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Artificial Intelligence

AI for Online Content Moderation

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More and more people are exploring various online platforms that allow for uploading user-generated content. Every day millions are uploading content either in the form of blogs, images, or videos to online platforms. Some are even making a living out of user-generated content and have made online platforms an integral part of their lives. But unlike the moderation rules associated with traditional offline media, the content on the internet is not subject to any editorial controls. This means users can post content that is cruel and insensitive to others especially children, pornographic content, or the ones that promote violence or terrorism.

There has arisen a need for moderation of content that goes live every other day over the internet. And the onus falls on the online platforms to review the content and flag and remove the inappropriate ones. These platforms are employing thousands of content moderators to vet any new content that is uploaded online. Globally, more than 100,000 people are moderating online content. Facebook for instance has employed 7500 moderators who moderate the content uploaded on their platform based on the rules set by the company.

What is the need for AI in Content Moderation?

The pace at which the user-generated content has been uploaded to the online platforms has made it difficult to identify and remove harmful content using the traditional human-based moderation. AI-based automation systems can assist humans in online content moderation and offer the scale and speed required to match the pace at which online content has been uploaded. This has been possible with the recent advancements in AI along with the availability of data and low-cost computational power needed to create new and improved algorithms.

AI-based moderation systems follow two approaches – content-based and context-based.

Content-based moderation systems can review text, image as well as videos. Named Entity Recognition, an important technique in Natural Language Processing is used for recognizing harmful content such as fake news, hate speech, harassment, etc. While sentiment analysis is used for classifying and labeling content based on the level of emotions involved. Semantic Segmentation, object detection – techniques of computer vision are used for analyzing images and videos.

Context-based moderation involves making the AI learn to understand the context or in simpler terms reading between the lines from various sources.

AI-Based Online Content Moderation Challenges

AI is aiding humans in online content moderation and helping to improve the pace at which content is moderated daily. But still, there are certain challenges that the machines have to overcome to perform efficiently and accurately in the long run.

There is a broad range of content that can be classified as harmful content ranging from child abuse content to spam, insensitive, violent and graphic content, extreme content, hate speech, and others. Some of these can be identified from the content alone while most of it requires the need to understand the context. A wide range of factors such as cultural, societal, political, and historical factors play a role in understanding the context and these contextual considerations vary as per the law of the land and what societies deem as acceptable. So, interpreting the context consistently is a challenge for AI-based systems.

Role of humans in training AI on content moderation

Since context plays an important role in moderating the content online, the role of training the AI-based system to read between the lines has fallen on humans. The rise of human data labelers has aided in the development of AI-based automated content moderation systems. Humans curate and organize the data as part of the data labeling process. They will first comb through the data and label what is appropriate and flag that’s inappropriate content. This helps to train the machines to recognize harmful content and process and moderate billions of user-generated content on online platforms. For AI to moderate content effectively, a mix of human data labelers and moderators is the need of the hour.

This is where data labeling companies like Data Labeler come into the picture. With 1000+ human data labelers working around the clock, we provide the labeled data to help train your AI-based systems for content & context-based moderation. Our team of data labelers will label the data as per your set specific guidelines and objectives to meet your company’s standards and policies. Contact us now for high-quality training datasets required for developing contextually aware AI-based moderation systems.

Categories
Deep Learning

Attention Mechanism in Deep Learning

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The introduction of Attention Mechanism has revolutionized the way we work with deep learning models. It is one of the most valuable developments that has given rise to many recent advancements in Natural Language Processing like the Transformer model and Google’s BERT. In this blog, we will explore the concepts behind Attention, its type, and applications in Transformers.

What is Attention?

Attention generally refers to the process of selectively focusing on a specific thing or a topic while ignoring all others. The Attention Mechanism in Deep Learning is also based on a similar concept where it selectively focuses on certain factors during data processing while ignoring the remaining factors. It is the main component of a network’s architecture that helps to manage and measure the quantity of interdependence between the input and output elements and within the input elements.

Why Attention is better than the standard sequence-to-sequence model?

