DAN: TSX-V (Canada)JE9N: FSE (Germany)DRRSF: OTC (USA)

SAGUENAY, QC, April 8, 2021 /CNW Telbec/ - Arianne Phosphate (the "Company" or "Arianne") (TSXV: DAN) (OTC: DRRSF) (FRANKFURT: JE9N), a development-stage phosphate mining company, advancing the Lac Paul project in Quebec's Saguenay-Lac-Saint-Jean region, is pleased to announce significant advancements in the design of its future tailings facility. Arianne partnered with the Quebec Center of Geomatics (CGQ), to advance research and development on a new method for the design and future monitoring of the Company's tailings operations. This work will use geomatic and remote sensing tools combined with artificial intelligence that should greatly improve the safety aspects of Arianne's operations.

"The Lac Paul mine is projected to be one of the most environmentally friendly phosphate mines in the world," said Jean-Sbastien David, COO of Arianne Phosphate. "From the project's onset, Arianne's design process had the goal of constructing a mine using best environmental practices and, the integration of technology was vital in this regard. Further, our reliance on renewable hydro-electricity will allow us to greatly diminish our production of greenhouse gases [GHG] with the goal of ultimately being GHG neutral. Our most recent endeavors have also added to the safety and structural integrity of our tailings facility."

Brian Ostroff, CEO of Arianne Phosphate added, "we take seriously our responsibilities surrounding environmental and safety issues. We know that many challenges surrounding mining operations stem from failures at their tailings facilities and, our work here goes a long way towards reducing these threats. Arianne will produce a high-purity, low-contaminant phosphate concentrate that provides for society's needs but, in as safe and effective manner as possible."

Arianne partnered with the Quebec Center of Geomatics (CGQ), a group within the College of Chicoutimi, in the Province of Quebec, to develop a new way to use geomatic and remote sensing instruments and monitor this information through a solution that uses artificial intelligence. It is during the construction process that sensors will be built in throughout the tailings dam that will measure, in real-time, data points such as moisture and pressure and, make adjustments as necessary to allow for smooth and safe operations, allowing for greater safety.

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This research project was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through its Partnership Engage Grants.

Qualified PersonJean-Sbastien David, P.Geo., Qualified Person by NI 43-101, has approved this release. Mr. David is also the Company's Chief Operating Officer.

About Arianne PhosphateArianne Phosphate ("Arianne Phosphate Inc.") (www.arianne-inc.com) is developing the Lac Paul phosphate deposits located approximately 200 km north of the Saguenay/Lac St. Jean area of Quebec, Canada. These deposits will produce a high-quality igneous apatite concentrate grading 39% P2O5 with little or no contaminants (Feasibility Study released in 2013). The Company has 173,354,669 million shares outstanding.

Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.

Follow Arianne on:Facebook: https://www.facebook.com/ariannephosphateTwitter: http://twitter.com/arianne_danYouTube: http://www.youtube.com/user/ArianneResourcesFlickr: http://www.flickr.com/photos/arianneresourcesResource Investing News: http://resourceinvestingnews.com/?s=Arianne

Cautionary Statements Regarding Forward Looking Information

This news release contains "forward-looking statements" and "forward-looking information" within the meaning of applicable securities regulations in Canada and the United States (collectively, "forward-looking information"). Forward-looking information includes, but is not limited to, anticipated quality and production of the apatite concentrate at the Lac Paul project. Often, but not always, forward-looking information can be identified by the use of words such as "plans", "expects, "is expected", "budget", "scheduled", "estimates", forecasts", "intends", "anticipates", or "believes", or the negatives thereof or variations of such words and phrases or statements that certain actions, events or results "may", "could", "would", "might", or "will" be taken, occur or be achieved. Forward-looking information is subject to known and unknown risks, uncertainties and other factors that may cause the actual results, level of activity, performance or achievements of the Company to be materially different from those expressed or implied by such forward-looking information, including but not limited to: volatile stock price; risks related to changes in commodity prices; sources and cost of power facilities; the estimation of initial and sustaining capital requirements; the estimation of labour and operating costs; the general global markets and economic conditions; the risk associated with exploration, development and operations of mineral deposits; the estimation of mineral reserves and resources; the risks associated with uninsurable risks arising during the course of exploration, development and production; risks associated with currency fluctuations; environmental risks; competition faced in securing experienced personnel; access to adequate infrastructure to support mining, processing, development and exploration activities; the risks associated with changes in the mining regulatory regime governing the Company; completion of the environmental assessment process; risks related to regulatory and permitting delays; risks related to potential conflicts of interest; the reliance on key personnel; financing, capitalization and liquidity risks including the risk that the financing necessary to fund continued exploration and development activities at Lac Paul project may not be available on satisfactory terms, or at all; the risk of potential dilution through the issue of common shares; the risk of litigation. Forward-looking information is based on assumptions management believes to be reasonable at the time such statements are made, including but not limited to, continued exploration activities, no material adverse change in commodity prices, exploration and development plans proceeding in accordance with plans and such plans achieving their stated expected outcomes, receipt of required regulatory approvals, and such other assumptions and factors as set out herein. Although the Company has attempted to identify important factors that could cause actual results to differ materially from those contained in the forward-looking information, there may be other factors that cause results not to be as anticipated, estimated or intended. There can be no assurance that such forward-looking information will prove to be accurate, as actual results and future events could differ materially from those anticipated in such forward-looking information. Accordingly, readers should not place undue reliance on forward-looking information. Forward-looking information is made as of the date of this press release, and the Company does not undertake to update such forward-looking information except in accordance with applicable securities laws.

