Understanding OCR

OCR is a technology used to convert scanned documents or images containing text into computer-readable text, allowing automated data extraction and analysis.

Real-life Applications of OCR in Automation Testing

Extracting Data: Extract crucial information like invoice numbers from invoices, receipts, or forms. By using this, we can perform validations, ensuring that software correctly processes and stores such information.

Test Data Generation: Reads test data from legacy systems or documents and creates test scenarios and test cases, reducing manual effort in data preparation.

Example 1: Extract product details, prices, and customer information from invoices and purchase orders. This is used to perform end-to-end testing, ensuring accurate order processing and improving customer experience.

Example 2: Digitize prescriptions and medical reports which are used in automated testing of EHR systems, guaranteeing the correct storage and recovery of patient information, medications, and treatment histories.

Introduction to NLP

NLP is a branch of artificial intelligence that helps computers understand, interpret, and generate human language. Its role is to bridge the gap between human communication and machine understanding, allowing software to process, analyze, and respond to text and speech data in a way that resembles human language comprehension.

Real-Time Examples of NLP in Automation Testing

Log Analysis: Identifies patterns and errors in log data, automates the detection of exceptions, and reduces the need for physical log inspection.

Test Case Generation: Converts natural language requirements into executable test cases. By translating textual descriptions of desired functionalities, NLP streamlines test case creation, ensuring that test cases accurately reflect intended behavior and reducing the time required for test design and scripting.

Chatbot Testing: By simulating user conversations with natural language, NLP ensures the chatbot’s understanding and ability to provide appropriate responses, improving overall functionality and user experience.

Accessibility Testing: Assesses the clarity and correctness of textual content for screen readers and visually impaired users.

Localization Testing: Automatically compares source and target language content to ensure that localized versions of software or websites accurately reflect the original text and cultural requirements for various global audiences.

Integration of OCR and NLP

Combining OCR and NLP in automation testing allows for advanced capabilities, such as extracting and comprehending text from images or documents, enabling sophisticated data validation and test case generation.

Extracting Text from Images: OCR can extract text from images, making content machine-readable. NLP can then analyze the extracted text, allowing automation scripts to validate the information in image-based UI testing.

Sentiment Analysis on User Reviews: NLP can perform sentiment analysis on user reviews, categorizing opinions as positive, negative, or neutral. Combined with OCR, you can extract textual reviews from images or unstructured data sources, enabling automation to assess user sentiment without manual data entry.

Benefits of Using OCR and NLP in Automation Testing

The integration of OCR and NLP minimizes manual effort in data entry and test case generation, allowing testing teams to focus on higher-level tasks. Additionally, these technologies excel at handling complex scenarios, such as analyzing vast amounts of textual and visual data, enhancing test coverage, and overall testing effectiveness.

Conclusion

In conclusion, the synergy of OCR and NLP in automation testing promises a transformative leap in efficiency, accuracy, and coverage, ushering in a new era of software quality assurance where intricate testing challenges can be met with ease, precision, and speed.

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Pre-trained models based on Transformers have achieved exceptional performance across a spectrum of tasks within Natural Language Processing (NLP). However, these models often comprise billions of parameters, resulting in a resource-intensive and computationally demanding nature. Consequently, their suitability for devices with constrained capabilities or applications prioritizing low latency is limited. In response, model compression has emerged as a viable solution, attracting significant research attention.

This article provides a comprehensive overview of Transformer compression, centered on the widely acclaimed BERT model. Within, we delve into the most recent advancements in BERT compression techniques, offering insights into the optimal strategies for compressing expansive Transformer models. Furthermore, we aim to illuminate the mechanics and effectiveness of various compression methodologies.

Fig. Pre-training large scale models

Introduction

Tasks such as sentiment analysis, machine reading comprehension, question answering, and text summarization have benefited from pre-training large-scale models on extensive corpora, followed by fine-tuning for specific tasks. While earlier methods like ULMFiT and ELMo utilized recurrent neural networks (RNNs), more recent approaches leverage the Transformer architecture, which heavily employs the attention mechanism.

Prominent pre-trained Transformers like BERT, GPT-2, XLNet, Megatron-LM, Turing-NLG, T5, and GPT-3 have significantly advanced NLP. However, their size poses challenges, consuming substantial memory, computation, and energy. This becomes more pronounced when targeting devices with lower capacity, such as smartphones or applications necessitating rapid responses, like interactive chatbots.

