Data Analysis Archives - AiThority

Expedera NPUs Run Large Language Models Natively on Edge Devices

PRNewswire — Mon, 08 Jan 2024 14:49:07 +0000

Expedera NPU IP adds native support for LLMs, including stable diffusion

Expedera, Inc, a leading provider of customizable Neural Processing Unit (NPU) semiconductor intellectual property (IP), announced that its Origin NPUs now support generative AI on edge devices. Specifically designed to handle both classic AI and Generative AI workloads efficiently and cost-effectively, Origin NPUs offer native support for large language models (LLMs), including stable diffusion. In a recent performance study using the open-source foundational LLM, Llama-2 7B by Meta AI, Origin IP demonstrated performance and accuracy on par with cloud platforms while achieving the energy efficiency necessary for edge and battery-powered applications.

LLMs bring a new level of natural language processing and understanding capabilities, making them versatile tools for enhancing communication, automation, and data analysis tasks. They unlock new capabilities in chatbots, content generation, language translation, sentiment analysis, text summarization, question-answering systems, and personalized recommendations. Due to their large model size and the extensive processing required, most LLM-based applications have been confined to the cloud. However, many OEMs want to reduce reliance on costly, overburdened data centers by deploying LLMs at the edge. Additionally, running LMM-based applications on edge devices improves reliability, reduces latency, and provides a better user experience.

“Edge AI designs require a careful balance of performance, power consumption, area, and latency,” said Da Chuang, co-founder and CEO of Expedera. “Our architecture enables us to customize an NPU solution for a customer’s use cases, including native support for their specific neural network models such as LLMs. Because of this, Origin IP solutions are extremely power-efficient and almost always outperform competitive or in-house solutions.”

Expedera’s patented packet-based NPU architecture eliminates the memory sharing, security, and area penalty issues that conventional layer-based and tiled AI accelerator engines face. The architecture is scalable to meet performance needs from the smallest edge nodes to smartphones to automobiles. Origin NPUs deliver up to 128 TOPS per core with sustained utilization averaging 80%—compared to the 20-40% industry norm—avoiding dark silicon waste.

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

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fullthrottle.ai Introduces SafeMatch to Easily Resolve First-Party Data-Driven Household Identity and Attribution

AIT News Desk — Thu, 04 Jan 2024 12:20:43 +0000

First-of-its-kind attribution platform builds on patented technology to provide new clarity into customer advertising and transaction journeys while maintaining privacy and safety compliance

FullThrottle Technologies, LLC, an end-to-end, first-party data-powered technology company that helps marketers identify, curate, and target audiences, announced the launch of SafeMatch, a new solution for ingesting household transaction data and safely resolving customer identities at the household level without using cookies. Leveraging fullthrottle.ai’s patented technology for household address resolution, SafeMatch empowers fullthrottle.ai customers to easily and safely connect first-party purchase information to customer profiles at the household level.

As signal loss continues to present significant challenges to the industry—especially for smaller companies without the resources or tools to quickly and effectively navigate the changing landscape—SafeMatch™ offers a scalable household transaction ingestion system that is easy to use and offers multi-dimensional outcome matching, at a person and product level. With its foundation in superior parallel processing technology, SafeMatch™ ensures supply path optimization with rapid and efficient first-party data operations compared to traditional sequential processing.

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The new methodology affords enhanced matching capabilities where households can now match to multiple transactions per month across multiple household buyers, ensuring comprehensive data capture. The transaction match period also now mirrors the evolving and extended buyer journey in the post-pandemic era. With SafeMatch™, marketers benefit from new levels of clarity regarding the path of each customer while meeting the highest standards for privacy and safety.

Tailored to meet the needs of advertisers of all sizes navigating an increasingly complex media and digital commerce landscape, SafeMatch™ ’s easy to use user-interface leverages time-aware technology and multi-unit learning algorithms to provide insightful data on household activities and resident density—facilitating product and household-level reporting. This approach to first-party data analysis connects site visitors to attribution data later in the customer journey and over an extended time, providing more accurate and actionable insights for customers to guide and refine their advertising strategies and investments.