SOURCE Arianne Phosphate Inc.

Cision

View original content: http://www.newswire.ca/en/releases/archive/April2021/08/c6273.html

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Technological Innovation and Artificial Intelligence Will Provide for Increased Operational Safety at the Arianne Phosphate Project - Yahoo Finance

NEW YORK--(BUSINESS WIRE)--CB Insights today named Dataiku, the world's most advanced Enterprise AI platform, to the fifth annual AI 100 ranking, showcasing the 100 most promising private artificial intelligence companies in the world.

This is the fifth year CB Insights has recognized the most promising private artificial intelligence companies with the AI 100, and this is one of the most global groups we've ever seen. This year's cohort spans 18 industries, and is working on everything from climate risk to accelerating drug R&D, said CB Insights CEO Anand Sanwal. Last year's AI 100 companies had a remarkable run after being named to the list, with more than 50% going on to raise additional financing (totaling $5.2B), including 16 $100 million+ mega-rounds. Many also went on to exit via M&A, SPAC or IPO. As industry after industry adopts AI, we expect this years class will see similar levels of interest from investors, acquirers and customers."

Were honored to be included on this exceptional list of companies solving some of the worlds greatest challenges through AI, said Florian Douetteau, CEO of Dataiku. Its exciting recognition for our team, which since Dataiku was founded in 2013 has succeeded in creating a best-in-class data science and machine learning platform that democratizes AI within the enterprise.

Through an evidence-based approach, the CB Insights research team selected the AI 100 from a pool of over 6,000 companies based on several factors including patent activity, investor quality, news sentiment analysis, proprietary Mosaic scores, market potential, partnerships, competitive landscape, team strength, and tech novelty. The Mosaic Score, based on CB Insights algorithm, measures the overall health and growth potential of private companies to help predict a companys momentum.

Dataiku supports the AI strategies of more than 400 companies worldwide, enabling organizations across pharmaceuticals, healthcare, financial services, transportation, the public sector, manufacturing, retail, and more to massively scale their AI efforts. Dataiku employs more than 600 people worldwide between offices in New York, Paris, London, Munich, Sydney, and Singapore, and plans to hire another 300 employees in 2021. Its roots are in France, its headquarters is in New York, and its culture is a dynamic melting pot of all of its international influences.

Quick facts on the 2021 AI 100:

About CB Insights

CB Insights builds software that enables the world's best companies to discover, understand and make technology decisions with confidence. By marrying data, expert insights and work management tools, clients manage their end-to-end technology making process on CB Insights. To learn more, please visit http://www.cbinsights.com.

About Dataiku

Dataiku is one of the worlds leading AI and machine learning platforms, supporting agility in organizations data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. Hundreds of companies use Dataiku to underpin their essential business operations and ensure they stay relevant in a changing world, including models driving fraud detection, customer churn prevention, predictive maintenance, supply chain optimization, and much more. Dataiku is built for companies looking to democratize AI across their organization, bringing agility and preparedness to the business through the use of data by everyone from analysts to data scientists.

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Dataiku Named to the 2021 CB Insights AI 100 List of Most Innovative Artificial Intelligence Startups - Business Wire

A team of researchers from the University of Toronto and LG AI Research have developed an explainable artificial intelligence (XAI) algorithm. The algorithm can help identify and eliminate defects in display screens.