To contextualize, training GPT-3, a potent and sizable Transformer model, on 300 billion tokens costs well over 12 million USD. Moreover, utilizing such models for fine-tuning or inference demands high-performance GPU or multi-core CPU clusters, incurring significant monetary expenses. Model compression offers a potential remedy.

Breakdown of BERT

Bidirectional Encoder Representations from Transformers, commonly known as BERT, constitutes a Transformer-based model that undergoes pre-training using extensive datasets sourced from Wikipedia and the Bookcorpus dataset. This pre-training involves two key objectives:

• Masked Language Model (MLM): These objectives aid BERT in grasping sentence context by learning to predict masked-out words within the text.

• Next Sentence Prediction (NSP): BERT also learns relationships between two sentences through NSP, which predicts whether one sentence follows the other in a given text.

Subsequent iterations of Transformer architectures have refined these training objectives, resulting in enhanced training techniques.

Fig. BERT model

The processing flow of the BERT model divides input sentences into WordPiece tokens, a type of tokenization that strengthens input vocabulary representation while condensing its size. Subworlds are used to break apart complex words to do this. Notably, these subworlds can create new words not in the training set, strengthening the model’s resistance to terms that aren’t in its lexicon. BERT is characterized by a classification token ([CLS]) before input tokens. The output corresponding to this token is used for tasks aiming at the whole input. Sentence pairs involved in situations are concatenated with a separator character ([SEP]) between them.

Each WordPiece token in BERT is encoded using three vectors: ticket, segment, and position embeddings. These embeddings are summed and fed through the model’s core, the Transformer backbone. This results in output representations directed into the final layer, tailored to the specific application (for instance, a sentiment analysis classifier).

The Transformer backbone comprises stacked encoder units, each featuring two primary sub-units: a self-attention sub-unit and a feed-forward network (FFN) sub-unit. Both sub-units possess residual connections for enhanced learning. The self-attention sub-unit incorporates a multi-head self-attention layer alongside a fully connected layer before and after. Meanwhile, the FFN sub-unit exclusively employs thoroughly combined layers. Three hyper-parameters define BERT’s architecture:

• The number of encoder units (L),
• The embedding vector (H) size and the number of attention heads in each self-attention layer (A).
• L and H determine the model’s depth and width, respectively, while A, an internal hyper-parameter, influences the contextual relations each encoder focuses on.

Compression Methods

Various compression methods address BERT’s complexity. Quantization reduces unique values for weights and activations, lowering memory usage and potentially enhancing inference speed. Pruning encompasses unstructured and structured approaches, removing redundant weights or architectural components. Knowledge Distillation trains smaller models using larger pre-trained models’ outputs. Other techniques like Matrix Decomposition, Dynamic Inference Acceleration, Parameter Sharing, Embedding Matrix Compression, and Weight Squeezing contribute to compression efforts.

1. Quantization

Quantization involves the reduction of unique values necessary to depict model weights and activations. This reduction enables their representation using fewer bits, leading to diminished memory usage and reduced precision in numerical computations. Quantization might enhance runtime memory consumption and inference speed, especially when the foundational computational hardware is engineered to handle lower-precision numerical values. An example is the utilization of tensor cores in recent Nvidia GPU generations. Programmable hardware like FPGAs can also be meticulously tailored to optimize bandwidth representation. Furthermore, quantization to intermediate outputs and activations can expedite model execution further.

2. Pruning

Pruning methodologies for BERT predominantly fall within two distinct categories:

(i). Unstructured Pruning: Also referred to as sparse pruning, unstructured pruning involves removing individual weights identified as least crucial within the model. The significance of these weights can be assessed based on their absolute values, gradients, or customized measurement metrics. Given BERT’s extensive employment of fully connected layers, unstructured pruning holds potential efficacy. Examples of unstructured pruning methods encompass magnitude weight pruning, which discards weights close to zero; movement-based pruning, which eliminates weights tending towards zero during fine-tuning; and reweighted proximal pruning (RPP), which employs iteratively reweighted ℓ1 minimization followed by the proximal algorithm to separate pruning and error back-propagation. Due to its weight-by-weight approach, unstructured pruning can result in arbitrary and irregular sets of pruned weights, potentially reducing the model size without significantly improving runtime memory or speed unless applied on specialized hardware or utilizing specialized processing libraries.

(ii). Structured Pruning: Structured pruning targets the elimination of structured clusters of weights or even entire architectural components within the BERT model. This approach simplifies and reduces specific numerical modules, leading to enhanced efficiency. The focal areas of structured pruning comprise Attention Head Pruning, Encoder Unit Pruning, and Embedding Size Pruning.

