"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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.
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.
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.
"WE'RE ALWAYS 30 days away from going out of business," is a mantra of Jen-Hsun Huang, co-founder of Nvidia, a semiconductor company. That may be a little hyperbolic coming from the boss of a company whose market value has increased from $31bn to $486bn in five years and which has eclipsed Intel, once the world's mightiest chipmaker, by selling high-performance chips for gaming and artificial intelligence (AI). As Mr Huang observes, Nvidia is surrounded by "giant companies pursuing the same giant opportunity". To borrow a phrase from Intel's co-founder, Andy Grove, in this fast-moving market "only the paranoid survive". Constant vigilance has served Nvidia well.
As with any new technology, machine learning implementation brings challenges and risks that businesses need to face and mitigate before moving forward. Another challenge of an ML-based system is that it is heavily dependent on data rather than human insight. Such a system can easily lead to a "winner takes all market," where large organizations with easy access to large quantities of data grow exponentially while the smaller players are left behind. The crucial aspect of this technological change is to use the machine capabilities along with human capabilities to create an organization that can thrive and grow its people as well as culture. With the amount of personal data being continually created, ML algorithms that analyze the data can pose a significant challenge to privacy.
Agriculture is a both major industry and the foundation of the economy. Artificial Intelligence (AI) techniques are widely used to solve a variety of problems and to optimize the production and operation processes in the fields of agriculture, food, and bio-system engineering. The use of artificial intelligence in the agriculture supply chain is becoming more and more important while involving Artificial Intelligence ML algorithms. The main four clusters are preproduction, production, processing, and distribution. In fact, in the preproduction, ML technologies are used, especially for the predictions of given features.
AI is hungry for data. Training and testing the machine-learning tools to perform desired tasks consumes huge lakes of data. More data often means better AI. Yet gathering this data, especially data concerning people's behavior and transactions, can be risky. For example, In January of this year, the US FTC reached a consent order with a company called Everalbum, a developer of photography apps.