ResinDek flooring panels are designed for elevated platforms such as mezzanines, pick modules, and work platforms. They have the proven structural integrity to support dynamic and static rolling limits from 2,000 to 8,000 lbs. ResinDek flooring panels are available in a multitude of options that are customized for load capacities, required finish type, volume and type of traffic including heavy rolling pallet jack loads and robotic traffic with AGVs and AMRs.
The world's first fast-food chain signs on for additional deployments of autonomous kitchen assistant - Flippy - to improve customer and team member experience in 'new normal'
While investment in automation is seen as vital for companies to remain competitive, supply chain leaders of American companies still envision a strong need for human capital in the workplace, according to a Honeywell
How can deep learning revolutionize medical image analysis beyond segmentation? In this article, we will see a couple of interesting applications in medical imaging such as medical image reconstruction, image synthesis, super-resolution, and registration in medical images
Although Ethical Risk Assessment (ERA) is not new – it is after all what research ethics committees do – the idea of extending traditional risk assessment, as practised by safety engineers, to cover ethical risks is new. ERA is I believe one of the most powerful tools available to the responsible roboticist, and happily we already have a published standard setting out a guideline on ERA for robotics in BS 8611, published in 2016.
Before looking at the ERA, we need to summarise the specification of our fictional robot teddy bear: RoboTed. First, RoboTed is based on the following technology:
RoboTed is an Internet (WiFi) connected device,
RoboTed has cloud-based speech recognition and conversational AI (chatbot) and local speech synthesis,
RoboTed’s eyes are functional cameras allowing RoboTed to recognise faces,
RoboTed has motorised arms and legs to provide it with limited baby-like movement and locomotion.
And second RoboTed is designed to:
Recognise its owner, learning their face and name and turning its face toward the child.
Respond to physical play such as hugs and tickles.
Tell stories, while allowing a child to interrupt the story to ask questions or ask for sections to be repeated.
Sing songs, while encouraging the child to sing along and learn the song.
Act as a child minder, allowing parents to both remotely listen, watch and speak via RoboTed.
The tables below summarise the ERA of RoboTED for (1) psychological, (2) privacy & transparency and (3) environmental risks. Each table has 4 columns, for the hazard, risk, level of risk (high, medium or low) and actions to mitigate the risk. BS8611 defines an ethical risk as the “probability of ethical harm occurring from the frequency and severity of exposure to a hazard”; an ethical hazard as “a potential source of ethical harm”, and an ethical harm as “anything likely to compromise psychological and/or societal and environmental well-being”.
(1) Psychological Risks
(2) Security and Transparency Risks
(3) Environmental Risks
For a more detailed commentary on each of these tables see our full paper – which also, for completeness, covers physical (safety) risks. And here are the slides from my short ICRES 2020 presentation:
Through this fictional case study we argue we have demonstrated the value of ethical risk assessment. Our RoboTed ERA has shown that attention to ethical risks can
suggest new functions, such as “RoboTed needs to sleep now”,
draw attention to how designs can be modified to mitigate some risks,
highlight the need for user engagement, and
reject some product functionality as too risky.
But ERA is not guaranteed to expose all ethical risks. It is a subjective process which will only be successful if the risk assessment team are prepared to think both critically and creatively about the question: what could go wrong? As Shannon Vallor and her colleagues write in their excellent Ethics in Tech Practice toolkit design teams must develop the “habit of exercising the skill of moral imagination to see how an ethical failure of the project might easily happen, and to understand the preventable causes so that they can be mitigated or avoided”.
The northern goshawk is a fast, powerful raptor that flies effortlessly through forests. This bird was the design inspiration for the next-generation drone developed by scientists of the Laboratory of Intelligent Systems of EPFL, led by Dario Floreano. They carefully studied the shape of the bird's wings and tail and its flight behavior, and used that information to develop a drone with similar characteristics.
Stanford University researcher Mac Schwager entered the world of penguin counting through a chance meeting at his sister-in-law's wedding in June 2016. There, he learned that Annie Schmidt, a biologist at Point Blue Conservation Science, was seeking a better way to image a large penguin colony in Antarctica. Schwager, who is an assistant professor of aeronautics and astronautics, saw an opportunity to collaborate, given his work on controlling swarms of autonomous flying robots.
On the surface, anti-static sounds like the right choice. Anti-static means no static, right? Not quite. Read on to learn the key differences between anti-static flooring and electrostatic dissipative (ESD) flooring.
An exoskeleton must be lightweight, compact, powerful, reliable, and easy to use – what could be easier? Many developers around the world dabble in this discipline with different degrees of success.
The feverish race to produce the shiniest, safest, speediest self-driving car has spilled over into our wheelchairs, scooters, and even golf carts. Recently, there's been movement from land to sea, as marine autonomy stands to change the canals of our cities, with the potential to deliver goods and services and collect waste across our waterways.
