As one of the first authors to publish a book on generative AI, she has emerged as a sought-after speaker helping global leaders, businesses, and institutions understand and adapt to this transformative moment.

We spoke to Nina to explore the future of AI-driven innovation, its ethical and political dimensions, and how organisations can lead in this rapidly evolving landscape.

In your view, how will generative AI redefine the foundational structures of business and economic productivity in the coming decade?

I believe generative AI is absolutely going to transform the entire economy as we know it. This moment feels quite similar to around 1993, when we were first being told to prepare for the Internet. Back then, some thirty years ago, we didn’t fully grasp, in our naivety, how profoundly the Internet would go on to reshape business and the broader global economy.

Now, we are witnessing something even more significant. You can think of generative AI as a kind of new combustion engine, but for all forms of human creative and intelligent activity. It’s a fundamental enabler. Every industry, every facet of productivity, will be impacted and ultimately transformed by generative AI. We’re already beginning to see those use cases emerge, and this is only the beginning.

As AI and data continue to evolve as forces shaping society, how do you see them redefining the political agenda and global power dynamics?

When you reflect on just how profound AI is in its capacity to reshape the entire framework of society, it becomes clear that this AI revolution is going to emerge as one of the most important political questions of our generation. Over the past 30 years, we’ve already seen how the information revolution — driven by the Internet, smartphones, and cloud computing — has become a defining geopolitical force.

Now, we’re layering the AI revolution on top of that, along with the data that fuels it, and the impact is nothing short of seismic. This will evolve into one of the most pressing and influential issues society must address over the coming decades. So, to answer the question directly — AI won’t just influence politics; it will, in many ways, become the very fabric of politics itself.

There’s been much discussion about the Metaverse and immersive tech — how do you see these experiences evolving, and what role do you believe AI will play in architecting this next frontier of digital interaction?

The Metaverse represents a vision for where the Internet may be heading — a future where digital experiences become far more immersive, intuitive, and experiential. It’s a concept that imagines how we might engage with digital content in a far more lifelike way.

But the really fascinating element here is that artificial intelligence is the key enabler — the actual vehicle — that will allow us to build and scale these kinds of immersive digital environments. So, even though the Metaverse remains largely an untested concept in terms of its final form, what is clear right now is that AI is going to be the engine that generates and populates the content that will live within these immersive spaces.

Considering the transformative power of AI and big data, what ethical imperatives must policymakers and society address to ensure equitable and responsible deployment?

The conversation around ethics, artificial intelligence, and big data is one that is set to become intensely political and highly consequential. It will likely remain a predominant issue for many years to come.

What we’re dealing with here is a technology so transformative that it has the potential to reshape the economy, redefine the labour market, and fundamentally alter the structure of society itself. That’s why the ethical questions — how to ensure this technology is applied in a fair, safe, and responsible manner — will be one of the defining political challenges of our time.

For business leaders navigating digital transformation, what mindset shifts are essential to meaningfully integrate AI into long-term strategy and operations?

For businesses aiming to digitally transform, especially in the era of artificial intelligence, it’s critical to first understand the conceptual paradigm shift we are currently undergoing. Once that foundational understanding is in place, it becomes much easier to explore and adopt AI technologies effectively.

If companies wish to remain competitive and gain a strategic edge, now is the time to start investigating how generative AI can be thoughtfully and effectively integrated into their business models. This includes identifying priority areas where AI can deliver long-term value — not just short-term.

If you put together a generative AI working group to look into this, your business will be transformed and able to compete with other businesses that are using AI to transform their processes.

As one of the earliest voices to articulate the societal implications of generative AI, what catalysed your foresight to explore this space before it entered the mainstream conversation?

My interest in AI didn’t come from a technical background. I’m not a techie. My experience has always been in analysing macro trends that shape society, geopolitics, and the wider world. That perspective is what led me to AI, as it quickly became clear that this technology would have far-reaching societal implications.

I began researching and writing about AI because I saw it as more than just a technological shift. Ultimately, this isn’t only a story about innovation. It’s a story about humanity. Generative AI, as an exponential technology built and directed by humans, is going to transform not just the way we work, but the way we live. It will even challenge our understanding of what it means to be human.

Photo by Heidi Fin on Unsplash

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A key advocate for responsible AI governance and diversity in tech, Wendy has played a crucial role in global discussions on the future of AI.

In our Q&A, we spoke to her about the gender imbalance in the AI industry, the ethical implications of emerging technologies, and how businesses can harness AI while ensuring it remains an asset to humanity.

The AI sector remains heavily male-dominated. Can you share your experience of breaking into the industry and the challenges women face in achieving greater representation in AI and technology?

It’s incredibly frustrating because I wrote my first paper about the lack of women in computing back in 1987, when we were just beginning to teach computer science degree courses at Southampton. That October, we arrived at the university and realised we had no women registered on the course — none at all.

So, those of us working in computing started discussing why that was the case. There were several reasons. One significant factor was the rise of the personal computer, which was marketed as a toy for boys, fundamentally changing the culture. Since then, in the West — though not as much in countries like India or Malaysia — computing has been seen as something nerdy, something that only ‘geeks’ do. Many young girls simply do not want to be associated with that stereotype. By the time they reach their GCSE choices, they often don’t see computing as an option, and that’s where the problem begins.

Despite many efforts, we haven’t managed to change this culture. Nearly 40 years later, the industry is still overwhelmingly male-dominated, even though women make up more than

half of the global population. Women are largely absent from the design and development of computers and software. We apply them, we use them, but we are not part of the fundamental conversations shaping future technologies.

