Hi Steve, welcome to the AiThority Interview Series in 2023. Please tell us about your two decades of tech experience so far. How did you arrive at Mastercard?

My career has had several phases. For the first 10 years or so after graduating, I worked at various – mostly small – independent, software companies and consultancies. My position evolved over the years; I started as a developer before advancing to the role of a software engineering manager and then eventually becoming a CTO.

Next, I worked for Science Foundation Ireland (SF), Ireland’s national science funding agency, where I led our investments in topics such as computer science, data science, software engineering, and artificial intelligence.

In 2014 I started at Mastercard, which is where I still currently work today. Initially, I supported and grew a team called Start Path, an engagement program for innovative startups in the fintech space. A few years later, I joined Mastercard Foundry, the innovation and R&D arm within the company, leading research and development for AI, ML, and now also quantum computing. This July, I was appointed to Mastercard’s first class of Distinguished Engineers, a recognition for select Senior Vice President technical experts as part of the company’s continuing commitment to technology, innovation, and career growth. With this distinction, I continue my work with a focus on artificial intelligence and quantum computing. 

You are in charge of Mastercard R&D’s strategy and execution of AI and ML in new product development efforts. What is the biggest challenge to Digital Transformation in the market you cater to?

Of the many years that I’ve worked in technology – this current period is distinct for the speed and scale of innovation taking place. This dynamism is exciting because we’ve only just scratched the surface of what is possible for businesses and consumers, but with it also comes new challenges for enterprises.

For one – leveraging emerging technologies to build new products and services.

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Part of the equation is to understand and introduce technologies like web3, spatial computing, PETs, and AI/ML, while also maintaining and upgrading legacy systems, and aligning with ever-evolving legislation and regulation governing their application.

AI specifically has been top of mind for our market, especially following the fairly recent explosion of generative AI.  Mastercard has been putting AI to work for years, particularly in our products and solutions across open banking, routing, personalization, and fraud that enhance the safety and security of the payments ecosystem.  Although the step change between AI and generative AI is exponential in terms of what you can do with it, our deep roots in AI have afforded us the capabilities, talent, framework, and partnerships to keep a pulse and execute on emerging technologies.  

As a leader in the payments space, and as with any nascent technology, Mastercard has a responsibility to set the precedent for exploring generative AI responsibly.  We developed an AI governance program and guidelines for our data scientists to minimize risks in AI and best serve our customers, invested in partnerships with key institutions like RIT In Dubai and Howard University, and actively encouraged our employees to safety test and learn. 

What technologies within AI and computing are you interested in?

The idea of being able to control a computer system and anything connected to it through programming has fascinated me since I was a teenager.

Today I’m looking at how AI, mixed reality, spatial computing, and web3 have unlocked an entirely new frontier in technology. We’re likely to see several key trends, such as the rapid increase in computational power, both at the edge and in the cloud, and the tokenization of assets to start to coalesce around some of these new computing paradigms.

For AI, the incredible advances born from generative AI and Large Language Models (LLMs) are also contributing to the transformative period we’re in.

Currently, Mastercard is engaging in test-and-learn with generative AI applications to enhance operational efficiency and improve data quality, aggregation, entity resolution, and categorization.

We’re also using ML for certain models that support our open banking solutions, such as credit scoring, financial management insights, account opening, and payments. It enables us to extract, identify, and classify data quickly and more efficiently than rules-based models alone.

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In the longer term, I’m paying close attention to both quantum computing and AGI. With quantum, I’m tracking developments in both hardware and software, to understand how these new devices will help us to solve ever more complex computational problems, and what use cases will arise in our industry. With AGI, at some stage, we may be looking at the prospect of human-like machines that can solve a wide range of complex tasks at scale. 

In the current analysis, it is reported the global quantum computing (QC) market will be at $900 million. How do you see QC disrupting the digital market in the next couple of years? 

At $900m, quantum computing is still a very small part of the overall computing market.

Over the next few years, we’ll see quantum computers – inclusive of quantum annealers – get progressively more performant, capable, and reliable.

Currently, our best guess is that the earliest use cases in banking and payments will most likely be in the optimization space, with other applications such as machine learning coming later.

Rather than a disruption, it’s more probable that we’ll see quantum technology adopted gradually, across industries and companies, as the technology continues to improve, becomes more usable, and its primary use cases become more evident. 

What steps can young technology professionals take to enhance their proficiency in collaborating effectively with Cloud, Automation, and AI-based tools? 

Nothing beats getting “hands-on-keyboard” experience using these technologies.  One of the amazing benefits that all young tech professionals have today is readily available online learning materials. There are tutorials on YouTube for just about every emerging technology imaginable, and through cloud computing, there’s also access to the resources required to explore those areas. Many cloud and tech companies also offer cheap or free trial accounts to help young developers learn their technologies at little or no cost.

On the Mastercard Developers platform, for example, you’ll find a quick start guide that will walk you through how to create a new project using Mastercard’s APIs, and gain access to the Sandbox environment. So, armed with nothing more than a laptop and an internet connection, people can get access to all the technology they could imagine, even quantum computers!

One of the tried and tested ways to build skills in these areas has been through the open-source community – whether it’s contributing to an existing project that you find interesting or relevant, or starting a personal project that scratches your own itch. 

