Monthly Archives: July 2021

The strange, failed fight to rein in civil forfeiture in Washington – Crosscut

Posted: July 14, 2021 at 1:34 pm

The process, called civil asset forfeiture, has been a staple of the drug war. Like other aspects of the drug war and like police violence, forfeiture tends to disproportionately affect Black Americans and other minorities. Unlike with police violence, however, forfeitures haven't sparked widespread public outrage.

Civil forfeiture is one of those technocratic things that happen in the dark, says Wesley Hottot, a litigator with the Institute for Justice, a libertarian public-interest law firm. Nobody can be made to care about it until it happens to them.

Nevertheless, the institute and other civilliberties advocates have campaigned against civil forfeitures, and newspaper exposs have documented abuses elsewhere in the country. In 2019, in an Indiana case argued by Hottot, the U.S. Supreme Court ruled unanimously that the Eighth Amendments ban on excessive fines extended to property seizures, so police couldnt takean expensive Land Rover in a minor drug case that carried a much smaller criminal penalty.

This is the second of three storiesin aCrosscut series examining civil asset forfeiturein Washington state. Skip toPart1orPart3.

Legislatures in other states have moved to limit civil asset forfeitures or impose stricter requirements on them. Three New Mexico, Nebraska and North Carolina have effectively eliminated civil forfeiture altogether.

But in Washington theres hardly been a peep. A small cadre of legislators has tried for two decades to reform the states forfeiture lawsbut failed to gain traction in Olympia or much attention outside it. When forfeiture makes local news at all, it tends to be routine, police-centered crime news U.S. attorneys touting annual forfeiture hauls and big border busts, or the closure and seizure of motels accused of harboring drugs and prostitution.

The silence around forfeiture in this state is not for lack of forfeitures. Washingtons cities and counties took in a near-record $11.9 million in forfeiture revenues last year, minus a 10% cut to the state Treasurers Office. All but $300,000 came from alleged drug offenses.

The most optimistic view is that law enforcement here is not engaging in the kind of wholesale abuse seen elsewhere. says Hottot, whos based in Seattle. The other possibility is that the abuse is just not apparent. It could be there's no abuse, or it could be it's under the radar.

One would-be reformer, state Rep. Roger Goodman, D-Kirkland, thinks its the latter.

The forfeiture authority has been grossly abused, says Goodman, who chairs the House Law and Justice Committee, in particular in the war on drugs, and particularly by several federally funded drug task forces multiagency outfits devised in the 70s and 80s to franchise the federal war on drugs to local governments.

These task forces perform a sizable share of Washingtons civil forfeitures. One, the Kent-based Valley Narcotics Enforcement Team, took in $726,000 in 2019. The Grays Harbor Drug Task Force, whose members include police from Aberdeen, Hoquiam and Grays Harbor County, forfeited more than $1.6 million in 2018, a bumper year.

That Grays Harbor take reflected a 2017 operation dubbed Green Jade, one of the largest police operations against cannabis in Washington since marijuana was legalized in 2012. The task force raided 44 sites and seized more than 20 houses. Its commander, Hoquiam Police Assistant Chief Joe Strong, calls it a blow against transnational crime: What we have seen nationwide is organized crime, Chinese nationals coming in to grow marijuana.

Strong acknowledges that forfeitures support the task forces operations, but denies that it seizes property to pay the bills. Sometimes well draw from forfeitures to equip our teams, he says, but theyre not our main funding source.

Forfeitures real value, he insists, is as a deterrent. Sometimes you have to take away the assets of drug dealers and hurt them where it hurts the most, Strong told me. They can often do their one to two years behind bars standing on their heads no problem. But when you take away a gun or car or $5,000 in illicit proceeds, thats what hurts.

Recent experience in other states, however, undercuts the oft-repeated contention that forfeiture dissuades would-be criminals. In 2015, New Mexico effectively eliminated civil forfeitures by requiring that money raised through the practice go into the state general fund rather than to law enforcement; policeeased off seizing property once they couldnt keep what they seized. Five years later, researchers with the Institute for Justice found that New Mexicos crime rates held steady relative to those of neighboring Colorado and Texas, which continued forfeiting on an industrial scale.

The system turns police and prosecutors into tax farmers when they should be addressing violent crimes, argues Hottot, the attorney with the institute. It treats property owners as worse than criminals. It's far easier to take their stuff than it is to try to put a person behind bars and more lucrative.

Eight federal agencies, including the Internal Revenue Service, Fish & Wildlife Serviceand Justice and Treasury departments, perform civil forfeitures. The Institute for Justice, which has compiled the most complete database of forfeitures nationwide, calculates that federal agencies forfeited about $46 billion worth of property from 2000 to 2019. In recent years, Justice alone has taken in $1.4 billion to $4.5 billiona year $2.2 billion in 2019.

Another agency, the U.S. Postal Inspection Service, gets less attention but is a prolific forfeiter. Postal inspectors routinely seize cash sent by mail; a recent Postal Inspection Service notice of forfeitures lists cash seizures of as little as $1,200, as well as hundreds of seized money orders. Forfeitees have 30 days to petition to have their money returned assuming they even know it's been seized.

People may have innocent, if naive, reasons both for mailing cash and for not claiming it. In June 2015, postal inspectors seized $8,700 mailed to a man who had recently moved to Everett from Alabama to take a job at Boeing. Hottot, whom the man spoke with at the time, says the cash was the mans savings, which he had his sister send him once he settled in.

Innocent people were at worst foolish sending it through the mail, says Hottot, who offered to help the man recover his money. They weren't criminal. But going to court to claim it back that's a daunting proposition. He told me, Look, I grew up as a Black man in Alabama. My relationship with the government is very different from your relationship. I don't want to get involved in this.

State and local law enforcement agencies received a fifth of the federal take, about $8 billion, between 2000 and 2019. Agencies in Washington state received about $87 million from federal forfeitures in the past two decades. Its a significant dividend for some departments; six agencies, including the Seattle Police Department, the King County Sheriffs Office and the Spokane Regional Drug Task Force, got six-figure payouts in 2018.

Fewer than half of the states reported their own forfeitures during that 20-year period, but those that did forfeited $23 billion more. Washington police agencies took in nearly $157 million.

For police, traffic stops are an especially easy way to rake in forfeiture dollars. They don't require surveillance, raids or GPS trackers; officers just pull cars over, look for drugs or suspicious cash, and seize everything. Drivers with legitimate reasons to carry large sums can still face forfeiture or, in one Wyoming case, be pressured to donate the money to police in order to be released. The Institute for Justice has documented a number of victims, including a Karen Christian refugee from Myanmar who was touring churches in Oklahoma to collect donations for an orphanage back home.

Cops have a way of talking their way into a car, says Robert Schiffner, a veteran Moses Lake attorney who has handled many forfeiture and stop-and-search cases, including one appeal that established new limits on vehicle searches in Washington. If a person is upside down on the car, they wont seize it. If it's a piece of junk, they won't seize it. Ive jokingly asked detectives why they didnt seize my clients car. One said, Have you seen it?

Ryan Rectenwald, the undersheriff of Grant County, where Moses Lake is located, disputes Schiffner's claims: The value of a vehicle doesnt matter. If a 2005 Porsche Carrera and a 1988 Subaru show the same sort of nexus to crime, well forfeit them both.

For decades, the road winding down to the Gorge Amphitheater, also in Grant County, was the highway interdiction equivalent of fishing in a barrel. But court decisions in the state have put a damper on baseless stops. As a result, Rectenwald says, vehicle stops [to search for drugs] aren't something we do.

