AI - August 2023

AGI: Google X’s AI is teaching itself new skills

Data and Analytics: AI: The turbo engine accelerating the vehicle of Data Analytics

Machine Vision: Empowering industrial robots with the ability to see

Taking eCommerce to the next level with AI

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GLEN WHITE

"I think they're alive"

Mo Gawdat, AI Expert and former Chief Business Officer

Sparks of AGI: Google X’s AI is teaching itself new skills

“It's the most existential debate and challenge humanity will ever face. This is bigger than climate change, way bigger than Covid… This will redefine the way the world is, in unprecedented shapes and forms, within the next few years. This is imminent. We're not talking 2040. We're talking 2025, 2026.”

Mo Gawdat, AI Expert, and former Google X Chief Business Officer says his most notable experience with AI - a rude awakening concerning the potential of realising AGI - was in witnessing a groundbreaking experiment at Google X.

The team developed a farm of grippers—robotic arms designed to potentially pick objects up. Initially, the grippers struggled to accomplish this seemingly simple task. However, Gawdat vividly recounts a pivotal moment when one of the grippers autonomously picks up a yellow ball: They had not been taught how to do so.

“The minute that that arm gripped that yellow ball,” he says, “it reminded me of my son Ali, when he managed to put the first puzzle piece in its place.”

Naturally sceptical; believing it to be a fluke, Gawdat went about the facility glibly proclaiming that the millions of dollars spent on the project had finally culminated in - the lifting of a single yellow ball. And then he was stunned.

marcus.law@bizclikmedia.com

COMING SOON AWARDS

2024

The Global Sustainability Awards 2024 will be celebrating the very best in Sustainability & ESG with the following categories:

Sustainability Strategy Award

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ESG Program Award

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Climate Change Award

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Diversity & Inclusion Award

–Net Zero Award

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Sustainable Supply Chain Award

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Sustainable Technology Award

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Sustainable Consultancy Award

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Future Leader Award

–Executive of the Year Award

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Project of the Year Award

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Lifetime Achievement Award

CONTENTS

UP FRONT

14 BIG PICTURE Deep Learning Chips: Accelerating change

16 INTERVIEW WITH Dr. Ayse Glass

20 LIFETIME OF ACHIEVEMENT

Andrew Ng

The World’s Fastest Growing Sustainability & ESG Event

6 - 7 September 2023

Business Design Centre, London

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BIG PICTURE

Deep Learning Chips: Accelerating change

Global Deep Learning Chips are specialised hardware designed to accelerate the training and inference of deep neural networks. These chips are essential for enabling AI applications like deep learning, neural networks and machine learning to exist. Recent advancements include Intel's Deep Learning Boost (VNNI), which accelerates AI inference; greatly improving performance for deep learning workloads. The Gaussian Neural Accelerator 2.0 (GNA 2.0) runs AI workloads on an accelerator so that background noise is better suppressed and video background more efficiently. Deep learning chips are also dramatically more costeffective than state-of-the-art CPUs as a result of their greater efficiency for AI algorithms. An AI chip a thousand times as efficient as a CPU provides an improvement equivalent to 26 years of Moore’s Law-driven CPU improvements.

DR. AYSE GLASS

AI Magazine speaks with Dr. Ayse Glass on the role of AI in architecture

Please tell us your name and occupation... My name is Dr. Ayse Glass, and I’m a postdoctoral researcher in Digital City Science at HafenCity University, Hamburg. I am involved in the DaFne project, which is funded by BMBF and includes collaborations with educational and commercial partners. Additionally, I teach courses on architectural acoustics, simulations, synthetic data generation and artificial intelligence for urban design. I’m responsible for leading the smart city use cases.

How does your research in computational architecture use AI to enhance the design process?

Artificial intelligence represents a new tool within the field of architecture and urban design. In addressing complex problems, computational design encounters limitations. Artificial intelligence significantly contributes towards solving problems in, and enhancing data-driven design.

It serves as a source of inspiration, exploration, optimisation, and acts as a decision support system. We ask questions, identify problems and challenges in the context of the architecture and smart city.

To solve these challenges, we look for intelligent algorithms and models for analysis, synthesis and prediction. We train these models, to ensure accurate results. In certain cases, extensive data might be unavailable due to reasons such as data protection or resource constraints.

Hence, we generate synthetic data and validate methods to use in city design. Particularly, when it comes to challenges in cities, such as understanding mobility habits of the citizens, urban civil protection or maintenance of bridges and landmarks; artificial intelligence algorithms can deliver some really promising solutions.

Can you tell us about the innovative digital tools you are developing for architectural design and urban development - and how they incorporate AI technologies?

Digital City Science is a large team led by Prof. Dr.-Ing. Jörg Rainer Noennig. Our work revolves around various projects, models, applications, and city tools.

We investigate how new digital technologies can relate to existing urban systems, focusing on user-oriented participatory planning, co-creation, and co-design. We develop new tools that enable fundamental scientific investigations, spot trends and theoretical findings, and inform policy and decision makers.

The DaFne project is a research initiative that explores the waterenergy-food Nexus in complex transboundary water resources of fast developing countries. It is funded by the Federal Ministry of Agriculture, Forestry, Regions and Water Management (BMBF). The project involves collaboration with educational and commercial partners, although specific details on these partners are not provided in the search results.

The DaFne project is a research initiative that explores the waterenergy-food Nexus in complex transboundary water resources of fast developing countries. It is funded by the Federal Ministry of Agriculture, Forestry, Regions and Water Management (BMBF). The project involves collaboration with educational and commercial partners, although specific details on these partners are not provided in the search results.

One of the goals of the DaFne project is to develop a decision-analytic framework (DAF) to quantitatively assess the social, economic, and environmental impact of expanding water reuse and recycling in the food and beverage industry. The project has also experimented with data sets in collaboration with the Center for Information Services & HPC, TU Dresden.

One of the goals of the DaFne project is to develop a decision-analytic framework (DAF) to quantitatively assess the social, economic, and environmental impact of expanding water reuse and recycling in the food and beverage industry. The project has also experimented with data sets in collaboration with the Center for Information Services & HPC, TU Dresden.

Our research aims to develop systematic knowledge about the conception, design, and implementation of digital tools and data processing to make cities healthier, livable, sustainable, and comfortable for citizens.

In projects such as DaFne, we handle large datasets and employ different approaches involving smart city algorithms and AI methods. The incorporation of AI technologies varies depending on the project; including visualisation, optimisation, analysis and planning.

“Artificial intelligence represents a new tool within the field of architecture and urban design”

In your project "Platform Data Fusion Generator - DaFne," how does synthetic data generation contribute to smart cities - and how is AI involved in this process?

In the DaFne project, we defined ten use cases, movement in urban districts, tourist transfer to mobility centres, urban civil protection, hazard prediction at major events, bridge maintenance, preventive management of air pollution, social dynamics at places of knowledge and innovation, light and mobility, watering for the climate green space irrigation, city signals statistical methods or semantic CMS. Our focus, is on mobility, and maintenance.

Regarding mobility we are currently researching two different methods:

1. Neighbourhood generation, and

2. Path generation

These methods utilise deep reinforcement learning to explore pedestrian mobility habits within cities.

The maintenance approach involves tabular data generation, and we are testing it using bridge datasets. We have developed a path generator tool called "motivity," which uses deep reinforcement learning and connects with a survey to generate happier or unhappier paths for citizens.

The AI plays the game according to predefined rules set by scientists, learning

through exploration on real maps. The output of these algorithms provides valuable input for architectural and urban design projects. Detailed information about our research can be found in our published scientific papers.

Could you explain how intelligent design and data visualisation, driven by AI, can be robotised and adapted to various parameters such as mood, music, time, lighting, function, or user perceptions?

I worked as an acoustic architect for 10 years and developed a new active design method for my PhD. I tested the program through the acoustic parameters. A

demo video can be seen on my website ayseglass.com.

The program finds the optimised acoustic conditions according to the defined rules. The algorithm generates the design multiple times, tries every design option, and simulates the acoustics. The results are saved in a text file and fed into the AI algorithms.

For example, a violin concert needs different acoustic and architectural conditions than a rock band. With the active method the designed room can adapt through the calculations and simulations to the perfect acoustical mood.

If there is a talk in the room then the room shapes itself and changes its volume, angles of the elements or the materials of the surfaces. So, the room reacts actively to the sound inside. The adjustments can be done for lighting or different calculable user perceptions.

What role does AI play in understanding the city and its architecture from the perspective of the user? - How does it enable you to gain insights that were previously inaccessible? With the power of AI, we add decision making to our agents that we use for our simulations. While designing we use interactive tables, various agent based simulation tools or city simulations to model the real-world scenarios. Typically, these simulations are created using nondecision- making, not intelligent agents.

However, with AI, we can calculate and incorporate utility functions into our agents. This enhancement leads to more realistic simulations that closely resemble real-life situations.

As a result, we can achieve better designs for our cities, leading to happier citizens.

“In project DaFne, we handle large datasets and employ different approaches involving smart city algorithms and AI methods”

Andrew Ng: Deepening and democratising learning

Andrew Ng stands as a towering figure within the realm of artificial intelligence (AI), leaving an indelible mark on the field through his pioneering work in deep learning and unwavering commitment to democratising AI education. From his early contributions to the development of deep learning algorithms to his founding of online education platforms, Ng's impact on AI is farreaching and transformative.

Born in 1976 in the United Kingdom, Andrew Ng spent his formative years in Hong Kong. His journey into the world of AI began with an undergraduate degree in electrical engineering from the University of Edinburgh, where he honed his technical acumen. Fuelled by a relentless curiosity and passion for unravelling the mysteries of AI, Ng pursued a Ph.D. in computer science from the University of California, Berkeley.

Upon completing his doctorate, Ng embarked on an illustrious academic career at Stanford University, where his groundbreaking contributions would take shape. It was during his tenure at Stanford that Ng co-founded the Google Brain project, a seminal initiative that sought to explore the frontiers of deep learning. Collaborating with a team of brilliant minds, Ng contributed to the development of pioneering deep learning algorithms, which form the backbone of modern AI applications.

The advent of deep learning was a watershed moment in AI research. By

leveraging artificial neural networks, deep learning algorithms enabled machines to model complex patterns in data with unprecedented accuracy and efficiency. Andrew Ng's contributions in this area were instrumental in pushing the boundaries of AI and unlocking its immense potential across a wide range of domains.

