The Red Ferret Journal - 5 new articles

Fruit harvesting is one of the trickiest parts of the agricultural spectrum. The fruit not only has to be selected when it's at the ideal ripeness, but also has to be picked very carefully so it doesn't bruise or suffer damage. Now Israeli made Tevel Autonomous harvest robots promise to revolutionize the process of fruit harvesting in a big way. continue reading

The post Tevel Autonomous Fruit Harvesting Bots – produce pickers face a fruitless future appeared first on The Red Ferret Journal.

    

Shutterstock, the $2 billion market cap digital content company, has just announced the launch of a free AI generative art service. The announcement wasn't exactly unexpected, but even so it's going to rock the foundations of the creative arts industry to its core.

It's one thing to watch small upstart companies like Stability AI and Midjourney unveil artificially generated imagery. It's altogether another to see a publicly quoted giant like Shutterstock embrace the technology as a first mover advantage. continue reading

The post Shutterstock AI launches – And Just Like That, The Revolution Starts appeared first on The Red Ferret Journal.

    

[caption id="attachment_62184" align="alignnone" width="1280"] Image by F. Muhammad from Pixabay[/caption]

Californian company Brimstone has announced the development of the world's first carbon negative cement product. It's a bold statement, and if true could revolutionize the global building industry and lead to a new era of carbon neutral construction.  Cement is a big deal.

Every year and estimated 4.4 billion tons of the stuff is produced to craft our buildings. But there's a hidden cost to the environment. If cement was a country, it would be the third largest CO2 emitter in the world, after the US and China. In fact, according to the BBC, it accounts for a massive 8% of all global carbon emissions. continue reading

The post Brimstone – the world’s first carbon negative portland cement appeared first on The Red Ferret Journal.

    

[caption id="attachment_62167" align="alignnone" width="1280"] Image by Gerd Altmann from Pixabay[/caption]

Happy New Year to everyone. Here's to a spectacular fresh year and lots of cake. continue reading

The post Happy New Year 2023 appeared first on The Red Ferret Journal.

    

[caption id="attachment_62162" align="alignnone" width="1280"] Image by Gabe Raggio from Pixabay[/caption]

A new system called Pomelo has been developed to estimate population densities during emergencies with unerring accuracy using artificial intelligence. The system has been designed to provide a fast and accurate mini census in times of emergency, such as natural disasters or war.

Pomelo, created as a joint venture between the International Red Cross, Lausanne Polytechnic and Bin Khalifa University in Qatar, uses large sets of data from remote sensing systems, and uses a neural network to identify likely population densities. Typically such data is hard to gather, either because of lack of time or limited resources, or the fact that numbers change so fast in a crisis situation.

The software can gather data from average building sizes, road maps, lighting and other factors, and crunch the numbers to provide a close estimate. This can then be used to calculate the relief volumes that are needed, or how to allocate resources such as food, medicine and other essentials.

Pomelo Does It Better

The key thing about Pomelo (population mapping by estimation of local occupancy rates) is the fact that it doesn’t need precise census data to start. The program can take rough regional data, then combine it with public data sets and sensors to produce numbers down to the sub-hectare level. In the chart above, we can see the comparison of estimated population densities on the left images, with the census data on the right hand side.

The engineering team tested their program in several African countries, and it proved to be significantly more accurate at producing estimates compared with other systems. The machine learning algorithm can be trained over various scenarios, for example densities in proximity to forest areas, and then apply that training to different locations.

As with everything AI, it’s crucial that the training is as good as it can be. The more countries that can be used to train, the better the overall accuracy. The model sees the similarities in different countries/regions and can then make very good estimates based on the data being examined. The team hope to keep adding potential data sources, such as anonymous Facebook data, and open source map data as time goes on. In this way the accuracy should be improved significantly.

It's good to see that AI can actually be used for more than just producing artsy anime models or game landscapes. Hopefully this is the start of a general deployment of genuinely useful machine learning systems around the world. continue reading

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