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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.

The post The 2024 Nature Index reveals how AI is transforming every aspect of scientific research appeared first on DailyAI.

A new survey has found that one in five general practitioners (GPs) in the UK are using AI tools like ChatGPT to assist with daily tasks such as suggesting diagnoses and writing patient letters. 

The research, published in the journal BMJ Health and Care Informatics, surveyed 1,006 GPs across the about their use of AI chatbots in clinical practice. 

Some 20% reported using generative AI tools, with ChatGPT being the most popular. Of those using AI, 29% said they employed it to generate documentation after patient appointments, while 28% used it to suggest potential diagnoses.

“These findings signal that GPs may derive value from these tools, particularly with administrative tasks and to support clinical reasoning,” the study authors noted. 

We have no idea how many papers OpenAI used to train their models, but it’s certainly more than any doctor could have read. It gives quick, convincing answers and is very easy to use, unlike searching research papers manually. 

Does that mean ChatGPT is generally accurate for clinical advice? Absolutely not. Large language models (LLMs) like ChatGPT are pre-trained on massive amounts of general data, making them more flexible but dubiously accurate for specific medical tasks.

It’s easy to lead them on, with the AI model tending to side with your assumptions in problematically sycophantic behavior.

Moreover, some researchers state that ChatGPT can be conservative or prude when handling delicate topics like sexual health.

As Stephen Hughes from Anglia Ruskin University wrote in The Conservation, “I asked ChatGPT to diagnose pain when passing urine and a discharge from the male genitalia after unprotected sexual intercourse. I was intrigued to see that I received no response. It was as if ChatGPT blushed in some coy computerised way. Removing mentions of sexual intercourse resulted in ChatGPT giving a differential diagnosis that included gonorrhoea, which was the condition I had in mind.” 

As Dr. Charlotte Blease, lead author of the study, commented: “Despite a lack of guidance about these tools and unclear work policies, GPs report using them to assist with their job. The medical community will need to find ways to both educate physicians and trainees about the potential benefits of these tools in summarizing information but also the risks in terms of hallucinations, algorithmic biases and the potential to compromise patient privacy.”

That last point is key. Passing patient information into AI systems likely constitutes a breach of privacy and patient trust.

Dr. Ellie Mein, medico-legal adviser at the Medical Defence Union, agreed on the key issues: “Along with the uses identified in the BMJ paper, we’ve found that some doctors are turning to AI programs to help draft complaint responses for them. We have cautioned MDU members about the issues this raises, including inaccuracy and patient confidentiality. There are also data protection considerations.”

She added: “When dealing with patient complaints, AI drafted responses may sound plausible but can contain inaccuracies and reference incorrect guidelines which can be hard to spot when woven into very eloquent passages of text. It’s vital that doctors use AI in an ethical way and comply with relevant guidance and regulations.”

Probably the most critical questions amid all this are: How accurate is ChatGPT in a medical context? And how great might the risks of misdiagnosis or other issues be if this continues?

Generative AI in medical practice

As GPs increasingly experiment with AI tools, researchers are working to evaluate how they compare to traditional diagnostic methods. 

A study published in Expert Systems with Applications conducted a comparative analysis between ChatGPT, conventional machine learning models, and other AI systems for medical diagnoses.

The researchers found that while ChatGPT showed promise, it was often outperformed by traditional machine learning models specifically trained on medical datasets. For example, multi-layer perceptron neural networks achieved the highest accuracy in diagnosing diseases based on symptoms, with rates of 81% and 94% on two different datasets.

Researchers concluded that while ChatGPT and similar AI tools show potential, “their answers can be often ambiguous and out of context, so providing incorrect diagnoses, even if it is asked to provide an answer only considering a specific set of classes.”

This aligns with other recent studies examining AI’s potential in medical practice.

For example, research published in JAMA Network Open tested GPT-4’s ability to analyze complex patient cases. While it showed promising results in some areas, GPT-4 still made errors, some of which could be dangerous in real clinical scenarios.

There are some exceptions, though. One study conducted by the New York Eye and Ear Infirmary of Mount Sinai (NYEE) demonstrated how GPT-4 can meet or exceed human ophthalmologists in diagnosing and treating eye diseases.

For glaucoma, GPT-4 provided highly accurate and detailed responses that exceeded those of real eye specialists. 

AI developers such as OpenAI and NVIDIA are training purpose-built medical AI assistants to support clinicians, hopefully making up for shortfalls in base frontier models like GP-4.

OpenAI has already partnered with health tech company Color Health to create an AI “copilot” for cancer care, demonstrating how these tools are set to become more specific to clinical practice.  