Amol Waishampayan, CPO of fullthrottle.ai, remarked, “SafeMatch™ represents our unwavering and simultaneous dedication to innovation, data compliance, and consumer protection. By listening to our clients and partners, many of whom are short on technical resources of their own, we’ve developed a first-of-its-kind methodology that exceeds current industry standards. SafeMatch™ is set to revolutionize how we understand and utilize first-party household data. The result is a comprehensive solution that makes it easier for advertisers to connect exposure to sales and to understand their customer journey at a detailed level that allows them to maximize the return on their marketing investment.”

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

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AiThority Interview with Steve Flinter, Distinguished Engineer, Artificial Intelligence & Quantum Computing, Mastercard Foundry R&D

Pooja Choudhary — Wed, 27 Dec 2023 10:22:37 +0000

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.

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Scienaptic AI and SentiLink Strengthen Partnership, Advancing AI-Powered Fraud Detection Capabilities

Business Wire — Mon, 18 Dec 2023 14:09:11 +0000

A Game-Changer in Preventing Multimillion-Dollar Losses for Lenders

Scienaptic AI, the leading AI-powered credit underwriting platform provider, and SentiLink, the leading identity verification and fraud detection solutions provider, are excited to announce an enhanced product partnership. This product alliance comes in response to the escalating challenges of identity theft and the growing threat of synthetic identity fraud in the digital lending environment. The strengthened partnership is set to equip credit unions and lenders with advanced tools to combat fraud effectively.

Identity fraud remains a significant cause of losses for credit unions, and the risk presented by synthetic identities is a growing concern. Recent studies reveal a shocking 1.8 million consumer credit accounts potentially compromised by synthetic identity fraud within just a year. Alarmingly, over 30% of these accounts were at risk of major delinquency or charge-offs, resulting in average losses of $8,000 – $10,000 per incident.

In light of this alarming trend, the partnership between Scienaptic and SentiLink is set to revolutionize fraud detection. By equipping credit unions and lenders with cutting-edge tools, this alliance aims to intercept fraudulent applications at the point of origination, thereby preserving the integrity of the lending process and protecting the experiences of genuine members. Credit unions are also able to leverage SentiLink’s solutions to re-verify identities of existing members when, for example, personal information is changed on an established account.

“By combining SentiLink’s data with Scienaptic’s AI, we’re equipping financial institutions with the necessary tools to effectively mitigate fraud risks and enhance the decision-making process behind their loans,” stated Vivek Ahuja, Head of Alliances Development at SentiLink. “Our analysis has shown that over 15% of synthetic identities specifically target credit unions. Our risk solutions, which are designed to detect identity theft and synthetic fraud at the point of application, form the core of this partnership. This collaboration enables financial institutions to approve more members safely, without introducing unnecessary friction into the onboarding process.”

Steve Perez, Loan Production Manager at Altura Credit Union, added, “Through SentiLink and Scienaptic, we’re revolutionizing fraud detection and prevention with AI-powered data analysis and pattern recognition. The platform scrutinizes origination data, delivering predictive scores, attributes, and insights to pinpoint fraudulent applications. As a result, we are experiencing diminished fraud losses and an enhanced member experience.”

“Our partnership with SentiLink is a cornerstone in our ongoing commitment to revolutionizing the technology used in credit decisioning,” said Eric Steinhoff, EVP Client Impact at Scienaptic AI. “The integration of our technologies offers a holistic underwriting approach that harnesses fraud data and AI. This fusion creates highly predictive signals, effectively reducing fraud risk, optimizing decision-making processes, and ultimately leading to a rise in approvals for deserving members. We stand united with credit unions in our shared mission to combat financial fraud.”

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

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WiMi Announced Asymmetric Spectral Network Algorithm

GlobeNewswire — Sun, 17 Dec 2023 15:51:42 +0000

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that its R&D team proposed an asymmetric spectral network algorithm. The algorithm employs asymmetric coordinate spectral spatial feature fusion to provide a novel, end-to-end feature learning method for hyperspectral image classification tasks. The algorithm’s adaptive feature fusion method is capable of extracting discriminative spectral spatial features, and unlike common feature fusion methods, the algorithm is more adaptable to multi-hop connectivity tasks while eliminating the need for manual parameterization.

