To be clear, we are not talking about gaming the system to get around the need for quality content and authentic engagement. Trust us, plug-ins, ploys, and hacks won’t work in the long run. LinkedIn’s machine learning is smarter than you think, and they are pretty diligent about cracking down on things that violate their policy. Instead, we advocate working with the system to create a great experience for your audience.
So, let’s explore some of the elements that influence LinkedIn’s algorithm, so you can make the most of it. A basic understanding of how LinkedIn decides to remove or amplify content is an important part of improving your reach on the platform.
Currently, LinkedIn utilises a four-step process that incorporates both machine learning and human decision-making to determine what content is distributed across its network:
INITIAL FILTER Every time you post to LinkedIn, their system immediately analyses the content and classifies it as spam, low quality, or approved.
AUDIENCE TESTING The second stage filter measures the reactions a sample audience has towards low-quality and approved content. This sample audience is matched with your post based on ‘multi-objective optimisation’ — such as the content they’ve liked and shared before, who they’ve interacted with, and how frequently.
CONTENT SCORING Based on the test audience reaction, LinkedIn scores your content. Different user actions have different weights in the algorithm. For example, a ‘like’ may get your content one point, while a comment earns it two points. LinkedIn is serious about fighting spam accounts, so they also consider your profile and credibility. The better you score, the more visible your content becomes and vice-versa.
EDITOR ASSESSMENT LinkedIn employees may also do manual spot checks that affect whether your post continues to appear in the news feed. If the post is working particularly well (i.e. it is of high-quality and driving engagement), then the editors can recommend that the algorithm share your content with people outside your network (e.g. showing in ‘trending content’).
YouTubesaid on Monday it is taking broader steps to make the video service more appealing to educators and learners and also provide more monetization avenues to the creators.Google unveiled Courses, a feature that will seek to bring a structured learning experience on YouTube
Teachers/Creators will be able to publish and organize their videos and provide text reading materials and questions right on the video app. Viewers who buy a Course/Learning Module will be able to watch the videos without ads. They will be allowed to offer the content for free or charge a fee. The feature is currently in beta and will roll out to users in India “soon,” and will represent a “new monetization option for Indian creators,”
Edtech in India has grown phenomenally in the last couple of years, making India the edtechcapital of the world. The private sector is playing a key role with the public sector acting as a facilitator. India’s education sector saw a boom in edtechfunding during the pandemic. There were/are certainly some advantages of edtech over conventional learning that were be considered.
In India are several different models/startups of eLearning that are used india, depending on the needs of the learners and the goals of the learning in Indian Audience. Some commonly picked eLearning models/startups include:
Self-paced learning: This is a model in which learners are given access to course materials and can work through them at their own pace. Open EdTech Models like Immerse, Kide Science and ABA English.
Instructor-led online courses: This model is similar to a traditional classroom, with a teacher or instructor leading the course and interacting with students through video conferencing or other online tools like Smart Course, Miho and GrabGuidance.
Blended learning: This model combines online and in-person instruction, with students participating in both types of learning like Athena Education, GuruQ and STEMROBO.
Flipped classroom: This model involves students completing course materials on their own time, then coming together for in-person or online discussions and activities with the instructor like PlayPosit, Verso and ClassFlow.
Gamified learning: This model uses game-based elements, such as points, badges, and leaderboards, to engage and motivate learners BYJU’s, Khan Academy and SoloLearn.
‘Thinking Digital to being Digital’ – The rise of Indian EdTech The country has witnessed growth in the IT sector and a shift towards education; between January 2014 and September 2019, more than 4,450 EdTech startups have been launched in India.
The sector got a much-needed push thanks to digital adoption; close to half a billion internet and smartphone users contributed to it. The EdTech sector in India can be compared in two phases – pre-COVID-19 and during/post-COVID-19. Prior to the pandemic, the EdTech sector was growing in India but at a relatively slower rate as online education was still met with some resistance. The lack of technology posed a major concern of reskilling and upskilling for teachers. While the pandemic has wreaked havoc across sectors and industries in the Indian market, it has been a watershed moment for India’s EdTech sector.
