While LLMs like ChatGPT have demonstrated impressive performance on various tasks, they often struggle with problems requiring numerical or symbolic reasoning.

NLEPs follow a four-step problem-solving template: calling necessary packages, importing natural language representations of required knowledge, implementing a solution-calculating function, and outputting results as natural language with optional data visualisation.

This approach offers several advantages, including improved accuracy, transparency, and efficiency. Users can investigate generated programs and fix errors directly, avoiding the need to rerun entire models for troubleshooting. Additionally, a single NLEP can be reused for multiple tasks by replacing certain variables.

The researchers found that NLEPs enabled GPT-4 to achieve over 90% accuracy on various symbolic reasoning tasks, outperforming task-specific prompting methods by 30%

Beyond accuracy improvements, NLEPs could enhance data privacy by running programs locally, eliminating the need to send sensitive user data to external companies for processing. The technique may also boost the performance of smaller language models without costly retraining.

However, NLEPs rely on a model’s program generation capability and may not work as well with smaller models trained on limited datasets. Future research will explore methods to make smaller LLMs generate more effective NLEPs and investigate the impact of prompt variations on reasoning robustness.

The research, supported in part by the Center for Perceptual and Interactive Intelligence of Hong Kong, will be presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics later this month.

(Photo by Alex Azabache)

See also: Apple is reportedly getting free ChatGPT access

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First reported by The Verge, the group of eight publications owned by Alden Global Capital (AGC) – including the Chicago Tribune, New York Daily News, and Orlando Sentinel – allege the companies have purloined “millions” of their articles without permission and without payment “to fuel the commercialisation of their generative artificial intelligence products, including ChatGPT and Copilot.”

The lawsuit is the latest legal action taken against Microsoft and OpenAI over their alleged misuse of copyrighted content to build large language models (LLMs) that power AI technologies like ChatGPT. In the complaint, the AGC publications claim the companies’ chatbots can reproduce their articles verbatim shortly after publication, without providing prominent links back to the original sources.

“This lawsuit is not a battle between new technology and old technology. It is not a battle between a thriving industry and an industry in transition. It is most surely not a battle to resolve the phalanx of social, political, moral, and economic issues that GenAI raises,” the complaint reads.

“This lawsuit is about how Microsoft and OpenAI are not entitled to use copyrighted newspaper content to build their new trillion-dollar enterprises without paying for that content.”

The plaintiffs also accuse the AI models of “hallucinations,” attributing inaccurate reporting to their publications. They reference OpenAI’s previous admission that it would be “impossible” to train today’s leading AI models without using copyrighted materials.

The allegations echo those made by The New York Times in a separate lawsuit filed last year. The Times claimed Microsoft and OpenAI used almost a century’s worth of copyrighted content to allow their AI to mimic its expressive style without a licensing agreement.

In seeking to dismiss key parts of the Times’ lawsuit, Microsoft accused the paper of “doomsday futurology” by suggesting generative AI could threaten independent journalism.

The AGC publications argue that OpenAI, now valued at $90 billion after becoming a for-profit company, and Microsoft – which has seen hundreds of billions of dollars added to its market value from ChatGPT and Copilot – are profiting from the unauthorised use of copyrighted works.

The news publishers are seeking unspecified damages and an order for Microsoft and OpenAI to destroy any GPT and LLM models utilising their copyrighted content.

Earlier this week, OpenAI signed a licensing partnership with The Financial Times to lawfully integrate the newspaper’s journalism. However, the latest lawsuit from AGC highlights the growing tensions between tech companies developing generative AI and content creators concerned about the unchecked use of their works to train profitable AI systems.

(Photo by Wesley Tingey)

See also: OpenAI faces complaint over fictional outputs

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.

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“With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today,” said Meta in a blog post announcing the release.

The initial Llama 3 models being opened up are 8 billion and 70 billion parameter versions. Meta says its teams are still training larger 400 billion+ parameter models which will be released over the coming months, alongside research papers detailing the work.

