Microsoft launches Azure Arc machine learning and container services

Microsoft Azure

At Ignite 2021, Microsoft unveiled two new Azure Arc services aimed at data scientists and enterprises with containerized workloads.

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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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IBM’s Arin Bhowmick explains why AI trust is hard to achieve in the enterprise

IBM AI Trust

While appreciation of the potential impact AI can have on business processes has been building for some time, progress has not nearly been as quick as many initial forecasts led many organizations to expect.
Arin Bhowmick, chief design officer for IBM, explained to VentureBeat what needs to be done to achieve the level of AI explainability that will be required to take AI to the next level in the enterprise.

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Exploring AWS SageMaker’s new features — Clarify, Pipelines, Feature Store

Feature store comparison

Welcome to part 2 of our two-part series on AWS SageMaker. If you haven’t read part 1, hop over and do that first. Otherwise, let’s dive in and look at some important new SageMaker features:
Clarify, which claims to “detect bias in ML models” and to aid in model interpretability
SageMaker Pipelines, which help automate and organize the flow of ML pipelines
Feature Store, a tool for storing, retrieving, editing, and sharing purpose-built features for ML workflows.
Clarify: debiasing AI needs a human element

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AI progress depends on us using less data, not more

Data globe

To avoid serious downsides, the data science community has to start working with some self-imposed constraints: specifically, more limited data and compute resources. A minimal-data practice will enable several AI-driven industries — including cyber security, which is my own area of focus — to become more efficient, accessible, independent, and disruptive.

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C3.ai exec says lack of automation is holding back AI progress

Model driven architecture

After more than a decade of providing a platform-as-a-service (PaaS) environment for building and deploying AI applications, C3.ai launched an initial public offering (IPO) in December 2020. Earlier this month, in partnership with Microsoft, Shell, and the Baker Hughes unit of General Electric, the company launched the Open AI Energy Initiative to enable organizations in the energy sector to more easily share and reuse AI models.

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IBM quantum computing development roadmap envisions applications running 100 times faster

IBM believes it has an achievable timetable to advance its quantum hardware to reach the power and reliability that will allow for commercial applications within 5 years. The challenge then is enabling the tools and the environment to let companies and developers start experimenting with writing the applications that will allow them to harness this power.

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Microsoft launches Custom Neural Voice in limited access

Microsoft today announced the general availability of Custom Neural Voice, an Azure Cognitive Services product that lets developers create synthetic voices with neural text-to-speech technology. It’s in limited access, meaning customers must apply and be approved by Microsoft, but it’s ready for production and available in most Azure cloud regions.

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Weights and Biases raises $45 million to advance MLOps governance

Weights and Biases, provider of a platform for enabling collaboration and governance across teams building machine learning models, today revealed it has raised a $45 million series B round led by Insight Partners. The company provides a software-as-a-service (SaaS) platform designed to make it easier for AI teams to first reproduce results and then ultimately explain how an AI model actually works, Weights and Biases CEO Lukas Biewald said.

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Here’s where AI will advance in 2021

Artificial intelligence continues to advance at a rapid pace. Even in 2020, a year that did not lack compelling news, AI advances commanded mainstream attention on multiple occasions. OpenAI’s GPT-3, in particular, showed new and surprising ways we may soon be seeing AI penetrate daily life. Such rapid progress makes prediction about the future of AI somewhat difficult, but some areas do seem ripe for breakthroughs. Here are a few areas in AI that we feel particularly optimistic about in 2021.

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University of Pisa leans into the I/O challenge AI applications create

At a time when workloads that employ machine and deep learning algorithms are being built and deployed more frequently, organizations need to optimize I/O throughput in a way that enables those workloads to cost-effectively share the expensive GPU resources used to train AI models. Case in point: the University of Pisa, which has been steadily expanding the number of GPUs it makes accessible to AI researchers in a green datacenter optimized for high-performance computing (HPC) applications.

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Microsoft researchers tap AI for anonymous data sharing for health care providers

The use of images to build diagnostic models of diseases has become an active research topic in the AI community. But capturing the patterns in a condition and an image requires exposing a model to a rich variety of medical cases. It’s well-known that images from a source can be biased by demographics, equipment, and means of acquisition, which means training a model on such images would cause it to perform poorly for other populations.

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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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The AI Incident Database wants to improve the safety of machine learning

AI systems’ failures have become a recurring theme in technology news. Credit scoring algorithms that discriminate against women. Computer vision systems that misclassify people with darker skin. Recommendation systems that promote violent content. Trending algorithms that amplify fake news.

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Google trained a trillion-parameter AI language model

Parameters are the key to machine learning algorithms. They’re the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well. For example, OpenAI’s GPT-3 — one of the largest language models ever trained, at 175 billion parameters — can make primitive analogies, generate recipes, and even complete basic code.

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