Jun-19-2017


9 Experts Answer Your Top Data Science ;amp; Machine Learning Questions

#artificialintelligence

Internet of Things (IoT) and Machine-to-Machine (M2M) communication technologies offer remote device access to most if not all household appliances. However, each communication technology differs in the way they achieve remote device access. On the other hand, IoT relies on IP-based networks to interface device data to a middleware platform or cloud. Since the rise of cloud apps, sensors, and wireless networks, the global IoT and M2M market has gained a strong advantage.


Training the next generation of AI-driven chatbots - Enterprise IT Watch Blog

#artificialintelligence

The conversational intelligence of virtual digital assistants--aka chatbots--depends on the extent to which their statistical algorithms have been trained with the most relevant, high-quality data for the task at hand. Fortunately for chatbot developers, training resources are amply available for building and tuning the smarts of your AI-driven digital assistants 24 7. From a data science standpoint, training chatbots to emulate dialogue naturalism is an approach that goes beyond merely building rule-driven conversational scripts and a deep lexicon into the software. For a larger discussion of how you acquire and prepare training data for chatbots and other AI projects, check out my recent KDNuggets column here.


Top 10 Most Promising Toronto AI Startups

#artificialintelligence

As an effort to retain talent and make Toronto a global supplier of AI capability, the University of Toronto gathered a team of globally renowned researchers and founded the Vector Institute. Google and Uber are also investing in their own Artificial Intelligence hubs: Google Brain Toronto, which is the second Google Brain satellite office based in Canada, and Toronto division of Uber's Advanced Technologies Group. According to the team: "Meta is a tool that helps researchers understand what is happening globally in science and shows them where science is headed. Artificial Intelligence based Tenant Screening platform.


Fjord Voice UI Guide

#artificialintelligence

The progression of natural language processing, deep learning algorithms and significantly improved microphones means we are beginning to see interfaces that can understand and accommodate the rigid structure of human conversation. But these aren't dry, dull electronics. Companies are developing personalities for their virtual assistants, which have mostly arrived as a set of female characters – embodied in phones, home assistants and navigation systems – personifying AI via voice. However, it's important to note that applying this gendered identity has ramifications, especially because the resulting impulse is to then add a "her" to every product we can.


How Artificial Intelligence Is Taking Over The World [VIDEO]

#artificialintelligence

Giant tech companies, like Google, Apple, and Amazon, believe that the next economic wave will be driven by artificial intelligence. For example the UK only spent $53.5 billion on research and development while Russia spent $47.6 billion. Aside from spending truckloads on research and development, these tech companies are also acquiring companies in different industries. For example, Amazon has acquired giant retail Whole Foods which has scared a lot of grocery stores around the world.


3 ways AI is already impacting ecommerce

#artificialintelligence

Algorithmic technology and AI can be incredibly helpful tools to grow sales and optimize various aspects of ecommerce operation, from pricing to demand planning. AI solves this problem by repricing merchandise using complex learning algorithms that continuously assess the market dynamics and changes in competitive environment. They can identify key factors that affect the velocity of orders, and monitor the factors' impact to accurately model velocity and inventory requirements. Logistics used to be the core competency of retail; today, algorithms constantly crunch data, predict market trends, and respond to market changes in real time.


Machine Learning Could Help When Sentencing Criminals - If Used Right Articles Big Data

#artificialintelligence

Loomis appealed the sentence, arguing that neither he nor the judge could examine the formula for the risk assessment as it was a trade secret. This is a bit like saying a game of chess is fairer if neither player knows the rules. But the Wisconsin Supreme Court upheld Loomis's sentence, reasoning that the risk assessment was only one part of the rationale for the sentence. It also said it wanted to carry on allowing judges to consider the COMPAS score as one part of their sentencing rationale, even if they had no clue how it was calculated.


Becoming One Of Tomorrow's Unicorns In The World Of Artificial Intelligence - BI Insight - Business Intelligence

#artificialintelligence

Everyone is buzzing about the impact of AI on work, and many leaders feel insecure about what it will mean in terms of their own career development and roles. Deep learning, machine learning, automation and robotics are creating a seismic shift across organizations. "We're now living in an age where [deep learning is] going to be mandatory for people building sophisticated software applications," according to Frank Chen, a partner at venture capital firm Andreessen Horowitz, who was quoted in a recent Fortune article. Soon, he notes, people will demand, "'Where's your natural-language processing version?'


How to Build an Email Sentiment Analysis Bot: An NLP Tutorial

@machinelearnbot

There are many ways to accomplish this, but for the sake of simplicity, let's set up a simple web server and use Sendgrid's inbound parse hook to pipe emails to the server. So we have a sentiment analyzer program written in Java and an email bot written in Python. We have built an email bot that is able to receive emails, perform sentiment analysis, and determine if an email requires immediate attention. In this article, you learned how to build an email sentiment analysis bot using the Stanford NLP library.


Time Series Analysis with Generalized Additive Models

@machinelearnbot

These correlations between past and present values demonstrate temporal dependence, which forms the basis of a popular time series analysis technique called ARIMA (Autoregressive Integrated Moving Average). Therefore, google search trends for persimmons could well be modeled by adding a seasonal trend to an increasing growth trend, in what's called a generalized additive model (GAM). Because GAMs are based on functions rather than variables, they are not restricted by the linearity assumption in regression that requires predictor and outcome variables to move in a straight line. Figure 3 shows the resulting functions for overall growth, special events, and seasonal variations: We can see that overall page views of the DST Wikipedia article is generally decreasing across the years, possibly due to competing online sources explaining DST.