August 2021 Summaries

BERT is a powerful natural language processing tool that can handle a wide range of tasks, including named entity recognition, sentiment analysis, and classification. It was trained using a simple strategy known as word masking, where words in sentences were randomly masked and the model was asked to predict what each masked word was. By wrapping BERT in TensorFlow Serving, developers can optimize it for memory-efficient, low-latency settings. The process involves adding an extra layer on top of the final layer in the encoder stack of the architecture, transforming the output from (batch_size, max_seq_length, hidden_units) to (batch_size, max_seq_length, vocab_size). The model is then saved into the SavedModel format using the Estimator class's export_savedmodel function. After saving the model, it can be hosted on a Docker container with TensorFlow Serving as the base image, allowing for low-latency predictions to be made by sending a REST API request to the served model.

The evrmore app is an AI-powered social platform that aims to help Gen Z navigate key moments in life and build confidence by capturing their thoughts through voice journaling. The app uses Empathy AI features to identify patterns in users' thoughts, allowing them to overcome difficult situations. To achieve this, the company partnered with Symbl.ai, a provider of conversation APIs, to create an API that can capture emotional nuances in human conversation. The partnership enables evrmore to provide robust and engaging experiences for its users, while also promoting tech for good and transparency. With the Trackers API, evrmore aims to identify patterns through phrases with similar meaning, further enhancing its ability to support young people's mental health and well-being.

The accuracy of video and audio transcription is crucial to ensure that the final output is as close to the spoken word as possible, making it professional and helpful. The frequency and quality of the audio are key variables that can affect transcription accuracy. Various methods can be used to improve transcription accuracy, including pre-feeding custom vocabulary, setting up different streams of audio, identifying dialects and accents, keeping audio clean, avoiding noise cancellation, not using automatic gain control, positioning the speaker close to the microphone, and avoiding audio clipping. Symbl.ai provides a conversation intelligence platform with real-time and asynchronous transcription capabilities that can help achieve better accuracy in transcriptions, often reaching up to 90% audio transcription accuracy.

Conversation intelligence is a powerful tool that enables real-time analysis of conversations at a contextual level, providing optimized intelligence. Workflow automation is the ability to automate repetitive manual tasks and have machines perform them instead, freeing humans from mundane work and allowing them to focus on more creative tasks. Conversation intelligence can help businesses improve efficiency, productivity, profit, and customer satisfaction by analyzing conversations, identifying patterns and trends, and automating tasks such as generating documents and payments. Symbl.ai's conversation intelligence platform provides real-time and contextual AI capabilities, programmable APIs, and SDKs for developers, enabling businesses to automate workflow tasks and drive business growth. By leveraging conversation intelligence, businesses can gain a competitive edge by improving customer satisfaction, reducing errors, and increasing productivity.

Symbl's Conversation API provides developers with everything they need to deploy amazing experiences beyond basic speech recognition. The current challenges of transcription alone, such as relying on rule-based systems or deep learning, are not enough to provide valuable insights and contextual details. Symbl.ai offers a plug-and-play solution that extracts intelligence from transcriptions in real time and asynchronously, providing features such as sentiment analysis, trackers, entities, action items, ideas, questions, and follow-ups, topics of discussion, parent-child hierarchy of topics, and more, which can help businesses drive meaningful change by uncovering important contextual details. By leveraging these features, developers can build a model to improve transcriptions but may still end up with errors, making buying the right solution a better option.

There's a surplus of sentiment analysis APIs available, but most lack contextual understanding and can't translate captured sentiments into actionable business results. To get started with sentiment analysis, you need an API that handles natural language processing complexities while offering simplicity, granularity, and flexibility. Choosing the right API for real-time conversations is challenging due to the grand majority of APIs not optimizing sentiment analysis for specific business objectives. Key considerations when selecting a sentiment analysis API include setting clear objectives, measuring accuracy vs. performance, deciding whether machine learning is needed, and checking for real-time analysis capabilities. A suitable API should be able to handle contextual understanding, provide granular insights, and surface useful information in real time. Symbl.ai's Sentiment Analysis API is an example of a comprehensive platform that offers aspect-based sentiment analysis, customizable polarity values, out-of-the-box integrations, and extensibility.

