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VergeSense secures $60M to help businesses monitor office usage during the pandemic

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VergeSense, a San Francisco, California-based company developing technologies to track physical office usage, today announced that it raised $60 million in a series C round led by Scale Venture Partners. According to CEO Dan Ryan, the proceeds will be used to help support R&D and growth as VergeSense looks to expand its international customer base.

As some workers return to the office, often in hybrid work arrangements, a growing number of companies are investing in technologies to monitor office space usage for health and logistics reasons. According to a recent PricewaterhouseCoopers survey, most executives (68%) believe that people should be in the office at least three days a week to maintain “a distinctive company culture” — at least once the pandemic is no longer a concern. But as analysts at McKinsey note, organizations will need to reimagine and redesign their existing spaces now to meet pandemic-era needs, like managing which employees can come into the office, when they can enter and take their places, how often the office is cleaned, and whether the airflow is sufficient.

Founded in 2017 by Ryan and Kelby Green, VergeSense leverages AI including computer vision to help businesses understand how their office spaces are being used. The startup’s “sensor-as-a-system” platform consists of sensor hardware coupled with a cloud platform for pretraining machine learning models that run on the hardware, process data, and report occupancy metrics back to VergeSense’s cloud.

Building analytics

VergeSense collects data via wired and battery-powered wireless optical sensors that count people and recognize objects, using a passive infrared sensor to detect motion. A convolutional neural network and “several patented deep learning algorithms,” all running on-sensor, spot people and objects like bags, chairs, laptops, coats, and more.

VergeSense

Above: One of VergeSense’s tracking sensors.

The sensors create a mesh network to communicate with each other and feed data to workplace management systems, space reservation systems, and other digital workplace tools. VergeSense’s software provides a view into office utilization across campuses, buildings, floors, rooms, and individual desks, helping employees find and reserve an available seat in real time.

VergeSense says that it collects millions of data points across over 40 million square feet in more than 29 countries every day.

In response to questions about how VergeSense preserves the privacy of the workers that its sensors track, the company says that it processes only low-resolution imagery from the sensors and doesn’t capture any personally identifiable information. Raw data captures are destroyed on-device, and metadata is based on an assigned number as opposed to tied to “geographically identifiable information.”

Space management

Rent, capital costs, facilities operations, maintenance, and management make real estate the largest cost category outside of compensation for many organizations. According to McKinsey, real estate often amounts to 10% to 20% of total personnel-driven expenditures.

VergeSense pitches its platform as a way to make sure office resources are being used effectively by collecting data on movement of people throughout spaces, where people are congregating, and the condition of each space. With vacancy rates expected to climb over the next two to five years — Morgan Stanley forecasts that rates in New York will reach 10% to 12% — it’s VergeSense’s assertion that companies will need to make data-driven decisions about how to use a mix of owned spaces, standard leases, flexible leases, flex spaces, and co-working spaces.

VergeSense

Above: A screenshot from the VergeSense dashboard.

Of course, VergeSense isn’t the only company vying for a slice of the nascent office analytics market. London-based OpenSensors is developing technology that measures air quality and space occupancy, tapping sensors that can be placed on desks to monitor motion and heat. Infogrid offers similar AI-powered, usage-tracking sensor and software technologies, as does Kleiner Perkins-backed Density.

But VergeSense has managed to secure several high-profile clients including Quicken Loans, Cisco, BP, Fresenius, RBC, Rapid7, and Autodesk. The startup recently inked an agreement with JLL, a commercial real estate services company in the U.K., to resell VergeSense solutions to JLL’s customers. And in September, VergeSense reported that annual recurring revenue grew 400% from Q2 2020 to Q2 2021.

“When the hybrid work model hit its stride, we were standing ready to assist with the data organizations need to power a frictionless digital workplace. Our rapid growth means faster product innovation to address timely hybrid work use cases,” Ran said in a statement. “We are energized to grow our footprint and expand our offering to serve our customers both now and well into the future.”

To date, VergeSense has raised over $82 million in venture capital.

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AI

Device42 launches AI recommendation engine for cloud usage

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Device42, a cloud discovery platform, this month launched a multicloud migration and recommendation engine the company claims is the first to support all major cloud providers. Using machine learning to drive its suggestions, Device42 says the service can perform real-time discovery of IT resources to create an inventory, leveraging dependency mapping to show the relationship and impact of resources on business units.

Organizations often face risks of business outages and disruptions when attempting to migrate to the cloud. And according to IDG research, only 25% achieve their initial goals. Additional reporting by Unisys has found that more than one-third of businesses fail to capture “notable benefits” from their cloud computing projects.

