- December 9, 2020
- Posted by: InApp
- Category: Artificial Intelligence
Artificial Intelligence (AI) can solve specific problems by generalizing over a given data. There are two main AI areas: Machine Learning (ML) and Deep Learning (DL). It takes time, effort, technical infrastructure, and knowledge to build AI applications. That is why there was a time when only the big corporations with big budgets could invest in AI applications, which were beyond the reach of small and medium companies. But no more the big companies have monopolies over the AI applications.
With the advent of cloud computing, the application of AI for various services has been democratizing for some time. Now companies of all sizes can have access to AI. Within a limited budget, AI is offering many opportunities for improving the business and the lives of people. It has all become possible with the emergence of a relatively new business niche: AIaaS, i.e., AI-as-a-Service, a third-party offering of AI outsourcing.
The main attraction of AIaaS is that the businesses need not invest massively to build AI applications massively. Still, it can continue focusing on their core business while taking advantage of AI technologies for varied solutions. The other advantages include reduced investment risks, reduced development time, and increased strategic flexibility. And the major disadvantage is the dependence on service providers.
AIaaS is helping businesses leverage AI for several applications at reduced risk and expense. Although being around for years with around $2.3 billion in 2017, the market of AIaaS is expected to reach $77 billion in 2025, with the forecasted CARG of 56.7% in the period 2018-25. So far, the main players in the industry proving AIaaS are IBM (Developer Cloud), Google (Cloud Platform), Amazon (Web Services), and Microsoft (Azure).
High level and Low-level AI as a Service
Low -Level AI focuses on a narrow task. It requires expert knowledge for the implementation with the advantage of the creation of innovative solutions for the problems that are unsolved and non-standardized. It consists of data collection, pre-processing, training, testing, parameter optimization, etc., and comprises Machine Learning algorithms such as Classification, Clustering, Regression, Representation Learning, Reinforcement Learning, etc…
In high-level AI, also called strong AI, a machine, instead of focusing on a single problem, applies intelligence to any problems. Akin to a conscious mind, strong AI can perform a cognitive function that a human mind can. It comprises Deep Learning with Convolutional neural network (CNN), Artificial Neural Networks (ANNs), Autoencoder, Recurrent neural network (RNN), etc.
With the advances in AI in recent years, the applications of AIaaS have widened, and advantages have also increased manifold. However, the creation of AI applications is expensive, and even the programmers need to create AI algorithms are hard to find around. Besides, as AI works on past good data, not all organizations may have created data in the past. So in such a scenario, AIaaS comes as a very viable and economical alternative. The following are some of the benefits of the AIaaS:
Cost-Effectiveness and Lesser Implementation Time: The main advantage of AIaaS is that it reduces cost and implementation time significantly compared with AI applications from scratch. The setting up of an application with the development of algorithms, their training requires huge costs. But with AIaaS, businesses have to contact service providers for getting access to readymade infrastructure and pre-trained algorithms. The service providers make use of the existing infrastructure, thus, decreasing financial risks and increasing the strategic versatility.
Transparency: In AIaaS, you pay for what you are using, and costs are also lower. This brings in transparency. Users don’t have to run AI nonstop.
Scalability: AIaaS lets you begin with smaller projects to learn along the way to find suitable solutions eventually. You can customize your service and scale up or down as project demands change.
Application Areas of AIaaS
Digital Assistance & Bots: These applications frees a company’s service staff to focus on more valuable activities. Chatbots use natural language processing (NPL) algorithms to learn from human speech and then provide responses by mimicking the language’s patterns. This is the most common use of AIaas.
Cognitive Computing APIs: Developers use APIs to add new features to the application they are building without starting everything from scratch. Some of the APIs solutions are speech, NPL, knowledge mapping, translation, computer vision, search, and emotion detention.
Machine Learning Frameworks: AIaaS is being used for developing Machine Learning (ML) models. Using AIaaS, developers can develop ML models without the use of big data. Organizations can build models suited to their requirements without using large amounts of data. These models learn quickly from the organization’s data over time.
Fully-Managed ML Services: These services provide custom templates, pre-built models, and code-free interfaces and enhance the accessibility of machine learning capabilities to non-technologies enterprises not interested in investing in developing tools.
Challenges before AIaaS
Currently, AIaaS is facing some challenges that make it difficult for organizations worldwide to realize their full potential. The first challenge is to overcome already set high expectations from AIaaS. Enterprises have huge expectations from AI. However, the advancements in AI are not still incommensurate with the expectations. With the right expectations, there will be more successful adoption.
- The user organizations are generally completely unaware of the algorithm and parameters used in AI as a service. Initially, it may create problems for the businesses to get adept at working with AIaaS solutions.
- Some organizations’ crucial operations get dependent on the service provider, which stokes apprehensions among businesses about adverse consequences in the future.
- The implementation of the AIaaS software is not bug-free and requires a lot of effort for successful implementation.
- Some organizations are not so keen to share their data with the service providers and are unsure of the future implications if they share data. Additionally, user organizations have difficulty generating quality data, which is one of the most important factors for AI success. So it will remain a challenge for the service providers to convince the organizations to create trust and generate quality data.
- Some organizations are interested in using AIaaS for their business but may not have the necessary talent for implementation and constant maintenance.
- The businesses are concerned about reduced data security. AIaaS also puts some constraints on innovation as a result of standardization in the process.
AI Enhancing Jobs not Replacing
When people think of AI, human-like intelligent machines come to mind. If we look at Gartner’s report, which predicts that by 2024, AI will be handing 67% of all routine work being currently done by managers, it seems disconcerting that AI may replace jobs and may make many people jobless. On the contrary, smart businesses are gradually learning to strike a balance between humans and machines. Moreover, AI is predicted to create a new job market not existing before. By leveraging cloud services for AI, the businesses will be able to offer unlimited solutions to many of the current problems.
AIaaS can be used for diverse applications without the deep knowledge of AI algorithms, and it’s working. People working on AIaaS need not be area experts. However, to get deeper and proceed more efficiently, what is important is that the data be interpreted correctly. An understanding of data science or consultancy with experts is suggested. Also, it is well known in the AI area that if you want to get better results, the data should be of high quality.
The year 2020 is certainly not conducive for the high powered growth of the world economy. However, within a year or two, the economy will bounce back with an expected high growth rate, opening up the unforeseen possibilities for applications of AI. With customers having high expectations and more awareness, a lot of churning in data is needed to meet their requirements and expectations.
The banking sector is one of the most conspicuous consumers of AIaaS. It brings better customer service, customer onboarding, improvement in processes, and mitigation of risk and fraud detection in the banking sector.
A mid-sized company can’t compete with the already established tech giants directly. However, by powering their services with AIaaS, they have good opportunities to meet customer expectations in their niche areas without any significant technology investment.