Gorge concerts still yield many drug arrests and vehicle seizures, but police now make them via undercover buys on the grounds, typically with informants posing as fans asking other concertgoers if they have a bit of MDMA to spare. People who literally have one or two tabs of ecstasy in their trunks can lose their cars, says Schiffner. The police, he adds, love Dave Matthews concerts everybody drives a BMW.

There and elsewhere in Washington, those who contest forfeitures go before a police-appointed hearing examiner typically a police officer, sometimes the chief. Rectenwald, who serves as hearing examiner in Grant County, says hes able to stay sufficiently neutral because as undersheriff I dont supervise the arresting officers.

Who cares? replies Seattle attorney Douglas Hiatt, who handles forfeiture cases around the state. He knows them all. Hes a cop. Hes hardly an impartial intermediary. Hiatt and other experienced attorneys routinely bypass the hearing examiners by having forfeiture cases moved to district court.

Law enforcement may use the threat of criminal charges to make sure seizures arent contested. Prosecutors aren't allowed to offer such swaps outright, notes Schiffner, but theyre implicitly on the table: It's a game. The prosecutor says, 'Make me an offer.

Gary Ernsdorff, a deputy prosecutor with the King County Prosecuting Attorneys Office, agrees that such global resolutions, where a prosecutor might say, Well offer to reduce charges if you concede to a forfeiture action, are a serious concern. In some jurisdictions, weve seen conflicts with criminal and civil actions going on at same time. Thats why this office doesnt do any forfeitures.

Two King County deputy prosecutors do in fact specialize in forfeitures. One is assigned to the Sheriff's Office and the other to the Valley Narcotics Enforcement Team. Ernsdorff says those assignments keep the two prosecutors at arms length: Theyre supervised by our office, but operate separately.

Thats bullshit! Hiatt exclaimed when he heard Ernsdorffs contention, noting that the deputy assigned to the Sheriffs Office still works for Attorney Dan Satterberg. Shes on loan to the sheriff. She has an office in the Sheriffs Office but reports to the prosecutor. This is Satterbergs policy. Thats all there is to it.

A 2017 email exchange between Hiatt and Satterberg shows that, despite his image as a humane, progressive prosecutor, Satterberg strongly supports forfeiture actions. Our criminal case response is insufficient to deter Chinese money from fueling black market grows, so the tool for accountability and deterrence we are left with is forfeiture, he wrote to Hiatt, who had urged him to get out of the forfeiture business.

I doubt you'll get much public sympathy for these enormous grows and huge sums of money involved to send tons of black market weed back East, Satterberg continued in the email, which Hiatt provided. I will be interested to see what you come up with, but I am not inclined to interfere with the local police desire to forfeit these houses and proceeds.

Not all the legislators trying to reform Washingtons forfeiture laws want to interfere either, but theyd at least like to know exactly what is being forfeited, from whomand on what grounds. They represent an unlikely marriage of ideological opposites.

In the House, liberal Democrat Goodman teamed up with then-Rep. Matt Shea, R-Spokane Valley, before Sheasinvolvement in what a House report called domestic terrorism made him too toxic for the Republican caucus. In the Senate, one of the chambers most conservative members, Republican Mike Padden, also from Spokane Valley, has allied with Democratic Seattle liberal Bob Hasegawa.

It's an interesting example where the libertarian right and the left come together around issues of privacy and civil rights, Goodman notes.

I am generally a strong supporter of law enforcement, Padden told me last year. But I am concerned that property can be taken away even without any conviction for a crime. I would rather let there be a conviction first.

Theyre two in a long line of legislators who have tried to rein in Washington's civil forfeitures, only to be blocked by the police lobby. In 2002, a bipartisan group of nine House members sponsored a bill that would have effectively done away with civil forfeituresabsent a criminal conviction. Proceeds would have gone exclusively for drug treatment, not to the police. That bill, like many to follow, never made it to the House floor.

In the 2019 session, Goodman signed on to Sheas more modest bill, which would have let owners collect legal costs when they successfully contest forfeitures and made clear that the burden of proof falls on the agencies doing the seizing rather than on owners trying to recover their property. It would also have required that the hearing examiners not be members of the police agencies performing the forfeiture.

Goodman says law enforcement signed off on that legislation. But when they brought it forward, both the law enforcement point people on the issue, James McMahan of the Washington Association of Police Chiefs and Kent Prosecutor Michele Walker, opposed it, claiming it wasnt what they'd agreed to. They insisted they were open to change but, in McMahan's words, wanted to make sure we do not lose the ability to make sure crime does not pay. In his final and most curious argument against the bill, McMahan, who like Walker did not respond to repeated requests for comment, objected to language requiring that, when forfeitures get overturned, agencies return property in the same condition they seized it.

If we return a car we seized, do we change the oil, check the tires, charge the battery? McMahan asked rhetorically. Would the sheriff have had to water and trim 40,000 marijuana plants?

The bill passed out of committee but never made it to the House floor. It was still on the docket in 2020 but went nowhere. Sheas sponsorship may have been a kiss of death. But the issue did not go away when he did, and forfeitures keep on coming into police coffers.

This story was produced with support from the Economic Hardship Reporting Project, https://economichardship.org.

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Icertis Recognized as the Winner of 2021 Microsoft AI Partner of the Year – Yahoo Finance

Posted: at 1:34 pm

- First and Only Contract Lifecycle Management Company Recognized by Microsoft for AI Leadership and as a Global ISV Partner of the Year Finalist

BELLEVUE, Wash., July 14, 2021 /PRNewswire/ -- Icertis, the contract intelligence company that pushes the boundaries of what's possible with contract lifecycle management (CLM), announced today that it has won the Artificial Intelligence (AI) 2021 Microsoft Partner of the Year Award and been named a Finalist of the Global ISV Microsoft Partner of the Year Award for the fourth year in a row. Icertis was honored among a global field of top Microsoft partners for demonstrating excellence in innovation and implementation of customer solutions based on Microsoft technology.

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"We are elated to be recognized with the top honor for AI innovations by the most-respected software company in the world. No Microsoft partner in the contract management space has been named a finalist or winner in this categoryuntil today," said Samir Bodas, CEO and Co-founder of Icertis. "In a world of endless data, contract data reigns supreme. Contracts are the foundation of all commerce, and the Icertis Contract Intelligence (ICI) platform sets the standard for AI use in discovering, visualizing, and optimizing this critical resource across enterprises and ecosystems."

The Microsoft Partner of the Year Awards recognize partners that have developed and delivered outstanding Microsoft-based solutions during the past year. Awards were classified in various categories, with honorees chosen from among 4,400 submissions across more than 100 countries worldwide. Icertis' decade-long and growing Microsoft relationship, was recognized in two award categories this year:

1. AI Partner of the Year: This award recognized the top Microsoft partner that designed, developed, and deployed high- value, customer-centric AI Solutions using Azure AI. Icertis AI applications apply machine learning to build predictive models that optimize contracting processes and uncover contracting insights that are critical in strategic decision making. The models are trained on the more than 10,000,000 contracts Icertis manages for its customers worldwide.

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A recent McKinsey survey on "The State of AI in 2020" found that 50% of respondents have implemented AI in at least one business function and some leading adopters of these technologies attribute 20% or more of their organizations' earnings before interest and taxes (EBIT) to AI. Icertis' high-value AI applications enable companies to address previously intractable contract challenges, including digitizing legacy contracts at scale and analyzing contract terms to improve sales negotiations and gain intelligent insights about terms, obligations, and risks. Notable examples of customers that are benefiting from the Microsoft and Icertis partnership and solutions in AI include:

HERE Technologies - "The Icertis DiscoverAI app rapidly digitized and analyzed more than 70,000 legacy HERE and third-party contracts, allowing us to unlock critical contractual terms to improve our business." Simon Anolick, Director Legal Counsel at location data and technology platform company HERE Technologies.

