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Introduction Developers have a lot of tools and technologies at their disposal that are meant to make work faster and easier. Since its release in 2021, GitHub Copilot has been a star. It does more than just speed things up. It shines when it comes to making complicated code easier to understand and making switching between computer languages easier.
Compared to large language models (LLMs), which are limited in size, speed, and ease of customization, small language models (SLMs) would be a more economical, efficient, and space-saving AI technology for users with limited resources. With fewer parameters (usually less than 10 billion), SLMs are assumed to have lower computational and energy costs.
Deep Convolutional Generative Adversarial Networks (DCGANs) – a subclass of Generative Adversarial Networks (GANs) – have utilized convolutional neural networks (CNNs) to synthesize good-quality images. The architecture was established by Radford et al. in 2015, significantly improving the original GANs from their earlier forms as it innovates these architectural changes that lead to stabilizing the training process and also further the quality of generated images.
Salesforce’s Sales Cloud is a set of tools that are meant to help sales teams work faster and better. It has many features that make you more productive, such as managing leads and pipelines, forecasting, getting insights from AI, coaching tools for managers, integration with communication channels (like phone calls, text messages, and LinkedIn InMail), sales cadences for easier outreach, and a powerful to-do list for keeping track of your daily tasks.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
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