Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g., radiology report generation), but there remain barriers that hinder the ability to deploy these solutions broadly. These include the cost of extensive computational requirements for developing large scale models, expertise in the development of sophisticated AI models, and the difficulty in accessing substantially large, high-quality datasets that adequately represent the population in which the LVLM solution is to be deployed. The HOPPR Medical-Grade Platform addresses these barriers by providing powerful computational infrastructure, a suite of foundation models on top of which developers can fine-tune for their specific use cases, and a robust quality management system that sets a standard for evaluating fine-tuned models for deployment in clinical settings. The HOPPR Platform has access to millions of imaging studies and text reports sourced from hundreds of imaging centers from diverse populations to pretrain foundation models and enable use case-specific cohorts for fine-tuning. All data are deidentified and securely stored for HIPAA compliance. Additionally, developers can securely host models on the HOPPR platform and access them via an API to make inferences using these models within established clinical workflows. With the Medical-Grade Platform, HOPPR's mission is to expedite the deployment of LVLM solutions for medical imaging and ultimately optimize radiologist's workflows and meet the growing demands of the field.

This commentary piece reviews the recent Open AI Democratic Inputs programme, which funded 10 teams to design procedures for public participation in generative AI. While applauding the technical innovations in these projects, we identify several shared assumptions including the generality of LLMs, extracting abstract values, soliciting solutions not problems and equating participation with democracy. We call instead for AI participation which involves specific communities and use cases and solicits concrete problems to be remedied. We also find it important that these communities have a stake in the outcome, including ownership of data or models.

The chipmaker Nvidia has extended its lead in artificial intelligence with the unveiling of a new "superchip", a quantum computing service, and a new suite of tools to help develop the ultimate sci-fi dream: general purpose humanoid robotics. Here we look at what the company is doing and what it might mean. The main announcement of the company's annual develop conference on Monday was the "Blackwell" series of AI chips, used to power the fantastically expensive datacentres that train frontier AI models such as the latest generations of GPT, Claude and Gemini. One, the Blackwell B200, is a fairly straightforward upgrade over the company's pre-existing H100 AI chip. Training a massive AI model, the size of GPT-4, would currently take about 8,000 H100 chips, and 15 megawatts of power, Nvidia said – enough to power about 30,000 typical British homes.

Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide accurate responses to questions. To address these challenges, a fine-tuning process is employed, involving feedback and examples to refine models. The objective is to enhance AI models through continuous feedback loops, utilizing metrics such as cosine similarity, LLM evaluation and Rouge-L scores to evaluate the models. Leveraging LLMs like GPT-3.5, GPT4ALL, and LLaMA2, and Claude, this approach is benchmarked on financial datasets, including the FinanceBench and RAG Instruct Benchmark Tester Dataset, illustrating the necessity of fine-tuning. The results showcase the capability of fine-tuned models to surpass the accuracy of zero-shot LLMs, providing superior question and answering capabilities. Notably, the combination of fine-tuning the LLM with a process known as Retrieval Augmented Generation (RAG) proves to generate responses with improved accuracy.

Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation. However, their popularity raises concerns about environmental impact and inefficient model selection. This paper introduces GreenRunnerGPT, a novel tool for efficiently selecting deep learning models based on specific use cases. It employs a large language model to suggest weights for quality indicators, optimizing resource utilization. The tool utilizes a multi-armed bandit framework to evaluate models against target datasets, considering tradeoffs. We demonstrate that GreenRunnerGPT is able to identify a model suited to a target use case without wasteful computations that would occur under a brute-force approach to model selection.

Target Audience: Content creators, writers, bloggers, marketers, and businesses who want to optimize their content creation process and increase productivity. Reframe your prompts with assistants to generate more powerful results. Customizable presets and priority support to streamline your workflow. What would you like to generate? Target Audience: Content creators, writers, bloggers, marketers, and businesses who want to optimize their content creation process and increase productivity.

As the field of AI and machine learning continues to evolve, pre-trained language models like ChatGPT/GPT-4 have emerged as powerful tools for natural language processing tasks. In a recent tweet, Daniel Bourke posed the question, "Why bother building your own custom ML models when ChatGPT/GPT-4 will be better?" It's a valid question, given the impressive capabilities of pre-trained language models like ChatGPT/GPT-4. However, there are still several compelling reasons to build custom ML models, even in the face of such impressive technology. If you are interested in learning more about AI and machine learning, you may want to check out Daniel Bourke's Twitter and Medium accounts, as well as his YouTube channel.

Distributed ledger systems have become more prominent and successful in recent years, with a focus on blockchains and cryptocurrency. This has led to various misunderstandings about both the technology itself and its capabilities, as in many cases blockchain and cryptocurrency is used synonymously and other applications are often overlooked. Therefore, as a whole, the view of distributed ledger technology beyond blockchains and cryptocurrencies is very limited. Existing vocabularies and ontologies often focus on single aspects of the technology, or in some cases even just on one product. This potentially leads to other types of distributed ledgers and their possible use cases being neglected. In this paper, we present a knowledge graph and an ontology for distributed ledger technologies, which includes security considerations to model aspects such as threats and vulnerabilities, application domains, as well as relevant standards and regulations. Such a knowledge graph improves the overall understanding of distributed ledgers, reveals their strengths, and supports the work of security personnel, i.e. analysts and system architects. We discuss potential uses and follow semantic web best practices to evaluate and publish the ontology and knowledge graph.

Ankush Sabharwal, Founder & CEO of CoRover.ai, a human-centric conversational AI platform being used by 1 Billion users. Conversational AI chatbots are revolutionizing the way businesses interact with their customers. These AI-powered chatbots can understand and respond to customer queries in a natural and human-like manner, making the customer experience more efficient and personalized. Conversational AI started with click-based UI. From there it went to keyword-based search to AI/NLU-based intent classification and entry extractions, and now it has reached deep learning/NLG-based LLM/generative AI, which is the reason conversational AI is producing headlines today.

While everybody was focused on crypto and web3 during the last two years, behind the scenes something that might have an impact of the same magnitude on the web and perhaps even more was rooting: Generative AI. But it's during the past months that everything seemed to accelerate. It's like every hope we had for AI in the last 20 years has come 10x closer to reality in a matter of weeks. This article is the first of a series of two medium posts regarding Generative AI. Today I'll focus on explaining what it is, how it works, how it emerged and what could be the underlying use cases.