Machine learning is a subset of artificial. Intelligence (ai) that, at the time of writing, has little. To do with climate change or our efforts. To combat excessive waste. However, many predict that this is about to change. Recent developments. In machine learning have. Real societal value: doctors. Are now using machine learning to make more. Accurate diagnoses , and self-driving cars are becoming. Increasingly common. Machine learning continues. To transform industries, and if we use it correctly. It will become a vital tool in our efforts. To lead better, more sustainable lives.
The problem, however, country email list is that many of. Us don’t know what machine learning. Is and can’t imagine what an ai-driven. Future might look like. We know what it means. To put recycling in the right bins, and. We only need to turn on the news to see. The direct impact of too much waste. But what is machine learning? And how can it help us. Reduce waste and emissions?
E-Waste and its effect on our world
E-waste is a disturbing byproduct of our rapidly changing technological world . When an electronic product breaks or is deemed to be worn out, it becomes e-waste. Every phone, laptop, household product, or electronic product we throw away adds to the already staggering pile of e-waste in landfills.
A recent United Nations study estimated that in 2019 alone, what is omnichannel marketing? ultimate guide we threw away 50 million tons of e-waste, but only recycled about 10 million tons. The e-waste issue is clearly spiraling out of our control and poses a significant threat to the environment and vulnerable human populations around the world. We simply need intelligent solutions that can address the growing problem of e-waste. Many believe that these solutions will come from artificial intelligence technologies that can calculate and analyze the massive amounts of data involved in e-waste.
Definition of AI + Machine Learning
For those of us less familiar with artificial intelligence and machine learning , it might be helpful to take a step back and provide some definitions.
Artificial intelligence
Nils J. Nilsson defines AI as “that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.”
In other words, ai is the use of computer systems to generate. Intelligence that mimics, and sometimes surpasses. Human thinking. More specifically, chile business directory the usefulness of. Ai lies in the fact that computers can store and analyze massive. Amounts of data more efficiently than humans. As such, ai is better suited. To completing tasks that involve large amounts of data. A human can’t sift through 50 million. Tons of electronic waste a year. But a properly programmed ai certainly can.
machine learning
Machine learning is a subfield of artificial intelligence that mimics human learning. Just as we learn things through trial and error, machine learning requires input of data to learn. This data is collected through data labeling . The interesting thing about machine learning is that it doesn’t require a human at every stage: when done correctly, machine learning allows computers to learn and adapt on their own. This is useful because it means that computers, which can quickly process large amounts of data more efficiently than humans, can learn and respond to various inputs to create solutions that draw from huge volumes of data.
Computer scientist Hilary Mason points out that the real value of machine learning is that it “gives us the ability to learn things about the world from large amounts of data that we as humans cannot possibly study or appreciate.” Mason also explains that the challenge of machine learning is not simply to create programs, but rather to find ways to effectively apply machine learning to the real world and all of its chaotic and unpredictable problems.
As far as unpredictable and chaotic problems go, climate change is something of a final boss. As such, it requires software engineers who specialize in the subfield of machine learning. Machine learning engineers differ slightly from software engineers in that they are required to take multidisciplinary approaches to data science. Simply put, machine learning engineers must consider the real world that exists beyond the program and find ways to adapt to meet the needs of society.