If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
If you have ever done a Kaggle competition, these would be commonly referred to as evaluation metrics. Typically, the lower the loss, the better the performance of your model. So if,for example, you were predicting house prices and using Mean Squared Error, and your cost was $25000, that means that your model is performing poorly as it is making a prediction error of $25000. Going back to our analogy, if you imagine that instead of a mountain there is a U-shaped curve, and instead of a person there is the cost function with maybe an initial cost value of 25,500. The aim of Gradient Descent would be to minimise this cost to either 0(global minimum), or something much smaller(local minimum).
Finland has released a free online artificial intelligence (AI) course which aims to educate public administration, businesses and the general public about the technology and to consider what it should be used for. The Ethics of AI course offered by the University of Helsinki has been designed in a partnership with the cities of Helsinki, Amsterdam and London as well as Finland's Ministry of Finance. Questions pertaining to the ethics of AI are topical, as many people are already making ethical choices in their work, for example, on data use. The course sets out to help them understand what the ethical use of artificial intelligence means and what it requires from both society and individuals. "These questions include how our data is used, who is responsible for decisions made by computers and whether, say, facial recognition systems are used in a way that acknowledges human rights. In a broader sense, it's also about how we wish to utilise advancing technical solutions," said Anna-Mari Rusanen, course coordinator for Ethics of AI.
Researchers at the Allen Institute for Artificial Intelligence have developed an AI-powered model that summarises scientific papers into a few sentences. In other words, it condenses a research paper into TLDR (Too Long; Didn't Read) format so you can decide which papers are worth reading. It does this by extracting the most important parts from the abstract, introduction, and conclusion sections, creating a snippet to describe the paper.
Add in a dash of Data Science tinkering ("I think I ran my Jupyter Notebook cells out of order") -- and it's no surprise 9 out of 10 Data Science projects never see the light of day. Given that the only data product I've deployed thus far is this clustering-based Neighborhood Explorer dashboard, I'll leave it to the more experienced to walk you through the deployment process. Data Scientists should understand how to deploy and scale their own models…Overspecialization is generally a mistake. She recommends courses and reading material on Kubernetes for Data Science. This container management tool represents the dominant force in cloud deployment.
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Goodhart's Law is an adage which states the following: "When a measure becomes a target, it ceases to be a good measure." This is particularly pertinent in machine learning, where the source of many of our greatest achievements comes from optimizing a target in the form of a loss function. Updates of this form have led to a series of breakthroughs from computer vision to reinforcement learning, and it is easy to see why it is so popular: 1) it is relatively cheap to compute using backprop 2) it is guaranteed to locally reduce the loss at every step and finally 3) it has an amazing track record empirically. However, we wouldn't be writing this if SGD was perfect! In fact there are some negatives.
Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.
This article was published as a part of the Data Science Blogathon. In this digital world, spam is the most troublesome challenge that everyone is facing. Sending spam messages to people causes various problems that may, in turn, cause economic losses. By spamming messages, we lose memory space, computing power, and speed. To remove these spam messages, we need to spend our time.