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Facebook to pay $5 million to local journalists in newsletter push

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By Sheila Dang

(Reuters) -Facebook Inc on Thursday said it will give $5 million to pay local journalists in multiyear deals as part of its new publishing platform to help independent writers attract an audience and make money through the social media network.

The move is part of Facebook’s answer to the trend of email newsletters, led by platforms like Substack, as it focuses on reporters “who are often the lone voice covering a given community,” the company has said.

The publishing platform, which Facebook announced last month, will be integrated with Facebook Pages and include a free self-publishing tool for journalists to send out newsletters or create their own website.

Independent journalists in the United States can apply to the program beginning on Thursday, and priority will be given to reporters who plan to cover “Black, Indigenous, Latinx, Asian or other audiences of color,” in locations that lack an existing news source, Facebook said.

Journalists will be able to earn additional money from publishing stories using Facebook’s tools, starting with subscriptions, and each writer can set their own price, the company added.

Emily Bell, director of the Tow Center for Digital Journalism at Columbia Journalism School, tweeted Thursday that while journalists and their communities will, hopefully, benefit from Facebook’s funding, the news industry will “have to reckon with the economic foundation of this Faustian pact,” because Facebook has been used as a tool to oppress reporters around the world and its business relies on news workers like content moderators who are “not unionized or paid well.”

The growth of paid newsletters has shaken up the news media world, as high-profile journalists from outlets including the New York Times and Vox Media have left to strike out on their own on platforms such as Substack and Patreon, lured by cash advances and uncapped earning potential from subscriptions.

Substack announced “Substack Local” this month, a $1 million program to pay up to 30 local reporters to build their own subscription-supported business.

Facebook said it would partner with the Washington-based International Center for Journalists and National Association of Hispanic Journalists to evaluate applications, and would give the journalists selected access to experts and services to help them build a news business.

The social network has long had a strained relationship with the news industry, which came to a head in February after a showdown with the Australian government over paying news outlets for content. Following the conflict, Facebook pledged to invest $1 billion in the news industry globally over the next three years.

(Reporting by Sheila Dang; Editing by Kenneth Li, Alison Williams and Jonathan Oatis)

Image Credit: Reuters

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Source: https://datafloq.com/read/facebook-pay-5-million-local-journalists-newsletter-push/14301

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SAS flexes AI ambitions with Viya platform expansion

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Elevate your enterprise data technology and strategy at Transform 2021.


During its Virtual SAS Global Forum 2021 event, SAS today announced an expansion for its SAS Viya platform, which is used for analyzing data and building AI models. The platform will now be available on Amazon Web Services (AWS) and Google Cloud, along with existing support for Microsoft Azure.

In addition, SAS pledged to support the Red Hat OpenShift platform based on Kubernetes that many organizations are starting to employ as the foundation for their hybrid cloud computing environments.

SAS also today unveiled Virtual Victim Assistance Network (ViViAN), a chatbot it built in collaboration with the Identity Theft Resource Center (ITRC) to assist victims of fraud. Developed under a grant provided by the Office of Victims of Crime within the U.S. Department of Justice (DOJ), ViViAN employs natural language processing (NLP) and linguistic rules, along with machine learning and deep learning algorithms to enable ITRC to better aid victims of identity theft. ITRC is a nonprofit organization that provides both victim assistance and consumer education via a toll-free call center, a website, and social media platforms.

Greater reach

ITRC claims to have helped more than 11,000 people in the last year through its contact center alone and says its website had 1.1 million unique visitors. ViViAN was built using the low-code SAS Conversation Designer tool the company makes available via the SAS Viya platform.

The goal is to make analytics universally accessible via speech interfaces without requiring anyone to run a report, SAS CTO and COO Bryan Harris said. “Think of it as having a conversational interface to SAS,” he said.

In general, SAS has been making a case for an integrated approach to building analytics and AI models that includes the management tools organizations require to handle massive amounts of data. Those data management capabilities are particularly important as organizations begin collecting data from internet of things (IoT) environments and other sources.