AI is even worse in this regard. If you want to work in machine learning, you need a degree in mathematics or computer science, which means we are funnelling an already male-dominated sector into an even more male-dominated pipeline.

But AI is about more than just machine learning and programming. It’s about application, ethics, values, opportunities, and mitigating potential risks. This requires a broad diversity of voices — not just in terms of gender, but also in age, ethnicity, culture, and accessibility. People with disabilities should be part of these discussions, ensuring technology is developed for everyone.

AI’s development needs input from many disciplines — law, philosophy, psychology, business, and history, to name just a few. We need all these different voices. That’s why I believe we must see AI as a socio-technical system to truly understand its impact. We need diversity in every sense of the word.

As businesses increasingly integrate AI into their operations, what steps should they take to ensure emerging technologies are developed and deployed ethically?

Take, for example, facial recognition. We still haven’t fully established the rules and regulations for when and how this technology should be applied. Did anyone ask you whether you wanted facial recognition on your phone? It was simply offered as a system update, and you could either enable it or not.

We know facial recognition is used extensively for surveillance in China, but it is creeping into use across Europe and the US as well. Security forces are adopting it, which raises concerns about privacy. At the same time, I appreciate the presence of CCTV cameras in car parks at night — they make me feel safer.

This duality applies to all emerging technologies, including AI tools we haven’t even developed yet. Every new technology has a good and a bad side — the yin and the yang, if you will. There are always benefits and risks.

The challenge is learning how to maximise the benefits for humanity, society and business while mitigating the risks. That’s what we must focus on — ensuring AI works in service of people rather than against them.

The rapid advancement of AI is transforming everyday life. How do you envision the future of AI, and what significant changes will it bring to society and the way we work?

I see a future where AI becomes part of the decision-making process, whether in legal cases, medical diagnoses, or education.

AI is already deeply embedded in our daily lives. If you use Google on your phone, you’re using AI. If you unlock your phone with facial recognition, that’s AI. Google Translate? AI. Speech processing, video analysis, image recognition, text generation, and natural language processing — these are all AI-driven technologies.

Right now, the buzz is around generative AI, particularly ChatGPT. It’s like how ‘Hoover’ became synonymous with vacuum cleaners — ChatGPT has become shorthand for AI. In reality, it’s just a clever interface created by OpenAI to allow public access to its generative AI model.

It feels like you’re having a conversation with the system, asking questions and receiving natural language responses. It works with images and videos too, making it seem incredibly advanced. But the truth is, it’s not actually intelligent. It’s not sentient. It’s simply predicting the next word in a sequence based on training data. That’s a crucial distinction.

With generative AI becoming a powerful tool for businesses, what strategies should companies adopt to leverage its capabilities while maintaining human authenticity in their processes and decision-making?

Generative AI is nothing to be afraid of, and I believe we will all start using it more and more. Essentially, it’s software that can assist with writing, summarising, and analysing information.

I compare it to when calculators first appeared. People were outraged: ‘How can we allow calculators in schools? Can we trust the answers they provide?’ But over time, we adapted. The finance industry, for example, is now entirely run by computers, yet it employs more people than ever before. I expect we’ll see something similar with generative AI.

People will be relieved not to have to write endless essays. AI will enhance creativity and efficiency, but it must be viewed as a tool to augment human intelligence, not replace it, because it’s simply not advanced enough to take over.

Look at the legal industry. AI can summarise vast amounts of data, assess the viability of legal cases, and provide predictive analysis. In the medical field, AI could support diagnoses. In education, it could help assess struggling students.

I envision AI being integrated into decision-making teams. We will consult AI, ask it questions, and use its responses as a guide — but it’s crucial to remember that AI is not infallible.

Right now, AI models are trained on biased data. If they rely on information from the internet, much of that data is inaccurate. AI systems also ‘hallucinate’ by generating false information when they don’t have a definitive answer. That’s why we can’t fully trust AI yet.

Instead, we must treat it as a collaborative partner — one that helps us be more productive and creative while ensuring that humans remain in control. Perhaps AI will even pave the way for shorter workweeks, giving us more time for other pursuits.

Photo by Igor Omilaev on Unsplash and AI Speakers Agency.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The partnership will involve developing a bespoke AI foundation model to supercharge L’Oréal’s Research & Innovation (R&I) teams in creating eco-friendly formulations using renewable raw materials. In turn, this initiative is designed to reduce both energy and material waste.

Described as the cosmetics industry’s first formulation-focused AI model, this effort is a glimpse into a future where cutting-edge technology drives environmentally-conscious solutions.

Stéphane Ortiz, Head of Innovation Métiers & Product Development at L’Oréal R&I, said: “As part of our Digital Transformation Program, this partnership will extend the speed and scale of our innovation and reformulation pipeline, with products always reaching higher standards of inclusivity, sustainability, and personalisation.”  

AI and beauty: A perfect match

By marrying L’Oréal’s expertise in cosmetic science with IBM’s AI technologies, the companies aim to unlock new pathways in both cosmetic innovation and sustainability. The role of AI in tailoring and personalising products is well-established, but diving deeper into its role in crafting renewable and sustainably-sourced formulations underscores a broader ecological mission. 

Matthieu Cassier, Chief Transformation & Digital Officer at L’Oréal R&I, commented: “Building on years of unique beauty science expertise and data structuring, this major alliance with IBM is opening a new exciting era for our innovation and development process.”

Foundation models serve as the technological backbone for this collaboration. These AI systems are trained on vast datasets, enabling them to perform various tasks and transfer learnings across different applications.

Although these models are perhaps most known for revolutionising natural language processing (NLP), IBM has advanced their use cases beyond text, including applications in chemistry, geospatial data, and time series analysis.

