Ben Westlake’s Post

As the use of AI in our lives increases every day, can you recognise its limitations? Now more than ever, the use of large language models (LLMs) like ChatGPT and Claude has become increasingly common worldwide. However, many people have started worrying that AI will come after their jobs. But this might not be the case. Ironically, almost all LLM-based systems fail at simple and straightforward tasks. VentureBeat describes the 'strawberry problem', where various LLM-based systems flounder at counting the number of 'r's in the word 'strawberry'. This is also the case for 'm's in 'mammal', and 'p's in 'hippopotamus'. Although LLMs might not be able to 'think' or logically reason, they are adept at understanding structured text such as computer code of many programming languages. AI models cannot 'think' like a human, and therefore may need human interventions in various scenarios. Recognising its limitations is crucial for its responsible usage and for realistic expectations of these models. Learn more here: https://heyor.ca/DhCsCY For bespoke technology solutions, contact us at ByteBox for more information. #LLM #AI #LLMBasedSystems

How ‘Chain of Thought’ Makes Transformers Smarter https://lnkd.in/dvR7GVrN Large Language Models and Advanced Reasoning Large Language Models (LLMs) like GPT-3 and ChatGPT excel in complex reasoning tasks like mathematical problem-solving and code generation, surpassing standard machine learning techniques. The key to unlocking these abilities lies in the “chain of thought” (CoT), allowing models to generate intermediate reasoning steps before arriving at the final answer, similar to how humans break down problems into smaller steps. Practical Solution and Value The “chain of thought” approach significantly enhances the reasoning capabilities of transformer models like GPT-3, enabling them to handle complex tasks requiring sequential logic that parallel models would struggle with. Understanding the Power of “Chain of Thought” Even generating incorrect or random intermediate steps improves the model’s reasoning ability. This approach allows transformers to perform more serial computations, making them powerful enough to solve almost any computationally hard problem, at least from a theoretical standpoint. Practical Implementation of AI Solutions Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI. Define KPIs: Ensure AI endeavors have measurable impacts on business outcomes. Select an AI Solution: Choose tools aligned with your needs and provide customization. Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. Spotlight on a Practical AI Solution Consider the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom #artificialintelligence #ai #machinelearning #technology #datascience #python #deeplearning #programming #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #business #engineering #robot #datascientist #art #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer

When you envision Large Language Models (LLMs), what do you see? A chatbot or a brain? LLMs, which are commonly associated with chatbots, are in fact much more than AI assistants or response generators. While it's true that AI assistants and chatbots like ChatGPT have become invaluable tools in the workplace, in the industrial context LLMs can be leveraged to automate complex tasks that involve decision-making. This can be achieved by employing LLM agents, chains, and their function calling capabilitites. To show some of these capabilities, in this video (link provided below), I use a chatbot that utilizes a GPT-3.5 model to make multiple decisions on demand: 1. Operate as a standard chatbot, similar to ChatGPT. 2. Utilize the DuckDuckGo search engine to conduct web searches based on user queries, including news, images, videos, or general website information. (It has access to 10 different Python functions that it can choose from.) 3. Generate a summary of an entire website upon request. 4. Set up a full RAG pipeline for a website, enabling the user to ask questions and chat with the website. And this example merely touches the tip of the iceberg. Imagine you have access to a brain that can make accurate decisions 24/7. How would you use such a powerful tool within your organization? Link to the video: https://lnkd.in/dT_-uxu7 Link to the code: https://lnkd.in/ehez98FF (Project name: WebRAGQuery) P.S: the user interface was designed using #Chainlit and #Langchain was used for RAG.   #LLM #agents #ai #application #generativeai #chatbot #gpt #RAG #duckduckgo #chatgpt #openai

Exciting news from OpenAI! Sam Altman, the visionary CEO of OpenAI, has just unveiled an incredible new GPT-4o (omni) model. This is the next generation of AI, and it's going to change the world! It's like an AI assistant from the movie Her, and it's going to be the new flagship model for OpenAI. Key features of the new GPT-4o: 1. this game-changing technology brings GPT-4 level capabilities to all free users. 2. the model is native modal, so it's trained with texts, sounds (voices), images, photos, and videos. 3. it supports 50 languages, and the translation functionality among languages is real-time. 4. dest of all, there are no big time lags before an answer, and the answer starts to come in just 0.3 seconds! 5. the overall ELO index is an impressive 1310 (with the nearest GPT-4-turbo at 1253). This represents a 100-point difference, which is a clear indication of the 66/34 relation of wins between GPT-4o and GPT-4-turbo. 6. amazingly, it's twice as fast and costs half as much (just $7 per million tokens)! Plus, it has five times the rate limits. 7. we've got a new tokenizer with compression! For instance, Arabic is now 2x less, Tamil is 3.3x, Hindi is 2.9x, and so on. It's based on an expanded dictionary, so it's even more powerful than before! 8. contex window lenght = 128K. 9. it can even understand your emotions and express them back to you! 10. and there's more! You can access it via API (an access is already available) and in ChatGPT (free!) We've got more updates on the way! Stay tuned! This is going to be a game-changer in the world of artificial intelligence! official link: https://lnkd.in/g5ipDmtv additional info link: https://lnkd.in/gr_NxQa7 Sam's blog: https://lnkd.in/g3wvWTTm #openai #ai #gpt #artificialintelligence #ceo #gpt4 #ChatGPT

