Kozminski Techblog

Have you ever thought how little we know about the Universe? Every day we make efforts to understand it more and more, and gather so much information that we don’t know what to do with. Satellites gather hundreds of terabytes of data each year, and Large Synoptic Survey Telescope that’s is currently under construction in Chile will provide 15 terabytes of pictures of space every night. We are not capable of sifting through all the information, it’s just impossible for the astronomers to do it. As astronomer Carlo Enrico Petrillo told The Verge: “Looking at images of galaxies is the most romantic part of our job. The problem is staying focused.” That’s why he trained an AI program to do the hard work for him.

Petrillo and his team of astronomers were searching for a phenomenon that’s commonly referred to as space telescope. When a massive object (a galaxy or a black hole) comes between a distant light source and an observer on Earth, it bends the space and light around it, creating a lens that gives astronomers a closer look at incredibly old, distant parts of the Universe that should be blocked from view. Astronomers refer to it as gravitational lens, and they might be the key to understand the secrets of the Universe, its origins and what it’s made of. Unfortunately, the process of manually looking for them is extremely tedious and slow. This is where AI comes in, with the ability to spend less than a second for looking at a picture. The program is fed data in form of visual information, and starts to recognize a certain pattern, which it later uses to find objects that we are looking for. As of now, the program is capable of identifying gravitational lenses in less than a second.

So, what are other uses of AI in astronomy? Researchers developed many tools that help them. Some, like Petrillo’s, are taking on the job of identification: classifying galaxies, for example. Some have been used to identify pulsar stars, locate unusual exoplanets, or sharpen up low-res telescope imagery. Some tools are used for even more than just organizing and sifting through data, and create the information by filling the spots in our observations.

But are astronomers worried that we put so much trust into AI?  Petrillo says he’s not bothered. “In general, humans are more biased, less efficient, and more prone to mistakes than machines.” A more common implementation of Artificial Intelligence might allow us to explore the stars sooner that expected.

Sources:

https://www.theverge.com/2017/11/15/16654352/ai-astronomy-space-exploration-data

https://www.lsst.org/

Cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. Globally, about 1 in 6 deaths is due to cancer. To fight it, the key is detecting it early on. The task is extremely difficult, as cancer in the early stage of growth is extraordinarily difficult to detect. Standard screening methods such as radiological imaging can miss signs of cancer or return a false negative (as it does in 20-30% of cases). Hereditary testing is another detection method that determines genetic predisposition to cancer, but this does not provide much detail and cannot reveal if a person has cancer now. It takes a lot of time and resources to find developing cancer cells. But there might be a solution.

In recent study, researchers trained an algorithm to detect early signs of cancer cells forming. AI can not only greatly improve the accuracy of image detection for cancer, but could also open up entirely new fields between genomics and cancer screening.

Founded by six deep-learning experts from KAIST University in South Korea in 2013, Lunit trained their INSIGHT algorithm on chest x-rays and mammography images to detect lung and breast cancer. Boasting a 97% detection rate for lung and breast cancer, Lunit put their success down to training: “rather than guiding our algorithm to a specific location, we provided a region and said “there is a nodule there, try to find it,” and let the algorithm learn by itself,” says Brandon Suh, Lunit’s CEO.  “It is difficult for doctors to find small nodules hidden behind ribs or organs in chest x-rays” says Suh, but their algorithm searches for cancer patterns to dramatically reduce the chance of a false negative or missed case of cancer.

This unique approach to accurately detect and treat cancer is part of a wave of AI implementation in healthcare that does not look likely to stop. Wide-scale implementation of AI could lead to a fully proactive healthcare system, that responds to diseases preemptively istead of being focused on treating sick people. Unfortunately, there are some obstacles. Aside from a lack of data, mindset and professional changes are major obstacles for AI in healthcare. But if applications like Lunit can make themselves known, and more importantly understood by the healthcare community, then AI will be a powerful weapon in the fight against cancer.

Sources:

https://www.forbes.com/sites/charlestowersclark/2019/04/30/the-cutting-edge-of-ai-cancer-detection/#2582c7e27336

https://www.who.int/news-room/fact-sheets/detail/cancer

https://m.medicalxpress.com/news/2019-12-artificial-intelligence-previously-unknown-features.html

New Story, a housing non-profit based in San Francisco, and Icon, a construction-technology company that designs 3D printers produced first 3D printed house, that costs 10000. The printing of the prototype took only 48 hours.

At 350 square feet, the house was far cheaper than the average tiny home, which has a price tag hovering at around $25,000.

Half a year later, the first 3D printed neighbourhood has its first complete houses. Residents of a rural part of Mexico are now moving into 500-square-foot homes with two bedrooms, a kitchen, a living room space, and an indoor bathroom with plumbing. They’re all built in one sweep of the 3D printer, ICON’s Vulcan II, which pipes a special concrete mixture to form exterior and interior walls. Software monitors the weather conditions, and the machine can adjust the mixture. “In the morning it might be drier, and then late in the afternoon, maybe it’s more humid, and then you’ll adjust that mixture a little bit in accordance to that that you get the viscosity that you need in order to have the same print quality throughout the day,” says New Story cofounder Alexandria Lafci.

Choosing to go into rural Mexico for the company’s first project was intentional, too. The organization wanted to show it could get to a challenging location that also had regular earthquake activity. Where makeshift shacks are extremely vulnerable to any seismic activity, the New Story homes are more resilient.

Based on national reports, it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. However, about 1.6 billion, more than 20 percent of the world’s population, may lack adequate housing. New Story believes that the new construction process could be part of the solution for affordable housing in some of the poorest communities in the world.

New Story considers its homes in Mexico to be proof of concept in a way, both as a sign that its 3D printer technology is sound, and as encouragement to other groups that may enter the affordable housing fray and solve homelessness problem forever.

References:

https://www.popularmechanics.com/technology/infrastructure/a30198742/3d-printed-houses-neighborhood/

https://www.fastcompany.com/90440406/the-worlds-first-3d-printed-neighborhood-now-has-its-first-houses