The Year 2020 has been through tough times, which almost lead the businesses to come to a standstill. Organizational processes involving human interference were suddenly adjourned due to the pandemic, which depicted huge adversities in economic growth and development. The surge of unemployment increased, and there felt a need to overcome this to save economic disruption. This is what paved the way to machine learning trends. It wasn’t that these didn’t exist before, but they surely received undue importance post-pandemic and lead to a drastic hype in its adaption.
Artificial Intelligence (AI) is the one that is demonstrated by Machines, unlike the Natural Intelligence displayed by Humans and Animals. The only differentiators are ‘Consciousness’ and ‘emotionality’, being present only in the latter.
Machine Learning (ML) is a part of Artificial Intelligence. It’s a study of Computer Algorithms which helps self-improvisation through experiences. It builds a model based on Sample data and is designed to make predictions and decisions without being programmed for it.
Deep Learning is an AI Function that involves imitation of the human brain in processing data and creating patterns for use in decision making. It’s a subset of ML which is capable of learning from unstructured data.
- Machine Learning Trends in 2021
- AI Technologies
1. Machine Learning Trends in 2021
According to the research conducted by Gartner, Emerging AI, and Machine Learning Trends in 2021 depict three major themes:
- People Centricity: Business needs of Human Resources are demanding the assistance of IT.
- Location Independence: Traditional Brick-Portal model is demolishing, and organizations are witnessing a revolution in the methodology of the work done.
- Resilient Delivery: Businesses need to adapt to changing scenarios to survive.
Various machine learning trends to be seen in 2021 are not independent functionalities, rather work together to achieve desired business objectives.
- Distributed Cloud: Cloud services are the backbone of Data Storage in the changing tech needs. Availability of various services over the cloud as per need on a pay-per-use basis is the most economic option available for upcoming business technology requirements. A Distributed Cloud system enables the usage of Cloud services at multiple locations governed by a Single service provider, which in turn helps organizations with cost-efficiency.
- Anywhere Operations: Elimination of physical space, rather, enforcing digitized means for regular business process and operations is the prime focus of the trend. I must enable employees to access Organization data from anywhere, customers to access products and services, and Business partners to be able to manage strategies and operations from anywhere just at the click of a button. That means, Contactless business.
- Cybersecurity: Enhancing Cybersecurity is of utmost importance where the spotlight lies on the ‘Identity of a person or thing’. Security Policies will have to be orchestrated, and proper enforcement regimes are dictated. Traditional methods of Cybersecurity may fade away eventually.
- AI Engineering: For AI models to deliver full value for investments, it is very essential to develop definite AI Engineering strategies which help facilitate reliability, compatibility, integrity, objectivity, scalability, and performance efficiency. AI Engineering encompasses the roadmap to make AI a part of DevOps projects rather than featuring them as separate projects. Amongst major concerns of AI Engineering is its Governance which demands more Trust, transparency, ethics, compliance, interpretability, and fairness.
- Hyper-automation: Business Processes are radically changing to replace the existing Workforce-driven growth towards Automation-driven development. Initially, Hyper-automation may seem taxing, and organizations at the beginning fail to implement the techniques correctly to streamline the Machines to their Business needs. However, if not adapted to, the business may lack behind the race.
- Intelligently Composed Business: It would be high time for Businesses to realize that inefficient processes are to be reviewed and eradicated and, more important to be given to quick responses and agile business environment where process time is effective and available data is well organized and managed to the advantage of the business.
- Privacy in Computation: Data privacy has been the most important factor to be taken care of in the world of digitalization. Data Computation is prone to threats at stages such as Processing, Analyzing and, encryption. Functioning paired with confidentiality is the need of the hour.
- Reinforcement Learning and AI: Where AI programming is designed with various conditions that characterize what sort of activity will be performed by the software, and through its operations, actions, and results, the software self-learns ways to achieve desired objectives. A Chatbot is the best example of RL.
2. AI Technologies
- Speech Recognition
- Machine Learning
- Deep Learning
- Natural Language Processing
- AI Optimized Hardware
It is seen that AI and ML are both very essential currently for businesses and organizations to boost. Adapting Automation into business processes at early stages is of utmost importance.
Also, along with the goods comes to the evils. Issues concerning AI and Machine Learning can be enumerated as below:
- The difficulty is setting long-term business goals and understanding the best suitable AI Strategy for the same.
- Determining what specific function the system must attempt to learn
- How much training Data required?
- Selection of best algorithm
- Alignment of objectives of learner and target Functions.
There are no right or wrong ways of learning AI and ML technologies – the more, the better! These valuable resources can be the starting point for your journey on how to learn Artificial Intelligence and Machine Learning. Do pursuing AI and ML interest you? If you want to step into the world of emerging tech, you can accelerate your career with this Machine Learning And AI Courses by Jigsaw Academy.