WiMi’s asymmetric spectral network algorithm solves the spectral noise problem through adaptive feature fusion. The algorithm allows the network to adaptively fuse multiple pieces of information to extract discriminative spectral-spatial features. Unlike traditional feature fusion, this algorithm does not require manual parameterization and is adapted to multi-hop connectivity tasks. This adaptivity helps to efficiently handle complex spectral data and improves the algorithm’s ability to recognize real signals.

In terms of the band correlation problem, the asymmetric spectral network algorithm introduces a coordinate and strip pooling module. Coordinates are used to obtain accurate coordinate and channel information, which helps the network to better understand the spatial structure of the data. Meanwhile, the strip pooling module is used to avoid introducing irrelevant information. The combination of these two techniques makes the network more adaptive and better able to handle the complex band correlations present in hyperspectral images.

WiMi’s asymmetric spectral network algorithm focuses on simplicity, which is to reduce the model complexity with less training time. The algorithm successfully reduces the complexity of the algorithm through an asymmetric learning model and adaptive feature fusion while maintaining high classification performance. This makes the algorithm more suitable for practical application scenarios and provides higher efficiency for hyperspectral image classification tasks.

WiMi’s asymmetric spectral network algorithm focuses not only on static scenes but also on dynamic scenes. Its end-to-end feature learning approach and adaptive feature fusion method enable the algorithm to better adapt to the ever-changing information in hyperspectral images, thus improving the classification accuracy in dynamic scenes. It effectively overcomes the technical challenges in hyperspectral image classification and brings a more efficient and accurate solution.

In addition, it introduces the key technology of asymmetric coordinate spectral spatial feature fusion. The algorithm learns the feature representation of hyperspectral images end-to-end through an asymmetric learning model. Compared to traditional methods, this asymmetric learning approach better captures the complex relationships between pixels, enabling the model to more accurately understand the non-uniformity of the spatial distribution, thus improving the classification accuracy.

The successful development of WiMi’s asymmetric spectral network algorithm provides greater feasibility for real-world application scenarios. By reducing model complexity and improving training and inference efficiency, the algorithm can be better adapted to real-world requirements, especially in decision-making and monitoring scenarios that require fast response, demonstrating significant advantages. The introduction of the algorithm will drive hyperspectral image classification technology into a new stage of development. This is expected to stimulate more research and innovation and drive the whole field forward.

WiMi’s asymmetric spectral network algorithm provides a more accurate and efficient solution for hyperspectral data analysis and processing in the fields of crop detection and geological exploration. In the future, with the further optimization of the algorithm, it will be applied to a wider range of fields, such as environmental monitoring, weather prediction, etc., providing more powerful support for various industries. asymmetric spectral network algorithm will accelerate the deep integration of scientific research and industry.

Considering the prevalence of dynamic scenes in hyperspectral image classification tasks, WiMi will continue to optimize the adaptability of the asymmetric spectral network algorithm. By further improving the end-to-end learning approach and adaptive feature fusion method, the algorithm is better adapted to rapidly changing environments and improves classification accuracy in dynamic scenes. WiMi’s asymmetric spectral network algorithm opens up new horizons in the field of hyperspectral image classification, and will continue to play an important role in scientific research, industrial applications, and technological innovation.

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

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AnswerRocket Unveils Skill Studio to Empower Enterprises with Custom AI Analysts for Enhanced Business Outcomes

Business Wire — Wed, 13 Dec 2023 06:34:46 +0000

AnswerRocket, an innovator in GenAI-powered analytics, announced the launch of Skill Studio, which empowers enterprises to develop custom AI analysts that apply the business’ unique approach to data analysis.

“Skill Studio has immense potential to transform our approach to analytics,” stated Stewart Chisam, CEO of RallyHere Interactive, a platform for game developers to run multi-platform live service games. “With Skill Studio, we can create a customized AI analyst that deeply understands the nuances of the gaming industry and how we analyze our data. Its ability to automate complex analyses like game performance, player interactions, and usage patterns is groundbreaking. The insights generated by Max can help drive strategic decisions to enhance both our platform and user experience.”