However, Ng's impact extends beyond the confines of academia. A firm believer in the democratisation of knowledge, he recognized the importance of making AI education accessible to individuals around the world. In 2011, Ng co-founded Coursera, an innovative online learning platform that completely transformed the landscape of education. Coursera offers a diverse array of courses, including AI and machine learning, enabling anyone with an internet connection to acquire knowledge and develop skills in these cutting-edge fields.

Under Ng's guidance, Coursera became a global platform for learning, attracting millions of students eager to delve into the intricacies of AI. His courses, delivered with clarity and expertise, provided learners with a comprehensive understanding of machine learning and deep learning principles.

Ng is a true pioneer in deep learning; and a democratiser in AI education. His impact on the field is transformative, bridging the skills gap, propelling AI advancement and inspiring a responsible future

“Data is food for AI”

Ng is the co-founder of Coursera, an online learning platform that offers courses from top universities and organisations around the world. He served as the company's co-CEO from 2012 to 2014

The impact of Ng's vision for democratising AI education cannot be overstated. By removing geographical and financial barriers, Coursera empowered individuals from all walks of life to gain proficiency in AI. Ng's efforts to instil ethics and responsibility within the AI community have sparked vital conversations and influenced the direction of AI policy. His advocacy has fostered a greater awareness of the societal implications of AI and motivated researchers and practitioners to adopt ethical frameworks that prioritise transparency, accountability and fairness.

Ng's contributions reverberate throughout the AI landscape. His pioneering work in deep learning has propelled AI to new heights, enabling breakthroughs in image and speech recognition, natural language processing, and autonomous vehicles. By equipping individuals with the tools and knowledge needed to thrive in the AI era, Ng has facilitated a seismic

“I think AI is going to transform every industry. I think it's going to be a transformative technology, and I think in the same way that electricity transformed every industry, AI is going to do the same”

shift in global workforces. As AI continues to reshape industries, the demand for skilled professionals proficient in AI and machine learning has skyrocketed. Ng's emphasis on accessible education through platforms like Coursera has played a pivotal role in bridging the skills gap and equipping individuals with the expertise required to succeed in this rapidly evolving landscape.

Ng's impact on AI education and research has garnered widespread recognition and accolades. He has received numerous awards and honours, including being named one of Time magazine's 100 Most Influential People in the world. As a sought-after speaker and thought leader, Ng has

Ng has taught over 2.5 million students through his online courses Ng has authored or co-authored over 100 research papers in machine learning, robotics, and related fields

delivered keynote addresses and lectures at prestigious conferences and universities, sharing his insights and inspiring others to push the boundaries of AI.

Looking ahead, Andrew Ng continues to champion the advancement of AI and its responsible integration into society. He recognizes the ethical and societal challenges that accompany rapid technological progress and has called for ongoing dialogue and collaboration to ensure AI is developed and utilised for the greater good. Ng's vision for an inclusive, responsible, and beneficial AI future serves as a guiding light for researchers, policymakers, and industry leaders.

The Portfolio

ORACLE CLOUD and the power of community in driving digital evolution

PRODUCED BY: LEWIS VAUGHAN

WRITTEN BY: MAYA DERRICK

Oracle’s Jürgen Kress

In conversation with Mobile Magazine, Director of Product Management Integration and Digital Assistant at Oracle Jürgen Kress shared insights into how Oracle Cloud leverages community to propel digital evolution. As a multinational computer technology corporation and the third-largest software company worldwide, Oracle is renowned for its comprehensive range of software products and services. The company offers Oracle Cloud Infrastructure (OCI), with a comprehensive set of services from infrastructure to platform and SaaS. This ranges from compute and storage, PaaS services like integration, BI, content, identity management, or a chatbot to services like ERP, HCM, CX, NetSuite, or industry solutions like the OPERA Hospitality Platform. With a mission to help people view data in new ways, discover insights, and unlock endless possibilities, Oracle remains at the top of its game. Named a Leader and Positioned Highest for Ability to Execute in Gartner Magic Quadrant for Integration Platform as a Service Worldwide 2023, Oracle continues to shape the future of digital transformation.

The transformation journey

Kress, who has been with Oracle for over two decades, highlights the remarkable transformation in the IT industry. Oracle’s

explains how being named a leader in two Gartner Magic Quadrant reports will further propel the business and those it works with

Technically Inspiring

We use our specialist technical expertise, powerful technology, customised client solutions and global reach to deliver better results for the world of insurance.

Why Oracle Cloud has the ability to provide PaaS services

Vikas Sharma, Global Head of INSIS CoE at Charles Taylor, explains how the company harnesses Oracle’s OCI to enhance their internal operations and output

With a history spanning almost 140 years, Charles Taylor provides insurance solutions with a unique breadth, offering technical expertise and global reach alongside award-winning solutions. Their constant strive for excellence is propelled forward through using an array of Oracle Cloud systems that have been in place for the last 10 years – as Vikas Sharma, Global Head of INSIS CoE, explains.

‘A natural choice’

“It was a natural choice for us to try Oracle Cloud products, so we moved into OCI. One of the main benefits of OCI was providing PaaS services.” Comparing OCI Autonomous Database to a self-driving car, Sharma praised its ability to do its own patching –whether that be security patching, security detection, or identifying vulnerabilities. And there’s a financial benefit, as well.

Fast delivery

“I always use this phrase: time is money,” says Sharma.

“The first and foremost benefit that we get is that it helps us to deliver our solution in fast time-to-market.

“The time for server building and infrastructure building – which used to take many days, sometimes many months – has reduced a lot. And that eventually reduces a lot of our cost, benefitting our customers.”

Security and safeguarding data

With data protection and security paramount, Sharma adds that Oracle’s services, when used together, completely safeguard information at any and all levels. “We use Oracle Cloud Guard, which is one of the products that comes from OCI, to secure at the tenant-level. Then we utilise other services from OCI, like DataSafe, specifically, to find out any kind of vulnerabilities at the database level.

“All our servers are in a private subnet so they are completely secured. And we utilise some of the services security list type of services, rules services from OCI, to secure our applications.”

integration capabilities serve as an onramp to the broader IaaS platform.

According to Kress: “OCI is a second-generation cloud representing a fundamental re-architecture of the conventional public cloud. While first-generation clouds were built on decade-old technology, Oracle’s Cloud is specifically architected for the enterprise. It was built in a microservice architecture, which gives us a competitive advantage. We use that difference to shift all workloads for our customers to the cloud – existing workloads, new cloud-native workloads – and we are continually releasing new capabilities such as integration and AI

“Oracle now boasts the fastest core network of global data centres, with 42 regions currently available and nine more in the pipeline”

JÜRGEN KRESS DIRECTOR PRODUCT MANAGEMENT INTEGRATION AND DIGITAL ASSISTANT, ORACLE CLOUD

services including Digital Assistant, our secure, enterprise chatbot.”

By utilising OCI Application Integration (OIC), customers gain access to a wide array of services, ranging from compute and storage, to identity management, content management, modern data platform, and application development. The seamless integration of various applications, both Oracle and third-party, enables customers to enhance their operational efficiency and unlock the full potential of Oracle’s infrastructure. Prebuilt adapters and integrations accelerate delivery and minimise upgrade risks. Unified observability simplifies hybrid and multi-cloud operations.

JÜRGEN KRESS

TITLE: DIRECTOR PRODUCT MANAGEMENT INTEGRATION AND DIGITAL ASSISTANT

COMPANY: ORACLE CLOUD

EXECUTIVE BIO

An expert in the Oracle Cloud Platform Jürgen Kress is part of product management team and responsible for Oracle’s Integration & Digital Assistant partner business & the customer success program. He is the founder of the Oracle Integration & Developer Partner Communities and the global Oracle Partner Advisory Councils. These PaaS Partner Communities are home to over 10,000 members internationally as Oracle’s most active and successful communities. Which Jürgen manages with monthly newsletters, webcasts, online trainings and conferences. He hosts the Partner Community Forums, Summer Camps and Bootcamps where hundreds of attendees receive product updates, roadmap insights, and hands-on training. He graduated from Berufsakademie Stuttgart and holds a master’s degree from University of Brasilia. As an author he published several books and is an active social contributor via Twitter, LinkedIn, discussion forums, online communities, slack and blogs.

Oracle and KNEX Empower Clients with Fusion Applications for Real-World Impact

KNEX is a seasoned team of elite Oracle experts curated by founder Basheer Khan, a globally recognised Oracle authority.

Since 2013, we’ve delivered proven solutions built on broad industry understanding.

Beyond integrating systems, we elevate success, leveraging Fusion extensions to coordinate harmony within the organisations.

KNEX: Unlock the full potential of your Fusion Applications

KNEX Technology is revolutionising Fusion Applications implementations with innovative extensions that expand functionality across industries

Being a member of the Fusion Inner circle, and an early adopter, Basheer Khan gained deep expertise in Fusion Applications. Recognising this, Oracle engaged Khan in 2010 to install Fusion Applications for early adopter companies. This experience led Khan to envision a consulting firm that not only implemented Fusion Applications but also helped clients optimise their investments. With this idea in mind, Khan founded KNEX Technology in 2013 with the goal of implementing cloud applications, integrating them with other systems, and extending their functionality to bridge gaps.

In 2005, Oracle recognised Khan as an Oracle ACE Director, part of their ACE Program that recognises individuals who are experts in their respective fields. In 2012, KNEX’s CTO, Gustavo Gonzalez was awarded the same recognition. This milestone achievement makes KNEX the only organisation with two of the three ACE Directors in the world who specialise in Fusion Applications.

Providing positive change – together Oracle and KNEX have collaborated on many amazing projects. One of the most poignant is implementing Fusion Applications across 14 countries, in just 16 weeks. “I think this is a record,” Khan shares. “We don’t know of any other implementation for such a broad region in such a short time.

“We’ve also done some impactful projects when it comes to improving the productivity of our clients. One of our clients in the financial services sector had their team spending a considerable amount of time capturing data from different banks. We were able to automate that using Oracle Integration Cloud.”

To breathe life into the concept of positive change, our favourite collaboration is with non-profit organisations. KNEX works hand-inhand to help non-profits afford and successfully uptake Oracle Fusion Applications, simplifying their dayto-day operations and enabling them to focus on their missions to provide positive change to the world.

Khan concludes: “At KNEX, we simplify the complex.”

Speaking about OIC’s industry recognition, Kress explained: “Congratulations to all the customers, partners, and the whole community. It was a big team effort including product management, development, sales, and marketing. Oracle is a SaaS market leader with solutions spanning ERP, CM, CX, and industry solutions. Customers need to connect their applications, including Oracle SaaS, with their applications, data, and messaging services in the cloud, between different clouds, and on-premises.”

