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) …
Let's talk about a topic that people are only whispering about at the moment, Active Learning. Active learning is a sub-field of Artificial Intelligence which is based on the fact that curious algorithms are better learners both in terms of efficiency and expressivity. The core idea is to let the algorithm pick examples to be trained on rather than the model be trained on all available training data. Active Learning is perhaps one of the simplest ideas in the field of AI. There are multiple variations to this idea but all of them have the stated theme.
Starting out as a data scientist, I struggled to understand the value economics brings. Now that I understand that data science is far more than knowing how to code, I've been able to identify the value that economists such as myself can bring to data science and machine learning. This article is an effort to help economists explain the value they can bring to machine learning roles, as well as help non-economists in data science understand what an economist can bring to the table. If you've ever taken an economics class, you may have heard of economics defined like this: "the branch of knowledge concerned with the production, consumption, and transfer of wealth." The field however -- and something I love about economics -- is far broader than these terms lead on.
This is the second unsupervised machine learning algorithm that I'm discussing here. This time, the topic is Principal Component Analysis (PCA). At the very beginning of the tutorial, I'll explain the dimensionality of a dataset, what dimensionality reduction means, main approaches to dimensionality reduction, reasons for dimensionality reduction and what PCA means. Then, I will go deeper into the topic PCA by implementing the PCA algorithm with Scikit-learn machine learning library. This will help you to easily apply PCA to a real-world dataset and get results very fast. In a separate article (not in this one), I will discuss the mathematics behind the principal component analysis by manually executing the algorithm using the powerful numpy and pandas libraries.
Hearing aid maker Starkey Hearing Technologies has introduced Livio AI – the first device to track physical activity and cognitive health. Livio AI hearing aid tracks brain and body health, has a natural user interface with tap control, language translation and advanced environmental detection using integrated sensors and Artificial Intelligence (AI). The new Hearing Reality technology provides as much as 50 per cent reduction in noisy environments, significant reduced listening effort, and newly enhanced clarity of speech, while the use of AI and integrated sensors enabled it to optimise the hearing experience. "What makes today a pivotal moment in the hearing industry, is that with Livio AI, we have transformed a single-use device into the world's first multi-purpose hearing aid, a Healthable with integrated sensors and artificial intelligence. Livio AI is so much more than just a hearing aid, it is a gateway to better health and wellness," Starkey Hearing Technologies President Brandon Sawalich said.
The Sequence Scope is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Data scientists, scholars, and developers from Microsoft Research, Intel Corporation, Linux Foundation AI, Google, Lockheed Martin, Cardiff University, Mellon College of Science, Warsaw University of Technology, Universitat Politècnica de València and other companies and universities are already subscribed to TheSequence. Years ago, cloud startup Heroku was able to successfully challenge cloud providers such as Amazon and Microsoft by providing a super simple model for building cloud applications. For years, Heroku was able to remain competitive in the midst of AWS and Azure growth, until it was acquired by Salesforce for $212 million.
If we want to calculate the physical properties of matter (such as the electronic state), we need to describe the state of the electron. The equations of motion that we are familiar with cannot describe the states of small objects such as electrons, so we need to use something called quantum mechanics. In quantum mechanics, the state of an electron is described by a complex function, the "wave function". "wave function" is, roughly speaking, electrons' orbitals. The equation below, called the Schrödinger equation, is a basic equation in quantum mechanics that shows the relationship between the wave function and the energy (Hamiltonian, H-hat on the right side), where ψ is the wave function.
Application service providers manage huge and complex infrastructures. Like any complex systems, things could go wrong from time to time, due to various reasons (for example, network connection response problems, infrastructure resource limitations, software malfunctioning issues, and so on). As a result, the question of how to quickly resolve issues when they happen becomes critical to help improve customer satisfaction and retention. Note: Performance numbers claimed in this post are based on public data sets and not specific to a particular project or organization. Recently, the fast advancement of natural language processing (NLP) algorithms have helped solve many practical problems by analyzing text information.
Machine learning (ML) algorithms are often categorized as either supervised or unsupervised, and this broadly refers to whether the dataset being used is labelled or not. Supervised ML algorithms apply what has been learned in the past to new data by using labelled examples to predict future outcomes. Essentially, the correct answer is known for these types of problems and the estimated model's performance is judged based on whether or not the predicted output is correct. In contrast, unsupervised ML algorithms refer to those developed when the information used to train the model is neither classified nor labelled. These algorithms work by attempting to make sense out of data by extracting features and patterns that can be found within the sample.
As organizations invest more in their AI and data capabilities, employees understand the growing influence of these technologies on their companies and careers. But despite their best efforts, many of these employees will not have the right training and qualifications to work effectively with AI. It's important for organizations to establish education and training requirements for their AI practitioners. Data scientists have varying qualifications, and not all have sufficient training in mathematics or computer science for AI projects. Even an employee with a Ph.D. might have studied a narrow field that isn't relevant to a particular company's needs.