Llama 3 has been over two years in the making with significant resources dedicated to assembling high-quality training data, scaling up distributed training, optimising the model architecture, and innovative approaches to instruction fine-tuning.

Meta’s 70 billion parameter instruction fine-tuned model outperformed GPT-3.5, Claude, and other LLMs of comparable scale in human evaluations across 12 key usage scenarios like coding, reasoning, and creative writing. The company’s 8 billion parameter pretrained model also sets new benchmarks on popular LLM evaluation tasks:

“We believe these are the best open source models of their class, period,” stated Meta.

The tech giant is releasing the models via an “open by default” approach to further an open ecosystem around AI development. Llama 3 will be available across all major cloud providers, model hosts, hardware manufacturers, and AI platforms.

Victor Botev, CTO and co-founder of Iris.ai, said: “With the global shift towards AI regulation, the launch of Meta’s Llama 3 model is notable. By embracing transparency through open-sourcing, Meta aligns with the growing emphasis on responsible AI practices and ethical development.

”Moreover, this grants the opportunity for wider community education as open models facilitate insights into development and the ability to scrutinise various approaches, with this transparency feeding back into the drafting and enforcement of regulation.”

Accompanying Meta’s latest models is an updated suite of AI safety tools, including the second iterations of Llama Guard for classifying risks and CyberSec Eval for assessing potential misuse. A new component called Code Shield has also been introduced to filter insecure code suggestions at inference time.

“However, it’s important to maintain perspective – a model simply being open-source does not automatically equate to ethical AI,” Botev continued. “Addressing AI’s challenges requires a comprehensive approach to tackling issues like data privacy, algorithmic bias, and societal impacts – all key focuses of emerging AI regulations worldwide.

”While open initiatives like Llama 3 promote scrutiny and collaboration, their true impact hinges on a holistic approach to AI governance compliance and embedding ethics into AI systems’ lifecycles. Meta’s continuing efforts with the Llama model is a step in the right direction, but ethical AI demands sustained commitment from all stakeholders.”

Meta says it has adopted a “system-level approach” to responsible AI development and deployment with Llama 3. While the models have undergone extensive safety testing, the company emphasises that developers should implement their own input/output filtering in line with their application’s requirements.

The company’s end-user product integrating Llama 3 is Meta AI, which Meta claims is now the world’s leading AI assistant thanks to the new models. Users can access Meta AI via Facebook, Instagram, WhatsApp, Messenger and the web for productivity, learning, creativity, and general queries.  

Multimodal versions of Meta AI integrating vision capabilities are on the way, with an early preview coming to Meta’s Ray-Ban smart glasses.

Despite the considerable achievements of Llama 3, some in the AI field have expressed scepticism over Meta’s motivation being an open approach “for the good of society.” 

However, just a day after Mistral AI set a new benchmark for open source models with Mixtral 8x22B, Meta’s release does once again raise the bar for openly-available LLMs.

See also: SAS aims to make AI accessible regardless of skill set with packaged 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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency.

Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,

“An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results.

Expanding market footprint

Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem. 

“By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.”

Bringing AI to the masses

SAS is democratising AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions.

Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges.

“Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year.

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.

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This is according to a study conducted in early 2024 by Coleman Parkes Research and sponsored by data analytics firm SAS, which surveyed 300 US GenAI strategy or data analytics decision makers to pulse check major areas of investment and the hurdles organisations are facing.

Marinela Profi, strategic AI advisor at SAS, said: “Organisations are realising that large language models (LLMs) alone don’t solve business challenges. 

“GenAI should be treated as an ideal contributor to hyper automation and the acceleration of existing processes and systems rather than the new shiny toy that will help organisations realise all their business aspirations. Time spent developing a progressive strategy and investing in technology that offers integration, governance and explainability of LLMs are crucial steps all organisations should take before jumping in with both feet and getting ‘locked in.’”

