In this episode, Abate interviews Josh Lessing, co-founder and CEO of RootAI. At RootAI they are developing a system that tracks data on the farm and autonomously harvests crops using soft grippers and computer vision. Lessing talks about the path they took to build a product with good market fit and how they brought a venture capital backed startup to market.
Josh Lessing
Josh is one of the world’s leading minds on developing robotics and AI systems for the food industry, previously serving as the Director of R&D at Soft Robotics Inc. His current venture, Root AI, is integrating advanced robotics, vision systems and machine perception to automate agriculture. Josh was a Postdoctoral Fellow in Materials Science & Robotics at Harvard University, having earned his Ph.D. studying Biophysics & Physical Chemistry at the Massachusetts Institute of Technology and received an Sc.B. in Chemistry from Brown University.
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By Conn Hastings, science writer
Controlling a swarm of robots to paint a picture sounds like a difficult task. However, a new technique allows an artist to do just that, without worrying about providing instructions for each robot. Using this method, the artist can assign different colors to specific areas of a canvas, and the robots will work together to paint the canvas. The technique could open up new possibilities in art and other fields.
What if you could instruct a swarm of robots to paint a picture? The concept may sound far-fetched, but a recent study in open-access journal Frontiers in Robotics and AI has shown that it is possible. The robots in question move about a canvas leaving color trails in their wake, and in a first for robot-created art, an artist can select areas of the canvas to be painted a certain color and the robot team will oblige in real time. The technique illustrates the potential of robotics in creating art, and could be an interesting tool for artists. This human-swarm interaction modality may also provide a basis for collaborative studies combining the arts and other sciences.
Creating art can be labor-intensive and an epic struggle. Just ask Michelangelo about the Sistine Chapel ceiling. For a world increasingly dominated by technology and automation, creating physical art has remained a largely manual pursuit, with paint brushes and chisels still in common use. There’s nothing wrong with this, but what if robotics could lend a helping hand or even expand our creative repertoire?
“The intersection between robotics and art has become an active area of study where artists and researchers combine creativity and systematic thinking to push the boundaries of different art forms,” said Dr. María Santos of the Georgia Institute of Technology. “However, the artistic possibilities of multi-robot systems are yet to be explored in depth.”
This latest study looks at the potential for robot swarms to create a painting. The researchers designed a system whereby an artist can designate different regions of a canvas to be painted a specific color. The robots interact with each other to achieve this, with individual robots traversing the canvas and leaving a trail of colored paint behind them, which they create by mixing paints of different colors available on-board.
“The multi-robot team can be thought of as an “active” brush for the human artist to paint with, where the individual robots (the bristles) move over the canvas according to the color specifications provided by the human,” explained Santos.
In their experiments, the researchers used a projector to simulate a colored paint trail behind each robot, and they plan to develop robots that can handle liquid paint in the future. As a result of the developed system, even when some robots didn’t have access to all the pigments required to create the assigned color, they were still able to work together and approximate the color reasonably well.
This system could allow artists to control the robot swarm as it creates the artwork in real time. The artist doesn’t need to provide instructions for each individual robot, or even worry whether they have access to all the colors needed, allowing them to focus on creating the painting.
In the current study, the resulting images are abstract, and resemble a child’s crayon drawing. They show unique areas of color that flow into each other, revealing the artist’s input, and are pleasing to the eye. Future versions of the system may allow for more refined images.
Most importantly, the images confirm that it is possible for an artist to successfully instruct a robot swarm to paint a picture. The technique may also have potential in other fields where easily controlling the actions of a swarm of robots could be valuable. Robot orchestra, anyone?
Credit: M. Santos and coauthorsThis article was initially published on the Frontiers blog. Original article: Interactive Multi-Robot Painting Through Colored Motion Trails
Artificial intelligence is becoming mainstream and many organizations, startups and big corporates alike, are now starting internal AI projects or are incooperating AI into other existing IT. There's just one problem. Leading AI projects are very different from leading in traditional IT.
As a result many AI projects either fail or ignite frustration with project participants, users of the AI and the management involved. They are unaware that they are now in a new paradigm and should have different expectations than usually have to IT. Even implementing off-the-shelf AI components or systems can cause problems that are not usual to the organization.
This is very understandable. A few years ago AI was still something a few big techs and the universities were engaged with but for the masses a distant future. So very few leaders and project managers have experience in the AI-field, that they are suddenly thrown into
The keyword here is uncertainty. The big difference between traditional IT and AI is how much certainty is available. The low certainty is due to the experimental nature of AI. The AI paradigm is experimental in the sense that you can’t predict the road to the finished product. You can't plan the inner workings of AI models before you have created it and you don't know exactly what data you will need or how much data. Lastly you don’t know how well the AI will work. So setting up expectations for the finished solution can be very difficult. In many ways AI development is very much comparable with vaccine development. It's impossible to know in advance if your project will even be successful and most of the insights you need will be acquired while developing. This is very much in contrast the IT paradigm we know. The consensus of traditional IT projects is to precise planning, estimation and achieve a preset list of business objectives as timely and accurate as possible. For that reason we have developed an expertise in for example, planning tools and estimation techniques. But suddenly with the entrance of AI a lot of these skills are no longer useful. In fact they can be downright damaging in an experimental paradigm. If management demands deadlines and accurate estimates the project is bound to fail as it will never be able to deliver.
In order to lead in the AI domain you first of all, must acknowledge that this is a new paradigm and you must speak about it openly. When working in a new paradigm the most important tool is to be vocal about the new rules of engagement. With very little certainty in AI, expectation management is already approaching an art. So if you are not even in a dialogue with the project stakeholders about how the new way of working is, expectations will never align. So be very clear about this up front. Even as you’re making the business case you have to be clear that you cannot know either the costs nor revenue generated by an AI project. Not everyone will like it and you will see resistance but it’s much better that you take the conflicts before the project starts.
The enabler to get acceptance from stakeholders to work in this kind of uncertainty is the right organizational culture. As a leader it is your responsibility to massage and try to shift the culture in a direction that works with experimental AI projects. If there’s a mismatch between the paradigm you work in and the culture, you will get in trouble in no time.
One of the important features in an experimental culture is the willingness to accept null results exactly like the scientific community does. This is not just the usual preach about accepting failure and mistakes. This is a culture that sees a lot of hard work amounting to no more than the knowledge that a specific solution is not viable.
The experimental culture that fits AI development is very much in line with the learning culture, a cultural style found in the eight distinct culture styles of corporate culture. Other cultural styles such as Results culture and Safety culture can be in sharp contrast to the learning style on crucial points when working with AI. The styles are respectively very keen on achieved results and accurate planning. With AI that offers no certainty for either results or predictability this can quickly conflict.
When leading under uncertainty, leading through a strong vision is very effective. Leading through a strong vision is very much in line with the Purpose culture style. I like to compare it to Columbus' journey to America. Not knowing what to expect on the way or if the journey would even see any kind of success, Columbus still managed to get funding and a reliable crew. Columbus was well known for being extremely strong in his vision and I would attribute at least some of his voyage success to a strong vision. The trick here is to be specific about what you envision on the other side of uncertainty. How will everything look and feel when the project is done?
In conclusion it’s very effective to actively use the culture to support AI development since the alternative might be that it will have that working against you. And organizational culture is definitely one of the stronger forces of the universe.