Sanofi - "With its intuitive interface and the ability to be customized to suit an individual's unique requirements, users have quickly adopted the new system and are now creating 400 contracts within ICI each month." Celine Arquizan, Head of Strategy and Development for the Contracting Center of Excellence at Sanofi.

2. Global ISV Partner of the Year: This award recognized the top Microsoft globally managed Independent Software Vendor (ISV) that has demonstrated strong customer focus and success by partnering deeply with Microsoft on a global scale. Icertis was named a Finalist in this category for the fourth consecutive year, standing out among the more than 100,000 partners in Microsoft's extensive ecosystem. This builds on Icertis' previous Microsoft award recognitions, including the 2018 Microsoft US Partner of the Year Award and the 2019 Microsoft US Partner of the Year Award for Manufacturing & Resources.

The relationship that Icertis has with Microsoft empowers every organization to transform their contracts into strategic advantage by connecting them to the systems and processes they touch organization-wide. The Icertis Contract Intelligence (ICI) platform is natively-built and runs exclusively on Microsoft Azure and integrates with Microsoft products and more than 60 Azure services including Azure AI, Microsoft 365, Dynamics 365, and Microsoft Teams.

"I am honored to announce the winners and finalists of the 2021 Microsoft Partner of the Year Awards," said Rodney Clark, corporate vice president, Global Partner Solutions, Channel Sales and Channel Chief, Microsoft. "These remarkable partners have displayed a deep commitment to building world-class solutions for customersfrom cloud-to-edgeand represent some of the best and brightest our ecosystem has to offer."

The award recognitions follow numerous CLM market leadership accolades so far this year, including being the only CLM vendor named to the Forbes AI 50 list every year since its inception, winning SAP Partner of the Year: SAP Store Category, and being named a Leader in both the 2021 Gartner Magic Quadrant for Contract Life Cycle Management (CLM), as well as The Forrester Wave: Contract Lifecycle Management for All Contracts, Q1 2021.

Icertis will showcase its award-winning ICI platform and speak at Microsoft Inspire July 14-15.

For more information about Icertis, visit http://www.icertis.com.

About Icertis

With unmatched technology and category-defining innovation, Icertis pushes the boundaries of what's possible with contract lifecycle management (CLM). The AI-powered, analyst-validated Icertis Contract Intelligence (ICI) platform turns contracts from static documents into strategic advantage by structuring and connecting the critical contract information that defines how an organization runs. Today, the world's most iconic brands and disruptive innovators trust Icertis to govern the rights and commitments in their 10 million+ contracts worth more than $1 trillion, in 40+ languages and 90+ countries.

Contact:

Liza Colburn Director of Corporate Communications, Icertis Liza.colburn@icertis.com +1 (781) 562-0111

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Mimecast launches AI-enabled solution designed to help organizations protect against the most evasive and hard-to-detect email threats – Yahoo Finance

Posted: at 1:34 pm

The Mimecast CyberGraph solution uses AI to help improve detection and reduce risk

LEXINGTON, Mass., July 14, 2021 (GLOBE NEWSWIRE) -- Mimecast Limited (NASDAQ: MIME), a leading email security and cyber resilience company, today announced the Mimecast CyberGraph solution, a new add-on for Mimecast Secure Email Gateway (SEG) that is engineered to use Artificial Intelligence (AI) to help detect sophisticated phishing and impersonation attacks. CyberGraph creates an identity graph which is built to store information about relationships between all senders and recipients. The graph is designed to detect anomalies and leverages machine learning technology to help organizations stay one step ahead of threat actors by alerting employees to potential cyber threats.

Phishing and impersonation attacks are getting more sophisticated, personalized and harder to stop. If not prevented, these attacks can have devastating results for an enterprise organization, said Josh Douglas, VP, Product Management for Threat Intelligence at Mimecast. Security controls need to be constantly updated and improved to outsmart threat actors. CyberGraph leverages our AI and machine learning technologies to help keep employees one step ahead with real-time warnings, directly at the point of risk. What makes this exciting is that we are embedding the technology for existing email security customers, they do not need to look for other vendors to fill the gap with technologies that only work to solve part of this challenge.

The workplace is always the top target of cybercriminals, but in the remote working era, the problem has intensified. The State of Email Security Report found that email threats rose by 64% and employees are clicking on three times as many malicious emails as they had before the COVID-19 pandemic. Security controls need to evolve to help evade cybercriminals relentless and crafty approach. CyberGraph includes three key capabilities engineered to help prevent cyber threats:

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Renders embedded trackers useless During the reconnaissance phase of an attack, threat actors embed trackers into emails that communicate with an illegitimate remote server, disclosing important information that can be used to create a targeted social engineering attack. CyberGraph is built to blocks this communication, mask the email recipients location, and prevents attempts to understand engagement levels with the email content.

Uses machine learning to protect from targeted email threats CyberGraph is designed to create an identity graph by learning about relationships and connections between all senders and recipients. This intelligence is combined with the outputs from machine learning models to detect anomalies that could be indicative of a malicious email.

Engages users with contextual, dynamic warning banners CyberGraph is engineered to engage users at the point of risk with color-coded banners that indicate the potential nature of a threat. Users are empowered by seeing whether an email is safe or potentially nefarious. CyberGraph is built to crowd-sources threat intelligence, which helps to reinforce the machine learning model. As the risk associated with any given delivered email changes, banners embedded in any similar emails are updated with the latest information, providing ongoing engagement and protection for users.

Availability

CyberGraph for Mimecast SEG is available now in the US and the UK, with more regions to follow.

Mimecast: Relentless protection. Resilient world.

Mimecast (NASDAQ: MIME) was born in 2003 with a focus on delivering relentless protection. Each day, we take on cyber disruption for our tens of thousands of customers around the globe; always putting them first, and never giving up on tackling their biggest security challenges together. We are the company that built an intentional and scalable design ideology that solves the number one cyberattack vector email. We continuously invest to thoughtfully integrate brand protection, security awareness training, web security, compliance and other essential capabilities. Mimecast is here to help protect large and small organizations from malicious activity, human error and technology failure; and to lead the movement toward building a more resilient world. Learn more about us at http://www.mimecast.com.

Mimecast Social Media ResourcesLinkedIn: MimecastFacebook: MimecastTwitter: @MimecastBlog: Cyber Resilience Insights

Press ContactTim Hamilton Press@Mimecast.com603-918-6757

Investor Contact Robert Sanders Investors@Mimecast.com617-393-7074

Mimecast and Cybergraph are either registered trademarks or trademarks of Mimecast Services Limited in the United States and/or other countries. All other products and/or services referenced are trademarks of their respective companies.

Safe Harbor for Forward-Looking Statements

Statements in this press release regarding managements future expectations, beliefs, intentions, goals, strategies, plans or prospects, including, without limitation, the statements relating to the effectiveness of the features and functionality in Mimecast Cybergraph technology, the future financial impact of the new features and functionality in Mimecast Cybergraph technology, and the overall impact of the Cybergraph technology on Mimecasts business and operations, may constitute forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 and other federal securities laws. All statements, other than statements of historical fact, are statements that could be deemed forward-looking statements, including statements containing the words predicts, plan, expects, anticipates, believes, goal, target, estimate, potential, may, might, could, see, seek, forecast, and similar words. Mimecast intends all such forward-looking statements to be covered by the safe harbor provisions for forward-looking statements contained in Section 21E of the Exchange Act and the Private Securities Litigation Reform Act of 1995. Such forward-looking statements involve known and unknown risks, uncertainties and other factors including those risks, uncertainties and factors detailed in Mimecasts filings with the Securities and Exchange Commission. As a result of such risks, uncertainties and factors, Mimecasts actual results may differ materially from any future results, performance or achievements discussed in or implied by the forward-looking statements contained herein. Mimecast is providing the information in this press release as of this date and assumes no obligations to update the information included in this press release or revise any forward-looking statements, whether as a result of new information, future events or otherwise.