Organisations are hitting stumbling blocks in four key areas of implementation:

• Increasing trust in data usage and achieving compliance. Only one in 10 organisations has a reliable system in place to measure bias and privacy risk in LLMs. Moreover, 93% of U.S. businesses lack a comprehensive governance framework for GenAI, and the majority are at risk of noncompliance when it comes to regulation.

• Integrating GenAI into existing systems and processes. Organisations reveal they’re experiencing compatibility issues when trying to combine GenAI with their current systems.

• Talent and skills. In-house GenAI is lacking. As HR departments encounter a scarcity of suitable hires, organisational leaders worry they don’t have access to the necessary skills to make the most of their GenAI investment.

• Predicting costs. Leaders cite prohibitive direct and indirect costs associated with using LLMs. Model creators provide a token cost estimate (which organisations now realise is prohibitive). But the costs for private knowledge preparation, training and ModelOps management are lengthy and complex.

Profi added: “It’s going to come down to identifying real-world use cases that deliver the highest value and solve human needs in a sustainable and scalable manner. 

“Through this study, we’re continuing our commitment to helping organisations stay relevant, invest their money wisely and remain resilient. In an era where AI technology evolves almost daily, competitive advantage is highly dependent on the ability to embrace the resiliency rules.”

Details of the study were unveiled today at SAS Innovate in Las Vegas, SAS Software’s AI and analytics conference for business leaders, technical users and SAS partners.

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.

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Could you tell us a little bit about Ikigai Labs and how it can help companies?

Ikigai is helping organisations transform sparse, siloed enterprise data into predictive and actionable insights with a generative AI platform specifically designed for structured, tabular data.  

A significant portion of enterprise data is structured, tabular data, residing in systems like SAP and Salesforce. This data drives the planning and forecasting for an entire business. While there is a lot of excitement around Large Language Models (LLMs), which are great for unstructured data like text, Ikigai’s patented Large Graphical Models (LGMs), developed out of MIT, are focused on solving problems using structured data.  

Ikigai’s solution focuses particularly on time-series datasets, as enterprises run on four key time series: sales, products, employees, and capital/cash. Understanding how these time series come together in critical moments, such as launching a new product or entering a new geography, is crucial for making better decisions that drive optimal outcomes. 

How would you describe the current generative AI landscape, and how do you envision it developing in the future? 

The technologies that have captured the imagination, such as LLMs from OpenAI, Anthropic, and others, come from a consumer background. They were trained on internet-scale data, and the training datasets are only getting larger, which requires significant computing power and storage. It took $100m to train GPT4, and GP5 is expected to cost $2.5bn. 

This reality works in a consumer setting, where costs can be shared across a very large user set, and some mistakes are just part of the training process. But in the enterprise, mistakes cannot be tolerated, hallucinations are not an option, and accuracy is paramount. Additionally, the cost of training a model on internet-scale data is just not affordable, and companies that leverage a foundational model risk exposure of their IP and other sensitive data.  

While some companies have gone the route of building their own tech stack so LLMs can be used in a safe environment, most organisations lack the talent and resources to build it themselves. 

In spite of the challenges, enterprises want the kind of experience that LLMs provide. But the results need to be accurate – even when the data is sparse – and there must be a way to keep confidential data out of a foundational model. It’s also critical to find ways to lower the total cost of ownership, including the cost to train and upgrade the models, reliance on GPUs, and other issues related to governance and data retention. All of this leads to a very different set of solutions than what we currently have. 

How can companies create a strategy to maximise the benefits of generative AI? 

While much has been written about Large Language Models (LLMs) and their potential applications, many customers are asking “how do I build differentiation?”  

With LLMs, nearly everyone will have access to the same capabilities, such as chatbot experiences or generating marketing emails and content – if everyone has the same use cases, it’s not a differentiator. 

The key is to shift the focus from generic use cases to finding areas of optimisation and understanding specific to your business and circumstances. For example, if you’re in manufacturing and need to move operations out of China, how do you plan for uncertainty in logistics, labour, and other factors? Or, if you want to build more eco-friendly products, materials, vendors, and cost structures will change. How do you model this? 