Aspect-based sentiment analysis is a sophisticated technique that categorizes data by feature (aspect) and identifies the attributable opinion (sentiment), automating tedious tasks, working in real-time, scaling easily, and providing an unbiased customer-centric experience. It differs from traditional sentiment analysis, which only classifies sentiments as positive, negative, or neutral. Aspect-based sentiment analysis allows users to link sentiments and aspects, extracting opinions about specific features or attributes of a product or service. This technique is valuable in areas such as customer feedback, market research, and understanding customer experiences, enabling businesses to gain insights, automate tedious sorting and analysis, and create better customer experiences. With Symbl.ai's APIs, developers can integrate aspect-based sentiment analysis into voice and video applications, providing real-time analysis of sentiments, speaker data, and other conversational metrics.

Sentiment analysis is a process that helps businesses understand customer needs and attitudes, but it's challenging for developers to implement accurately with existing APIs. However, Symbl.ai's Sentiment API offers flexibility, extensibility, and user-friendliness, making it suitable for real-time sentiment analysis over WebSockets. The API applies aspect-based sentiment analysis, which categorizes data by topic and identifies the sentiment attributed to each one. It's non-assumptive, non-invasive, fully extensible, and user-friendly, allowing developers to create actionable analytics on a user-by-user or call-by-call basis. With Symbl.ai's APIs, developers can integrate real-time transcription and capture sentiments in conversations, providing valuable insights for businesses and applications.

Natural language processing has made significant progress in recent years, but existing solutions are often task-specific and lack contextual understanding. Building models that can process spoken conversations is crucial for extracting valuable insights. Developers can leverage existing tools to create more intelligent conversation intelligence by assembling them strategically. To build a model that generates accurate conversation summaries, it's essential to identify topics, build timelines, and create summaries using a text summarization algorithm. By taking these steps, developers can overcome the limitations of task-specific solutions and create contextual understanding of everyday language.

Symbl.ai, a conversation intelligence platform for developers, has published its public workspace on Postman, enabling easy testing and exploration of its APIs. The workspace provides access to Symbl.ai's APIs for real-time voice, video, chat, or broadcast experiences, including authentication, async APIs for ingest and processing, telephony API for capturing conversations in real-time, conversation APIs for extracting contextual insights, and management APIs for creating custom insights and intents. With Postman as the "GitHub of the API world," developers can create new opportunities for connecting, transforming, or visualizing conversations with Symbl.ai's APIs, and even fork, clone, or submit pull requests against the public workspace to customize it. The platform also supports cross-API playgrounds and provides visualizations for data, including mindmaps for topics, enabling developers to create human-readable forms of data. Symbl.ai has been included in Postman's Startups to Watch list, highlighting promising young startups with potential to disrupt their respective industries.

Symbl.ai's conversation intelligence API provides a plug-and-play solution for optimizing webinar experiences, offering features such as real-time transcription, sentiment analysis, speaker separation, and topic detection. This enables creators to create structured, searchable, and interactive content that is optimized for online learning, while providing attendees with a complete post-event summary experience relevant to their specific interests. By incorporating Symbl.ai's conversation intelligence API into webinars, developers can improve the quality and value of the experience for both creators and attendees, addressing common pain points such as creating unbiased content and finding topics for growth. With Symbl.ai's flexible API, developers can skip hours of manual training and focus on releasing AI capabilities in their webinar platforms.

Voice data from business communications such as video calls can be captured using speech analytics, providing insights into human-to-human conversations. This data can be used to create apps that better serve customers or provide answers to patients in medical settings. Voice data can be captured asynchronously or in real-time and contains valuable information like follow-up meetings, transcriptions, action items, sentiment analysis, and more. Symbl.ai provides APIs to help capture and analyze voice data, making it easy to get started with voice analytics and unlocking insights from conversations across various industries.