This AI newsletter is all you need #28
Last Updated on January 25, 2023 by Editorial Team
Author(s): Towards AI Editorial Team
Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.
What happened this week in AI by Louis
- AI-assisted code can be inherently insecure, study finds
According to a new study involving several developers, code-generating algorithms were found to be more prone to being insecure compared to the “handmade” solutions created by the control group. Neil Perry, a Ph.D. candidate at Stanford and the lead co-author of the study, also advises developers to double-check generated code. Here’s why:
- What we learned about AI and deep learning in 2022
As deep learning and generative models continue to make progress, the scientific community is divided on how to interpret these advances. To move forward, it is necessary to understand what is currently missing from AI systems and the architecture for the next generation of AI systems.
- Machines that think like humans: Everything to know about AGI and AI Debate 3
The online AGI Debate #3, hosted by Vincent Boucher, the president of Montreal AI, and AI researcher Gary Marcus, discussed the lessons from cognitive science and neuroscience and the path toward achieving commonsense reasoning in AI. Here is a summary of the key points covered during the debate:
Three 5-minute reads/videos to keep you learning
- Here’s a no-code tutorial on how you can create 3D assets using Generative AI technology
In this Twitter thread, @angrypenguinPNG shares a step-by-step tutorial on how to create 3D assets using generative AI technology. The tutorial covers all the prerequisites, essential tools, and the complete process for creating 3D models.
- How to do video scripts on ChatGPT
ChatGPT’s advanced conversational capabilities are making quite the buzz, but for it to produce accurate results, it needs precise and clear instructions. In this Twitter thread, @scottcmillard explains how to do it right and what factors to consider, including the inputs, variables, and more.
- The Brief History of Artificial Intelligence: The World Has Changed Fast — What Might Be Next?
In this article, Dr. Max Roser traces the brief history of AI systems’ language and image recognition capabilities and uses long-term trends to predict the future of AI. The article also discusses an important aspect of AI: creating a public resource to facilitate necessary public conversation.
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The Learn AI Together Community section!
Meme of the week!
Featured Community post from the Discord
DrDub#0108, an independent researcher, has just achieved over 2000 citations in the fields of natural language processing, AI, ML, and information retrieval. A big shoutout to Pablo! You can find a list of all the papers here and join in on the conversation here.
AI poll of the week!
TAI Curated section
Article of the week
The Ever-evolving Pre-training Tasks for Language Models by Harshit Sharma
Self-Supervised Learning (SSL) is the backbone of transformer-based pre-trained language models. This paradigm involves solving pre-training tasks (PT) that help model the natural language. The author has made an effort to put all the popular pre-training tasks at a glance.
Our must-read articles
Lazypredict: Run All Sklearn Algorithms With a Line Of Code by Travis Tang
Markov Algorithm For Time Series by Ashutosh Malgaonkar
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