AI

Multi-channel transcription streaming is a feature of Amazon Transcribe that can be used in many cases with a web browser. Creating this stream source has it challenges, but with the JavaScript Web Audio API, you can connect and combine different audio sources like videos, audio files, or hardware like microphones to obtain transcripts. In this […]

This is a guest post co-authored by Evan Miller, Noah Kershaw, and Valerie Renda of Kepler Group At Kepler, a global full-service digital marketing agency serving Fortune 500 brands, we understand the delicate balance between creative marketing strategies and data-driven precision. Our company name draws inspiration from the visionary astronomer Johannes Kepler, reflecting our commitment

After almost 10 years, Google Assistant was recently axed in favor of Gemini. Siri gets a bad rap—now is the time for Apple to make a change too.

Recordings of business meetings, interviews, and customer interactions have become essential for preserving important information. However, transcribing and summarizing these recordings manually is often time-consuming and labor-intensive. With the progress in generative AI and automatic speech recognition (ASR), automated solutions have emerged to make this process faster and more efficient. Protecting personally identifiable information (PII)

As companies and individual users deal with constantly growing amounts of video content, the ability to perform low-effort search to retrieve videos or video segments using natural language becomes increasingly valuable. Semantic video search offers a powerful solution to this problem, so users can search for relevant video content based on textual queries or descriptions.

GPUs are a precious resource; they are both short in supply and much more costly than traditional CPUs. They are also highly adaptable to many different use cases. Organizations building or adopting generative AI use GPUs to run simulations, run inference (both for internal or external usage), build agentic workloads, and run data scientists’ experiments.

Businesses rely on precise, real-time insights to make critical decisions. However, enabling non-technical users to access proprietary or organizational data without technical expertise remains a challenge. Text-to-SQL bridges this gap by generating precise, schema-specific queries that empower faster decision-making and foster a data-driven culture. The problem lies in obtaining deterministic answers—precise, consistent results needed for

Palantir threatened to call police on a WIRED reporter and kicked out other journalists from a recent conference following reports of the data analytics firm’s work with the Trump administration.

This post is co-written with Qing Chen and Mark Sinclair from Radial. Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. With over 30 years of industry expertise, Radial tailors its services and solutions to align strategically with each brand’s unique needs. Radial

For an AI model to perform effectively in specialized domains, it requires access to relevant background knowledge. A customer support chat assistant, for instance, needs detailed information about the business it serves, and a legal analysis tool must draw upon a comprehensive database of past cases. To equip large language models (LLMs) with this knowledge,