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TL;DR We’re all FUCKED, actually ! Note: This is a conversation with GPT-4 regarding its capabilities and limits. Would you consider the success and progress of AI and LLMs (large language models) to be more akin to the introduction of electricity or more like the first working telephone? The development and success of AI and …
We use GPT-4 to automatically write explanations for the behavior of neurons in large language models and to score those explanations. We release a dataset of these (imperfect) explanations and scores for every neuron in GPT-2.
In today’s digital world, business and IT leaders are turning to automation to improve operational efficiency, increase employee productivity and, ultimately, boost business performance. At IBM, we believe that organizations need AI coupled with automation to help developers reduce time to productivity. By empowering employees with automation and AI technologies like machine learning, deep learning, …
Read more “Reshaping IT automation with IBM Watson Code Assistant”
Amazon SageMaker Serverless Inference allows you to serve model inference requests in real time without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. You can let AWS handle the undifferentiated heavy lifting of managing the underlying infrastructure and save costs in the process. A Serverless Inference endpoint spins up …
Read more “Announcing provisioned concurrency for Amazon SageMaker Serverless Inference”
Generative AI is poised to transform and streamline many of the most common processes and tasks each of us performs every day at work — like how we communicate, how we analyze data, how we interface with apps, or even how we summarize content and conversations. At Google Cloud, our open approach to generative AI …
Read more “Bringing Google Cloud generative AI to popular workplace apps”
Artificial intelligence (AI) is polarizing. It excites the futurist and engenders trepidation in the conservative. In my previous post, I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. This blog continues the discussion, now investigating the …
With Amazon SageMaker, you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. This post provides a solution tailored to customers that are already using MLflow, an open-source platform for managing ML workflows. …
Read more “Securing MLflow in AWS: Fine-grained access control with AWS native services”
TL;RD There is no more excuse not to have a great marketing plan. Prompt: “Craft a marketing plan in the style of Allan Dib’s 1-Page Marketing Plan for our AI blog.” This is the prompt we used – simply replace “AI blog” with your product or service to generate a plan suitable to your product(s) …
“In my opinion, AutoGPT / babyAGI / AgentGPT is the real disruption point for the use of AI for complex problem-solving. Keep an eye out for these terms popping up in the news in the coming months.” That’s what Lars said, and rightfully so! Autonomous AI agents will introduce a new world of research and finding …
In this post, we discuss a machine learning (ML) solution for complex image searches using Amazon Kendra and Amazon Rekognition. Specifically, we use the example of architecture diagrams for complex images due to their incorporation of numerous different visual icons and text. With the internet, searching and obtaining an image has never been easier. Most …
Read more “Build an image search engine with Amazon Kendra and Amazon Rekognition”