User input: ChatGPT takes in user input in the form of natural language text, such as a question or statement.
Preprocessing: The user input is preprocessed to prepare it for input to the GPT-3 model. This might involve tokenizing the text, adding special tokens to mark the beginning and end of the input, or performing other types of preprocessing.
GPT-3 generation: The preprocessed user input is fed to the GPT-3 model, which generates a response based on the input and its knowledge of the language.
Postprocessing: The generated response is post processed to clean up any errors or inconsistencies and to make it more appropriate for use in a chatbot context. This might involve removing repetitive or nonsensical text, adding appropriate punctuation, or other types of postprocessing.
Output: The final, postprocessed response is output to the user.
The GPT-3 model used by ChatGPT is a deep learning model that uses a transformer architecture. It is trained on a large dataset of natural language text and is able to generate human-like responses to a wide range of inputs.
GPT (Generative Pre-trained Transformer) is a family of large, deep learning-based language generation models developed by OpenAI. The GPT models are trained on a massive dataset of natural language text and are able to generate human-like responses to a wide range of inputs.
There are several versions of the GPT model, including:
GPT: The original GPT model, released in 2018.
GPT-2: An updated version of the GPT model, released in 2019.
GPT-3: The latest version of the GPT model, released in 2020. GPT-3 is significantly larger and more powerful than the previous versions of the model.
Each version of the GPT model is based on the transformer architecture, which is a type of neural network that is particularly well-suited for language processing tasks. The specific details of the GPT model architectures and training processes are not publicly disclosed by OpenAI.
How does Chat GPT process outputs on the basis of training data? ChatGPT (and GPT-3 more generally) processes data that it has been trained on to generate responses. GPT-3 is a large, deep learning-based language generation model that is trained on a massive dataset of natural language text. The model is designed to learn the patterns and structures present in the training data and use that knowledge to generate human-like responses to a wide range of inputs. When you send an input to ChatGPT (or any other service that uses GPT-3), the input is processed by the model and used to generate a response based on the patterns and structures learned during training. The generated response is then output to the user.In this way, ChatGPT (and GPT-3 more generally) relies on the training data to generate responses, as the model has learned from that data and uses that knowledge to generate appropriate responses to new inputs.
What is the huss all about? Generative AI refers to artificial intelligence systems that are able to generate new content or data that is similar to a training dataset. This can include generating text, images, music, or other types of data.
Generative AI systems use a variety of techniques, such as deep learning, to learn the patterns and structures present in the training data and then use that knowledge to generate new, original content that is similar in style or content to the training data. These systems can be used for a wide range of applications, including language translation, image generation, and music composition.
Some examples of generative AI include language translation models that can translate text from one language to another, image generation models that can create new images based on a given set of inputs, and music generation models that can create original compositions based on a set of musical styles or genres.
How does it work? Generative AI systems work by learning the patterns and structures present in a training dataset, and then using that knowledge to generate new, original content that is similar in style or content to the training data.
There are a number of ways that can be used to build generative AI systems, including deep learning, which involves training a neural network on a large dataset and then using the learned patterns to generate new content.
To train a generative AI model, the model is typically fed a large dataset of training examples. The model then analyzes the dataset and learns the patterns and structures present in the data. Once the model has learned these patterns, it can use that knowledge to generate new, original content that is similar to the training data.
For example, if a generative AI model is trained on a dataset of images of animals, it might learn to recognize patterns such as the shape of an animal’s head, the color of its fur, and the way it moves. Once the model has learned these patterns, it can generate new images of animals that are similar to the training data but also unique and original.
There are many different approaches to building generative AI systems, and the specific techniques used will depend on the type of data being generated and the goals of the model.
There are several types of generative AI systems, including:
Autoregressive models: These models generate new data by predicting the next value in a sequence based on the previous values. For example, an autoregressive model might be used to generate new text by predicting the next word in a sentence based on the previous words.
