"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
In the first part of this series, we explored the capabilities of ChatGPT, a state-of-the-art language model developed by OpenAI, in assisting data scientists with tasks such as data cleaning, preprocessing, and code generation. In this second part, we will delve deeper into what ChatGPT generated and why it didn't work. We will discuss the specific challenges that come with using AI-generated code, and how to effectively address these issues to ensure the reliability and accuracy of the final product. Whether you're a data scientist or a developer, this post will provide valuable insights into how to use ChatGPT to improve your workflow and streamline your development process.
In 2023, radiologists in hospitals around the world will increasingly use medical images--which include x-rays and CT, MRI, and PET scans--that have been first read and evaluated by AI machines. Gastroenterologists will also be relying on machine vision during colonoscopies and endoscopies to pick up polyps that would otherwise be missed. This progress has been made possible by the extensive validation of "machine eyes"--deep neural networks trained with hundreds of thousands of images that can accurately pick up things human experts can't. This story is from the WIRED World in 2023, our annual trends briefing. Read more stories from the series here--or download or order a copy of the magazine.
Keya Medical has launched the DeepVessel FFR, a software device that utilizes deep learning to facilitate fractional flow reserve (FFR) assessment based on coronary computed tomography angiography (CCTA). Cleared by the Food and Drug Administration (FDA), the DeepVessel FFR provides a three-dimensional coronary artery tree model and estimates of FFR CT value after semi-automated review of CCTA images, according to Keya Medical. The company said the DeepVessel FFR has demonstrated higher accuracy than other non-invasive tests and suggested the software could help reduce invasive procedures for coronary angiography and stent implantation in the diagnostic workup and subsequent treatment of coronary artery disease. Joseph Schoepf, M.D., FACR, FAHA, FNASCI, the principal investigator of a recent multicenter trial to evaluate DeepVessel FFR, says the introduction of the modality in the United States dovetails nicely with recent guidelines for the diagnosis of chest pain. "I am excited to see the implementation of DeepVessel FFR. It comes together with the 2021 ACC/AHA Chest Pain Guidelines' recognition of the elevated diagnostic role of CCTA and FFR CT for the non-invasive evaluation of patients with stable or acute chest pain," noted Dr. Schoepf, a professor of Radiology, Medicine, and Pediatrics at the Medical University South Carolina.
For many machine learning use-cases, organizations rely solely on tabular data and tree-based models like XGBoost and LightGBM. This is because deep learning is simply too hard for most ML teams. As a result, teams miss out on valuable signals hidden within unstructured data like text and images. New declarative machine learning systems--like open-source Ludwig started at Uber--provide a low-code approach to automating ML that enables data teams to build and deploy state-of-the-art models faster with a simple configuration file. Specifically, Predibase--the leading low-code declarative ML platform--along Ludwig make it easy to build multi-modal deep learning models in 15 lines of code.
Even if you think you are good at analyzing faces, research shows many people cannot reliably distinguish between photos of real faces and images that have been computer-generated. This is particularly problematic now that computer systems can create realistic-looking photos of people who don't exist. Recently, a fake LinkedIn profile with a computer-generated profile picture made the news because it successfully connected with US officials and other influential individuals on the networking platform, for example. Counter-intelligence experts even say that spies routinely create phantom profiles with such pictures to home in on foreign targets over social media. These deep fakes are becoming widespread in everyday culture which means people should be more aware of how they're being used in marketing, advertising and social media.
When it comes to learning, artificial intelligence has come a long way. In the early days of AI, learning was limited to simple tasks carried out by basic algorithms. But thanks to advances in computation and data storage, AI can now tackle much more complex problems using deep learning. Deep learning is a subset of machine learning that uses algorithms to model high-level patterns in data. By doing so, deep learning can enable machines to carry out tasks that would be difficult or impossible for traditional AI methods.
When ChatGPT debuted, it literally stopped the internet. Developed by OpenAI, GPT (Generative Pre-trained Transformer) is a machine learning model that uses a transformer neural network to generate natural language text. According to OpenAI, ChatGPT has had more than 1 million users since its debut within a few days of its launch on November 30, 2022. Fifteen days later, more than two million users were already testing OpenAI's service. People everywhere were sharing their results on Twitter, LinkedIn and other channels.
OpenAI's ChatGPT chatbot can fix software bugs very well, but its key advantage over other methods and AI models is its unique ability for dialogue with humans that allows it to improve the correctness of an answer. Researchers from Johannes Gutenberg University Mainz and University College London pitted OpenAI's ChatGPT against "standard automated program repair techniques" and two deep learning approaches to program repairs: CoCoNut, from researchers at the University of Waterloo, Canada; and Codex, OpenAI's GPT-3 based model that underpins GitHub's Copilot paired programming auto code completion service. "We find that ChatGPT's bug fixing performance is competitive to the common deep learning approaches CoCoNut and Codex and notably better than the results reported for the standard program repair approaches," the researchers write in a new arXiv paper, first spotted by New Scientist. That ChatGPT can solve coding problems isn't new, but the researchers highlight that its unique capacity for dialogue with humans gives it a potential edge over other approaches and models. The researchers tested ChatGPT's performance using the QuixBugs bug fixing benchmark.
This article belongs to the series "Probabilistic Deep Learning". This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e., know what they do not know. In this article, we will introduce the concept of probabilistic logistic regression, a powerful technique that allows for the inclusion of uncertainty in the prediction process. We will explore how this approach can lead to more robust and accurate predictions, especially in cases where the data is noisy, or the model is overfitting.