3. Knowledge Distillation

Knowledge Distillation involves training a compact model (referred to as the student) by utilizing outputs generated by one or more extensive pre-trained models (referred to as the teachers) through various intermediate functional components. This exchange of information might occasionally pass through an intermediary model. Within the context of the BERT model, numerous intermediate outcomes serve as potential learning sources for the student. These include the logits within the concluding layer, the outcomes of encoder units, and the attention maps.

4. Other methods

1. Matrix Decomposition

2. Dynamic Inference Acceleration

3. Parameter Sharing

4. Embedding Matrix Compression

5. Weight Squeezing

Effectiveness of Compression Methods

Quantization and unstructured pruning offer the potential to decrease the model size. Yet, their impact on runtime inference speed and memory consumption remains limited, unless applied on specialized hardware or using specialized processing libraries. Conversely, when deployed on suitable hardware, these techniques can significantly enhance speed while maintaining performance levels with minimal compromise. Therefore, it’s crucial to consider the target hardware device before opting for such compression methods in practical scenarios.

Knowledge distillation has demonstrated strong compatibility with various student models, and its unique approach sets it apart from other methods, making it a valuable addition to any compression strategy. Specifically, distilling knowledge from self-attention layers, if feasible, holds integral importance in Transformer compression.

Alternatives like BiLSTMs and CNNs boast an additional advantage in terms of execution speed compared to Transformers. Consequently, replacing Transformers with alternative architectures is a more favorable choice when dealing with stringent latency requirements. Additionally, dynamic inference techniques can expedite model execution, as these methods can be seamlessly integrated into student models sharing a foundational structure akin to Transformers.

A pivotal insight from our preceding discussion underscores the significance of amalgamating diverse compression methodologies to realize truly effective models tailored for edge environments.

Applications of BERT

BERT’s capabilities are extensive and versatile, enabling the development of intelligent and efficient search engines. Through BERT-driven studies, Google has advanced its ability to comprehend the intent behind search queries, delivering relevant results with increased accuracy.

Text summarization represents another area where BERT’s potential shines. BERT can be harnessed to facilitate textual content summarization, endorsing a well-regarded framework that encompasses both extractive and abstractive summarization models. In the context of extractive summarization, BERT identifies the most significant sentences within a document, forming a summary. This involves a neural encoder creating sentence representations, followed by a classifier that predicts which sentences merit inclusion as part of the summary.

The advent of SCIBERT underscores the significance of BERT in medical literature. Given the exponential growth in clinical resources, NLP powered by SCIBERT has become a vital tool for large-scale data extraction and system learning from these documents.

BERT’s contribution extends to the realm of chatbots as well. It played a pivotal role in enhancing the Stanford Question Answering Dataset (SQuAD), which involves reading comprehension tasks based on questions posed to Wikipedia articles. Leveraging BERT’s functionality, chatbot capabilities can be extended from handling small to substantial text inputs.

Moreover, BERT’s utility encompasses sentiment analysis, which involves discerning sentiments and emotions conveyed in textual content. Additionally, BERT excels in tasks related to text matching and retrieval, where it aids in identifying and retrieving relevant textual information.

The post Text Analytics with low latency and high accuracy: BERT – Model Compression appeared first on Indium.

At the beginning, we had simple apps that did very little. However, everything has changed in the last decade as a result of the Smartphone revolution.

These mobile apps influence everything from your daily chores to your social interactions to your business strategy.

When we think of artificial intelligence (AI), the first names that pop up are probably Siri, Bixby, Cortana or Alexa.

According to the most recent McKinsey Global Institute reports, Google and Apple have invested billions of dollars in artificial intelligence. According to the report, AI advancements brought in $139 billion in investment in 2020, which was more than three times the money invested in AI three years prior.

The concept of a ‘smart assistant’ which can solve everyday tasks has captivated millions of users across all business sectors, not to mention education, healthcare, and finance. However, AI is not limited to smart assistance; it is progressing at a rapid pace. Many mobile apps are now utilising AI to improve user satisfaction.

AI is continuing to improve mobile apps by acting as a catalyst. It enables the evolution of mobile apps by transforming them into intelligent pieces of software capable of predicting user behaviour and making decisions. AI algorithms also enable mobile apps to learn from user-generated data.

It is important to note that AI in this context does not refer to pure self-aware intelligence machines. Rather, it is a catch-all term for a variety of applications used by website and mobile app developers.

