A team of researchers from the Harbin Institute of Technology along with partners at the First Affiliated Hospital of Harbin Medical University, both in China, has developed a tiny robot that can ferry cancer drugs through the blood-brain barrier (BBB) without setting off an immune reaction. In their paper published in the journal Science Robotics, the group describes their robot and tests with mice. Junsun Hwang and Hongsoo Choi, with the Daegu Gyeongbuk Institute of Science and Technology in Korea, have published a Focus piece in the same journal issue on the work done by the team in China.
In recent years, robots have gained artificial vision, touch, and even smell. "Researchers have been giving robots human-like perception," says MIT Associate Professor Fadel Adib. In a new paper, Adib's team is pushing the technology a step further. "We're trying to give robots superhuman perception," he says.
This research project was originated from the collaboration between two institutions with their respective expertise: The TIPs laboratory of the ULB, in Belgium, which is a group dedicated to the study of transport phenomena and fluid interfaces, and the AS2M department of the FEMTO-ST institute, in France, specialized in microrobotics. And thus, ThermoBot was born, a new kind of manipulation platform working on the air-water interface. ThermoBot uses an original actuation mechanism, an infrared laser that locally heats the air-water interface, triggering so-called thermocapillary flows. Combining our specialties in interfacial phenomena and robotics, we were able to use this flow to displace floating components in a controlled manner.
When it comes to powering mobile robots, batteries present a problematic paradox: the more energy they contain, the more they weigh, and thus the more energy the robot needs to move. Energy harvesters, like solar panels, might work for some applications, but they don't deliver power quickly or consistently enough for sustained travel.
Last year, a team of biologists and computer scientists from Tufts University and the University of Vermont (UVM) created novel, tiny self-healing biological machines from frog cells called "Xenobots" that could move around, push a payload, and even exhibit collective behavior in the presence of a swarm of other Xenobots.
The introduction of modern automation technology into the sensitive production areas of pharmaceutical and medical technology was rapid. However, the conditions for the entry of systems, components and robots into this sector are anything but easy to meet.
While a Mars rover can explore where no person has gone before, a smaller robot at the University of the Sunshine Coast in Australia could climb to new heights by mimicking the movements of a lizard.
Not long after the 1918 Spanish flu pandemic, Czech writer Karel Čapek first introduced the term "robot" to describe artificial people in his 1921 sci-fi play R.U.R. While we have not yet created the highly intelligent humanoid robots imagined by Čapek, the robots most commonly used today are complex systems that work alongside humans, assisting with an ever-expanding set of tasks.
The entire face of team communications has changed, escalating the need for alternate ways to communicate with the growing mobile workforce. Internal and external communications are merging as businesses strive to reconnect with disconnected teams off-site.
In this episode, Lilly interviews Amanda Prorok, Professor of Computer Science and Technology at the University of Cambridge. Prorok discusses her research on multi-robot and multi-agent systems and learning coordination policies via Graph Neural Networks. They dig into her recent work on self-interested robots and finding explainability in emergent behavior.
Amanda Prorok
Amanda Prorok is an Assistant Professor (University Lecturer) in the Department of Computer Science and Technology, at Cambridge University, and a Fellow of Pembroke College. She serves as Associate Editor for IEEE Robotics and Automation Letters (R-AL) and Associate Editor for Autonomous Robots (AURO). Prior to joining Cambridge, Prorok was a postdoctoral researcher at the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, USA, where she worked with Prof. Vijay Kumar. She completed her PhD at EPFL, Switzerland, with Prof. Alcherio Martinoli.
Gold Solo Triple Twitter is an ultra-high-current servo drive, capable of delivering up to 270A/60VDC, 240A/80VDC, 210A/100VDC and 100A/200VDC. The drive delivers up to 17 kW electrical power in a compact package (EtherCAT: 152.68 cm3 or CAN: 144.32 cm3). This advanced, high power density servo drive provides top performance, advanced networking and built-in safety, as well as a fully featured motion controller and local intelligence. As part of the Gold product line, it is fully programmable with the Elmo Motion Control language. The Gold Solo Triple Twitter is available in a variety of models. There are multiple power rating options, different communication options, a number of feedback options and different I/O configuration possibilities. The Gold Solo Triple Twitter can be used in a variety of industrial applications, including medical, robotics, semiconductors and material handling fields.
