Machine learning is widely applied in finance for tasks like credit scoring, fraud detection, and trading. It helps analyze big financial data to spot trends, predict outcomes, and automate decisions, boosting efficiency and profits. This course recommends top machine learning courses for finance professionals aiming to harness these techniques for better decision-making and performance. Machine…
Language modeling, a core component of machine learning, involves predicting the likelihood of a sequence of words. This field primarily enhances machine understanding and generation of human language, serving as a backbone for various applications such as text summarization, translation, and auto-completion systems. Efficient language modeling faces significant hurdles, particularly with large models. The main…
Generative AI (GenAI) tools have come a long way. Believe it or not, the first generative AI tools were introduced in the 1960s in a Chatbot. Still, it was only in 2014 that generative adversarial networks (GANs) were introduced, a type of Machine Learning (ML) algorithm that allowed generative AI to finally create authentic images,…
Multimodal large language models (MLLMs) represent a cutting-edge intersection of language processing and computer vision, tasked with understanding and generating responses that consider both text and imagery. These models, evolving from their predecessors that handled either text or images, are now capable of tasks that require an integrated approach, such as describing photographs, answering questions…
Applications that take advantage of machine learning in novel ways are being developed thanks to the rise of Low-Code and No-Code AI tools and platforms. AI can be used to create web services and customer-facing apps to coordinate sales and marketing efforts better. Minimal coding expertise is all that’s needed to make use of Low-Code…
In a recent study, a team of researchers from Imperial College London and Dell introduced StyleMamba, an effective framework for transferring picture styles that uses text prompts to direct the stylization process while maintaining the original image content. The computational needs and training inefficiencies of the current text-guided stylization techniques have been addressed in this…
The inherent risks associated with AI systems, especially in applications like autonomous driving and medical diagnosis, where errors can have severe consequences, should be handled carefully, keeping the risk factor under control. The key challenge lies in developing dependable models and ensuring their reliable execution, including innovative approaches to mitigate these risks effectively. Researchers from…
Large language models (LLMs) have revolutionized natural language processing, enabling groundbreaking advancements in various applications such as machine translation, question-answering, and text generation. However, the training of these models poses significant challenges, including high resource requirements and long training times due to the complexity of the computations involved. Previous research has explored techniques like loss-scaling…
Language models (LMs) have gained traction as aids in software engineering, where users act as intermediaries between LMs and computers, refining LM-generated code based on computer feedback. Recent advancements depict LMs functioning autonomously in computer environments, potentially expediting software development. However, the practical application of this autonomous approach still needs to be explored. Code generation…
ChatGPT – GPT-4 GPT-4 is the latest LLM of OpenAI, which is more inventive, accurate, and safer than its predecessors. It also has multimodal capabilities, i.e., it is also able to process images, PDFs, CSVs, etc. With the introduction of the Code Interpreter, GPT-4 can now run its own code to avoid hallucinations and provide…