Category Archives: Ai

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LinkSquares, a contract management and analytics tool for legal and finance teams, today announced that it raised $40 million in a series B funding round led by Sorenson Capital. The company, whose total funding stands at $61.4 million, says itll use the financing to expand its workforce, advance its technology, and develop strategic business partnerships.

During the pandemic, legal departments and contract negotiators faced a critical period of transformation. Legal was expected to contribute data-driven analyses while contending with the gap between executed contract analytical platforms and legal request business process flows. More than half of the worlds major companies face lost revenue and missed business opportunities as a result of inefficiencies in their handling of contracting processes, according to an EY Law survey.

Just this past year, legal and compliance teams raced to analyze their business exposure to major events across PDFs, paper contracts, and other documents. The pandemic caused a surge in interest from prospective customers who needed digital contract management. These companies couldnt afford to use on-premises solutions locked away in an office anymore, and they needed our help to ensure fully remote implementations that were quick and painless, and we came through, LinkSquares CEO Vishal Sunak told VentureBeat via email.

According to Sunak, the inspiration for LinkSquares came during his experiences with the manual work associated with contracts over the course of the acquisition business continuity firm Datto planned during Sunaks time at Backupify, a cloud data backup company. Datto hoped to migrate Backupifys customer data to its cloud infrastructure, but the team first had to understand each signed customer contract and determine if Datto had the right to move the data without permission.

Above: The LinkSquares platform.

Image Credit: LinkSquares

The idea to review each contract, read the provision related to data transfer, and store the answer seemed straightforward at first. In reality, because Backupify had negotiated more than 2,000 contracts, the act of finding all the contracts and looking for the provision language was an impossible undertaking, Sunak explained. When our team explored the types of contract management products available for post-signed contracts, there was a spark of innovation: most of the tools that could surface answers and insights from executed agreements focused on contracts that hadnt been finalized yet, mainly in the pre-signature stage. And so, LinkSquares and its AI for signed contracts was born.

LinkSquares platform performs searches for keywords, contract terms, and phrases across documents using AI. It extracts data from contracts (e.g., parties, effective and termination dates, payment terms, governing states, and limitations and liabilities), and its email-based notification function reminds teams of important dates and obligations. Optical character recognition transforms scanned PDFs into a searchable format, while custom user roles let admins control data access. And a clause library enables real-time searches for contract clauses.

Were starting to understand the real benchmarks of what is being agreed to and how people are agreeing to it in their contracts. Because were an AI company were starting to see macro trends in legal language emerge, Sunak said. Modern legal teams need to be data-driven and move beyond their perception as a cost center and gatekeeper. Corporate counsels and mergers and acquisitions teams rely on LinkSquares to elevate their internal value, reputation, confidence, and productivity by eliminating time spent on manual and ineffective processes.

LinkSquares customers include over 400 brands like Fitbit, Twilio, TGI Fridays, Wayfair, and Cogito. Growth over the last two years exceeded 1,000%, and the company recently announced a technology partnership with Xerox PARC (Palo Alto Research Center), the R&D lab behind laser printing and electronic ink.

Cogito estimates saving $30,000 per year [with LinkSquares, while] Asurion reduced time spent on searching for information by 50%, Sunak said. We overcame a lot of adversity this past year with employees and customers and we needed new levels of empathy and flexibility for a workforce that was 100% remote overnight. And we had customers who had a lot of uncertainty about their financial situations, so I personally worked something flexible out with several. That way, no one had to choose between paying their own employees and maintaining access to their critical legal insights.

Sunak expects that Boston, Massachusetts-based LinkSquares annual recurring revenue will grow 100% year-over-year by the end of 2021, up from well over $10 million. Other backers in the companys latest round included Catalyst Investors, Xerox, Bottomline Technologies, DraftKings founders and key legal and compliance executives, Hyperplane Venture Capital, MassMutual Ventures, and First Ascent Ventures.