Integration

Harris said this integrated approach has helped SAS drive past the $3 billion revenue mark in 2020, despite the economic downturn brought on by the COVID-19 pandemic. He added that a full 27% of the revenue SAS generates is plowed back into research and development. SAS also pledged last year to invest $1 billion in AI over a three-year span to advance its overall strategy.

As part of that strategy, SAS plans to extend its ability to integrate with Microsoft cloud application environments such as Microsoft 365 and Dynamic. “We’ll integrate with existing ecosystems,” Harris said.

In 2020, SAS also acquired Boemska, a provider of low-code/no-code application deployment and analytic workload management tools for the SAS Viya platform. This deal was part of an effort to accelerate AI integration in third-party applications that will be offered via a SAS online marketplace.

SAS isn’t the only provider of analytics platforms to reveal such ambitions. However, the company is betting that a composite approach to AI will prevail as organizations employ multiple data sources residing in the cloud and in on-premises IT environments.

It’s already apparent that AI requires the management of large amounts of data at a level of consistency few organizations have been able to achieve and maintain. Rather than asking organizations to integrate a separate data management platform with multiple frameworks for building analytics applications infused with AI capabilities, SAS is driving an approach that ultimately reduces the total cost of building and managing these applications.

It’s far too early to say which approach will prevail, but with a large installed base of enterprise customers, SAS is likely to be an AI force for years to come.

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Source: https://venturebeat.com/2021/05/18/sas-flexes-ai-ambitions-with-viya-platform-expansion/

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Top 5 Trends in Facilities Management

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facilities trends
Illustration: © IoT For All

In 2021, most people have access to more technology than ever before. From solutions that make jobs more fulfilling to innovations that improve personal life, people rely on technology for an increased standard of living.

The modern tenant is drawn to digital solutions that provide comfort and convenience. Combined with the constant demand for cost and energy efficiency in facilities management, new trends can offer insights and ideas to help you reach your goals. This article takes a closer look at some of the important facilities management trends right now.

Sensor Technology

The “Internet of Things” (IoT) has been a buzzword for over a decade now. As a telltale sign for its widespread adoption, the term itself is hardly used in facilities management. Today, service providers in real estate and facilities managers have swapped buzzwords for practical deployment of sensor technologies to deliver measurable operational benefits.

Sensor technology is IoT put in action. It allows a building’s assets to communicate their operational and health status without human intervention. Advances in sensor and battery technology have introduced wireless devices with little or no configuration and maintenance effort to be deployed in minutes.

Size does matter. Sensors should not be overly noticeable to tenants and occupiers. While it may be simple to fit any size of the sensor in a newly built property, smaller sensors are better for retrofitting existing assets, furniture, pipes, and plant room equipment.

In 2021, sensor technology is used to gather data for many aspects of any given commercial building. New technical data points include granular ambient temperature, humidity, and the technical building status for its HVAC, electrical, and water treatment systems. Discreet or invisible sensors deliver live insight into desk and space occupancy, washroom usage, and cleaning status. Tenants are even able to express satisfaction and ask for support using wireless click and feedback panels.

Data Analytics

Even without sensors, facilities managers have access to vast amounts of data. This data can be harvested for various purposes and by different systems. However, making decisions that lead to operational savings based on this data alone can be challenging and time-consuming.

Many facilities managers are deploying or implementing building and facilities management systems to solve this challenge, where data is translated into work orders or automated actions. As an example, consider the monitoring of space occupancy in a building. Using new sensor-based occupancy data points, building management systems will adjust lights, ventilation, and temperature according to how many people are present at any given time, which will dramatically reduce energy spend. Sensor-driven building optimization will typically achieve savings of 15% to 40% of the total energy spent.

Increasingly, building management systems are augmented by separate data analytics platforms. These platforms constantly analyze building data to extract usage and consumption patterns. Artificial Intelligence (AI) is used to understand anomalies and generate predictions. This provides a deep understanding of what is happening in your buildings and why. This insight helps owners and occupiers identify warning signs in need of proactivity and enables them to prevent operational and safety issues such as power outages or water hygiene problems.

If you are managing a portfolio of buildings, you will be able to use data from one building to make decisions in others. Data analytics and AI will help you identify why one building’s energy use is more efficient than another’s and streamline efficiency efforts.

