--Open-source Vision-Language Models show immense promise for enterprise applications, yet a critical disconnect exists between academic evaluation and enterprise deployment requirements. Current benchmarks rely heavily on multiple-choice questions and synthetic data, failing to capture the complexity of real-world business applications like social media content analysis. This paper introduces VLM-in-the-Wild (ViLD), a comprehensive framework to bridge this gap by evaluating VLMs on operational enterprise requirements. We define ten business-critical tasks: logo detection, OCR, object detection, human presence and demographic analysis, human activity and appearance analysis, scene detection, camera perspective and media quality assessment, dominant colors, comprehensive description, and NSFW detection. T o this framework, we bring an innovative BlockWeaver Algorithm that solves the challenging problem of comparing unordered, variably-grouped OCR outputs from VLMs without relying on embeddings or LLMs, achieving remarkable speed and reliability. Besides, ViLD's methodology avoids traditional bounding boxes, which are ill-suited for generative VLMs, in favour of a novel spatial-temporal grid system that captures localisation information effectively for both images and videos. T o demonstrate efficacy of ViLD, we constructed a new benchmark dataset of 7,500 diverse samples, carefully stratified from a corpus of one million real-world images and videos. ViLD provides actionable insights by combining semantic matching (both embedding-based and LLMas-a-judge approaches), traditional metrics, and novel methods to measure the completeness and faithfulness of descriptive outputs. By benchmarking leading open-source VLMs (Qwen, MIMO, and InternVL) against a powerful proprietary baseline as per ViLD framework, we provide one of the first industry-grounded, task-driven assessment of VLMs capabilities, offering actionable insights for their deployment in enterprise environments. Vision-Language Models (VLMs) have fundamentally transformed the landscape of artificial intelligence, enabling systems to understand and reason about visual content through natural language.

It's easy to think of LLMs (large language models) as just'hallucinating' or mere generators of text. A glorified LSTM so to speak. While there are some limitations of LLMs (and indeed they are evolving), a far more interesting question to explore is: How can LLMs be used in enterprise applications? In many ways, enterprise applications of LLMs can overcome some of the problems. One possible solution is a combination of Azure Cognitive Search and Azure OpenAI Service. Taking a B2B perspective, the solution involves "chatting with your own data".

Artificial intelligence (AI) and machine learning (ML) are ubiquitous in consumers' lives, from the "up next" suggestions from your streaming service to routes suggested by your GPS when you plug an address into your phone for directions. Less visible impacts of AI and ML include the use of AI to control data center efficiency and cooling or the management of restaurant wait times, as some companies use AI to make decisions about how many burgers to cook for the day's lunch rush. Whereas AI refers to the ability of a computer to emulate human decision-making, ML is the algorithm-driven foundation that enables AI. We can think of automation as the application of AI to develop a series of repeatable tasks or actions designed to accomplish a certain task or execute a process. Companies use automation for transporting products to warehouse workers for packing, processing invoices, and assisting with many other repetitive business tasks that humans have historically performed.

In the 1935 science fiction novel Pygmalion's Spectacles by Stanley Weinbaum, the main character Dan Burke puts on a pair of magic spectacles and enters a virtual world where he interacts with other virtual characters and can taste, touch and smell what they do. Fast forward nearly seven decades, the virtual world now has a name, in the 1992 novel Snow Crash by bestselling science fiction author Neal Stephenson. The main character, Hiro Protagonist, moves between Los Angeles and a location called the "metaverse" to gather information about a dangerous drug. While both Weinbaum and Stephenson were futurists depicting the virtual reality technology that was to come, the 21st century has yet to meet up to the expectations about what a virtual world or the metaverse could be. Both novelists imagined a metaverse where people can act just like they would in the physical world, whether they're wearing a special device such as virtual reality goggles or not.

By Esther Rietmann, Director of Content and Programming at Data Science Salon Enterprises leverage machine learning (ML) to improve business operations, adapt to changing business requirements, and gain insights into market trends. A 2021 ML market study indicates that 59% of all large enterprises are deploying ML solutions. ML techniques can enable fast and accurate decision-making to avoid costly corrective measures and ensure solid business reputation. We at Data Science Salon spoke with some of the experts who will be speaking at Data Science Salon Miami Hybrid on September 21 to expand the understanding of ML in the enterprise, its key challenges and trends. Read this post to gain insightful answers that will guide you in the AI adoption journey!

AI is becoming increasingly ubiquitous -- from enterprises to the edge. It's a movement accelerated by the pandemic, which sped up many companies' planning and implementation of AI projects. Some 86% of respondents surveyed by consulting firm PwC reported that AI is becoming a mainstream technology at their companies. Companies had to adapt quickly to a whole new business landscape, faster than ever. Yet, while AI is making rapid inroads as a tool to solve complex business challenges, many enterprises still struggle with the move from testing to deployment.

Deep learning and machine learning are both important methods for analyzing data and making predictions. Each has its own advantages and disadvantages.

As an example of the efficiency that AI can introduce, let's consider production-line workers and engineers who manage reject rates in the pulley assembly process. This used to be a multi-step, manual process,

It's been years since developers found that Nvidia's main product, the GPU, was useful not just for rendering video games but also for high-performance computing of the kind used in 3D modeling, weather forecasting, or the training of AI models--and it's on enterprise applications such as those that CEO Jensen Huang will focus his attention at the company's GTC 2022 conference this week. For some applications, a simple database may suffice to record a product's service history--when it was made, who it shipped to, what modifications have been applied--while others require a full-on 3D model incorporating real-time sensor data that can be used, for example, to provide advanced warning of component failure or of rain. Two groups of researchers are already using Nvidia's Modulus AI framework for developing physics machine learning models and its Omniverse 3D virtual world simulation platform to forecast the weather with greater confidence and speed, and to optimize the design of wind farms. To help other enterprises build and maintain their own digital twins, later this year Nvidia will offer OVX computing systems running its Omniverse software on racks loaded with its GPUs, storage, and high-speed switch fabric. The option to securely process such data on a GPU, even in a public cloud or a colocation facility, could enable enterprises to speed up the development and use of machine learning models without scaling up capital spending. It's been years since developers found that Nvidia's main product, the GPU, was useful not just for rendering video games but also for high-performance computing of the kind used in 3D modeling, weather forecasting, or the training of AI models--and it's on enterprise applications such as those that CEO Jensen Huang will focus his attention at the company's GTC 2022 conference this week.

It's been years since developers found that Nvidia's main product, the GPU, was useful not just for rendering video games but also for high-performance computing of the kind used in 3D modeling, weather forecasting, or the training of AI models--and it's on enterprise applications such as those that CEO Jensen Huang will focus his attention at the company's GTC 2022 conference this week. Nvidia is hoping to make it easier for CIOs building digital twins and machine learning models to secure enterprise computing, and even to speed the adoption of quantum computing with a range of new hardware and software. Digital twins, numerical models that reflect changes in real-world objects useful in design, manufacturing, and service creation, vary in their level of detail. For some applications, a simple database may suffice to record a product's service history--when it was made, who it shipped to, what modifications have been applied--while others require a full-on 3D model incorporating real-time sensor data that can be used, for example, to provide advanced warning of component failure or of rain. It's at the high end of that range that Nvidia plays.