What are your predictions for AI/ML and other smart technologies heading beyond 2024?

As machine and deep learning evolve, so too will their role within our sector. This past year has been about experimentation. In 2024, we expect generative AI to continue to gradually integrate into business operations and products.

Companies are currently focused on internal generative AI applications, like software development co-pilots, knowledge bots and operational efficiency drivers that are serving as testbeds and laying the groundwork for what’s to come. This phase is likely to continue throughout the year, as companies start building the foundations for implementation. As challenges like data privacy, information accuracy and bias are addressed, we anticipate that the range of use cases will expand to include more ambitious and public-facing deployments.

 

One of the most compelling use cases for generative AI in the financial services industry is in open banking. With the aid of fine-tuned LLMs, generative AI can enable the cleaning and categorization of data at a significantly higher through-put and with more accuracy than previously available.

In line with informed data consent protocols, generative AI could streamline personal financial management, for example, by acting as a personal wealth manager to create an encompassed view of an individual’s financial well-being, help formulate college savings plans, procure loans and implement financial strategies – empowering people to navigate their financial lives more adeptly.

Thank you, Steve! That was fun and we hope to see you back on AiThority.com soon.

• About Steve
• About Mastercard

About Steve

Steve is an IT professional with more than 25 years of industry experience in payments, government, and academia. He is currently responsible for leading Mastercard Foundry’s R&D initiatives in emerging technologies, including artificial intelligence, machine learning, quantum computing, 5G and Web3. In this role, Steve leads a team of talented data scientists, data engineers and software engineers to bring new products and services to market.

About Mastercard

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

The post AiThority Interview with Steve Flinter, Distinguished Engineer, Artificial Intelligence & Quantum Computing, Mastercard Foundry R&D appeared first on AiThority.

With the first stages of Google’s ‘Cookiegeddon’ less than two months away, digital advertisers are still scrambling to find solutions that will enable them to continue to deliver effective campaigns. Indeed, despite the threat of the deprecation of third-party cookies hanging over the industry for several years, more than 40% of advertisers remain unprepared.

This is quite a damning reflection on the industry. Especially, before the news that Google Chrome would be removing third-party cookies, there was a conspicuous shift toward consumer privacy, with other browsers having waved goodbye to user tracking. Not to mention that GDPR is now over half a decade old.

Nonetheless, it’s not too late for digital advertisers to get all their ducks in a row, and overcome the challenges presented by a privacy-first online experience. Indeed, the end of third-party cookies doesn’t mean the end of effective digital advertising. Instead, it poses an opening for innovative solutions to make the ecosystem a better place for consumers and advertisers alike.

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New age contextual

The new era of digital advertising will see the end of the repetitive, frustrating, low-quality ads that have plagued the lives of consumers for many years. Instead, the conversation has shifted back to the role of creativity and context in driving truly successful advertising. This is thanks to the increasing recognition within the industry of attention and engagement metrics as being the best way to measure the effectiveness of digital advertising. This has helped bring creativity back to the forefront of the industry’s arsenal, having previously been neglected in favor of an approach that favored hyper-targeting over the look and feel of an ad.

Greater penetration of artificial intelligence (AI) into contextual advertising has not only made it far more sophisticated than it once was but much more accessible to all. Technologies now can serve the most relevant creatives to precisely targeted audiences, based on their interests and behaviors, without ever putting the personal data of consumers at risk.

AI technology can analyze millions of web pages in seconds, identifying the brands, products, semantics, and intent associated with those pages and using that information to serve the right ad at the right time. Moreover, AI can harness data in real-time around anything from product lists to sports scores, from weather to train times, within the creative, meaning true relevance can be delivered at the moment.

The combination of this in-depth contextual analysis and heightened creative prowess helps advertisers align their targeting and creativity in real time at an impression level. These ads, with their AI-powered personalization, seamlessly integrate into the user’s browsing and create an experience that drives attention and engagement. No longer will consumers be negatively impacted by low-quality ads lacking relevance.

Advertisers should be experimenting with AI-based solutions now, to help them to not only produce the most visually engaging ads but also to ascertain which creatives would be most relevant to specific audiences and via which channels – all the while maintaining customer privacy.

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Putting Creativity Back On Top

Furthermore, the introduction of automation to the creative process means that brands can navigate the laborious work required to create multiple ads that cater to the different needs of each consumer. Having the technology in place to carry out this essential work means ads can be more easily delivered across the marketing funnel – whether the goal is to raise brand awareness or edge the consumer toward conversion – and return on investment (ROI) can be maximized.

In a world where advertisers are having to adjust to significant changes, the best way to overcome the challenges they are facing is by focusing on contextual relevance and engaging creative that adds to the user’s browsing experience, rather than lowering the quality.

AI-powered technologies exist to ensure that consumers are served the most relevant and appealing ads and help the industry get back to what made people enjoy advertising in the first place. Creativity has always been what truly made advertising special, and now it’s time for it to once again be the number one priority for brands, with AI here to ensure that target audiences truly engage with the ads served to them.

[To share your insights with us, please write to sghosh@martechseries.com]

The post The Cookie’s Already Crumbled – So What Are Advertisers Waiting For? appeared first on AiThority.

