December 2022 Summaries

The year 2022 brought significant advancements for AssemblyAI, including the launch of its V9 Core Transcription Model and new Summarization models. Other highlights include the release of the AssemblyAI CLI, additional language support, a $58 million funding round, the introduction of the AssemblyAI Playground, and growth initiatives that expanded their team from 20 to 62 members. Additionally, they held their first AI Hackathon with over 440 participants. In terms of research, there were notable advancements in physics-inspired Diffusion Models and natural language processing. AssemblyAI was also recognized as a G2 High Performer and Momentum Leader in the Voice Recognition Software category for Fall, Summer, and Spring 2022. They experienced substantial growth on their YouTube channel and blog, with over 110 pieces of content published across various topics. The top pieces of content included introductions to Diffusion Models, running Stable Diffusion locally, and an overview of the top free speech-to-text APIs. AssemblyAI also launched its Creators Program, a community for AI creators in the developer community.

ChatGPT is based on the Reinforcement Learning with Human Feedback (RLHF) methodology, which consists of three main steps: supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and evaluating the resulting model. In the SFT step, a pre-trained language model is fine-tuned on high-quality instruction data. In the RLHF step, the model is trained with an additional reward model based on human feedback to optimize its output for human preferences. Finally, the performance of the resulting model is evaluated by human labelers on several criteria including helpfulness, truthfulness, and harmlessness.

Advancements in artificial intelligence (AI) are driving innovation across various industries. As a result, AI-first companies that integrate AI into their products or platforms are surpassing competitors. Product-led growth companies are also leveraging AI to create powerful features for increased adoption and expansion. Google's acquisition of Alter, an AI avatar startup, exemplifies this trend. The guide provides best practices for product teams looking to incorporate AI effectively into their products and services. It covers potential challenges and solutions, choosing the right AI partner, and successful use cases of AI-first products and features.

During a recent hackathon organized by AssemblyAI, 440 participants from 84 countries worked on over 150 projects. The event included guest judges and mentors such as Nat Friedman, Daniel Gross, Lenny Rachitsky, Omar Sanseviero, Nick Frosst, Chanin Nantasenamat, Daniel Kornaś, Jaydeep Karale, Sophia Yang, and Shubham Saboo. Winning projects included Superpaint (first place), Toy Story Creator (second place), OperatorAI (third place), Pupil.ai (best project built with AssemblyAI), and Docspace (best project built with Cohere). Other notable projects were Supercut, Photostudio, Scaling Potato, MaiBook, MidJournal, and several honorable mentions. The event was a success in fostering creativity for AI-first products and the participants are thanked by AssemblyAI.

AssemblyAI has released its new v9 transcription model, marking a significant improvement over the previous version and showing increased performance across various audio types. The new model also provides the foundation for the upcoming v10 model, expected to deliver radical improvements in speech recognition accuracy. The v9 model demonstrates an 11% average improvement in Word Error Rate (WER) compared to its predecessor, and it shows improved transcription quality beyond quantitative metrics such as WER. It also offers marked improvements with respect to proper nouns. The new model is currently deployed and automatically used for all transcriptions going forward.

AI Summarization Model is a technology that automatically generates concise summaries from longer pieces of text or transcriptions, such as audio recordings or videos. It can be used to analyze and interpret conversational data in call coaching features by identifying key points and summarizing them for quick review and analysis. AssemblyAI's Conversational Summarization Model is a state-of-the-art model designed specifically for conversations, providing accurate and efficient summaries that highlight important details from each interaction.

Stable Diffusion 1.5 outperforms Stable Diffusion 2 in generating highly detailed and complex stylized images when using an "augmented" prompt that references specific artists or art styles. However, for more realistic and less stylized images, SD 2 is comparable to SD 1.5 and performs better at text coherence and upscaling images.

- The new Summarization models are now available to use for all AssemblyAI API users. - There are four types of summaries currently offered, including "headline", "paragraph", "gist", and "bullets". - We have seen various innovative ways in which customers are using summarization capabilities to improve their business processes and decision making abilities. - The use cases for Summarization models span across industries such as call centers, meetings, podcasting, media monitoring, news aggregation, social media monitoring, among others. - To utilize the new Summarization models, one can simply send a POST request to the AssemblyAI API. - By integrating AI-powered summarization into their operations, companies can stay ahead of the competition and optimize their processes more efficiently.