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UAE President Sheikh Mohamed bin Zayed al-Nahyan will travel to Washington on Monday for a landmark meeting with US President Joe Biden.

This is the first-ever UAE presidential visit to the US capital in seven years, and AI is at the forefront of discussions as the oil-rich Gulf nation seeks to reinvent itself as an AI powerhouse.

The UAE’s AI push, spearheaded by Abu Dhabi-based firm G42, has raised eyebrows in Washington. 

G42, chaired by UAE National Security Adviser Sheikh Tahnoon bin Zayed al-Nahyan, recently unveiled “Jais,” touted as the world’s most advanced Arabic large language model. However, the company’s past ties with Chinese company Huawei set off alarm bells in the US.

“AI and new changes in cloud computing are going to change the way the world looks,” says Anwar Gargash, Sheikh Mohamed’s diplomatic adviser. “We cannot let this sort of wave of technological breakthroughs pass by us.” 

For the UAE, AI isn’t just about staying technologically relevant – it’s about economic survival in a post-oil world.

However, US export restrictions imposed in 2022 have thrown a wrench in the UAE’s AI aspirations. The Biden administration added Gulf states to a list of countries facing limitations on importing advanced US-made AI chips, citing concerns about technology leaks to China. 

This slowed the UAE’s AI projects, adding urgency to Sheikh Mohamed’s mission.

The UAE is now pivoting towards US tech partnerships. Microsoft’s recent $1.5 billion investment in G42 signals growing US interest in the Emirates’ AI ecosystem. G42 has also announced a partnership with US chip manufacturer Nvidia for a weather forecasting initiative.

Yet, these moves have intensified scrutiny from US lawmakers. Republican politicians have requested an intelligence assessment of the Microsoft-G42 deal, worried about potential exports of restricted AI chips. 

In response, G42’s CEO Peng Xiao has pledged to phase out Huawei technology, stating, “We simply cannot do much more work with Chinese partners.”

Sheikh Mohamed and Biden are expected to hammer out a broad framework for US-UAE AI cooperation. Sources suggest the UAE wants to secure a “road map” ahead of the upcoming US election, hoping to lock in progress regardless of who occupies the White House come January.

The talks will also cover pressing issues like the Gaza conflict, Sudan, climate change, and clean energy. 

This push for AI dominance isn’t unique to the UAE. Flush with oil wealth, Middle Eastern nations are racing to build competitive AI industries. Gulf nations were among the biggest buyers of Nvidia chips last year.

Earlier this year, OpenAI CEO Sam Altman wanted to strike deals with the UAE for an outlandish trillion-dollar chip manufacturing strategy, though we’ve heard little of it since.  

An analyst explained in August 2023, “The UAE wants to own and control its computational power and talent, develop their platforms, and remain independent from the influence of major players like the Chinese or the Americans. With the capital and energy resources at their disposal, they’re also attracting the top global talent.”

UAE’s AI minister Omar Sultan Al Olama reflects on past mistakes: “We over-regulated the printing press… The Middle East banned it for 200 years.” The region is determined not to repeat history with AI.

As Sheikh Mohamed makes his case in Washington, the stakes are high. Success could catapult the UAE to the forefront of global AI development, while failure might leave it languishing in a technological backwater. 

The outcome of Monday’s talks could shape not just the future of US-UAE relations, but the balance of technological power in the Middle East and beyond.

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The Open Source Initiative (OSI) has released an updated draft definition of what constitutes open-source AI and says Meta’s models don’t qualify despite the company’s claims.

Mark Zuckerberg has been vocal about Meta’s commitment to what he says is open-source AI. However, while models like Llama 3.1 are less opaque than the proprietary models from OpenAI or Google, discussions in the OSI community suggest Meta is using the term loosely.

At an online public town hall event on Friday, the OSI discussed the criteria it believes a truly open-source AI model should conform to. The OSI refers to these criteria as “4 Freedoms” and says an open-source AI “is an AI system made available under terms and in a way that grant the freedoms to:

Use the system for any purpose and without having to ask for permission.
Study how the system works and inspect its components.
Modify the system for any purpose, including to change its output.
Share the system for others to use with or without modifications, for any purpose.”

To be able to modify an AI model, the OSI’s open AI definition says the weights and source code should be open, and the training data set should be available.

Meta’s license imposes some restrictions on how its models can be used and it has declined to release the training data it used to train its models. If you accept that the OSI is the custodian of what “open-source” means, then the implication is that Meta distorts the truth when it calls its models “open”.

The OSI is a California public benefit corporation that relies on community input to develop open-source standards. Some in that community have accused Mark Zuckerberg of “open washing” Meta’s models and bullying the industry into accepting his version rather than the OSI’s definition.

Chairman of Open Source Group Japan, Shuji Sado said “It’s possible that Zuckerberg has a different definition of Open Source than we do,” and suggested that the unclear legal landscape around AI training data and copyright could be the reason for this.