We all know that today’s online shoppers are looking for e-commerce businesses to connect with them on a personal level. This approach is especially important for businesses to stand out from the competition. By delivering relevant, targeted content and experiences, e-commerce companies can increase conversion rates, order values and ultimately, customer loyalty.

This is known as personalization, and it’s all about getting inside a customer’s head and appealing to each person based on a knowledge of what content resonates with and interests that person the most. Up until a few years ago, e-commerce companies relied heavily on third-party cookies to accumulate data gathered between browsing sessions across various websites to create a clear picture of a user, but this is now set to change.

In 2020, Google announced its plans to make third-party cookies obsolete on the internet, in response to increasing consumer privacy demands and new regulations.

Just last month, Google unveiled its timeline to begin migrating Google users to the Privacy Sandbox by early 2024, with the official, complete deprecation of all third-party cookies on track for the second half of 2024.

Whether they want to or not, e-commerce marketers are going to have to adapt to an environment free of third-party cookies. Many are filling this void by leveraging first-party cookies and zero-party data coming from a variety of sources, including sales interactions, social media, web forms and more. Still, many e-commerce marketers are left wondering – what will fill the void in personalization after third-party cookies are no longer available?

Generative AI – A Saviour for Personalization in a Cookie-Less World

As cookies are being phased out, generative AI is being looked at as the next great personalization enabler. Generative AI is a subset of machine learning that uses algorithms to make new content, and allows marketers to automate content creation and produce impactful visuals, captivating videos, persuasive copy and unique and engaging advertisements, all at scale.

With generative AI, marketers can create dozens of different personalized campaigns extremely quickly, which can all be A/B tested in order to determine which direction works best with the widest number of people.  In this sense, this tactic is not really about individual personalization anymore – you don’t need one-to-one personalization so much as a clear connection for the majority of users.

In fact, generative AI is set to dramatically alter the e-commerce marketing landscape as we know it. Never before have e-commerce companies been able to iterate and test so quickly, and they can rapidly experiment to determine what content is the best at driving conversions at scale, without necessarily needing to collect data on consumers and infringe upon their privacy.  The key intuition here is that generative content creation is fast enough that you can test alternative experiences for users in minutes. Do that right, and you know what works for shoppers today, not last month.

If it sounds too good to be true, it may be – because there are several challenges ahead. The primary one regards the lack of personnel needed to handle these opportunities.

According to our recent survey, a large number of marketers are frustrated with the availability of developer resources and believe it takes far too long for code changes to be made. To be successful, real-time personalization must be dynamic; fast and accessible; and highly agile. This can’t happen if an e-commerce business takes days or weeks to change content or experiment by conducting A/B tests.

This need for agility is especially critical when e-commerce marketers are investing in paid social media (and paying for each click to their landing page). Demand on social networks is highly fickle and fleeting in nature, changing rapidly from one moment to the next. Generative AI can help e-commerce companies capitalize on sudden demand spikes.

For example, it can be highly advantageous to create a corresponding campaign on the main website if a product suddenly goes viral on social media – but storefronts need to be able to keep up, changing content on the fly.

Think Like a Marketer, Act Like a Developer

In this context, e-commerce marketers have to ‘think like marketers and act like developers.’ This means – being able to change site content on the fly and continuously experiment in order to determine what content drives the most conversions at scale, and being able to make adjustments in near real-time. Development dependencies must be eliminated in order to get conversion-driving storefronts to market faster, which means e-commerce marketers need tools enabling them to make quick changes and adjustments on their own.

Generative AI looks to be the next big wave in personalization as third-party cookies become obsolete. But the power of generative AI will be severely hampered if our ability to move quickly and make rapid website changes remains hamstrung by a lack of personnel or other technical limitations. Empowering e-commerce marketers will be the key to unlocking the new personalization landscape where generative AI is king.

The post Generative AI: The Next Wave of Personalization Demands Greater Agility appeared first on AiThority.

As automated systems become commonplace throughout businesses, there is a natural progression to integrate these systems to achieve even higher productivity levels. Often referred to as hyper- automation, this is one of those terms that is less of a technology and more of a methodology or a goal. Laying the groundwork for a “Connected Enterprise”, hyper-automation is the strategic orchestration and integration of various automation technologies, creating an interconnected business model with heightened productivity. Industry experts describe hyper-automation as “a discipline that helps to combine several technologies in an orchestrated manner to deliver end-to-end, intelligent, event-driven automation.” The goal of that approach is aimed at reducing manual efforts and errors, improving efficiency, and increasing the speed of business operations through the integration of various automation technologies, including automation, robotic process automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), and other advanced solutions to automate business processes end-to-end.

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Hyper-automation enables departments across the enterprise to automate workflows – from data extraction and processing to decision-making and analysis, without human intervention. This can reduce the time and cost associated with business operations while improving accuracy and consistency.

The foundational technological elements for hyper-automation are already available to businesses in all sectors and functions.

Leading analyst firm Gartner recently predicted just last year that “By 2024, 90% of integration-platform-as-a-service vendors will enable process automation, while almost all RPA vendors will offer integration via APIs.” However, integrating advanced automated systems poses significant obstacles for entire enterprises. Challenges include complexity, technical expertise, data compatibility, security, scalability, and cost. Despite these challenges, integrating multiple automation technologies can significantly benefit efficiency, accuracy, and cost savings. By addressing these challenges, organizations can create a connected enterprise that unites technology, processes, and people, driving a more intelligent and efficient business environment.

