AI

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,

As organizations look to incorporate AI capabilities into their applications, large language models (LLMs) have emerged as powerful tools for natural language processing tasks. Amazon SageMaker AI provides a fully managed service for deploying these machine learning (ML) models with multiple inference options, allowing organizations to optimize for cost, latency, and throughput. AWS has always

This post is co-written with Tatia Tsmindashvili, Ana Kolkhidashvili, Guram Dentoshvili, Dachi Choladze from Impel. Impel transforms automotive retail through an AI-powered customer lifecycle management solution that drives dealership operations and customer interactions. Their core product, Sales AI, provides all-day personalized customer engagement, handling vehicle-specific questions and automotive trade-in and financing inquiries. By replacing their