Contributions Of AI to Mobile Application Development

Facial recognition: Because of the ‘easy to use’ nature and added layer of security, face recognition lock has become one of the most popular features on Android smartphones. These systems use AI and ML-based algorithms to recognise a person’s face to unlock the phone and the various apps that are installed on it.

Smartphone manufacturers are expected to implement even more advanced AI and ML in the coming years to identify a person as their facial features change, such as growing a beard or wearing glasses.

Search Engines on mobile phones: The use of voice search and voice commands is perhaps one of the most common and popular advancement in artificial intelligence and machine learning. Customers used to type their queries into search bars. It is now as easy as asking your virtual assistant to look for something for you.

Instead of signing into your computer or unlocking your phone, something as easy and simple as “hey google what’s the best restaurant near me?” provides users with the quick answer they seek while also directing them to your business. Voice command allows you to respond to text messages without having to type.

Smart Camera apps: The smartphone camera is one of the most important areas in which custom android app development personnel and android mobile manufacturers are making significant advances in AI and ML. These advanced cameras can detect the subject within the frame, such as faces, fireworks, or food, and adjust the settings to produce the best possible image.

Artificial intelligence and machine learning can now automatically identify and enhance facial features for outstanding portrait images. More advanced features can even count the calories you eat from a simple photo of your food or provide information to businesses about how and where their products are being used when photos are shared on social media platforms.

Emotion recognition: Emotion recognition is a rising star in AI development. We have now advanced to the point where we can incorporate ML & AI into apps and capture micro& macro expressions. Through image and voice data processing, software can now read human emotions through the capturing of subtle variations, body language cues and vocal inflection. Companies can use these analytics to enhance consumer experiences by identifying the need for a product or service or to get new ideas for new product.

Real-time translation: There is a vast array of translation apps available. However, the majority of these apps are inoperable without access to the internet. AI could allow smartphones to translate & transliterate different languages in real-time without requiring an internet connection.

AI can provide a language instruction tool that allows sentences and phrases to be translated almost instantly without a time lag, similar to how interpreters work. The translation tool can be adjusted for latency using AI. This means that a user can specify the amount of time between a spoken word and its translation. This would be incredibly beneficial for languages that require a longer time lag for accurate translation.

Advantages of implementing AI

1. AI assists you in completing monotonous tasks quickly
2. Accuracy and completeness
3. Enhanced customer experiences
4. Intelligent interactions with users
5. User retainment

Personalized user experiences

The advancement of AI technology has enabled mobile users to completely redesign the value benchmark of existing user experience. Users are starting to demand more detailed and personalised mobile app performance.

Retail brands such as Tommy Hilfiger, Starbucks, Nike, etc can deliver personalised experiences that include recommendations unique to each user by collecting and analysing customer data based on purchases and locations.

In reference to Tommy Hilfiger’s chatbot, users can use the chatbot to browse their most recent collections or get a behind-the-scenes look at the most recent fashion show. The chatbot also employs natural language processing to provide style advice and product recommendations in addition to responding to customer inquiries. The bot gathers information about the user’s style preferences by asking a series of questions and then suggests an outfit based on the information gathered.

Smartphones have GPS tracking capabilities, as well as microphone and camera features, making them an ideal platform for AI applications. Furthermore, Apple revealed that the iPhone XR, 11, and 12 will include an A12 Bionic chip with a neural engine designed to use AI hardware in previously unimaginable ways.

When AI technology is combined with these built-in features, apps become more relevant and personalised. Using artificial intelligence to contextualise user behaviour will make each app session better than the previous one.

Wind-Up

AI opens up a plethora of opportunities for innovation in the mobile app industry. AI is the wave of the future in mobile app development. Users’ interactions with app services and products are changing as a result of artificial intelligence. Users of mobile apps will also be linked to an ecosystem of intelligent applications that will collaborate to provide a personalised user experience.

The greater role of AI in mobile apps has demonstrated its value in terms of business growth and user engagement. Here are some examples of how AI can help you understand your users:

• AI can collect and store user data by analysing user behaviour and interactions with the app.
• AI collects essential data such as location, contacts, and daily actions to better serve users.
• AI products level up the user experience.

A smart ecosystem will collect a large amount of social data and behavioural interest, which can be used to boost revenue and improve user experience. It is not an exaggeration to say that AI is reshaping the smartphone industry. As a result, it is critical to include AI in your business and mobile applications.

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