Complexity

First and foremost, organizations as a whole need to be prepared to tackle the complexity of implementing these technologies. The complexity of implementing hyper-automation can be immense, especially when envisioning it as the key to a Connected Enterprise. To give a sense of how complex this process can be, let’s use a hypothetical example of a large financial institution that has decided to implement hyper-automation to enhance its loan approval process. To achieve this goal, they needs to integrate multiple tools and technologies, such as RPA, AI, ML, natural language processing (NLP), and advanced analytics. That’s assuming that the organization already has these solutions in place to begin with.

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What’s more, the technology implementation team must manage the cultural and organizational changes that come with implementing hyper-automation. They need to collaborate with different departments, provide training and support, and address any concerns or resistance from employees.

Taken together, these elements present a significant challenge. Gartner again provides two key steps to take prior to any actual work. First, “Create a hyper-automation capability map by working with your business peers to identify all capabilities required to achieve task automation, process automation and augmentation goals.”

And second, “Select technologies by prioritizing the key hyper-automation capabilities required to deliver the identified use cases and by following a decision framework to determine the right combination of technologies.” Following these two steps will lay the groundwork for any successful implementation. Technical expertise

Teams across the enterprise must ensure seamless integration of these diverse technologies, which can be challenging as each tool may have different requirements, architecture, and protocols. The team must create a unified platform that allows these tools to work together efficiently. In order to achieve this goal, organizations need to make sure they have the right personnel for the job before taking this next step.

Data compatibility, consistency, and quality 

With data being gathered from multiple sources, it’s essential for the entire enterprise to ensure data consistency and quality. In a Connected Enterprise, data compatibility, consistency, and quality are of paramount importance.  They need to establish processes for data cleansing, validation, and integration to avoid inconsistencies and inaccuracies that could compromise the automation process.

For example, data management team can standardize data formats and structures to ensure that data is consistent across different systems and applications. This can be achieved by defining a set of data standards, such as data type, format, and length, and enforcing them across the organization.

Similarly, data management team can also leverage data integration tools to extract, transform, and load data from various sources into a unified data model. This can help to ensure that data is consistent, accurate, and up to date across different systems and applications.

Establishing data governance policies will also be important. Solid data governance policies ensure high-quality data that is managed and used in a consistent and secure manner across the organization. This can include defining data ownership, access, security, and privacy policies, as well as establishing data stewardship roles and responsibilities.

Finally, it’s the responsibility of the data stewardship division to continuously monitor data quality to implement quality checks and to ID and resolve issues before they arise. This can be done by implementing data quality metrics and dashboards, as well as establishing data quality management processes and procedures.

Security and compliance

While secure systems are a priority for just about every organization, hyper-automation brings new wrinkles to this issue given the lack of human involvement, particularly with often sensitive financial, personal, or healthcare data being processed.  As the enterprise becomes more connected and automated, the security and compliance aspects gain higher importance. Robust access control, data encryption, and auditing mechanisms are critical tools to ensuring your automated systems are safe from attack.

Scalability

As the organization grows and transforms into a more Connected Enterprise, it’s vital for the whole enterprise to ensure that the hyper-automation platform can scale to accommodate increasing workloads and adapt to changing business needs. This requires constant monitoring, updates, and optimization of the automation infrastructure. Selecting vendors and technologies at the outset that are capable of growing as your implementation grows is also very important.

Overall, tackling the challenges of hyper-automation will, in the end, deliver a raft of important benefits to the enterprise. Successfully implemented hyper-automation paves the way for a Connected Enterprise, eliminating bottlenecks across all operations, optimizing processes, eliminating the need for time-consuming manual tasks, and fostering a more productive, driven, and motivated workforce. The journey toward a Connected Enterprise may be complex, but the benefits it brings make it worth the effort. Once implemented, it’s the responsibility of the entire organization to regularly evaluate the effectiveness of the hyper-automation solution and fine-tune it to ensure maximum efficiency and return on investment. This may involve monitoring performance metrics, identifying bottlenecks, and implementing improvements. Organizations must plan carefully, involve stakeholders, and continually optimize their hyper-automation strategies.

However, when successfully implemented, enterprises can expect significant benefits; bottlenecks eliminated across all operations, optimized processes, obviating the need for time-consuming manual tasks, and a more productive, driven, and motivated workforce.

The post The Road to Hyper-automation; Overcoming its Greatest Challenges appeared first on AiThority.

Although artificial intelligence (AI) has existed for decades, it is now at the forefront of information technology due to its level of maturity, increasingly widespread adoption, and possible revolutionary impact on insights and productivity. The surge of interest in AI also is due in no small part to the introduction of generative AI products such as ChatGPT and Dall-E by OpenAI, which are among the first to provide an intuitive, common, direct, and easy interface through voice and keyboard so that almost anyone can experience, experiment and work with AI.

While AI is receiving so much attention and is here to stay, another potentially important development in information technology, the metaverse — the immersive internet of virtual and augmented reality — received similar attention in 2021, but its adoption and awareness dropped steeply and now has been eclipsed by the conversation around generative AI.

Why the Metaverse Lags Behind AI.