Cloud-Based Data Collection

In 2021, we expect to access data and information 24/7 and from any corner of the world. This also holds in the commercial built environment.

Using cloud-based services greatly simplifies data collection, as facilities managers and service providers do not need to find ways to gather and store the data. The expectation is to plug in the equipment and have the data available for productive use. Only cloud-based services can deliver this experience.

Cloud-based solutions will offer granular and tailored access to data. A site manager may not require the same visibility on building data as the estate manager. Multi-tenant platforms with project-based access control to individual building assets are key features expected of any modern cloud-based solution.

Data loss risk mitigation is another area that cloud-based services address with new elegance and simplicity—no need to worry about failures or backups, as data is collected and stored for you.

Security is paramount – look for solutions that encrypt all sensor and analytics data in transit and at rest, and ask for a third-party security and penetration test by a well-known security consultancy firm.

Remote Monitoring

If there is anything that 2021 has taught us so far, it’s important to work remotely. Similarly, remote property and asset monitoring have quickly become one of the biggest drivers in facilities management. The three trends discussed in the previous sections create the backdrop for the remote monitoring of your building estate.

With smart sensors, cloud technology, and data analytics, you can get a full overview of key assets and environmental parameters within all your buildings, regardless of where you are. Service providers can reduce the dispatch of on-site engineers greatly through sensor-based condition monitoring.

Furthermore, insights generated from vacant buildings due to virus outbreaks or tenant churn can drive energy savings far beyond the 15% that can be expected when a building is fully occupied.

Environmental and safety monitoring parameters for vacant buildings include water ingress and flooding detection, temperature, dampness and humidity, the protection of assets and goods, and door and window openings.

Digital Workplace Services

Digital workplace services have been turning into a hallmark of a modern company’s office environment.

Until recently, digital workplace services have been mostly centered around employee and tenant comfort and experience. In a global labor market competing for talent, companies want to be sure to deliver an exceptional workplace experience.

Typical services are powered by sensors, analytics, digital signage and apps and include:

  • Desk availability and way-finding
  • Meeting room availability and booking/release
  • Canteen occupancy
  • Office temperature and climate
  • Feedback panels
  • Service request buttons around printers, coffee machines, communal equipment
  • Meeting room assistance buttons and panels
  • Washroom cleaning status and improved cleaning regimes

Given the recent pandemic, digital workplace services turn from being experience-driven to being essential for health and safety. As employees return to the office, sensors and data analytics enable informed choices around distancing measures as well as high standards around hygiene and transmission safety:

  • Reduced and controlled desk occupancy levels
  • Washroom occupancy and distancing
  • Event-driven cleaning regimes
  • Tracking temperature and relative humidity (40-60% rule) for minimal viral spread
  • Reduce necessary face-to-face interactions through smart assets and panels

While mature sensor technology already exists for all of these new applications, the analytics and software to power them are being developed at breakneck speed. New apps, signage screens, and analytics platforms are powered to achieve the new goal of keeping our workplaces attractive, safe and interactive.

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Source: https://www.iotforall.com/top-5-trends-in-facilities-management

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Data and AI operations startup Coiled nabs $21M

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Coiled, a DataOps and AIOps platform that seeks to increase access to scalable computing, today announced that it raised $21 million in series A funding led by Bessemer Venture Partners. Alongside the financing, Coiled unveiled Coiled Cloud, which lets data scientists build machine learning models using Python tools on a laptop, then transfer the model to any cloud environment to avoid vendor lock-in. 

Enterprises‌ ‌have‌ ‌long struggled‌ ‌to‌ ‌collaborate‌ ‌well ‌around‌ ‌their‌ ‌data. This hinders their ability to adopt‌ ‌applications‌ ‌like‌ ‌AI, but the ‌evolution‌ ‌of‌ ‌‌DataOps‌ and AIOps ‌could‌ ‌solve that problem. Research firm Gartner calls them major trends encompassing steps in the data and machine learning lifecycles. DataOps is the automated, process-oriented methodology to improve the quality and reduce the cycle time of data analytics, while AIOps is the application of AI in IT operations — a combination of big data and machine learning.