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How AI can be used to streamline DAM workflows – ITProPortal

Posted: at 1:34 pm

It wasnt too long ago that artificial intelligence (AI) technology was merely a concept from science fiction movies. But today, AI is changing the status quo of entire industries, including marketing technology (martech).

In the marketing sphere, we are increasingly seeing AI capabilities leveraged in martech tools: the use of automation capabilities used to support chatbots, personalize content, and even manage social media platforms.

As consumer expectations shift, and agile, disruptive retail players enter the ecosystem, marketers need to ensure that they are still able to compete for consumers attention across an increasing number of channels. For these marketing teams, this means ensuring any visual assets used are both accurate and compelling enough in a competitive landscape to convert consumers into paying customers.

One way in which businesses have been streamlining their content strategies to allow for AI is through digital asset management (DAM), built to support marketing teams to organize, find, distribute, and analyze digital content. With AI capabilities on the rise, we ask how AI can be used to streamline DAM workflows?

In order to be effective, DAM software relies on metadata, or descriptive information about each piece of content, to provide structure and information to make digital assets findable. Metadata is crucial to a systems success but can often be time-consuming to add and is prone to error without a clear process.

When working with metadata, fields are used to answer different questions about each asset including identifiers like the file name, photography type, description, and usage rights. A system of metadata fields is called a metadata schema. Creating a schema with fields relevant to a specific company improves the search experience and helps ensure that metadata is added accurately. And with the help of image recognition software, AI has a valuable and growing role in system management through its ability to automatically tag assets with relevant metadata during the upload process.

Create accuracy:

Employing AI to automate part of the image-tagging process can improve categorization, power the accuracy of related assets, and offer advanced searching options for users. Not to mention, saving hours of manual tagging efforts.

The ability to automatically categorize and tag images extends to a range of scenarios. It can recognize faces or demographics including the age, ethnicity, or gender of persons in the image. It can also apply keywords for specific industries, such as travel, food, and apparel.

Adding the power of image recognition to a DAM system makes metadata creation simpler, faster, and most importantly, better. Image recognition software can reduce human errors and inconsistencies, and avoid assets being uploaded onto the DAM system without any metadata.

Furthermore, AI can make searching more effective as thorough metadata allows search tools to return accurate results, quickly.

Save time:

As well as creating accurate content, AI enables marketers to ultimately save time on the more remedial and security-based tasks, spotting anomalies and flagging potential errors. Image recognition software includes features that allow DAM administrators to:

Automating a manual process reduces the time needed to tag assets, meaning workflows can be better streamlined. All of these efficiencies translate to business cost savings. After all, less time spent tagging and searching means more time for creative and strategic work.

Support brand agility:

Perhaps unsurprisingly, following the effects of Covid-19, it is estimated that marketing teams will lose around US$222 billion from budgets, and around 30 percent of their staff, by the end of 2021. This is according to a 2020 Forrester report, which studied the effects the pandemic is predicted to have on US marketers. In contrast, it is estimated that by 2023, marketing automation budgets will hit an all-time high of US$25 billion, up from the US$11.4 billion in 2017. As with any uncertainty, businesses will need to remain flexible and agile in order to not only stay afloat, but to keep up with consumer demands. This means that the automation of an organizations workflow processes will soon be a necessity, not an option.

In recent months, weve seen several examples where AI has been labeled as biased. Most recently, Twitter was accused of having biased artificial intelligence image-cropping algorithms. To stay away from such negative headlines, the tech giant announced that it would simply do away with the image-cropping software in favor of its users now cropping their own images as they see fit.

With such negative headlines regularly making press, theres no doubt that we run the risk of marketers being too fearful to truly amerce their workflow process in AI in fear that they too will become subject to biased AI, resulting in a PR crisis.

And yet, there is a way that businesses can ensure they do not fall victim to such demises. Marketers and developers need to not only have a thorough understanding of the data being used, but also the patterns that the data generates. In doing so, AI tools can successfully be harnessed to automate a marketing teams system management both effectively and efficiently.

Ultimately, it is not the artificial intelligence technology that is biased but the algorithms built by human teams that are its downfall.

Despite some peoples hesitations, there is no denying we are in fact moving into a digitally transformed, online world. But although technology is helping a lot of companies work with greater efficiency and speed, it is also clear that this cannot be achieved without some element of human intervention. Technology alone is not able to fix an organizations workflow processes. A strategy must be put in place to align the workforce, processes, and technology such as AI in order to achieve the desired marketing outcomes.

Tools like image recognition require a balance between automation and human touch. While it can free up time and resources for other value-driven projects, the workforce is still needed to ensure accuracies across all data inputs, as well as informing and guiding both the technology and its users.

As we continue to see developments in this space, we will see the partnership between AI and the human workforce grow more important to marketing teams. Not only to streamline and scale DAM workflows but to successfully create cost savings, too.

Jake Athey, VP of Marketing and Customer Experience, Widen

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AI, the Biggest Existential Threat to Humankind says Elon Musk – Analytics Insight

Posted: at 1:34 pm

Artificial Intelligence, is all about the theory and development of computer systems that are able to perform tasks normally without human intervention but requiring human intelligence for visual perception, speech recognition, decision-making, and translation between languages. It is an interdisciplinary competence with numerous perspectives. Today the development of AI is creating a progressive shift in the division of the tech industry.

Although AI is announced as one of the evolving technologies having the most extraordinary benefits, many business tycoons and tech enthusiasts still consider it a significant threat to humankind and thus calls for more investigation on its collective consequences.

This article will take through the views on real-world AI expressed by Elon Musk, Tesla, and SpaceX Chief executive.

Many signatories, including Musk, expressed their concern regarding the serious consequences of AI. They think that digital machines with the incorporation of AI will rapidly outmaneuver humans. Elon Musk calls AI the most dangerous warning for humanity. He says he is not against digital transformation but when it is about AI he has a very different view. He also claimed to be careful about the advancement of AI.

Musk says, with machine learning a self-driving car cannot be taught to detect unusual conditions like spotting a plastic bag accompanying the breeze on a jammed road or predicting the movement of a biker, such situations are difficult to teach a robot.

Going forward with robust AI systems is a more significant mistake that can result in threatening conditions for humanity, says Musk.

Elon Musk says adopting AI is like Summoning the Demon. He believes that artificial intelligence can evoke the next world war and result in dominating the world and that robot leadership is a threat to the world. He also warns that the world will never be able to escape when those intelligent AIs will become deathless authoritarians.

One of his statements included that we need to be super careful with AI as they are potentially more dangerous than nukes. He also recommends being energetic in regulation rather than being reactive when everything ends.

He believes that development of AI should be done with improved regulations and controlling structure. He wants AI to be controlled and structured even for his own brand Tesla.

Elon Musk considers AI as the scariest problem. He had regularly cautioned that AI will rapidly become as clever as humans and once it does, humankinds existence will be at stake.

He also claimed that he already has experience working with AI so he knows what he is saying and has confidence in it. He further said that the world will proceed in a state where AI is more intelligent than humans. He also mentioned the time frame which is not more than 5 years when AI will take over the world and things would get weird and unstable. Musk claims AI will outsmart humanity and overtake human civilization in less than five years and also that AI and robots will take over everyones jobs and will do everything better than us.

According to him, AI is the real existential risk to humankind and people should not cherish that. He also asked the government department to take preventive and proactive governmental steps before it gets too late.