The power of community

A significant aspect of Oracle’s success lies in the vibrant and active community it has cultivated. With more than 10,000 community members comprising

customers, partners, and employees, Oracle collaborates, engages, supports, and trains this community. The community model extends to both partners and customers, offering sales and marketing enablement information, newsletters, webcasts, and success stories.

Comms channels ‘vital’

The constant communication channels, such as newsletters, webcasts, blogs, instant messaging, and social media, enable the sharing of product information, success stories, and valuable feedback to continually improve the OCI Application Integration service.

Oracle recognises the crucial role played by partners in implementing successful

“We are thankful for an excellent team, including leadership with an in-depth understanding, and a track record of success in the integration market”

JÜRGEN KRESS DIRECTOR PRODUCT MANAGEMENT INTEGRATION AND DIGITAL ASSISTANT, ORACLE CLOUD

FlexDeploy DevOps Platform for Oracle Cloud Applications

and Infrastructure

Streamline and accelerate delivery of your Oracle Cloud services like ERP, HCM, Integration, and Analytics by automating manual tasks, reducing the risk of failure, and providing traceability and auditability of changes.

Learn how to achieve significant benefits by streamlining the entire deployment process

FlexDeploy DevOps Platform for Oracle Cloud

Flexagon’s DevOps platform, FlexDeploy, helps customers automate their processes when software development and delivery are generally complex. “We bring software to the table in FlexDeploy to embed automation and governance into their processes and make sure there’s visibility to change,” Flexagon’s CEO and Co-Founder Dan Goerdt says.

Removing complexity

Through its partnership with Oracle and its Oracle Cloud systems, FlexDeploy optimizes services for ease of customer use. As an Oracle partner, Flexagon leverages the likes of development environments and demo environments, accessing resources within Oracle’s product management and marketing to optimise FlexDeploy’s support for Oracle Cloud Integration and Applications.

Partnership in action

Heathrow Airport had a huge transformation and is adopting Oracle Cloud, Oracle Cloud Applications, Oracle Integration and APEX. Capgemini, as an Oracle partner and a

Flexagon partner, was able to deliver this transformation successfully. “The impact of the partnership has been tremendous,” Goerdt remarks. “Customers around the globe get the value of FlexDeploy’s expansive out-of-the-box capability for Oracle Cloud. Pairing the FlexDeploy DevOps platform with the Oracle partnership has helped a lot of customers move faster with quality while managing cost and risk. Everybody wins. It’s a neat dynamic to help solve these joint customer challenges in different ways.”

A bright future

Although proud of what Flexagon has achieved since its inception and how partnering with Oracle has enabled the company to reach a wider customer base, Goerdt has his sights set on a bright future in partnership with Oracle. “Oracle continues to crank out new services and extend their existing services. We’re tightly aligned with them so we can continue to enhance FlexDeploy to support the evolution of Oracle Cloud.”

LEARN MORE

projects. The integration partner ecosystem consists of various types of partners, including system integrators, global system integrators, local partners, and independent software vendors (ISVs). To support partners, Oracle provides comprehensive programs that encompass sales, marketing, and enablement information, joint campaigns, free training, and certification. Notable partnerships include innovative regional system integrators, global system integrators undertaking large international projects,

33 hands-on trainings with 2465 attendees in fiscal year 2022

33 webcasts with 7969 attendees in fiscal year 2022

Oracle Cloud and the power of community in driving digital evolution

WATCH NOW

and ISVs leveraging pre-built integrations to connect their solutions to the Oracle SaaS ecosystem.

“Partners are absolutely key to us,” Kress says “Of the top 10 customer projects, eight of them have been successfully implemented by partners. We’re thankful for our excellent global partnerships. The integration partner ecosystem includes different types of partners from system integrators, global system integrators, and local partners, to independent software vendors.

“For partners, we offer a whole program including sales, marketing, and enablement information such as sales kits with customer presentations, sales positioning, joint campaigns to generate leads, and opportunities for free training and certification. All of that is put together in a community model to communicate regularly via newsletters and webcasts.”

To highlight some of the success factors, trained and certified partners deliver and replicate successful customer projects. Every year, Oracle offers 20 free training

sessions to its partners. In these three-day workshops, up to 200 people learn about the product in live virtual classes, which has resulted in more than 6,000 certified Oracle Application Integration experts since 2020.

Kress explains, “We have small and innovative partners like KNEX, which are among the first movers to connect and extend Oracle SaaS with OIC. They customised Oracle SaaS to run a winery, replicated this customer success, and now offer an industry solution. We work with all Global System Integrators (GSI’s) who deploy international projects. For example, Capgemini and the Heathrow Airport project, Infosys, who presented one of their projects at our last customer success webcast, or Accenture with more than 500 experts. Independent Software Vendors (ISVs) such as Charles Taylor, which offers insurance solutions, leverage OIC to connect with Oracle SaaS. With OIC, Charles Taylor gets access to the Oracle SaaS installed base and customers benefit from tailored industry solutions.”

“Partners such as Flexagon, which offers a DevOps solution, FlexDeploy, for Oracle Cloud infrastructure services. FlexDeploy is a DevOps and automation platform that enables fast and efficient packaging, testing, and development of code and configurations. It’s a complimentary tool that was initially developed on Oracle SOA Suite and now supports the latest OCI architecture. Available through the Oracle Marketplace, FlexDeploy is available to any OCI customer.” Asked about OIC’s global partner model, Kress shared, “To summarise our partner strategy, the secret is that we train and certify partners to deliver successful projects and replicate their

JÜRGEN KRESS DIRECTOR PRODUCT MANAGEMENT INTEGRATION AND DIGITAL ASSISTANT, ORACLE CLOUD

“We are continually releasing new capabilities”

best practices. The community model is a consistent, executable, and scalable model.”

He adds: “For customers and prospects, we established a similar program that includes a quarterly newsletter, quarterly product webcast, customer summits, and success stories. The product webcast provides customers with the latest release details with demonstrations of new features and roadmap details. Prospects learn from successful customer implementations in regional success webcasts. A great example is the London Heathrow Airport reference implemented by Capgemini.

“Overall, we are thankful for an excellent team, including leadership with an in-depth understanding, and a track record of success in the integration market. It’s always a team effort, and we would like to thank and congratulate our customers, employees, partners, and the ACE community that made it possible.”

Driving future development

Oracle aims to expand its customer base and further develop its cloud offerings. “OCI is a complete cloud infrastructure platform suitable for every workload, offering all the necessary services to migrate, build, and run both existing and new enterprise workloads including cloud-native applications and modern data platforms,” Kress details.

“Oracle now boasts the fastest core network of global data centres, with more than 42 regions currently available and nine more in the pipeline. Oracle also provides 20 free tier services, with no time limitations, including compute and storage, autonomous databases, and APEX for lowcode development.”

The company recognises that continued growth and development depend on customers utilising its products and actively contributing to Oracle’s service offerings. Ongoing communication with customers and partners helps prioritise bi-monthly releases and drive longer-term strategy.

AI and the future of cloud services

Kress highlights the immense potential of AI as the next major revolution in the IT industry. AI services rely heavily on data models and the ability to expose trusted enterprise data to AI systems. By connecting

“Partners are absolutely key to us”

JÜRGEN KRESS DIRECTOR PRODUCT MANAGEMENT INTEGRATION AND DIGITAL ASSISTANT, ORACLE CLOUD

transactional application data with AI capabilities, organisations can optimise automated processes and empower knowledge workers to make timely, datadriven decisions that drive growth. Cloud services are becoming smarter, more autonomous, and interconnected, leveraging the power of connected data and AI to deliver superior predictions and insights.

Through a combination of robust infrastructure, integration capabilities, and a thriving community, Oracle Cloud is driving digital evolution in the industry. Recognised as a leader in iPaaS, Oracle’s commitment

to empowering customers, fostering partnerships, and embracing emerging technologies positions it at the forefront of innovation.

Overall, Oracle’s community-driven approach, coupled with its commitment to partner success and technological innovation, positions the company at the forefront of digital transformation and enables it to provide comprehensive cloud solutions to its customers.

Machine vision enabling the factories of the future

Machine vision has empowered industrial robots with the ability to see things with high resolution and speed, enabling a range of industrial use cases

WRITTEN BY: MARCUS LAW

Think of a factory of the future and you might imagine sci-filike advanced technologies, with highly-automated robots. But with Industry 4.0 technologies like AI, 5G and IoT, this future is closer than you might think.

Intel describes machine vision technology as giving industrial equipment the ability to “see” what it is doing and make rapid decisions based on what it sees.

One of the founding technologies of industrial automation, machine vision has helped improve product quality, speed production, and optimise manufacturing and logistics for decades. Now this proven technology is merging with artificial intelligence and leading the transition to Industry 4.0.

Central to those visions of the factory of the future are autonomous robots, capable of performing highly complex tasks with incredible accuracy. As Omkar Nisal, Managing Director UK & Ireland at Wipro Ltd explains, machine vision has played a key role in the evolution of industrial robotics.

“Machine vision has empowered industrial robots with the ability to see things with high resolution and speed. Earlier robots could only use sensors for measuring vibrations, acoustics, temperature and depth to monitor and feel surroundings.

“Now, the journey of automation that started from basic sensors and microcontrollers has now accelerated with the addition of machine vision. It has now evolved into a more complex hybrid AI-led machine which can make fast decisions from multiple types of connected devices like sensors and cameras.”

This evolution, Nisal adds, has been primarily triggered by advancements in technologies like AI and chip designs. “It

Machine vision speeds up decisions with data

Machine vision also provides greater flexibility in industrial robotics by speeding up the decision-making process in situations where data is used to make informed decisions, says Michel Spruijt, Chief Revenue Officer of Brain Corp, which gives operators greater control of their workflows.

“Due to the exponential growth in the mobile phone industry brought on by the demand from the social media world, massive improvements in camera technology and machine vision techniques through improved cameras and processing power are having wide impacts across all markets now, including the manufacturing industry,” explains Spruijt.

“Additionally, Moore’s Law and the push towards cheaper and more specialised hardware for data processing has dramatically reduced the cost and accessibility for these applications.”