In this context, the custom AI model being developed for L’Oréal will process a massive database of cosmetic formulas and raw material components. From creating new products to reformulating existing ones and scaling up for production, the model will accelerate critical tasks for the company’s R&D teams.  

“This collaboration is a truly impactful application of generative AI, leveraging the power of technology and expertise for the good of the planet,” said Alessandro Curioni, IBM Fellow and VP for Europe and Africa, as well as Director at IBM Research Zurich.

“At IBM, we believe in the power of purpose-built, customised AI to help transform businesses. Using IBM’s latest AI technology, L’Oréal will be able to derive meaningful insights from their rich formula and product data to create a tailored AI model to help achieve their operational goals and continue creating high-quality and sustainable products.”

One of the more fascinating dimensions of this collaboration is its potential to deepen understanding of renewable ingredient behaviour within cosmetic formulations.

Guilhaume Leroy-Méline, IBM Distinguished Engineer and CTO of IBM Consulting France, said: “This alliance between highly specialised expertise in artificial intelligence and cosmetics seeks to revolutionise cosmetic formulation. It embodies the spirit of AI-augmented research, emphasising sustainability and diversity.” 

For IBM, this partnership reflects its broader strategy to extend AI applications into industries requiring bespoke solutions. As Curioni pointed out, custom AI has the potential to reshape businesses on multiple levels.

By co-developing this bespoke formulation model, IBM and L’Oréal are setting the stage for a beauty industry that prizes both sustainability and cutting-edge innovation. If successful, the partnership could very well serve as a blueprint for other industries looking to bring AI’s transformative potential to bear on sustainability efforts.  

(Photo by Kelly Sikkema)

See also: Cisco: Securing enterprises in the AI era

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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“AI has been advancing at an incredible pace,” Huang said. “It started with perception AI — understanding images, words, and sounds. Then generative AI — creating text, images, and sound. Now, we’re entering the era of ‘physical AI,’ AI that can perceive, reason, plan, and act.”

With NVIDIA’s platforms and GPUs at the core, Huang explained how the company continues to fuel breakthroughs across multiple industries while unveiling innovations such as the Cosmos platform, next-gen GeForce RTX 50 Series GPUs, and compact AI supercomputer Project DIGITS. 

RTX 50 series: “The GPU is a beast”

One of the most significant announcements during CES 2025 was the introduction of the GeForce RTX 50 Series, powered by NVIDIA Blackwell architecture. Huang debuted the flagship RTX 5090 GPU, boasting 92 billion transistors and achieving an impressive 3,352 trillion AI operations per second (TOPS).

“GeForce enabled AI to reach the masses, and now AI is coming home to GeForce,” said Huang.

Holding the blacked-out GPU, Huang called it “a beast,” highlighting its advanced features, including dual cooling fans and its ability to leverage AI for revolutionary real-time graphics.

Set for a staggered release in early 2025, the RTX 50 Series includes the flagship RTX 5090 and RTX 5080 (available 30 January), followed by the RTX 5070 Ti and RTX 5070 (February). Laptop GPUs join the lineup in March.

In addition, NVIDIA introduced DLSS 4 – featuring ‘Multi-Frame Generation’ technology – which boosts gaming performance up to eightfold by generating three additional frames for every frame rendered.

Other advancements, such as RTX Neural Shaders and RTX Mega Geometry, promise heightened realism in video games, including precise face and hair rendering using generative AI.

Cosmos: Ushering in physical AI

NVIDIA took another step forward with the Cosmos platform at CES 2025, which Huang described as a “game-changer” for robotics, industrial AI, and AVs. Much like the impact of large language models on generative AI, Cosmos represents a new frontier for AI applications in robotics and autonomous systems.

“The ChatGPT moment for general robotics is just around the corner,” Huang declared.

Cosmos integrates generative models, tokenisers, and video processing frameworks to enable robots and vehicles to simulate potential outcomes and predict optimal actions. By ingesting text, image, and video prompts, Cosmos can generate “virtual world states,” tailored for complex robotics and AV use cases involving real-world environments and lighting.

Top robotics and automotive leaders – including XPENG, Hyundai Motor Group, and Uber – are among the first to adopt Cosmos, which is available on GitHub via an open licence.

Pras Velagapudi, CTO at Agility, comments: “Data scarcity and variability are key challenges to successful learning in robot environments. Cosmos’ text-, image- and video-to-world capabilities allow us to generate and augment photorealistic scenarios for a variety of tasks that we can use to train models without needing as much expensive, real-world data capture.”

Empowering developers with AI models

NVIDIA also unveiled new AI foundation models for RTX PCs, which aim to supercharge content creation, productivity, and enterprise applications. These models, presented as NVIDIA NIM (Neural Interaction Model) microservices, are designed to integrate with the RTX 50 Series hardware.

Huang emphasised the accessibility of these tools: “These AI models run in every single cloud because NVIDIA GPUs are now available in every cloud.”

NVIDIA is doubling down on its push to equip developers with advanced tools for building AI-driven solutions. The company introduced AI Blueprints: pre-configured tools for crafting agents tailored to specific enterprise needs, such as content generation, fraud detection, and video management.

“They are completely open source, so you could take it and modify the blueprints,” explains Huang.

Huang also announced the release of Llama Nemotron, designed for developers to build and deploy powerful AI agents.

Ahmad Al-Dahle, VP and Head of GenAI at Meta, said: “Agentic AI is the next frontier of AI development, and delivering on this opportunity requires full-stack optimisation across a system of LLMs to deliver efficient, accurate AI agents.

“Through our collaboration with NVIDIA and our shared commitment to open models, the NVIDIA Llama Nemotron family built on Llama can help enterprises quickly create their own custom AI agents.”

