The release of ChatGPT by OpenAI in November 2022 marked another significant milestone in the evolution of AI. ChatGPT, a large language model capable of generating human-like text, demonstrated the potential of AI to understand and generate natural language. This capability opened up new possibilities for AI applications, from customer service to content creation. The world responded to ChatGPT with a mix of awe and excitement, recognizing the potential of AI to transform how humans communicate and interact with technology to enhance our lives. Today, the rise of agentic AI -- systems capable of advanced reasoning and task execution -- is revolutionizing the way organizations operate.

Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based. Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources.

Large language models capable of writing poems, summaries, and computer code are driving the demand for "natural language processing (NLP) as a service." As these models become more capable -- and accessible, relatively speaking -- appetite in the enterprise for them is growing. According to a 2021 survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their NLP budgets grew by at least 10% compared to 2020, while a third -- 33% -- said that their spending climbed by more than 30%. Well-resourced providers like OpenAI, Cohere, and AI21 Labs are reaping the benefits. As of March, OpenAI said that GPT-3 was being used in more than 300 different apps by "tens of thousands" of developers and producing 4.5 billion words per day.

DeepMind says that teaching machines to realistically mimic human language is more complex than simply throwing increasing amounts of computing power at the problem, despite that being the predominant strategy in the field. In recent years, most progress in building artificial intelligences (AIs) has come from increasing their size and training them with ever more data on the biggest computer available. But this makes the AIs expensive, unwieldy and hungry for resources. A recent system created by Microsoft and Nvidia required more than a month of supercomputer access and almost 4500 high-power graphics cards to train, at a cost of millions of dollars. In a bid to find alternatives, AI firm DeepMind has created a model that can look up information in a vast database, in a similar way that a human would use a search engine.

Let the OSS Enterprise newsletter guide your open source journey! At its fall 2021 GPU Technology Conference (GTC) today, Nvidia announced that it's making Megatron 530B, one of the world's largest language models, available to enterprises for training to serve new domains and languages. First detailed in early October, Megatron 530B -- also known as Megatron-Turing Natural Language Generation (MT-NLP) -- contains 530 billion parameters and achieves high accuracy in a broad set of natural language tasks, including reading comprehension, commonsense reasoning, and natural language inference. "Today, we provide recipes for customers to build, train, and customize large language models, including Megatron 530B. This includes scripts, code, and 530B untrained model. Customers can start from smaller models and scale up to larger models as they see fit," Nvidia VP of AI software product management Kari Briski told VentureBeat via email.

Microsoft and chip manufacturer Nvidia have created a vast artificial intelligence that can mimic human language more convincingly than ever before. But the cost and time involved in creating the neural network has called into question whether such AIs can continue to scale up. The new neural network, known as the Megatron-Turing Natural Language Generation (MT-NLG) has 530 billion parameters, more than tripling the scale of OpenAI's groundbreaking GPT-3 neural network that was considered the state of the art up until now.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Microsoft and Nvidia today announced that they trained what they claim is the largest and most capable AI-powered language model to date: Megatron-Turing Natural Language Generation (MT-NLP). The successor to the companies' Turing NLG 17B and Megatron-LM models, MT-NLP contains 530 billion parameters and achieves "unmatched" accuracy in a broad set of natural language tasks, Microsoft and Nvidia say -- including reading comprehension, commonsense reasoning, and natural language inferences. "The quality and results that we have obtained today are a big step forward in the journey towards unlocking the full promise of AI in natural language. The innovations of DeepSpeed and Megatron-LM will benefit existing and future AI model development and make large AI models cheaper and faster to train," Nvidia's senior director of product management and marketing for accelerated computing, Paresh Kharya, and group program manager for the Microsoft Turing team, Ali Alvi wrote in a blog post.