Device42’s new recommendation engine aims to help with cloud migration via AI-driven analysis. It works by first performing a discovery of all resources and apps, creating a directory. Once the inventory finishes, the engine delivers a cost analysis to recommend which apps to move to the cloud and which cloud — Amazon Web Services (AWS) or VMWare on AWS, Microsoft Azure, GCP, or Oracle — might be best for each app.

“We know migration is a big challenge for many organizations, and we’ve heard it loud and clear from our customers. We built this engine to help our customers automate the processes and help them reduce risk,” Device42 founder and CEO Raj Jalan said in a statement.

‘Right-sizing’ cloud deployments

According to RightScale, in 2017 26% of enterprises with more than 1,000 employees spent over $6 million a year in the public cloud. But it’s estimated that a fair amount of that enterprise cloud spend is going to waste. The same report found that the average waste in cloud costs was 35%, netting out to $10 billion each year across AWS, Azure, and GCP.

Device42’s engine can provide data about the cost of resources and their performance impact, as well guidelines to support best practices. It helps determine the most efficient course of action, including whether to re-architect apps, and works to identify the right sizes for cloud instances.

According to Jalan, the engine matches operating systems from on-premises solutions to the cloud so apps function after migration. Savings come from reservation purchase options and algorithms that factor in networking and storage costs, along with CPU and memory.

“The [engine] provides the visibility and information users need to make key decisions across cloud instances,” Jalan continued.

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AI

Onestream: Data analysis, AI tools usage increased in 2021

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CFOs and other finance executives are optimistic that economic recovery is on the horizon: three-quarters (73 percent) expect that they will return to normal growth by the end of 2021, according to the latest Enterprise Financial Decision-Making Report from OneStream, a provider of corporate performance management solutions for mid-sized and large enterprises. Companies have significantly increased their data analysis tool investments and usage over the past year, the report found.

Above: Over half of companies are using data analysis tools more than before the pandemic. Companies in the IT and finance industries, and those with over 1,000 employees, are using data analysis tools significantly more than others.

Image Credit: Onestream Software

OneStream’s study targeted finance leaders across North America and identified the factors driving their priorities, budgets and technology adoption plans for 2021. The survey found that the COVID-19 pandemic created a heightened need for agile forecasting, predictive planning and digital transformation. The ability to quickly reforecast budgets and shift workflows has become essential.

The 2021 report found that finance executives have significantly increased their data analysis tool investments and usage. Companies commonly invested in artificial intelligence (59 percent) and increased their use of cloud-based planning and reporting solutions (65 percent). Most companies already use (69 percent) or plan to use (18 percent) low-code development platforms, which enable business users and citizen developers to take on new roles while circumventing complicated coding requirements. For return-to-office budgets, data privacy tools are the most common priority (18%), followed by hybrid cloud technologies.

Compare the results with OneStream’s 2020 Enterprise Financial Decision-Making Report where less than half (46 percent) of the finance executives reported using cloud-based solutions regularly, while less than a quarter used machine learning (21 percent) and artificial intelligence (20 percent) solutions.

Many finance executives are evaluating their workforce, technology and supply chain needs for a post-pandemic reality. However, the political and social landscape have also heavily impacted investment decisions, leading executives to prioritize sustainability and diversity initiatives as well.

The commissioned study, conducted by Hanover Research in April of 2021, sourced insights from 340 finance decision makers in the United States, Canada and Mexico. All individuals hold management position (C-level executive (CFO), VP, Director, Controller) in finance. Respondents work at companies across numerous industries and varying revenues, with 24 percent employed by companies with over $1 billion in annual revenue.

Read the full Onestream report Enterprise Financial Decision-Making Report 2021 — North America.

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AI

Cloudera partners with Nvidia to expand GPU usage across AI applications

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Cloudera and Nvidia announced a collaboration that will allow organizations to use GPUs in more areas across the AI development lifecycle.

Cloudera will integrate its Cloudera Data Platform with Nvidia’s accelerated Apache Spark 3.0 libraries. The integration will make it easier to add machine learning workflows to processes and create architectures without requiring GPU customization. Enterprises will be able to make changes to their data science workflows without having to also update the Nvidia integration manually.

GPUs have shown tremendous promise in enhancing the data science side of AI development, enabling enterprises to run some types of workloads on top of GPUs. However, analytics often involve processes that span multiple teams, forcing enterprises to invest in customizing GPU integrations for those use cases.

Gartner has predicted that creating new architecture patterns that help operationalize data science and ML pipelines will be one of the major trends in 2021.

Benefits to accelerating GPUs

The partnership will allow enterprises to use GPUs across modern data workflows that span data preparation, data science, and analytics tasks. The typical workflow includes many steps including data ingestion, data curation, data pipeline automation, data science exploration, model development, testing, deployment, model monitoring and retraining, and delivery into the business. Cloudera has been busy in making these processes and the handoffs between them much easier over the last year.