Philipp Herzig, Chief AI Officer at SAP, added: “AI agents that collaborate to solve complex tasks across multiple lines of the business will unlock a whole new level of enterprise productivity beyond today’s generative AI scenarios.

“Through SAP’s Joule, hundreds of millions of enterprise users will interact with these agents to accomplish their goals faster than ever before. NVIDIA’s new open Llama Nemotron model family will foster the development of multiple specialised AI agents to transform business processes.”

Safer and smarter autonomous vehicles

NVIDIA’s announcements extended to the automotive industry, where its DRIVE Hyperion AV platform is fostering a safer and smarter future for AVs. Built on the new NVIDIA AGX Thor system-on-a-chip (SoC), the platform allows vehicles to achieve next-level functional safety and autonomous capabilities using generative AI models.

“The autonomous vehicle revolution is here,” Huang said. “Building autonomous vehicles, like all robots, requires three computers: NVIDIA DGX to train AI models, Omniverse to test-drive and generate synthetic data, and DRIVE AGX, a supercomputer in the car.”

Huang explained that synthetic data is critical for AV development, as it dramatically enhances real-world datasets. NVIDIA’s AI data factories – powered by Omniverse and Cosmos platforms – generate synthetic driving scenarios, increasing the effectiveness of training data exponentially.

Toyota, the world’s largest automaker, is committed to using NVIDIA DRIVE AGX Orin and the safety-certified NVIDIA DriveOS to develop its next-generation vehicles. Heavyweights such as JLR, Mercedes-Benz, and Volvo Cars have also adopted DRIVE Hyperion.

Project DIGITS: Compact AI supercomputer

Huang concluded his NVIDIA keynote at CES 2025 with a final “one more thing” announcement: Project DIGITS, NVIDIA’s smallest yet most powerful AI supercomputer, powered by the cutting-edge GB10 Grace Blackwell Superchip.

“This is NVIDIA’s latest AI supercomputer,” Huang declared, revealing its compact size, claiming it’s portable enough to “practically fit in a pocket.”

Project DIGITS enables developers and engineers to train and deploy AI models directly from their desks, providing the full power of NVIDIA’s AI stack in a compact form.

Set to launch in May, Project DIGITS represents NVIDIA’s push to make AI supercomputing accessible to individuals as well as organisations.

Vision for tomorrow

Reflecting on NVIDIA’s journey since inventing the programmable GPU in 1999, Huang described the past 12 years of AI-driven change as transformative.

“Every single layer of the technology stack has been fundamentally transformed,” he said.

With advancements spanning gaming, AI-driven agents, robotics, and autonomous vehicles, Huang foresees an exciting future.

“All of the enabling technologies I’ve talked about today will lead to surprising breakthroughs in general robotics and AI over the coming years,” Huang concludes.

(Image Credit: NVIDIA)

See also: Sam Altman, OpenAI: ‘Lucky and humbling’ to work towards superintelligence

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According to Google Cloud’s data, 86% of enterprise companies currently using generative AI in production environments have witnessed increased revenue, with an estimated average growth of 6%. 

This metric has driven the tech giant’s latest innovation push, resulting in the introduction of Veo – its most sophisticated video generation model to date – and Imagen 3, an advanced text-to-image generation system.

Breaking ground

Veo, now available in private preview on Vertex AI, represents a milestone as Google becomes the first hyperscaler to offer an image-to-video model. The technology enables businesses to generate high-quality videos from simple text or image prompts, potentially revolutionising video production workflows across industries.

Imagen 3 – scheduled for release to all Vertex AI customers next week – promises unprecedented realism in generated images, with marked improvements in detail, lighting, and artifact reduction. The model includes new features for enterprise customers on an allowlist, including advanced editing capabilities and brand customisation options.

Transforming operations

Several major firms have begun implementing these technologies into their operations.

Mondelez International, the company behind brands such as Oreo, Cadbury, and Chips Ahoy!, is using the technology to accelerate campaign content creation across its global portfolio of brands.

Jon Halvorson, SVP of Consumer Experience & Digital Commerce at Mondelez International, explained: “Our collaboration with Google Cloud has been instrumental in harnessing the power of generative AI, notably through Imagen 3, to revolutionise content production.

“This technology has enabled us to produce hundreds of thousands of customised assets, enhancing creative quality while significantly reducing both time to market and costs.”

Knowledge sharing platform Quora has developed Poe, a platform that enables users to interact with generative AI models. Veo and Imagen are now integrated with Poe.

Spencer Chan, Product Lead for Poe at Quora, commented: “We created Poe to democratise access to the world’s best gen AI models. With Veo, we’re now enabling millions of users to bring their ideas to life through stunning, high-quality generative video.”

Safety and security

In response to growing concerns about AI-generated content, Google has implemented robust safety features in both models. These include:

• Digital watermarking through Google DeepMind’s SynthID.
• Built-in safety filters to prevent harmful content creation.
• Strict data governance policies ensure customer data protection.
• Industry-first copyright indemnity for generative AI services.

The launch of these new models signals Google’s growing influence in the enterprise AI space and suggests a shift toward more sophisticated, integrated AI solutions for business applications.

(Imagery Credit: Google Cloud)

See also: Alibaba Marco-o1: Advancing LLM reasoning capabilities

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The report reveals a stark contrast in how the C-suite and V-suite view the potential of generative AI. While the C-suite focuses on visible use cases such as customer experience, service, and sales, the V-suite sees opportunities across various functional areas, including operations, HR, and finance.

Risk perception

The divide extends to risk perception as well. Fifty-one percent of C-level respondents expressed more concern about the risk and ethics of generative AI than other emerging technologies. In contrast, only 23 percent of the V-suite shared these worries.

