Google’s Model Search automatically optimizes and identifies AI models

Google Building

Google today announced the release of Model Search, an open source platform designed to help researchers develop machine learning models efficiently and automatically. Instead of focusing on a specific domain, Google says that Model Search is domain-agnostic, making it capable of finding a model architecture that fits a dataset and problem while minimizing coding time and compute resources.

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Sentry raises $60 million to monitor app performance in real time

Sentry Platform

Application performance monitoring startup Sentry today announced it has secured $60 million in series D financing for a post-money valuation of $1 billion. Sentry says the funds will fuel product development and go-to-market functions, as well as hiring across the company’s San Francisco, Toronto, and Vienna offices.

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DeepMind & UCL’s Alchemy Is a ‘Best-of-Both-Worlds’ 3D Video Game for Meta-RL

Deepmind Alchemy

n recent years, reinforcement learning (RL) has garnered much attention in the field of machine learning. The approach does not require labelled data and has yielded remarkable successes on a wide variety of specific tasks. RL unfortunately continues to struggle with issues such as sample efficiency, generalization, and transfer learning. To address these drawbacks, researchers have been exploring meta-reinforcement learning (meta-RL), in which learning strategies can quickly adapt to novel tasks by using experience gained on a large set of tasks that have a shared structure.

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Facebook makes Multilingual Speech Recognition Model Open Source

Facebook AI has released a massive speech recognition database and training tool called Multilingual LibriSpeech (MLS) as an open-source data set. MLS combines more than 50,000 hours of audio in eight languages from public domain audiobooks with pre-trained language models and other data useful for automatic speech recognition development.

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Why ML in production is (still) broken and ways we can fix it

By now, chances are you’ve read the famous paper about hidden technical debt by Sculley et al. from 2015. As a field, we have accepted that the actual share of Machine Learning is only a fraction of the work going into successful ML projects. The resulting complexity, especially in the transition to “live” environments, lead to large amounts of failed ML projects never reaching production.

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The Social Dilemma in 2021: Personal Data Privacy

It’s needless to say we are living in the digital era. Nowadays our life is not as difficult as it was before the age of technological advancements. We can connect to the world in seconds. Social media and the Internet helps us a lot. All social media and messaging platforms are free. But nothing is truly free. If you are not paying for it, you’re not the customer; you’re the product being sold.

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Feature store repositories emerge as an MLOps linchpin for advancing AI

A battle for control over machine learning operations (MLOps) is beginning in earnest as organizations embrace feature store repositories to build AI models more efficiently. A feature store is at its core a data warehouse through which developers of AI models can share and reuse the artifacts that make up an AI model as well as an entire AI model that might need to be modified or further extended. In concept, feature store repositories play a similar role as a Git repository does in enabling developers to build applications more efficiently by sharing and reusing code.

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Google: The Language Interpretability Tool (LIT): Interactive Exploration and Analysis of NLP Models

As natural language processing (NLP) models become more powerful and are deployed in more real-world contexts, understanding their behavior is becoming increasingly critical. While advances in modeling have brought unprecedented performance on many NLP tasks, many research questions remain.

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Anyscale raises $40 million to launch a managed service for distributed computing workloads

Anyscale, the startup behind the open source project Ray, today closed a $40 million funding round. A company spokesperson says the capital will be put toward growing the ecosystem around Ray and promoting Anyscale’s first commercial offering, a managed Ray platform.

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Facebook’s open source M2M-100 model can translate between 100 different languages

Facebook today open-sourced M2M-100, an algorithm it claims is the first capable of translating between any pair of 100 languages without relying on English data. The machine learning model, which was trained on 2,200 language pairs, ostensibly outperforms English-centric systems on a metric commonly used to evaluate machine translation performance.

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Spotify open-sources Klio, a framework for AI audio research

This week at the 2020 International Society for Music Information Retrieval Conference, Spotify open-sourced Klio, an ecosystem that allows data scientists to process audio files (or any binary files) easily and at scale. It was built to run Spotify’s large-scale audio intelligence systems and…

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LinkedIn open-sources GDMix, a framework for training AI personalization models

LinkedIn recently open-sourced GDMix, a framework that makes training AI personalization models ostensibly more efficient and less time-consuming. The company says it’s an improvement over LinkedIn’s previous release in the space — Photon ML — because it supports deep learning models.

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AI data tracker encourages Scientific Research into COVID-19 non-pharmaceutical interventions

There are a few questions about the wide range of non-pharmaceutical interventions (NPIs) that have been applied by governments, globally. Since the onset of the pandemic, these NPIs have been implemented in various degrees, with the intention of reducing the transmission of COVID-19.

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WhyLabs raises $4 million to grow AI and data monitoring platform

WhyLabs is launching out of stealth today with $4 million to grow its platform for data scientists who need help monitoring and troubleshooting problems they encounter with datasets or AI models. The goal is to help teams managing machine learning models save time and catch problems before they make trouble for businesses or customers.

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Intellectual Property Rights for Data Scientists

Data Scientists use software they didn’t write and data they don’t own pretty much all the time. It is only thanks to open source that they can use programming languages. This should not at all be taken for granted. In fact, given how important and ubiquitous intellectual property is in the data science world, it is not being discussed enough I believe. This is why I wrote this blog post.

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Why Deep Learning is still too difficult

Deep Learning is still too difficult

While deep learning has great potential, building practical applications powered by deep learning remains to be too expensive and too difficult for many organizations. In this article, we will describe some of the challenges to broader adoption of deep learning.

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Let’s talk about Open Data…

According to the International Open Data Charter, it defines open data as those digital data that are made available with the technical and legal characteristics necessary so that they can be freely used, reused and redistributed by anyone, at anytime and anywhere. But what are the bases that are governed to comply with the definition of open data, the International Open Data Charter gives us the principles.

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