Vehicle make and model recognition (VMMR) is an important task in intelligent transportation systems, but existing approaches struggle to adapt to newly released models. Contrastive Language-Image Pretraining (CLIP) provides strong visual-text alignment, yet its fixed pretrained weights limit performance without costly image-specific finetuning. We propose a pipeline that integrates vision language models (VLMs) with Retrieval-Augmented Generation (RAG) to support zero-shot recognition through text-based reasoning. A VLM converts vehicle images into descriptive attributes, which are compared against a database of textual features. Relevant entries are retrieved and combined with the description to form a prompt, and a language model (LM) infers the make and model. This design avoids large-scale retraining and enables rapid updates by adding textual descriptions of new vehicles. Experiments show that the proposed method improves recognition by nearly 20% over the CLIP baseline, demonstrating the potential of RAG-enhanced LM reasoning for scalable VMMR in smart-city applications.

The term "deep learning" has firmly worked its way into the enterprise lexicon. Deep learning is a class of machine learning algorithms that uses a deep neural network in order to learn. A deep neural network is a collection of artificial neurons, which are simply trainable mathematical units. These neurons collectively "learn" complex mathematical functions to map raw input to an output. Neural networks have existed in machine learning since the 1950s, but today's "deep" networks have many more stacked layers of neurons than older networks.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! These days, conversational artificial intelligence (AI) chatbots are everywhere on websites, SMS and social channels. Conversational AI-supported chatbots that use natural language processing (NLP) help customers deal with everything from product recommendations to order questions. Enterprises love conversational AI chatbots, too: According to a recent Gartner report, by 2027 chatbots will become the primary customer service channel for roughly a quarter of organizations.

We rely on experts all the time. If you need financial advice, you ask an expert. If you are sick, you visit a doctor, and as a juror you may listen to an expert witness. In the future, however, artificial intelligence (AI) might replace many of these people. In forensic science, the expert witness plays a vital role.

We rely on experts all the time. If you need financial advice, you ask an expert. If you are sick, you visit a doctor, and as a juror you may listen to an expert witness. In the future, however, artificial intelligence (AI) might replace many of these people. In forensic science, the expert witness plays a vital role.

Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?

Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?

Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?

The Army used a cutting-edge Israeli anti-drone system to defeat the unmanned aerial vehicle (UAV) that brought misery to hundreds of thousands of people at Gatwick airport. The British Army bought six'Drone Dome' systems for £15.8 million in 2018 and the technology is used in Syria to destroy ISIS UAVs. Police had been seen on Thursday with an off-the-shelf DJI system that tracks drones made by that manufacturer and shows officers where the operator is (DJI is the most popular commercial drone brand.) However, the drone used at Gatwick is thought to have been either hacked or an advanced non-DJI drone, which rendered the commercial technology used by the police useless. At that point, the Army's'Drone Dome' system made by Rafael was called in.

The car-buying business has changed a lot since Cars.com Today it announced a new matchmaking experience that uses AI to help car buyers find the ride of their dreams. "We're treating people like human beings with distinct emotional nuances, not just site users, as we build a more relevant, personalized car shopping experience," Cars.com chief product officer Tony Zolla said in a statement. "Early-stage car shoppers don't know what they're looking for. In fact, an overwhelming majority are undecided on make and model, yet nearly all online car search experiences force people to select make or model as the first step in their journey."