Stretch’s mobile base allows it to go to where repetitive box lifting is required - unloading trucks, building pallets of boxes and order building. Stretch makes warehouse operations more efficient and safer for workers.
AI comes in many different shapes and sizes. That applies to the use cases, the underlying technologies as well as the approaches to adopting AI in your organization. As many organizations are looking to adopt AI, an increasing need for tangible frameworks to understand the technology in a business perspective is requested by leaders in all industries.
Some of the key questions asked by leaders are simple. How much time and money is required to adopt AI and solve business problems via AI and what returns do we get for those efforts? That is more than reasonable questions but answering these questions have been an issue in two parts. Firstly the answers have been a moving target with the technology being in an exponential development and as a result the answers of yesterday seem antique today. Secondly the intangible and explorative nature of AI has made it hard to provide such answers at all.
But as AI has matured as a technology, and been packaged into products and ready-to-use solutions, these questions are ready to be answered. The products and solutions might come in different levels of abstractions but they are nevertheless ready for being applied to business problems without much hassle.
The three main AI approaches
To make it easy to understand the efforts and the outcomes of AI it can be divided into three core approaches; Off-the-shelf-AI, AutoAI and Custom AI. The idea is simple. AI has reached a point where some solutions are ready to use out of the box and others need a lot of work before being applied. All approaches come with their own benefits and drawbacks so the trick is to understand these properties and know when to apply what kind. These core AI adoption strategies provide a more concrete foundation for predicting costs, risks and returns when applying AI.
Off-the-shelf-AI
Some AI solutions are ready to use out of the box and need little to no adjustment. Examples can be the Siri in your iPhone, an invoice capture software or speech-to-text solutions. These solutions take minutes to get started and the business models are often AI-as-a-Service making the initial investments low. Often these services are pay-per-use models and consequently implies low risk. The challenge of course is that you get what you get and you shouldn’t get upset. The options for adjusting and making necessary changes for the business problem are usually as low as the costs. More and more of these AI-services are blooming in the AI ecosystem with the large cloud providers as Google and Microsoft taking the lead.
AutoAI
Also known as AutoML, a more technical name, this solution is the hybrid solution giving both freedom to shape the AI as one wishes to a certain extent but also not having to invent the wheel once again. With AutoAI a business can take it’s own data such as documents, customer data or even pictures of products. This data is then used to train AI’s in pre-made environments that have the ability to pick the right algorithms for the job and deploy the AI ready to use in the cloud. As it can be costly to acquire data so there are some efforts required with AutoAI but at least it rarely requires a small army of data scientists. The drawback is also the inflexibility that is inherent in standardized tools. AutoAI also will be challenged when aiming for an AI with the highest possible accuracy.
Custom AI
With Custom AI almost everything is built from scratch. It is a job for data scientists, machine learning engineers and more of a task for R&D than any other place in the organization. The Custom AI approached is usually the weapon of choice when extremely high accuracy is required. Everything can be built and the possibilities are endless. This also usually means at least months or even years of work and experiments. Costly and time consuming. As the AutoAI and Off-the-shelf-AI is becoming more and more available and advanced, Custom AI is more suitable for companies building AI solutions that compete with other AI solutions. With all these extra efforts you might get the small edge that will win you the market.
A final note
For solving problems with AI the most usual approach is advancing towards off-the-shelf and AutoAI. An even more likely future is that a combination of these will be the favorite choice for many organizations adopting AI.
The concept of these approaches is not unique to AI. Almost all other technologies have been through the same natural progression and now the time has come to AI. It is a sign of maturity and that AI is in a state of public property and no longer hidden behind the ivy walls of the top universities.
Of course these approaches are not set in stone and the boundaries between them are fluid and inconsistent. But applying this framework of approaches to the conversation when adopting AI helps the almost magic aura of artificial intelligence become closer to a tool in the toolbox business. And that is where the value starts mounting.