The algorithm outperformed comparable approaches on industry benchmarks and was developed through an ongoing AI research collaboration between LG and the University of Toronto.

According to the researchers, the XAI algorithm could be applied in other fields, primarily those which require details into how machine learning makes its decisions, including data interpretation from medical scans.

Kostas Plataniotis, a professor at the Edward S. Rogers Sr. department of electrical and computer engineering, believes that explainability and interpretability are about meeting the quality standards we set for ourselves as engineers and are demanded by the end-user.

The research team also included recent the University of Toronto Engineering graduate Mahesh Sudhakar and masters candidate Sam Sattarzadeh and researchers led by Jongseong Jang at LG AI Research Canadas global research-and-development arm.

XAI is an emerging field addressing problems with the black box approach of machine learning strategies. In a black-box model, a computer may be given a set of training data in the form of millions of labeled images. The algorithm learns to relate certain features of the input with specific outputs by analyzing the data. It can then correctly attach labels to images it has never seen before.

The machine decides itself which aspects of the image to pay attention to and which to ignore. Thus, its designers never know exactly how it arrives at a result. Therefore, a black-box model presents challenges when its applied to areas such as health care.

XAI is thus designed to be a glass box approach that makes the decision-making process transparent. Traditional Algorithms and XAI algorithms are run simultaneously to examine the validity and the level of their learning performance. The method also provides opportunities to carry out debugging and find training efficiencies.

There are two methods to develop an XAI algorithm. The first, called back-propagation, relies on the underlying AI architecture to quickly calculate how the networks prediction corresponds to its input. The second method, known as Perturbation, sacrifices some speed for accuracy. It involves changing data inputs and tracking the corresponding outputs to determine the necessary compensation.

The teams resulting XAI algorithm SISE (Semantic Input Sampling for Explanation) is detailed in a research paper introduced at the 35th AAAI Conference on Artificial Intelligence.

Source:https://techxplore.com/news/2021-04-artificial-intelligence-algorithm.html

Paper: https://arxiv.org/pdf/2010.00672.pdf

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Researchers From the University of Toronto and LG AI Research Develop Explainable Artificial Intelligence (AI) Algorithm - MarkTechPost

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American households are increasingly connected internallythrough the use of artificially intelligentappliances.1But who regulates the safety of thosedishwashers, microwaves, refrigerators, and vacuums powered byartificial intelligence (AI)? On March 2, 2021, at a virtual forumattended by stakeholders across the entire industry, the ConsumerProduct Safety Commission (CPSC) reminded us all that it has thelast say on regulating AI and machine learning consumer productsafety.

The CPSC is an independent agency comprised of fivecommissioners who are nominated by the president and confirmed bythe Senate to serve staggered seven-year terms. With the Bidenadministration's shift away from the deregulation agenda ofthe prior administration and three potential opportunities to staffthe commission, consumer product manufacturers, distributors, andretailers should expect increased scrutiny andenforcement.2

The CPSC held the March 2, 2021 forum to gather information onvoluntary consensus standards, certification, andproduct-specification efforts associated with products that use AI,machine learning, and related technologies. Consumer producttechnology is advancing faster than the regulations that govern it,even with a new administration moving towards greater regulation.As a consequence, many believe that the safety landscape for AI,machine learning, and related technology is lacking. The CPSC,looking to fill the void, is gathering information through eventslike this forum with a focus on its next steps for AI-relatedsafety regulation.

To influence this developing regulatory framework, manufacturersand importers of consumer products using these technologies mustunderstand and participate in the ongoing dialogue about futureregulation and enforcement. While guidance in these evolving areasis likely to be adaptive, the CPSC's developing regulatoryframework may surprise unwary manufacturers and importers who havenot participated in the discussion.

The CPSC defines AI as any method for programmingcomputers or products to enable them to carry out tasks orbehaviors that would require intelligence if performed byhumans and machine learning as an iterative processof applying models or algorithms to data sets to learn and detectpatterns and/or perform tasks, such as prediction or decisionmaking that can approximate some aspects ofintelligence.3To inform the ongoingdiscussion on how to regulate AI, machine learning, and relatedtechnologies, the CPSC provides the following list ofconsiderations:

These factors and corresponding questions will guide theCPSC's efforts to establish policies and regulations thataddress current and potential safety concerns.