Simon James, Managing Director of Data & AI at Publicis Sapient, said: “It’s likely the C-suite is more worried about abstract, big-picture dangers – such as Hollywood-style scenarios of a rapidly-evolving superintelligence – than the V-suite.”

The report also highlights the uncertainty surrounding generative AI maturity. Organisations can be at various stages of maturity simultaneously, with many struggling to define what success looks like. More than two-thirds of respondents lack a way to measure the success of their generative AI projects.

Navigating the generative AI landscape

Despite the C-suite’s focus on high-visibility use cases, generative AI is quietly transforming back-office functions. More than half of the V-suite respondents ranked generative AI as extremely important in areas like finance and operations over the next three years, compared to a smaller percentage of the C-suite.

To harness the full potential of generative AI, the report recommends a portfolio approach to innovation projects. Leaders should focus on delivering projects, controlling shadow IT, avoiding duplication, empowering domain experts, connecting business units with the CIO’s office, and engaging the risk office early and often.

Daniel Liebermann, Managing Director at Publicis Sapient, commented: “It’s as hard for leaders to learn how individuals within their organisation are using ChatGPT or Microsoft Copilot as it is to understand how they’re using the internet.”

The path forward

The report concludes with five steps to maximise innovation: adopting a portfolio approach, improving communication between the CIO’s office and the risk office, seeking out innovators within the organisation, using generative AI to manage information, and empowering team members through company culture and upskilling.

As generative AI continues to evolve, organisations must bridge the gap between the C-suite and V-suite to unlock its full potential. The future of business transformation lies in harnessing the power of a decentralised, bottom-up approach to innovation.

See also: EU introduces draft regulatory guidance for AI models

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The report found that nearly half (46%) of Wazoku’s 700,000-strong network of problem solvers had utilised generative AI (GenAI) to work on innovative ideas over the past year. This network – known as the Wazoku Crowd – comprises a diverse group of professionals including scientists, pharmacists, engineers, PhD students, CEOs, start-ups, and business leaders.

Perhaps more strikingly, almost a quarter (22%) of respondents reported using GenAI or LLM tools such as ChatGPT and Claude for at least half of their idea submissions, with 8% employing these technologies for every single submission. Of those using GenAI, 47% are leveraging it specifically for idea generation.

The Wazoku Crowd’s collective intelligence is harnessed to solve ‘challenges’ – requests for ideas submitted by enterprises – with an impressive success rate of over 80%.

Simon Hill, CEO of Wazoku, commented on the findings: “There’s an incredible amount of hype with GenAI, but alongside that there is enormous curiosity. Getting immersed in something and being curious is an innovator’s dream, so there is rich potential with GenAI.”

However, Hill also urged caution: “A note of caution, though – it is best used to generate interest, not solutions. Human ingenuity and creativity are still best, although using GenAI can undoubtedly make that process more effective.”

The study revealed that the most common application of GenAI was in research and learning, with 85% of respondents using it for this purpose. Additionally, around one-third of the Wazoku Crowd employed GenAI for report structuring, writing, and data analysis and insight.

The research was conducted in partnership with Oguz A. Acar, Professor of Marketing and Innovation at King’s Business School, King’s College London. Professor Acar viewed the study as a crucial first step towards understanding AI’s potential and limitations in tackling complex innovation challenges.

“Everyone’s trying to figure out what AI can and can’t do, and this survey is a step forward in understanding that,” Professor Acar stated. “It reveals that some crowd members view GenAI as a valuable ally, using it to research, create, and communicate more effectively.”

“While perhaps it’s no surprise that those open to innovation are curious about new tools, the survey also shows mixed opinions. Most people haven’t used GenAI tools yet, highlighting that we’re only beginning to uncover AI’s potential in innovative problem-solving.”

Wazoku collaborates with a range of customers, including Sanofi, A2A, Bill & Melinda Gates Foundation, and numerous global enterprise businesses, government departments, and not-for-profits, to crowdsource ideas and innovation.

Recently, Wazoku launched its own conversational AI to aid innovation. Dubbed Jen AI, this digital innovation assistant has access to Wazoku’s connected innovation management suite—aimed at accelerating decision-making around innovation and enhancing productivity to deliver consistent, scalable results.

“The solutions to the world’s problems are complex, and the support of AI brings vast benefits in terms of efficiency, creativity, and insight generation,” explained Hill.

As the adoption of AI in innovation processes continues to grow, it’s clear that – while these tools offer significant potential – they are best used to augment rather than replace human creativity and problem-solving skills.

(Photo by Ally Griffin)

See also: Ivo Everts, Databricks: Enhancing open-source AI and improving data governance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The post King’s Business School: How AI is transforming problem-solving appeared first on AI News.

The evaluation framework – which focuses on Retrieval Augmented Generation (RAG) – assessed 22 prominent Gen AI LLMs from major players including OpenAI, Anthropic, Google, and Meta. This year’s index expanded significantly, adding 11 new models to reflect the rapid growth in both open- and closed-source LLMs over the past eight months.

Vikram Chatterji, CEO and Co-founder of Galileo, said: “In today’s rapidly evolving AI landscape, developers and enterprises face a critical challenge: how to harness the power of generative AI while balancing cost, accuracy, and reliability. Current benchmarks are often based on academic use-cases, rather than real-world applications.”

The index employed Galileo’s proprietary evaluation metric, context adherence, to check for output inaccuracies across various input lengths, ranging from 1,000 to 100,000 tokens. This approach aims to help enterprises make informed decisions about balancing price and performance in their AI implementations.

