You may think you're a great judge of character, but how much do you really know about someone after a single meeting? Despite the importance of trust in any relationship, business or otherwise, it can be hard to ascertain, especially in a short period of time. So to help you make better, more informed decisions about people you may want to work with, tech company Trooly has launched its Instant Trust rating service, which claims to help "businesses verify, screen, and predict trustworthy relationships and interactions." It's all based on machine learning and the wealth of information available within your digital footprint, and Trooly says it hopes to fill the "trust gap" that results from the "speed of modern commerce and community." Available to both businesses and consumers, Trooly uses data that is generally publicly available to better understand an individual's -- or a company's -- personality and behavior traits.

With all the talk about A.I. what is missing is a definition of the I. Intelligence, I believe, is about self-awareness. We don't know what causes it. We can't isolate it for study. What we have in the works are systems to mimic the complexity of thought. To do that, we have to build in biases.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning.

Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype associations. We develop a computational framework for prediction tasks using quantitative microbiome profiles, including species-level relative abundances and presence of strain-specific markers.

I'm co-founder and CTO at Overleaf, a successful SaaS startup based in London. From August 2014 to December 2015, I manually tracked all of my work time, minute-by-minute, and analysed the data in R. Like most people who track their time, my goal was to improve my productivity. It gave me data to answer questions about whether I was spending too much or too little time on particular activities, for example user support or client projects. The data showed that my intuition on these questions was often wrong. There were also some less tangible benefits. It was reassuring on a Friday to have an answer to that usually rhetorical question, "where did this week go?" I feel like it also reduced context switching: if I stopped what I was doing to answer an chat message or email, I had to take the time to record it in my time tracker. I think this added friction was a win for overall productivity, perhaps paradoxically. This post documents the (simple) system I built to record my time, how I analysed the data, and the results.

LVMH-owned beauty retailer Sephora is doubling down on augmented reality and artificial intelligence sales tactics by enabling shoppers to virtually try on false lashes, watch tutorials using their own image and engage via a chatbot to trial and purchase lip color. With Sephora's customers virtually trying on more than 70 million lip shades using the Virtual Artist in-app functionality that was introduced earlier this year, false lashes are being added to expand the program. Users of the Sephora application can also now experience live step-by-step makeup application tutorials using their own uploaded images and augmented reality technology. "This is a significant expansion because we are adding elements that we know will help empower and educate our clients' purchase making decisions, and they're done in a way that is fun and engaging," said Bridget Dolan, vice president of Sephora Innovation Lab. "The new Live Tutorials especially are a game changer for our users," she said.

There's a new web app that uses advanced "deep learning" research to magically auto-colorize black-and-white photos. The app uses the Colorful Image Colorization algorithm that's being developed by a team at UC Berkeley led by PhD student Richard Zhang. We first reported on the technology back in March 2016, and now there's an online demo that anyone can try on any photograph. Simply paste a URL to a photo into the website and press the purple "Colorize It" button. After some processing and a short wait, the page displays a side-by-side comparison of the B&W and colorized photos that you can switch between.

Algorithms have become a ubiquitous and essential part of business operations. Uber uses algorithms to determine customer demand and set pricing accordingly, while Amazon and Netflix use algorithms to nudge their customers to purchase a product or stream a new video they might like. And these examples are just the tip of the iceberg. Interestingly, the use of these algorithms can not only increase an enterprise's internal efficiency, but often algorithmic software or machine learning algorithms can also be used to deepen consumer loyalty and trust. Viewers on Netflix trust algorithms to deliver content they'll enjoy, just as customers trust Amazon to offer only useful products for purchase.

When Liz Jurcik of Seattle felt a sharp pain in her side and back in January of 2013, she didn't think much about it. Jurcik, a 31-year-old human resources professional at Boeing, ran regularly and was in good shape. She thought it was probably a strained muscle from a workout. But the pain got worse, and by early February she could barely stand up. "I had the absolutely worst pain in my life," she said.

It's been called the'sexiest job of the 21st century', the'hottest job of the decade', and is the fastest-growing field in tech at the moment – the impact of Data Science in today's world cannot be overstated. As a discipline, data science involves the collection and study of data – both structured and unstructured – to gain insights and information that can be used by organizations to devise effective strategies. By collating data over a period of time, patterns can be identified that enable companies to find new market opportunities, enhance efficiency, reduce costs, and place themselves at a competitive advantage in their industry. Due to rapid technological advances, especially in areas like mobile advertising, social media, and website personalization, a massive amount of data is being generated on a daily basis. These data volumes have resulted in industries having to become data-savvy & adapt to the new landscape – or risk falling behind the competition.