Artificial Intelligence & Latest Trends
There was a time when Artificial Intelligence (AI) was often portrayed as robots. Machines that exhibited human-like characteristics (learning and decision making) with an artificial 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 all the sectors for the better.
Until today, artificial intelligence has outperformed humans in specific tasks. To quote a few examples-
Smartphones to navigate around the world
Amazon Go stores to make your shopping experience easy
Flippy for flipping hamburgers at the CaliBurger restaurant in California.
The worldwide AI market is expected to grow by $120 billion by the end of 2025. The AI software market, on the other hand, is expected to touch $22.6 billion in the year 2020, says TechJury.
Now, researchers and technologists are all set to build artificial intelligence that would outperform humans in every cognitive task. Therefore, machine learning and deep learning are essential aspects of AI; deep learning being the latest breakthrough. It has left no stone unturned in the application, whether it is our personal, professional, or social life.
And AI-powered education seems like a great substitute for a better future.What Makes AI So Special?
The growing popularity of AI is both the cause and consequence for tech giants and businesses investing heavily in it. Intelligent and profitable digital transformation has now transformed our industries. Moreover, fascinating controversies and myths around the future of AI also continue to trigger the curiosity in humans.
The question is what drives intelligence and why is it important for us to know that?
Well, the major role of AI is to make accurate predictions after learning from consumer activities. This connects us back to AI machine learning and deep learning. Every possible data input contributes to the process of decision making. AI algorithms generate business analytics based on different types of annotated data. So, data is the lifeblood of AI. A vast pool of “data” from multiple sources drives this intelligence. This is also known as the “Big Data pool”.
No wonder why AI and Big Data are now seemingly inseparable!How AI & Data Annotation are Related
It is no secret that AI or Machine learning models work on a large proportion of training data. Just like humans, machines, and algorithms also learn with rigorous training. But the process of data input isn’t as straightforward as in humans. This indicates how data annotation is important for thriving in the AI world.Labeling the content of various forms i.e., text, speech, or multimedia, making it discernable to machines is called data annotation. Now that the world is going through a paradigm shift. All the AI and ML companies require large 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 is a critical factor in ensuring that our AI and machine learning projects scale. Even the most technically advanced algorithm is not applicable without data. Data annotation gives us a leg up on the machines as we are responsible for identifying annotating data to the machine. Certain sources reveal how in present-day AI projects, 80% of total project time is spent in data preparation and even the slightest of errors can cause huge damage. Providing machines and algorithms, that access to learnable data is the biggest competitive advantage and will remain so for years to come. This is a reason enough to claim that data annotation is a prospect career and is yet to thrive.
Types of Data Annotation
This makes us wonder what are the different types of data annotations and their implications.
Here’s the answer-
1) Text Annotation
Text annotation is employed for Natural Language processing by machines. AI virtual assistants and chatbots are common applications of text annotation. There are many sub-categories of text annotation, based on different applications.
2) Audio annotation
It is similar to text annotation with the only difference to make Natural Language processing for “speech”, to make it understandable to the machines. In other words, the time-stamping of speech data and transcription for clear identification of pronunciation, modulation, and language is a part of this process.
3) Image annotation
For applications including face recognition, computer/robot vision, the ML model should be able to interpret images or objects. This annotation finds common applications in healthcare services and smart devices.
4) Video annotation
Video annotation, as the name suggests is done to make movement recognizable to machines. For example, video annotation is used to train the visual perception in autonomous vehicles.
Education During & Post Pandemic
The virus has accelerated the trend that was already underway. The global e-learning market was already booming in the last few months. This significantly high growth of adoption is indicated by how global tech investments reached US$18.66 billion in 2019, as indicated in the World Economic Forum.
The student community is also amongst many “hard-hit sections of the pandemic”. As schools and universities close, remote learning emerged as a new normal. Over 1.2 billion students across 186 countries are affected by school closures.
According to the World Economic Forum, report, the overall market for online education is projected to reach $350 Billion by 2025. Thanks to the pandemic. Whether it is online learning software/apps like Coursera, virtual tutoring, language apps, or video conferencing tools as zoom and Google meets, there has been a significant, in usage since COVID-19. Many virtual education platforms, for example, “Byjus” have offered free access to students in response to their high demand. Some other platforms like the Singapore based “Lark” have ramped up their global infrastructure to offer one-stop facilities to teachers and students for effective e-learning experience. But, this unplanned rapid shift towards online education, with hardly any preparation and insufficient bandwidth, will only be unconducive for growth. For this, we rely on AI. It will transform education for better user experience and effective learning.
AI-Powered Education Post Pandemic While social, economic disturbances continue to exist in the pandemic, adaptive learning technologies have the power to orchestrate students towards their careers. The first and foremost challenge lies in providing equal importunity for all students to access live classes, course material, and abundant online learning platforms. Many students (the figures vary across different countries) struggle to participate in digital learning due to unreliable internet connection. There is a significant gap between those who come from privileged backgrounds and those who come from disadvantaged backgrounds.
Secondly, the platform changes, learning, and teaching styles also change. Now that the traditional classes are shut, the world needs to think of sustainable teaching and learning strategies to entrust students with education. Mere telepresence without regulation and adaptability won’t yield much. A controlled yet the unrestricted environment is a must for a fruitful learning experience. And “Adaptive learning” seems like the best antidote.
What is adaptive learning?
Just the one where course material is available online?
Well, that would be an incomplete answer. Adaptive learning is not just about online courses or automated quizzes. It is a broader term that encompasses performance tracking through continuous assessment and immediate and personalized feedback. It is almost like a course material personalized to students’ learning techniques and pace.
Online learning, although, seems more effective if students have access to the right technology. Yet, a structured environment is needed to ensure the effectiveness of online learning. We need to think beyond just replicating the physical classrooms via online video lectures. Intelligent tools for collaboration and student engagement are a rescue for making the trend last longer!
Another important concern is how our institutes should think beyond just rot learning and focus more on “critical thinking” and “adaptability”. The current transition to online education is definitely not a hindrance, rather the first-hand experience to learn these skills.
Besides all the tools that we use today, here’s how AI-powered education will make online learning sustainable