Key findings from the index include:

• Anthropic’s Claude 3.5 Sonnet emerged as the best overall performing model, consistently scoring near-perfect across short, medium, and long context scenarios.
• Google’s Gemini 1.5 Flash ranked as the best performing model in terms of cost-effectiveness, delivering strong performance across all tasks.
• Alibaba’s Qwen2-72B-Instruct stood out as the top open-source model, particularly excelling in short and medium context scenarios.

The index also highlighted several trends in the LLM landscape:

• Open-source models are rapidly closing the gap with their closed-source counterparts, offering improved hallucination performance at lower costs.
• Current RAG LLMs demonstrate significant improvements in handling extended context lengths without sacrificing quality or accuracy.
• Smaller models sometimes outperform larger ones, suggesting that efficient design can be more crucial than scale.
• The emergence of strong performers from outside the US, such as Mistral’s Mistral-large and Alibaba’s qwen2-72b-instruct, indicates a growing global competition in LLM development.

While closed-source models like Claude 3.5 Sonnet and Gemini 1.5 Flash maintain their lead due to proprietary training data, the index reveals that the landscape is evolving rapidly. Google’s performance was particularly noteworthy, with its open-source Gemma-7b model performing poorly while its closed-source Gemini 1.5 Flash consistently ranked near the top.

As the AI industry continues to grapple with hallucinations as a major hurdle to production-ready Gen AI products, Galileo’s Hallucination Index provides valuable insights for enterprises looking to adopt the right model for their specific needs and budget constraints.

See also: Senators probe OpenAI on safety and employment practices

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Anthropic to Google: Who’s winning against AI hallucinations? appeared first on AI News.

The C2PA standard allows digital content to be certified with metadata proving its origins, whether created entirely by AI, edited using AI tools, or captured traditionally. OpenAI has already started adding C2PA metadata to images from its latest DALL-E 3 model output in ChatGPT and the OpenAI API. The metadata will be integrated into OpenAI’s upcoming video generation model Sora when launched more broadly.

“People can still create deceptive content without this information (or can remove it), but they cannot easily fake or alter this information, making it an important resource to build trust,” OpenAI explained.

The move comes amid growing concerns about the potential for AI-generated content to mislead voters ahead of major elections in the US, UK, and other countries this year. Authenticating AI-created media could help combat deepfakes and other manipulated content aimed at disinformation campaigns.

While technical measures help, OpenAI acknowledges that enabling content authenticity in practice requires collective action from platforms, creators, and content handlers to retain metadata for end consumers.

In addition to C2PA integration, OpenAI is developing new provenance methods like tamper-resistant watermarking for audio and image detection classifiers to identify AI-generated visuals.

OpenAI has opened applications for access to its DALL-E 3 image detection classifier through its Researcher Access Program. The tool predicts the likelihood an image originated from one of OpenAI’s models.

“Our goal is to enable independent research that assesses the classifier’s effectiveness, analyses its real-world application, surfaces relevant considerations for such use, and explores the characteristics of AI-generated content,” the company said.

Internal testing shows high accuracy distinguishing non-AI images from DALL-E 3 visuals, with around 98% of DALL-E images correctly identified and less than 0.5% of non-AI images incorrectly flagged. However, the classifier struggles more to differentiate between images produced by DALL-E and other generative AI models.

OpenAI has also incorporated watermarking into its Voice Engine custom voice model, currently in limited preview.

The company believes increased adoption of provenance standards will lead to metadata accompanying content through its full lifecycle to fill “a crucial gap in digital content authenticity practices.”

OpenAI is joining Microsoft to launch a $2 million societal resilience fund to support AI education and understanding, including through AARP, International IDEA, and the Partnership on AI.

“While technical solutions like the above give us active tools for our defences, effectively enabling content authenticity in practice will require collective action,” OpenAI states.

“Our efforts around provenance are just one part of a broader industry effort – many of our peer research labs and generative AI companies are also advancing research in this area. We commend these endeavours—the industry must collaborate and share insights to enhance our understanding and continue to promote transparency online.”

(Photo by Marc Sendra Martorell)

See also: Chuck Ros, SoftServe: Delivering transformative AI solutions responsibly

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post OpenAI takes steps to boost AI-generated content transparency appeared first on AI News.

However, for many consumer brands, the divide between expectations and reality looms large. Marketers envisioning a seamless, magical customer experience must recognise that AI’s effectiveness depends on high-quality underlying data. Without that, the AI falls flat, leaving marketers grappling with a less-than-magical reality.

AI-powered marketing fail

Let’s take a closer look at what AI-powered marketing with poor data quality could look like. Say I’m a customer of a general sports apparel and outdoor store, and I’m planning for my upcoming annual winter ski trip. I’m excited to use the personal shopper AI to give me an experience that’s easy and customised to me.

I need to fill in some gaps in my ski wardrobe, so I ask the personal shopper AI to suggest some items to purchase. But the AI is creating its responses based on data about me that’s been scattered across the brand’s multiple systems. Without a clear picture of who I am, it asks me for some basic information that it should already know. Slightly annoying… I’m used to entering my info when I shop online, but I was hoping the AI upgrade to the experience would make things easier for me. 

Because my data is so disconnected, the AI concierge only has an order associated with my name from two years ago, which was actually a gift. Without a full picture of me, this personal shopper AI is unable to generate accurate insights and ends up sharing recommendations that aren’t helpful.

Ultimately this subpar experience makes me less excited about purchasing from this brand, and I decide to go elsewhere. 

The culprit behind a disconnected and impersonal generative AI experience is data quality — poor data quality = poor customer experience. 

