Category robots

No matter how expertly designed or rigorously tested, every robot is bound to encounter edge cases at some point or another. How the issues are handled and how field ops are done, determines the longevity of the robot system or product.

Artificial intelligence(AI) projects are becoming commonplace for big business and entrepreneurs alike. As a result many people with no prior experience with AI are now being put in charge of AI projects. Almost 5 years ago that happened to me for the first time and I’ve since learned a lot. So here’s six things I wish I had known, when I did my first AI project.

1. Data is the most expensive part

AI is often talked about as being technically very difficult requiring extensive resources to develop. But in fact that’s not the complete truth. The development can be costly but the vast majority of the work and resources needed is usually in acquiring, cleaning and preparing data for the development to take place. 

Data is also the most crucial element when trying to make the AI successfully do its job. As a result you should always prefer superior data over superior technology when making AI models.

So when budgeting for an AI project make sure that you set a side most of the time and money for getting a lot of good quality data. And remember that you might even need to acquire fresh data continuously if the domain you work in has changing conditions.

2. AI technology is more accessible than you think

In a very short time AI have made the jump from requiring specialist data scientists and machine learning engineers to where we can now make AI models without a single line of code. A multitude of AutoML(Automatic machine learning) vendors have appeared in the later years and they are rapidly improving. That means that getting started on AI doesn’t require as much investment as before. 

The data acquisition and the human processes, like training and onboarding, still requires hard work though and neither should be underestimated.

3. AI is experimental 

Developing AI is an experimental process. You cannot know how long it will take to develop what you have in mind or how good it will be. In some cases you cannot even be sure that AI is a feasible solution to your problem before trying. 

The best way to succeed with uncertain project conditions like this, is to time cap and milestone fund the project. Set short milestones and only release more funds for a project if the goals for each milestone has been met or at least that you see meaningful progress. If you fund the whole project up front you might end up pouring all your money into a dead end that could have been caught early. 

4. Be clear on what the succes is to your project 

Before getting started you should be very clear with your stakeholders what a successful project will look like. New technology like AI can quickly be held to golden standards that it will never achieve. If expectations are not aligned before the kick off you might end up thinking you made a fantastic solution while some of your stakeholders are disappointed. In my experience the exact same AI solution can amaze some people and seem novel to others.

A good way to deal with this is to make all stakeholders agree that the first version of the AI should just be able to deliver the status quo. From there you can improve and gradually increase the value.

5. Users will lose sense of control

It can be hard to explain the inner workings and the reasoning behind an AI’s output. At the same time you cannot exactly know what output it will give, given a specific input. That will make it feel just as or even more unpredictable than humans doing the same tasks. As the users of an AI cannot ask questions or know if feedback given the AI will make a difference, the users will often feel a lost sense of control. 

To avoid that feeling you must first of all prepare the user of this new paradigm. It’s much easier if they buy in on these conditions before they get to try the AI. If possible you can also provide feedback mechanisms so the users will at least feel that they can make a difference. Not that it will work every time but it’s better than nothing. 

It’s also a good idea to manage expectations through the right narrative. Make it clear if the AI is a decision system, that makes decisions on it’s own or a support system that is just suggesting. By clearly understanding the purpose of the AI the users usually get more comfortable with it quicker.

6. People have very different understandings of what AI is 

As a rule of thumb everyone has a different understanding of AI. The managers, users, developers and all other stakeholders will have their unique understanding of what AI actually is. That’s very fair since AI does not have one definite definition, but it will be a source of problems if everyone involved in a project has a different understanding of what is going on. So before you start a project, make sure not to take for granted that anybody thinks the way you do. Be explicit about what AI means to you and how you will approach it.

Vanderbilt University engineers have determined that their back-assist exosuit, a clothing-like device that supports human movement and posture, can reduce fatigue by an average of 29-47 percent in lower back muscles. The exosuit's functionality presents a promising new development for individuals who work in physically demanding fields and are at risk for back pain, including medical professionals and frontline workers.

Tensegrity is a design principle that has often been applied by artists, architects and engineers to build a wide range of structures, including sculptures, frames and buildings. This principle essentially describes the dynamics that occur when a structure maintains its stability via a pervasive tensional force.

The lifetime cost of ownership of a mobile robot is largely influenced by the design of its drivetrain. If the OEM uses well designed and proven components, the mobile robot will provide many years of service with minimal downtime for maintenance.

Researchers at Yale have developed a robotic fabric, a breakthrough that could lead to such innovations as adaptive clothing, self-deploying shelters, or lightweight shape-changing machinery.

An international team of Johannes Kepler University researchers is developing robots made from soft materials. A new article in the journal Communications Materials demonstrates how these kinds of soft machines react using weak magnetic fields to move very quickly—even grabbing a quick-moving fly that has landed on it.

There has been an increase in the demand of service robotics solutions across the world owing to greater need for automation of services. With the increasing use of cobots in service applications, the overall demand for robotics has increased drastically over the years.

Since the outbreak of the COVID-19 pandemic, we’ve seen many industries go down due to different reasons. However, some industries were able to get past these hurdles and find in the current pandemic the source for their growth. Robotics is one of these industries.

A combined team of researchers from Korea and Germany has built an AI-based curling robot that is able to compete at a professional level. In their paper published in the journal Science Robotics, the group describes how their robot was built, how it was trained and how well it performed when matched against professional human players. Johannes Stork with Örebro University has published a Focus piece discussing the work by the team in the same journal issue.

Back in 2013, local Brooklyn papers were excitedly reporting on a new initiative aimed at getting residents involved in cleaning up the highly polluted Gowanus Canal. Brooklyn Atlantis, as the project was known, was the brainchild of NYU Tandon Professor of Mechanical and Aerospace Engineering Maurizio Porfiri, who envisioned building and launching robotic boats to collect water-quality data and capture images of the infamous canal, which citizen scientists would then view and help classify. Those robotic boats ultimately led to the formation of the company Manifold Robotics, which aimed to further develop the unmanned surface vehicles (USVs) with sensor technology. (The fledgling company received support from PowerBridgeNY, a collaborative initiative to bring university research to market.) More recently, the startup has now branched out to develop a mobile data collection platform that allows unmanned aerial vehicles (UAVs) to operate safely in the sky near power lines.

The defence sector is a valuable contributor to the economy, providing a large number of jobs and investing in a number of industries. However the relationship is mutualistic, with advancements in industry benefitting the defence sector.

Building a custom training loop in Tensorflow and Python with checkpoints and Tensorboards visualizations

Robots will help H-E-B grocery stores keep up with the growing demand for online grocery services amid the pandemic.

Robots will help H-E-B grocery stores keep up with the growing demand for online grocery services amid the pandemic.