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) …
"Bias in AI" refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. This discrimination usually follows our own societal biases regarding race, gender, biological sex, nationality, or age (more on this later). Just this past week, for example, researchers showed that Google's AI-based hate speech detector is biased against black people. In this article, I'll explain two types of bias in artificial intelligence and machine learning: algorithmic/data bias and societal bias. I'll explain how they occur, highlight some examples of AI bias in the news, and show how you can fight back by becoming more aware.
The Outlook Calendar Scheduling team is currently looking for a highly motivated data scientist who can help build scalable prediction, machine learning, and AI to change the way people use calendar to organize their life. If you are passionate about designing and building the next generation time management intelligence and scheduling solution used by hundreds of millions of users every day then this is the job for you.
We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites.
In the modern-day, the impact that technology has on business operations is undeniable – no matter what industry the organisation is in. Allowing employees to be more productive and deliver a more personalised product to consumers, it's not something that should be ignored. Over the last few years, the global insurance industry has started to advance by utilising the benefits that come with updating their technology. Noticing the importance of customer expectations, many businesses are stepping away from the traditional processes in favour of the contemporary. However, not all insurance companies are keeping up with the advancement – which has led to them collaborating with long-established insurtech organisations or an insurtech startup.
In the past few years there has been a large increase in tools trying to solve the challenge of bringing machine learning models to production. One thing that these tools seem to have in common is the incorporation of notebooks into production pipelines. This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way. Let's start by defining what these are, for those readers who haven't been exposed to notebooks, or call them by a different name. Notebooks are web interfaces that allow a user to create documents containing code, visualisations and text.
Researchers at the University of Zurich's Brain Research Institute have recently developed a technique to automatically detect neurons of different types in a variety of brain regions at different developmental stages. They presented this deep learning-based tool, called DeNeRD, in a paper published in Nature Scientific Reports. Mapping the structure of the mammalian brain at the cellular level is an important, yet demanding task, which typically involves capturing specific anatomical features and analyzing them. In the past, researchers were able to gather several interesting observations and insights about the mammalian brain's structure using classical histological and stereological techniques. Although these methods have proved to be very useful for studying the anatomy of the brain, carrying out a truly brain-wide analysis typically requires a different approach.
Japanese companies are working to ensure that facial recognition and other technologies using artificial intelligence will not indirectly lead to discrimination. AI is making inroads into various aspects of people's lives. But concerns are growing about unexpected outcomes, including bias. Fujitsu established an outside ethics committee last month, comprising six experts. Its guidelines urge the firm to protect people's privacy and refrain from discrimination and inflicting harm.
In order to become and remain competitive, companies need to tap into the latest technologies and customer experience trends. Here are 6 customer experience trends every company must get ready for. It's no longer enough to have a surface-level understanding of customers. It's assumed every business should know who their customers are, where they live, how old they are, and other rudimentary info. Today, companies must go deeper, and with fast-advancing technology such as the Internet of Things (IoT), they are able to get a 360-degree view of customers and markets.
Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2020. Analysts presented their findings during Gartner IT Symposium/Xpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which is rapidly growing with a high degree of volatility reaching tipping points over the next five years. "People-centric smart spaces are the structure used to organize and evaluate the primary impact of the Gartner top strategic technology trends for 2020," said David Cearley, vice president and Gartner Fellow. "Putting people at the center of your technology strategy highlights one of the most important aspects of technology -- how it impacts customers, employees, business partners, society or other key constituencies. Arguably all actions of the organization can be attributed to how it impacts these individuals and groups either directly or indirectly. This is a people-centric approach."