Elon Musk is working hard to save the world from AI, as a result, he wants the technologies to be developed responsibly and with proper vision and oversight. He wants the government to develop teams and take a look over it. He also said that if the government cant then he is ready to take the charge by himself. In the past few years, Elon has spent enormous resources on tech industries that are responsible for the development of intelligent machinery. However, he is considered working on technology that would give humankind an acceleration in a possible AI catastrophe.

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AI and the COVID-19 Vaccine: Moderna’s Dave Johnson – MIT Sloan – MIT Sloan

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The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

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We tend not to be a company of half measures, notes Dave Johnson, chief data and artificial intelligence officer at Moderna, so when we decide were going to do something, were going to do it. This characterization certainly seems to fit the Cambridge, Massachusetts-based biotech company that made a name for itself in 2020 upon releasing one of the first COVID-19 vaccines approved by the U.S. Food and Drug Administration for emergency use to combat the coronavirus.

Dave Johnson is chief data and artificial intelligence officer at Moderna, where he is responsible for all enterprise data capabilities, including data engineering, data integration, data science, and software engineering. Johnson earned a doctorate in information physics and has more than 15 years of experience in software engineering and data science. He has spent more than a decade working exclusively in enterprise pharma and biotech companies.

In this bonus episode of the Me, Myself, and AI podcast, our hosts learn how Moderna used artificial intelligence to speed up development of the vaccine and how the technology has helped to automate other key systems and processes to build efficiencies across the organization. Dave also describes Modernas digital-first culture and offers insights around collaboration that can be applied across industries.

If youre enjoying the Me, Myself, and AI podcast, continue the conversation with us on LinkedIn. Join the AI for Leaders group today.

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Sam Ransbotham: What role did artificial intelligence have in helping combat the coronavirus pandemic? Find out today when we talk with an innovative company that used artificial intelligence to help solve the critical problem society faced in the last year.

Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. Im Sam Ransbotham, professor of information systems at Boston College. Im also the guest editor for the AI and Business Strategy Big Idea program at MIT Sloan Management Review.

Shervin Khodabandeh: And Im Shervin Khodabandeh, senior partner with BCG, and I colead BCGs AI practice in North America. Together, MIT SMR and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

Sam Ransbotham: Today were talking with Dave Johnson, chief data and artificial intelligence officer at Moderna. Dave, thanks for joining us. Welcome.

Dave Johnson: Thanks, guys, for having me.

Sam Ransbotham: Can you describe your current role at Moderna?

Dave Johnson: Im chief data and AI officer at Moderna. In my role, Im responsible for all of our enterprise data functions, from data engineering to data science integration. I also manage the software engineering team building unique custom applications to curate and create new data sets [and] also to then take those AI models that are created and build them into processes. So its kind of end-to-end everything to actually deploy an AI model to build, deploy, and put an AI model into production.

Sam Ransbotham: How did you end up in that role? I know you have physics in your background. I didnt hear any physics in what you just said.

Dave Johnson: Its a good point. So I have my Ph.D. in whats called information physics, which is a field closely related to data science, actually. Its about the foundations of Bayesian statistics and information theory a lot of what is involved in data science. My particular research was in applying that to a framework that derives quantum mechanics from the rules of information theory. So that part, youre right, is not particularly relevant to my day-to-day job. But the information theory part and the Bayesian stats [are] completely on target for what I do. In addition to that, I spent many years doing independent consulting in a software engineering data science capacity. And when I finished my Ph.D., I realized academia wasnt really for me; I wanted to do applications, and I ended up with a consulting firm doing work for large pharmaceutical companies.

So I spent a number of years doing that, and it turned out to be a real great marriage of my skill sets: understanding of science, understanding of data, understanding of software engineering. And so I did one project in particular for a number of years in research at a pharmaceutical company around capturing data in a structured, useful way in the preclinical space in order to feed into advanced data and advanced models so, very much what Im doing today. And about seven years ago, I moved over to Moderna. At the time, we were a preclinical stage company, and the big challenge we had was producing enough small-scale mRNA to run our experiments. And what were really trying to do is accelerate the pace of research so that we can get as many drugs in the clinic as quickly as possible. One of the big bottlenecks was having this mRNA for the scientist to run tests in. So, what we did is we put in place a ton of robotic automation, put in place a lot of digital systems and process automation and AI algorithms as well. And [we] went from maybe about 30 mRNAs manually produced in a given month to a capacity of about a thousand in a month period without significantly more resources and much better consistency in quality and so on. So then, I just kind of from there grew with the company and grew into this role that we have now, where Im applying those same ideas to the broader enterprise.

Shervin Khodabandeh: Thats great, Dave. And can you comment a bit on the spectrum of use cases that AI is being applied to here and is really making a difference?

Dave Johnson: For us, what weve seen a lot of is in the research space particularly. In Moderna, thats been because thats where we digitized early. We see that putting in digital systems and processes to actually capture homogeneous, good data that can feed into that is obviously a really important first step, but it also lays the foundation of processes that are then amenable to these greater degrees of automation. So thats where were seeing a lot of that value, is in this preclinical production we have kind of high throughput, we have lots of data, were able to start automating those steps and judgments that were previously done by humans. One example is our mRNA sequence design. Were coding for some protein, which is an amino acid sequence, but theres a huge degeneracy of potential nucleotide sequences that could code for that, and so starting from an amino acid sequence, you have to figure out whats the ideal way to get there. And so what we have [are] algorithms that can do that translation in an optimal way.

And then we have algorithms that can take one and then optimize it even further to make it better for production or to avoid things that we know are bad for this mRNA in production or for expression. We can integrate those into these live systems that we have, so that scientists just press a button and the work is done for them. And they dont know whats going on behind the scenes, but then poof! out comes this better sequence for them. And then weve seen it with quality-control steps as well.

Were also doing some work right now with our clinical partners in the clinical operation space in terms of optimal trial planning. Were doing some work right now around our call center planning. Now that were rolling our vaccine out across the whole world, more and more phone calls are coming in, and as we look to launching in new countries, we have to start planning our resources for that. Were looking at machine learning models to help predict the forecast of these calls so that we can then staff up appropriately. So we do see it across a variety of different areas.

Sam Ransbotham: You mentioned pressing the button scientists press the button, and some trials happen. What do these scientists think? I mean, youve suddenly taken away something that used to be something that they did, and youre having AI do it. Whats the reaction? Are they thrilled? Are they despondent? Somewhere in between?

Dave Johnson: Id say closer to the thrilled side. Usually how it works were a company that believes in giving people a lot of responsibility, and people work really hard. And what that leads to is people doing a lot of work. And so what often happens is, folks will come to us and say, Look, Im doing this activity over and over. I would really love some help to automate this process. And so, in that case, theyre thrilled. They dont want to be looking at some screen of data over and over and over again. They want to be doing something insightful and creative. And so thats where we really partner with them and take off that component of what they do.

Shervin Khodabandeh: Dave, I want to build on that, because I think youre putting your finger on something quite interesting. In addition to the financial impact that many get from AI, productivity, efficiency, and all of that you talked about some of those, Dave there is an impact in overall organizational culture and teams being more collaborative, higher morale, happier, more confident, etc. Are those some of the things that you guys are seeing as well?

Dave Johnson: For sure. I think one of the sure signs of that is we get a lot of repeat customers. If we do some particular algorithm for somebody, that person comes back with the next one or their team comes back time and time again. We dont think about AI in the context of replacing humans. We always think about it in terms of this human-machine collaboration, because theyre good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed. What we find [to be] the most successful projects are where we kind of put the two together have the machine do the parts of the job that its good at [and] let the humans take over for the rest of that.

Sam Ransbotham: With this freedom, what have people done? Youve opened up this time. What kind of new

Shervin Khodabandeh: I got two shots of that, what people have done with that freedom.

