Each day around a third of all food harvested or produced around the globe is wasted. This means that about 1.3 billion tons of food feeds no one.  IKEA’s restaurants serve 680 million people each year and the company takes food waste seriously.  IKEA has enlisted AI in its sustainability efforts.

IKEA is a proponent of the “circular economy”, which is an economic system based on minimizing waste and making the most of resources. It is basically the opposite of taking materials, manufacturing products (or food), and then disposing of the end product. Instead it is a regenerative approach that reduces waste.

Here’s a great article on R-CNN, object detection, and the ins and outs of computer vision.

After exploring CNN for a while, I decided to try another crucial area in Computer Vision, object detection. There are several methods popular in this area, including Faster R-CNN, RetinaNet, YOLOv3, SSD and etc. I tried Faster R-CNN in this article. Here, I want to summarise what I have learned and maybe give you a little inspiration if you are interested in this topic.

The University of California, San Francisco is developing and training an AI model that could help diagnose tears in knee cartilage, or the meniscus.  A meniscus tear can lead to long-term health challenges  and lifestyle changes, ranging from debilitation to limits on activity. One of the keys to mitigating the consequences of meniscus tears is identifying and treating tears in the meniscus early. Here’s an interesting look at the research currently going on.

While this goal is pretty simple, the path forward is rather complicated. To diagnose a torn meniscus, clinicians need to review and interpret hundreds of high-resolution 3D magnetic resonance imaging (MRI) slices showing a patient’s knee from different angles. Radiologists then assign a numerical score to indicate the presence of a tear and its severity. This labor-intensive, time-consuming process relies heavily on the skills and availability of clinical specialists, and the interpretation of the images themselves can be rather subjective.

Google Colaboratory (Colab) is a cloud service that can be used for free of cost. It supports a free GPU and is based on Google’s Jupyter Notebooks environment. If you’re looking for alternatives, then you’re in luck.  While I am partial to Azure Notebooks, especially when paired with a Data Science Virtual Machine, I appreciate this rundown of other alternatives by Analytics India Magazine.

It provides a way for your machine to not carry the load of heavy workout of your ML operations. It is one of the very popular platforms of the kind. But there are some others which form as efficient alternatives of Colab. These are the best alternatives available out there for Google colab.

Imagine a Raspberry Pi cluster computing kit for $128. Well, imagine no more. Just think of what AI-infused IoT geeky things that could be built with this.

Raspberry Pi computers have been quite the revolution for makers, encouraging experimentation and creativity thanks to their low cost and compact size. And while the tiny computers are by no means high-end in their processing power, they continue to get faster with each generation. Now, you can gang together […]

Here’s a great idea for powering the next generation of innovators on this planet and a few others.

Microsoft’s education arm and NASA have come together to create online lessons to get school students interested about space. The eight online lesson plans range from titles such as Designing Astro Socks to protect astronauts’ feet in microgravity to designing one’s own space station, CNET reported on Friday. The […]

While AI has made enormous strides in recent years, there’s quite a bit of “simple things” that animals can do that AI has serious challenges doing. Why is that?