Say hello to specialized AI copilots

AI copilots have emerged as a powerful tool for enterprises to access their data and streamline operations, but existing solutions fail to meet the unique data analysis needs of each organization or job role. Skill Studio addresses this gap by providing organizations with the ability to personalize their AI assistants to their specific business, department, and role, which enables users to more easily access relevant, highly specialized insights.

Skill Studio elevates Max’s existing AI assistant capabilities by conducting domain-specific analyses, such as running cohort and brand analyses. Key enhancements include:

Full Development Environment: End-to-end experience supporting the software development lifecycle for developers to gather requirements, develop, test, and deploy Skills to the AnswerRocket platform. Skill Studio allows developers to leverage the Git provider and integrated development environment (IDE) solution of their choice.
Low-Code UX: User-friendly interface for developers and analysts to create customized Skills for the end users they support.
Reusable Code Blocks: Accelerate custom Skill development by leveraging pre-built code blocks for analysis, insights, charts, tables, insights, and more.
Bring Your Own Models: Skill Studio extends the analytical capabilities of Max, enabling enterprises to deploy their existing machine learning algorithms within the Max experience.
Multi-source and Multi-modal Data Support: Analysts can perform complex analyses using multiple data sources, including structured or unstructured through a single tool. This allows businesses to glean insights from siloed data sources that were previously inaccessible.
Create Purpose-Built Copilots: Construct copilots designed for specific roles by giving them access to the Skills needed to perform a set of analytical tasks.
Quality Assurance & Answer Validation: Testing framework for validating accuracy of answers generated by Skills.

“AI copilots have revolutionized the way organizations access their data, but current solutions on the market are general-use and not personalized to specific use cases,” said Alon Goren, CEO of AnswerRocket. “Skill Studio puts the power of AI analysts back in the hands of our customers by powering Max to analyze their data in a way that helps them achieve their specific business outcomes.”

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A collaborative experience for creating fit-for-purpose AI assistants

Skill Studio enables cross-functional design, development, and deployment of AI copilots:

Data Scientists and Developers: Technical team members can democratize specialized data science algorithms and models as reusable Skills that can be leveraged by Max, enabling users to successfully retrieve the advanced answers they need quickly and securely.
Analysts: Analysts can customize Skills and Copilots to capture their company’s best practices for analyzing and retrieving insights from data. This allows repetitive, manual data analysis processes to be executed by Max for automated analyses.
Business Users: Users can enjoy an easy-to-use experience for interacting with their data by chatting with an AI analyst who understands their business, analytical processes, and insights needs.

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

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Almost 60 Percent of Organizations Are Implementing or Exploring Generative AI in Marketing

GlobeNewswire — Mon, 11 Dec 2023 10:10:22 +0000

Almost 60% of organizations are implementing or exploring generative AI in marketing And three quarters of organizations have either already allocated budget to integrate generative AI into marketing, or plan to do so in the next six months

The majority of marketers (62%) believe that generative AI will augment human creativity, enhancing unique human qualities such as intuition, emotion, and context understanding. Organizations already investing in generative AI for marketing dedicate 62% of their total marketing technology budget towards it, seeing this breakthrough technology as a catalyst for creativity and innovation in marketing. That’s according to Capgemini Research Institute’s latest report ‘Generative AI and the evolving role of marketing: A CMO’s Playbook’, which reveals that half of organizations have already set aside specific budgets and almost half (47%) have allocated teams for the implementation of generative AI in marketing.

The report found that 57% of marketers expect generative AI to act as a catalyst for unlocking new creative possibilities, particularly when collaborating between human and AI-driven innovation; 55% foresee this motivating teams to think beyond conventional boundaries. In the next two to three years, marketers already using generative AI expect it will be applied across data analysis (90%), search engine optimization (89%), customer services (89%), content creation (88%) and image and video generation (86%). As a result, marketing functions are actively establishing practices to use generative AI across marketing domains.

According to the research, organizations believe that this technology can help in building a unique brand image (67%), accurate analysis of customer and market trends (65%), reduction in marketing costs (66%), and increase efficiency in generating content and results (65%).