The Apache Spark 3.0 libraries are accelerated using Nvidia’s RAPIDS platform, which will dramatically accelerate much of the boring prep work required to bring new machine learning models into production. For example, the US Internal Revenue Service is already seeing a threefold improvement in data science workflows for fraud detection, said Joe Ansaldi, IRS technical branch chief for the Research Applied Analytics & Statistics Division, in a statement.

Speeding up data preparation tasks and training models faster will save on infrastructure costs as well. GPU-accelerated Apache Spark 3 runs natively on CDP and can plug into high performance compute tools, Cloudera said.

Comparison of CPU and GPU workloads

Above: Comparing the CPU and GPU powered workflows.

Image Credit: Cloudera

Cloudera’s data portfolio

Cloudera was a trailblazer in the development of data lakes built on top of the Hadoop platform. Cloudera merged with Hortonworks, another Hadoop vendor, in 2018 and combined the technologies into a modern architecture called the Cloudera Data Platform (CDP). At the time, many speculated this spelled the end of Hadoop data warehouses, but Cloudera has continued to innovate and extend Hadoop into a more nimble workflow.

Cloudera added Applied ML Prototypes (AMPs), a framework for packaging AI and ML models for data scientists, to CDP earlier this year. AMPs allow teams to take the guesswork out of ML projects with prebuilt business application templates for specific use cases, and they often run on Nvidia GPU hardware. Cloudera Data Engineering (CDE) streamlines the data engineering and prep work at the start of a project. This solved common problems data engineers face, such as scheduling and orchestration of complex data, troubleshooting and performance tuning tools for data flows, and improving collaboration with analytic and data science teams.

The RAPIDS Accelerator for Apache Spark will be available in CDP Private Cloud this summer. Nvidia and Cloudera will roll out additional accelerated offerings in CDP over time, starting with Accelerated Deep Learning and Machine Learning in CDP Public Cloud in May. “This means that no matter where customers require these GPUs (from on-prem to public cloud, to hybrid cloud and beyond), they’ll be able to leverage best-in-class GPUs out of the box,” said Santiago Giraldo, Cloudera director of product marketing for data engineering and machine learning.

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Google launches Lyra codec in beta to reduce voice call bandwidth usage

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Google today open-sourced Lyra in beta, an audio codec that uses machine learning to produce high-quality voice calls. The code and demo, which are available on GitHub, compress raw audio down to 3 kilobits per second for “quality that compares favorably to other codecs,” Google says.

While mobile connectivity has steadily increased over the past decade, the explosive growth of on-device compute power has outstripped access to reliable, fast internet. Even in areas with reliable connections, the emergence of work-from-anywhere and telecommuting have stretched data limits. For example, early in the pandemic, nearly 90 out of the top 200 U.S. cities saw internet speeds decline as bandwidth became strained, according to BroadbandNow.

It’s Google’s assertion that Lyra can make a difference in these scenarios.

Lyra’s architecture is separated into two pieces, an encoder and decoder. When someone talks into their phone, the encoder captures distinctive attributes, called features, from their speech. Lyra extracts these features in 40-millisecond chunks and then compresses and sends them over the network. It’s the decoder’s job to convert the features back into an audio waveform that can be played out over the listener’s phone.

According to Google, Lyra’s architecture is similar to traditional audio codecs, which form the backbone of internet communication. But while these traditional codecs are based on digital signal processing techniques, the key advantage for Lyra comes from the ability of its decoder to reconstruct a high-quality signal.

Lyra codec

Above: Lyra’s architecture in schematic form.

Image Credit: Google

Google believes there are a number of applications Lyra might be uniquely suited to, from archiving large amounts of speech and saving battery to alleviating network congestion in emergency situations.

“We are excited to see the creativity the open source community is known for applied to Lyra in order to come up with even more unique and impactful applications,” Google Chrome engineers Andrew Storus and Michael Chinen wrote in a blog post. “We [want] to enable developers and get feedback as soon as possible.”

The Lyra code is written in C++ using the Bazel build framework. The core API provides an interface for encoding and decoding at the file and packet levels, and the complete signal processing toolchain is provided, which includes filters as well as transforms. Google’s example code integrates with the Android NDK to show how Lyra can work with Java-based Android apps, and Google has also provided the weights and vector quantizers necessary to run Lyra.

“This release provides the tools needed for developers to encode and decode audio with Lyra, optimized for the 64-bit ARM android platform, with development on Linux,” Storus and Chinen continued. “We hope to expand this codebase and develop improvements and support for additional platforms in tandem with the community.”

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