📝 Editorial 

For years, Google I/O have been one of the most exciting conferences in the tech word given the number of exciting products that are regularly unveiled at this event. The 2022 edition of Google I/O took place last week and machine learning (ML) was front and center. Just like Microsoft’s Ignore AWS re:Invent, I/O provides a first row seat to the ML innovation happening at Google and the new additions to its ML stack. 

This year’s edition of I/O was packed with ML announcements across software, hardware, and research. On the hardware and infrastructure front, Google announced the general availability of its Cloud TPU VMs as well as what can be considered the biggest ML compute cluster available. The new Cloud ML Hub boosts an astonishing 9 exaflops computation power. On the software side, Google announced support for 24 new low-resource languages in Google Translate, new ML capabilities for Google  Maps as well as new libraries added to TensorFlow. Google also made available new versions of the LaMDA (Language Model for Dialog Applications) and Pathways Language Model (PaLM) which power systems such as the Google Assistant. Another interesting release was the AI Test Kitchen that provides users with an interactive experience to explore the capabilities of these models. Finally, there was an unexpected announcement of a new form of augmented reality glasses that leverage sophisticated computer vision and language models. 

I/O 2022 provided a glimpse of Google’s investments in ML research and technology. While there were no major new product lines announced, these incremental releases should consolidate Google’s positions as one of the main ML platform ecosystems on the market. 

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🗓 Next week in TheSequence Edge:

Edge#191: we discuss the fundamental enabler of distributed training: message passing interface (MPI); +Google’s paper about General and Scalable Parallelization for ML Computation Graphs; +the most relevant technology stacks to enable distributed training in TensorFlow applications. . 

Edge#192: a deep dive into Predibase, the first declarative ML platform.

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

🔎 ML Research

A Generalist Agent

DeepMind published a paper introducing Gato, a large model that can perform multiple tasks in highly heterogenous environments such as computer vision, physical embodiment or language →read more in this summary published by DeepMind

Foundation Models

IBM Research published a detailed blog post discussing the idea of foundational models being a way to build more reusable ML models →read more on IBM Research

Chain of Thought in Language Models

Google Research published a paper about a chain of thought method to enable reasoning in language models →read more on Google Research blog

Improved Model Based RL

Carnegie Mellon University published a paper describing a technique to improve the training data selection in model based RL models →read more on CMU ML Lab blog

🤖 Cool AI Tech Releases

Largest Available ML Cluster

Google announced what is effectively the largest, publicly available ML cluster with  8 TPU v4 pods which provide 9 exaflops of computer power →read more on Google Cloud blog

Cloud TPU VMs

At its I/O conference, Google announced the general availability of Cloud TPU VMs →read more on Google Cloud blog

New Languages in Google Translate

Google announced a new version of Google Translate with support for over 24 languages →read more from Google Translate team

On-Device ML Search

TensorFlow added a new library to enable on-device image, text or audio search across a large dataset →read more on TensorFlow blog

🛠 Real World ML

TensorFlow at Karrot

The engineering team of Karrot, the app of local products and communities, published details about their ML architecture based on TensorFlow →read more on their blog

ML Model Persistence at Walmart 

Walmart provided some insights about the techniques used for persisting and retrieving models using PySpark →read more on Walmart Global Tech blog

💸 Money in AI

ML&AI&Data

• Inflection AI, led by LinkedIn and DeepMind co-founders, raised $225 million, according to a filing with the U.S. Securities and Exchange Commission. Hiring remote/hybrid.

• Deep data analytics platform proteanTecs raised a $45 million financing round led by Addition. Hiring in Haifa/Israel.

• Declarative ML platform Predibase emerged from stealth with $16.25 million in Series A funding led by Greylock. Read our interview with its co-founder and CEO Piero Molino here.

• Data security governance platform Concentric Inc. raised $14.5 million in Series A funding led by Ballistic Ventures. Hiring in the US and in Bengaluru/India.

• Database automation and optimization platform OtterTune raised $12 million in Series A funding led by Intel Capital and Race Capital. Hiring remote in the US/Canada.

AI-powered

• Business intelligence platform Pyramid Analytics raised $120 million in a Series E funding round led by H.I.G. Growth Partners. Hiring globally.

• No-code contract intelligence platform Evisort raised $100 million in Ceries C funding round led by TCV. Hiring in the US and Canada.

• Biosciences company Brightseed (creator of computational platform Forager) raised $68 million in Series B funding led by Temasek. Hiring in San Francisco and Raleigh/US.

• Background screening solutions Certn raised $50 million in Series B funding led by B Capital. Hiring remote.

• Workplace safety company Voxel secured $15 million in a Series A funding round led by Eclipse Ventures. Hiring in Bay Area/US.

• Indoor positioning platform Oriient raised an $11 million Series A funding round. Hiring in Tel Aviv/Israel.

• Customer service automation startup Lang.ai raised $10.5 million Series A led by Nava Ventures. Hiring in the US and Spain.