Theres no shortage of startups developing AI-driven contract creation and management tools. Others in the $2.9 billionmarket includeConcord, which raised $25 million for its digital contract visualization and collaboration tools in 2019. Thats not to mention Icertis, which recently snagged $115 million; DocuSign, which invested $15 million in AI contract discovery startupSeal Software;andEvisort, which nabbed over $15 million to develop its solutions.

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LinkSquares nabs $40M to expand its AI-powered contract platform - VentureBeat

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GovExec Daily: Using AI to Solve Problems in Health Care and Beyonfd - GovExec.com

People of all ages and mixed ethnicity groups standing together

In a recent article, WIRED senior writer Tom Simonite talked to Kate Crawford, author of Atlas of AI, to explore the ethical issues facing artificial intelligence and machine learning technologies.

Were relying on systems that dont have the sort of safety rails you would expect for something so influential in everyday life, notes Crawford. There are tools actually causing harm that are completely unregulated.

When people that arent in the industry hear me say that artificial intelligence and machine learning can become forces for positive change in society, they ask me to explain why these technologies have been mired in controversy for more than a decade. And why ethical issues seem to be getting worse versus getting better.

Indeed, in recent years several high-profile cases of ML technologies causing harm to marginalized parts of society have captured headlines. Household name brands like Amazon, Apple, Facebook, Google have been accused of algorithmic bias, thus affecting society. As a result, there is a growing sentiment that systems designed to improve everything from peoples financial lives to their physical well-being have become a threat to many populations already suffering.

To answer these questions, I draw from my own experience and observations in the field. Theres never one answer to such a complex set of issues. Still, one contributor is that, historically, the teams responsible for the systems that make millions of life-changing decisions every second have been largely homogeneous, built without extreme care for whether they are reflective of all of society.

In other words, there is an entire generation of data scientists and engineers in our industry that are building systems that impact groups of society they dont understand.

To be fair, theres nothing wrong with a company reflecting on solving some of the worlds most vexing problems by hiring the best and brightest talent to do so. However, unless theres a conscious effort to build diversity into the fabric of an organization, the result is a pool of talent in the ML community that isnt being discovered and nurtured, and their skills and experience are being wasted.

More importantly, teams that lack a diversity of backgrounds, experiences, and perspectives not only perpetuate workplace inequality but also serve as a barrier to solving many of the problems that ML and AI technologies have the potential to address.

Although no single company or team can solve the ethical AI dilemma, the hope for all companies in our industry is that they embrace fairness, transparency, and accountability in their hiring and R&D processes so developments in AI advance positive outcomes for all people and societies.

Some recent developments suggest that there is good reason to believe many companies will adopt these principles. More and more organizations are investing in teams to ensure algorithmic accountability and ethics with an ultimate eye towards improving how their products impact the world.

Yet, these steps are just the beginning. We know that implementing systems that are free from bias and ethical concerns is essential; however, achieving this goal requires direct action in the following areas:

Conclusion

In the world of AI and machine learning, we are quickly learning that data and models can often obscure the hard truths of a persons lived experience. This is particularly true if the models are built by teams that are not representative of race, gender, sexual orientation, or socioeconomic status. Suppose we imagine different outcomes, have the readiness to pursue them, and start with the people behind the products first. In that case, we can create a new reality where ML and AI technologies truly serve all people fairly and without harm.

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Unleashing The Power Of A Diverse Team To Build More Ethical AI Technologies - Forbes

Join executive leaders at the Conversational AI & Intelligent AI Assistants Summit, presented by Five9. Watch now!

The thing about fraud is that its constantly changing looking at a past attack doesnt guarantee the next attack will look the same or target the same kind of victim and defenders have to continuously adapt. Visa utilizes artificial intelligence to analyze all of the transactions that go across the network and track large-scale transactional changes as part of its fraud detection efforts, Melissa McSherry, Visas senior VP and global head of data, security, and identity products, said at VentureBeats Transform 2021 virtual conference on Monday.

Visa scores all of the transactions that go across the Visa network, which allows the company to define a set of behaviors that would be considered normal. The team is constantly updating the models view of history and updates the model itself to reflect the data on a fairly regular basis, McSherry said.