Sam Ransbotham: Yeah, actually, theres at least one product thats in the market now, isnt there? I think Ive heard something on the news.

Dave Johnson: Theres one, yeah. You know, I always like to joke that work is like a gas that always expands to fill the container. So if you take something off somebodys plate, theres all this mountain of work that they didnt even realize just wasnt being done. And so people are always relieved to then go on and find the next mountain to climb and the next thing to do.

Sam Ransbotham: But what are these kinds of things? How are people choosing how to expand to fill that space?

Dave Johnson: Well, if you think of the examples like the preclinical quality-control steps that weve automated the reality is, [with] one operator stretched over a huge amount of work, its really hard for them to really do really in-depth inspection of these samples. And so by taking off a bulk of that work 80%, 90%, for the algorithm to do that what theyre able to do is just do a better, more thorough job of inspecting the samples that are left. It also means were not hiring a whole bunch of other people just to go look at screens of data. So its a bit of an immediate gain for the people who are there and then kind of this longer-term gain on our head count plans.

Some folks talk about AI in the pharma space being like, I just want an algorithm that can predict, from the structure of a small molecule, the efficacy in humans, like thats the entire drug discovery process. Thats just not going to happen; thats completely unrealistic. So we just think about the fact that there are countless processes, its a very complicated process to bring something to market, and there are just numerous opportunities along the way. Even within a specific use case, youre rarely using one AI algorithm. Its often, For this part of the problem, I need to use this algorithm, and for this, I need to use another.

Shervin Khodabandeh: Dave, I want to ask you something about the talent base and people. You commented that Moderna is the kind of company that likes to give people a lot of freedom [a] highly motivated, smart, ambitious team working to do the best it can. How do you bring and cultivate that talent, and what are you finding to be some of the lessons learned in terms of how to build a high-performing team?

Dave Johnson: Its a good question. I dont know that if we look across the company as a whole there is one particular place where we hire people. We get people from biotechs, [from] five people to pharmas of 100,000 people and everywhere in between, [from] inside the industry and outside the industry. I think for us, its always about finding the right person for the job, regardless of where they come from and their background. I think the important thing for us is to make sure that we set expectations appropriately as we bring them in, and we say, Look, this is a digital company. Were really bold. Were really ambitious. We have really high quality standards. And if we set those expectations really high, it does start to self-select a lot of the people who want to come through that process.

Sam Ransbotham: I want to flip over and talk You mentioned some of the infrastructure, I would call it, that you put in place that suddenly the world benefited from a few months ago. How did people know to get those things set up in the first place? You mentioned being able to scale from, I think, 30 to a thousand different How did you know that was the direction, or that was the vision to get those things set up?

Dave Johnson: Thats a great point. The whole COVID vaccine development, were immensely proud of the work that weve done there, and were immensely proud of the superhuman effort that our people went through to bring it to market so quickly. But a lot of it was built on just what you said: this infrastructure that we had put in place where we didnt build algorithms specifically for COVID; we just put them through the same pipeline of activity that weve been doing. We just turned it as fast as we could. When we think about everything we do at Moderna, we think about this platform capability. We were never going to make one drug; that was never the plan. The plan was always to make a whole platform around mRNA because, since its an information-based product, all you do is change the information encoded in the molecule, and you have a completely different drug. We knew that if you can get one in the market, you can get any number of them to the market. And so all the decisions we made around how we designed the company and how we designed the digital infrastructure was all around this platform notion that were not going to build this for one thing were going to build a solution that services this whole platform. And so thats exactly why we built this early preclinical staff where we can just crank through quite a few of these. Thats why we built these algorithms to automate activities. Anytime we see something where we know that scale and making it parallel is going to improve things, we put in place this process.

Sam Ransbotham: The proof is certainly in the pudding. One thing that Im kind of finding fascinating is how normal this all is. I guess Im just surprised at how much that seems to be part of your Can I use the word DNA here?

Dave Johnson: Its totally fine.

Sam Ransbotham: RNA.

Shervin Khodabandeh: mRNA. Its part of their mRna.

Dave Johnson: Yeah, no, its true. We were founded as a digital biotech and a lot of companies say things and put taglines on stuff, but we really meant it. And we have pushed on this for many years, and weve built out this for many years.

Shervin Khodabandeh: Its the platform you built, and now its running.

Dave Johnson: Its the platform approach we take to our data science and AI projects as well. I hear a lot of struggles from folks around, Great, I built a model and a Jupiter notebook. Now what do I do with it? As they resolve this data cleansing and data curation to even get it to be in a useful state, then they dont know where to go from it to deploy it. And we took the same platform approach to our data science activities. We spent a lot of time on the data curation, data ingestion, to make sure the data is good to be used right away. And then we put a lot of tooling and infrastructure in place to get those models into production and integrated. So this platform mentality is just so ingrained into how we think.

Sam Ransbotham: Take us back to early in the COVID race for a vaccine. What was it like being part of that team and a part of that process? I mean, what were the emotions like when the algorithms or when the people find something that seems to work or that seems promising? Does that lead to a massive appetite for more artificial intelligence and more algorithms? Tell us a little bit about that story.

Dave Johnson: I think if you look at how people felt in general at the time, it was a real sense of honor and pride. We felt very uniquely positioned. Wed spent a decade getting to this point and putting all of this infrastructure in place and putting things in the clinic before this to get to this moment. And so we just really felt truly honored to be in that position. And for those of us on the digital side who have kind of contributed to this and built it, this is why we did it. This is why were here: to help bring as many patients [these vaccines] as quickly and safely as possible [throughout] the world. But there was always the question of, Would this thing work in the real world? And thats where the proof came in the clinical data, and we were all anxiously waiting like everybody else to see that readout.

Shervin Khodabandeh: Was AI always front and center at Moderna, or has it become more critical as a pillar of growth and innovation over time?

Dave Johnson: I think its always been there, though we probably didnt call it that in the early days; its become obviously much more of a hot marketing term than it used to be. But the notion of algorithms taking over decision-making and data science capability was absolutely always there. We were very thoughtful about how we built this digital landscape, such that were collecting structured data across all these steps, knowing full well that what we want to do is then turn those into algorithms to do things. So it was very purposeful for that. But I do think its also come into a greater focus because weve seen the power of it very recently, obviously. Weve seen how this digital infrastructure and how these algorithms can really help push things forward. And so its gotten that kind of a renewed focus and importance in the company.

We tend not to be a company of half measures, so when we decide were going to do something, were going to do it. Its been a very strong message from our senior leadership, about This is the future of the company injecting digital and AI into everything we do. Under no uncertain terms, this is happening. To the point that, as we think about the fact that were growing really fast as a company we just doubled; were probably going to double again were bringing in a lot of new folks from outside the company, to grow, who are not necessarily familiar with this digital culture that weve had. And so, what were working on right now is actually developing what were calling an AI academy, which we intend to be a very thorough, in-depth training for our company, from people who would use and interact with AI models on a daily basis to senior leaders who would be responsible [for] a portfolio of potential projects in their areas. And that just shows the level of serious commitment we have about this. We were built on this concept of having a smaller company thats very agile and can move fast. We see digital as a key enabler for that and AI as a key enabler for that. So the hope is that helps us to compete in ways that other companies cant. And that is certainly the intention here.

Sam Ransbotham: Dave, thanks so much for talking with us today. We really enjoyed I mean, you mentioned Moderna hires smart people, and we know that from a sample size of one thats clearly true. Thanks for taking the time to talk with us today.

Shervin Khodabandeh: Thank you so much.

Dave Johnson: Absolutely, guys. I really appreciate it.

Shervin Khodabandeh: Sam, that was an awesome conversation with Dave. What do you think?

Sam Ransbotham: Impressive. Im glad hes around. Im glad Moderna is around.

