This AI newsletter is all you need #29
Last Updated on January 25, 2023 by Editorial Team
Author(s): Towards AI Editorial Team
Originally published on Towards AI.
What happened this week in AI by Louis
Our Learn AI Together Discord community has grown to 35,000 members and we are excited to see the engagement in our new AI Technical Questions forum format where members of our community and team are there to try to help with any AI questions, theory or ops.
Building on this we have several exciting new features in the pipeline for the Community this year starting with Community Events.
Given the success of the graduate seminar on Neural Networks Architectures that Pablo Duboue (DrDub) taught last year in Argentina, he has decided to reiterate the seminar this year, this time in English in a 9 part series through the Towards AI Discord server. We are excited to host it and hope you will join us and learn with us! Add our Google calendar to see all our free AI events! (More information in our community section below!)
If you are interested in hosting an Event or live video seminar with us in our server — please reach out!
- Why ChatGPT is not a threat to Google Search
The New York Times reports that Google’s management has declared a “code red” in response to the potential disruption ChatGPT may cause. However, it may be premature to say that ChatGPT will surpass Google. The technology still has a long way to go, and even when it matures, Google Search may be well-positioned to benefit from language model technologies.
- 2023 tech predictions: AI and machine learning will come into their own for security
As AI technologies continue to advance, there are several predictions about their impact on security and its impact on the economy. Some security and technology professionals predict that artificial intelligence and machine learning will have mass applications for security and detection in the near future, while others expect new technologies and services to be budget-conscious.
- The Fourth Age Of Programming
The emergence of software that can write, draw and speak as humans has often been seen as a sign that machines are about to revolutionize our society and completely alter the value and nature of labor. It is crucial to understand the current capabilities of machine learning code generation, the different eras of programming, and how it has evolved to this point.
Three 5-minute reads/videos to keep you learning
- Introduction to Graph Machine Learning
This is an introductory guide on the concept of graphs, their uses, and effective ways to represent them. It also covers the representation of graphs using machine learning techniques, such as graph neural networks and graph transformers, and provides additional learning resources on the topic.
- How to translate a website into different languages with GPT-3
GPT-3 has demonstrated potential for use in a wide range of fields and with the right prompts and adequate preparation, it can be used to translate text into multiple languages. While manual tweaks and adjustments may be necessary to optimize performance, this article provides a step-by-step guide to streamline the process and achieve high-quality results.
- The End of Programming
In this paper, Matt Welsh discusses the evolution of programming as a field. He argues that the traditional concept of “writing a program” is on the verge of obsolescence. In fact, he predicts that for all but highly specialized applications, most current software will be replaced by AI systems that are trained rather than programmed. The paper outlines the progression of this transition.
Enjoy these papers and news summaries? Get a daily recap in your inbox!
The Learn AI Together Community section!
Neural Networks Architectures seminar series by Pablo Duboue (DrDub)
Format: There will be 9 introductory lectures of about 30' to 40' of presentation followed by a discussion with the attendees.
The lectures take place on Tuesdays at 8 pm California time. That is 11 pm New York time, 9:30 am India time. The first lecture is on January 10th and will then be held weekly.
The recording of the lectures will be available as a YouTube playlist.
The chronogram and content follow this (updated page coming soon).
Target audience: Deep learning researchers and practitioners interested in improving their understanding of neural network architectures across the board. This is not an introductory course nor it deals with implementation.
Follow the event and get notified when we are live:
Some questions people might ask:
– Is there any cost associated with this? No, but if you enjoy the lectures please present a paper yourself. The goal is to learn together.
– There will be certificates awarded to attendees? No.
– Will the lectures cover architecture so and so? Most probably not but take a look at the chronogram. Your best bet is to prepare a paper on that architecture for the seminar and we can discuss it all together.
– Will coming to this seminar help me with “extremely precise needs arising from requirements at work or university”? Most certainly not. And in the off chance it does, you’ll be sitting through tons of material that is not relevant. The discord server has very good question forums, your best bet is to post there.
– Where can I find the event? It will be hosted on the server. Check out the Event feature at the top left of your screen!
Any more questions ping @DrDub on the server.
Meme of the week!
Featured Community post from the Discord
A new weekly reading group hosted by Nex#5992 and Big Bux Chungus#1729 only on Learn AI Together! Papers, approaches, libraries, good practices, and more will be presented weekly every Friday 6 pm EST! Follow the announcements on the #ai-reading-group-text channel and join the first event here.
Please reach out in the #ai-reading-group-text channel if you’d like to present anything to the weekly reading group! (~30 min presentations)
AI poll of the week!
TAI Curated section
Towards AI Article of the week
The author explains how to construct an app that can perform the function of paraphrasing in two distinct ways, each of which has its own advantages. The first approach is to make use of the FastAPI, while the second approach is to make use of Streamlit.
Other must-read Towards AI articles
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