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E-learning was driven due to the current state of things and the COVID-19 pandemic and global lockdown. According to the forecast even before the coronavirus, the worldwide e-learning market was expected to grow to $325 billion in 2025. The technology evolves with new features added and possibilities opened. If you want to know how AI (artificial intelligence) can improve your e-learning solutions, then this article is a must-read to the end.

8 Ways Artificial Intelligence is transform e-learning

AI market is forecasted to reach $190.61 billion by 2025, reshaping a lot of industries, including e-learning. Thus, AI is a trend in 2020 and beyond. The implementation of AI technologies in e-learning allows us to leverage e-learning solutions (LMSs, LXPs, LAPs, etc.) to a greater extent. Yes, AI is transforming the way we learn, unlocking new and new educational possibilities. And here are 8 ways AI dramatically changes the e-learning industry.

Define Learner’s Pathways

One of the key features of AI is the ability to gather and analyze the data of the individuals each time they interact with the technology. That’s what can help to define the learner’s pathways through the education process more efficiently. According to the questions asked by the end-user, AI technology can:

• Determine the next level to achieve by the learner;
• Direct him/her to that level by providing the relevant content;
• Help to get there in line with the learner’s already acquired skills, knowledge, experience as well as individual learning style and pace.

Personalized Tutoring Session

It’s no secret anymore that the learning process is not a one-size-fits-all solution. Every learner has its own learning style, pace, and abilities. Today we used to get personalized shopping and entertainment, so why not personalize the education?

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Just like Amazon or Netflix personalize the content, list of recommendations, and ads according to the user’s preferences, likes, previous buys/watched movies, AI technology can customize the tutoring sessions to the learners. So that AI implemented in the e-learning solution can:

1. Track the learner’s previous performance;
2. Identify the gaps in the proficiency of each learner;
3. Use the data for alerting the educational materials accordingly, thus personalizing the learning experience.

Content Analytics

Online learning means the great pool of materials inaccessibility: texts, documents, media, audio, images, and so on. Usually, the major part of all the data is unstructured and it’s a challenge for teachers and administrators to process it accurately. AI has the ability to process and analyze large data sets quickly and efficiently, providing both teachers and users with the ability to get more out of the course materials.

Thus, the technology implemented can spot patterns and trends; get insights about the individual learning style, pace, and gaps of the learner; and after that highlight that to the user for interpretation of the data and further decisions.

More Targeted Marketing

Currently, many businesses collect more user’s information than they need to and know what to do with. Such big data collected can be really overwhelming for companies because it must be safely stored somewhere. Besides, due to the fact the companies can’t properly process all the gathered info, they can’t offer targeted marketing.

First and foremost, AI can identify which data points are crucial and relevant, and then stop gathering unnecessary user information. Besides that, technology can analyze the data more efficiently to deliver better-targeted marketing materials and advertising. Thus, the most engaged audience will see your online course ads.

AI-based Virtual Assistance

Virtual assistants have become a common thing in our life and work. They provide help, answer the questions, guide us on various matters, and so on. Thus, AI-based chatbots are cost-effective, fast, and accurate assistants in different fields like shopping, healthcare, etc. They can be also used for providing real-time support in education.

Besides the around-the-clock counseling, AI-based assistants can understand the human language and respond in a conversational manner thanks to machine learning (ML) and natural language processing (NLP). Due to the NLP, the virtual assistants can monitor the user’s behavior, provide additional educational materials, impart expert knowledge, give custom-tailored feedback and guidance, and on and on. Thereby the technology boosts the engagement and productivity of the users.

Deeper engagement with Virtual Reality (VR)

A combination of VR and AI technologies can level up online education. With a mix of the solutions, you can create online training simulations and provide the users to go through in detail real-life scenarios. Thus, the learners can immerse themselves in a learning environment, study the subject deeper, get some practice, and gauge the understanding of the subject, etc. Besides that, such interaction can enhance the user experience and improve their engagement.

Automatic Grading

Besides all the aforementioned capabilities of the AI technologies, by integrating the solution you can also leverage powerful automation tools that facilitate grading. Integration of the solution can hasten the process of checking and grading the learners’ papers in different languages. It frees the time of the teachers for preparing materials for online courses, communication with the learners, and so on.

