Chances are, you’ve been experiencing some of the hottest weeks of the summer and are thinking about how to better manage your home’s energy use to save money on your utility bills.

The good news is that SmartThings Energy offers many features that allow you to take advantage of automation to become a more efficient energy consumer. Flex Connect, available in California and New York, is one example of how SmartThings Energy’s AI Energy Mode feature automates user devices to help them save energy. 

With Flex Connect, Earn a Reward for Helping Out the Grid

The recently released Flex Connect program, a demand response initiative powered by SmartThings Energy, offers residents in New York and California a smarter way to save energy. By signing up for the Flex Connect program and connecting your smart home devices to the SmartThings app, you can earn a reward for helping the energy grid.

All it takes is entering your zip code to see if you qualify to get started.

How SmartThings Flex Connect Works

SmartThings offers functionality that enables eligible users to connect Samsung home appliances, TVs, and other compatible devices through Flex Connect. During peak energy demand periods, these devices automatically reduce their energy usage. For example, when electricity demand surges due to extreme weather, your eligible smart home devices will consume less energy during these peak times. 

This reduced strain on the grid helps balance the supply and demand of energy. Residents of California and New York now have even more ways to save energy while earning rewards effortlessly with Flex Connect. Click here to check your eligibility and enroll.

How All SmartThings Users Can Take Advantage of SmartThings Energy

Energy savings and the ability to earn rewards aren’t just available for New York and California residents. If you have Samsung home appliances, TVs1, and other compatible devices connected to SmartThings, you can take advantage of SmartThings Energy features available today, such as automation and energy usage data.

Automations built into SmartThings Energy elevate energy-saving features to a whole new level. For instance, AI Energy Mode is an exciting feature that learns your routines and adjusts your device’s energy usage in real-time. It uses machine learning to detect when your energy use exceeds the targets you’ve set in SmartThings Energy and provides insights into why this happens, so you don’t have to lift a finger (and likely won’t notice the difference).

With SmartThings Energy, you can also view energy usage data for all of your paired devices and learn how to optimize energy use in your home. For example, if you forget to turn off a light when you leave for work, you can be notified and turn it off right from the app. Thanks to Electricity Maps’ partnership with SmartThings, you can even see how your energy usage translates to carbon emissions. All of this helps you take control of your energy management. 

Even More Ways to Have Fun and Save Energy With SmartThings

Built into the latest release, you can now enjoy gamification elements, including earning Energy Stamps, for taking steps towards saving energy with the SmartThings AI Energy Mode feature.

Once you’ve leveraged your supported devices in the app, you can earn one Energy Stamp per day for every 400Wh of electricity saved using AI Energy Mode. Each Energy Stamp can be converted into 20 Samsung Rewards Points. You can earn up to one Energy Stamp per day, totaling 365 stamps over the course of a year, which can accumulate to 7,300 Samsung Rewards Points. These points can be used to purchase products on Samsung.com.

This is all just scratching the surface of what SmartThings Energy can do for you. #DoTheSmartThings and download the SmartThings app in the Google Play or Apple Store.

Activate SmartThings Energy and start living a more sustainable life.

Getting the best from your business in the modern era is often a matter of knowing when to automate. There are certain processes and tasks that demand human attention – but then there are other (usually more mundane) tasks, that can be performed fairly mindlessly by machines.

Thanks to the advent of artificial intelligence, however, it’s become difficult to see where the line should be drawn. Here, let’s take a closer look at what automation can do for you, and where it might be appropriate to use it.

Enhancing Operational Efficiency

Automation is at its best when it’s removing drudgerous labour from human hands, and leaving those hands free to pursue other, more creative and fulfilling tasks. You might think of an office worker who is asked to name documents in a particular way, based on the contents of a spreadsheet.

When this task takes up a significant number of working hours each week, automating it can lead to huge amounts of extra productivity and value. But even if it’s just a small task, a little bit of automation can help to free up mental energy, and make a given job seem that much less onerous.

Improving Accuracy and Reducing Errors

In many cases, automation can do things more accurately, as well as more quickly. Those documents we’ve mentioned would be renamed precisely as the spreadsheet would dictate. Of course, this would sometimes mean that any errors are also carried over from one location to the other.

But in some cases, modern artificial intelligence, informed by large language models like GPT-4, might be able to spot and correct these errors in the same way that a human being would – and then notify a human being that a correction has been made, so that the action taken can be approved.

In industries where accuracy is critical, like engineering and finance, the role of automated systems in eliminating human error can be hugely beneficial.

Enhancing Employee Satisfaction and Engagement

Freeing workers from mundane tasks can help to lift their wellbeing and morale. This can have a widespread impact, lifting the mood of a given workplace, even among workers who are not directly benefiting from automation.

The end result here is typically higher rates of productivity, lower absenteeism, and reduced rates of staff turnover. After all, no-one wants to perform dull, repetitive work.

You might think of the role that an automated people-first platform for HR might have in overseeing a workplace. It would allow human HR professionals to focus on the people being described by the system, rather than being distracted by the system itself.

