A new study has revealed that not all are doomed in a robot takeover. Researchers have discovered that people who are more intelligent and who showed an interest in the arts and sciences while in high school are less likely to fall victim to automation. The team concluded that these individuals are more likely to choose jobs that are more creative or have a higher degree of complexity that is not routine - two areas where robots fall short. Researchers at the University of Houston analyzed personality and background factors in order to determine whether a person will select jobs that are more likely to be automated in the future. The team used a dataset of 346,660 people from the American Institutes of Research.

In our recent post, we described our encounter with the Google Cloud Natural Language API. Let's see where we can find the desired information: With this analysis at our disposal, we make a few experiments to qualitatively compare the Google Natural Language API and the Stanford engine. Stanford CoreNLP also provides similar feature, allowing to perform entity linking of detected entities to their Wikipedia page. The Natural Language API returned "negative", "positive" and "positive" for these inputs.

The Microsoft Build 2017 conference starts tomorrow in Seattle! You can watch the conference live starting at 8:00AM Pacific Time and get the scoop. Only a few days to go before we reach zero bugs. Great progress has been made, but we are still 163 bugs away from zero. In February we took a camera crew to the Microsoft Alumni Network's big .NET 15th birthday bash and caught up with team members past and present.

Convolutional encoders for neural MT go as far back as (Kalchbrenner, Blunsom 2013) and convolutional encoders decoders in LM and MT appear first in (Kalchbrenner et al, 2016) and with pooling also in (Bradbury et al, 2016).

Document classification is a fundamental machine learning task. It is used for all kinds of applications, like filtering spam, routing support request to the right support rep, language detection, genre classification, sentiment analysis, and many more. To demonstrate text classification with scikit-learn, we're going to build a simple spam filter. While the filters in production for services like Gmail are vastly more sophisticated, the model we'll have by the end of this tutorial is effective, and surprisingly accurate. Spam filtering is kind of like the "Hello world" of document classification. However, something to be aware of is that you aren't limited to two classes.

A contest aimed at automating the detection of lung cancer shows how machine learning may be poised to overhaul medical imaging. The challenge offered $1 million in prizes for the algorithms that most accurately identified signs of lung cancer in low-dose computed tomography images. The winning algorithms won't necessarily be adopted by clinicians, but they could inspire algorithmic innovations that find their way into medical imaging.

BootstrapLabs, a leading Venture Capital firm focused on Applied AI, announces its annual Applied Artificial Intelligence Conference on May 11th in San Francisco. The Applied AI Conference 2016 was a great success, and this year, the BootstrapLabs community will gather again for one day of discovery, exchanging ideas, and networking with over 600 AI thought leaders, corporate executives, founders and investors. "The conference is a great opportunity to learn about practical applications and the commercialization of AI technologies across industries such as Transportation & Logistics, Internet of Things (IoT), Future of Work (FoW), Financial Technologies (FinTech), CyberSecurity, and Healthcare Technologies (HealthTech)," said Benjamin Levy, Co-Founder, BootstrapLabs. The world's leading AI investors and companies will be at this year's conference, including Intel, Facebook, LinkedIn, Google, Salesforce, Cylance, Ford, Visa, GE, Brighterion, Bloomberg, Capgemini, Intuit, Unity, Open.AI, UC Berkeley, Stanford, D-Wave Systems and many others. "We are at the epicenter of a new revolution, and for BootstrapLabs, working with the incredible community of Artificial Intelligence thought leaders and experts gives our portfolio companies an unfair advantage. The Venture Capital industry is evolving, with some of the most exciting opportunities emerging in the early stages. We created a way to effectively bring the value add of larger Venture Capital funds to early stage companies," said Nicolai Wadstrom, CEO and Founder of BootstrapLabs.

How does one become a hacker, neuroscientist, pilot, radio host and storyteller? "A random sequence of events with no planning, that you try to weave into a story in hindsight." Thus does neuroscientist and Kellogg School of Management Marketing professor Moran Cerf describe his life's path. It's an observation that many successful people will appreciate. Rather than strategy driving implementation, strategy often emerges from actions, experiments and unexpected events -- and if we're smart, a lot of awareness and learning along the way.

Chatbots could help trim business costs by more than $8 billion per year by 2022, according to new research which is anticipating a surge in automated customer service programs as companies move to embrace artificial intelligence (AI). Health care and banking, industries which manage large volumes of human interaction, are set to benefit most from the new technology, the study published Tuesday from analysis firm Juniper Research suggests. It predicts that between three-quarters and 90 percent of queries in these areas will be dealt with by "chatbots" within the next five years, resulting in cost savings of up to $0.70 per interaction. "We believe that health care and banking providers using bots can expect average time savings of just over 4 minutes per enquiry, equating to average cost savings in the range of $0.50-$0.70 per interaction," the research company said. Chatbots currently account for business cost savings of $20 million globally, but their ability to deal comprehensively with human problems is limited.

Broadly, there are 3 types of Machine Learning Algorithms.. 1. Supervised LearningHow it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression,Decision Tree, Random Forest, KNN, Logistic Regression etc. 2. Unsupervised LearningHow it works:In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.