Real-Time Questioning

AI is fast, accurate, and efficient when it comes to responding to queries. Sometimes, it’s vital to get some answers immediately without needing to go to the teacher or searching the info on the net and disrupting the learning process. AI technologies can process user queries and respond to them in real-time with all the reasoning and clarification conveyed. AI integrated into the e-learning courses also reveals the resources, suggests learning assets, and eliminates the efforts and time needed to do all that manually.

Bottom Line

Such emerging technologies as artificial intelligence are transforming a lot of industries, including e-learning. AI implemented into the e-learning solutions helps to:

• Create custom-tailored learning paths;
• Personalize the online courses, providing the relevant materials to appropriate learners;
• Analyze the content for improving the engagement of the learners;
• Make the learning process and grading more automated and efficient.

Definitely, AI has boundless potential in the education industry. So, the integration of technology becomes vital for staying competitive. Certainly, it takes some time to take over the entire industry, yet AI technology is reshaping the way we learn here and right now.

Don’t forget to give us your ? !

The Role of Artificial Intelligence in E-learning was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Self-driving cars need more precise visual training to detect or recognize the objects on the street and ride in the right lane to avoid collisions. Actually, autonomous vehicles can visualize the entire scenario of the natural environment to take action while running on the road. And to perceive the different objects the AI model used for self-driving needs to be trained with accurate machine learning data sets.

From a high-resolution camera to LIDAR sensors, an autonomous vehicle needs a huge amount of information to perceive it’s surrounding to keep moving safely. So, right here we will discuss the leading image annotations techniques that help to detect, recognize or classify the different types of objects allowing the self-driving vehicles to drive without the help of humans.

Types of Annotations for Autonomous Vehicle Training

Though, there are diverse image annotation techniques, but few of them like bounding box, cuboid annotation or semantic segmentation is widely used for creating the training data sets for such highly sensitive visual perception models. Hence, we have discussed only the most useful technique that can help you to improve your computer vision-based autonomous driving vehicle model.

2D Bounding Boxes for Object Detection

Video: Bounding Box Annotation for Machine Learning

Using bounding box, various objects can be annotated including traffic lights, cyclists, pedestrians and other makingsuch objects recognizable to the autonomous vehicle through computer vision. It is basically a 2D bounding box technique for object detection.

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3D Cuboid Annotation for in-depth Recognition of Objects

Using the 3D cuboid annotation, self-driving cars can sense the distance of each object from the vehicle and measure the spacing avoiding the chances of collision with them. Anolytics provides 3D cuboid annotation with the best level of accuracy for in-depth dimensional object detection.

Semantic Segmentation for Better Understanding of Surroundings

It can segment various objects on the street likea street lamp,road,vehicle, building, pedestrian, sky, etc. to aid in scene understanding. Anolytics can create data sets of such high-resolution images with semantic segmentation to identify the objects and events for situational understandings accurately.

Also Read: How To Label Data For Semantic Segmentation Deep Learning Models?

Line or Polyline Annotation for Precise Lane Detection on Road

Video: Polylines Image Annotation Services for Machine Learning & AI

Anolytics can annotate road lanes including shoulder lanes, single lane, broken lane, double lane and sidewalks or edge roads for accurate lane detection by self-driving cars. It can develop training data sets with edge-to-edge marking through the polyline annotation technique at best pricing.

Polygon Annotation for Irregular Shaped Objects Recognition

Anolytics are providing the polygon annotation with the right mix of semantic segmentation for asymmetrical objects detection observing by visual perception models more accurate.It can annotate road marking, road signboards, logos and other vehicles in polygon shapes.

Video: Polygon Annotation Service for Computer Vision

These image annotations techniques can help your computer vision-based algorithm for autonomous vehicles to better understand the scenario and work without any trouble. Each image is annotated with world-class tools and software to create high-quality training data sets for autonomous vehicles and self-driving cars at best pricing with timely delivery of projects.

Also Read: Five Reasons Why You Need To Outsource Your Data Annotation Project

Don’t forget to give us your ? !

How to Improve Computer Vision in Autonomous Vehicles using Image Annotation Services? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/how-to-improve-computer-vision-in-autonomous-vehicles-using-image-annotation-services-9839dda662b6?source=rss—-5e5bef33608a—4