Generative adversarial networks (GANs): These models consist of two neural networks: a generator and a discriminator. The generator generates new data, while the discriminator tries to distinguish the generated data from real data. The generator and discriminator are trained together, with the generator trying to produce data that is difficult for the discriminator to identify as fake, and the discriminator trying to accurately identify fake data.
Variational autoencoders (VAEs): These models consist of an encoder and a decoder. The encoder takes in data and maps it to a latent space, while the decoder takes a latent representation and generates new data. VAEs can be used to generate new data that is similar to the training data, as well as to perform tasks such as data compression and denoising.
Normalizing flow models: These models use a series of invertible transformations to map data from a simple distribution (such as a standard normal distribution) to a more complex distribution (such as the distribution of the training data). Normalizing flow models can be used to generate new data that is similar to the training data.
These are just a few examples of the types of generative AI systems that exist. There are many other approaches to building generative AI models, and the specific techniques used will depend on the type of data being generated and the goals of the model.
How do we train a dataset for AI applications?
To train a generative AI model, you will need a training dataset that contains examples of the type of data that you want the model to generate. For example, if you want to train a generative AI model to generate images, you will need a dataset of images.
To train the model, you will typically follow these steps:
Preprocess the training data: This might involve cleaning the data, formatting it in a specific way, or performing other types of preprocessing to make it suitable for training.
Split the training data into a training set and a validation set: The training set is used to train the model, while the validation set is used to evaluate the model’s performance during training.
Choose a model architecture and hyperparameters: The model architecture refers to the structure of the model, including the number and size of layers, the type of activation functions used, and other details. The hyperparameters are values that are set before training, such as the learning rate and the batch size.
Train the model: This involves feeding the training data to the model and using an optimization algorithm to adjust the model’s weights and biases so that it can learn to generate data that is similar to the training data.
Evaluate the model on the validation set: This involves using the model to generate data and comparing the generated data to the validation data to see how well the model is performing.
Fine-tune the model: If the model’s performance is not satisfactory, you may need to adjust the model architecture, hyperparameters, or other aspects of the model to improve its performance. This process is known as fine-tuning.
Once the model is trained and fine-tuned, you can use it to generate new, original data that is similar to the training data.
What is a chatbot? A chatbot is a type of computer program that uses natural language processing (NLP) to simulate human conversation. It is designed to understand the intent behind user input and provide an appropriate response. Chatbots can be integrated into messaging platforms, websites, and mobile apps, and they can be used for a variety of purposes, such as customer service, information gathering, and entertainment. The technology behind chatbots has advanced significantly in recent years, with the use of deep learning algorithms and other techniques to improve their ability to understand and respond to user input.
This has led to the development of more sophisticated chatbots that are better able to handle complex conversations and provide more relevant and accurate responses. One key aspect of chatbot technology is natural language processing, which is the ability of a computer program to understand and interpret human language. This involves analyzing the structure and meaning of a user’s input and determining the appropriate response. NLP algorithms can be trained on large amounts of data to improve their accuracy and ability to handle a wide range of input. Another important aspect of chatbot technology is the user interface, which is the way in which the user interacts with the chatbot.
This can take many forms, such as text-based messaging, voice input, or visual elements such as buttons or menus. The user interface should be designed to be intuitive and easy to use, so that users can easily initiate conversations and navigate the chatbot’s capabilities. Overall, chatbot technology has the potential to revolutionize the way that businesses and organizations interact with their customers and users.
By providing quick and convenient access to information and services, chatbots can improve the user experience and help organizations to better meet the needs of their customers.
What are Deep Learning Algorithms?
Deep learning algorithms are a type of machine learning algorithm that uses artificial neural networks to learn from data and make predictions or decisions. These algorithms are called “deep” because they use multiple layers of interconnected nodes, which allow them to learn and represent complex patterns in the data.
Deep learning algorithms are often used for tasks such as image and speech recognition, natural language processing, and time series prediction. They have been shown to be particularly effective for these types of problems, as they are able to learn complex patterns in the data and make highly accurate predictions.
One of the key advantages of deep learning algorithms is their ability to automatically learn and extract features from the data, without the need for manual feature engineering. This makes them well-suited to problems where the data is complex and highly-structured, such as images or text.