These use cases are some of the ways companies are attempting to use AI to run their business and plan in an uncertain world. Finding specificity and tailoring the technology to your unique needs is probably the best way to use AI to find true competitive advantage.  

What are the main challenges companies face when deploying generative AI and how can these be overcome? 

Listening to customers, we’ve learned that while many have experimented with generative AI, only a fraction have pushed things through to production due to prohibitive costs and security concerns. But what if your models could be trained just on your own data, running on CPUs rather than requiring GPUs, with accurate results and transparency around how you’re getting those results? What if all the regulatory and compliance issues were addressed, leaving no questions about where the data came from or how much data is being retrained? This is what Ikigai is bringing to the table with Large Graphical Models.  

One challenge we’ve helped businesses address is the data problem. Nearly 100% of organisations are working with limited or imperfect data, and in many cases, this is a barrier to doing anything with AI. Companies often talk about data clean-up, but in reality, waiting for perfect data can hinder progress. AI solutions that can work with limited, sparse data are essential, as they allow companies to learn from what they have and account for change management. 

The other challenge is how internal teams can partner with the technology for better outcomes. Especially in regulated industries, human oversight, validation, and reinforcement learning are necessary. Adding an expert in the loop ensures that AI is not making decisions in a vacuum, so finding solutions that incorporate human expertise is key. 

To what extent do you think adopting generative AI successfully requires a shift in company culture and mindset? 

Successfully adopting generative AI requires a significant shift in company culture and mindset, with strong commitment from executive and continuous education. I saw this firsthand at Eightfold when we were bringing our AI platform to companies in over 140 countries. I always recommend that teams first educate executives on what’s possible, how to do it, and how to get there. They need to have the commitment to see it through, which involves some experimentation and some committed course of action. They must also understand the expectations placed on colleagues, so they can be prepared for AI becoming a part of daily life. 

Top-down commitment, and communication from executives goes a long way, as there’s a lot of fear-mongering suggesting that AI will take jobs, and executives need to set the tone that, while AI won’t eliminate jobs outright, everyone’s job is going to change in the next couple of years, not just for people at the bottom or middle levels, but for everyone. Ongoing education throughout the deployment is key for teams learning how to get value from the tools, and adapt the way they work to incorporate the new skillsets.  

It’s also important to adopt technologies that play to the reality of the enterprise. For example, you have to let go of the idea that you need to get all your data in order to take action. In time-series forecasting, by the time you’ve taken four quarters to clean up data, there’s more data available, and it’s probably a mess. If you keep waiting for perfect data, you won’t be able to use your data at all. So AI solutions that can work with limited, sparse data are crucial, as you have to be able to learn from what you have. 

Another important aspect is adding an expert in the loop. It would be a mistake to assume AI is magic. There are a lot of decisions, especially in regulated industries, where you can’t have AI just make the decision. You need oversight, validation, and reinforcement learning – this is exactly how consumer solutions became so good.  

Are there any case studies you could share with us regarding companies successfully utilising generative AI? 

One interesting example is a Marketplace customer that is using us to rationalise their product catalogue. They’re looking to understand the optimal number of SKUs to carry, so they can reduce their inventory carrying costs while still meeting customer needs. Another partner does workforce planning, forecasting, and scheduling, using us for labour balancing in hospitals, retail, and hospitality companies. In their case, all their data is sitting in different systems, and they must bring it into one view so they can balance employee wellness with operational excellence. But because we can support a wide variety of use cases, we work with clients doing everything from forecasting product usage as part of a move to a consumption-based model, to fraud detection. 

You recently launched an AI Ethics Council. What kind of people are on this council and what is its purpose? 