AI-powered marketing for the win

Now, let’s revisit this outdoor sports retailer scenario, but imagine that the personal shopper AI is powered by accurate, unified data that has a complete history of my interactions with the brand from first purchase to last return. 

I enter my first question, and I get a super-personalised and friendly response, already starting to create the experience of a one-on-one connection with a helpful sales associate. It automatically references my shopping history and connects my past purchases to my current shopping needs. 

Based on my prompts and responses, the concierge provides a tailored set of recommendations to fill in my ski wardrobe along with direct links to purchase. The AI is then able to generate sophisticated insights about me as a customer and even make predictions about the types of products I might want to buy based on my past purchases, driving up the likelihood of me purchasing and potentially even expanding my basket to buy additional items. 

Within the experience, I am able to actually use the concierge to order without having to navigate elsewhere. I also know my returns or any future purchases will be incorporated into my profile. 

Because it knew my history and preferences, Generative AI was able to create a buying experience for me that was super personalised and convenient. This is a brand I will keep returning to for future purchases.

In other words, when it comes to AI for marketing, better data = better results.

So how do you actually address the data quality challenge? And what could that look like in this new world of AI?

Solving the data quality problem

The critical first element to powering an effective AI strategy is a unified customer data foundation. The tricky part is that accurately unifying customer data is hard due to its scale and complexity — most consumers have at least two email addresses, have moved over eleven times in their lifetimes and use an average of five channels (or if they are millennials or Gen Z, it’s actually twelve channels).

Many familiar approaches to unifying customer data are rules-based and use deterministic/fuzzy matching, but these methods are rigid and break down when data doesn’t match perfectly. This, in turn, creates an inaccurate customer profile that can actually miss a huge portion of a customer’s lifetime history with the brand and not account for recent purchases or changes of contact information. 

A better way to build a unified data foundation actually involves using AI models (a different flavour of AI than generative AI for marketing) to find the connections between data points to tell if they belong to the same person with the same nuance and flexibility of a human but at massive scale. 

When your customer data tools can use AI to unify every touchpoint in the customer journey from first interaction to last purchase and beyond (loyalty, email, website data, etc…), the result is a comprehensive customer profile that tells you who your customers are and how they interact with your brand. 

How data quality in generative AI drives growth

For the most part, marketers have access to the same set of generative AI tools, therefore, the fuel you input will become your differentiator. 

Data quality to power AI provides benefits in three areas: 

• Customer experiences that stand out — more personalised, creative offers, better customer service interactions, a smoother end-to-end experience, etc.
• Operational efficiency gains for your teams — faster time to market, less manual intervention, better ROI on campaigns, etc.
• Reduced compute costs — better-informed AI doesn’t need to go back and forth with the user, which saves on racking up API calls that quickly get expensive

As generative AI tools for marketing continue to evolve, they bring the promise of getting back to the level of one-to-one personalisation that customers would expect in their favourite stores, but now at a massive scale. That won’t happen on its own, though — brands need to provide AI tools with accurate customer data to bring the AI magic to life.

The dos and don’ts of AI in marketing

AI is a helpful sidekick to many industries, especially marketing — as long as it’s leveraged appropriately. Here’s a quick ‘cheat-sheet’ to help marketers on their GenAI journey:

Do:

• Be explicit about the specific use cases where you plan to use data and AI and specify the expected outcomes. What results do you expect to achieve?
  
• Carefully evaluate if Gen AI is the most appropriate tool for your specific use case.
  
• Prioritise data quality and comprehensiveness — establishing a unified customer data foundation is essential for an effective AI strategy.

Don’t:

• Rush to implement GenAI across all areas. Start with a manageable, human-in-the-loop use case, such as generating subject lines.

(Editor’s note: This article is sponsored by Amperity)

The post Why data quality is critical for marketing in the age of GenAI appeared first on AI News.

The Blackwell platform promises up to 25 times lower cost and energy consumption compared to its predecessor: the Hopper architecture. Named after pioneering mathematician and statistician David Harold Blackwell, the new GPU architecture introduces six transformative technologies.

“Generative AI is the defining technology of our time. Blackwell is the engine to power this new industrial revolution,” said Jensen Huang, Founder and CEO of NVIDIA. “Working with the most dynamic companies in the world, we will realise the promise of AI for every industry.”

The key innovations in Blackwell include the world’s most powerful chip with 208 billion transistors, a second-generation Transformer Engine to support double the compute and model sizes, fifth-generation NVLink interconnect for high-speed multi-GPU communication, and advanced engines for reliability, security, and data decompression.

Central to Blackwell is the NVIDIA GB200 Grace Blackwell Superchip, which combines two B200 Tensor Core GPUs with a Grace CPU over an ultra-fast 900GB/s NVLink interconnect. Multiple GB200 Superchips can be combined into systems like the liquid-cooled GB200 NVL72 platform with up to 72 Blackwell GPUs and 36 Grace CPUs, offering 1.4 exaflops of AI performance.

NVIDIA has already secured support from major cloud providers like Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure to offer Blackwell-powered instances. Other partners planning Blackwell products include Dell Technologies, Meta, Microsoft, OpenAI, Oracle, Tesla, and many others across hardware, software, and sovereign clouds.

Sundar Pichai, CEO of Alphabet and Google, said: “We are fortunate to have a longstanding partnership with NVIDIA, and look forward to bringing the breakthrough capabilities of the Blackwell GPU to our Cloud customers and teams across Google to accelerate future discoveries.”

The Blackwell architecture and supporting software stack will enable new breakthroughs across industries from engineering and chip design to scientific computing and generative AI.

Mark Zuckerberg, Founder and CEO of Meta, commented: “AI already powers everything from our large language models to our content recommendations, ads, and safety systems, and it’s only going to get more important in the future.