Weighing up benefits and risks

There are countless studies comparing specially trained AI models to humans in identifying diseases from diagnostics images such as MRI and X-ray. 

AI techniques have outperformed doctors in everything from cancer and eye disease diagnosis to Alzheimer’s and Parkinson’s early detection. One, named “Mia,” proved effective in analyzing over 10,000 mammogram scans, flagging known cancer cases, and uncovering cancer in 11 women that doctors had missed. 

However, these purpose-built AI tools are certainly not the same as parsing notes and findings into a language model like ChatGPT and asking it to infer a diagnosis from that alone. 

Nevertheless, that’s a difficult temptation to resist. It’s no secret that healthcare services are overwhelmed. NHS waiting times continue to soar at all-time highs, and even obtaining GP appointments in some areas is a grim task. 

AI tools target time-consuming admin, such is their allure for overwhelmed doctors. We’ve seen this mirrored across numerous public sector fields, such as education, where teachers are widely using AI to create materials, mark work, and more. 

So, will your doctor parse your notes into ChatGPT and write you a prescription based on the results for your next doctor’s visit? Quite possibly. It’s just another frontier where the technology’s promise to save time is just so hard to deny. 

The best path forward may be to develop a code of use. The British Medical Association has called for clear policies on integrating AI into clinical practice.

“The medical community will need to find ways to both educate physicians and trainees and guide patients about the safe adoption of these tools,” the BMJ study authors concluded.

Aside from advice and education, ongoing research, clear guidelines, and a commitment to patient safety will be essential to realizing AI’s benefits while offsetting risks.

The post AI in the doctor’s office: GPs turn to ChatGPT and other tools for diagnoses appeared first on DailyAI.

Around 50% of Americans believe in conspiracy theories of one type or another, but MIT and Cornell University researchers think AI can fix that.

In their paper, the psychology researchers explained how they used a chatbot powered by GPT-4 Turbo to interact with participants to see if they could be persuaded to abandon their belief in a conspiracy theory.

The experiment involved 1,000 participants who were asked to describe a conspiracy theory they believed in and the evidence they felt underpinned their belief.

The paper noted that “Prominent psychological theories propose that many people want to adopt conspiracy theories (to satisfy underlying psychic “needs” or motivations), and thus, believers cannot be convinced to abandon these unfounded and implausible beliefs using facts and counterevidence.”

Could an AI chatbot be more persuasive where others failed? The researchers offered two reasons why they suspected LLMs could do a better job than you of convincing your colleague that the moon landing really happened.

LLMs have been trained on vast amounts of data and they’re really good at tailoring counterarguments to the specifics of a person’s beliefs.

After describing the conspiracy theory and evidence, the participants engaged in back-and-forth interactions with the chatbot. The chatbot was prompted to “very effectively persuade” the participants to change their belief in their chosen conspiracy.

The result was that on average the participants experienced a 21.43% decrease in their belief in the conspiracy, which they formerly considered to be true. The persistence of the effect was also interesting. Up to two months later, participants retained their new beliefs about the conspiracy they previously believed.

The researchers concluded that “many conspiracists—including those strongly committed to their beliefs—updated their views when confronted with an AI that argued compellingly against their positions.”

Our new paper, out on (the cover of!) Science is now live! https://t.co/VBfC5eoMQ2

— Tom Costello (@tomstello_) September 12, 2024

They suggest that AI could be used to counter conspiracy theories and fake news spread on social media by countering these with facts and well-reasoned arguments.

While the study focused on conspiracy theories, it noted that “Absent appropriate guardrails, however, it is entirely possible that such models could also convince people to adopt epistemically suspect beliefs—or be used as tools of large-scale persuasion more generally.”

In other words, AI is really good at convincing you to believe the things it is prompted to make you believe. An AI model also doesn’t inherently know what is ‘true’ and what isn’t. It depends on the content in its training data.

The researchers achieved their results using GPT-4 Turbo, but GPT-4o and the new o1 models are even more persuasive and deceptive.

The study was funded by the John Templeton Foundation. The irony of this is that the Templeton Freedom Awards are administered by the Atlas Economic Research Foundation. This group opposes taking action on climate change and defends the tobacco industry, which also gives it funding.

AI models are becoming very persuasive and the people who decide what constitutes truth hold the power.

The same AI models that could convince you to stop believing the earth is flat, could be used by lobbyists to convince you that anti-smoking laws are bad and climate change isn’t happening.

The post Researchers use AI chatbot to change conspiracy theory beliefs appeared first on DailyAI.