The metaverse is behind AI in mindshare and adoption for four reasons:

• The metaverse is a place or at the very least a medium. Generative AI is a means to an end or ends.
• Despite this important distinction, the ease of use that chat GPT has brought to the interface to connect with their generative AI cannot be understated. Nearly everyone is used to typing (or using speech-to-text, etc.) into a chat box. The interface of virtual and augmented reality headsets creates friction because it requires considerable change for us to use the headsets, interact with a medium, and indeed inhabit a different realm than what we’re used to.
• The language components of the metaverse and its visual components such as avatars are not sufficiently lifelike to inspire mass adoption, especially in retail environments that need to closely resemble in-person experiences to succeed.
• After three years in relative isolation during the pandemic, there is a strong desire to return to real life rather than inhabit a virtual world in addition to many online meetings.
• It’s much more difficult to create a compelling, convincing, immersive virtual experience that approximates real life than it is to apply AI algorithms to various discrete tasks such as copywriting, analytics, and decision support.

Economics and practical applications also are important factors in the widespread adoption of AI.

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With AI, deeper and more accurate insights from improved analytics can lead to better decisions which usually provide economic and productivity benefits and ROI. With the metaverse, immersive experiences such as reviewing a virtual product design or shopping in a virtual store with virtual clothing try-ons are mostly further out in the future and nowhere near rivaling or replacing current e-commerce and in-person retail experiences and revenue.

Generative AI Can Unlock The Metaverse.

The lagging metaverse now has a newfound enabling technology in generative AI to achieve its potential for widespread adoption, lifelike immersive experiences, and the scalability required to deliver such experiences across languages, cultures, and markets. Now that we can create any imaginable image just by speaking or typing it and automated language translation services are increasingly maturing and common, the metaverse has the potential to come to fruition to a point where the equipment and behavioral change required will be worth it to receive its benefits. It’s important to note that image generation via AI still is too low quality and not true-to-life enough to enable visual experiences in the metaverse to be suitable for mass adoption. That will change with time, and when it does, there will be a dramatic uptick in metaverse adoption as the visuals in those virtual spaces will become indistinguishable from real-world spaces (e.g., you will look like you, not an avatar).

Possible future benefits of the metaverse in a retail environment include generating and interacting with lifelike virtual versions of the human customers and salespeople as though they were in-person and in the culture and language of the customer. All of the complexities and nuances of in-person communication will be a major challenge to emulate virtually, but generative AI increasingly will be capable of closely approximating so many subtleties.

Rather than the current avatars that are fuzzy approximations of a person or downright “bizarro” versions of a person, future avatars will look exactly like that person and will enable virtual try-on of clothing, jewelry, hairstyle, makeup, and accessories that look exactly like their real-world analogies. That should lead to the final promise of higher rates of customer satisfaction and conversion and lower return rates.

Look Before You Leap with Generative AI Metaverse

However, a note of caution: The lifelike metaverse and its potential for widespread adoption are closer than ever but still in infancy.

Therefore, proceed slowly and invest prudently, and, most importantly, understand the problem or goals first before examining and adopting potential solutions.

Although generative AI has countless use cases for customer service, customer experience, and metaverse applications, carefully evaluate the possibilities and decide whether any use cases are right for your business now. While some organizations may have a problem that can be solved or a goal that can be achieved through generative AI or other AI or metaverse applications, it may not be the right timing for your business. Many of us remember or know about the e-commerce meltdown at the turn of this century when many e-commerce startups went out of business due to a lack of sufficient revenue; let’s not repeat it by overinvesting in new technologies without the fundamentals to support them.

The pressure on startups from investors sometimes is immense to dedicate resources to the current or next big thing, while established companies sometimes are perceived to lack innovation compared to disruptive startups.

Both innovative startups and more cautious established organizations need to apply the same thinking: Define problems and goals, and then determine the best methods and technologies to address them within appropriate and allocated budgets.

The post Generative AI Will Bring the Metaverse to Fruition appeared first on AiThority.

The headlines speak for themselves: Artificial Intelligence (AI) is the moment. Between self-driving cars, facial recognition, and chatbots – it seems like AI is everywhere. With new advancements being announced seemingly every day, tech pioneers are calling for a temporary halt to advance AI development and increase standardizations across industries. And while some may think that AI will take away jobs from humans, it’s predicted that AI will actually create 97 million new jobs by 2025. There’s no doubt that in the coming years, AI will completely reshape the world.

Although there is hesitation around how quickly things are advancing, the opportunity for AI is unprecedented. And with that, AI is an opportunity to empower marketers specifically. 

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But how can marketers simultaneously leverage AI to see results, while also keeping humanity and trust within their marketing campaigns? The issue is traditional AI tools aren’t created with the long-term in mind and therefore don’t maintain a lasting impact. While these platforms succeed in closing today’s sales and boosting quick wins, they don’t create true customer-brand relationships, which is ultimately the goal of marketing.

To create a lasting relationship with customers, brands need to leverage a different kind of AI—one that offers a relationship-building strategy to create a better customer experience, builds loyalty, and leads to higher lifetime value.

Say Goodbye to Legacy Processes

The traditional AIs found on the market are built for sales, not marketing. This is because they rely on reinforcing previous actions, instead of setting up personalized content that’s original to the customer to encourage their continued engagement.

