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This AI newsletter is all you need #4
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This AI newsletter is all you need #4

Towards AI
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July 25, 2022
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Last Updated on July 25, 2022 by Editorial Team

What happened this week in AI

The International Conference on Machine Learning (ICML) 2022 conference is happening this week — you can stay tuned to a couple of articles on our side covering the most exciting news and research shared there, including “Make-A-Scene” which we cover on this week’s “papers of the week” section.

ICML is a big conference in the field and many breakthroughs are published there. We will share the top paper of the conference. Unfortunately, we do not have anyone from the team there, in person. Let us know if you’d like us to send someone from the Toward’s AI team at these events to publish a recap and a “how it’s like” kind of article to share our in-person experience with those of you that might be interested in going to such events.

Hottest News

  1. PLEX: a framework to improve the reliability of deep learning systems
    Google introduced PLEX, a framework for reliable deep learning as a new perspective about a model’s abilities; this includes a number of concrete tasks and datasets for stress-testing model reliability. They also introduce Plex, a set of pre-trained large model extensions that can be applied to many different architectures.
  2. Tesla’s AI director Andrej Karpathy leaves the company after 5 years of autonomous vehicles research
    Since 2017, Autopilot has progressed from keeping Teslas in lanes to navigating city streets, he noted. I am excited to see who’s gonna be the next AI director at Tesla, and even more exciting to see what Andrej will work on next as he mentioned he wanted to “spend more time revisiting [his] long-term passions around technical work in AI, open source and education.”
  3. “I posted my project on Reddit and received 9 job offers”!
    A Reddit user shared his project and received 9 official job offers. The moral of this story is? Work on personal projects and share them online! That’s the best way to learn and improve your portfolio!

Most interesting papers of the week

  1. MegaPortraits: One-shot Megapixel Neural Head Avatars
    They bring megapixel resolution to animated face generations (neural head avatars), focusing on the “cross-driving synthesis” task: when the appearance of the driving image is substantially different from the animated source image.
  2. ProDiff: Progressive Fast Diffusion Model for High-Quality Text-to-Speech
    Yes, a diffusion model for high-quality text-to-speech! ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation in accelerating sampling, requiring only 2 iterations to synthesize high-fidelity mel-spectrograms.
  3. Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
    “Make-A-Scene”: a fantastic blend between text and sketch-conditioned image generation. Learn more in our article!

Enjoy these papers and news summaries? Get a daily recap in your inbox!

This issue is brought to you thanks to our fantastic community:

Join us on discord! Learn AI Together and Towards AI have recently partnered to build a community that is engaging, inclusive, and mutually beneficial. In addition, the community will serve as a platform for the sharing of AI skills, knowledge, and developments, enabling students, engineers, entrepreneurs, academics, and others to work collaboratively to make AI more accessible to everyone.

Interested in becoming a Towards AI sponsor and being featured in this newsletter? Find out more information here or contact sponsors@towardsai.net!

The Learn AI Together Community section!

Meme of the week!

The Iron Law of AI. Meme shared by RobKnight#4276. Join the conversation and share your memes with us!

Featured Community post from the Discord

We love to see you share your events in our community and help spread the world ! If you are in Vienna this week, check it out, looks super interesting!

Create compelling Disco Diffusion artworks in one line.

Learn more about the event.

AI poll of the week!

Let us know what you think: join the discussion on Discord!

TAI Curated section

Article of the week

PySyft is a Framework for Private Deep Learning: The framework uses differential privacy in models built on PyTorch and TensorFlow.

The significance of privacy in deep learning applications is discussed in this article. The PySyft open-source framework tackles the difficulties of moving toward more distributed architectures, which in turn guarantees effective privacy protections in deep learning models. The author does a fantastic job of explaining PySyft and raising privacy concerns in deep learning applications.

This week we published 28 new articles and welcomed 5 new writers to Towards AI. If you are interested in publishing at Towards AI, please sign up here and we will publish your blog to our network if it meets our editorial policies and standards.

Lauren’s Ethical Take on LaMDA’s potential for sentience

I may have missed the peak of the conversation around the news of Google’s LaMDA and engineer Blake Lemoine’s assertion of its sentience, but there is still much to be discussed!

One of my gut reactions to the news was that the need for a sentience Turing test has skyrocketed. I was introduced to AI ethics primarily through researching forms of a moral Turing Test with my philosophy advisor a few years ago, and surveyed a good chunk of the ethical work on what we need to do to adapt the Turing Test to determine moral agency in AI. I was reminded of a quote from Karsten Weber’s 2013 paper titled What is it like to encounter an autonomous artificial agent?:

“…a successful interaction of human beings and autonomous artificial agents depends more on which characteristics human beings ascribe to the agent than on whether the agent really has those characteristics.”

Based on this definition, LaMDA created a highly successful interaction with a human, to have been deemed sentient. This reminds us that AI is almost inherently anthropomorphic, due to the biomimicry of neural networks and our standard for intelligence is almost always human. Determining intelligence on our ability to be fooled by them (ie the original Turing Test) is not a model that will work for determining sentience. There is much work to be done to create models that help fill this need.

Going forward in this exciting discussion, remember that the ascription of sentience to AI is not something that we can just dismiss anymore, whether it’s correct or not. These conversations can be unsettling, but a bit of compassion, patience and due diligence will take us far.

Join the Learn AI community

Featured Jobs this week

Senior Machine Learning Scientist @ Atomwise ( San Francisco — USA)

Senior ML Engineer — Algolia AI @ Algolia (Hybrid remote)

Senior ML Engineer — Semantic Search @ Algolia (Hybrid remote)

Machine Learning Engineer @ Gather AI (Remote — India)

Deep Learning Engineer (R&D — Engineering) @ Weights & Biases (Remote)

Solutions Architect — ML Specialist, MLOps, DevOps @ Qwak (New York City, New York and Tel Aviv, Israel)

Interested in sharing a job opportunity here? Contact sponsors@towardsai.net or post the opportunity in our #hiring channel on discord!

This AI newsletter is all you need #4 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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',*/ ]; var replaceText = { '': '', '': '', '
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