Another advantage of deep learning algorithms is their ability to handle large amounts of data, which is essential for many applications such as natural language processing and image recognition. By using multiple layers of interconnected nodes, deep learning algorithms can learn from very large datasets and make highly accurate predictions.
Overall, deep learning algorithms are a powerful tool for solving a wide range of problems in fields such as computer vision, natural language processing, and time series analysis. By using artificial neural networks to learn from data, these algorithms can extract complex patterns and make highly accurate predictions.
What are Deep Learning Algorithms Types Currently there in the market?
There are several types of deep learning algorithms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning (DRL) algorithms. Each of these types of algorithms has its own unique characteristics and is well-suited to specific types of problems.
Convolutional neural networks (CNNs) are a type of deep learning algorithm that is commonly used for image recognition and processing tasks. These algorithms are based on the structure of the visual cortex in the human brain, and they use a combination of convolutional and pooling layers to learn and extract features from images.
Recurrent neural networks (RNNs) are a type of deep learning algorithm that is well-suited to problems involving sequential data, such as natural language processing and time series prediction. RNNs use feedback connections to allow the network to remember and incorporate information from previous inputs, which makes them effective for handling variable-length sequences of data.
Deep reinforcement learning (DRL) algorithms are a type of deep learning algorithm that uses reinforcement learning principles to learn from experience and make decisions. These algorithms are commonly used for problems involving complex decision-making, such as game playing and robotics.
In addition to these three types of deep learning algorithms, there are also other variations and architectures that have been developed for specific applications, such as autoencoders, generative adversarial networks (GANs), and transfer learning. These algorithms may have different structures and characteristics, but they all share the common goal of using deep learning techniques to learn from data and make predictions or decisions.
Statistical confidence algorithms are computer programs that are used to calculate the likelihood that a given result is accurate. These algorithms are commonly used in a variety of different contexts, such as market research, political polling, and scientific experiments.
The exact details of how a statistical confidence algorithm works can vary, but typically it involves analyzing a sample of data to determine the likelihood that the sample accurately represents the larger population. For example, a market research firm may use a statistical confidence algorithm to determine the likelihood that a sample of 1,000 people accurately reflects the opinions of the entire population of a given country.
One of the key benefits of using a statistical confidence algorithm is that it can help to improve the accuracy of results. By calculating the likelihood that a given sample is representative of the larger population, the algorithm can help to identify potential biases or errors in the data. This can help researchers and analysts to make more informed decisions and improve the reliability of their results.
In addition to improving accuracy, statistical confidence algorithms can also help to save time and resources. By using data and computer science to calculate the likelihood of a given result, the algorithm can help researchers and analysts to focus their efforts on the most reliable results. This can help to reduce the need for additional research or experimentation, which can ultimately save time and resources.
Overall, statistical confidence algorithms are a valuable tool for researchers and analysts who need to determine the accuracy of their results. By using data and computer science to calculate the likelihood that a given result is reliable, these algorithms can help to improve the accuracy of results and save time and resources.
A sorting algorithm is a computer program that is used to rearrange a sequence of items in a specific order. These algorithms are commonly used to arrange data in a way that makes it easier to search, analyze, or process.
The exact details of how a sorting algorithm works can vary, but typically it involves comparing items in the sequence and swapping them based on certain rules. For example, a simple sorting algorithm might compare each item in the sequence with its neighbors and swap them if they are in the wrong order. This process is repeated until the entire sequence is sorted.
One of the key benefits of using a sorting algorithm is that it can help to improve the efficiency of data processing. By rearranging the data in a specific order, the algorithm can make it easier and faster to search or analyze the data. This can be particularly useful for large datasets that would be difficult to process manually.
In addition to improving efficiency, sorting algorithms can also help to improve the accuracy of data analysis. By arranging the data in a specific order, the algorithm can make it easier to identify patterns or trends in the data. This can help analysts to make more informed decisions and improve the accuracy of their results.