Shervin Khodabandeh: Thats right.

Sam Ransbotham: This is real.

Shervin Khodabandeh: Hes not paying lip service to buzzwords and this, that, and the other. Hes just, Yeah, we started this way. Thats why were doing it. We would not have existed without digital and AI and data and analytics. Of course its real. Thats where we are. He said Moderna is a digital company. Thats what he said.

Sam Ransbotham: Its just part of their process. Some of the questions, it didnt even occur to him that it was artificial intelligence; thats just the way they do things. I wonder if thats the new industry after industry, are we going to see the Moderna-type approaches come into industries and just be dominant? The vestiges of historical Oh, weve been around for 100 years are almost a liability versus a plus.

Shervin Khodabandeh: I think this contrast, Sam, that you were trying to get at, which is, How come its so easy for you guys, and what about the pre/post and the transformation? Hes like, Well, we actually started this way. We said we wanted to be a small company.

Sam Ransbotham: They started post.

Shervin Khodabandeh: Yeah, We started post. We wanted to be agile, we wanted to be small, we wanted to do a lot more with everything that we had, and so that had to be platform-centric, data-centric, AI-centric, and thats how we built [the company]. So AI is everywhere. Why are you surprised, Sam, that AI is everywhere? Of course, its everywhere. We do it for planning and the trials and sequencing and Its quite energizing and intriguing how its just a very different mindset toward AI.

Sam Ransbotham: Right. And I know that we dont want to make everything AI. Theres a lot thats going on there thats not artificial intelligence, so I dont want to paint it as entirely [AI], but that certainly was a big chunk of the speed story here, and its pretty fascinating.

Allison Ryder: Thanks for joining us for this bonus episode of Me, Myself, and AI. Well be back in the fall with new episodes for Season 3. In the meantime, stay in touch with us on LinkedIn. Weve created a group called AI for Leaders specifically for audience members like you. You can catch up on back episodes of the show, meet show creators and hosts, tell us what you want to hear about in Season 3, and discuss key issues about AI implementation with other like-minded people. Find the group at https://mitsmr.com/AIforLeaders, which will redirect you to LinkedIn, where you can request to join. Well put that link in the show notes as well, and we hope to see you there.

Sam Ransbotham (@ransbotham) is a professor in the information systems department at the Carroll School of Management at Boston College, as well as guest editor for MIT Sloan Management Reviews Artificial Intelligence and Business Strategy Big Ideas initiative. Shervin Khodabandeh is a senior partner and managing director at BCG and the coleader of BCG GAMMA (BCGs AI practice) in North America. He can be contacted at shervin@bcg.com.

Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rdinger.

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Nordstrom CTO on AI-powered analytics and consumer insights – VentureBeat

Posted: at 1:34 pm

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Customer service is Nordstroms bedrock, and that wasnt going to change just because the retail giant was engaging with customers online and not in the store, Edmond Mesrobian, Nordstroms chief technology officer and chief information officer, said during VentureBeats Transform 2021 virtual conference event today. In conversation with Hari Sivaraman, head of AI content strategy at VentureBeat, Mesrobian shared how Nordstrom revamped its data infrastructure in ways designed to enhance customer experience.

When customers are in the store environment, it is easy to have a compelling personalized experience, thanks to the attentions of a stylist or a salesperson. It is more of a challenge to connect digitally when the online presence tends to be more of a cold and distant experience. This difference forced Nordstrom to take steps to find out how to become closer with customers.

We had to figure out how to get analytical data to make predictions that help us make additions, so that we can actually offer the most compelling experience to our customers, whether online or in-store, and thats the journey we embarked on, Mesrobian said.

Nordstrom Analytical Platform (NAP), the analytics platform that Nordstrom employs, is a real-time, event streamingcentric analytical platform that provides insights on everything from customer services to credit. Reflecting on the start of this project, Mesrobian said that the most important thing is to remember that presenting the information is not about reporting data, but about collecting events that can be translated into actions.

Nordstrom starts by getting the business events created, organized, and streamed in real time. Then the multi-layered analytical models translate the events into predictions that ultimately lead to customer benefits and services. NAP employs open source technology and cloud computing, stitching existing components together to create an analytical platform that drives machine learning in a robust way.

Its off-the-shelf commodity components with a lot of special sources, which essentially translate our business events into a semantically rich object, something that could then be actioned through a model, Mesrobian said.

Before adding the sources, however, Mesrobian needed to make sure that the data was clean and readable to use. That is, the business events had to be completely and accurately represented to start with. As a result, when those events arrive at a point of being transformed into high-level objects, they are accurate and searchable.

We want to transfer the responsibility from the data engineers in the past to the application owners to make sure that their business events were pure, curated, and correct to begin with, and [they are] also aligned with their business goals, Mesrobian explained. The shift in responsibility, he said, is key to the platform architecture process.

With more than 100 AI models leveraged daily at Nordstrom, an end user will indirectly see the benefit from the timely delivery of information. While users have control over their privacy and permission settings, Nordstrom collects user preference information and employs it to provide better selection, better dynamic looks, better style boards, better choices, and so on.

Through AI, the company presented a more robust way of driving discovery and personalization. Nordstrom also launched a fashion map effort to take a natural language-based approach with deep learning so that the model gets to know customers better through conversation, as opposed to taxonomy-driven keyword searches.

In terms of the various AI technologies that Nordstrom uses in building NAP, Mesrobian listed examples such as linear classifier algorithms for computer vision. Those algorithms generate attribution vectors about the product based on pictures, and those vectors are used to drive dynamic loops. The company also uses computer vision algorithms that take shots of customer closets and give them insights about whats there. Tree-based models are also used to detect abnormal signals and fraud. Name it, and we are probably using it, he said.

When building an analytical platform, Mesrobian pointed out that it is important to keep in mind that data is not the crucial aspect, since the end result is to present predictions and leverage them in real-time events. Building from that broad recognition, the three takeaways from the platform include:

When talking about data privacy, Mesrobian said that developers need to be clear about customer proposition and about what information is being collected. We are very mindful of those regulations to make sure that [the customer] is always aware of what we are collecting, and that you have access to that information, he said. In the end, the data needs to create accessible value for the customer, he concluded.

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Xero introduces AI-powered analytics tool – Accounting Today

Posted: at 1:34 pm

Xero has released Analytics Plus, a new suite of planning and forecasting tools.

Powered by artificial intelligence, Analytics Plus combines cash flow forecasting featuring advanced predictions and business reporting tools, directly available in Xero.

The offering comes at a time when business appetites for strong data insights are renewed, following the difficult pandemic year.

The suite was developed in collaboration through testing and conversation with the broader Xero community during 2020.

Xero has also released Analytics, a free tool for all Xero Business Edition subscribers that combines the existing short-term cash flow tool, which visually projects cash flow over 30 days, and business snapshot tool, providing up to date insights on their business performance. Since their initial release in pilot last year, the cash flow tool has been redesigned with a new look and the ability to view future scheduled invoices and bills, while the business snapshot report can now be viewed on a cash or accrual basis.

To truly grow and thrive, every business needs to have access to trusted, insightful data that helps them understand where they are now, make decisions for today and where they might be headed in the future, said Xero chief product officer Anna Curzon in a statement. Over the past two years, weve worked closely with small businesses and advisors all around the world to build simple, beautiful and powerful tools and reports that give them access to that data, all on the Xero platform. As our business scale has grown, we now are able to provide powerful AI-powered products that use rich, trusted data, to help small businesses not only understand their current position, but make decisions with confidence based on their future potential.

Some features of Xero Analytics Plus include:

Xero Analytics Plus will be included and available in Established/Premium plans. More information on pricing can be found here.