As indicated at the March 2, 2021 forum, the CPSC is taking someof its cues for its fledgling initiative from organizations thathave promulgated voluntary safety standards for AI, includingUnderwriters Laboratories (UL) and the International Organizationfor Standardization (ISO). UL 4600 Standard for Safety for theEvaluation of Autonomous Products covers fully autonomoussystems that move such as self-driving cars along with applicationsin mining, agriculture, maintenance, and other vehicles includinglightweight unmanned aerial vehicles.5Usinga claim-based approach, UL 4600 aims to acknowledge the deviationsfrom traditional safety practices that autonomy requires byassessing the reliability of hardware and software necessary formachine learning, ability to sense the operating environment, andother safety considerations of autonomy. The standard coverstopics like safety case construction, risk analysis, safetyrelevant aspects of the design process, testing, toolqualification, autonomy validation, data integrity, human-machineinteraction (for non-drivers), life cycle concerns, metrics andconformance assessment.6While UL 4600mentions the need for a security plan, it does not define whatshould be in that plan.

Since 2017, ISO has had an AI working group of 30 participatingmembers and 17 observing members.7This group,known as SC 42, develops international standards in the area of AIand for AI applications. SC 42 provides guidance to JTC 1aspecific joint technical committee of ISO and the InternationalElectrotechnical Commission (IEC)and other ISO and IECcommittees. As a result of their work, ISO has published sevenstandards that address AI-related topics and sub-topics, includingAI trustworthiness and big data referencearchitecture.8Twenty-two standards remain indevelopment.9

The CPSC might also look to the European Union's (EU)recent activity on AI, including a twenty-six-page white paperpublished in February 2020 that includes plans to propose newregulations this year.10On the heels of theGeneral Data Protection Regulation, the EU's regulatoryproposal is likely to emphasize privacy and data governance in itsefforts to build[] trust inAI.11Other areas of emphasis include humanagency and oversight, technical robustness and safety,transparency, diversity, non-discrimination and fairness, societaland environmental wellbeing, and accountability.12

***

Focused on AI and machine learning, the CPSC is contemplatingpotential new consumer product safety regulations. Manufacturersand importers of consumer products that use these technologieswould be well served to pay attention toand participateinfuture CPSC-initiated policymaking conversations, or riskbeing left behind or disadvantaged by what is to come.

Footnotes

1See Crag S. Smith, A.I. Here, There,Everywhere, N.Y. Times (Feb. 23, 2021), https://www.nytimes.com/2021/02/23/technology/ai-innovation-privacy-seniors-education.html.

2 Erik K. Swanholt & Kristin M. McGaver,Consumer Product Companies Beware! CPSC Expected to Ramp upEnforcement of Product Safety Regulations (Feb. 24, 2021), https://www.foley.com/en/insights/publications/2021/02/cpsc-enforcement-of-product-safety-regulations.

385 Fed. Reg. 77183-84.

4Id.

5Underwriters Laboratories, Presenting theStandard for Safety for the Evaluation of Autonomous Vehicles andOther Products, https://ul.org/UL4600 (last visited Mar. 30,2021). It is important to note that autonomous vehicles fall underthe regulatory purview of the National Highway Traffic SafetyAdministration. See NHTSA, Automated DrivingSystems, https://www.nhtsa.gov/vehicle-manufacturers/automated-driving-systems.

6 Underwriters Laboratories, Presenting theStandard for Safety for the Evaluation of Autonomous Vehicles andOther Products, https://ul.org/UL4600 (last visited Mar. 30,2021).

7 ISO, ISO/IEC JTC 1/SC 42, ArtificialIntelligence, https://www.iso.org/committee/6794475.html(last visited Mar. 30, 2021).

8ISO, Standards by ISO/IEC JTC 1/SC 42,Artificial Intelligence, https://www.iso.org/committee/6794475/x/catalogue/p/1/u/0/w/0/d/0(last visited Mar. 30, 2021).

9Id.

10 See Commission White Paper on ArtificialIntelligence, COM (2020) 65 final (Feb. 19, 2020), https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf.

11 European Commission, Policies, A European approachto Artificial Intelligence, https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence(last updated Mar. 9, 2021).

12Commission White Paper on ArtificialIntelligence, at 9, COM (2020) 65 final (Feb. 19, 2020), https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf.

Originally Published by Foley & Lardner, March2021

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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The CPSC Digs In On Artificial Intelligence - Consumer Protection - United States - Mondaq News Alerts