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In the first half of the last century, commercial robots like the single-armed Goliath dominated space robotics. Even though they were very busy and served a lot of people, the retail business was not so good. Opening the door to the second half of the 20th century, beautiful humanoid robots made their debut. Today, more and more robots are being stored in offices, hospitals, and schools, and especially in heavy-duty workers such as warehouses, satisfaction centers, and small factories. They are also taking advantage of opportunities on the streets and flying over them.

Smarter AI Through Quantum, Neuromorphic, and High-Performance Computing


The current AI and Deep Learning of the present era have a few shortcomings like training a deep net can be very time-consuming, cloud computing can be costly and unavailability of sufficient data can also be a problem. To be rid of these, the scientists are all set in their search for a smarter version of AI, and there seem to be three ways they can progress in the future. Within the process of improving AI, the most focus is on high-performance computing. It is based on the deep neural net but aims to make them faster and easier to access. It aims to provide better general-purpose environments like TensorFlow, and greater utilization of GPUs and FPGAs in larger and larger data centers, with the promise of even more specialized chips not too far away.

Prediction of Submucosal Invasion for Gastric Neoplasms in Endoscopic Images Using Deep-Learning – Digital Health and Patient Safety Platform


Endoscopic resection is recommended for gastric neoplasms confined to mucosa or superficial submucosa. The determination of invasion depth is based on gross morphology assessed in endoscopic images, or on endoscopic ultrasound. These methods have limited accuracy and pose an inter-observer variability. Several studies developed deep-learning (DL) algorithms classifying invasion depth of gastric cancers. Nevertheless, these algorithms are intended to be used after definite diagnosis of gastric cancers, which is not always feasible in various gastric neoplasms.

AutoX launches new version of its fully driverless system production for 'RoboTaxis'


AutoX has launched the production of its latest "Gen5 system for fully driverless RoboTaxis". The AutoX Gen5 was launched at the Crowne Plaza Shanghai Anting recently when AutoX founder and CEO, Dr Jianxiong Xiao, unveiled the technology behind the Gen5 system. The Gen5 system has 50 sensors in total, as well as a vehicle control unit of 2,200 TOPS computing power. There are 28 cameras capturing a total of 220 million pixels per second, six high resolution LiDAR offering 15 million points per second, and 4D radar with 0.9-degree resolution encompassing 360 degrees around the vehicle. Using camera and LiDAR fusion perception blind spot modules, the Gen5 system "covers the entire RoboTaxi body with zero blind spots", according to AutoX.

Bias in Artificial Intelligence


Artificial Intelligence (AI) has been swiftly infiltrating each aspect of our days and civilization. AI plays a notable role in human society, from hiring employees to the healthcare system and even criminal justice. Shaped by human norms and individual preferences, AI algorithms can lead to biases that are frequently subliminal, flawed. It is the outcome of the inadequate view of the world that individuals possess. Bias in AI is what we encounter when a machine-learning algorithm exhibits a systematically inaccurate result.

The Internet of Living Things Helps Put Food on the Table - Manufacturing Solutions


Today, advances in agronomy combined with smart agriculture technology have improved crop yields and sustainability. Enhancements in animal husbandry technology, improved breeding, nutrition and disease management help ensure optimal growth and performance of livestock. In spite of these innovations, the agricultural industry still faces significant challenges in producing enough food and getting it safely to market. These include changing weather patterns, water shortages, urbanization, population growth, complex environmental regulations, and dwindling available agricultural land, among others. In addition, food waste is a significant drain on the global food supply.

Researchers use AI to predict risk of developing type 2 diabetes


Artificial intelligence could be used to predict who is at risk of developing type 2 diabetes--information that could be used to improve the lives of millions of Canadians. Researchers at the University of Toronto used a machine learning model to analyze health data, collected between 2006 to 2016, of 2.1 million people living in Ontario. They found that they were able to use the model to accurately predict the number of people who would develop type 2 diabetes within a five-year time period. The machine learning model was also able to analyze different factors that would influence whether people were high or low risk to develop the disease. The results of the study were recently published in the journal JAMA Network Open.

What Are Deep Fakes And Why Are They Dangerous? - AI Summary


Fake videos generated by artificial intelligence — also known as deep fakes — are becoming more common and harder to detect. But some deep fakes are being used for a good cause. Karina Bafradzhian has the story.

Huawei teams up with GAC Motor for level 4 smart SUV


Chinese automaker GAC Group and Huawei have joined forces to work towards producing a smart SUV that is planned to hit the market in 2023. Labelled as the "first joint product of the two enterprises", the electric vehicle will have level 4 autonomy and "exciting new energy capabilities". While GAC will be providing the chassis, Huawei will be bringing the computing and communication aspects, with the pair planning "eight models and multiple series" of electric vehicles. In June, GAC, Huawei, and Didi said they would be "pooling resources, data, and scientific research". "One exciting project that GAC is working towards in cooperation with Huawei and Didi is'Level 4' autonomous vehicles, which can operate almost entirely without input from humans (current driver-assist mechanisms are classed as Level 2 autonomy)," the trio said at the time.

What Is Training Data? How It's Used in Machine Learning


Training data is the initial dataset used to train machine learning algorithms. Models create and refine their rules using this data. It's a set of data samples used to fit the parameters of a machine learning model to training it by example.