Google has hired former Intel executive Uri Frank to lead its custom chip division. Apart from Google, many companies have taken to chipmaking in the last few years to build competitive moats. The Intel veteran will serve as the Vice-President of Engineering for server chip design at Google. Uri Frank has over two decades of experience in custom CPU design and delivery experience. His expertise in design engineering at Intel will come in handy for Google.

Technology commentators are predicting that 2020 is going to see an acceleration of new tech entering the market including 5G wireless connectivity, blockchain technology (beyond cryptocurrency) and AI/ML. While each of these technologies has its own applications, what's more, interesting is how they can work together, especially with IoT already moving out of the hype cycle and entering a phase of rapid growth. Here is IoT in 2020 and how it's all coming together. How are the new technologies going to impact on the IoT? We may not have a crystal clear picture yet but we are in a position where we can start making some confident sketches.

In countries like the US, artificial intelligence is already being used at a large scale to evaluate student essays, saving educational institutes money and time. According to reports, at least 21 states in America have deployed some type of automated scoring, from middle school to college level. Students are being graded on their essays using such AI systems designed by different vendors for highly important tests like the Graduate Record Examinations (GRE). While educators in the US say they are not going back to using human teachers for essay grading, it has received major backlash from parents particularly those from state school systems. But, it's not all great when it comes to automated grading.

An artificial intelligence (AI) algorithm can transform low-dose CT (LDCT) scans into high-quality exams that radiologists may even prefer over LDCT studies produced via commercial iterative reconstruction techniques (Nature Machine Intelligence 10.1038/s42256-019-0057-9). A team of researchers from Rensselaer Polytechnic Institute (RPI) in Troy, NY, and Massachusetts General Hospital (MGH) in Boston developed a deep-learning model called a modularized adaptive processing neural network (MAP-NN), which progressively reduces noise on LDCT images with guidance from the radiologist until the optimal level of image quality is achieved. Testing on images from three different vendors, three radiologists found the algorithm produced images that were either better or comparable to images processed with iterative reconstruction. The deep-learning method also processed images much faster. "The deep-learning approach can thus already effectively compete with iterative reconstruction solutions and potentially replace the iterative reconstruction approach," wrote the group, led by Hongming Shan of RPI.