Open Source AI Definition – Weekly update September 23 https://t.co/flbb3yGCmx

— Open Source Initiative @osi@opensource.org (@OpenSourceOrg) September 23, 2024

Words matter

This might all sound like an argument over semantics but, depending on the definition the AI industry adopts, there could be serious legal consequences.

Meta has had a tough time navigating EU GDPR laws over its insatiable hunger for users’ social media data. Some people claim that Meta’s loose definition of “open-source AI” is an attempt to skirt new laws like the EU AI Act.

The Act provides a limited exception for general-purpose AI models (GPAIMs) released under open-source licenses. These models are exempt from certain transparency obligations although they still have to provide a summary of the content used to train the model.

On the other hand, the proposed SB 1047 California AI safety bill disincentivizes companies like Meta from aligning their models with the OSI definition. The bill mandates complex safety protocols for “open” models and holds developers liable for harmful modifications and misuse by bad actors.

SB 1047 defines open-source AI tools as “artificial intelligence model[s] that [are] made freely available and that may be freely modified and redistributed.” Does that mean that an AI model that can be fine-tuned by a user is “open” or would the definition only apply if the model ticks all the OSI boxes?

For now, the vaguery allows Meta the marketing benefits and room to negotiate some legislation. At some point, the industry will need to commit to a definition. Will it be defined by a big tech company like Meta or by a community-driven organization like the OSI?

The post Open Source Initiative disagrees with Meta on ‘open’ AI appeared first on DailyAI.

This conference, taking place in London, offers a unique opportunity to explore the latest trends and innovations in marketing technology across the B2B and B2C sectors.

The event, which will take place November 12-13, 2024, at Convene 155 Bishopsgate, London, will feature insightful case studies, panel discussions, fireside chats, keynotes, and Q&As focused on the latest MarTech trends. 

With 400+ attendees, over 85% in senior leadership roles, the summit provides unparalleled learning and networking opportunities with CMOs, Heads, Directors, and more.

Attendees will acquire cutting-edge knowledge on MarTech advancements, real-world implementation strategies, and how technology is transforming marketing approaches and workflows across both B2B and B2C sectors.

Why attend The MarTech Summit London 2024?

There are numerous reasons for attending the MarTech Summit London 2024:

Explore five key themes across three stages over two days
Gain insights from 70+ speakers and industry experts
Network with 400+ attendees, including 85% senior management
Participate in discussion roundtables to share and gain diverse perspectives
Evaluate new technologies for your MarTech stack from leading service providers

Who should attend?

The MarTech Summit London 2024 is designed for senior-level executives in functions such as:

Marketing and technology
Customer experience (CX) and engagement
Brand loyalty and retention
Data and consumer insights
E-commerce marketing
Digital strategy
Omni-channel
Innovation
Social media
Content strategy and storytelling
CRM
Product marketing
Automation
Digital transformation and growth

Key speakers at the event

Some of the key speakers in the lineup include, but aren’t limited to:

Sivan Einstein, Industry Head – Omnichannel Retail, Google
Neil Robins, Head of Digital & Media, Kenvue
Cat Daniel, Senior Director, Growth & Engagement, Monzo
Sam Lewis-Williams, Head of Marketing Automation, Financial Times
Namita Mediratta, CMI Director, Beauty, EMETU, Unilever
Richard Jones, Director of eCommerce, Carlsberg
Kumar Amrendra, Head of Digital Marketing, Planning & Data Science, Sky
James Brindley-Raynes, Head of Digital Customer Journey, Maersk
Clive Head, Head of CRM and Loyalty, Santander UK
Marie Tyler, Global Customer Engagement Leader, Honeywell

Featured themes and topics from The MarTech Summit London 2024

The event features five main themes across three stages:

Day 1 – Plenary Room:

MarTech Trends (including Marketing Automation, AI in Marketing, Digital Transformation)

Day 2 – B2C Marketing Stage:

Customer Experience (including Omnichannel Personalisation, E-Commerce, Digital Customer Experience)
Data, Analytics & Insights (including First-Party Data, Customer Data Platform, Data Privacy & Security)

Day 2 – B2B Marketing Stage:

Sales Enablement (including Revenue Enablement, Sales Content Optimisation, Account-Based Marketing)
Demand Generation (including B2B Marketing Metrics, Precision Demand Marketing, Content Marketing)

Key details about the event

Mark your calendars and prepare to be part of this fantastic event:

Date: November 12-13, 2024
Venue: Convene 155 Bishopsgate, Second Floor, London EC2M 3YD
Tickets: Available on the event website here

Visit the official MarTech Summit London 2024 website to secure your spot, view the full agenda, and explore sponsorship opportunities. 

The post The MarTech Summit London 2024: AI marketing in focus appeared first on DailyAI.