Gagandeep Gadri, Managing Director of frog, part of Capgemini Invent, said, “The future of marketing will undeniably be influenced by the widespread adoption of generative AI to deliver personalized content and communication; it’s value realization will need a fusion of strategic choices, human centered creativity and a good understanding of the art of the possible in a very fast-moving space. In many ways this feels like the Digital boom from 20 years ago, where the brands that succeeded were those that stayed true to their values but were brave and bold on how digital could deliver growth for their business. The same will apply for generative AI.”

Addressing ethical and regulatory issues will be key

As AI algorithms become increasingly sophisticated, marketers will continue to face complex ethical considerations around issues such as the responsible use of customer data, the transparency of AI-driven decision-making processes, and ensuring algorithms do not reinforce social inequalities.

The research indicates that only 30% of organizations have implemented clear guidelines for the use and oversight of AI systems and less than half consider attributes of trust, privacy, and responsibility when selecting AI systems for marketing activities. Less than half of organizations (42%) are implementing measures to protect themselves from challenges related to the use of generative AI in marketing, such as monitoring or searching for AI-derived versions of their work, including logos and artwork.

Bridging the generative AI skills gap

According to the report, the majority of organizations (71%) anticipate that certain marketing roles will be significantly or moderately impacted by generative AI including SEO specialists, digital marketing and creative directors, PR/communication specialists, copywriters, and customer insight specialists.

The majority (63%) of organizations recognize that the demand for generative AI skills in marketing significantly outstrips supply. To address this skills gap and harness the full potential of generative AI in marketing, organizations are implementing internal and external strategies. On average, 53% of companies are planning to provide generative AI training for their marketing teams in the next six months, with companies in the Netherlands, India, Australia, and the US most likely to implement this initiative. Sectors such as media, insurance, automotive, and life sciences also show higher-than-average commitment to generative AI training for their marketing teams.

Marketing is emerging as a strategic force to drive organizational success

The research shows that marketing has transformed in recent years, with Chief Marketing Officers (CMOs) increasingly playing a central role in strategic decision-making processes. In business-to-consumer (B2C) sectors, 71% of C-suite respondents see marketing as a strategic partner in driving business growth and 72% of B2C organizations involve the CMO in critical decisions to drive business goals and objectives. CMOs have assumed greater direct responsibility for contribution to revenue growth (49%) and profit-related decisions (44%) over the past two years, increasing by 25 and 19 percentage points from 2021 respectively.

Almost 60% of organizations are integrating generative AI into their marketing efforts, out of which 37% are actively implementing it across various initiatives, while an additional 21% are in the experimental phase. In order to succeed, organizations are adopting diverse AI strategies tailored to their specific marketing needs and available resources. Nearly a quarter of organizations rely solely on external applications and platforms for generative AI in marketing, however, half of them are in the process of either developing or using in-house applications alongside external tools.

Methodology

The Capgemini Research Institute surveyed 1,800 executives overseeing marketing strategies across diverse organizations worldwide with an annual revenue of over USD 1 billion. The organizations came from a range of sectors, including automotive, banking, consumer goods, insurance, retail, telecom, utilities, high-tech, manufacturing, life sciences, public sector and media. They are based in 14 countries across North America, Europe, and APAC. In addition, 25 in-depth interviews were conducted with Chief Marketing Officers and marketing leaders with firsthand knowledge or awareness of their organizations generative AI initiatives. Charts comparing 2021 and 2023 data exclude respondents from B2B sectors, as they were not part of the 2021 survey. The covered sectors include automotive, banking, consumer goods, insurance, retail, telecom, and utilities.

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

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SEOUL FINTECH LAB Supports Participation of 10 Promising Seoul-based Startups at the ‘Singapore Fintech Festival’

GlobeNewswire — Fri, 08 Dec 2023 07:31:20 +0000

SEOUL FINTECH LAB successfully supported the participation of 10 promising fintech companies based in Seoul at the ‘Singapore Fintech Festival (SFF)’, held for three days from November 15 to 17.