Take grocery for example: when the challenge isn’t offer-driven or substitution-driven (i.e. getting customers to substitute store brand items for name brands), then you need a different approach. How do we get them to shop in more aisles? What products are the key ones to finding the competitive advantage for each customer and creating loyalty to my store?

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Generative AI can adopt the above framework to create a content quality report for brands. It can generate a lot of information about what creative/products are good ‘teasers’ for helping to move customers on their loyalty journey. This feedback loop is ideal for marketers because they can use it to help drive what creatives they create and what offers they make. It also creates an understanding of how often to touch a customer over time to maximize engagement with a channel, which can maximize lifetime value without overly relying on frequent short-term deals and discounts.

For companies that are looking to adopt a more long-term marketing vision, leveraging a sophisticated AI tool allows them to step away from the reliance on offering short-term gimmicks.

Building Generative AI Trust: A New Culture of Experimentation

For marketers, instead of mindlessly implementing campaigns, the vast movement to AI and machine learning has created a culture of A/B testing that has allowed them to experiment with how to perfect their campaigns. The ability for marketers to learn from AI, but make their own decisions on the best route of action, puts the power back in their hands.

For customers to commit to a brand relationship, they need to know the brand – with AI, not only will each customer learn about an overarching brand story, the points that resonate with them will be emphasized. This way not only is their value proposition highlighted, but the specific ways they can serve every individual customer will be showcased.

Unsurprisingly, almost 70% of individuals are likely to stick with a brand if they feel an emotional connection. Therefore, learning their unique individual customer needs with AI will maximize both short and long-term revenue gains for marketers.

That’s exactly what we do with Da Vinci. The engine is discovery-oriented and learns about the customer, rather than just echoing what it’s heard before. By learning about the products and who they resonate with for each customer, marketers can engage with them earlier in the funnel, not only when they’re close to purchasing. Through this, they can build a long-term relationship, tell the brand’s story, and ultimately, see profit gain (which Da Vinci achieved in H1 2022, earning 21% + lift in conversion rates and 26% + lift in revenue).

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Building Trust in AI

In the marketing/e-commerce space, Generative AI is about getting leverage from your investments — not about being a substitute for producing terrific brand-building creative. For customers, mistrust is the result of setting-up AI to compete with human intelligence.

However, this doesn’t need to be the case. In order to foster trust, humans need to grow in their collaborative experience with AI.

AI is a tool for assisting humans to build great things. It is a long way from building anything truly great itself, but it is a great aid for helping humans get the leverage they need to use their own creativity to differentiate themselves. By way of illustration: imagine a marketing team looking to deploy email communications. If a marketer decides to blindly trust generative AI to produce or create something from scratch, the results will be mediocre.

Marketers should instead use algorithms to rank their creative investments. Simply put, this means taking the top-performing creatives and using generative AI to produce variants of that creative. Now, the marketer can send multiple variations of a high-quality piece of content to their targeted customer. Backed by data, they know it’s a good fit but potentially risk losing brand appeal if they send the same thing twice. This is where generative AI can be used to reinforce the brand message to build trust instead of eroding it.

The Future of AI 

Leveraging AI, marketers are empowered to continually evolve their messaging to surprise and please their customers and build a long-lasting relationship that establishes meaningful brand loyalty.

Acknowledging the power and potential of AI, there’s no doubt that in the coming years, it will completely reshape the world. And when done correctly, it can maximize companies’ capabilities and liberate customers from undesired communications.

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In recent years we have seen the perception of PC and console gaming pivot from unsociable and, let’s face it, a bit nerdy, to an activity that is universally enjoyed, with a growing community of women and older generations getting involved.

This shift was accelerated by the pandemic, when people were searching for new and more entertaining ways to connect. However, despite mobile gaming already establishing an effective marketing tool via in-game programmatic ads, PC and console gaming has been relatively under-utilized, especially considering users often play for two or more hours in one sitting. This is because it is more challenging to reach consumers in an environment in which they value their experiences greatly and do not wish to be interrupted.

However, by understanding how consumers value video games and the experience they demand, and learning from past successes and failures, brands can leverage the opportunity and optimize engagement and campaign effectiveness among gamers.

Gaming:  Surge in Popularity

Gaming surged in the spring of 2020. Gen Z and Millennials, seeking entertainment during lockdowns, spent 23% and 21% more time respectively gaming as of March 2020, while a far wider demographic was also exposed to gaming during this time. There was a spread to non-traditional audiences and to a greater proportion of the younger generation. The number of people aged 60 and over searching for games increased by 200%, and 93% of under-18s admitted to gaming regularly. At the same time, gaming moved away from an isolated, unsociable stereotype, instead being enjoyed by all ages, alone and as a community activity.  New groups emerged; ‘down-timers’ were parents or young professionals looking for a break, whilst ‘social gamers’ utilized gaming as a new way to connect with friends.

Fast forward to 2022 and we saw a slowdown as global inflation tightened purse strings, and chip shortages led to supply issues with gaming hardware. Even so, the global gaming market’s revenue increased by 45% from 2019 to 2022, and the sector is predicted to be worth a staggering $321 billion by 2026.

