The Sequence Scope: Keeping Up with AI Research and Technology

Weekly newsletter that discusses impactful ML research papers, cool tech releases, the money in AI, and real-life implementations.

📝 Editorial: Keeping Up with AI Research and Technology

Keeping up with new developments in the world of artificial intelligence (AI) is brutally hard. You take a class, finish a complete course, and a few months later many of the technologies you learned have become semi-obsolete. Every week, there are dozens of relevant research papers that are pushing the boundaries of AI capabilities and new technology platforms that improve the capabilities of AI solutions. How can we possibly keep up?

One of TheSequence’s goals is to help you learn about concepts of the AI world and link it to relevant research papers and technology platforms. The other goal is to keep you up-to-date with the newest research and technological solutions, but sometimes it’s hard to incorporate it in The Sequence Edge if it doesn’t match the concept we are discussing in the current issue. To improve on this, we will be making a minor change to the current format of The Sequence Edge.

Enter “What’s New in AI”. Every Thursday, we will devote The Sequence Edge towards discussing a recent technology release or research paper that can be impactful in modern AI applications. We promise to structure our analysis using simple and relatable narratives that can be followed by anyone, without diminishing the technical aspects. We think this small change will create a vehicle for all of us to stay up to date with the latest AI research and tech without having to spend long hours reading papers or technical documentation. Can’t wait to hear your feedback.

We will deep dive into this topic next week.

🔺🔻TheSequence Scope — our Sunday edition with the industry’s development overview — is free. To receive high-quality educational content every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻

🗓 Next week in TheSequence Edge:

Edge#37: the concept of model drift; the pillars of robust machine learning by DeepMind; Fiddler, an ML monitoring platform with built-in model drift detection.

New Format! Edge#38: deep dive into Ludwig 3.0, a fast-growing low-code machine learning platform.

Now, let’s review the most important developments in the AI industry this week.

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🔎 ML Research

Prosody and Synthetized Speech

Researchers from Amazon published two papers proposing methods to improve prosody (rhythm, intonation) in synthetic speech systems ->read more on Amazon Research blog

Adapting to Changes in Training Data Distribution

Training dataset distribution changes every time the models are exposed to new environments or user-bases. Berkeley AI Research (BAIR) lab published a paper presenting a method for deep learning models to deal with that ->read more on the BAIR team blog

Solving RPM Visual Cognition Test

Researchers from Facebook AI Research (FAIR) and Tel Aviv University developed a deep learning method to solve the famous Raven Progressive Matrix (RPM) test, which is typically used to assess visual reasoning and cognition ->read more in the original research paper

🤖 Cool AI Tech Releases

Iris Landmark tracking for TensorFlow.js

Iris tracking just got way cooler! Google released an update to TensorFlow.js that includes an impressive model to track landmarks from the iris and pupil ->read more on TensorFlow blog

Document AI Suite

Google released Document AI Suite, a platform that uses AI to extract data from complex documents->read more on the Google Cloud team blog

💸 Money in AI

  • Autonomous driving startup raised another $267 million in funding, in addition to $400 million raised in February. It’s hard to scope what ML technologies are used in such companies, as there are so many. Autonomous transportation is one of the industries that exists because of ML and AI developments. It’s worth keeping an eye on what scientists and developers are working on in such companies. Getting back to — they are hiring.
  • Safety technologies startup Provizio has raised $6.2 million in a seed round. The company leverages the technologies developed for autonomous cars, which still belong to the near future, to make our current cars safer. Using its proprietary long-range imaging sensors & AI on-the-edge, they perceive, predict and help prevent automotive accidents in real-time.
  • Intel keeps acquiring startups to strengthen its machine learning and AI operations. This week it’s, a startup that builds a unified solution for data scientists to manage, build and automate the entire ML lifecycle from research to production. The financial terms were not disclosed.
  • Business automation startup Ushur raised $25 million in Series B funding. Utilizing conversational AI and intuitive workflows, Ushur created a toolset that helps companies automate customer interactions.
  • Employee experience platform Leena AI has raised $8 million in its Series A round (kudos to our premium subscribers from Leena.AI 👏). From HR chatbots, the startup came to redefine every aspect of employee experience. Various AI tools are used to automate employee experience, increase engagement, extract insights, and cater to other needs of an enterprise and its employees. They are hiring.

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CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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