After reading popular headlines in the media, we can assume that artificial intelligence (AI) has permanently conquered our world. It is used in almost all areas of human life. Today, we will try to figure out what changes await us next year and what technological changes to expect from AI.
The Growing Role Of AI And ML
AI and ML continued to be on a roll in 2020, finding applications in a wide area from quantum computers and medical diagnostics systems to consumer electronics and smart personal assistants. AI can be used everywhere. Particular applications of AI include expert systems, Speech Recognition, Machine Learning, and Machine Vision. Here you can read about the 10 Best AI Apps.
Grand View Research predicts global revenue from AI hardware, software, and services to reach the compound annual growth rate (CAGR) up to 42.2% from 2020 to 2027.
A growing role in hyper-automation
Hyper-automation — the IT buzz word, the process of which is described as the convergence of RPA, AI, BPM, and just about every other technological building block of the digital age. Gartner views hyper-automation as one of the top 10 strategic technology trends for 2020.
Hyper-automation involves using all advanced technologies to implement process automation and improve people’s work increasingly. It includes a wide range of tools that can be automated while taking into account the increasing complexity of automation, analytics, and constant change. Thus, hyper-automation is a new point of view, not a new structure or separate technology.
Artificial intelligence, machine learning, and robotic process automation are the essential components and the main hyper-automation drivers. They will assist in completing tasks and processes faster, more efficiently, and with less error.
The Internet of Things will become the Intelligence of Things
In 2021, the integration of AI will be the main challenge on the Internet of Things, and its solution will allow the transition from the “Internet of things” to “intelligence of things.”
Innovations in areas such as deep learning and computer vision will enable smart device software and hardware upgrades. Given the industry’s dynamics, economic incentives, and demand for remote access, the Internet of Things is expected to come to smart manufacturing and smart healthcare. In terms of smart manufacturing, the introduction of contactless technology is expected to accelerate the 4th generation emergence. As smart factories strive for resiliency, flexibility, and efficiency, AI integration will make critical devices (like cobots and drones) more accurate and control over production, thus transforming automation into autonomy.
Computing to improve privacy
With this trend, you can collaborate on research with different companies without sacrificing your company’s confidential data. Three technologies have become the primary sources of data protection:
The first creates a secure environment where data is processed or analyzed;
The second helps to perform analytics and data processing in a decentralized manner;
The third helps to encrypt algorithms and data before any analytics and processing.
Security automation will empower us to detect, investigate, and remediate cyber threats without supervision. The system will identify incoming threats, triaging, and prioritizing alerts as they emerge.
The essence of a distributed cloud is to distribute cloud services across different physical entities but leave responsibility for the development, use, and management of the public cloud provider. This helps with cost savings, low latency scenarios, and compliance with storage laws.
This provides a more flexible way to leverage the cloud while also meeting the need for companies to bring cloud resources closer to the offices where their main business operations occur. A distributed cloud also provides the means to implement a unified cloud strategy that includes location-based service options.
By 2025, most cloud providers will provide some share of distributed services, which will be a natural development of cloud technologies.
AI in driving control systems
Automotive safety technology has evolved from external devices to interior devices, and sensor technology is also changing. In this way, driver monitoring systems are integrated with the collection of environmental data. Similarly, AI automotive applications are evolving and have outgrown entertainment to become an indispensable aid to vehicle safety. In the future, the development of reliable and accurate camera-based systems will gain momentum. These systems can determine in real-time whether the driver is tired, distracted from driving, or is driving improperly through pupil tracking and behavior monitoring. Cars with integrated driver monitoring systems are expected to be mass-produced soon.
The total experience can be a game-changer for the businesses as it combines all types of experiences of the customer, employee, and user. TE’s main intention is to improve the overall experience where all of these components intersect, from technology to users, customers, and employees.
This trend allows organizations to profit from the disrupters of COVID-19, including remote workers, mobile, virtual, and distributed clients.
Gartner predicts that TX-providing organizations will outperform the competition on key satisfaction metrics over the next three years.
Conclusions: AI engineering as a development strategy
When implementing newly developed AI systems and machine learning models, many projects face serviceability and scalability issues.
Specialists realize the need for a clear AI engineering strategy to improve AI models’ performance, scalability, and interpretability while delivering a full return on investment in AI.
AI development becomes part of the entire DevOps process, rather than a collection of isolated specialized projects, Gartner points out. A well-structured AI engineering process involving elements of DataOps, ModelOps, and DevOps is required.