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Category: Generative AI

Deployment Parameters in Azure AI Foundry

Published 2024-12-06 by Kevin Feasel

Tomaz Kastrun continues a series on Azure AI:

Give the model instructions about how it should behave and any context it should reference when generating a response. You can describe the assistant’s personality, tell it what it should and shouldn’t answer, and tell it how to format responses. There’s no token limit for this section, but it will be included with every API call, so it counts against the overall token limit

Click through for a description of each part of the deployment parameters section.

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Deployment in Azure AI Foundry

Published 2024-12-05 by Kevin Feasel

Tomaz Kastrun continues a series on Azure AI:

When you are in Azure AI Foundry, on the left navigation bar, select “Model Catalog”.

For this demo, I will be selecting multimodal model “gpt-4” that can work with images and text.

Click “> Deploy” and select the deployment type and also customize the deployment details.

Tomaz has some step-by-step instructions, a bit of detail on deployment types, and a bit of info on how to consume the results.

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Creating a Project in Azure AI Foundry

Published 2024-12-04 by Kevin Feasel

Tomaz Kastrun continues a series on Azure AI:

Azure AI models inference service provides access to the most powerful models available in the Azure AI model catalog. Coming from the key model providers in the industry including OpenAI, Microsoft, Meta, Mistral, Cohere, G42, and AI21 Labs; these models can be integrated with software solutions to deliver a wide range of tasks including content generation, summarization, image understanding, semantic search, and code generation.

The Azure AI model inference service provides a way to consume models as APIs without hosting them on your infrastructure. Models are hosted in a Microsoft-managed infrastructure, which enables API-based access to the model provider’s model. API-based access can dramatically reduce the cost of accessing a model and simplify the provisioning experience.

Read on to learn more about what you get when you create a project.

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A Review of the Azure AI Foundry

Published 2024-12-02 by Kevin Feasel

Tomaz Kastrun starts a new series:

Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Azure AI Foundry brings these services together in a single unified experience for AI development on the Azure cloud platform.

Until now, developers needed to work with multiple tools and web portals in a single project. With Azure AI Foundry, these tasks are now simplified and offers same environment for better collaboration.

Read on to see more about the Azure AI Foundry.

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Generative AI Answers: Do Not Trust, Do Verify

Published 2024-11-01 by Kevin Feasel

Erik Darling speaks wisdom:

Here’s what I’ve used it for with some success:

  • Creating images for Beer Gut Magazine
  • Summarizing long documents
  • Writing boilerplate stuff that I’m bad at (sales and marketing drivel, abstracts, lists of topics)

But every time I ask it to do that stuff, I really have to pay attention to what it gives me back. It’s often a reasonable starting place, but sometimes it really goes off the rails.

That’s true of technical stuff, too. Here’s where I’ve had a really bad time, and if there’s anything you know deeply and intimately, you’ll find similar problems too.

Click through for Erik’s experience. That’s pretty close to my own, and is a big part of why I refer to generative AI models as being akin to drunken interns: sure, give them assignments, but you’d better double-check every part of it.

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Security Risk Profile in AI-Generated Code

Published 2024-10-29 by Kevin Feasel

Jerome Robert reviews the papers:

As such, nowadays, almost all developers use some form of AI-generated code — and they absolutely should. AI tools make developers’ lives easier by leveraging the knowledge cultivated by the development community over time and across the globe to overcome obstacles that, while potentially new and challenging to them, have long been addressed. They can reasonably trust that code to perform the function they want to achieve — and can test it to be sure.

But can they trust that code to be secure? Absolutely not. With all that time and work spent committing functional code, just as much, if not more, is spent navigating the security backlog afterward.

Click through for a summary of two recent academic papers, as well as links to the papers themselves.

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Generating Embeddings in Oracle from a Function or Trigger

Published 2024-09-20 by Kevin Feasel

Brendan Tierney continues a series on generative AI in Oracle:

In my previous post, I gave examples of using Cohere to create vector embeddings using SQL and of using a Trigger to populate a Vector column. This post extends those concepts, and in this post, we will use OpenAI.

Warning: At the time of writing this post there is a bug in Oracle 23.5 and 23.6 that limits the OpenAI key to a maximum of 130 characters. The newer project-based API keys can generate keys which are greater than 130 characters. You might get lucky with getting a key of appropriate length or you might have to generate several. An alternative to to create a Legacy (or User Key). But there is no guarantee how long these will be available.

Assuming you have an OpenAI API key of 130 characters or less you can follow the remaining steps. This is now a know bug for the Oracle Database (23.5, 23.6) and it should be fixed in the not-too-distant future. Hopefully!

Read on to learn more.

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Myths and Reality of Copilot for Power BI

Published 2024-09-11 by Kevin Feasel

Kurt Buhler puts together an essay:

However, recent months reveal rising skepticism, concern and possibly even disillusionment with generative AI tools, both from investors (especially from investors) and from the public. Despite the massive investment, enthusiasm, and promotion, these tools seem to be seeing limited adoption and aren’t yet showing the measurable value that fulfills their promises. And yet, paradoxically, many professionals will agree anecdotally that they use generative AI tools regularly, and that these tools seem to help them be more productive in certain tasks. Furthermore, there are concrete success stories where generative AI is bringing value, such as the models like the latest versions of Alphafold (from Google) and ESMfold (from Meta) that aid in protein folding for pharmaceutical companies more effectively find potential new drug candidates. So, who are these tools for, what problems do they solve, and how can we use them effectively? This is too big of a topic for even Bink and Bonk the Data Goblins to solve, so let’s narrow the focus, a bit.

This is a must-read, and Kurt even provides a de-goblinified PDF version for management.

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