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NVIDIA Aims to Bring AI to the 5G Edge – IT Business Edge

Posted: at 1:34 pm

NVIDIA is gearing up to bring artificial intelligence to the 5G wireless edge starting next year. The company at the Mobile World Congress (MWC) event revealed its plans to make available network cards based on Arm processors that will have the compute power required to run AI inference engines on edge computing platforms. At the core of that effort is a NVIDIA Aerial software development kit (SDK) that NVIDIA is making available to developers.

The NVIDIA Aerial A100 AI-on-5G computing platform will be based on 16 Arm Cortex-A78 processors into the NVIDIA BlueField-3 A100 network card scheduled to become available in the first half of 2022. Those network cards are based on a data processing unit (DPU) that NVIDIA is bringing to market to offload network, storage, security, and now AI workloads from servers.

NVIDIA is in the middle of trying to acquire Arm as part of an effort that would create a behemoth large enough to counter Intel. In addition to running AI on graphical processor units (GPUs), NVIDIA is betting organizations will find it more cost efficient to offload AI inference engines on to Arm processors deployed in a variety of edge computing environments.

Also read: NVIDIA Extends Scope of AI Software Ambitions

Most AI models are trained in the cloud. However, when it comes time to deploy an application infused with AI an inference engine is required. The closer that inference engine runs to the point where data is being collected the better the overall application experience becomes, notes Gilad Shainer, vice president of marketing for NVIDIA. This is where the DPU shines, he said.

Offloading tasks from servers is hardly a new idea. NVIDIA is taking the concept a step further by weaving together graphics processor units (GPUs), traditional CPUs, and DPUs together under a common software architecture. Ultimately, the goal is to create a framework for training AI models using GPUs that then spawn inference engines optimized for processors that also happen to be from NVIDIA and its allies. In most cases, AI models are trained in cloud, but NVIDIA has also been making the case for certified GPU systems that can be deployed in on-premises IT environments.

One way or another the amount of compute horsepower available at the network edge for running AI models is about to substantially increase. The challenge now is optimizing best machine learning operations (MLOps) practices to reduce the friction that many organizations experience when building and deploying AI models today. In most cases, AI models are built by data scientists. Aligning their efforts with application development teams to make sure AI models are ready when applications need to be deployed has proven challenging. In fact, its not clear to what degree MLOps represents a truly separate IT discipline or is simply an aberration that will ultimately be folded into existing IT operations.

One way or another, however, AI is coming to the network edge. The next issue is deciding how best to deliver, manage, secure and update it once it gets there.

Read next: Open Source Platforms Vie with IT Vendors for Management of MLOps

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AI leaders talk intersectionality, microaggressions, and more at Transform Women in AI Breakfast – VentureBeat

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At VentureBeats third annual Women in AI Breakfast at Transform 2021, leaders in AI and machine learning across industries came together to discuss some of the most urgent questions in the tech sector today, including what responsible AI and engineering means, and the roles and responsibilities of corporates, academia, governments, and society as a whole in getting more diverse voices into the tech sector, and more.

This year the breakfast, presented by Capital One, was moderated by Noelle Silver, the founder of Women in AI. The breakfast opened with a talk about what inclusive engineering means for the tech sector and why it cant just be the responsibility of those who are underrepresented.

Diversity must encapsulate diversity of race and ethnicity and religion, but also economic diversity, diversity of experience, and diversity of education, said Teuta Mercado, director of the responsible AI program at Capital One. And inclusive engineering means ensuring that all voices are represented. Many corporations are starting to do the outreach necessary to bring more diversity into this field with recruiting efforts and imperatives, she noted.

At Capital One, inclusive engineering means ensuring that our products and our services really reflect our customer base, and are accessible to all, said Mercado. Banking customers represent all facets of society, and everyone needs credit services, so its really important that we have diverse teams that are working on and building AI and machine learning so that we can build for all our customers, and not just for some.

Kathy Baxter, principal architect of ethical AI practice at Salesforce, has a background in psychology and human factors engineering, noting that shes benefited from the strong mix of genders seen in psychology and user experience research.

So, when moving into AI, Ive been able to leverage those different experiences of working across roles, she said. Seeking out people with a large variety of expertise and disciplines to contribute and participate in our discussions of what it means to create fair and equitable AI and identifying unintended consequences of AI systems.

Tiffany Deng, who leads the program management team for Responsible AI at Google, explained that her time in the U.S. Army as an intelligence officer prepared her for what she does today: bringing different voices to the table in order to find solutions.

That is what inclusive engineering is about its about giving voice to the underrepresented, and ensuring not only that their voice is heard, but its acted upon, Dang said. And then thinking about going into communities and understanding how a problem may present itself differently in that subtle context, as opposed to what we already think or what weve already seen.

At the World Economic Forum, we think about inclusive engineering globally, said Kay Firsh Butterfield, head of AI and machine learning, and executive committee member of the World Economic Forum.

Companies need to have multi-stakeholder teams so when theyre thinking about policy, that should include academics, nonprofits, governments, and businesses in the room but thats not enough, she said.

Weve been doing some work with a number of the big tech companies to ask what does responsible use of technology actually look like, she said. One of the things we know is that you need diverse product teams. We should all be at the table, with different backgrounds, so that our products use good, non-biased AI.

When talking about increasing the diversity of teams, it comes back to the pipeline issue, or encouraging people of marginalized backgrounds or underrepresented minorities to participate, Baxter added.

I dont want us putting all of the onus on women and other minorities to have to push their way into the door, and I feel like thats what often comes of these discussions, she said. Instead we need to look within our own companies, when we are hiring. Really taking significant time and effort to ensure we have a diversity of sources.

That means not hiring the very first person who meets the job criteria, but continuing to talk to a wide range of people who might be interested in these jobs which takes time and effort, but needs to be done. And then, once individuals are in the companies, its making sure that the environment is actually inclusive.

Too often, there are the micro aggressions and toxicities that not only impact an individuals ability to participate in these conversations, but also to keep them long-term, so that we lose the contributions that they could have given to the company, Baxter said.

There are so many different perspectives around every single day that weve always tried to tap into, in order to come to a better solution for our users, Deng said. A thing that really keeps me going is this idea of intersectionality.

We all have different facets, and identify in very different ways, and companies need to continue to build upon that in truly inclusive ways for these different communities, to ensure that all voices brought into the fold are heard, she said.

Its not enough just to have the women. We have to have men who are in positions of power, also supporters, who also understand that there is a role in their companies for us and that they actually begin to do much better if they have diversity within their country, their cabinet, or within the C-suite, Firth Butterfield said.

So many have struggled against bias in their field, Mercado said, but many things have kept her going.

One, the network: just surrounding yourself with women and men, people who care about what theyre doing, who are passionate about ethical AI or passionate about just doing whats right for our customers for people who are using the products, she said. And then the biggest thing for me is working for a company that has a culture of empowerment, allowing you to do the right thing.

Diversity and inclusion in AI, the erasure of bias in machine learning algorithms, and inclusive engineering is an issue that directly impacts the bottom line, but its so much bigger than that, said Deng.

Me being a Black woman, I think about my children and how can I make the world better for them, and how can I make the world safer for them, she said. AI is omnipresent; its a part of how we go to school, how we district, how things and services are distributed in our communities. And so I want to make that better. This is really an inflection point, and a really great opportunity for us to have deep impact, in all those different types in places in society.

Dont miss the full discussion, from how bias has impacted womens careers in male-dominated fields to how men often miscalculate what women can bring to the table, to how everyone must continue pushing traditional boundaries of these sectors, building truly inclusive community and ensuring that all perspectives are considered at every moment of the design process, and more.

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AI leaders talk intersectionality, microaggressions, and more at Transform Women in AI Breakfast - VentureBeat

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