The drawback of the seq2seq models was its inability to process long input sequences accurately. This is due to its limitation of considering only the last state of the encoder RNN as the context vector for the decoder. Attention mechanism was introduced as a solution to overcome this problem. During the process of decoding, it retains and uses all the hidden states of the encoder RNN and maps the output of the decoder to all the hidden states of the input sequence.

Types of Attention Models

The attention models can be categorized into two major types: Bahdanau Attention and Luong Attention. The major differences between these models lie in their computations and architecture while the underlying principles remain the same.

Bahdanau Attention

This model is also called as an Additive model and was proposed by Dzmitry Bahdanau in one of his papers that was aimed at improving the seq2seq model in Machine Learning tasks. It attempted to align the decoder with the right input sentences and then implementing the Attention mechanism.

Here’s how the attention mechanism was implemented in Bahdanau’s paper:

  1. The encoder creates hidden states for each element of the input sequence
  2. Alignment scores are calculated between each of the encoder’s hidden states and the previous decoder hidden state
  3. The alignment scores of each encoder hidden state are combined and converted into a single vector post which it is softmaxed
  4. A context vector is created by multiplying the encoder hidden states and their alignment scores
  5. The new output is produced by concatenating the context vector with the previous decoder output and fed along with the previous decoder hidden state into the decoder RNN for a particular time step
  6. The steps from 2 to 5 repeat itself for each of the decoder’s time step until the output is beyond the specified max length or a token is generated.

Luong Attention

This type is also called Multiplicative Attention and was built on top of the Bahdanau Attention. It was proposed by Thang Luong. The main differences between the two lie in their ability to calculate the alignment scores and the stage at which the Attention mechanism is introduced in the decoder.

Here’s how the attention mechanism was implemented in Luong’s paper:

  1. The encoder creates hidden states for each element of the input sequence
  2. A new hidden state is created for a particular time step by passing the previous decoder output along with its hidden state
  3. Alignment scores are calculated using the encoder hidden states and the newly created decoder hidden state
  4. A single vector is created by combining alignment scores for each encoder hidden state which is then softmaxed
  5. A context vector is generated by multiplying the encoder hidden states and their alignment scores
  6. The new output is produced when the decoder hidden state created in step 2 is concatenated with the context vector
  7. The steps from 2 to 6 repeat itself for each of the decoder’s time step until the output is beyond the specified max length or a token is generated.

Looking for a FREE consultation? Reach out to us at sales@datalabeler.com for top-quality data labeling services.

Categories
Machine Learning

How to Choose the Right Machine Learning Algorithm?

Datalabeler_Machine Learning Algorithm

Choosing the right Machine Learning algorithm is a tough task as it plays a major part in the success of your AI project. You have to choose over a range of factors before deciding on the one that best suits your use case or business problem. In this blog, we will take you through a list of major factors that helps you in selecting the right model for a particular task. 

Before we start, let’s have a look at the different types of Machine Learning algorithms:

Supervised Learning

In supervised learning, the algorithm uses training data having both input and output labels to create a mathematical model

Unsupervised Learning

In unsupervised learning, the algorithm uses data that only has input features without any output labels to build a model.

Reinforcement Learning

In reinforcement learning, the model performs a set of actions and makes decisions. It then improvises itself by learning from the feedback from its previous actions and decisions.

Important Factors Worth Considering While Choosing a ML Algorithm

Data

The first and foremost factor you need to consider while choosing an algorithm is your data. You need to understand the data type, its characteristics, and size by visualizing the data and identifying the hidden patterns in it.

You can categorize your data into input and output data. If the input data is labeled, then it is best to use a supervised learning model, or if otherwise, an unsupervised learning model will fit in. The type of your output data can also help in determining the right ML model. For instance, the regression model works better for numeric output data while for a set of groups, the clustering model is the best.

The means by which your data is formed also plays a role. For linear data, you may require a linear model whereas, for complex data, an algorithm like random forest will work.

The performance of your algorithm depends on the size of your training datasets. Algorithm having high bias or low variance classifiers work better for shorter datasets whereas, for larger datasets, algorithms with low bias or high variance will work better. 

Accuracy

The accuracy of a model can be defined as its ability to predict the right outcome from its observation that can be close enough to the actual response for a particular observation set. The accuracy of your model is determined by the type of problem you are trying to solve.  