Matthew Rocklin — the creator of Dask, a Python framework for deploying open source libraries like Pandas, NumPy, Jupyter, XGBoost, and Scikit-Learn — cofounded Coiled in 2020. He describes the New York-based company as the result of “deep collaboration” between enterprises in operations support systems and Python, with the goal of integrating Python and operations support systems into enterprise environments.

Coiled

Above: Cluster information in the Coiled web interface.

Image Credit: Coiled

“The Coiled team are passionate open source developers, maintainers, and advocates that are helping customers harness the power of distributed computing while making it easy and approachable,” Rocklin said in a blog post. “I started out as a scientist at Sandia National Labs and developed a deep respect and admiration for people who take on the complex challenges facing the world today in research, business, and science. Coiled exists to take complexity out of their day-to-day jobs so they can focus on solving those problems.”

Product lineup

Coiled offers two products to customers: the new Coiled Cloud and Coiled Enterprise.

Coiled Cloud, which runs primarily on Amazon Web Services, takes care of deploying containers, hooking up the requisite networking automatically. Coiled offers a web interface and Python environment that runs from other web services and automated jobs, managing GPUs and CPUs to allow mixing and matching with architectures and libraries such as Facebook’s PyTorch.

As for Coiled Enterprise, it runs in the cloud as well as on-premises and allows customers to work on their own personal devices as well as GPU servers in their infrastructure. Coiled Enterprise can create containers that can move between environments and manage server clusters — setting when the clusters run, for how long, how many processor cores each team member can use, and how long cores can be active. Coiled Enterprise also enables the sharing of work across team members and provides built-in telemetry and authentication to prevent unauthorized access and generate reports.

Coiled

Above: Monitoring performance using Coiled’s dashboard.

Image Credit: Coiled

Coiled Enterprise includes optimization services starting at 2 hours per month for Python libraries like Rapids, Scikit-Learn, and XGBoost, plus training credits for custom machine learning model training. Coiled Enterprise also includes discounted pricing on Coiled Cloud and access to run Coiled on on-premises hardware and synchronize containers in the cloud.

One Coiled customer, Zebra Medical Vision, says they were able to get an initial reduction across their data curation, automated experiments, and other pipelines from 66 hours to 15 minutes using Coiled products. To date, more than 100 million tasks have been hosted on Coiled, the startup claims, including from data science teams at Capital One and Anthem Health.

Coiled’s latest funding round brings its total raised to $26 million to date. Previously, the company closed a $5 million seed round led by Costanoa Ventures.

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Source: https://venturebeat.com/2021/05/18/data-and-ai-operations-startup-coiled-nabs-21m/

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Data prep platform Explorium raises $75M

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Explorium, a startup developing an automated data and feature discovery platform, today closed a $75 million funding round led by Insight Partners with participation from existing investors. The capital, which brings the company’s total raised to $127 million to date, will be used to expand Explorium’s platform after a year in which it doubled its customer base and more than quadrupled revenue, according to CEO Maor Shlomo.

In machine learning, a feature is a property or characteristic of the phenomenon being observed. Features are usually numeric, but structural features such as strings and graphs can be used in pattern recognition. Feature engineering — the process of using domain knowledge to extract features from raw data via data-mining techniques — is often arduous. According to a Forbes survey, data scientists spend 80% of their time on data preparation, and 76% view it as the least enjoyable part of their work. It’s also expensive. Trifecta pegs the collective data prep cost for organizations at $450 billion.

San Mateo, California-based Explorium aims to solve this by acting as a repository for a company’s information, connecting siloed internal data to thousands of external sources on the fly. Using machine learning, the company claims to automatically extract, engineer, aggregate, and integrate the relevant features from data to power sophisticated predictive algorithms, evaluating hundreds before scoring, ranking, and deploying the top performers.

“Explorium was founded by three Israeli entrepreneurs, Omer Har, Maor Shlomo, and Or Tamir. Maor and Or met while serving in the Israeli Defense Forces. They recruited Omer, and together, the three set out to create a platform for data science professionals,” a spokesperson told VentureBeat via email. “Explorium provides access to thousands of data sources and more important, identifies the data signals that mattered most, aligning those signals with internal data so they can be immediately incorporated into any analytical process.”