The Singapore Fintech Festival, now in its 8th year, is the world’s largest fintech exhibition, led by the Monetary Authority of Singapore (MAS). This year’s event focused on the growth of artificial intelligence (AI) and ESG, topics of global interest. The festival saw participation from over 62,000 individuals and over 10,000 institutions from 134 countries, including international financial institutions and IT companies, with multi-national company executives delivering speeches at the conference.

SEOUL FINTECH LAB, a Seoul City facility supporting fintech startups and a leading incubator for fintech startups in South Korea, provided comprehensive support to these promising domestic companies. This included providing startup office space, customized growth plans for each company, open innovation support, assistance with international expansion, Demo Days, Investment Relations (IR) presentations, and reverse pitching as part of its specialized acceleration programs.

SEOUL FINTECH LAB also spared no effort supporting the ten selected fintech companies for this event. The lab provided pre-event support, such as market fit and business plan validation for international expansion, pitch deck design, and speech consulting to enhance the global capabilities of these startups.

The companies that participated in the festival include Wavebridge (virtual asset management service ‘Dolfin’), Grade Health Chain (healthcare finance service), Treasurer (investment in luxury and high-value collectibles), WinkStone Partners (SME P2P Platform), Quantit (comprehensive asset management platform ‘FINTER’), FinInsight (investment data analysis ‘Insight Page’), Alchemi Lab (AI-based investment ‘ZOLBO’), Hanul Company (blockchain-based music IP trading platform ‘Broove’), Whatssub (subscription expense management), and Mofin (robo-advisor ‘Mofin RA’).

On November 14, the day before the festival, SEOUL FINTECH LAB hosted the ‘SEOUL FINTECH DEMODAY,’ enabling these startups to build networks with various investors, potential partners, and buyers, including local investors SC (Standard Chartered) Ventures and Genting Venture. This also allowed them to introduce their services and competitive strengths to stakeholders.

Kim Han-sam, CEO of Alchemi Lab, shared his experience: “It was a fascinating experience. I had the chance to meet financial institutions, media, and investors from all over Asia at once.” He added, “Some institutions have already inquired about investment plans, and I am scheduled to appear on a Singaporean economic radio broadcast, which brings me back to Singapore. I am committed to capitalizing on this opportunity for growth.”

A representative from SEOUL FINTECH LAB stated, “Through this participation, we realized the deep interest foreign companies have in the Korean market and the confidence that Korean startups are competitive on the global stage.” The representative added, “We plan to explore ways to connect domestic and international startups further. We will continue to support startups in seizing more growth opportunities.”

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

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New TDWI Research Report Examines Applications and Best Practices for Diverse Data Types

GlobeNewswire — Thu, 30 Nov 2023 17:51:47 +0000

Report explores key challenges facing users of diverse data and describes innovative practices from successful organizations

TDWI Research has released its newest TDWI Best Practices Report: Harnessing the Power of Diverse Data for Business Growth. This original, survey-based report focuses on the factors driving today’s organizations to use diverse data and how they can successfully manage, analyze, and govern it.

The report’s author, TDWI’s vice president and senior research director for advanced analytics, Fern Halper, explains that those companies analyzing and using diverse data tend to be more successful with analytics. However, enterprises are struggling to unify diverse data for analysis, govern the data, and manage complex pipelines.

In the report, Halper points out that “respondents cite numerous opportunities for diverse data, [including] having more accurate analytics for better customer insights, improving operational efficiencies, creating a more data-driven culture, and improving innovation and collaboration.” The report explains the most common challenges enterprises are facing in managing and analyzing diverse data, and also explores the value of overcoming those challenges.

The report discusses the most common types of diverse data that enterprises are collecting and working with and the implications of new technologies for data analysis, including generative AI.

Report Highlights

Among this comprehensive report’s key findings:

Most respondents are collecting structured data; text data is already mainstream while other unstructured data types are only beginning to enter mainstream adoption
One-third (30%) of respondents are using data marketplaces to source diverse data; these respondents are more likely to monetize their data
More than half of respondents (59%) are struggling with data quality issues; 42% said they lack necessary skills for analyzing diverse data
Forty-eight percent of respondents agreed that generative AI might be a game-changer for analyzing unstructured data

The report concludes with best practice recommendations for ensuring success in using new data types.

