Tag Archives: AI

Don’t forget about GPT-4

By: Logan Kilpatrick

Re-posted from: https://logankilpatrick.medium.com/dont-forget-about-gpt-4-d5ab8c9493fc?source=rss-2c8aac9051d3------2

Exploring the model that changed the path of AI and machine learning history

Image created by Author and DALL-E 3

The age of powerful language-based AI is upon us, and few players compare to the might and potential of OpenAI’s GPT-4. Let’s delve into the intricacies, capabilities, and potential applications of this revolutionary language model.

Picture the Power of GPT-4

Image captured from source video [1]

GPT-4 has truly broken barriers with its ability to generate up to 25,000 words of text, a monumental increase of about eight times compared to its predecessor, chat GPT. This leap forward enhances GPT-4’s abilities in handling long passages of text, making it a significant tool for a range of applications requiring long-duration interactions or wide-spanning narratives.

Advanced Image Understanding

Image captured from source video [1]

GPT-4’s advance into understanding, interpreting, and coherently describing images revolutionizes the idea of automated systems. Imagine snapping a picture of a scene, uploading it to GPT-4, and having the AI describe the visual elements perfectly. The idea that an AI can not only “see” an image but also make sense of different elements and predict outcomes, like explaining that cutting the strings of balloons would make them fly away, is fascinatingly next-gen.

GPT-4’s ability to understand images makes it an invaluable assistant in several fields — from virtual education to diverse areas where describing visuals in word processing is required.

Unique Challenges and Improvements

Image captured from source video [1]

Like any technology, AI language models come with their challenges, including adversarial usage, unwanted content, and privacy concerns. However, OpenAI has put substantial effort into mitigating these issues. With GPT-4, the team has implemented further measures for safety, alignment, and usefulness to make the model more user-friendly and secure.

Groundbreaking Applications in Education

GPT-4’s potential in revolutionizing education is immense. Imagine enriching every classroom with a personal AI tutor capable of addressing questions on a wide range of subjects. Or a fifth-grader getting unlimited time for personalized math tutoring with this AI that never gets tired or impatient. GPT-4 makes tailor-made tutoring accessible to all, directly in the comfort of their homes.

Image captured from source video [1]

Ultimately, GPT-4 elevates everyday life through advancements in AI. Whether it’s boosting productivity, teaching new skills, or simply organizing our days, AI like GPT-4 stands to ameliorate our lives in countless ways.

Shaping the Future of AI with Microsoft

The strategic partnership between OpenAI and Microsoft is aimed at transforming AI technology into useful tools accessible to everyone. Their concerted efforts lay the groundwork for harnessing AI’s full potential to enhance productivity, ultimately leading to an improved quality of life. GPT-4, a product born from the convergence of numerous technology advances, holds incredible promise for the future.

From enhancing education with AI-powered tutors to bringing valuable assistance into our lives, GPT-4 is on the verge of redefining our interactions with technology. As with any tool, ensuring that AI serves us correctly and safely is essential to leverage its benefits fully. As we sculpt the future of AI, learning, updating, improving, and transparency stand as our guiding tenets.

As we eagerly anticipate wider access to GPT-4 and similar AI, it’s critical to approach this revolutionary technology with informed understanding and responsible usage. OpenAI’s breakthrough serves as a testament to humanity’s unyielding prowess to innovate and evolve, even in the realms of artificial intelligence. Happy coding!

Source video [1]: https://www.youtube.com/watch?v=–khbXchTeE

Note: This blog post was generated by a GPT-4 pipeline as part of a demo for the AI Engineer Summit presentation in collaboration with Simon Posada Fishman.

Deep Learning on the New Ubuntu-Based Data Science Virtual Machine for Linux

Authored by Paul Shealy, Senior Software Engineer, and Gopi Kumar, Principal Program Manager, at Microsoft.

Deep learning has received significant attention recently for its ability to create machine learning models with very high accuracy. It’s especially popular in image and speech recognition tasks, where the availability of massive datasets with rich information make it feasible to train ever-larger neural networks on powerful GPUs and achieve groundbreaking results. Although there are a variety of deep learning frameworks available, getting started with one means taking time to download and install the framework, libraries, and other tools before writing your first line of code.

Microsoft’s Data Science Virtual Machine (DSVM) is a family of popular VM images published on the Azure marketplace with a broad choice of machine learning and data science tools. Microsoft is extending it with the introduction of a brand-new offering in this family – the Data Science Virtual Machine for Linux, based on Ubuntu 16.04LTS – that also includes a comprehensive set of popular deep learning frameworks.

Deep learning frameworks in the new VM include:

  • Microsoft Cognitive Toolkit
  • Caffe and Caffe2
  • TensorFlow
  • H2O
  • MXNet
  • NVIDIA DIGITS
  • Theano
  • Torch, including PyTorch
  • Keras

The image can be deployed on VMs with GPUs or CPU-only VMs. It also includes OpenCV, matplotlib and many other libraries that you will find useful.

Run dsvm-more-info at a command prompt or visit the documentation for more information about these frameworks and how to get started.

Sample Jupyter notebooks are included for most frameworks. Start Jupyter or log in to JupyterHub to browse the samples for an easy way to explore the frameworks and get started with deep learning.

GPU Support

Training a deep neural network requires considerable computational resources, so things can be made significantly faster by running on one or more GPUs. Azure now offers NC-class VM sizes with 1-4 NVIDIA K80 GPUs for computational workloads. All deep learning frameworks on the VM are compiled with GPU support, and the NVIDIA driver, CUDA and cuDNN are included. You may also choose to run the VM on a CPU if you prefer, and that is supported without code changes. And because this is running on Azure, you can choose a smaller VM size for setup and exploration, then scale up to one or more GPUs for training.

The VM comes with nvidia-smi to monitor GPU usage during training and help optimize parameters to make full use of the GPU. It also includes NVIDIA Docker if you want to run Docker containers with GPU access.

Data Science Virtual Machine

The Data Science Virtual Machine family of VM images on Azure includes the DSVM for Windows, a CentOS-based DSVM for Linux, and an Ubuntu-based DSVM for Linux. These images come with popular data science and machine learning tools, including Microsoft R Server Developer Edition, Microsoft R Open, Anaconda Python, Julia, Jupyter notebooks, Visual Studio Code, RStudio, xgboost, and many more. A full list of tools for all editions of the DSVM is available here. The DSVM has proven popular with data scientists as it helps them focus on their tasks and skip mundane steps around tool installation and configuration.


To try deep learning on Windows with GPUs, the Deep Learning Toolkit for DSVM contains all tools from the Windows DSVM plus GPU drivers, CUDA, cuDNN, and GPU versions of CNTK, MXNet, and TensorFlow.

Get Started Today

We invite you to use the new image to explore deep learning frameworks or for your machine learning and data science projects – DSVM for Linux (Ubuntu) is available today through the Marketplace. Free Azure credits are available to help get you started.

Paul & Gopi