conversion. After this, the computer performs part-of-speech tagging using a set of lexicon rules and converts the coded text into speech. Apart from human language processing, NLP can be applied to any written text. Data scientists at Endgame (US) apply NLP to security by building upon advanced NLP techniques to better identify and understand malicious code, moving toward an NLP methodology specifically designed for malware analysis—a malicious language processing framework. Major virtual assistants such as Google Assistant, Alexa, and Siri apply NLP to interpret speech and respond in everyday language.
What is Context Aware Computing in the best Artificial Intelligence Software market?
Context-aware computing is an integral part of AI systems. The development of sophisticated hard and soft sensors has accelerated the growth of context-aware processing, which, nowadays, features in most smartphones. Context awareness allows companies to cope with emerging threats as they evolve their business requirements for greater openness. It involves the use of supplemental information to improve security decisions at the time they are made, resulting in more accurate security decisions capable of supporting dynamic business and IT environments. Device context awareness provides information about connectivity, bandwidth, printers, and displays. User context awareness provides information about user profile, location, nearby people, social situation, and various other activities.
In the automotive application, LIDAR sensors are used in autonomous and semiautonomous vehicles for pedestrian detection applications to avoid possible collisions. The complete driving behaviour is a combination of intricate and complex interactions between the driver, the vehicle, and the environment, and this behaviour cannot be linearly predicted. However, a context-aware system could assist the driver in augmenting the probability of undertaking safe behaviour. In-vehicle context-aware systems aim at considering more contextual information related to the driving task to produce adapted or customized actions.
In manufacturing plants, the production quantity depends on many uncertain factors, which include internal and external factors, such as random orders, sudden equipment failure, supply modification, and runtime restriction. Context-aware technology finds customized solutions and arranges the machines accordingly for abovementioned issues.
Artificial Intelligence Software - By Industries
Artificial intelligence (AI) is already in use, and its effects are expected to be magnified in the next 5 years, as various end-user industries are expected to transform their core processes and business models to take advantage of AI. The major applications of AI include patient data & risk analysis, inpatient care & hospital management, and healthcare imaging & diagnostics in healthcare; predictive maintenance & machinery inspection, quality control, and material movement in manufacturing; HMI in automotive; precision farming and drone analytics in agriculture; product recommendation and planning, payment services management, and supply chain management and demand planning in retail; antivirus and identity and access management in security; and virtual assistant in HR.
The best Artificial Intelligence Software market for healthcare is categorized into various applications, such as patient data & risk analysis, medical imaging & diagnostics, precision medicine, lifestyle management & monitoring, drug discovery, inpatient care & hospital management, virtual assistant, wearables, and research. Growth in patient data owing to the increasing adoption of EMRs and various advantages, such as predictive analytics and risk management, offered by AI systems to healthcare providers and payers are supporting the growth of the patient data & risk analysis segment. The market for the medical imaging & diagnostics segment is expected to grow at the highest CAGR during the forecast period. Factors such as the presence of a large volume of imaging data, advantages offered by AI systems to radiologists in diagnosis and treatment management, and the influx of a large number of start-ups in this segment are fueling the growth of medical imaging & diagnostics.
AI in manufacturing is beneficial in the design and planning stages of the manufacturing process. Increasing data volume derived from the manufacturing value chain has led to the involvement of AI-enabled data analytics in the manufacturing sector. In addition, several industry initiatives, such as Industry 4.0, a connected manufacturing initiative by the Government of Germany, have proliferated the growth of AI-enabled devices in manufacturing. The best Artificial Intelligence Software market for manufacturing is classified into various applications, such as material movement, predictive maintenance and machinery inspection, production planning, field services, reclamation, and quality control. Increasing demand to reduce operational costs caused by early-life failure of machines is driving the growth of predictive maintenance and machinery inspection in the AI market. The market for quality control is expected to grow at the highest CAGR during the forecast period. Increasing demand for minimizing on-field failure of finished products is propelling the growth of AI in manufacturing and further driving the growth of the best Artificial Intelligence Software market.
Automotive is among the major applications for AI; it includes various technologies, such as ML, NLP, and image processing. Nowadays, several vehicles are being developed using AI technology software and a set of hardware sensors, including LIDAR, video camera, position estimators, and distance sensors. The best Artificial Intelligence Software market for automotive is divided into multiple applications, such as autonomous driving, human–machine interface (HMI), and semiautonomous driving. Increasing demand for enhanced user experience and convenience features is the key factor driving the growth of HMI in the best Artificial Intelligence Software market. Moreover, intelligent car concept, which gives users an experience of advanced technology with effective vehicle controls such as advanced cruise control and self-parking, is further elevating the growth of HMI in the AI market. The AI market for autonomous driving is projected to grow at the highest CAGR during the forecast period. Increasing demand to improve road safety and reduce mishaps caused by human intervention is resulting in the growth of autonomous driving. In addition, increasing participation of various automotive and IT giants to develop autonomous cars is flourishing the growth of autonomous driving in the best Artificial Intelligence Software market.
