This week on TechRepublic's Business Technology Weekly podcast, hosts Dan Patterson and Bill Detwiler discuss how swarm AI won the Kentucky derby, and the real world, practical impact of artificial intelligence. Headlines: Swarm AI predicts the 2016 Kentucky Derby Hope Reese Big news in the AI world this week! HOME NEWS Baidu to Shift to AI After Government Probe Baidu is planning to switch toward developing artificial intelligence after a government probe that affected its core business. BERLIN, GERMANY - SEPTEMBER 04: Visitors look at smartphones at the Lenovo stand at the 2015 IFA consumer electronics and appliances trade fair on September 4, 2015 in Berlin, Germany. The PC maker posted its first loss in six years in 2015.

This article is for software developers--particularly those coming from a Ruby or Java language background--who are facing their first machine learning implementation. I was recently given the assignment to create an RSS feed categorization subsystem for a client. The goal was to read dozens or even hundreds of RSS feeds and automatically categorize their many articles into one of dozens of predefined subject areas. The content, navigation, and search functionality of the client website would be driven by the results of this daily automated feed retrieval and categorization. The client suggested using machine learning, perhaps with Apache Mahout and Hadoop, as she had recently read articles about those technologies. Her development team and ours, however, are fluent in Ruby rather than Java technology. This article describes the technical journey, learning process, and ultimate implementation of a solution. My first question was, "what exactly is machine learning?"

Data Science Middle East (#DSME) in partnership with PAPIs are excited to announce this 2-day hands-on Machine Learning Workshop. Most Machine Learning courses are given from the perspective of a researcher/academic and focus on the theory and mathematics of the machine learning models. This workshop takes the perspective of learning by working on real machine learning problems using open source tools and platforms. We'll go all the way from data preparation to the integration of predictive models in applications and their deployment in production. "Just like development where you don't need to know a thing about computability or big-O notation to write code and ship useful and reliable software, you can work machine learning problems end-to-end without a background in statistics, probability and linear algebra."

It's hard to make a right turn down San Francisco's crowded streets these days without running into a story about bots, conversational commerce, or conversational commerce bots. Bots have reached kale levels of hype. But amidst the noise, there's something real going on here -- we just have to decipher it. You may have heard of conversational commerce -- it's a catchall term for a future of technology driven by messaging (and voice) interactions that transcend current communications modalities. It's a convenient moniker but also confusing because there isn't one trend to follow.

How to unlock "Not provided" keywords in analytics report? AFS Analytics provides an innovative solution with an algorithm using artificial intelligence and data provided by Google Search Console. Today, secure searches account for 95% of Google's traffic. The other search engines follow the same trend. Keywords or search terms don't show in the referrer's URL, the web analytics services, including Google Analytics, cannot collect this data for their clients and display the message "Keyword not provided".

I think we can all agree that this assumption is usually violated in practice (think temporal auto-correlation for instance, as observed when dealing with time series). My question is therefore: where exactly does the i.i.d. I'm asking this because I can think of many models (e.g. Actually the results usually stay the same, it is the inferences that one can draw that change (e.g. My guess is therefore that the i.i.d.

Machine learning can eliminate the need for a lot of programming. These first steps simplify the daunting process of applying it to your business. Even with advances in machine learning (ML) techniques, computing power and programming accessibility in the last five years, approaching an ML question can be a daunting process. How do you take reams of messy, unstructured or incomplete data and turn it into something of value? This is a question my company -- a machine learning solutions provider -- has been working on for years, and one that I've spent a lot of time thinking about.

Anomaly detection is a method used to detect outliers in a dataset and take some action. Example use cases can be detection of fraud in financial transactions, monitoring machines in a large server network, or finding faulty products in manufacturing. This blog post explains the fundamentals of this Machine Learning algorithm and applies the logic on the Spark framework, in order to allow for large scale data processing. Indeed, this was a real SMS I received from my bank after trying to deposit some money to an online payment system I had never used before. If Spark is new to you, it is an top-level Apache project for large-scale data processing.

NCODED are working on a Non-Profit project which aims to solve a serious problem which has affected the UK and part's of Europe for over 50 years. The problem is actually getting worse and spreading further into Europe. In our opinion the current "solution" is both in-practical and will be non-effective for the long term. NCODED is using AI based Machine Vision within an embedded device to offer a new long term solution, along with work that other's are conducting within the BioTech and other industries. We belive that we have a Unique product and therefore until a fully working prototype is finished, and all IP has been secured, then we will not be disclosing any further information about this project.

Alphabet's Google recently revealed Google Home, its response to Amazon's Echo. Like the Echo, the Home is an always-on speaker which listens for search queries, plays music, and controls connected home automation devices. Home is powered by Google Assistant, which can be useful for users who store lots of data on Google services like Gmail and Calendar. Responses to search queries are optimized for audio, which produces more concise results than text. It also synchronizes with any device using the Cast standard, so it can communicate with Chromecasts to play videos, music, or other content.