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Creating impactful AI apps is still a frontier, filled with challenges and requiring pivotal decisions on cost management, technology selection, and more.
The no-code development phenomenon has drastically changed with the emergence of generative AI, with the industry projected to reach $187 billion by 2030.
Generative AI now boasts over 600 use cases that could significantly advance the United Nations’ sustainable development goals.
Agentic workflows are all about making LLMs smarter by integrating them into structured processes. This helps the AI deliver higher-quality results.
Vector embeddings are the very basis of how large language models function. They open doors to various use cases like semantic search, natural language processing, and more
LangChain is a popular and powerful tool that simplifies building LLM applications by employing abstractions.
Generative AI will impact the education system as a whole whereby the lives of students, teachers, administrators, and course creators will be largely affected.
The process of building a RAG-powered LLM application is nuanced. Explore various challenges you can experience in different stages of your RAG pipeline
RAG is a framework that connects external sources of information with the LLM so that its responses are context-aware and accurate.
Explore the changing landscape of the AI chips industry after the rise of large language models | Major competitors in the industry | NVIDIA’s growth debunked
Open-source LLMs are not only proving to be helpful for organizations, but they are also the source of innovation and democratization of AI across the world.
Small language models are scaled-down versions of larger AI models like GPT-4. They are designed to perform similar tasks but with fewer parameters.
Sora is a first of its kind Text-to-Video model released by OpenAI. The model is capable of producing videos for up to 1 minute in high definition.
As interesting as it sounds from its name, Mixtral seems to top the leaderboards challenging models created by big tech such as Meta’s LLaMA 2, OpenAI’s GPT 3.5, and Google’s Gemini Pro.
LLMs do not process information like we humans do. To put it simply, they are like big probabilistic models trying to predict the best responses possible. And because of how they process information and predict outputs, they often behave weirdly.
The strength of the transformer model is in its ability to understand the significance and context of every word in a sentence, unlike RNNs which primarily assess the connection of a word with its immediate neighbor.
Generative AI is poised to automate 60-70% of current employee work time in the future creating a world where people will have personal assistants that can help them increase their productivity.
Throughout 2023, generative AI made striking progress globally, with several key players, including Amazon, Google, Microsoft, OpenAI, and Anthropic engaging in a heated competition.
Large language models are very powerful. However, the tools that use them can sometimes not leverage them fully. To avoid such dumb encounters, there are several methods that bolster the performance of LLM-powered tools.
The European Union passed the draft for AI Act which aims to implement detailed regulations on AI systems, focusing on high-risk AI with obligations for data governance, and more.
Multimodality is the future of generative AI. The power to not only understand text but audio, video, and more allows these models to master tons of use cases.
GPT-4 is the first language model that shows sparks of AGI including strong reasoning, problem-solving, and abstract thinking abilities.
The rise of large language models has sparked a heated debate about whether open-source AI is a reliable option or we should prefer closed-source options.
Learn how AI tools like ChatGPT, MidJourney, Gemini and more can revolutionize your work: Explore use cases in the functions of data science, content writing, programming & more!
An LLM is an advanced machine learning algorithm capable of identifying, condensing, translating, predicting, and generating various forms of text and content using extensive datasets.
Data science has taken over the world and it is not surprising. Over 33% of companies now rely on data science as a vital component of their decision-making process.
From modest beginnings to a household name, these trailblazing advancements collectively propelled AI to transform industries and open up new opportunities for exploration and innovation.

The Data-Driven Dispatch

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