Top Twitter Topics by Data Scientists #18

Trending this week: Self-supervised video object segmentation; Image segmentation on edge devices using deep learning; Artificial intelligence that can detect sarcasm in social media.

Every week we analyze the most discussed topics on Twitter by Data Science & AI influencers.

The following topics, URLs, resources, and tweets have been automatically extracted using a topic modeling technique based on Sentence BERT, which we have enhanced to fit our use case.

Want to know more about the methodology used? Jump into this article for more details, and find the codes in this Github repository!


This week, Data Science and AI influencers on Twitter have talked about:

  • Deep Learning Updates
  • Artificial Intelligence: The Road Ahead
  • AI Societal Applications

The following sections provide all the details for each topic.

Deep Learning Updates

This week, again, the influencers have shared research papers and articles about deep learning. These updates are presented in the following paragraphs:

Aran Komatzusaki has shared a research paper titled “ResMLP: Feedforward networks for image classification with data-efficient training”. This publication introduces ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. According to this paper, when trained with a modern training strategy using heavy data-augmentation and optionally distillation, ResMLP attains surprisingly good accuracy/complexity trade-offs on ImageNet. Code based on the Timm library will be shared and pre-trained models too.

Dr. Ganapathi Pulipaka has shared an article that talks about bringing image segmentation to edge devices using deep learning. This post presents a new neural network architecture designed by artificial intelligence researchers at DarwinAI and the University of Waterloo that will make it possible to perform image segmentation on computing devices with low-power and -compute capacity.

Mike Tamir has shared a research paper talking about Self-supervised Video Object Segmentation by Motion Grouping. This publication introduces a computer vision system able to segment objects by exploiting motion cues, i.e. motion segmentation. According to this paper, the approach presented achieves superior results compared to previous state-of-the-art self-supervised methods, while being an order of magnitude faster. It was evaluated on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favorably to the top supervised approach.

Artificial Intelligence: The Road Ahead

This week a lot of influencers spoke about the road ahead for Artificial intelligence and the importance of AI ethics in deciding the future of AI.

Ipfconline shared an article on how AI is helping companies hire women, and helping to proscribe the impact of the pandemic on dilating gender inequality. They also shared an article ‘Navigate the road to Responsible AI’ which talks about what it would take to deploy AI ethically and responsibly.

Nige Willson shared a report citing the New Zealand PM, where she calls for “ethical algorithms” to fight online extremism.

He also shared an article reporting that biased algorithms and moderation are censoring activists on social media.

Tamara McCleary shared an article on the same issue titled “We need to be specific to address the issues of AI ethics”. This article talks about the ‘broad umbrella’ of AI ethics and some of the major trends occurring in the voice-tech industry.

She shared another interesting piece about how the AI sector can use nonprofits as an ethical example. The article says, if you’re in the business of building AI, a good place to start is by reading the Code of Ethics and about Blackman’s seven steps to ethical AI.

Terence Mills also shared an article that was trying to deep dive into the causes of AI’s ethical problems. The article talks about the issue not just for practical reasons but for specifically ethical ones as well.

AI Societal Applications

This week, the influencers have shared articles talking about societal applications of artificial intelligence.

In particular, Nige Willson has shared the following articles presenting AI applications:

First, he shared an article talking about Using AI to help achieve Sustainable Development Goals. This post states that AI is sometimes portrayed in apocalyptic terms as the technology that will take over our jobs or even our lives. But what if it could also become a valuable tool in the worldwide efforts to achieve the UN’s Sustainable Development Goals? It explains how AI capabilities are already being used in various ways to further societal goals, and it provides some concrete use cases of applications of AI in our societies.

Then, he shared an interesting article presenting how Microsoft and Darktrace partnership will help keep organizations secure using AI that learns “self”. This post explains that their business, which was founded in Cambridge, UK, in 2013, provides best-in-class cyber AI using self-learning artificial intelligence to protect organizations against attacks of all kinds — including insider threats, espionage, supply chain attacks, phishing, and ransomware at machine speed.

Finally, he shared a funnier post presenting how Researchers Develop Artificial Intelligence That Can Detect Sarcasm in Social Media. This article explains that computer science researchers at the University of Central Florida have developed a sarcasm detector using a newly developed artificial intelligence algorithm that can accurately detect sarcasm in comments written on social media platforms.