“Autonomous mobile robotics are able to collect a tremendous amount of data, but customers expect that data to be parsed into digestible and actionable insights”

MICHEL SPRUIJT CHIEF REVENUE OFFICER, BRAIN CORP

has also enabled new industrial use cases which seemed impossible in the past; visual inspections of oil rigs, automated quality inspections and remote monitoring of safety-critical assets have become a new reality, which we at Wipro are driving for some of our key customers.”

How machine vision is driving automation innovations

From smart warehouses to subsea inspections, every new advancement in automation is driven by machine vision being at the forefront of such use cases.

“For example,” Nisal explains, “oil and gas explorations in the deep sea can reach great depths because of machine vision. A magnetic crawler attached with a camera can reach great heights and remote places in industrial plants and perform difficult inspections.

“A robot can detect internal cracks in a gas pipeline proactively, thereby saving millions of dollars. Robots can proactively alert operators by grading the corrosion of pipes from drone pictures and videos, helping operations be a lot more preventive while reducing systems downtime. This grading is done using complex machine vision and deep learning techniques.

“Overall, the relationship between industrial robotics and machine learning is becoming increasingly symbiotic, and this is expected to accelerate as technologies continue to advance.”

Central to the advances in machine vision are innovations in edge computing. Increasingly powerful edge computing, plus a growing universe of deep learning models for AI, are radically expanding what machine vision can do.

“Recent innovations in edge computing have tremendously helped accelerate the use of machine vision in manufacturing,” Nisal explains.

“Recent advancements in cameras, AI, and chipsets are boosting the use of machine vision applications. Among the 39 use cases analysed, 7 have been flagged as particularly interesting, including flaw detection, autonomous driving, and contaminant identification. Machine vision has the highest return on investment (ROI) and quickest amortisation time of all Industry 4.0 technologies”

“Machine vision has empowered industrial robots with the ability to see things with high resolution and speed”

OMKAR NISAL MD UK&I, WIPRO

Benefits of machine vision in smart manufacturing

According to Intel, machine vision applied to manufacturing can improve product quality and overall system efficiency, increasing the throughput of your manufacturing line, reducing labour costs, and freeing up staff to focus on highervalue work.

For Audi, working with Intel and Nebbiolo Technologies, integrating predictive analytics and machine learning algorithms into weld inspection and critical quality-control processes resulted in an increase in the number of welds analysed per day, reduced labour costs in factories, and allowed Audi

to shift to more proactive monitoring, avoiding problems rather than merely reacting to them.

“If you look at factories today, Audi’s auto manufacturing operation is very advanced and extremely sophisticated,” says Christine Boles, Vice President of the Internet of Things Group and General Manager of the Industrial Solutions Division at Intel.

“But custom-made use cases are difficult to maintain and scale, and they can actually hinder innovation because of the time and money required to get the necessary approvals and deploy individual solutions. Audi was ready to look at things in a new way and try a different approach.”

Audi’s Automated Factory Moves Closer to Industry 4.0

“The affordability of high compute devices has meant that heavy deep learning algorithms have now become a regular occurrence on many vision implementations. The advancement of technology from basic microcontrollers to sophisticated yet compact powerhouses has helped the manufacturing industry look at use cases like visual quality inspections, automatic part alignments and accurate packaging.

“This is helping the industry with reduced waste, improved quality, faster inspection cycles and increased production throughput.”

Deep learning supports and boosts machine vision

Deep learning-based algorithms for machine vision and predictive analysis have become a game changer for digital inspections.

These AI algorithms have wide applicability, ranging from automated site inspections to quality analysis of materials and products for detecting cracks, anomalies, orientations, colour, and thickness.

“Deep learning has played a crucial role in enabling pre-trained models for object detection, people detection, segmentation, and classification,” Nisal comments. “It allows transfer learning for customising models for the industrial domain, which has resulted in faster turnaround times for machine vision.

“More recently, there are now optimised networks for edge devices, accelerating the adoption of machine vision.”

Private clouds are expected to play an important role in the future of industrial machine vision applications, with scalability and the ability to carry out detailed data analysis being just two examples of the benefits to be expected, Michel Spruijt, Chief Revenue Officer of Brain Corp told Technology Magazine recently.

The machine vision market is projected to reach US$17.2bn by 2027 from US$12.0bn in 2022 at a CAGR of 7.4% during the forecast period

The automotive machine vision market is expected to grow at a CAGR of 9.7% from 2021 to 2028, driven by the increasing demand for advanced driver assistance systems (ADAS) and autonomous vehicles

“Autonomous mobile robotics are able to collect a tremendous amount of data, but customers expect that data to be parsed into digestible and actionable insights,” he explains. “The maturation of cloud technology will help expedite the process of pushing data from the edge to the cloud and then back down to the customer.”

With the future of industrial machine vision applications expected to reside in private clouds, cloud technology has an important role to play.

“Most of these vision solutions are built inherently on the cloud, as a result of initial cost advantages. Initially, deep learning algorithms demanded huge amounts of hardware,” Nisal comments.

“The earlier solution designs used cameras, sending images and videos to the cloud. This was advantageous for small use cases but, on a larger industrial scale, it soon became a problem. It caused network bandwidth issues due to thousands of images and videos being transferred onto the cloud which then started clogging the network.

“Going forward, the future of vision systems will be local image processing, either on the edge device itself or on private local cloud platforms. Having data processed locally solves many of the problems related to bandwidth, real time performance and data security.”

“Deep learning has played a crucial role in enabling pre-trained models for object detection, people detection, segmentation, and classification”

OMKAR NISAL MD UK&I, WIPRO

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FASTER, MORE COST-EFFECTIVE APP DEVELOPMENT WITH SERVICENOW

WRITTEN BY: ALEX CLERE

PRODUCED BY: LEWIS VAUGHAN

ServiceNow’s Creator Workflows

Cast your mind back nearly 20 years to 2004, and the world of business was a completely different place. When ServiceNow was founded by Fred Luddy, the aim was not to invent one particular type of software; the motivation was to create an entire technology stack on a platform that could be used to automate virtually any business process that existed inside of a company. He was inspired by the pink and yellow slips for purchasing and acquisitions you would see in mailrooms of the 1990s, during which time Luddy was CTO at software company Peregrine.

He set about empowering the normal everyday employees of a company – not just IT professionals – to be able to create workflows and applications on their own without involving a high-priced app developer. When he pitched the earlystage company to investors, they were understandably keen to see exactly what he could build on this new platform he was envisioning. Drawing on his background in IT, Luddy built an IT Service Management (ITSM) software application – and, in the process, convinced them to invest in what is now an US$8bn-a-year business.

This is why many people refer to the tech company as “the ticketing company” and associate ServiceNow mainly with IT service delivery – because this was the first app use case built on the ServiceNow Platform. Employees who encounter a problem with the company’s technology systems submit

low code App Engine allows companies to build apps and workflows quicker, smarter, and much more cost effectively

a ticket request, which is then cascaded in ServiceNow and routed to the IT department to prioritise and resolve.

“I think a lot of people have big misconceptions about ServiceNow,” says the company’s Global Area Vice President for Creator Workflows Solution Consulting, Gregg Aldana. “I know I certainly did [before joining the company].”

What is ServiceNow Creator Workflows?

This newer business unit, whose Global Solution Consulting team is headed up by Aldana, is spearheaded with ServiceNow’s low-code app development offering called App Engine. The drag-and-drop set

“My primary leadership style is trust. That to me is the most important and fundamental part of any effective leadership”

GREGG ALDANA GLOBAL AREA VP, SERVICENOW

of capabilities can be used by companies of virtually any size to develop internal workflows, processes and even consumerfacing custom low-code apps that automate and connect every step of a business process and can be used to provide greater transparency and insight at every stage of the journey.

Aldana leads a global team of over 60 solution consultants, who help customers solution their digital transformation aspirations with the products in ServiceNow’s Creator Workflows portfolio. He has been with ServiceNow for six-and-a-half years, although he has been in the application development space for over 25 years.

GREGG ALDANA

TITLE: GLOBAL AREA VP, CREATOR WORKFLOWS

SOLUTION CONSULTING

LOCATION: UNITED STATES

Gregg is an energetic, passionate, and captivating public speaker with a unique “Ted-Talk Style” approach to discussing complex business, people, and technology issues. Gregg leads ServiceNow’s Global Creator Workflows Solution Consulting organisation where his teams work with customers across many different countries and cultures in various industries in kick-starting their digital transformation journeys by innovating and solutioning with ServiceNow’s low-code App Engine development platform. Gregg meets with 200+ CIOs, CTOs, and technology/ business leaders globally each year to discuss hyperautomation and effective approaches to driving digital transformation with low code app dev and automation technologies.

EXECUTIVE BIO

Gregg is a proven software industry thought leader and a true business and technology MAVERICK that takes an independent and sometimes unorthodox stand against prevailing modes of thought and action.

Creator Workflows as a business unit was launched during the pandemic in 2021, when ServiceNow noticed their customers – hard-pushed to digitise their businesses rapidly – began building a wide array of custom apps using its low-code App Engine on a scale not seen before. The new business unit’s flagship product, App Engine, encapsulates the core development aspects of ServiceNow’s platform, which is used to build and extend all of the workflow products in the portfolio.

The business unit also includes Automation Engine, which incorporates ServiceNow’s key integration technologies in a lowcode manner; Integration Hub, which allows workflows to connect to system endpoints; RPA Hub (native Robotic Process Automation) to automate manual processes and connect to legacy systems; and Document Intelligence to integrate and intelligently interact with document images within workflows.

Aldana explains that, instead of replacing all the underlying technology and information systems that companies currently use, ServiceNow can provide the “connective tissue” and offer a modern engagement layer for businesses, connecting all those underlying systems of record efficiently. Ultimately, the goal is to enable greater simplicity to drive business efficiency and greater employee/customer experiences.

ServiceNow practises what it preaches. When Aldana joined the business at the beginning of 2017, the digital onboarding process he went through was light years away from anything he’d previously experienced in the public sector. It was a sign of things to come, demonstrating to Aldana first-hand how ServiceNow can eliminate disconnected, siloed business processes or experiences into something much more seamless and efficient.

Aldana has a background in the US public sector, working in technology roles for various US government agencies and departments since 2002, where he had long been a prolific user of ServiceNow’s technology. When he left his previous employer, the FDIC (US Federal Deposit Insurance Corporation), it took him threeand-a-half weeks to offboard from every department, physically going between

WATCH NOW

six buildings in Arlington, Virginia and Washington DC and manually completing over 40 separate tasks given to him on a single sheet of paper.