Our AI Ethics Council is all about making sure that the AI technology we’re building is grounded in ethics and responsible design. It’s a core part of who we are as a company, and I’m humbled and honoured to be a part of it alongside such an impressive group of individuals. Our council includes luminaries like Dr. Munther Dahleh, the Founding Director of the Institute for Data Systems and Society (IDSS) and a Professor at MIT; Aram A. Gavoor, Associate Dean at George Washington University and a recognised scholar in administrative law and national security; Dr. Michael Kearns, the National Center Chair for Computer and Information Science at the University of Pennsylvania; and Dr. Michael I. Jordan, a Distinguished Professor at UC Berkeley in the Departments of Electrical Engineering and Computer Science, and Statistics. I am also honoured to serve on this council alongside these esteemed individuals.  

The purpose of our AI Ethics Council is to tackle pressing ethical and security issues impacting AI development and usage. As AI rapidly becomes central to consumers and businesses across nearly every industry, we believe it is crucial to prioritise responsible development and cannot ignore the need for ethical considerations. The council will convene quarterly to discuss important topics such as AI governance, data minimisation, confidentiality, lawfulness, accuracy and more. Following each meeting, the council will publish recommendations for actions and next steps that organisations should consider moving forward. As part of Ikigai Labs’ commitment to ethical AI deployment and innovation, we will implement the action items recommended by the council. 

Ikigai Labs raised $25m funding in August last year. How will this help develop the company, its offerings and, ultimately, your customers? 

We have a strong foundation of research and innovation coming out of our core team with MIT, so the funding this time is focused on making the solution more robust, as well as bringing on the team that works with the clients and partners.  

We can solve a lot of problems but are staying focused on solving just a few meaningful ones through time-series super apps. We know that every company runs on four time series, so the goal is covering these in depth and with speed: things like sales forecasting, consumption forecasting, discount forecasting, how to sunset products, catalogue optimisation, etc. We’re excited and looking forward to putting GenAI for tabular data into the hands of as many customers as possible. 

Kamal will take part in a panel discussion titled ‘Barriers to Overcome: People, Processes and Technology’ at the AI & Big Data Expo in Santa Clara on June 5, 2024. You can find all the details here.

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 Kamal Ahluwalia, Ikigai Labs: How to take your business to the next level with generative AI appeared first on AI News.

The company says the 132 billion parameter DBRX model surpasses popular open-source LLMs like LLaMA 2 70B, Mixtral, and Grok-1 across language understanding, programming, and maths tasks. It even outperforms Anthropic’s closed-source model Claude on certain benchmarks.

DBRX demonstrated state-of-the-art performance among open models on coding tasks, beating out specialised models like CodeLLaMA despite being a general-purpose LLM. It also matched or exceeded GPT-3.5 across nearly all benchmarks evaluated.

The state-of-the-art capabilities come thanks to a more efficient mixture-of-experts architecture that makes DBRX up to 2x faster at inference than LLaMA 2 70B, despite having fewer active parameters. Databricks claims training the model was also around 2x more compute-efficient than dense alternatives.

“DBRX is setting a new standard for open source LLMs—it gives enterprises a platform to build customised reasoning capabilities based on their own data,” said Ali Ghodsi, Databricks co-founder and CEO.

DBRX was pretrained on a massive 12 trillion tokens of “carefully curated” text and code data selected to improve quality. It leverages technologies like rotary position encodings and curriculum learning during pretraining.

Customers can interact with DBRX via APIs or use the company’s tools to finetune the model on their proprietary data. It’s already being integrated into Databricks’ AI products.

“Our research shows enterprises plan to spend half of their AI budgets on generative AI,” said Dave Menninger, Executive Director, Ventana Research, part of ISG. “One of the top three challenges they face is data security and privacy.

“With their end-to-end Data Intelligence Platform and the introduction of DBRX, Databricks is enabling enterprises to build generative AI applications that are governed, secure and tailored to the context of their business, while maintaining control and ownership of their IP along the way.”

Partners including Accenture, Block, Nasdaq, Prosus, Replit, and Zoom praised DBRX’s potential to accelerate enterprise adoption of open, customised large language models. Analysts said it could drive a shift from closed to open source as fine-tuned open models match proprietary performance.