“We’re looking forward to using NVIDIA’s Blackwell to help train our open-source Llama models and build the next generation of Meta AI and consumer products.”

With its massive performance gains and efficiency, Blackwell could be the engine to finally make real-time trillion-parameter AI a reality for enterprises.

See also: Elon Musk’s xAI open-sources Grok

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post NVIDIA unveils Blackwell architecture to power next GenAI wave  appeared first on AI News.

With mounting research demonstrating that gender diverse teams outperform their peers time and time again, we turned the conversation over to the exceptional women thought leaders who are at the forefront of shaping the narrative surrounding Gen AI and marketing. 

Let’s dive into their insights and experiences:

Lisa Gately, Principal Analyst at Forrester 

Gately has 20 years of experience in B2B technology content, communications, events, and services marketing. She helps Forrester clients build and optimise their B2B content engines and transform them into competitive differentiators. Lisa is an evangelist for audience-centric content strategy, content marketing, and content operations.

“During the past year, we’ve seen Gen AI capabilities appear in the martech stack along with a rise of multimodal capabilities, where AI models can understand, interpret, and generate content across multiple formats like text, images, audio, and video. It can be overwhelming to understand which systems do what tasks and to determine which ones to embrace. However, making time to learn about these capabilities is important. Gen AI brings more power to content creation, audience engagement, and personalisation. Content use cases aren’t only a practical entry point for scaling Gen AI adoption; they also represent a large part of an organisations’ activities and offer enormous potential for enhancing the customer experience and speeding time to market. Acting now is essential because the pace of change for Gen AI will only accelerate.”

Julie Shainock, Managing Director Travel, Transport & Logistics (TTL) at Microsoft

Shainock is responsible for developing Microsoft’s point of view and future strategy for our WW Travel and Transport Industry. She is focused on leading the airlines, hospitality companies, cruise and freight logistics and rail companies to driving innovation that will enhance the customer and employee journey, while driving increased productivity and cost reduction with the use of Microsoft’s technology and its ecosystem of solution partners.

“Generative AI is set to revolutionise the travel, transport, and logistics industries by delivering unprecedented levels of personalisation, efficiency, and innovation. It’s not just about automation; it’s about creating intuitive, seamless customer experiences and unlocking new levels of operational efficiency. For organisations to tackle the full potential of Gen AI effectively, establishing a clean data foundation and a clear strategic vision for desired outcomes is critical.”

Adiela Aviram, Cookieless Marketing Transformation Practice Leader at Deloitte Digital

Aviram is an award-winning digital marketer and a Senior Manager in Deloitte Digital’s Advertising, Marketing and E-Commerce offering. Beyond her career, she is a dedicated Fellow at The Black Wealth Club (BWC), actively contributing to the group’s mission of wealth generation and community reinvestment.

“The only thing constant in marketing, much like in every field, is change. Reflecting on one of my initial roles as a search marketer, I can draw some parallels. I had no formal training in search marketing, and the idea of learning an entire system to advertise on search engines seemed bizarre. Now, much of that same field is supported by generative AI. I’m excited for all the things Gen AI will enable for marketers. It will allow us to focus on more strategic, less repetitive, and energizing areas of our work. However, marketing will always need the human element. Customer experience, by its very nature, is human, and Gen AI will not stand in the way of that.” 

Joyce Gordon, Head of Generative AI, Amperity

Gordon leads Amperity’s generative AI product development and strategy. She’s also worked on the product development for many investments across the ML and ML Ops spaces, including launching Amperity’s predictive models and infrastructure used by many of the world’s top brands.

“Gen AI is only as good as the data that powers it. And since customer data is notoriously complex, it takes a different AI process to unify it into accurate, comprehensive profiles that can feed your Generative AI tools to get the best results.  

Customer data tools can use AI to power critical processes behind the scenes, including data unification, insights, and predictions, so you have the answers to the questions “Who are my customers? What did they do? And what should I do next?” 

In a world where the same Generative AI tools for activation are available to everyone, the data you feed into your Gen AI systems will be a key competitive differentiator, since it determines the quality of the output. In short, it’s not enough to have AI tools at the last mile — it needs to be part of your approach from the first step.”

Teresa Sperti, Founder & Director, Arktic Fox

Sperti is a highly seasoned and regarded digital and eCommerce leader with over 20 years’ experience working with and for leading brands including Coles, Officeworks and World Vision among others. Since establishing Arktic Fox four years ago, Teresa and her team have been partnering with leading brands and scale up retailers and CPG | FMCG brands to drive eCommerce adoption and capability build, to enable better utilisation of data to enhance experience and deliver better omnichannel experiences and supported brands to utilise and invest in tech to underpin their strategy.

“There are pivotal moments in the digital era that fundamentally alter the trajectory of the industry – the birth of social media and the launch of the iPhone were some of those moments. And I believe the launch of ChatGPT is another one that really lit a match to the adoption of Gen AI.

“In the tech world, we are already seeing strong adoption of Gen AI within marketing platform interfaces to automate creative development and support and enable smarter decision making. However, it is still very much the early days for Gen AI , and I think we’re just scratching the surface. Brace yourself for a whirlwind of change in this space over the next 12 to 24 months.”

The future of Gen AI in marketing

The female leaders in marketing have spoken, and their insights demonstrate the importance of embracing Gen AI not only as a tool for innovation but as a fundamental pillar for cultivating growth and establishing meaningful relationships within the rapidly transforming marketing landscape. Let’s make 2024 the year we harness Gen AI to its fullest potential and unleash lasting, genuine change for the better.

(Editor’s note: This article is in association with Amperity)

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