A Fine Slice Of SQL Server
In this video, we walk through the code in an Azure Machine Learning project and see how the pieces fit together.
There are a few more videos to go in this Azure ML series and I would recommend going through them in order to understand how we got to this video, but this one is what I’ve been building toward.
Comments closedFacebook has announced a new library:
Today, we’re introducing SeamlessM4T, the first all-in-one multimodal and multilingual AI translation model that allows people to communicate effortlessly through speech and text across different languages. SeamlessM4T supports:
- Speech recognition for nearly 100 languages
- Speech-to-text translation for nearly 100 input and output languages
- Speech-to-speech translation, supporting nearly 100 input languages and 36 (including English) output languages
- Text-to-text translation for nearly 100 languages
- Text-to-speech translation, supporting nearly 100 input languages and 35 (including English) output languages
The open source library is available on GitHub and you can also get the model itself on HuggingFace. The nicest thing about all of this is that, unlike existing translation services, you can run it entirely offline and perform the inference on local compute.
Comments closedSabyasachi Samaddar continues a series on generating video from a series of text prompts:
Welcome back to the second part of our journey into the world of Azure and OpenAI! In the first part, we explored how to transform text into video using Azure’s powerful AI capabilities. This time, we’re taking a step further by orchestrating our application flow with Semantic Kernel.
Semantic Kernel is a powerful tool that allows us to understand and manipulate the meaning of text in a more nuanced way. By using Semantic Kernel, we can create more sophisticated workflows and generate more meaningful results from our text-to-video transformation process.
In this part of the series, we will focus on how Semantic Kernel can enhance our application and provide a smoother, more efficient workflow. We’ll dive deep into its features, explore its benefits, and show you how it can revolutionize your text-to-video transformation process.
Read on for an understanding of how Semantic Kernel fits in and what you can do with it.
Comments closedMichael Mayer has a new R package for us:
This post is mainly about the third approach. Its beauty is that we get information about all interactions. The downside: it is as good/bad as partial dependence functions. And: the statistics are computationally very expensive to compute (of order n^2).
Different R packages offer some of these H-statistics, including {iml}, {gbm}, {flashlight}, and {vivid}. They all have their limitations. This is why I wrote the new R package {hstats}:
Click through for an overview of the package and an example of how it works.
Comments closedSabyasachi Samaddar has an interesting project:
In today’s digital age, video content has become a powerful medium for communication and storytelling. Whether it’s for marketing, education, or entertainment purposes, videos could captivate and engage audiences in ways that traditional text-based content often cannot. However, creating compelling videos from scratch can be a time-consuming and resource-intensive process.
Fortunately, with the advancements in artificial intelligence and the availability of cloud-based services like Azure Open AI and Cognitive Services, it is now possible to automate and streamline the process of converting text into videos. These cutting-edge technologies provide developers and content creators with powerful tools and APIs that leverage natural language processing and computer vision to transform plain text into visually appealing and professional-looking videos.
This document serves as a comprehensive guide and a starting point for developers who are eager to explore the exciting realm of Azure Open AI and Cognitive Services for text-to-video conversion. While this guide presents a basic implementation, its purpose is to inspire and motivate developers to delve deeper into the possibilities offered by these powerful technologies.
Click through for a guide on how to do it.
Comments closedShivansh Mundra, et al, report on some research:
With the rise of AI-generated synthetic media and text-to-image generated media, fake profiles have grown more sophisticated. And we’ve found that most members are generally unable to visually distinguish real from synthetically-generated faces, and future iterations of synthetic media are likely to contain fewer obvious artifacts, which might show up as slightly distorted facial features. To protect members from inauthentic interactions online, it is important that the forensic community develop reliable techniques to distinguish real from synthetic faces that can operate on large networks with hundreds of millions of daily users, like LinkedIn.
There are some interesting findings here.
Comments closedObaro Alordiah gives us a primer:
The Azure OpenAI Service has been a trending topic in the tech world this year as it combines the power of OpenAI’s advanced generative AI models with the comprehensive suite of services available on the Azure cloud. It has given developers the opportunity to create and embed high performing AI models into the Azure environment to deliver more efficient, insightful & innovative solutions. In this blog, we will take a high level look at some of the key features within the Azure OpenAI playground and how we can get the best out of it.
Generative AI via OpenAI is an area in which Microsoft is putting an inordinate amount of focus.
Comments closedJi Shen shows off how to perform discrete time series in SAS:
The HMM procedure in SAS Viya supports hidden Markov models (HMMs) and other models embedded with HMM. PROC HMM supports finite HMM, Poisson HMM, Gaussian HMM, Gaussian mixture HMM, the regime-switching regression model, and the regime-switching autoregression model. This post introduces Poisson HMM, the latest addition to PROC HMM in the SAS Viya 2023.03 release.
Count time series is ill-suited for most traditional time series analysis techniques, which assume that the time series values are continuously distributed. This can present unique challenges for organizations that need to model and forecast them. As a popular discrete probability distribution to handle the count time series, the Poisson distribution or the mixed Poisson distribution might not always be suitable. This is because both assume that the events occur independently of each other and at a constant rate. In time series data, however, the occurrence of an event at one point in time might be related to the occurrence of an event at another point in time, and the rates at which events occur might vary over time.
HMM is a valuable tool that can handle overdispersion and serial dependence in the data. This makes it an effective solution for modeling and forecasting count time series. We will explain how the Poisson HMM can handle count time series by modeling different states by using distinct Poisson distributions while considering the probability of transitioning between them.
Read on for an overview of Hidden Markov Models (in general and the Poisson variation in particular) and some of the challenges you can run into when performing this test.
Comments closedI recently came across an insightful research paper titled “Moving Fast With Broken Data” by Shreya Shankar, Labib Fawaz, Karl Gyllstrom, and Aditya G. Parameswaran from UC Berkeley and Meta. The paper addresses the significant issue of data corruption in machine learning (ML) pipelines, which often leads to decreased model accuracy. The authors present an automatic data validation system implemented at Meta that aims to solve this problem.
Sounds like I have some beach reading.
Ed. Note: He’s kidding, right?
Ed. 2 Note: About going to the beach maybe.
Ed. & Ed. 2 Note: HAHAHAHAHAH.
Yeah, I hired Statler and Waldorf as my editors. Worst Best decision of my life.
Steven Sanderson has a series on using the tidyAML Model Builder. Part 1 builds a simple model:
The first reactive expression,
data, reads in the data file uploaded by the user or selects a built-in dataset, depending on which option the user chooses. If the user uploads a file, theread.csv()function is used to read the data file into a data frame. If the user selects a built-in dataset, theget()function is used to retrieve the data frame associated with that dataset. In both cases, the column names of the data frame are used to update the choices in thepredictor_colselect input, so that the user can select which column to use as the predictor variable.
Part 2 builds on it by adding new regression algorithms:
Comments closedYesterday I spoke about building
tidymodelsmodels using my package{tidyAML}and{shiny}. I have made an update to it, and will continue to make updates to it this week.I have added all of the supported engines for regression problems only, NOT classification yet, that will be tomorrow’s work. I will then add a drop down for users to pick which backend function they want to use from
{parsnp}likelinear_reg().