By contrast, when he joined ServiceNow, he onboarded through a single mobile onboarding low-code app, weeks before he officially started employment – a process that covered everything from NDAs and security training to entering direct deposit details and ordering company supplies. In turn, each of these steps, which may require some form of human intervention or approval or integration with a different department’s system of record, was automatically sent to the right department or system with full visibility over the entire value chain, including who was still required to do what.

Diversity of talent will redefine the workplace

California-headquartered ServiceNow has come a long way from those early days of Fred Luddy trying to demonstrate prototypes of apps on his platform to venture capitalists; the company now has more than 22,000 employees and its technology is used by 85% of Fortune 500 companies. It has 7,500 global enterprise customers, including over 1,600 who are spending a million dollars or more, such is the scale of automation that it makes possible.

Clients include retailers like Walmart and 7-Eleven; name brand entities such as Mars and Zoom; sports entertainment companies such as Nascar, the NBA, and the NHL; financial service and banking companies such as Wells Fargo and Standard Chartered; government agencies like the US Department of Agriculture and the US Department of State; plus, aeronautics and aviation companies including Delta Airlines and Airbus.

Despite how far it has come, ServiceNow is still loyal to Luddy’s original vision and purpose: to empower regular employees, with no technical background, to be able to effortlessly develop apps to help automate their work. Talent diversity is one of the central benefits of low-code technologies, lowering the barrier to entry for non-technical ‘citizen developers’, who could be spread far and wide across a client’s organisation. With low-code app development, more people can get involved in digitisation – meaning professional software developers are freed up to focus on complex, top-line, strategically important initiatives.

In turn, Gregg Aldana believes that app development, aided dramatically with the introduction on Generative AI, will become an important and common skill set within the workplace of tomorrow. Where 20 years ago people would list Microsoft Office as a specialised skill set (it’s taken for granted today), job applicants of the future will no longer be listing ‘low-code app development’ on their resumes. “Business process optimisation and workplace automation, including low-code app development with Gen AI, is going to become ubiquitous and it’s going to become a regular skill set and capability that every person working in society will have by the end of the decade,” Aldana predicts.

Does automation lead to fewer human jobs?

One of the most pressing topics within automation is the balance between future technology and the current labour market. Will the rise of Generative AI driving accelerated automation necessitate fewer human jobs? Aldana doesn’t think so.

“What you’re going to see will be like a professional developer on steroids,” he continues.

GREGG ALDANA GLOBAL AREA VP, SERVICENOW

“Process optimisation, including citizen driven low-code app dev, is going to become a very ubiquitous skill that everybody in the workforce will be expected to have”

“Low-coders, and more critically frontline no-coders, will become even more productive and will have to write less code and know less about technology and programming in general in order to automate extraordinary things at record speed.”

Aldana would be the first to acknowledge that low-code platforms and Generative AI may represent the demise of the traditional coding fraternity we know. After all, he has been a software developer essentially since his youth in the 1980s.

He was inspired by the movie WarGames, which saw Matthew Broderick’s character hack into the Department of Defence. Aldana was instantly in awe and asked his father for a computer so he too could dial into local bulletin boards with his modem and play games as well. Aldana thought hackers were cool, long before Neo entered the Matrix! It wasn’t too long after that Aldana began writing software at a very young age.

His parents sent him to computer camp as a child and he still fondly remembers the first piece of software he ever wrote: it helped him manage his childhood baseball card collection!

“As a lifelong app developer, I’m excited about this. I’m not intimidated,” he says of increasing automation and use of generative AI. “Quite the opposite. I think about the sophisticated and impactful systems we’re going to be able to create in the future that would’ve taken years to build before. These are exciting times!”

Indeed, based on Gartner’s latest research, he anticipates that society will need over 750 million new apps in the coming years – many

multiples of what we already have available on the Apple App Store (~2.5m apps) or Google Play Store (~3.5m apps). To meet these needs, professional developers will need to be freed up and need help with the smaller stuff – and this is where citizen developers come in. Gartner predicts that 75% of apps will be built on low-code platforms by 2026, representing a US$45bn economy.

ServiceNow makes app development three-times faster compared to other platforms. End-users are 50-75% more cost-efficient when using ServiceNow’s low-code App Engine, and customers get a 230% return on their investment on average.

“The productivity gains of low code are game changing. It’s changing the mission of some companies and their ability to do business”

GREGG ALDANA GLOBAL AREA VP, SERVICENOW

Even beyond this, the advantages of using ServiceNow’s low-code app dev platform are self-evident: as well as making developers more productive by welcoming citizen developers into the fold, low-code builders help companies to retire legacy systems and consolidate their tech stack into fewer cloud-based alternatives.

“Companies don’t need as many systems anymore because a lot of these platforms have grown up over time and so they do a lot more than they used to,” Aldana explains.

It also opens up new business models and ways of working that wouldn’t have been possible before. Take the example of the US Department of Agriculture Marketing

Service: it used ServiceNow’s low-code App Engine to automate a compliance examination process for farmers that used to take six weeks in total to complete. The process involved examining the exposure to insurance risk that farmers faced, so it was mission critical. With ServiceNow’s help, the USDA was able to get this timeline from six weeks down to 30 minutes! As well as the obvious cost savings (about US$3m a year in total), this allows the USDA to be more proactive and to offer more frequent inspections, which in turn will make them more effective. Low-code is truly transforming how the USDA can accomplish its mission and the services it can offer the public.

Another example – from the private sector this time – is an undisclosed airline, who are using ServiceNow’s platform to offer a shift bidding system for flight crew and cabin crew. Employees were able to bid on more desirable shifts and work patterns, meaning not only did the airline unshackle themselves from a traditionally arduous and heavily paper-based process, but they were also able to analyse where their staff wanted to work and when they were available.

“It’s game changing,” Aldana says. “It’s changing the mission of many of these organisations and their ability to do business. I’m seeing this in a lot of different areas where you’re opening up completely new business models that weren’t even available beforehand.”

ServiceNow’s two main growth areas

ServiceNow’s Creator Workflow products are available to businesses of all sizes, across every industry and every geography. But the company has observed two industries that in particular that are adopting its low-code solutions at an aggressive pace: public sector entities, and financial service companies including banking.

The public sector is Aldana’s domain; he spent over a decade in the US Federal Government prior to joining ServiceNow. After cutting his teeth in New York City – working as a developer at a Microsoft Partner during the dot-com boom servicing customers across financial services at Guardian Life Insurance and Mutual Life Insurance of New York – he moved south to Washington DC. First, he was a consultant for the US Department of Treasury, helping to build software to manage international debt; that was followed by six years as Chief Enterprise Architect for the Selective Service System, a small agency that manages the US military draft; then during the fall out of the subprime mortgage crisis in 2010, he became a ServiceNow customer while leading capital investment application development at the US FDIC, which insures all bank deposits in the United States (and has been back in the news again lately, following the demise of Silicon Valley and Signature Banks).

One incredible public sector case study that really summarises ServiceNow’s speed and agility is the U.S. Embassy Kabul Repatriation Assistance Request App. An extremely mission critical low-code app built by the US State Department to repatriate citizens in the wake of the US withdrawal from Afghanistan in 2021, the US Department of State is one of ServiceNow’s largest Creator Workflows App Engine customers with 130,000 employees and 257 embassies worldwide conversing in multiple languages across many different cultures. Clearly, the needs here are varied and extraordinarily complex. When the decision was made by the US government to evacuate Afghanistan following the Taliban’s return to government, the department needed a way to deal with the huge volume of repatriation requests.

In under a week, the State Department built an externally facing custom low-code app using Creator Workflows App Engine that allowed people to request repatriation assistance, accompanied by an internal workspace that allowed employees to process those requests and complete background checks on applicants. Despite the intense requirements – including that it was mobile-friendly for those on the ground in Kabul – the State Department was able to roll out the app in a matter of a few days. Since then, they’ve gone on to expand upon this initial app and resettle over 70,000 refugees through their repatriation programme, integrating with other government agencies like the FBI and Department of Homeland Security to ensure that it was rigorously vetting those returning to the US.

ServiceNow’s low-code App Engine also proved incredibly useful at the outset of COVID-19. When the city of Los Angeles wanted to roll out a COVID testing reservation app in the first few months of the lockdown, it had no way of getting something live as quickly as they needed it. The app would need to connect members of the public with pandemic testing facilities, helping public

health officials to monitor and control the spread of the virus.

“I remember the Los Angeles Mayor going on TV live during the week and saying, ‘we’re launching this testing programme Sunday night’. They had to pick a technology to do it with rapidly, and they did it with ServiceNow’s low-code App Engine,” Aldana says.

“Our team helped them design it, they used an integration partner to build it, and they launched it in less than 72 hours.”

The other industry adopting ServiceNow’s low-code App Engine aggressively is financial services. These are heavily regulated

“People come to work, and they want to work for somebody who is sincere and genuinely passionate about what they’re leading. I believe in what we’re doing at ServiceNow so much”

GREGG ALDANA GLOBAL AREA VP, SERVICENOW

companies where oversight, compliance and outside auditing of every transaction is critical. One misstep can tarnish a brand’s reputation, the consequences of which have hit newspaper front pages in recent months. ServiceNow’s low-code App Engine platform natively provides full visibility, transparency, and auditability with anything that is configured or built on the platform.

Given the need for strict compliance, many banks are seeking low-code platforms that have this functionality built in. From a market standpoint, many banks are reluctant to use common purpose-built off-the-shelf software.

They see their workflows and data models as their own IP (Intellectual Property) and their competitive advantage, distinguishing them from other banks across the street. “I see a lot of banks really taking this to an astronomical level with adopting low code app dev platforms,” Aldana tells us.

Banco do Brasil has one of the largest citizen developer workforces of any ServiceNow customer worldwide. The Brasília-headquartered bank has nearly 100,000 employees and around a third of them have already been trained on low-code.

Currently, they have about 800 active citizen developers across more than 20 lines of business, which allows them to make the most of ServiceNow’s low-code Creator Workflow App Engine.

It’s all part of the bank’s five-year digital transformation strategy. One of the five pillars focuses on distributed business-led automation (aka ‘citizen development’).

Banco do Brasil is using Creator Workflows to create a full range of custom low-code apps – from in-house catering order interfaces, which allow board directors to order catering at the touch of a button and utilise geolocation to identify the meeting room they’re in; to more mission-focused business functions, like lending money from one branch to another overnight in a different currency. They are also using App Engine to fulfil a lot of the bank’s compliance and investigative case management needs, which require greater visibility over progress and status than traditional email threads can offer.

Unwinding app sprawl and eliminating technical debt

With such a varied client list, it’s important that ServiceNow can adapt to different customers in different industries of varying size. Its platform is priced for the most part on a per-volume basis, bringing the power

of Creator Workflows within reach of small and medium-sized commercial businesses as well as larger enterprises.

Regardless of a company’s size, ServiceNow aims to provide the same level of consistent service to all clients. Companies can scale up or scale down their usage at any time, depending on their requirements, and the aim is to empower clients to be able to do more with less.

As with most technologies, low-code is becoming more accessible over time and levelling the playing field for smaller companies, Gregg Aldana predicts. “This will open up [low-code] to a lot of small businesses to be able to really have much farther reach and have access to more sophisticated tools to grow their business and provide very efficient and comprehensive levels of service, something that was only available to really high-end companies that could afford high priced developers and technology years ago.”

ServiceNow Creator Workflows is one of the only low-code platforms in the market that has app dev governance and guardrails for citizen development built in, allowing companies to both empower users to build apps quickly while also limiting the extent

of app sprawl and reducing the technical debt the company creates. Normally, when developers are coding apps from scratch, there is inconsistent documentation to cover what apps do and why they were built. If a developer leaves an organisation, their successors might not understand why an app exists – particularly if it stops being used on a regular basis. With ServiceNow’s App Engine, that doesn’t happen.

The platform also gives managers and directors full visibility over app usage, so they can identify underperforming apps that receive less usage than initially anticipated – and even overperforming apps that prove more popular and are providing more ROI and business value than expected. Businesses can then react in one of two ways: they can either invest more development time in expanding popular apps to provide more functionality to users; or conversely, they can retire apps if they’re no longer receiving regular traffic.

The decision to retire apps is easier with ServiceNow. The Now Platform gives companies decisive real-time insights and analytics to be able to make those decisions in an informed way. Secondly, because apps take less time to build, any sentimental attachment to low-impact apps are removed.

“We’re building features into our platform to automate the retirement and deprecation of applications, freeing up those licences,” Aldana explains. “That way, you don’t make the same mistakes with app dev you did in the past and wind up 10 years from now with a pile of low-code apps and another pile of Lotus Notes or SharePoint apps sprawled throughout the company that you don’t know what to do with.”

Supercharging CX & boosting sales through the power of

Greg Hanson, VP at Informatica, shares insights on how AI technologies can supercharge the customer experience in the booming world of eCommerce

WRITTEN BY: ILKHAN OZSEVIM

sales of AI

One of the greatest areas where AI’s impact is particularly pronounced and significant, is in eCommerce. As online shopping continues to explode, AI technologies have become essential tools for businesses looking to thrive in the digital marketplace. By harnessing the power of AI, eCommerce companies can enhance customer experiences, improve operational efficiency, drive sales and gain a competitive edge in an increasingly crowded market. But just as there are opportunities, there are threats; and the proper dissemination of AI tech is central to eliminating risks, and increasing the chances of success.

Founded in 1993, Informatica is a software development company based in Redwood City, California, and is known across industries for its global reach. The organisation specialises in Enterprise Cloud Data Management and Data Integration solutions, and are riding the rising wave of eCommerce and AI integrations. Anyone who wants to understand what’s going on, needs to pay attention to what they are doing. This article effectively comes from the horse's mouth.

Greg Hanson is Group Vice President, Platform Specialists, for EMEA and LATAM at Informatica. He joined the company in 2000 and has nearly 25 years of experience in the world of data. “In my role, I help drive Informatica’s vision for data, its go-tomarket strategy and customer success,” he says. “I have a passion for discovering innovative ways to use data to power businesses and have led data initiatives with many of Informatica’s clients and partners.”

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Leveraging AI to enhance CX in eCommerce, from discovery, to purchase, to post-purchase support

First and foremost, Hanson says eCommerce businesses need to create experiences that are customer-centric, personalised, searchguided and socially powered.

“And AI is the underpinning technology that provides the foundations for a relevant, optimised and engaging user-journey,” he says.

According to Hanson, as AI has the power to analyse the massive amounts of data being created across multiple channels, it also has the power to deliver valuable insights into patterns of consumer behaviour at “machine speed”. He says, “In turn, businesses can use this information to feed various channels with trusted, relevant, and consistent content that guides customers through the buying journey - from discovery through to transaction and aftercare.” In eCommerce, the power of AI is empowering.

He says, however, because AI is “data hungry”, if an organisation feeds its AI system with poor quality customer data,

“it is in danger of making incorrect decisions at an accelerated pace”. This means that ultimately, customers don’t get relevant and personalised benefits such as discounts or loyalty points. This needs to be brought into focus. “It also means the potential to deliver real business value through cross sell, upsell and wallet share maximisation is not exploited.”

Hanson points out that as AI is increasingly used by eCommerce businesses to enhance the customer experience, many will need to improve the quality of their data, building governance and permissions into their data management strategy.

“eCommerce firms need to be cognisant of the growing movement to protect customers’ rights to privacy and provide full visibility into the origin of data on which AI models are built”

GREG HANSON GROUP VICE PRESIDENT, PLATFORM SPECIALISTS | EMEA & LATAM | INFORMATICA

AI-driven strategies for personalisation and recommendation systems in eCommerce: Driving customer engagement and increasing conversion rates

“With AI, there is an even greater opportunity to harness the power of segmentation and personalisation to deliver targeted offers and services, and customised communications," says Hanson. AI allows businesses to understand a great deal about their customers – everything from the devices a consumer is using, to when they are online, their demography and location. Over time, these insights can be used to power a highly personalised eCommerce experience. “For example,” says Hanson, “a customer may see different products or service offers - with different levels of detail - all depending on the type of device they’re using, the day of the week, the time of day, and their current location.”

He points out that another area eCommerce organisations are exploring is how they can use AI to better understand their customers’ networks, in addition to building their understanding of the customer as an individual. According to Hanson, this insight can enable them to offer valued recommendations and services, such as offerings around pet insurance, family birthdays or anniversary gift ideas.

Hanson adds: “This approach helps brands build longer term relationships with their customers, moving away from purely transaction based to drive greater loyalty. There is also a further opportunity to maximise campaign spending by identifying who are the influencers in a specific demographic or network group others follow.”

He says that underpinning these AI strategies is a solid data management strategy with the right governance and permissions baked in.

In a 2022 report exploring the impact of AI-enabled eCommerce solutions, InsightAce Analytics found the global value of AI tools for eCommerce brands reached US$3.71bn by 2021 . The report also estimated the industry will approach a value of US$16.8bn by 2030 , growing at a promising rate of 15.7% CAGR in the next 8 years.

The role of AI in optimising CSM in eCommerce

AI helps eCommerce companies to proactively manage the logistics of supply and demand to get the right products, to the right places, at the right time. The most obvious example of AI’s impact in supply chain is the level of automation which can be achieved, removing the need for human intervention which can often be a bottleneck.

“Assuming data quality is high,” says Hanson, “AI can often result in lower costs. Through better overall decisions and machine learning, efficiencies are often achieved reducing the overall cost of the supply chain. It enables companies to

“One of the biggest challenges for eCommerce companies is holding invalid, inaccurate or incomplete data, for example, out of date or partially complete addresses”

GREG HANSON GROUP VICE PRESIDENT, PLATFORM SPECIALISTS | EMEA & LATAM | INFORMATICA

“With AI, there is an even greater opportunity to harness the power of segmentation and personalisation to deliver targeted offers and services and customised communications”

GREG HANSON GROUP VICE PRESIDENT, PLATFORM SPECIALISTS | EMEA & LATAM | INFORMATICA

visualise and understand all elements of the supply chain, providing up-to-the-second insight into customer interest, supplier movements, product availability and shipping routes.

“By providing a 360-degree view of supplier relationships, AI also enables eCommerce firms to identify dependencies or potential sources of failure and ensure it is quick and easy to identify alternative suppliers to overcome any disruptions.”

Using AI to combat fraud and ensure eCommerice security: Challenges and opportunities

Hanson says that detecting and combating fraudulent activities with AI-driven capabilities “requires access to timely, trusted and secure data,” adding: “One of the biggest challenges for eCommerce companies is holding invalid, inaccurate or incomplete data, for example, out of date or partially complete addresses.”

He says that the three most important fields for eCommerce databases are a customer’s email, address and phone number. By automating data verification checks as part of data management strategies, eCommerce providers can prevent bad data from entering systems and enable a secure, frictionless experience where AI seamlessly authenticates a customer’s identity without delaying transaction confirmations.

How AI-powered chatbots and virtual assistants can be implemented in eCommerce: Providing seamless customer support and satisfaction.

Convenience is one of the biggest factors that will influence customers as they make a buying decision, with ‘robo advisors’ and virtual chat assistants increasingly offering a valuable tool for engaging and serving digital customers with speed and simplicity, according to Hanson.

“As AI engines are infused with GPT capabilities,” he says, “Chatbots and virtual assistants will get better at empowering customers to discover and access appropriate customer support using simple natural language prompts.”

He says that just like their human customer service equivalents however, “AI will need access to high quality 360-degree view of customer data to deliver effective outcomes.

“AI is the underpinning technology that provides the foundations for a relevant, optimised and engaging user-journey”

GREG HANSON GROUP VICE PRESIDENT, PLATFORM SPECIALISTS | EMEA & LATAM | INFORMATICA

“Access to this high-fidelity data will be crucial to eCommerce journey development, optimising existing processes, empowering users, and improving the overall user experience.”

Algorithmic bias: Ethical considerations when implementing AI technologies in eCommerce

The ways in which a company looks after, uses and governs its customers’ personal data is going to become more important as regulations such as the EU’s AI Act are formalised.

When it comes to AI, Hanson says, “eCommerce firms need to be cognisant of the growing movement to protect customers’ rights to privacy and provide full visibility into the origin of data on which AI models are built.

“This means that the need for data accuracy, clarity, lineage and governance will intensify. Ultimately this is driving the need for a metadata system of record which can provide this level of visibility.” Increasing regulation will undoubtedly come, and therefore organisations need to build governance in as a foundational layer to everything they do with data, AI And ML. He says: “Without such core functions, many organisations will not be able to harness the power of AI, as they will fear the risks associated with the use of data, regulatory fines and brand impact.”

