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- Category: Machine Learning
AI Powered Education For A Better Tomorrow
There was a period when Artificial Intelligence (AI) was portrayed as a robot. A machine that exhibited human-like characteristics (learning and decision making) with a synthetic brain. Today, AI encompasses anything and everything. Be it vehicles, entertainment, corporations, smart homes, google search algorithms, education, law, or medical services, AI has transformed and improved all industry sectors. It has outperformed humans in specific tasks. To quote a few examples:
- Smartphones using Google maps to navigate around the globe.
- Amazon Go stores make the shopping experience user-friendly.
- Flippy for flipping burgers at CaliBurger restaurant..
The worldwide AI market is predicted to experience an expected growth of $120 billion by the close of 2025. The AI software market, however, is expected to reach $22.6 Billion in 2020, according to TechJury.
Researchers are now building AI that will outperform humans in every cognitive task. Therefore, machine learning and deep learning are fundamental aspects of AI – deep learning being the current breakthrough. It’s left no stone unturned in the application, whether it is our personal, professional, or social life, and AI-powered learning seems to be a great alternative for the better future.
What drives intelligence and why it’s important to understand this.
The main role of AI is to make accurate forecasts after learning from customer activities. This returns us to AI machine learning and to deep learning. Every possible data input contributes to the decision making process. AI algorithms help leaders to make business decisions based on different types of annotated data. Data is the lifeblood of AI. A vast pool of “data” from numerous sources drives this intelligence. This is also known as “Big Data pool”.
How AI & Data Annotation are Related
It’s no secret that Machine Learning models work on large proportions of training data. Just like people, machines and algorithms also learn with meticulous training. However, the process of data input isn’t as direct as it is in human beings. This suggests that data annotation is important for a successful AI world.
Labeling the content of diverse forms i.e., text, verbal, or multimedia, making it sensible to machines is called data annotation. Now that the world is going through a paradigm shift, AI and ML companies require huge volumes of annotated data for training their ML models. This indicates the urgent need for skilled data annotators. The skill is all about enhancing customer experience through advanced data annotation techniques.
Data annotation may be a crucial element in ensuring that AI and machine learning projects scale. Even technically advanced algorithms are not applicable without the use of data. Data annotation gives a leg up on the machines as human beings are responsible for identifying annotating data to the machine. Providing machines and algorithms that access to learnable data is the biggest competitive advantage and will remain so for years and years to come. This is reason enough to claim that data annotation is a prospect career but is yet to thrive.
Types of Data Annotation
There are different types of data annotations and their implications.
- Text Annotation – Text annotation is employed for Natural Language processing by machines. Virtual assistants and chatbots are some of the common applications of text annotation. There are various other categories of text annotation, based on diverse applications.
- Audio annotation – It’s similar to text annotation with the difference being to make Natural Language processing for “speech”, to make it understandable to machines. The time-stamping of speech data and transcription for clear identification of pronunciation, modulation, and language is a part of this process.
- Image annotation – For applications including face recognition, computer/robot vision, the ML model should be able to interpret images or objects. This annotation finds mutual applications in offering healthcare services and in smart devices.
- Video annotation – Video annotation, as the name suggests is done to make movement recognizable to machines. For example, video annotation is used to train visual perception in autonomous vehicles.
AI-powered education interfaces will assist teachers with their jobs and make them more productive. The ongoing trend is here to stay! COVID-19 has created an opportunity for us to experiment and deploy new technologies to make education a meaningful and delightful experience for learners. The transformation is real and AI has the capacity to make it ideal. In this regard, AI will empower.