Wowza! Microsoft just dropped a game-changer in the world of AI! They've unveiled a new family of small language models (SLMs) called Phi-3, starting with Phi-3-Mini. Here's the scoop: • Phi-3 models are more affordable & accessible than large language models like ChatGPT & Google's Gemini • Despite being smaller, Phi-3-Mini outperforms similar-sized models in language processing, reasoning, coding & math • Trained on a massive 3.3 trillion token dataset, Phi-3-Mini packs a punch with just 3.8 billion parameters • These compact models could run directly on smartphones without needing constant internet connectivity • Microsoft's goal? Democratize generative AI tech for small businesses & individual users You can read all about it in this article from MarTech: "Microsoft unveils a new small language model" https://lnkd.in/gyCWkFgN I'm excited to see how this development transforms the AI landscape & makes the tech more accessible to everyone! Happy Saturday! Let me know your thoughts. Casey Jones #AI #LanguageModels #Phi3 #Microsoft #Accessibility

Artificial Intelligence (A.I.), has become a buzzword lately, and most people can't differentiate between what ChatGPT does, as opposed to what it is. There are hundreds of technologies that provide tools to interpret and parse data of large language models (LLMs). OpenAI (GPT) is just one company who provides useful tools with regards to artificial intelligence. Google has a multimodal A.I. model called “Vertex Model Garden.” Vertex helps developers train their models on varies algorithms as well a deploy their LLMs into production. Google also has PaLM (Pathways Language Model) which is a 540 billion parameter transformer-based LLM that is capable of varies tasks such as commonsense reasoning, arithmetic reasoning, joke explanation, code generation, and translation.

Ignore the truly awful name, but OpenAI had just released GPT-4o mini, a smaller, more affordable version of GPT-4o. 90% cheaper than GPT-4o, in fact. It will likely appeal to companies and developers accessing it via its APIs, for uses where top notch performance isn't necessary, but speed is. Chat bots on web sites, for example. Full details below. In the meantime, when oh when will OpenAI hire a branding expert... #Marketing #ContentCreation #BusinessLeaders #AIMarketing #AIInnovation #GPT4oMini #TechUpdates #CostEfficiency #Chatbot #BusinessTechnology #OpenAI #Claude #Gemini #ChatBots #GPT4o

Meta released their most advanced frontier language model called Llama 3.1 just Wednesday this week. Llama 3.1 is the most advanced language model from the family of Llama models. The model has 405 billion parameters. This means that the machine learning model can consider 405 billion variables as part of the input, meaning it can identify the underlying characteristics of the input in 405 billion ways. The Llama model has been trained with 15 trillion tokens (data points) over 16,000 GPUs over several months. These 16,000 GPUs consumed enough electricity to power a small city. There are 126 layers in the model - consider layers like filters, the input passes through 126 filters to give the final output But what is the fuss all about? Llama is an open source model - essentially democratising access to the model’s inner workings for developers to build on top of the model Llama is not an end-consumer product - You can’t access Llama as easily as typing a URL on your browser like ChatGPT. Making its use limited to only those on who can build on the base of this model What’s the biggest raw material for machine learning models? Data Machine learning models require tonnes of data for successful model formation Llama can help generate synthetic data, and on top of the synthetic data niche specific models could be developed for very specific use cases. Llama 3.1 marks a significant development in the AI landscape. Looking forward to what the AI world has to offer next.

GPT-4o: OpenAI's recent “omni”-modality LLM 🔵 OpenAI, on 13.05.24 unveiled #GPT-4o, a game-changer for human-computer interaction. It understands and responds across audio, vision, and text in real-time! 💡 Key features: - Natural Interaction: Seamless communication through voice, vision, and text. It accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. - Superior vision and audio understanding. - Lightning-fast audio response: 232ms on average. - User Control & Accessibility: Free tier available, with greater user control over outputs. - Now available in 20 languages 📌 Availability: - Text & Image features rolling out in ChatGPT (free & Plus tiers) - Audio features coming soon (alpha) in ChatGPT Plus - Text & Vision access available for developers through OpenAI API. Bonus: GPT-4o better understands and responds with new tokenizer's compression mechanism in multiple languages, including 5 Indian regional languages (Hindi, Marathi, Tamil, Telugu, Gujrati)! Source: https://lnkd.in/g6vEuD4i #AI #GenerativeAI #OmniLLM #GPT4o

OpenAI has just unveiled GPT-4o, marking significant advancements over the previous models. This model introduces multimodal capabilities, enabling it to handle text, audio, and image inputs and outputs better. The best new features include: - Processes text, audio, and image inputs and outputs through a single neural network. This works multimodally, ie you can add all these inputs together. - This model is fast. It responds to audio inputs in as little as 232 milliseconds. - Incredibly superior capabilities in non-English text. - Developers can face a 50% reduction in API costs. - Trained across text, vision, and audio for comprehensive understanding. - Can engage in real-time language translation with *higher* accuracy. - Includes built-in safety protocols with rigorous evaluations. - Achieves significant token reduction across many languages. - It delivers double the speed, half the price, and higher rate limits compared to GPT-4 Turbo. - It has enhanced capabilities in visual analysis, translation, and interactive tasks. My favourite part of this model: Not only does it maintain the high performance of GPT-4 Turbo in English and coding tasks, but it also improves upon handling non-English text. The way AI agents are going to improve with this model is going to be unprecedented. Stay tuned for more agentive AI ideas, as well as new ways to help your business use this model to make more money and save more time. Read more about the update here: