Ai for Business

  Today I want to talk to you about an experiment conducted by some AI researchers to see if AI can learn language the way human babies do. With this in mind, they put a head mounted camera on an infant and tried to understand how human babies comprehend language with the help of the visual and auditory cues it gets from its environment and can AI learn on the same lines. We’ll discuss that experiment in a bit. If you come to think of it, learning a language is such a complex task and yet human babies do it pretty easily! Contrary to the belief that parents or caretakers teach a child to learn a language, children acquire language through interaction — not only with their parents and other adults, but also with other children. All normal children who grow up in normal households, surrounded by conversation, will acquire the language that is being used around them. And it is just as easy for a child to acquire two or more languages at the same time, as long as they are regularly int...

  ❓RAG demos are easy. RAG in production is much harder. Why is it so? Recently I wrote about Retrieval Augmented Generation (RAG), the technique that enables enterprises to leverage the power of large language models (link in comment ). Many companies have experienced that their technology teams are able to demonstrate the use of RAG quite rapidly. However, when the time comes to put the solution into production, they face difficulties. The users do not accept the solution easily and many don’t think it adds a lot of value. We will first see why the demos are easy and then reflect on the challenge of productionising RAG. Why are RAG demos easy? 👉 Simple architecture: The principle of RAG is quite simple to understand. In the basic RAG pipeline, there are very few components: vector database, source chunking, vector matching and LLM interface. It is easy for engineers to understand the architecture. 👉 Framework availability: To make matters simpler, helper frameworks such as Lang...

❓What is RAG, the technique that helps companies to leverage Generative AI? If you have used ChatGPT or Bard (now Gemini), you know how useful GenAI can be. It can answer your questions, summarize books 📘 and write articles. There are a host of other things that you can do with LLMs like ChatGPT and Gemini, by learning a bit about how to write prompts. All this is good for personal use. But what about the use of LLMs inside organizations 🏢? Not many applications of ChatGPT for companies come to mind. The idea 💡that helped companies to make use of LLMs is ‘augmentation’. In simple words, it means including additional information in the prompt. This additional information comes from a source within the company. It can be a contract 📃, records from a database or even a powerpoint presentation. We can also combine information from many sources and include it in the prompt. Of course, the additional information must be first fetched from the right source. A CXO wants to know the impli...

Sora: Creating video from text Sora is an AI model that can create realistic and imaginative scenes from text instructions. Read technical report All… openai.com Video taken from openai.com, Open AI’s Sora model’s landing page Open AI, the maker of Chat GPT, has launched a new Generative AI model called Sora which can instantly generate videos from text. The video that I have shared here is one such video generated by Sora. The quality of the videos is far superior to the ones generated by the previous models launched by the other tech giants. It is capable of generating videos in different resolutions and aspect ratios. It is not publicly available yet. While these videos appear more polished than those generated by earlier models, closer examination reveals occasional errors, such as discontinuity between frames and surreal scenes in realistic videos. You might see strange things like cats with three legs or a person’s arm disappearing into nowhere. You might notice a certain oddness...

  🌍 Experts on economy and technology think that AI will result in the growth of the global economy. In other words, it will boost the GDP of the world. For example, Accenture in its report ‘Why AI is the future engine of growth’ predicts that AI will double the global economic growth rate by 2035. PWC estimates that AI will add 15% to the world GDP by 2030. 📈 The contribution of AI to economic growth will come majorly from three factors: 💲 Capital investment in AI by the industry: most companies worldwide are spending on AI implementation because of its promise. Since capital expenditure is added to the GDP computation, this is one reason for the growth. ⏩ Productivity gain: AI enables automation of processes that are currently manual. This results in major productivity gains for the companies. Better productivity means better and cheaper products for the consumers. This boosts personal consumption which is a big part of the GDP calculation. 🆕 New products and services: AI wi...