Artificial Intelligence (AI) continues to shape the way we interact with video material, and Jockey, an open-source chat video agent, embodies these advancements. By integrating LangGraph and Twelve Labs APIs, Jockey enhances video processing and communication.
Twelve Labs provides advanced video comprehension APIs that draw out rich insights from video footage. Unlike traditional methods that use…
Model selection is a critical part of addressing real-world data science problems. Traditionally, tree ensemble models such as XGBoost have been favored for tabular data analysis. However, deep learning models have been gaining traction, purporting to offer superior performance on certain tabular datasets. Recognising the potential inconsistency in benchmarking and evaluation methods, a team of…
Research out of Princeton University makes a critical commentary on the current practice of evaluating artificial intelligence (AI) agents predominantly based on accuracy. The researchers argue that this unidimensional evaluation method leads to unnecessarily complex and costly AI agent architectures, which can hinder practical implementations.
The evaluation paradigms for AI agents have traditionally focused on…
The rise of generative AI technologies (GenAI) brings a critical decision for businesses - to buy an off-the-shelf solution or develop a custom one. This decision is influenced by several factors that impact the return on investment and overall effectiveness of the solution.
First, the specific use case must be clearly defined. Should the goal…
Function-calling agent models are a critical advancement in large language models (LLMs). They interpret natural language instructions to execute API calls, facilitating real-time interactions with digital services, like retrieving market data or managing social media interactions. However, these models often face challenges as they require high-quality, diverse and verifiable datasets. Unfortunately, many existing datasets lack…
Udacity, the online educational platform, offers a vast array of courses in Artificial Intelligence (AI), including technology and applications, catered towards both beginners and advanced learners. These in-depth courses teach foundational topics in AI like machine learning algorithms, deep learning architectures, natural language processing, computer vision, reinforcement learning, and even AI ethics. The learning extends…
Language modeling in the area of artificial intelligence is geared towards creating systems capable of understanding, interpreting, and generating human language. With its myriad applications, including machine translation, text summarization, and creation of conversational agents, the goal is to develop models that mimic human language abilities, thereby fostering seamless interaction between humans and machines. This…
ChatGPT and similar AI-powered tools are now vital in the modern business environment. They offer a multitude of benefits, allowing businesses to gain a competitive edge, boost productivity, and enhance their profit margins. In this article, 10 key use cases for ChatGPT that professionals, CxOs, and business owners can adopt extensively have been identified.
ChatGPT's application…
Large language models (LLMs) are becoming progressively more powerful, with recent models exhibiting GPT-4 level performance. Nevertheless, using these models for applications requiring extensive context, such as understanding long-duration videos or coding at repository-scale, presents significant hurdles. Typically, these tasks require input contexts ranging from 100K to 10M tokens — a great leap from the…
Qdrant, a pioneer in vector search technology, has unveiled BM42, a powerful new algorithm, aimed at transforming hybrid search. BM25, the algorithm relied upon by search engines like Google and Yahoo, has dominated for over 40 years. Yet, the rise of vector search and the launch of Retrieval-Augmented Generation (RAG) technologies have revealed the need…
Researchers from Stanford University have developed a new model to investigate the contributions of individual data points to machine learning processes. This allows an understanding of how the value of each data point changes as the scale of the dataset grows, illustrating that some points are more useful in smaller datasets, while others become more…
Overfitting is a prevalent problem when training large neural networks on limited data. It indicates a model's strong performance on the training data but its failure to perform comparably on unseen test data. This issue arises when the network’s feature detectors become overly specialized to the training data, building complex dependencies that do not apply…