The 2024 Nature Index supplement on Artificial Intelligence, released this week, reveals a scientific world in the throes of an AI-driven paradigm shift. 

This annual report, published by the journal Nature, tracks high-quality science by measuring research outputs in 82 natural science journals, selected by an independent panel of researchers.

The latest edition illustrates how AI is not just changing what scientists study, but fundamentally altering how research is conducted, evaluated, and applied globally. 

One of the most striking trends revealed in the Index is the surge in corporate AI research. US companies have more than doubled their output in Nature Index journals since 2019, with their Share (a metric used by the Index to measure research output) increasing from 51.8 to 106.5. 

However, this boom in R&D activity comes with a caveat – it still only accounts for 3.8% of total US AI research output in these publications. In essence, despite a major uplift in corporate AI R&D, we’ve not seen those efforts reflected in public research output. 

This raises questions about where corporate AI research is located. Are companies publishing their most groundbreaking work in other venues, or keeping it under lock and key?

The answer is one of competing names and narratives. OpenAI, Microsoft, Google, Anthropic, and a handful of others are firmly entrenched in the closed-source model, but the open-source AI industry, led by Meta, Mistral, and others, is rapidly gaining ground.

Contributing to this, the funding disparity between private companies and public institutions in AI research is staggering. 

In 2021, according to Stanford University’s AI Index Report, private sector investment in AI worldwide reached approximately $93.5 billion. 

This includes spending by tech giants like Google, Microsoft, and Amazon, as well as AI-focused startups and other corporations across various industries.

In contrast, public funding for AI research is much lower. The US government’s non-defense AI R&D spending in 2021 was about $1.5 billion, while the European Commission allocated around €1 billion (approximately $1.1 billion) for AI research that year.

This gaping void in resource expenditure is giving private companies an advantage in AI development. They can afford more powerful computing resources and larger datasets and attract top talent with higher salaries.

“We’re increasingly looking at a situation where top-notch AI research is done primarily within the research labs of a rather small number of mostly US-based companies,” explained Holger Hoos, an AI researcher at RWTH Aachen University in Germany.

While the US maintains its lead in AI research, countries like China, the UK, and Germany are emerging as major hubs of innovation and collaboration.

However, this growth isn’t uniform across the globe. South Africa stands as the only African nation in the top 40 for AI output, showing how the digital divide is at risk of deepening in the AI era. 

AI in peer review: promise and peril

Peer review ensures academic and methodological rigor and transparency when papers are submitted to journals.

This year, a nonsense paper with giant AI-generated rat testicles was published in Frontiers, indicating how the peer review process is far from impenetrable.

Someone used DALL-E to create gobbledygook scientific figures and submitted them to Frontiers Journal. And guess what? The editor published it. LOLhttps://t.co/hjQkRQDkal https://t.co/aV1USo6Vt2 pic.twitter.com/VAkjJkY4dR

— Veera Rajagopal  (@doctorveera) February 15, 2024

Recent experiments have shown that AI can generate research assessment reports that are nearly indistinguishable from those written by human experts. 

Last year, an experiment testing ChatGPT’s peer reviews versus human reviewers on the same paper found that over 50% of the AI’s comments on the Nature papers and more than 77% on the ICLR papers aligned with the points raised by human reviewers.

Of course, ChatGPT is much quicker than human peer reviewers. “It’s getting harder and harder for researchers to get high-quality feedback from reviewers,” said James Zou from Stanford University, the leader researcher for that experiment.

AI’s relationship with research is raising fundamental questions about scientific evaluation and whether human judgment is intrinsic to the process.  The balance between AI efficiency and irreplaceable human insight is one of several key issues scientists from all backgrounds will need to grapple with in the years ahead.

AI might soon be capable of managing the entire research process from start to finish, potentially sidelining human researchers altogether.

For instance, Sakana‘s AI Scientist autonomously generates novel research ideas, designs and conducts experiments, and even writes and reviews scientific papers. This tempts a future where AI could drive scientific discovery with minimal human intervention.

On the methodology side, using machine learning (ML) to process and analyze data comes with risks. Princeton researchers argued that since many ML techniques can’t be easily replicated, this erodes the replicability of experiments – a key principle of high-quality science. 

Ultimately, AI’s rise to prominence in every aspect of research and science is gaining momentum, and the process likely irreversible. 

Last year, Nature surveyed 1,600 researchers and found that 66% believe that AI enables quicker data processing, 58% that it accelerates previously infeasible analysis, and 55% feel that it’s a cost and time-saving solution.

As Simon Baker, lead author of the supplement’s overview, concludes: “AI is changing the way researchers work forever, but human expertise must continue to hold sway.”

The question now is how the global scientific community will adapt to AI’s role in research, ensuring that the AI revolution in science benefits all of humanity, and without unforeseen risks wreaking havoc on science.  

As with so many aspects of the technology, mastering both benefits and risks is challenging but necessary to secure a safe path forward.

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