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.

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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.” 

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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The trial, called Trusted Operation of Robotic Vehicles in Contested Environments (TORVICE), was held in Australia under the AUKUS partnership formed last year between the three countries. It aimed to test robotic vehicles and sensors in situations involving electronic attacks, GPS disruption, and other threats to evaluate the resilience of autonomous systems expected to play a major role in future military operations.

Understanding how to ensure these AI systems can operate reliably in the face of modern electronic warfare and cyber threats will be critical before the technology can be more widely adopted.  

The TORVICE trial featured US and British autonomous vehicles carrying out reconnaissance missions while Australia units simulated battlefield electronic attacks on their systems. Analysis of the performance data will help strengthen protections and safeguards needed to prevent system failures or disruptions.

Guy Powell, Dstl’s technical authority for the trial, said: “The TORVICE trial aims to understand the capabilities of robotic and autonomous systems to operate in contested environments. We need to understand how robust these systems are when subject to attack.

“Robotic and autonomous systems are a transformational capability that we are introducing to armies across all three nations.” 

This builds on the first AUKUS autonomous systems trial held in April 2023 in the UK. It also represents a step forward following the AUKUS defense ministers’ December announcement that Resilient and Autonomous Artificial Intelligence Technologies (RAAIT) would be integrated into the three countries’ military forces beginning in 2024.

Dstl military advisor Lt Col Russ Atherton says that successfully harnessing AI and autonomy promises to “be an absolute game-changer” that reduces the risk to soldiers. The technology could carry out key tasks like sensor operation and logistics over wider areas.

“The ability to deploy different payloads such as sensors and logistics across a larger battlespace will give commanders greater options than currently exist,” explained Lt Atherton.

By collaborating, the AUKUS allies aim to accelerate development in this crucial new area of warfare, improving interoperability between their forces, maximising their expertise, and strengthening deterrence in the Indo-Pacific region.

As AUKUS continues to deepen cooperation on cutting-edge military technologies, this collaborative effort will significantly enhance military capabilities while reducing risks for warfighters.

(Image Credit: Dstl)

See also: Experts from 30 nations will contribute to global AI safety report

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The use of AI in the military is highly controversial. In this context, Large Language Models (LLMs) and algorithms must be implemented as ethically as possible.

Palantir believes that’s where its AI Platform (AIP) comes in. AIP offers cutting-edge AI capabilities and claims to ensure that the use of LLMs and AI in the military context is guided by ethical principles.

AIP is able to deploy LLMs and AI across any network, from classified networks to devices on the tactical edge. AIP connects highly sensitive and classified intelligence data to create a real-time representation of the environment.

The solution’s security features let you define what LLMs and AI can and cannot see and what they can and cannot do with safe AI and handoff functions. This control and governance are crucial for mitigating significant legal, regulatory, and ethical risks posed by LLMs and AI in sensitive and classified settings.

AIP also implements guardrails to control, govern, and increase trust. As operators and AI take action on the platform, AIP generates a secure digital record of operations. These capabilities are essential for responsible, effective, and compliant deployment of AI in the military.

In a demo showcasing AIP, a military operator responsible for monitoring activity within Eastern Europe receives an alert that military equipment is amassed in a field 30km from friendly forces.

AIP leverages large language models to allow operators to quickly ask questions such as:

• What enemy units are in the region?
• Task new imagery for this location at a resolution of one metre or higher
• Generate three courses of action to target this enemy equipment
• Analyse the battlefield, considering a Stryker vehicle and a platoon-size unit
• How many Javelin missiles does Team Omega have?
• Assign jammers to each of the validated high-priority communications targets
• Summarise the operational plan

As the operator poses questions, the LLM is using real-time information integrated from across public and classified sources. Data is automatically tagged and protected by classification markings, and AIP enforces which parts of the organisation the LLM has access to while respecting an individual’s permissions, role, and need to know.

Every response from AIP retains links back to the underlying data records to enable transparency for the user who can investigate as necessary.

AIP unleashes the power of large language models and cutting-edge AI for defense and military organisations while aiming to do so with the appropriate guardrails and high levels of ethics and transparency that are required for such sensitive applications.

(Image Credit: Palantir)

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