A team of researchers is creating mobile robots for military applications that can determine, with or without human intervention, whether wheels or legs are more suitable to travel across terrains. The Defense Advanced Research Projects Agency (DARPA) has partnered with Kiju Lee at Texas A&M University to enhance these robots' ability to self-sufficiently travel through urban military environments.
With a training technique commonly used to teach dogs to sit and stay, Johns Hopkins University computer scientists showed a robot how to teach itself several new tricks, including stacking blocks. With the method, the robot, named Spot, was able to learn in days what typically takes a month.
More than one million American adults use wheelchairs fitted with robot arms to help them perform everyday tasks such as dressing, brushing their teeth, and eating. But the robotic devices now on the market can be hard to control. Removing a food container from a refrigerator or opening a cabinet door can take a long time. And using a robot to feed yourself is even harder because the task requires fine manipulation.
With so many activities and over 2,600 scheduled demos taking place at PACK EXPO Connects, here is every preparation tool attendees need to ensure a successful event.
Lobe.ai just released for open beta and the short story is that you should go try it out. I was lucky and got to test it in the closed beta so I figured i should review a short review.
Making AI more understandable and accessible for most people is something I spend a lot of time on and Lobe is without a doubt right down my alley. The tagline is “machine learning made simple” and that is exactly what they do.
Overall great tool and I see it as an actual advance in the AI technology by making AI and deep learning models even more accessible than the AutoML wave is already doing.
So what is Lobe.ai exactly?
Lobe.ai is an Automl tool. That means that you can make AI without coding. In Lobe’s case they work with image classification only. So in short you give Lobe a set of images with labels and Lobe will automatically find the most optimal model to classify the images.
Lobe is also acquired by Microsoft. I think that’s a pretty smart move by Microsoft. The big clouds can be difficult to get started with and especially Microsoft's current AutoML solutions is first of all only tabular data but also requires a good degree of technical skills to get started.
It’s free. I don’t really understand the business model yet, but so far the software is free. That is pretty cool but I’m still curious about how the plan on getting revenue to keep up the good work.
Features
Image classification
So far Lobe only has one main feature and that’s training and image classification network. And it does that pretty well. In all the tests I have done I have gotten decent results with only very little training data.
Speed
The speed is insane. The models are being trained in something that seems like a minut. That’s a really cool feature. You can also decide to train it for longer to get better accuracy.
Export
You can export the model to CoreML, TensorFlow, TensorFlow Lite and they also provide a local API.
Use Cases
I’m planning to use Lobe for both hobby and commercial projects. For commercial use I’m going to use it for the main three purposes:
Producing models
Since the quality is good and the export possibilities are ready for production I see no reason not to use this for production purposes when helping clients with AI projects. You might think it’s better to hand build models for commercial use, but my honest experience is that many simple problems should be solved with the simplest solution first. You might stay with a model build with Lobe for at least the first few iterations of a project and sometimes forever.
Teaching
As I’m teaching people about applied AI, Lobe is going to be one of my go to tools from now on. It makes AI development very tangible and accessible and you can play around with it to get a feeling about AI without having to code. When project and product managers get to try developing models themself I expect a lot more understanding of edge cases and unforeseen problems.
Selling
When trying to convince a potential customer that they should invest in AI development you easily run into decision makers that have no understanding about AI. By showing Lobe and doing live tests I hope to be able to make the discussion more leveled since there’s a chance we are now talking about the same thing.
Compared with other AutoML solutions
The bad:
Less insights
In short you don’t get any model analysis like you would with Kortical for example.
Less options
As mentioned Lobe only offers image classification. Compared to Google Automl, that does that and objectregocnition, text, tabular, video and so on, it is still limited in use cases.
The good:
It’s Easy
This is the whole core selling point for Lobe and it does it perfectly. Lobe is so easy to use that it could easily be used for teaching 3rd graders.
It’s Fast
The model building is so fast that you can barely get a glass of water while training.
The average:
Quality
When I compared a model I build in Google AutoML to one I build in Lobe, Google seemed to be a bit better but not by far. That being said the Google model took me 3 hours to train vs. minutes with Lobe.
Future possibilities
For me to see Lobe.ai can go in two different directions in the future. They can either go for making a bigger part of the pipeline and let you build small apps on top of the models or they can go for more types of models such as tabular models or text classification. Both directions could be pretty interesting and whatever direction they go for I’m looking forward to testing it out.
Conclusion
In conclusion Lobe.ai is a great step forward for accessible AI and already in it’s beta it’s very impressive and surely will be the first in a new niche of AI.
It doesn’t get easier than this and with the export functionality it’s actually a good candidate for many commercial products.
Make sure you test it out, even if it’s just for fun.