Mike O’Rourke, Head of AI and Data Services at NASDAQ, commented: “Databricks is a key partner to Nasdaq on some of our most important data systems. They continue to be at the forefront of the industry in managing data and leveraging AI, and we are excited about the release of DBRX.

“The combination of strong model performance and favourable serving economics is the kind of innovation we are looking for as we grow our use of generative AI at Nasdaq.”

You can find the DBRX base and fine-tuned models on Hugging Face. The project’s GitHub has further resources and code examples.

(Photo by Ryan Quintal)

See also: Large language models could ‘revolutionise the finance sector within two years’

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.

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Because LLMs have an ability to analyse large amounts of data quickly and generate coherent text, there is growing understanding of the potential to improve services across a range of sectors including healthcare, law, education and in financial services including banking, insurance and financial planning.

This report, which is the first to explore the adoption of LLMs across the finance ecosystem, shows that people working in this area have already begun to use LLMs to support a variety of internal processes, such as the review of regulations, and are assessing its potential for supporting external activity like the delivery of advisory and trading services.

Alongside a literature survey, researchers held a workshop of 43 professionals from major high street and investment banks, regulators, insurers, payment service providers, government and legal professions.

The majority of workshop participants (52%) are already using these models to enhance performance in information-orientated tasks, from the management of meeting notes to cyber security and compliance insight, while 29% use them to boost critical thinking skills, and another 16% employ them to break down complex tasks.

The sector is also already establishing systems to enhance productivity through rapid analysis of large amount of text to simplify decision making processes, risk profiling and to improve investment research and back-office operations.

When asked about the future of LLMs in the finance sector, participants felt that LLMs would be integrated into services like investment banking and venture capital strategy development within two years.

They also thought it likely that LLMs would be integrated to improve interactions between people and machines, for example dictation and embedded AI assistants could reduce the complexity of knowledge intensive tasks such as the review of regulations.

But participants also acknowledged that the technology poses risks which will limit its usage. Financial institutions are subject to extensive regulatory standards and obligations which limits their ability to use AI systems that they cannot explain and do not generate output predictably, consistently or without risk of error.

Based on their findings, the authors recommend that financial services professionals, regulators and policy makers collaborate across the sector to share and develop knowledge about implementing and using LLMs, particularly related to safety concerns. They also suggest that the growing interest in open-source models should be explored and could be used and maintained effectively, but that mitigating security and privacy concerns would be a high priority.

Professor Carsten Maple, lead author and Turing Fellow at The Alan Turing Institute, said: “Banks and other financial institutions have always been quick to adopt new technologies to make their operations more efficient and the emergence of LLMs is no different. By bringing together experts across the finance ecosystem, we have managed to create a common understanding of the use cases, risks, value and timeline for implementation of these technologies at scale.”

Professor Lukasz Szpruch, programme director for Finance and Economics at The Alan Turing Institute, said: “It’s really positive that the financial sector is benefiting from the emergence of large language models and their implementation into this highly regulated sector has the potential to provide best practices for other sectors. This study demonstrates the benefit of research institutes and industry working together to assess the vast opportunities as well as the practical and ethical challenges of new technologies to ensure they are implemented safely.”

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.

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Creator Fund also participated, along with, MMC Ventures, Moonfire Ventures and Rockmount Capital and leading angel investors. 

Stanhope AI was founded as a spinout from University College London, supported by UCL Business, by three of the most eminent names in neuroscience and AI research – CEO Professor Rosalyn Moran (former Deputy Director of King’s Institute for Artificial Intelligence), Director Karl Friston, Professor at the UCL Queen Square Institute of Neurology and Technical Advisor Dr Biswa Sengupta (MD of AI and Cloud products at JP Morgan Chase). 