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

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SAIC MOTOR Overseas Intelligent Mobility Selects AWS as Its Strategic Cloud Provider

Business Wire — Tue, 28 Nov 2023 14:16:30 +0000

China’s largest automaker relies on AWS to scale adoption of its SAIC i-SMART connected vehicle platform globally, optimize vehicle performance, and develop personalized features at the lowest cost

At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company announced that SAIC MOTOR Overseas Intelligent Mobility Technology Co., Ltd. (SAIC MOTOR), China’s largest automaker and a Fortune Global 500 company, selected AWS as its strategic cloud provider for its i-SMART connected vehicle platform. SAIC MOTOR is using AWS’s global infrastructure and portfolio of technologies, including high performance compute (HPC), storage, and Internet of Things (IoT), to power its i-SMART connected vehicle platform, which enables intelligent driving experiences in half a million vehicles across Australia, Europe, Middle East, New Zealand, and South America.

By running on AWS, SAIC i-SMART enables drivers to monitor, interact with, and upgrade in-vehicle features around the world using their mobile phone. SAIC MOTOR uses Amazon Simple Storage Service (Amazon S3), an object storage service with industry-leading durability, availability, security, and scalability, to store massive amounts of vehicle data. With Amazon Relational Database Service (Amazon RDS), SAIC MOTOR is able to extract additional value from vehicle data to enhance the driving experience. For example, this data is used to support remote features, such as remote door lock and unlock, as well as cabin preheating or cooling before drivers return to their vehicles. These personalized services can also analyze vehicle systems and driving behavior to suggest nearby gas or charging stations for drivers based on estimated driving distance, or perform a remote diagnostic scan to confirm a car is running at optimal performance and doesn’t need servicing.

SAIC MOTOR also uses Amazon Elastic Kubernetes Service (Amazon EKS), as well as serverless services, including AWS Lambda, to build an elastic and agile connected vehicle architecture that can scale globally. This architecture gives consumers seamless and low latency access, even during peak drive times like morning and evening commutes. In addition, SAIC MOTOR built serverless IoT applications based on AWS IoT Core to collect, process, and analyze vehicle data. AWS IoT Core allows the automaker to securely connect vehicles and devices in the cloud, ingest large volumes of data for real-time analysis, and perform remote fleetwide updates that enhance the driving experience. In the future, SAIC MOTOR plans to use AWS generative artificial intelligence (generative AI) services, including Amazon Bedrock, a service that makes multiple foundation models available via an API, to personalize the in-vehicle experience with virtual assistants that can provide tailored recommendations, such as routing and virtual tours, and to automatically diagnose vehicle issues using the owner’s manual.

In addition, SAIC MOTOR integrated Amazon Music, an audio entertainment service that connects fans, artists, and creators through music and culture, to further enhance the in-vehicle experience for its customers. The i-SMART connected vehicle platform equipped with Amazon Music is available on the MG ZS EV, MG5 EV, and MG4 EV models, offering drivers and passengers high-quality streaming media services as part of its in-vehicle listening experience.

“AWS has deep experience in driving innovation in the automotive industry, with years of expertise in developing connected vehicles and data analysis platforms,” said Jie Xu, CTO of SAIC MOTOR Overseas Intelligent Mobility Technology Co., Ltd. “AWS’s global infrastructure, extensive cloud services, and industry-leading safety and compliance practices helped SAIC MOTOR launch the i-SMART connected vehicle system in countries around the world. We look forward to continuing to work with AWS to innovate new driver experiences, expand our business, and bring connected vehicles to more consumers.”

“We’re excited to support SAIC MOTOR and its work to build and offer an industry-leading connected vehicle platform, bringing a customized and intelligent connected-car experience to overseas consumers,” said Wendy Bauer, vice president and general manager of Automotive and Manufacturing at AWS. “In the future, AWS will continue to help the automotive industry innovate new features and services that enhance driver experiences, improve safety standards, drive sustainable mobility, and create smart and connected car features for the global market.”

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

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