A New Zealand startup producing its own servers is expanding into the realm of artificial intelligence, creating machine learning solutions that carry out common tasks while relieving people of repetitive, unsatisfying work. Having spotted an opportunity for the development of low-cost, high-efficiency and environmentally sustainable hardware, Kauricone has more recently pivoted in a fascinating direction: creating software that thinks about mundane problems, so we don’t have to. These tasks include identifying trash for improved recycling, ‘looking’ at items on roads for automated safety, pest identification and – in the ultimate alleviation of a notoriously sleep-inducing task – counting sheep.
Managing director, founder and tech industry veteran Mike Milne says Kauricone products include application servers, cluster servers and internet of things servers. It was in this latter category that the notion emerged of applying machine learning at the network’s edge.
“Having already developed low-cost-low power edge hardware, we realised there was a big opportunity for the application of smart computing in some decidedly not-so-enjoyable everyday tasks,” relates Milne. “After all, we had all the basic building blocks already: the hardware, the programming capability, and with good mobile network coverage, the connectivity.”
Work is just another name for tasks people would rather not do themselves, or that we cannot do for ourselves. And despite living in a fabulously advanced age, there is a persistent reality of all manner of tasks which must be done every day, but which don’t require a particularly high level of engagement or even intelligence.
It is these tasks for which machine learning (ML) is quite often a highly promising solution. “ML collects and analyses data by applying statistical analysis, and pattern matching, to learn from past experiences. Using the trained data, it provides reliable results, and people can stop doing the boring work,” says Milne.
There is in fact more to it than meets the eye (so to speak) when it comes to computer image recognition. That’s why ‘Capcha’ challenges are often little more than ‘Identify all the images containing traffic lights’: because distinguishing objects is hard for bots. ML overcomes the challenge through the ‘training’ mentioned by Milne: the computer is shown thousands of images and learns which are hits, and which are misses.
“Potentially, there are as many use cases as you have dull but necessary tasks in the world,” Milne notes. “So far, we’ve tackled a few. Rocks on roads are dangerous, but monitoring thousands of kilometers of tarmac comes at a cost. Construction waste is extensive, bad for the environment and should be managed better. Sheep are plentiful and not always in the right paddock. And pests put New Zealand’s biodiversity at risk.”
Tackling each of these problems, Kauricone started with its own-developed RISC IoT server hardware as the base. Running Ubuntu and programmed with Python or other open-source languages, the servers typically feature 4GB memory and 128GB solid state storage, the solar-powered edge devices consume as little as 3 watts and run indefinitely on a single solar panel. This makes for a reliable, low-cost ‘field-ready’ device, says Milne.
The Rocks on Roads project made clear the challenges of ‘simple’ image identification, with Kauricone eventually running a training model around the clock for 8 days, gathering 35,000 iterations of rock images, which expanded to 3,000,000 identifiable traits (bear in mind, a human identifies a rock almost instantly, perhaps faster if hurled). With this training, the machine became very good at detecting rocks on the roads.
For a new project involving construction waste, the Kauricone IoT server will maintain a vigilant watch on the types and amounts of waste going into building-site skips. Trained to identify types of waste, the resulting data will be the basis for improving waste management and recycling or redirecting certain items for more responsible disposal.
Counting sheep isn’t only a method for accelerating sleep time, it’s also an essential task for farmers across New Zealand. That’s not all – as an ML exercise, it anticipates the potential for smarter stock management, as does the related pest identification test case pursued by Kauricone. The ever-watchful camera and supporting hardware manage several tasks: identifying individual animals, numbering them, and also monitoring grass levels, essential for ovine nourishment. Tested so far on a small flock, this application is ready for scale.
Milne says the small test cases pursued by Kauricone to date are just the beginning and anticipates considerable potential for ML applications across all walks of life. “There is literally no end to the number of daily tasks where computer vision and ML can alleviate our workload and contribute to improved efficiency and, ultimately, a better and more sustainable planet,” he notes.
The Rocks on Roads project promises improved safety with a lower ‘human’ overhead, reducing or eliminating the possibility of human error. Waste management is a multifaceted problem, where the employment of personnel is rendered difficult owing to simple economics (and potentially stultifying work); New Zealand’s primary sector is ripe for technologically powered performance improvements which could boost already impressive productivity through automation and improved control; and pest management can help the Department of Conservation and allied parties achieve better results using fewer resources.
“It’s early days yet,” says Milne, “But the results from these exploratory projects are promising. With the connectivity of ever-expanding cellular and low-power networks like SIGFOX and LoraWan, the enabling infrastructure is increasingly available even in remote places. And purpose-built low power hardware brings computing right to the edge. Now, it’s just a matter of identifying opportunities and creating the applications.”
For more information visit Kauricone’s website.