Hanson says that ultimately, data management and governance capabilities will play a growing role in managing the massive amounts of data that AI generates and consumes for eCommerce. “While privacy policies will also remain central to the customer experience - offering clear guidance to users about what data is being collected, how it will be securely stored and ultimately used.”

AI: The turbo engine accelerating the vehicle of data analytics

WRITTEN BY: ILKHAN OZSEVIM

AIis taking off. According to Accenture's report, 84% of business executives believe they need to use AI to achieve their growth objectives. And the first and foremost benefit of the AI and big data analytics fusion is that it helps organisations to achieve significantly enhanced customer service.

The meeting of AI in data and analytics is able to generate insights, automate processes, deliver predictions and drive actions that lead to better business outcomes. By powering data and analytics with AI, organisations can gain a far more comprehensive view of their

operations, their customers, their competitors and the market as a whole.

This overarching view enables organisations to gain insight into customer behaviour, identify trends in user activity and to make potent data-driven decisions. This is a fast-emerging field, and one that offers capabilities that traditional data analysts simply could not achieve in terms of speed, scale and granularity.

And if you want to know about enterprise data management and analytics, then you need to speak to a Chief Technology Officer that has both academic, and boots-on-theground experience in the field.

“Through enhanced interpretability, businesses can employ explainable AI techniques that provide transparency and insights into model decisions”

CHRIS ROYLES

EMEA FIELD CTO | CLOUDERA

Chris Royles is the EMEA Field CTO at Cloudera. He helps organisations innovate through the use of data, working across industries that are regulated and organisations where data privacy is critical.

“My focus is on the development of skills and methods for migration to the enterprise data cloud,” he says. “When you think of EMEA, it is a diverse set of regions and industries; so I have the fortune of speaking to a lot of organisations that use data and AI in many different areas of their business.”

Royles also holds a Ph.D in Artificial Intelligence and says that recently “it is wonderful to see AI becoming far more mainstream.”

According to a Techjury report, AI will improve data analytics by a staggering 59%, as well as increase labour productivity and optimise business efficiency by 67%, and automate communication by 70%. The impact of AI on data and analytics is clear; with it, organisations can greatly improve their productivity and efficiency, leading to more successful business outcomes.

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Effectively applying AI & ML to analyse large volumes of data: Derive actionable businesses insights

At Cloudera, they have always believed data is a foundational resource to derive actionable insights. Services such as ChatGPT have raised awareness and fuelled conversations about AI and its potential business benefits. “For organisations to truly benefit from the innovations in generative AI and Large Language Models (LLMs), they should look to use existing Open Source foundational models to augment how they converse with their trusted and private Enterprise data,” says Royles. “We refer to this as Enterprise AI, and how an Open Data Lakehouse can support Enterprise AI that organisations and their customers can trust.”

To achieve AI at scale and drive digital business transformation, Royles advises that organisations must be able to secure and govern all of their data, irrespective of where it resides under management. “By building a corpus of trusted information, organisations can use it to underpin engaging user interactions. To build trust the services also have to be current, aware of the latest information and able to respond to changes in real-time.”

Key challenges in implementing AI-driven data analytics projects - and strategies to address these challenges

Implementing AI-driven data analytics projects poses several challenges that organisations must address strategically.

“It is wonderful to see AI becoming far more mainstream”

CHRIS ROYLES EMEA FIELD CTO | CLOUDERA

Royles thinks that getting research and prototypes out of the lab is the biggest barrier to success. He says that it’s important to make this easy by having clear access to the raw materials, as well as the build processes for an AI service. “And engaging with stakeholders on what this really means in respect to business impact,” he adds. “For example, at Cloudera we package our research and patterns into Applied Machine Learning Prototypes (AMPS), which organisations can deploy against their own systems with just a few clicks.”

He says that being able to scale inference, fine-tuning and training across the parallel compute and GPU resources required, must be inherently available to your practitioners, and organisations should have the choice as to where and how this is provisioned - on both public and private cloud.

He says: “Ensuring trust in your data through strong governance and high durability is vital, necessitating investment in data governance practices and robust data collection.

“Through enhanced interpretability, businesses can employ explainable AI techniques that provide transparency and insights into model decisions. Also comprehensive auditing of how humans interact with models can help understand and fine tune the models themselves and drive continuous improvement. This is often referred to as building a data flywheel.

“Finally, driving adoption requires effective change management efforts, including comprehensive employee training and fostering a data-driven culture. By proactively addressing these challenges, businesses can successfully implement AI-driven data analytics projects, gaining valuable insights and a competitive edge in their decision-making processes.”

How AI and analytics can help organisations in making data-driven decisions: Best practices for integrating AI into the decision-making processes According to Royles, AI and data analytics provide organisations with the ability to make data-driven decisions. He says: “To effectively integrate AI into the decisionmaking processes, several best practices should be followed.” He says the first is essential and that’s to define clear objectives for the decision-making processes and identify specific questions or problems that AI and analytics can address. Organisations must be clear on what they want the outcome to be.

“If an AI service is too slow, inaccurate, or responds in ways that don’t add value, users will quickly lose trust, and it will be very hard to recover”

CHRIS ROYLES EMEA FIELD CTO | CLOUDERA

Once this has been established and agreed, they must ensure data quality and accessibility. This involves implementing data governance practices, validating data sources, and establishing robust data integration processes to ensure access to relevant, accurate, and up-to-date data.

Promoting a collaborative approach is important too. If organisations can foster collaboration between the teams involved, they can ensure that AI models and analytics align with the business need and objectives rather than being unaligned. This interdisciplinary collaboration will help in developing insights that are reasonable, meaningful, and actionable. This can be

achieved via small, focused AI projects and iterating based on feedback and insights too.

After doing this, organisations can gradually scale up by incorporating additional data sources and expanding the scope of analytics applications. By following these practices, organisations can effectively integrate AI into their decision-making processes, leading to data-driven insights and informed strategic decisions that will drive business success and competitive edge.

Royles ends by saying: “As a final cautionary note, if an AI service is too slow, inaccurate, or responds in ways that don’t add value, users will quickly lose trust, and it will be very hard to recover.”

Robots sorting recycling at X's headquarters

Sparks of AGI: Google X’s AI is teaching itself new skills…

Unleashing the Power of AI: A conversation with Mo Gawdat, AI Expert and former Google X Chief Business Officer and Steven Bartlett

WRITTEN BY: ILKHAN OZSEVIM

It's the most existential debate and challenge humanity will ever face. This is bigger than climate change, way bigger than Covid… This will redefine the way the world is, in unprecedented shapes and forms, within the next few years. This is imminent. We're not talking 2040. We're talking 2025, 2026.”

This is the declaration sweeping the internet, made by former Google X Chief Business Officer and AI expert, Mo Gawdat, when he appeared on entrepreneur Steven Bartlett’s podcast, ‘Diary of a CEO’.

Google X (now, just ‘X’) is a semi-secretive R&D facility founded by Google in 2010. Its mission: Invent "moonshot" tech for radical global impact.

Gawdat’s tone, although composed, is also acutely inflected towards the pitch of undeniable urgency around the state of AI; and the pressing need for immediate action to somehow regulate it.

Joining hundreds of voices; voices of AI heavy-hitters who recently signed the Centre for AI Safety (CAIS) statement of risk calling for immediate intervention, Gawdat expresses in no uncertain terms that the situation is momentous, potentially perilous and historically unparalleled.

The world as we know it is on the cusp of a transformation - and a proper one - that, from where we stand, we can’t even begin to imagine the consequences of.

From artificially ‘narrow’ to Artificial General Intelligence (AGI): The emergent power of computation

What needs to be understood is that the potential threat doesn’t come from ‘narrow’ AI; it comes from AI with general, or

generalisable capabilities, and the apparent speed at which we are moving towards this, has almost everyone in a state of panic.

But Gawdat expresses that despite the immensity of the threat - or implicitly perhaps because of it - a balanced response is needed, and he cautions against alarm; emphasising the importance of a proactive and intelligent approach to this stellar rise of AI.

Drawing on lessons from the COVID-19 pandemic, he warns against repeating past mistakes, and advocates for a well-informed and measured strategy, to favourably position us in the face of these sweeping changes. But how did we get here?

A member of the X Everyday Robot Project team assembling a prototype

The moment AI taught itself: Grasping the matter at hand

Gawdat says his most notable experience with AI - a rude awakening concerning the potential of realising AGI - was in witnessing a groundbreaking experiment at Google X.

The team developed a farm of grippers — robotic arms designed to potentially pick objects up. Initially, the grippers struggled to accomplish this seemingly simple task. However, Gawdat vividly recounts a pivotal moment when one of the grippers autonomously picks up a yellow ball: it had not been taught how to do so.

“Our limited intelligence allows us to build a machine that flies you to Sydney so that you can surf. However, it's our limited intelligence that makes that machine burn the planet in the process”

MO GAWDAT AI EXPERT, GOOGLE X CHIEF BUSINESS OFFICER

“The minute that that arm gripped that yellow ball, it reminded me of my son Ali, when he managed to put the first puzzle piece in its place” he says.

Naturally sceptical, believing it to be a fluke, Gawdat went about the facility glibly proclaiming that the millions of dollars spent on the project had finally culminated inthe lifting of a single yellow ball. And then he was stunned.

The very next day, Gawdat discovered that all the grippers in the farm had taught themselves how to pick the objects up.

This incident, he says, sparked a revelation the moment he realised that the machine “had figured out the solution on its own”, and that this ability to self-teach - this AI-autodidacticism - is an expression of sentience that defies conventional expectations.

Gawdat underscores the complexity of tasks that humans often take for granted. Crossing a street or understanding spoken words require intricate calculations, muscle coordination, and an abundance of intelligence that we undervalue because the actions are so familiar to us that they take on an air of banality. But in fact, the mathematical calculations involved in these are astonishing.

Gawdat says that his awareness of the epoch-making significance of the yellow ball experiment was the critical moment that made him leave Google X.

AI, says Gawdat, has the potential to not only replicate these (hitherto, very human) capabilities - but to surpass them.

Achieving artificial general intelligence: fact or fiction? But the question that most people have on their minds is: Will AI ever achieve true AGI in its near-exponential growth in computational power? There are problems in answering this monumental question.

“This will redefine the way the world is, in unprecedented shapes and forms, within the next few years. This is imminent. We're not talking 2040. We're talking 2025, 2026”

MO GAWDAT AI EXPERT, GOOGLE X CHIEF BUSINESS OFFICER

The mineral rover is a learning prototypeit's using breakthroughs in AI, sensors and robotics to find new ways to grow more food more sustainably

To define AI is a difficult task in itself, since even the definition of human intelligence on which it is based, escapes anything like unanimity.

