May 2024 Summaries

Building a large language model (LLM) from scratch has become increasingly feasible for organizations of all sizes, thanks to growing knowledge and resources. The process involves defining the use case, creating the model architecture, curating data, training the LLM, fine-tuning it, and evaluating its performance. Key factors influencing the complexity and time required include the intended use case, available computational resources, and quality of training data. Evaluating an LLM can be done using standardized benchmarks to measure various aspects such as knowledge, reasoning, natural language understanding, and more.

Cloud Court, a legal-tech software company, uses Symbl's APIs to extract topics and summaries from legal transcripts, improving knowledge consumption for case preparation and outcomes. The challenge lies in the time-consuming and error-prone manual process of analyzing depositions, information loss due to traditional search methods, and subjectivity in interpretation. By automating topic detection, Cloud Court enhances productivity and improves case outcomes by organizing legal transcripts into relevant topics for effective deposition preparation and defense.

Markets EQ, an AI startup, has launched a new feature called 'EQ MarketScore' that uses Symbl's Gen-AI capabilities to provide insights into earnings calls for Investor Relations Officers (IROs). The feature analyzes the tone, confidence, clarity, and intricacy of messages in earning calls, presentations, and executive communications. It is powered by Nebula, a conversation-centric large language model from Symbl. Markets EQ aims to improve corporate communications for businesses that face significant losses due to ineffective communication. The integration of Nebula's capabilities within the EQ MarketScore feature has led to reduced time to market for Markets EQ and greater control over communication for IROs, resulting in significant time savings.

Speech recognition is a vital application of AI due to its natural and intuitive nature, leading to widespread adoption in various industries. An end-to-end speech recognition model takes audio input and outputs textual transcripts without the need for explicit intermediate representations. These models work by processing acoustic signals, extracting relevant features, mapping them to phonetic or sub-word representations, predicting word sequences using a language model, and decoding the most probable text transcription of the audio signal. To build an end-to-end speech recognition system, one must first define its use case, which will determine the model's architecture and size. Popular applications include digital personal assistants, home automation, customer service, transcription services, translation tasks, and accessibility features. After selecting a neural network architecture, such as DeepSpeech or ESPnet, it is crucial to build a data pipeline for training the model. This involves collecting and preprocessing data, performing data augmentation, and dividing the data into training and evaluation sets. The speech recognition model is then trained using forward and backward propagation, followed by fine-tuning with domain or task-specific data. Finally, the model's performance is evaluated using metrics such as Word Error Rate (WER), Token Error Rate (TER), and Character Error Rate (CER). Despite challenges in processing audio signals, speech recognition remains an essential area of AI research and development due to its numerous applications and potential for improving human-to-machine communication.