Agriculture is one of the emerging applications of AI. AI in agriculture has evolved as a result of its extensive use in diagnosing and mapping, and evaluating crop health, and making real-time decisions. AI provides the status of crop water, along with soil water holding capacity. ML technology, along with computer vision technology, is a key element in the field of agriculture.
The combination of IoT and advanced analytics with AI further allows farmers to analyze real-time data of weather conditions, temperature, soil moisture, plant health, and crop prices in the market. The AI market for agriculture is classified into multiple applications, such as precision farming, livestock monitoring, drone analytics, and agricultural robots. Other applications include smart greenhouse management, soil management, and fish farming management.
The adoption of AI technologies for applications such as yield monitoring, field mapping, crop scouting, weather tracking and forecasting, and irrigation management is driving the growth of AI in precision farming application. Moreover, rising demand for improving farm productivity due to growing population and need for improving food quality are further escalating the growth of precision farming in the agriculture best Artificial Intelligence Software market. Moreover, there will be increased adoption of AI for drone analytics during the forecast period owing to rising use of drones in diagnosing and mapping crop health and making real-time decisions. Further, favourable government mandates for using drones in the agriculture field are expected to fuel the growth of AI in agriculture for drone analytics.
What are the most important features of Artificial Intelligence Software?
Natural Language Dialogue
The protracted communication amongst two or more members in a natural language is called as natural language dialogue. Many conversational AI tools also include language-processing techniques. In addition to being a well-organized conversion partner, this feature helps the users to engage data and determine new relations and insights.
Intelligent Data Discovery
Artificial Intelligence software come with conversational interactions, automated visions and a comprehensive view of data discovery features. This feature helps users in attaining a profound understanding of the data and also makes it easy to analyse.
Another important feature of the Artificial Intelligence software is self-service dashboards. This feature helps professionals to share their insights in the dashboard which can be smoothly created with the help of integral visualization techniques.
Text to Speech and Speech to Text
Best Artificial Intelligence software also provide with the functionality that helps in converting text into audio - in an extensive diversity of voices and languages. Likewise, they include features to excerpt text from voice or audio for quicker understanding.
This feature of Artificial Intelligence software allows users to evaluate the visual content of their videos and images with the help of machine learning.
What are the Current Trends in the Artificial Intelligence Software Market?
The next few years will see huge investments from global technology firms into AI technologies. In 2020, many factories of AI models and data will arise to help AI technology and related commercial solutions on a bigger scale assisting the enterprise.
Digital IQ will upsurge in this decade. Digital Intelligence is defined as the extent of how organizations comprehend the business processes and the content and data inside them from a diversity of critical viewpoints.
Increased Application of Deep Learning Technology
In 2020 and the next few years, deep learning will be applied across multiple industries at a scale to quicken transformation, upgrading and execute innovation.
Organizations are all set to welcome many digital robots willing to take up rule-based tasks at the workplace. Employees across geographies will cooperate with digital workers working together with them in the future.
Frequently Asked Questions
What is the role of Natural Language Processing in AI?Natural Language Processing can be referred to as a subcategory of artificial intelligence. NLP basically enables the AI software to comprehend the actual meaning of words. Its fundamental goal is to make computers or machines as smart as possible to understand the human language.
What are various type of Artificial Intelligence Software available in the market?Artificial Intelligence software is a broad space which can be further categorized as follows: AI Platform, Machine Learning, Chatbots and Deep Learning.
What are the most important features of an ideal AI software?The most important features of an AI software include Machine Learning, Speech and Voice Recognition, Smart Data Discovery, Visual Recognition, Predictive Abilities, Virtual Assistant and more.
What are the major benefits of using Artificial Intelligence Software?Implementing AI software can help businesses in several ways, some of which are: Businesses can automate customer interactions and offer real time assistance; AI software helps with data mining which can provide critical insights; AI software can predict outcomes and thereby can help in reducing any errors before it is late; AI Software can be used to identify frauds and unauthorized access; and more.
How is the Artificial Intelligence Software market projected to grow in the next few years?The AI Software market is expected grow from to USD 10.1 billion in 2018 to USD 126.0 billion in by 2025 at a staggering CAGR of 43.60%.
Amy Artificial Intelligence Software fosters meeting, scheduling business, doesn’t aid the data selling business. It facilitates encryption of every detail and maintains a secure, locked-down cloud service and aids to automatically reject all email attachments where no human intervention is needed in the loop.
Sia by Opentopic is a completely integrated marketing bot using cognitive technologies by IBM Watson. Sia enables you to gain deep, behavioral insights into your audience, automatically personalize campaigns at scale and obtain predictions on campaigns. Sia with Watson is a powerful cognitive engine that powers the Artificial Intelligence (AI) of IBM Watson and Opentopic Technologia Lab, empowering digital agencies and their clients, association professionals, and banks and guarantors to customize at a human and behavioral level at a very large scale.