Gaining a Competitive Advantage

Workplaces that are not automating will be disadvantaged, relative to those which are. Automation can allow workplaces to be more responsive, more efficient, and more pleasant to work within.

It might also allow operations to scale effectively, since it eliminates the administrative burden of expanding a workforce and procuring new premises.

The phrase “practice makes perfect” is usually reserved for humans, but it’s also a great maxim for robots newly deployed in unfamiliar environments.

Picture a robot arriving in a warehouse. It comes packaged with the skills it was trained on, like placing an object, and now it needs to pick items from a shelf it’s not familiar with. At first, the machine struggles with this, since it needs to get acquainted with its new surroundings. To improve, the robot will need to understand which skills within an overall task it needs improvement on, then specialize (or parameterize) that action.

A human onsite could program the robot to optimize its performance, but researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and The AI Institute have developed a more effective alternative. Presented at the Robotics: Science and Systems Conference last month, their “Estimate, Extrapolate, and Situate” (EES) algorithm enables these machines to practice on their own, potentially helping them improve at useful tasks in factories, households, and hospitals. 

Sizing up the situation

To help robots get better at activities like sweeping floors, EES works with a vision system that locates and tracks the machine’s surroundings. Then, the algorithm estimates how reliably the robot executes an action (like sweeping) and whether it would be worthwhile to practice more. EES forecasts how well the robot could perform the overall task if it refines that particular skill, and finally, it practices. The vision system subsequently checks whether that skill was done correctly after each attempt.

EES could come in handy in places like a hospital, factory, house, or coffee shop. For example, if you wanted a robot to clean up your living room, it would need help practicing skills like sweeping. According to Nishanth Kumar SM ’24 and his colleagues, though, EES could help that robot improve without human intervention, using only a few practice trials.

“Going into this project, we wondered if this specialization would be possible in a reasonable amount of samples on a real robot,” says Kumar, co-lead author of a paper describing the work, PhD student in electrical engineering and computer science, and a CSAIL affiliate. “Now, we have an algorithm that enables robots to get meaningfully better at specific skills in a reasonable amount of time with tens or hundreds of data points, an upgrade from the thousands or millions of samples that a standard reinforcement learning algorithm requires.”

See Spot sweep

EES’s knack for efficient learning was evident when implemented on Boston Dynamics’ Spot quadruped during research trials at The AI Institute. The robot, which has an arm attached to its back, completed manipulation tasks after practicing for a few hours. In one demonstration, the robot learned how to securely place a ball and ring on a slanted table in roughly three hours. In another, the algorithm guided the machine to improve at sweeping toys into a bin within about two hours. Both results appear to be an upgrade from previous frameworks, which would have likely taken more than 10 hours per task.

“We aimed to have the robot collect its own experience so it can better choose which strategies will work well in its deployment,” says co-lead author Tom Silver SM ’20, PhD ’24, an electrical engineering and computer science (EECS) alumnus and CSAIL affiliate who is now an assistant professor at Princeton University. “By focusing on what the robot knows, we sought to answer a key question: In the library of skills that the robot has, which is the one that would be most useful to practice right now?”

EES could eventually help streamline autonomous practice for robots in new deployment environments, but for now, it comes with a few limitations. For starters, they used tables that were low to the ground, which made it easier for the robot to see its objects. Kumar and Silver also 3D printed an attachable handle that made the brush easier for Spot to grab. The robot didn’t detect some items and identified objects in the wrong places, so the researchers counted those errors as failures.

Giving robots homework

The researchers note that the practice speeds from the physical experiments could be accelerated further with the help of a simulator. Instead of physically working at each skill autonomously, the robot could eventually combine real and virtual practice. They hope to make their system faster with less latency, engineering EES to overcome the imaging delays the researchers experienced. In the future, they may investigate an algorithm that reasons over sequences of practice attempts instead of planning which skills to refine.

“Enabling robots to learn on their own is both incredibly useful and extremely challenging,” says Danfei Xu, an assistant professor in the School of Interactive Computing at Georgia Tech and a research scientist at NVIDIA AI, who was not involved with this work. “In the future, home robots will be sold to all sorts of households and expected to perform a wide range of tasks. We can’t possibly program everything they need to know beforehand, so it’s essential that they can learn on the job. However, letting robots loose to explore and learn without guidance can be very slow and might lead to unintended consequences. The research by Silver and his colleagues introduces an algorithm that allows robots to practice their skills autonomously in a structured way. This is a big step towards creating home robots that can continuously evolve and improve on their own.”

Silver and Kumar’s co-authors are The AI Institute researchers Stephen Proulx and Jennifer Barry, plus four CSAIL members: Northeastern University PhD student and visiting researcher Linfeng Zhao, MIT EECS PhD student Willie McClinton, and MIT EECS professors Leslie Pack Kaelbling and Tomás Lozano-Pérez. Their work was supported, in part, by The AI Institute, the U.S. National Science Foundation, the U.S. Air Force Office of Scientific Research, the U.S. Office of Naval Research, the U.S. Army Research Office, and MIT Quest for Intelligence, with high-performance computing resources from the MIT SuperCloud and Lincoln Laboratory Supercomputing Center.