When most people hear the term Artificial Intelligence, the first thing they usually think of is robots or some famous science fiction movie like the Terminator depicting the rise of AI against humanity. Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning, analyzing, comprehending, and problem-solving. The applications of artificial intelligence in the real-world are perhaps more than what many people know. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal or defined operations. With the advancements of the human mind and their deep research into the field, AI is no longer just a few machines doing basic calculations.
In recent years, the media have been paying increasing attention to adversarial examples, input data such as images and audio that have been modified to manipulate the behavior of machine learning algorithms. Stickers pasted on stop signs that cause computer vision systems to mistake them for speed limits; glasses that fool facial recognition systems, turtles that get classified as rifles -- these are just some of the many adversarial examples that have made the headlines in the past few years. There's increasing concern about the cybersecurity implications of adversarial examples, especially as machine learning systems continue to become an important component of many applications we use. AI researchers and security experts are engaging in various efforts to educate the public about adversarial attacks and create more robust machine learning systems. Among these efforts is adversarial.js,
Welcome to my first blog on topics in artificial intelligence! Here I will introduce the topic of edge computing, with context in deep learning applications. This blog is largely adapted from a survey paper written by Xiaofei Wang et al.: Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. If you're interested in learning more about any topic covered here, there are plenty of examples, figures, and explanations in the full 35 page survery: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp & arnumber 8976180 Now, before we begin, I'd like to take a moment and motivate why edge computing and deep learning can be very powerful when combined: Deep learning is becoming an increasingly-capable practice in machine learning that allows computers to detect objects, recognize speech, translate languages, and make decisions. More problems in machine learning are solved with the advanced techniques that researchers discover by the day.
Every passing year brings the digital world a whole new crop of buzzwords, phrases and technologies. Machine learning has made a significant mark in 2020 with more people getting familiar with the technology and adapting it for better solutions. Machine learning is a form of artificial intelligence that automates data analysis, allowing computers to learn through experience and perform tasks without human invasion or explicit programming. Machine learning is an astonishing technology. Mastering machine learning tools will let people play with data, train models, discover new methods, and create own algorithms.
An increasing number of Twitter and LinkedIn influencers preach why you should start learning Machine Learning and how easy it is once you get started. While it's always great to hear some encouraging words, I like to look at things from another perspective. I don't want to sound pessimistic and discourage no one, I'll just give my opinion. While looking at what these Machine Learning experts (or should I call them influencers?) Maybe the main reason comes from not knowing what do Machine Learning engineers actually do.
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Fish farmers in Norway are using AI models designed to cut costs and improve the efficiency of their efforts to raise salmon, one of the country's major exports, thanks to efforts of the Norwegian Open AI Lab. The efforts are part of a growing trend to apply AI automation to aquaculture, which is the farming of fish, crustaceans, mollusks, aquatic plants, algae and other organisms. The AI models are designed to optimize feeding, keep the fish clean and healthy, and help companies make better decisions regarding farm operations, according to an account in WSJ Pro. The Norwegian Open AI Lab is run by Norwegian telecommunications carrier Telenor AS A, which along with other companies, provides technology services such as testing of 5G mobile connectivity, to salmon farms. Salmon exports in 2019 totaled some $11.3 billion, according to the Norwegian Seafood Council.
Deep learning models (aka neural nets) now power everything from self-driving cars to video recommendations on a YouTube feed, having grown very popular over the last couple of years. Despite their popularity, the technology is known to have some drawbacks, such as the deep learning "reproducibility crisis"-- as it is very common for researchers at one to be unable to recreate a set of results published by another, even on the same data set. Additionally, the steep costs of deep learning would give any company pause, as the FAANG companies have spent over $30,000 to train just a single (very) deep net. Even the largest tech companies on the planet struggle with the scale, depth, and complexity of venturing into neural nets, while the same problems are even more pronounced for smaller data science organizations as neural nets can be both time-and cost-prohibitive. Also, there is no guarantee that neural nets will be able to outperform benchmark models like logistic regression or gradient-boosted ones, as neural nets are finicky and typically require added data and engineering complexities.
Researchers at Google have developed a new AI tool called Chimera Painter that turns doodles into unusual creatures. This tool uses machine learning to create representation based on the user's rough sketches. Before this, Nvidia has used a similar concept with landscapes, and MIT and IBM have produced a similar idea with buildings. A high level of technical knowledge and artistic creativity is required to create art for digital video games. Game artists need to promptly iterate on ideas and develop many assets to meet tight deadlines.