Models can be categorized as flexible and restrictive based on the range of shapes they produce of the mapping function. Restrictive models produce a small range of shapes while flexible models produce a wide range of shapes. 

Restrictive models are preferred when inference is the goal and you would like to achieve interpretability. Flexible ones are preferred when high-accuracy is your goal. The interpretability of a model decreases as its flexibility increases.

Speed

Speed here generally refers to training time. If you want to achieve higher accuracy, then you may have to train your model using larger training data which again requires a longer time. Speed & accuracy are opposite to each other. If you are short on time, use a simpler algorithm and if accuracy is more important to you, a more complex algorithm will be useful for your AI project

Number of parameters & features

Parameters determine the behavior of an algorithm. Error tolerance, number of iterations, options between variants are some of the parameters that will affect how your algorithm behaves. Most of the time, the number of parameters determine the time needed to train and process the data. As the number of parameters increases, the training and processing time also increases.

Based on the number of data points, the number of features of a dataset varies. A dataset with a large number of features may bog down a few algorithms. It is best to use an algorithm such as SVM that will work for apps having a large number of features.

About Data Labeler

Data Labeler helps AI companies develop smart machine learning models by providing high-quality datasets that can train, validate, and test their models. If you are looking for the best data labeling companies in Philadelphia, drop a mail to sales@datalabeler.com

Categories
Natural Language Processing and Deep Learning

Transformers – A Deep Learning Model for NLP

Datalabeler_deep_learning

The Transformer is a Deep Learning Model that was introduced in 2017 and is mainly used for Natural Language Processing Tasks. It is mainly designed to handle sequential data for carrying out tasks such as text summarization and translation.

Let’s take a deep dive into its architecture and why it is considered better than the Recurrent Neural Networks.

Encoder & Decoder Architecture

Transformers has an encoder-decoder architecture. The encoder consists of two important components; a feed-forward neural network and a self-attention mechanism. The decoder consists of three important components; a feed-forward neural network, a self-attention mechanism, and an attention mechanism over the encodings

Both encoder and decoder are modular, having modules that can be stacked one on top of each other multiple times. Each encoder module processes the input to generate encodings which are then passed as inputs onto the next encoder module. The encodings generally contain information on the parts of the inputs that are relevant to each other.

The decoder modules on the other hand process the encodings and generates an output sequence by using the contextual information incorporated within the encodings. Each of the encoder and decoder layers uses the attention mechanism to weigh the relevancy of every input and extracts information from them accordingly to generate the output. Each decoder layer comes with an additional attention mechanism that helps to extract information from the outputs of previous decoders. This takes place before the decoder can even draw information from the encodings. Both the encoder and decoder layers rely on a feed-forward neural network for additional processing of the output.

Why Transformers Are Preferred Over RNNs?

Most of the Natural Language Processing systems till recently were dependent on gated recurring neural networks (RNNs) such as Long short-term memory (LSTMs) and gated recurrent units (GRUs) having additional attention mechanisms. But after the introduction of Transformers, it has started to replace the older RNNs like LSTMs.

Even though both RNNs and Transformers can handle sequential data, unlike the former, the latter doesn’t require the sequential data to be processed in the order. This means when a transformer model is processing a natural language sentence, it doesn’t have to process it from the beginning. Hence, Transformers allows for more parallelization when compared to RNNs, and therefore requires less training.

The transformers were built using attention technologies without using an RNN structure. This highlights the fact that the attention mechanism alone minus the recurrent sequential processing can achieve the performance of RNNs.

Since Transformers facilitate more parallelization than older RNNs, it can easily enable training on larger datasets thereby making the development of pre-trained systems possible such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT). These systems were trained using larger datasets of general language and as a result, can be customized to perform specific language tasks.

Trust Data Labeler with All Your Human Data Annotations Needs

Data Labeler specializes in building comprehensive datasets that are perfect for training your ML models. Even though Data Annotation is a very significant part of your AI/ML undertaking, you don’t have to worry about spending time annotating data yourself. We will do the heavy weight-lifting part while you focus on optimizing your AI/ML models to perfection. Write to us at sales@datalabeler.com for customized training datasets for your AI/ML projects.