More than 500 strains of bacteria have been found living in seven types of tumour. Understanding their behaviour may lead to new kinds of treatments.
When you think of the words “data” and “mine”, no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining — that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction.
Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need. It should come as little surprise then that mining and resource extraction companies are looking to robotics, autonomous systems, and AI applications of all sorts to minimize risk, maximize return, and also lessen the environmental impact that mining has on our ecosystem.
On a recent AI Today podcast episode, Antoine Desmet of mining technology and equipment company Komatsu shared how they’re using advanced forms of AI, automation, and robotics to make an impact on the organization’s operations. Antoine has an interesting background, starting his career as a telecom engineer and receiving a Ph.D in neural network engineering. After getting his Ph.D, he returned to Komatsu and started working in surface analytics. He states that at the time there was a lot of data to work with, but very few analytics in place. He decided to start implementing machine learning and in the last few years his company has seen significant growth through this approach, with his data science team growing from just one person to ten people.
Although we often think of knowledge as “knowing that” (for example, knowing that Paris is the capital of France), each of us also knows many procedures consisting of “knowing how,” such as knowing how to tie a knot or start a car. Now, a new study has found the brain programs that code the sequence of steps in performing a complex procedure.
In a just published paper in Psychological Science, researchers at Carnegie Mellon University have found a way to find decode the procedural information required to tie various knots with enough precision to identify which knot is being planned or performed. To reach this conclusion, Drs. Robert Mason and Marcel Just first trained a group of participants to tie seven types of knots, and then scanned their brains while they imagined tying, or actually tied the knots while they were in an MRI scanner. The main findings were that each knot had a distinctive neural signature, so the researchers could tell which knot was being tied from the sequence of brain images collected. Furthermore, the neural signatures were very similar for imagining tying a particular knot and planning to tie it.
Dr. Just said, “Tying a knot is an ancient and frequently performed human action that is the epitome of everyday procedural knowledge, making it an excellent target for investigation.”
The point of the expanded testing program is to try and blunt a potential second wave of COVID-19 before it develops.
Day 5 at the Artificial Intelligence Hub boot camp #TakeOver, Today’s python class was interesting as students learned to use strings and lists… This is part of our vision to domesticate Artificial intelligence in Africa.
Kelvin Dafiaghor added a new photo.
The Defense Advanced Research Projects Agency (DARPA), the Pentagon’s cutting-edge research and development branch, is funding one of the oddest robotic concepts yet: a robot that mimics an earthworm to dig underground tunnels. It’s all part of an effort to demonstrate robotic tunneling technologies that will provide a secure way of resupplying U.S. Army troops in battle zones.
Recently discovered ‘single-pixel vision’ in fish could help researchers understand how humans are able to spot tiny details in their environment—like stars in the sky.
In a paper published this week, researchers at the University of Sussex found that zebrafish are able to use a single photoreceptor to spot their tiny prey.
This photoreceptor is like an ‘eye pixel’ and seems to provide enough of a signal for the fish to go and investigate the stimulus.
A machine-learning algorithm has been developed by scientists in Japan to breathe new life into old molecules. Called BoundLess Objective-free eXploration, or Blox, it allows researchers to search chemical databases for molecules with the right properties to see them repurposed. The team demonstrated the power of their technique by finding molecules that could work in solar cells from a database designed for drug discovery.
Chemical repurposing involves taking a molecule or material and finding an entirely new use for it. Suitable molecules for chemical repurposing tend to stand apart from the larger group when considering one property against another. These materials are said to be out-of-trend and can display previously undiscovered yet exceptional characteristics.
‘In public databases there are a lot of molecules, but each molecule’s properties are mostly unknown. These molecules have been synthesised for a particular purpose, for example drug development, so unrelated properties were not measured,’ explains Koji Tsuda of the Riken Centre for Advanced Intelligence and who led the development of Blox. ‘There are a lot of hidden treasures in databases.’
Amsterdam-based Squad Mobility’s solar powered electric two-seater quickly caught our fancy the last time we covered it. Now the Squad Solar City Car is back with updated design options for the solar-powered microcar.