Analyzing datasets

For enterprises using predictive models to forecast consumer behavior, data drift was a major challenge in 2020 due to never-before-seen circumstances related to the pandemic. Organizations were forced to constantly retrain and update their machine learning models, and 12 months later, many are still wrestling with the challenge.

Indeed, a recent MIT survey suggests that just 13% of organizations are delivering on their data strategy, with challenges in managing the end-to-end lifecycle presenting the biggest barriers. Alation’s latest quarterly State of Data Culture Report similarly implies that, because of data quality issues, only a small percentage of businesses are using AI effectively across the organization.

With Explorium, lenders and insurers can discover predictive variables from thousands of data sources, while retailers can tap the platform to forecast which customers are likely to buy products. Data scientists can add custom code to incorporate domain knowledge and fine-tune AI models. And admins gain tools designed to uncover optimization-informing patterns from large corpora.

In April 2020, Explorium added a new set of signals to help organizations understand risk derived from the pandemic. By combining variables like internal company data, policy factors, and geographic factors that might affect a company’s repayment or operability, the platform generates an overall risk score. For instance, a health system that’s considered essential and receives federal aid would have a lower risk than a hotel that’s closed and not considered critical.

“Explorium offers hundreds of curated premium and public external data sources that have been validated, checked for regulatory compliance, normalized and distilled into thousands of proprietary signals. Explorium’s external data gallery covers multiple categories including, but not limited to, company data, people data, geospatial data, time-based data, and product data,” the spokesperson continued. “The platform automatically analyzes the user’s data in context, engineers and generates features, and presents the user with an optimal feature set through an internal ranking mechanism based on feature interactions, feature scoring, and proprietary algorithms.”

Explorium

Above: A screenshot of Explorium’s platform.

Image Credit: Explorium

More recently, 130-employee Explorium launched Signal Studio, a product that “pre-vets” data sources across companies, contacts, geospatial imagery, and more. Signal Studio spotlights data signals based on configuration settings and then matches and integrates the enriched data with a company’s internal datasets.

Pandemic-induced demand

According to market research firm Tractica, the global AI software market is expected to experience “massive” growth in the coming years, with revenues increasing from $9.5 billion in 2018 to an expected $118.6 billion by 2025. A number of startups are attempting to cash in on the trend (or have already done so), including Kaskada and Determined AI, which recently raised $11 million to further develop its deep learning model development tools for data scientists and AI engineers. Meanwhile, Iguazio nabbed $24 million for its suite of AI development and management tools, and Clusterone raked in $2 million for its DevOps for AI platform that operates with both on-premises servers and public cloud computing platforms like AWS, Azure, and Google Cloud Platform.

Explorium is also joined by a raft of tech giants in the burgeoning AutoML segment. Databricks just last year launched a toolkit for model building and deployment, which can automate things like hyperparameter tuning, batch prediction, and model search. IBM’s Watson Studio AutoAI — which debuted in June 2020 — promises to automate enterprise AI model development, as does Microsoft’s recently enhanced Azure Machine Learning cloud service, Google’s AutoML suite, and Amazon’s SageMaker Data Wrangler.

For Explorium’s part, the company claims to have doubled its customer base and quadrupled its revenue in the past 12 months. “Dozens” of brands now use the platform, Shlomo says, including Pepsi, Melio, and OnDeck.

“The pandemic accelerated Explorium’s business in two ways: (1) It made AI models and business analytics obsolete, forcing businesses to scramble for external data, and (2) it made the ongoing pain of obtaining external data impossible to ignore, inspiring many businesses to look for a long-term solution,” Shlomo told VentureBeat. “The pandemic was a turning point for a lot of companies. They realized just how much they needed external data, and how hard it was for them to get it and use it for their business.”

Zeev Ventures, Emerge, F2 Capital, 01 Advisors, and Dynamic Loop Capital also participated in Explorium’s latest financing round, a series C.

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VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

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Source: https://venturebeat.com/2021/05/18/data-prep-platform-explorium-raises-75m/

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