By using key neuroscience principles and applying them to AI and mathematics, Stanhope AI is at the forefront of the new generation of AI technology known as ‘agentic’ AI.  The team has built algorithms that, like the human brain, are always trying to guess what will happen next; learning from any discrepancies between predicted and actual events to continuously update their “internal models of the world.” Instead of training vast LLMs to make decisions based on seen data, Stanhope agentic AI’s models are in charge of their own learning. They autonomously decode their environments and rebuild and refine their “world models” using real-time data, continuously fed to them via onboard sensors.  

The rise of agentic AI

This approach, and Stanhope AI’s technology, are based on the neuroscience principle of Active Inference – the idea that our brains, in order to minimise free energy, are constantly making predictions about incoming sensory data around us. As this data changes, our brains adapt and update our predictions in response to rebuild and refine our world view. 

This is very different to the traditional machine learning methods used to train today’s AI systems such as LLMs. Today’s models can only operate within the realms of the training they are given, and can only make best-guess decisions based on the information they have. They can’t learn on the go. They require extreme amounts of processing power and energy to train and run, as well as vast amounts of seen data.  

By contrast, Stanhope AI’s Active Inference models are truly autonomous. They can constantly rebuild and refine their predictions. Uncertainty is minimised by default, which removes the risk of hallucinations about what the AI thinks is true, and this moves Stanhope’s unique models towards reasoning and human-like decision-making. What’s more, by drastically reducing the size and energy required to run the models and the machines, Stanhope AI’s models can operate on small devices such as drones and similar.  

“The most all-encompassing idea since natural selection”

Stanhope AI’s approach is possible because of its founding team’s extensive research into the neuroscience principles of Active Inference, as well as free energy. Director Indeed Professor Friston, a world-renowned neuroscientist at UCL whose work has been cited twice as many times as Albert Einstein, is the inventor of the Free Energy Theory Principle. 

Friston’s principle theory centres on how our brains minimise surprise and uncertainty. It explains that all living things are driven to minimise free energy, and thus the energy needed to predict and perceive the world. Such is its impact, the Free Energy Theory Principle has been described as the “most all-encompassing idea since the theory of natural selection.” Active Inference sits within this theory to explain the process our brains use in order to minimise this energy. This idea infuses Stanhope AI’s work, led by Professor Moran, a specialist in Active Inference and its application through AI; and Dr Biswa Sengupta, whose doctoral research was in dynamical systems, optimisation and energy efficiency from the University of Cambridge. 

Real-world application

In the immediate term, the technology is being tested with delivery drones and autonomous machines used by partners including Germany’s Federal Agency for Disruptive Innovation and the Royal Navy. In the long term, the technology holds huge promise in the realms of manufacturing, industrial robotics and embodied AI. The investment will be used to further the company’s development of its agentic AI models and the practical application of its research.  

Professor Rosalyn Moran, CEO and co-founder of Stanhope AI, said: “Our mission at Stanhope AI is to bridge the gap between neuroscience and artificial intelligence, creating a new generation of AI systems that can think, adapt, and decide like humans. We believe this technology will transform the capabilities of AI and robotics and make them more impactful in real-world scenarios. We trust the math and we’re delighted to have the backing of investors like UCL Technology Fund who deeply understand the science behind this technology and their support will be significant on our journey to revolutionise AI technology.”

David Grimm, partner UCL Technology Fund, said: “AI startups may be some of the hottest investments right now but few have the calibre and deep scientific and technical know-how as the Stanhope AI team. This is emblematic of their unique approach, combining neuroscience insights with advanced AI, which presents a groundbreaking opportunity to advance the field and address some of the most challenging problems in AI today. We can’t wait to see what this team achieves.” 

Marina Santilli, sasociate director UCL Business, added “The promise offered by Stanhope AI’s approach to Artificial Intelligence is hugely exciting, providing hope for powerful whilst energy-light models. UCLB is delighted to have been able to support the formation of a company built on the decades of fundamental research at UCL led by Professor Friston, developing the Free Energy Principle.” 

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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.

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