Few-shot image classification remains challenging due to the limited availability of labeled examples. Recent approaches have explored generating synthetic training data using text-to-image diffusion models, but often require extensive model fine-tuning or external information sources. We present a novel training-free approach, called DIPSY, that leverages IP-Adapter for image-to-image translation to generate highly discriminative synthetic images using only the available few-shot examples. DIPSY introduces three key innovations: (1) an extended classifier-free guidance scheme that enables independent control over positive and negative image conditioning; (2) a class similarity-based sampling strategy that identifies effective contrastive examples; and (3) a simple yet effective pipeline that requires no model fine-tuning or external captioning and filtering. Experiments across ten benchmark datasets demonstrate that our approach achieves state-of-the-art or comparable performance, while eliminating the need for generative model adaptation or reliance on external tools for caption generation and image filtering. Our results highlight the effectiveness of leveraging dual image prompting with positive-negative guidance for generating class-discriminative features, particularly for fine-grained classification tasks.

AI company Hugging Face has released a new open source JavaScript library that allows frontend and web developers to add machine learning capabilities to webpages and apps. Traditionally, Python notebooks are the toolkit for data scientists, but for most web and frontend developers, it's JavaScript. Until now, adding those functions meant a Python app on the backend that did the work, said Jeff Boudier, head of product and growth at the startup. Using JavaScript, the browser can request machine learning models to serve predictions and obtain answers for a visitor. "We provide some low code/no code tools, but if you want to dig in a little bit, you still have to whip out some Python notebooks, etc. And that's the traditional toolkit of data scientists," Boudier told The New Stack.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! The latest generation of artificial intelligence (AI) models, also known as transformers, have already changed our daily lives, taking the wheel for us, completing our thoughts when we compose an email or answering our questions in search engines. However, right now, only the largest tech companies have the means and manpower to wield these massive models at consumer scale. To get their model into production, data scientists typically take one to two weeks, dealing with GPUs, containers, API gateways and the like, or have to request a different team to do so, which can cause delay.

Fresh off a $100 million funding round, Hugging Face, which provides hosted AI services and a community-driven portal for AI tools and data sets, today announced a new product in collaboration with Microsoft. Called Hugging Face Endpoints on Azure, Hugging Face co-founder and CEO Clément Delangue described it as a way to turn Hugging Face-developed AI models into "scalable production solutions." "The mission of Hugging Face is to democratize good machine learning," Delangue said in a press release. "We're striving to help every developer and organization build high-quality, machine learning-powered applications that have a positive impact on society and businesses. With Hugging Face Endpoints, we've made it simpler than ever to deploy state-of-the-art models, and we can't wait to see what Azure customers will build with them." The demand for AI remains high.

As the competition for capturing the machine learning industry heats up, Microsoft is turning to a popular $2 billion startup to get an edge over rivals. The creators of Azure are rolling out an integration with Hugging Face, a popular data science startup that hosts some of the most-used machine learning models, to gain a new route into companies and grow business. The startup recently raised $100 million at a $2 billion valuation led by Lux Capital in a highly competitive funding round, with Addition and Sequoia participating. Microsoft is betting that Endpoints, Hugging Face's new integration, will help drastically simplify the time required to get machine learning models into place. The majority of efforts in machine learning die before seeing the light of day due to the number of people involved -- which Hugging Face is trying to drop to as small a number as possible by making it easy for a single person to share a model across the organization.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Today is a big day for AI announcements from Microsoft, both from this week's Build conference and beyond. But one common theme bubbles over consistently: For AI to become more useful for business applications, it needs to be easier, simpler, more explainable, more accessible and, most of all, responsible. Responsible AI is actually at the heart of a lot of today's Build news, John Montgomery, corporate vice president of Azure AI, told VentureBeat. Most notable is Azure Machine Learning's preview of a responsible AI dashboard, which brings together capabilities in use over the past 18 months, such as data explorer, model interpretability, error analysis, counterfactual and causal inference analysis, into a single view.

Conversational artificial intelligence serves as the interface between a person and a computer, through which organizations can achieve engaging two-way interactions. The technology is usually associated with call centers and virtual assistants/chatbots, although it can be applied in practically every vertical industry. Thanks to machine learning and AI breakthroughs during the last couple of years, conversational AI has grown beyond chatbots to include a variety of use cases. The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started.