Opportunities for Advertisers

A unique opportunity has emerged for advertisers to reach younger audiences in a non-traditional format. Gaming audiences skew younger: 74.2% of 18-24 year-olds play video games in the US, according to Insider Intelligence, while TV penetration for the same group is just 58.2% and falling. Deloitte’s media trends report showed that playing video games is Gen Z’s favorite entertainment activity in the five countries surveyed (US, UK, Brazil, Germany and Japan). In fact, Gen Z spends a quarter of their leisure time gaming, more than on any other medium.

Traditionally, mobile gaming has been the most effective channel, and today, 45% of all video gaming ad revenue comes from mobile. Programmatic in-game advertising is seen as the most sustainable ad business for most marketers, with technology capable of measuring and tracking ad performance. Meanwhile, PC and console gaming is the most attention-rich and immersive envirnoment, but has historically been under-utilized by brands to access a hard-to-reach audience.

The reality is, however, that as players invest significant time and money in gaming, they do not want to be slowed down or hampered by repetitive on-screen ads, like those seen all too often on free-to-play mobile gaming and traditional linear media. Basketball simulation game NBA 2K19 came in for criticism when players were disappointed to find un-skippable ads surfacing within the game.

At the other end of the spectrum is Fortnite. The trailblazing battle royale game has collaborated successfully with multiple brands to create interactive and immersive experiences for its users. From soccer skins and interactive pitches in celebration of the World Cup and cosmetic tie-ins with the NFL, to a Marvel tie-up creating Fortnite x Avengers crossover events, brand partnerships have exploded on the platform. It has hosted game modes for franchises such as Star Wars, John Wick and Avengers: Endgame, and created branding that no longer feels like advertising. Firstly, it is optional and secondly, it has an immersive nature, bringing new and exciting elements to the game as players are seamlessly familiarized with the brand or product. These innovations are a great example of how brands can effectively reach gamers within console and PC gaming.

2023 and Beyond: An Exciting Frontier

The outlook for 2023 and beyond is certainly positive for the gaming industry. The US economy is on the road to recovery and rapidly-growing economies in Asia and the Middle East are expected to see sustained mobile gaming growth. PC and console gaming will continue its multi-platform investment, along with further commercial diversification in the eSports industry.

Game publishers will seek to leverage technology to help create an ever more personalized gaming experience. Storytelling will become increasingly bespoke with the use of technology such as generative AI, machine learning and zero code platforms, and brands – as we’ve seen with Fortnite – will be most effective when facilitating the creation of such experiences, enhancing them with added value and benefits.

While the ‘one-size-fits-all’ approach that may have been effective across traditional media, it will not be appreciated in a landscape where gamers are unwilling to endure linear commercial activity that interrupts their game.

What’s clear is that marketers need to develop a comprehensive understanding of target games, the experience they offer the player and the content and culture surrounding each. Only then can they determine how their brand might create added value in a way that is engaging, interactive and memorable.

Thoughtful, tailored approaches that enhance, rather than detract, from the user’s experience will be the most successful. Get it wrong and it could be game over. Get it right and you’ll elevate your marketing to the next level.

The post Gaming: An Opportunity to Boost Your Brand Among Passionate Players appeared first on AiThority.

AI researchers have published a new paper stating the development of a deep learning-based EMG Sensing Technology, called EMGSense. The developments were published in the paper titled “EMGSense: A Low-Effort Self-Supervised Domain Adaptation Framework for EMG Sensing”. The new human-machine interface (HMI) model fundamentally aims to solve the problem of cross-user EMG sensing, which is linked to the degradation of sensing devices due to biological heterogeneity.

Let’s understand the concept of EMG sensing technology and how a deep learning model like EMGSense works.

What is EMG Sensing Technology?

Electromyography or EMG sensing technology tracks and measures the electromyographic (EMG) signals that your muscles generate during locomotion. These sensors can detect faintest of muscle movements such as bending of elbows, rotating wrists, twiddling with the thumb, or lifting the arm. EMG sensing technology is used in the medical field to detect degenerative nervous or muscular disorders causing numbness, tingling, weakness and cramping. With the rise of smartphone-based connectivity sensors, the scope of EMG Sensing technology has grown beyond medical applications. Today, this technology is widely used to study biomechanics, to create customized prosthetic limbs, to build gesture-controlled immersive video games and robotic control systems.

There are two types of EMG Sensors;

• Surface
• Intramuscular

EMG sensing systems suffer from the problem of degradation and limited cross-user heterogeneity. Deep learning technology has a solution, as researchers from CityU- Hong Kong showed in their latest paper. The framework is called EMGSense– a high-performance self-trained model that delivers accurate output from wearables. Researchers developed advanced self-supervised deep neural network (DNN) model to measure EMG data from gesture recognition and activity recognition. EMGSense is much more accurate than other EMG sensing technologies, outperforming other models by up to 17.4%.

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MCA Connect, LLC, an award-winning Microsoft Manufacturing and Supply Chain Partner, announced its acquisition of Pervicta, LLC, a Dallas, Texas based data consultancy specializing in Data Strategy and AI/Machine Learning.

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“Welcoming Pervicta to our team means we can reach more customers and bring increased AI/Machine Learning capabilities to help solve increasingly complex manufacturing challenges.”

Pervicta employs a seasoned team of data experts with deep expertise in building IP and providing consulting services to help companies turn their data into a strategic asset. “Now more than ever, data is at the heart of every customer conversation. The manufacturing industry has seen significant change over the past few years,” said Claude Watson, MCA Connect CEO. “Welcoming Pervicta to our team means we can reach more customers and bring increased AI/Machine Learning capabilities to help solve increasingly complex manufacturing challenges.”

