🗄 ML to Power a New Generation of Databases 

The Scope covers the most relevant ML papers, real-world ML use cases, cool tech releases, and $ in AI. Weekly.

📝 Editorial 

The history of the software industry has been the history of data infrastructure. Each relevant technology trend in the last five decades has been accompanied by incremental progress in database technologies. Server-side software coincided with the emergence of relational databases; social and mobile technologies powered the NoSQL movement; cloud computing was the main catalyzer for the emergence of big data platforms. In the era of machine learning (ML), we are likely to see the evolution of a new type of database platforms optimized for data science workloads. However, ML has the unique capability to not only improve the existing generation of database technologies but also reimagine the space with new databases we haven’t seen before.  

The influence that ML can have in database technologies is unique because it is bidirectional. Areas such as natural language processing (NLP) can power new query models for the existing database platform. Who doesn’t like the idea of interacting with data using natural language? However, the influence of ML in the database field can be more profound. Imagine embedding inference workloads as a native construct of a database engine. Google BigQuery ML is a great example of this type of concept. Taking these ideas further, ML pipelines themselves can benefit from a new type of database that is more optimized for training and evaluation workflows. Research in all these areas is accelerating at a frantic pace. Just this week, Facebook published a research paper unveiling what they call neural databases, a concept that combines the use of NLP for unstructured databases. Like previous technology trends, ML is likely to bring fresh ideas that power innovation in the world of databases.   


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🗓 Next week in TheSequence Edge:

Edge#119: we discuss the topic “Data Labeling - Build vs. Buy vs. Customize”; we explore how by identifying behaviors in previously labeled data we can build a pipeline to label the rest of the data; we overview Label Studio.

Edge#120: we go practical and talk through several use cases of data labeling customization.

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

🔎 ML Research

Neural Databases  

Facebook AI Research (FAIR) published a paper proposing Neural Databases, a new concept to search unstructured databases using natural language ->read more on FAIR team blog

Amazon PECOS 

Amazon Research published two papers detailing case studies for PECOS, their open-source framework for multilabel ranking ->read more on Amazon Research blog

🛠 Real World ML

Waymo Driver

The Waymo team published some details about Waymo Driver, their autonomous driving system that just expanded its testing in San Francisco ->read more on Waymo blog

ML at Headspace 

Meditation startup Headspace published some details about their real-time ML infrastructure ->read more on their blog

Using Data in Uber’s Rider App 

The Uber engineering team published a blog post detailing the data processing infrastructure powering their Rider app >read more on their blog

🤖 Cool AI Tech Releases

Telum Processor

IBM unveiled Telum, a new processor for large-scale deep learning inference ->read more on IBM Research blog

NVIDIA AI Enterprise

NVIDIA announced the general availability of its AI Enterprise platform that enables running a new set of tools and frameworks in the VMWare vSphere platform ->read more in NVIDIA press release

💎 We recommend*

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💸 Money in AI


  • Research-as-a-Service (RaaS) startup Atheneum raised $150 million in a round of funding led by Guidepost Growth Equity. Hiring globally.

  • Damage prevention software developer Urbint raised $60 million in a Series C funding round led by Energize Ventures. Hiring remotely. 

  • Answers automation company Shelf.io raised $52.5 million in a Series B round led by Tiger Global and Insight Partners.

  • Intelligent NFTs platform Alethea AI raised $16 million in a token co-led by Metapurse and Crypto.com Capital.

  • Customer intelligence platform Involve.ai raised $16 million in a Series A financing round led by Sapphire Ventures. Hiring in California.

  • Ultrasound analysis solutions provider DiA Imaging Analysis raised a $14 million Series B round of funding. Hiring.

  • Parallel compute platform for data workloads Bodo.ai raised a $14 million Series A funding round led by Dell Technologies Capital. Hiring in the US.

  • Conversation intelligence platform Level AI launches with a $13 million Series A led by Battery Ventures. Hiring in Dehli, India; and the US.

  • Real-time AI optimization for mobile devices CoCoPIE raised $6 million in a Series A funding round led by Sequoia China Seed Fund.

  • Food tech startup Ai Palette raised $4.4 million in a Series A round co-led by Exfinity Venture Partners and pi Ventures.


  • AI-automation startup for customer service Cleverly.ai was acquired by Zendesk. The companies agreed not to disclose the purchase price.

  • AI-based audio chat analysis platform Oto was acquired by Unity. The companies did not disclose terms.

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