HIRO is a general problem-solving AI technology developed by Arago US and is thoroughly tested in real commercial applications with a strong focus on business and IT optimization. It helps the management of the business and IT processes of companies from every industry with less headcount. It can manage processes such as IT infrastructure, software, applications, services, business processes, workflows, transactions and ensures performance and resilience. The platform has an Advanced AI which has been developed through 20 years of research and works with existing datasets and systems achieving results in weeks using Agile Automation.
THRON Artificial Intelligence Software is a product of the DAM family, it has been an agile platform for brand management, content delivery along with omnichannel platforms, with the touch of artificial intelligence which is applied to most of its key features. It has the capability to profile website visitors and make new potential buyers out of leads. The sole purpose is to manage the lifecycle and performance of contents, which allows the business to be in total control of the comprehensive delivery and first-party data collection.
The cloud-based deployment of Wordsmith along with delivering automated insights and reports corporate earnings, has done an ample job for many business owners. Chatbots are accessible to everyone, especially benefitting for the B2B businesses and SMEs, and the software produces about1.5 billions of content per year.
Indico Artificial Intelligence Software can ingest myriad contracts and annotate relevant clauses for developing custom models. It provides services in various sectors from finance to law and automates manual processes for maximizing efficiency and minimizing costs. It can create personalized marketing lists for enterprises by processing contracts and accurately classifying them. The platform is multi-featured and assists in all kinds of business practices, driving good sales, increasing productivity, and helping the enterprise grow.
The bundles of agile features in a single place like Site24x7 Artificial Intelligence Software have been useful for the users by date when it comes to small and medium-sized business and startups. It has the capability to cater to functional Inclusions and running web app or owner administrative tasks, site reliability engineering and also system Manager.
Ellie Artificial Intelligence Software provides the customer with seamless interaction between various digital communication devices for a unified experience. It helps the company reduce time and cost and increase revenue. It helps boost productivity and reduces website bounce rate by one fourth which comes in handy in a well-integrated and user-friendly system.
It is designed for businesses of all sizes - from small-scale enterprises to large-scale. 24*7 response feature, customisable templates, ticket raising, social media integration - all of the key features make it a customer-friendly application. The advanced machine learning algorithm suggests the most effective way to improve the design of ChatBot Artificial Intelligence Software and make it more user friendly. It is available at an affordable ChatBot pricing point.
Arya’s intelligence to need the automated list of top candidates and compare them to their tenure and growth at companies they’ve worked for, current industry trends, also as measure their behavior over a period of some time to condense the talent pool to candidates that are the only fit the prospect.
Create stories out of every relevant data. Data contain technical jargon which sometimes get difficult for customers to analyze and interpret. Quill Artificial Intelligence Software makes instant stories out of data and saves time and effort of customers. No requirement for lengthy configurations. Adding a story creates a 16% engagement lift amongst users.
Farrago Artificial Intelligence Software is built to replace data scientists who take weeks and months to make sense of data. It does their job and more in no time through automated machine learning which enables Farrago to remain a viable asset to any business or company for the foreseeable future. The instant results of machine learning speed up business decisions saving money and time for the users.
AlphaSense Artificial Intelligence Software helps users to uncover extremely relevant information in a very short span of time so that the business never loses any market opportunity due to time-consuming research processes. The platform uses statistical techniques allowing the software to interpret data in a similar manner to the way humans might interpret data. AlphaSense search engine also has features like comprehensive synonym recognition, accurate relevance scores and ability to search by proximity.
Auris Artificial Intelligence Software functions as a platform for professionals to get consumer insights in real-time using data. The sources of data are divided into two branches; one includes social media, news, review board, and all sorts of data available on blogs, and the second include the privacy of data that requires encryption both in a digital and offline way. The platform works by bringing the data together, deploy AI algorithms to curate insights and action in the short term and long term basis.
BAAR Artificial Intelligence Software helps better manage resources and risk. It ensures that the changes made to production follow organizational standards and sets a benchmark in providing access to databases via middleware. It not only finds issues and loopholes but assists the client in remediating them as well. It provides an extremely dedicated and multi-faceted platform for individual groups in an enterprise.
Aiden Artificial Intelligence Software is one of the most latest and viable platform options for growing the marketing strength for a Business. Automation of the workflow with the help of new-age AI analytics unlike traditional methods have made acquisition easier. Aggregating the overall marketing information, daily recommendations for consistent improvement and one-click activation are the agile features for the business owners as well as individuals.
Sesame Artificial Intelligence Software is designed to provide services to call centres for better understanding of clients and their needs. The voice biometric feature is a sublime feature which identifies callers over their voices. This AI is even capable of reading the client’s emotion and mood and bestow him/her with a solution suiting his/her need the best. With these features, Sesame provides high-end results with efficient details.
Kairos Artificial Intelligence Software helps developers and users to easily build sophisticated facial recognition software into their products by directly integrating with Kairos API. It is unique in its features and security provided at an affordable cost and beats competitors in terms of ease-of-use. It provides ethical solutions by using computer vision and deep learning and simplifies the process of combining human identity with software