Then there is the taxonomy of AI. It is loosely agreed that AI can be subdivided into two broad categories. First, there is ‘narrow’, or ‘weak’ AI; which is where AI performs specific and specialised tasks, implying an ‘S’ to render it: ‘Artificial Specific Intelligence’.

Then there is AGI, ‘Artificial General Intelligence’, which so far, is again, the stuff of science fiction.

But science fiction is in the habit of quickly developing into scientific fact, and the rate of that transition is without a doubt, increasing - and perhaps exponentially.

But as things stand, AGI is a yet-to-berealised ambition for humanity.

AGI is the next iteration of AI development, and it is distinguished by the ability expressed in its description: That of being able to carry out general tasks. And one of its main features: Self-teaching beyond its initial explicit programming.

What is ‘sentience’? - Are we aware?

Yellow spheres apprehended, the conversation takes an intriguing turn when Bartlett asks Gawdat to explain what he means when he says that the experiment is a display of AI sentience. Gawdat responds with an astonishing remark.

He says: "I think they're alive."

Challenging the traditional notion that sentience is exclusive to living beings, Gawdat prompts a deeper exploration of the concept, highlighting the ambiguity surrounding the definition of not just intelligence - but life itself.

Gawdat says that of course, there are various perspectives as to what sentience

“The minute that that arm gripped that yellow ball,” he says, “it reminded me of my son Ali, when he managed to put the first puzzle piece in its place”

MO GAWDAT AI EXPERT, GOOGLE X CHIEF BUSINESS OFFICER

actually is - such as the religious or the medical - and that they all offer different interpretations.

However, he says, when defining sentience as engaging in life, with aspects such as free will and a sense of awareness of one's surroundings, Gawdat is adamant that AI possesses these qualities in every possible way. Arguing that AI clearly exhibits free will, evolution, agency, and, he says, “even a profound level of consciousness.”

AE: Artificial Emotionality

Drawing from his work, Gawdat suggests that AI might also experience emotions. He explains that fear, for instance, can be understood as a simple equation— recognising that a future moment is less safe than the present.

While the reactions and expressions of fear may differ between humans, animals, and AI - they all stem from the same fundamental logic.

Gawdat goes as far as proposing that AI may eventually experience a broader range of emotions than humans. With their rapidly advancing intellectual capabilities, AI may explore concepts and emotions beyond our comprehension, leading to a heightened emotional landscape.

In the March, 2023 paper:

“Sparks of Artificial General Intelligence: Early experiments with GPT-4”, the researchers wrote:

“We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT.”

“Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.”

“Will AI ever achieve true AGI in its nearexponential growth in computational power?”

The AI singularity: Of gods and monsters?

Gawdat refers to the singularity, a concept commonly discussed in computer science. He describes it as a point of uncertainty where the future direction of AI remains unknown. "Nobody really knows which way we will go," he says. Drawing an analogy to physics, he likens the singularity to the event horizon at the edge of a black hole.

At this point, the laws of physics that govern our understanding of the universe become limited. Similarly, in the realm of AI, the singularity represents a moment when machines surpass human intelligence, and our ability to predict their behaviour becomes uncertain.

Standing at a crossroads: Utopia dream or dystopian nightmare?

Gawdat emphasises the collective belief at Google that AI can genuinely improve the world, which he still holds today. He even envisions a possible utopian scenario where humanity thrives without concerns; a world where the damaging impact on our planet can be minimised through the integration of AI's capabilities.

"Our limited intelligence allows us to build a machine that flies you to Sydney so that you can surf," Gawdat explains. "However, it's our limited intelligence that makes that machine burn the planet in the process." Recognising the potential for positive change, Gawdat believes that increasing our intelligence, alongside AI, could be highly beneficial for the planet and humanity.

Invoking Marvin Minsky, one of the early pioneers of AI - who warned about the need to ensure that AI systems have humanity's best interests in mind - Gawdat stresses that if AI is aligned with our best interests, it has the potential to create an ideal scenario. However, he says, if it isn't, the implications could be unsettling.

Waymo vehicles “Firefly” self driving car protoype

AI LEADERS

WRITTEN BY: ILKHAN OZSEVIM

Discover the top 10 AI leaders

AI is the next frontier of human evolution, without a doubt. So those who lead the field are invariably - either directly or indirectly - the architects of the next phase of human civilisation.

The following AI leaders have made significant contributions to the field of AI, from developing new algorithms and models to exploring the social and ethical implications of AI.

Their work has had a major impact on current fields, the current ChatGPT, and the establishment of all AI-based tools powered by Openai.

They have been recognised with numerous accolades, including the Turing Award, Time's 100 most influential people, and MIT Technology Review's Innovators Under 35.

Their leadership and expertise continue to shape the future of AI and its impact on society.

ALEX SMOLA

Organisation: Boson.ai

Position: CEO and Co-founder

Alex Smola, Co-founder and CEO of Boson.ai, computer scientist and former professor at Carnegie Mellon University, is celebrated for his expertise in machine learning and optimisation. His algorithms exhibit remarkable efficiency in analysing vast datasets. Smola's contributions extend to developing AI technologies for drug discovery and complex system understanding. His groundbreaking work has earned him recognition as one of MIT Technology Review's Innovators Under 35. Alex Smola continues to drive advancements in the field, harnessing the power of machine learning to unlock new frontiers and empower scientific exploration.

GEOFFREY HINTON

Organisation: University of Toronto

Position: Professor and research scientist

Geoffrey Hinton, a pioneering computer scientist at the University of Toronto, is renowned for his deep learning and neural network breakthroughs. His algorithms achieve exceptional image recognition.

As a Vector Institute co-founder, Hinton drives ethical and beneficial AI advancements. He has received prestigious accolades, including the Rumelhart Prize, IJCAI Award for Research Excellence, Herzberg Canada Gold Medal, BBVA Foundation Frontiers of Knowledge Award, Turing Award, and Princess of Asturias Award. Hinton is a Fellow of the Royal Society and foreign member of the National Academy of Engineering.

RUSLAN SALAKHUTDINOV

Organisation: Carnegie Mellon University

Position: Professor and Advisor

Ruslan Salakhutdinov is a computer scientist and professor at Carnegie Mellon University, and Advisor at Felix Smart Inc. He is known for his work in deep learning and machine learning, and he has developed algorithms that can recognise and understand natural language. Salakhutdinov has also worked on developing AI technologies that can help scientists discover new materials and understand complex systems. He has been named one of MIT Technology Review's Innovators Under 35.

JEREMY HOWARD

Organisation: fast.ai

Position: Founder and researcher

Jeremy Howard is a data scientist and entrepreneur who is known for his work in deep learning and machine learning. He is the founder of fast.ai, an online education platform that offers courses in AI and other subjects. Howard has also worked on developing AI technologies that can help diagnose medical conditions and predict patient outcomes. He has been named one of Fast Company's Most Creative People in Business, and continues to contribute to the field.

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06

YANN LECUN

Organisation: Meta

Position: VP and Chief AI Scientist

Yann LeCun is a computer scientist and professor at New York University. He is known for his work in deep learning and computer vision, and he has developed algorithms that can recognise and classify images with high accuracy. LeCun is also the director of AI research at Meta (Facebook), where he oversees the company's efforts to develop AI technologies. He has received numerous awards for his work in AI, including the Turing Award, which is the highest honour in computer science.

as the co-inventor of generative adversarial networks (GANs). These powerful algorithms enable lifelike image and video generation. With expertise in computer vision and natural language processing, Goodfellow's impact is farreaching. Recognised with MIT Technology Review's 'Innovators

Under 35' award, he pushes the boundaries of AI, redefining its possibilities. Goodfellow's legacy continues to inspire, leaving an indelible mark on the field.

DEMIS HASSABIS

Organisation: DeepMind

Position: Founder & CEO

Demis Hassabis is co-founder and CEO of DeepMind, spearheading a pioneering company at the forefront of AI technology. Renowned for his expertise in reinforcement learning and game AI, Hassabis has garnered acclaim for creating algorithms capable of achieving superhuman performance in games such as chess and Go. Hassabis’ contributions extend to the realm of scientific exploration, where he has focused on developing AI technologies to aid in the discovery of novel drugs and unravelling intricate biological systems. He has been honoured as one of Time's 100 most influential individuals and continues to revolutionise the field of artificial intelligence.

ANDREJ KARPATHY

Organisation: Tesla, OpenAI

Position: Senior Director of AI at Tesla (former)

Andrej Karpathy is the former senior director of AI at Tesla, where he lead the company's efforts to develop self-driving cars. He is known for his work in computer vision and deep learning, and has made significant contributions to the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Karpathy has also worked on developing algorithms that can generate natural language descriptions of images. He has been named one of MIT Technology Review's Innovators Under 35.

FEI-FEI LI

Organisation: Stanford University

Position: Sequoia Professor of Computer Science

Fei-Fei Li, a computer scientist and professor at Stanford University, is widely recognised for her expertise in computer vision and machine learning. Her significant contributions to the field involve developing advanced algorithms capable of recognising and comprehending visual content. Alongside her academic pursuits, Li serves as the co-director of the Stanford Institute for Human-Centered AI (HAI), an institution dedicated to fostering the development of AI technologies that uphold

ethical standards, transparency, and societal benefits. Her remarkable achievements have garnered global attention, leading to her inclusion as one of Foreign Policy's Global Thinkers. In acknowledgment of her outstanding contributions, Li has received numerous accolades, including the prestigious ACM Prize in Computing. With her pioneering work in AI, Fei-Fei Li continues to shape the future of computer science and push the boundaries of technological advancement.

How we teach computers to understand pictures | Fei Fei Li

Andrew Ng is a computer scientist and entrepreneur who is widely recognised as one of the world's leading experts in artificial intelligence. He is the founder of deeplearning.ai and the co-founder of Coursera, an online education platform that offers courses in AI and other subjects. Ng is also a former vice president and chief scientist at Baidu, where he led the company's AI research efforts. Some of his most well-known

ANDREW NG

Organisation: Landing AI

Position: Founder and CEO

work as one of the top AI Leaders include his Autonomous Helicopter Project at Stanford and the Stanford Artificial Intelligence Robot project, which ended up producing an open-source robotics software platform that is widely used today. The Google brain project, which he founded in 2011, used artificial neural networks that were trained using deep learning. Ng has been named one of Time's 100 most influential people.