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Top Twitter Topics by Data Scientists #27

Trending this week: Quantum Artificial Intelligence in 2021 in-Depth Guide; You should invest in machine programming; Why 90% of machine learning models never hit the market.

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? Check out this article for more details, and find the codes in this Github repository!

Overview

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

  • Evolution And Future Of AI
  • ML Discussions
  • Blockchain & Digital Currency

The following sections provide all the details for each topic.

Evolution And Future Of AI

AI & data science influencers have shared content talking about the evolution and the future of artificial intelligence (AI).

Ronald Van Loon has shared the following content:

An article discussing The Evolution of Neural Networks. This article recounts where the history of neural networks all started around the year 1943 and evolved with the increase of computational means and the emergence of new technologies. Then, it explains how artificial intelligence is already changing the present via applications in various different areas such as virtual assistants, medical research, self-driving cars, and online retail stores. Finally, it comes up with future advancements of AI as the premises of a bright future with augmented reality, machine learning, and big data technologies, that will allow researchers to create more advanced artificial intelligence systems that can adapt to new data as the human brain does.

A post on Quantum Artificial Intelligence in 2021 in-Depth Guide. This publication discusses how quantum computing can provide a computation boost to artificial intelligence, enabling it to tackle more complex problems and artificial general intelligence(AGI). In particular, it addresses the following questions:

  • What is quantum AI?
  • What is quantum computing?
  • Why is it important?
  • How does quantum AI work?
  • What are the possibilities of applying quantum computing in AI?
  • What are the critical milestones for quantum AI?

ipfconline has shared a research paper titled “Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks”. This paper introduces a novel molecular programming paradigm. It presents how researchers discovered a surprisingly tight connection between a popular class of neural networks (binary-weight ReLU aka BinaryConnect) and a class of coupled chemical reactions that are absolutely robust to reaction rates. They showed how a BinaryConnect neural network trained in silico using well-founded deep learning optimization techniques, can be compiled to an equivalent chemical reaction network. Which establishes a promising advance in bioengineering implementation.

ML Discussions

This week, influencers have also shared discussions on machine learning.

Nige Willson has shared:

A post discussing Why 90% of machine learning models never hit the market. This post explains how and why corporations are going through rough times when launching the development of machine learning models to improve their products. It explains the failure that most companies face in their ML approaches because of the lack of leadership support, effective communication between teams, and accessible data. It details the main causes of this failure, among which:

  • The disconnect between IT, data science, and engineering
  • The lack of cross-language and framework support
  • The fact that companies underestimate the difficulty of scaling up

Then, this post provides keys on how to stop trying and start deploying ML.

An article addressing Why machine programming should be the next technology you invest in. This article discusses the advantages of the emergence of machine programming, a new paradigm that uses machine learning and other methods to automate parts of the software development process. It provides examples of emerging tools like GitHub Copilot, which launched such a tool last month that suggests code while a programmer is developing it; Amazon who has also created CodeGuru, a tool to help automatically find performance bottlenecks in software; Facebook who has Aroma, which can also provide code-to-code recommendations. This post explains that by harnessing the power of code semantic similarity, the industry can develop automated systems to help CIOs ensure developer teams are maintaining the same level of productivity despite increased software and hardware complexity, all the while addressing the software developer talent shortage and combating burnout. It gives, in particular, some concrete cases where machine programming can make a difference:

  • Enabling language-to-language translations
  • Elevating novice developers, helping to fill the developer gap

On his side, Ronald Van Loon has shared a post titled “Is Explainable ArtificialIntelligence a Distant Dream?” This article states that transparency in AI’s working can be headache-inducing for organizations that incorporate the technology in their daily operations, and it addresses the following crucial question: what can they do to put their concerns surrounding explainable artificial intelligence (AI) requirements to rest? This article provides the reasons to make AI explainable, then gives several barriers to overcome:

  • The AI Accountability Paradox
  • The AI’s Black Box Problem
  • The compliance

Finally, this post provides some norms and standards that organizations can really benefit from making their AI models and systems compliant with. This is crucial as AI regulations all over the world become more and more stringent regarding AI accountability, organizations can really benefit from making their AI models and systems compliant with such norms and standards.

Blockchain & Digital Currency

Following the rumors that amazon might start accepting Crypto as a payment method. This week, a lot of Data Science and AI Influencers tweeted about the use of Crypto across various domains.

Alvin Foo shared an article that a job posting shows Amazon seeking a digital currency and blockchain expert. The role signals a shift toward cryptocurrency which Amazon still doesn’t accept as payment.

Then he shared a couple of tweets on the Amazon effect, and how it will impact the crypto market. He also shared that the bull is back in crypto with Bitcoin & Ethereum closing in on $40k and $2.4K respectively.

Fig. 3. Blockchain

Terence Mills shared an article by MIT on Unlocking the potential of Blockchain Technology. He also shared an article on Blockchain And Sustainability: Oxymoron Or Panacea? The article addresses the issue about the recent fragility in the Blockchain market and possible scenarios going forward.

On a similar note, Andreas Staub shared a working paper on the paradoxes of trust in Blockchain Tech.

Tamara McCleary shared an article on Blockchain and IoT. The article talks about how Blockchain Technology can benefit the Internet of Things and then the challenges of Integrating Blockchain Technology With IoT.