“Joining forces with MCA Connect enables us to expand our reach and bring advanced data capabilities to manufacturing companies seeking to optimize operations and improve business outcomes,” said James Roberts, CEO of Pervicta.

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The acquisition of Pervicta is a significant step in MCA Connect’s commitment to modern manufacturing, the Microsoft Cloud, and manufacturing excellence. The launch of the modern analytics platform MCA Connect Inspire Platform™, coupled with the acquisition of Pervicta enhances the breadth of manufacturing intelligence capabilities MCA Connect brings to its customers. MCA Connect’s best-in-class data and AI services coupled with industry IP such as Inspire Platform™ help customers become more agile, efficient, and profitable.

Lori Borg, MCA Connect Chief Growth Officer and SVP, Manufacturing Intelligence, said, “Combining the strength of our existing Manufacturing Intelligence sales and delivery teams with the Pervicta team allows us to serve our customers more holistically, continue our growth trajectory, and be in lockstep with Microsoft as their go-to Business Applications and Azure partner for all things manufacturing.”

Ryan Smiley, RLH Managing Director and MCA Connect Board Member, said, “This acquisition demonstrates our commitment to expanding the reach and capabilities of MCA Connect, enhancing their ability to serve US-based and global customers with world-class expertise. We believe the data strategy and advanced analytics capabilities Pervicta brings will further accelerate MCA Connect’s existing growth model.”

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[To share your insights with us, please write to sghosh@martechseries.com]

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Advanced Navigation, the world’s most determined innovator in artificial intelligence (AI) for robotic and navigation technologies, is announcing the largest subsea robotics facility in Australia, located in Balcatta, Western Australia (WA). The high tech manufacturing and R&D facility will accelerate the production of the company’s revolutionary underwater technologies, including its autonomous underwater robot, Hydrus.

See Advanced Navigation in action here:

“Now more than ever, there is a need to open up the earth’s oceans, to make data and knowledge more accessible to global communities, research institutions and governments. Western Australia has always been an exploration hub for ocean discoveries.

The new subsea center will help Advanced Navigation meet the growing demand for high-grade underwater data, bringing new and existing solutions to market far more quickly and efficiently. With the goal to grow our subsea team threefold, we are confident this investment will deepen and advance our understanding of the oceans.” said Xavier Orr, CEO and co-founder, Advanced Navigation.

Ocean Intelligence with Advanced Manufacturing 

The subsea center is located on a massive 5.5 acre site. The facility is split between development and manufacturing for high volume production and continued research and expansion of subsea navigation and robotics technologies. This includes the growth of its underwater artificial intelligence division.

Advanced Navigation is a proud stalwart for independent, in-house design and vertical integration that has ushered in many innovations, including extreme miniaturization of pressure-tolerant electronics, sophisticated sonar technologies and AI-based autonomous systems. The new center also includes full testing facilities with several marine simulation environments to ensure reliable performance and the highest quality production.

Reshaping the Blue Economy with Brilliant Minds

Advanced Navigation’s break-through underwater navigation and robotic technologies are utilized across the blue economy, supporting research, aquaculture, offshore renewable energy, transportation, surveillance, biotechnology and high-tech services.

The company’s recent autonomous underwater robot Hydrus continues to revolutionize undersea research, survey and exploration by making data capture far simpler and vastly more accessible. The Hydrus design synthesizes numerous cutting-edge navigational, sonar, propulsion and data capture technologies with highly developed and sophisticated artificial neural network (ANN) intelligence.

With support from prominent research institutions including the University of Western Australia, Curtin University and philanthropic organization Minderoo, Advanced Navigation continues to establish sustainable technologies to foster the growth of the blue economy, nationally and internationally.

“It’s exciting to see Advanced Navigation continue to grow its team of engineers in Western Australia. At UWA we are researching how natural and artificial reef structures can protect coastlines by dissipating wave energy – Hydrus is a key tool in mapping and surveying these underwater structures. The technology makes more efficient use of our funds and ultimately scales up our ability to collect high-resolution data,” said Justin Geldard, Coastal and Ocean Researcher, University of Western Australia Ocean Institute.

Autonomous underwater robot Hydrus deployed in UWA wave flume tank 

Future of the Autonomy Revolution

The subsea center is just one of several investments made by Advanced Navigation as it continues to expand its global reach and capabilities. The company has established headquarters in Sydney with research centers throughout Australia, including Brisbane for aerial drone technology, Canberra for photonic and laser technology and Newcastle for quantum sensing.

In addition to novel autonomous subsea robotics, Advanced Navigation delivers AI-enhanced navigation technologies for land, sea, air and space applications. The company is committed to developing innovative products and systems that will be catalysts of the autonomy revolution.

Today, Advanced Navigation is a supplier to some of the biggest companies in the world, including Airbus, Boeing, Google, Apple, and General Motors. Advanced Navigation is headquartered in Sydney, Australia with multiple research facilities throughout the country and sales offices around the world. Advanced Navigation is an Australian manufacturer exporting globally while maintaining carbon-neutral operations.

[To share your insights with us, please write to sghosh@martechseries.com]

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