Overall, sorting algorithms are a valuable tool for anyone working with large datasets. By using data and computer science to rearrange the data in a specific order, these algorithms can help to improve the efficiency and accuracy of data processing and analysis.
Matchmaking algorithms are computer programs that are used to match individuals together based on certain criteria. These algorithms are commonly used by dating websites to help match individuals with compatible partners. The exact details of how the algorithm works can vary, but typically it involves analyzing data such as age, location, interests, and other factors to determine the likelihood of a successful match.
One of the key benefits of using a matchmaking algorithm is that it can help to save time and effort for both individuals and the website itself. By using data and computer science to calculate compatibility, the algorithm can quickly and efficiently match individuals who are likely to be compatible. This can save users from having to spend time browsing through potential matches and instead allows them to focus on getting to know their matches better.
In addition to saving time, matchmaking algorithms can also help to improve the quality of matches. By analyzing a variety of different data points, the algorithm can help to identify matches that are more likely to be successful. This can help to reduce the likelihood of individuals being matched with partners who are not compatible, which can ultimately lead to better relationships.
Overall, matchmaking algorithms are a valuable tool for dating websites and individuals looking for compatible partners. By using data and computer science to calculate compatibility, these algorithms can help to save time and improve the quality of matches.
Humanistic psychology is a theoretical approach to understanding human behavior that emphasizes the inherent goodness of people and the importance of individual subjective experience. It is based on the ideas of humanistic psychologists such as Carl Rogers and Abraham Maslow, who believed that all individuals have an innate drive towards self-actualization, or the realization of their full potential.
According to humanistic psychology, people are not simply the product of their environment or their biology, but rather they have the capacity for self-direction and self-determination. This means that people have the ability to make choices and decisions about their own lives, and to take responsibility for their own actions. Humanistic psychologists believe that this capacity for self-direction is the key to personal growth and development, and that it is the foundation of mental health and well-being.
One of the key ideas in humanistic psychology is that people have a need for positive relationships and positive experiences in order to thrive. This means that people need to feel valued, accepted, and respected by others in order to develop a sense of self-worth and to reach their full potential. Humanistic psychologists believe that when people are able to form positive relationships and have positive experiences, they are able to grow and develop in healthy ways.
Humanistic psychology also emphasizes the importance of personal meaning and purpose in human life. According to this view, people need to have a sense of meaning and purpose in order to feel fulfilled and satisfied with their lives. This can come from a variety of sources, such as personal relationships, religious or spiritual beliefs, or personal goals and aspirations.
Overall, humanistic psychology is a holistic and compassionate approach to understanding human behavior that emphasizes the inherent goodness of people and the importance of personal growth and self-actualization. It provides a valuable perspective on mental health and well-being, and can help individuals to achieve their full potential and lead fulfilling lives.
There are many different case studies that have been published in the literature of humanistic psychology. Here are a few examples:
The case of Mary: Mary was a young woman who struggled with low self-esteem and feelings of inadequacy. Through the course of therapy, she was able to develop a more positive self-image and a greater sense of self-worth, which helped her to overcome her psychological problems and lead a more fulfilling life.
The case of Jake: Jake was a young man who struggled with anxiety and depression. Through the course of therapy, he was able to develop a greater sense of personal meaning and purpose, which helped him to overcome his psychological problems and find greater fulfillment in his life.
The case of Sarah: Sarah was a young woman who struggled with eating disorders and body image issues. Through the course of therapy, she was able to develop a more positive body image and a greater sense of self-acceptance, which helped her to overcome her psychological problems and lead a more fulfilling life.
The case of John: John was a middle-aged man who struggled with feelings of isolation and loneliness. Through the course of therapy, he was able to develop a greater sense of connection and belonging, which helped him to overcome his psychological problems and lead a more fulfilling life.
These case studies illustrate the ways in which humanistic psychology can help individuals to overcome psychological problems and achieve personal growth and self-actualization. By focusing on the inherent goodness of people and the importance of positive relationships and experiences, humanistic psychology provides a valuable perspective on mental health and well-being.
Mohammad Jazim 