Claire chatted to Emma Hart from Edinburgh Napier University about algorithms that ‘evolve’ better robot designs and control systems.
Emma Hart is a computer scientist working in the field of evolutionary computation. Her work takes inspiration from the natural world, in particular biological evolution, and uses this to develop algorithms that ‘evolve’ both the design and control systems of a robot, customised to a specific application. She was elected as a Fellow of the Royal Society of Edinburgh in 2022, and was awarded the ACM SIGEVO Award for Outstanding Contribution to Evolutionary Computation in 2023. She was invited to give a TED Talk on her work in 2021 that has over 1.8 million views.
Researchers developed FaceAge, an AI tool that calculate's a patient biological age from a photo of their face. In a new study, the researchers tied FaceAge results to health outcomes in people with cancer: When FaceAge estimated a younger age than a cancer patient's chronological age, the patient did significantly better after cancer treatment, whereas patients with older FaceAge estimates had worse survival outcomes.
Engineers developed a ping-pong-playing robot that quickly estimates the speed and trajectory of an incoming ball and precisely hits it to a desired location on the table.
Brown University researchers have developed an artificial intelligence model that can generate movement in robots and animated figures in much the same way that AI models like ChatGPT generate text.
A research team from AMOLF in Amsterdam has created a soft robot that walks, hops, and swims—all without a brain, electronics, or AI. Just soft tubes, air, and some clever physics.
You’re expected to support next-gen AI on infrastructure that was never designed for it.
In highly regulated industries like government, financial services, and healthcare, IT teams face growing pressure to drive innovation while staying compliant.
But traditional systems can’t keep up with the speed, scale, or complexity that generative and agentic AI demand.
The result is a widening gap between business expectations and what existing infrastructure can support.
This creates a two-fold challenge:
Ensuring continuous security and compliance across production systems.
Delivering AI fast enough to meet business needs, without blowing through time, cost, or resources.
And for most teams, starting from scratch with open source tooling isn’t realistic.
What’s needed is a composable, integrated AI foundation that works across environments, simplifies operations, and gives IT teams the tools and shortcuts to deliver outcomes fast, without compromising security or governance.
Why AI stalls in high-security environments
AI initiatives often lose momentum — or never reach production — due to barriers like:
Procurement and integration delays: Acquiring and deploying new tools takes too long, stalling adoption and impact.
Disconnected infrastructure: Disparate and siloed systems block access to the right data and make it hard to design a scalable, resilient AI stack.
Operational complexity: IT teams spend more time maintaining tools than enabling data science.
Rigid security and compliance demands: Every new component requires vetting, testing, and regulatory approval when AI demands quick iteration.
Poor observability and blind spots: Limited visibility leads to rework in the short term and audit delays down the line.
These challenges are deeply interconnected. Patchwork fixes won’t address them.
Delivering outcomes at scale requires a platform that simplifies infrastructure, embeds security and governance, and gives IT teams the tools and control they need to move fast, without compromising trust.
Validated by Nutanix. Built for regulated environments.
IT leaders need more than a patchwork of tools; they need a secure, scalable platform that works right out of the box.
That’s exactly what we’ve built with Nutanix.
We’re proud to be recognized as Nutanix’s 2025 Americas Technology Alliance Partner of the Year — a reflection of our shared focus on simplifying agentic AI for highly regulated, on-prem environments.
This recognition highlights the strength of our joint solution: a fully validated, enterprise-grade AI platform that runs securely on Nutanix infrastructure. It enables rapid deployment, built-in governance, and full-stack integration, helping teams accelerate adoption without compromising compliance or control.
Simplifying agentic AI delivery on-prem
Current tools and infrastructure weren’t built for the speed, scale, or security that AI demands — especially in highly regulated, on-prem environments.
DataRobot and Nutanix remove the friction with a fully integrated platform that simplifies setup, secures access to the latest models, and embeds observability and governance from the start.
Together, we offer a validated AI stack purpose-built for high-security on-prem and air-gapped environments. This end-to-end solution gives IT leaders the speed, and compliance needed to deliver AI value within budget, without sacrificing control.