The fraudsters do not stand still. And theyre always looking to innovate, McSherry said in a conversation with Jana Eggers, the CEO of synaptic intelligence company Nara Logics.

Being able to detect changes in the data is useful for authentication, McSherry said. A single phone and email address pair is likely associated with a legitimate transaction, especially if that same pair has been used for a lot of transactions. The next transaction that comes through with the pair will also likely be tracked as legitimate. But if that one phone number is associated with 500 email addresses, it is more likely that all the email addresses are associated with compromised accounts and that the phone number is not associated with a real identity, either.

It is absolutely the case that the data is constantly evolving, but we take advantage of the velocity of the enormous amount of data that we get and try to evolve our perspective with it, McSherry said.

Everyone knows Visa has an enormous amount of data, and McSherry was able to shed some light on how Visa uses it. Visa has been using neural networks for fraud detection since the 1990s. Eventually, the self-learning technology updated the frame of what was normal to identify big deviations in model distributions. More recently, the company is using convolution neural networks (CNN) and recurrent neural networks (RNN) to improve pattern recognition across the network. They arent just used as models but also around the models to identify areas that require more scrutiny or highlight changes that need to be made to the model, McSherry said.

Visa uses generative adversarial networks (GAN) to create virtual fraudsters and pit them against the anti-fraud tools to identify gaps in the fraud-detection models, McSherry said. The gaps can also be in tools provided by partners or in the business logic.

My experience has been that sometimes things dont work exactly the way that you think that theyre going to work the very first time that you use them, McSherry said. Consequently, new methods will be used in parallel or just for monitoring until they are better understood.

Incorporating AI requires commitment and follow-through, McSherry said. Visa consistently sees a 20% to 30% lift for advanced AI methods over more garden-variety technologies, but this requires heavy investment. Stakeholders need to remain engaged and focused because the first few attempts may not work exactly as planned. Experimentation and patience are key.

The first and most important thing is just making sure that the problem itself will benefit from those kinds of lifts, McSherry said. Its just really helpful if everybody understands that the value on the other side [of the implementation] is really worth quite a lot.

Having personnel learn about newer techniques will allow businesses to get the most out of machine learning. While it makes sense to hire new people with strong AI backgrounds, Visa also gave existing employees who understood the business the opportunity to experiment and learn new techniques.

I think that when people who really have the business context and long-term pride in the quality of the product [are combined] with a really good understanding of AI techniques, thats when you get something really special, McSherry said.

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Visa on using advanced AI such as unsupervised learning to fight fraud - VentureBeat

A Double-Edged Sword

Artificial intelligence is a double-edged sword when it comes to cybersecurity, with defenders using it to respond to and predict threats and attackers using it to launch even more refined attacks. For example, AI algorithms can send spear phishing tweets (personalized tweets sent to targeted users to trick them into sharing sensitive information) six times faster than a human and with twice the success.

The enlargement of attack surface and the increased sophistication of attacks has made AI a key weapon in thwarting cyberattacks, Capgemini found. Cyber analysts are finding it increasingly difficult to effectively monitor current levels of data volume, velocity, and variety across firewalls, prompting organizations to turn to artificial intelligence.

[RELATED: Artificial Intelligence Week 2021]

In fact, Capgemini found that 61 percent of organizations acknowledge they wouldnt be able to identify critical threats without AI. The increases in cyberattacks that can quickly compromise critical operations within an enterprise also require enhanced capabilities that can best be provided through AI, according to Capgemini.

From identifying fundamental traits that numerous threats share to classifying and responding to well-camouflaged malware to advising how organizations should allocate security resources, heres a look at how 10 AI security companies are making the world a safer place.

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The 10 Hottest AI Security Companies You Need To Know - CRN

- 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.

icertis Logo

"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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View original content:https://www.prnewswire.com/news-releases/icertis-recognized-as-the-winner-of-2021-microsoft-ai-partner-of-the-year-301332309.html

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

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

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

Excerpt from:

How AI can be used to streamline DAM workflows - ITProPortal

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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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.

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