Improving AI is complicated by data, as the amount of training data required for each new model release has increased significantly. This burden is further worsened by the growing problem of finding useful, compliant data in the open domain. However, with David AI’s data marketplace, AI developers can now focus on their core task of…
Hormesis Management in Agriculture: Leveraging AI for Crop Improvement: Plant stress negatively impacts crop productivity but can also be beneficial when controlled, a phenomenon known as hormesis. Hormesis management involves exposing crops to low doses of stressors to enhance traits like stress tolerance and metabolite production. However, the complexity of plant responses to stress limits…
State-of-the-art large language models (LLMs) are increasingly conceived as autonomous agents that can interact with the real world using perception, decision-making, and action. An important topic in this arena is whether or not these models can effectively use external tools. Tool use in LLMs will involve: Recognizing when a tool is needed. Choosing the correct…
The integration of language models into biological research represents a significant challenge due to the inherent differences between natural language and biological sequences. Biological data, such as DNA, RNA, and protein sequences, are fundamentally different from natural language text, yet they share sequential characteristics that make them amenable to similar processing techniques. The primary challenge…
Artificial intelligence (AI) has evolved into a powerful tool beyond simple automation, becoming a critical asset in scientific research. Integrating AI in scientific discovery is reshaping the landscape by enabling machines to perform tasks that traditionally require human intelligence. This evolution marks a shift towards a future where AI assists and autonomously drives scientific innovation.…
The rapid advancement of AI and machine learning has transformed industries, yet deploying complex models at scale remains challenging. This is particularly true for multimodal applications integrating diverse data types like vision, audio, and language. As AI applications grow more sophisticated, transitioning from prototypes to production-ready systems becomes increasingly complex. There is a pressing need…
Sarvam AI has recently unveiled its cutting-edge language model, Sarvam-2B. This powerful model, boasting 2 billion parameters, represents a significant stride in Indic language processing. With a focus on inclusivity and cultural representation, Sarvam-2B is pre-trained from scratch on a massive dataset of 4 trillion high-quality tokens, with an impressive 50% dedicated to Indic languages.…
Large Language Models (LLMs) have demonstrated remarkable effectiveness in addressing generic questions. An LLM can be fine-tuned using the company’s proprietary documents to utilize it for a company’s specific needs. However, this process is computationally intensive and has several limitations. Fine-tuning may lead to issues such as the Reversal Curse, where the model’s ability to…
Most advanced machine learning models, especially those achieving state-of-the-art results, require significant computational resources such as GPUs and TPUs. Deploying large models in resource-constrained environments like edge devices, mobile platforms, or other low-power hardware restricts the application of machine learning to cloud-based services or data centers, limiting real-time applications and increasing latency. Access to high-performance…
The research field of Spiking Neural P (SNP) systems, a subset of membrane computing, explores computational models inspired by biological neurons. These systems simulate neuronal interactions using mathematical representations, closely mimicking natural neuronal processes. The complexity of these models makes them valuable for advancing fields such as artificial intelligence and high-performance computing. By providing a…