There is a lot of buzz in the world of machine learning (ML) and as a layperson it can be hard to keep up with it all. Therefore, we decided to write down some of our thoughts and musings on how we are approaching ML at Netdata. We’ll touch on the current state of applied ML in industry in general, and zoom in on ML in the monitoring industry.
Hearing loss is a rapidly growing area of scientific research as the number of baby boomers dealing with hearing loss continues to increase as they age. To understand how hearing loss impacts people, researchers study people’s ability to recognize speech. It is more difficult for people to recognize human speech if there is reverberation, some hearing impairment, or significant background noise, such as traffic noise or multiple speakers.
Artificial Intelligence (AI, also called Machine Learning) is certainly making its way in the world. Technologies such as Voice Recognition, Face Recognition, Predictive Analytics, Self-driving cars, and Robotics are now becoming embedded into our society. With the advent of big-data, these technologies can become more and more powerful and more and more a part of our everyday lives. I'm sure that there is much controversy over this. I'm sure that many people consider it invasive.
More than 2.14 billion global consumers are expected to buy goods and services online in 2021, according to Statista. That is up 29% from 1.66 billion digital customers just six years ago. This rapid change in shopping habits is driving retailers’ digital transformations and ever more advanced technologies. Many retailers have begun automating back office functions like claims processing, accounting and inventory management.
Though we’re living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms that are decades, in certain cases 70 years old. Some might contend that many of these older methods fall into the camp of ‘statistical analysis’ rather than machine learning, and prefer to date the advent of the sector back only so far as 1957, with the invention of the Perceptron.
Machine learning and artificial intelligence (AI) systems have long been touted as the future of medicine. A patient can walk into a doctors office, and after a quick scan discover their risk for a variety of diseases, and be given information on how to prevent them from occurring. Patients suffering from diseases like cancer can have treatment decisions made by an AI that can optimize care and maximize likelihood of survival.
After talking to machine learning and infrastructure engineers at major Internet companies across the US, Europe, and China, two groups of companies emerged. One group has invested hundreds of millions of dollars into infrastructure to allow real-time machine learning and has already seen returns on their investments. The other group still wonders if there’s value in real-time machine learning.
Over the years, this biweekly letter has provided me with the opportunity to fully and fairly document just how much free time college students can have if they try. My college roommates tried really hard.
While the global spend on artificial intelligence (AI) and machine learning (ML) was $50 billion in 2020 and is expected to increase to $110 billion by 2024 per an IDC report, AI/ML success has been hard to come by—and often slow to arrive when it does. There are four main impediments to successful adoption of AI/ML in the cloud-native enterprise.