It’s fast to set up, easy to manage, and built to support secure, governed AI from development through deployment.
Nutanix: Build a scalable, secure foundation
Setup and scale securely: Deploy Nutanix Enterprise AI on CNCF-certified Kubernetes — scalable, cost-efficient, and ready for high-security workloads.
Simplify access to the latest models: Deploy open-source LLMs, including NVIDIA NIM, with streamlined model selection and management.
Manage and protect every endpoint: Stand up resilient endpoints with built-in security to test and run models safely.
DataRobot: Deliver business-ready agentic AI apps
Build enterprise-grade AI apps: Use prebuilt templates or create custom apps for high-security use cases within a flexible, production-ready development environment.
Ensure trust and compliance: Apply built-in guardrails and generate compliance documentation to support audit readiness.
Deploy and govern at scale: Move apps to production with continuous monitoring and full governance oversight.
DataRobot delivers mission-critical AI apps for federal agencies
Federal agencies can’t afford disruption. That’s why DataRobot delivers mission-critical AI apps designed to integrate with existing systems and deploy where your data lives, so you can move fast, stay compliant, and drive mission outcomes without overhauling your infrastructure.
Our AI applications tackle a wide range of critical use cases, including:
Budget insight and contract optimization: Identify underutilized funds and model future resource needs to drive smarter, faster budget decisions.
Fraud, waste, and abuse surveillance:Detect anomalies early with continuous data monitoring and automated compliance alerts.
Talent management and force readiness:Optimize recruitment, retention, and training investments to maintain a mission-ready workforce.
Operational Efficiency and Predictive Maintenance:Streamline procurement and predict equipment failures to boost fleet availability and lower costs.
Secure on-prem AI without compromise
For IT leaders in regulated industries, AI success often means tough trade-offs: speed vs. control, innovation vs. compliance, usability vs. security, budget overruns vs. mission outcomes.
In sectors like public, healthcare, and financial services, the challenge is meeting every requirement — without slowing down progress.
To move from AI ambition to real results, ask yourself:
Is your infrastructure fast, secure, and flexible enough to adapt?
Are security and compliance controls built in and simple to use?
Do your tools and apps enable teams to deliver on business needs?
This is the new operational baseline for secure AI delivery in highly regulated environments, and it’s exactly what Nutanix and DataRobot make possible.
With this platform, you can deploy AI securely, efficiently, and without compromise — turning ambition into measurable outcomes.
It's easy to take joint mobility for granted. Without thinking, it's simple enough to turn the pages of a book or bend to stretch out a sore muscle. Designers don't have the same luxury. When building a joint, be it for a robot or wrist brace, designers seek customizability across all degrees of freedom but are often restricted by their versatility to adapt to different use contexts.
Researchers have developed a more efficient chip as an antidote to the vast amounts of electricity consumed by large-language-model artificial intelligence applications like Gemini and GPT-4.
An edible robot leverages a combination of biodegradable fuel and surface tension to zip around the water's surface, creating a safe -- and nutritious -- alternative to environmental monitoring devices made from artificial polymers and electronics.
What makes people think an AI system is creative? New research shows that it depends on how much they see of the creative act. The findings have implications for how we research and design creative AI systems, and they also raise fundamental questions about how we perceive creativity in other people.
An edible robot made by EPFL scientists leverages a combination of biodegradable fuel and surface tension to zip around the water's surface, creating a safe and nutritious alternative to environmental monitoring devices made from artificial polymers and electronics.
A team of researchers has unveiled a cutting-edge Amphibious Robotic Dog capable of roving across both land and water with remarkable efficiency. The study, published in Bioinspiration and Biometrics, was inspired by mammals' ability to move through water as well as on land.
A human clearing junk out of an attic can often guess the contents of a box simply by picking it up and giving it a shake, without the need to see what's inside. Researchers from MIT, Amazon Robotics, and the University of British Columbia have taught robots to do something similar.
