Software


Kaia's motion-tracking workout app remembers which rep you're on

Engadget

Kaia Health caught our attention last year with an app that tracks your motion using your phone's camera in a bid to help you achieve perfect squat form, though we found it didn't quite hit the mark. Still, Kaia is elevating the concept with an updated version called Kaia Personal Trainer. It says the app will track your exercises and reps, create workout plans tailored to you and offer audio feedback in real time. It doesn't need any equipment other than an iPhone or iPad running iOS 12 (an Android version will arrive in the next few months), though you might still opt to use a fitness tracker. Once you get into position around seven feet away from your device, the app's AI uses a 16-point system to compare the way you move to optimal movement, looking at factors including the positions and angles of your limbs and joints.


How to use process data mining to improve DevOps

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DevOps combines the information technology and software development teams and increases communication and collaboration between the two groups. With DevOps, then, it becomes possible to adopt an approach to project management that allows for shorter times between new versions of apps or other products. As such, DevOps encourages continual evolution brought about by team or client needs and feedback. Something called process data mining -- analysing large amounts of data about processes and taking action accordingly -- could enhance DevOps practices in several ways. Data mining involves looking through collections of information and identifying patterns.


Salesforce Research: Knowledge graphs and machine learning to power Einstein

ZDNet

A super geeky topic, which could have super important repercussions in the real world. That description could very well fit anything from cold fusion to knowledge graphs, so a bit of unpacking is in order. If you're into science, chances are you know arXiv.org. In other words, it's where cutting edge research often appears first. Some months back, a publication from researchers from Salesforce appeared in arXiv, titled "Multi-Hop Knowledge Graph Reasoning with Reward Shaping."


Standardizing the Machine Learning Lifecycle

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Successfully building and deploying a machine-learning model can be difficult to do once. Enabling other data scientists (or yourself) to reproduce your pipeline, compare the results of different versions, track what's running where, and redeploy and rollback updated models, is much harder. In this eBook, we'll explore in greater depth what makes the ML lifecycle so challenging compared to the traditional software-development lifecycle, and share the Databricks approach to addressing these challenges. Key challenges faced by organizations when managing ML models throughout their lifecycle and how to overcome them. How MLflow, an open source framework unveiled by Databricks, can help address these challenges, specifically around experiment tracking, project reproducibility, and model deployment.


Era change brings Y2K-like computer dilemma to Japan

The Japan Times

Companies in Japan have a little more than six weeks to revamp their computer software to respond to the country's first era change in the digital age when a new Emperor is enthroned on May 1. The Ministry of Economy, Trade and Industry, or METI, is calling on companies to check where they use the Japanese calendar in their computer systems, modify necessary programs and carry out tests to detect potential problems. Eras are how Japan defines its history, so drivers' licenses, newspapers and a host of official documents use it to mark the years, with 2019 currently referred to as the "31st year of Heisei." The government will announce on April 1 the name of the new era, which will begin on May 1 in line with Crown Prince Naruhito's accession to the throne. According to the Information-Technology Promotion Agency, an independent administrative agency, systems using the current Heisei and other era names require program modifications.


Two thirds of Android antivirus apps don't work properly

Engadget

It can be wise to secure your Android phone with antivirus software, but which ones can you count on? You can rule out most of them, apparently. AV-Comparatives has tested 250 antivirus apps for Google's platform, and only 80 of them (just under one third) passed the site's basic standards -- that is, they detected more than 30 percent of malicious apps from 2018 and had zero false positives. Some of the apps that fell short would even flag themselves, according to the researchers. In some cases, the failure is a simple one: they're not really scanning app code.


What Is Artificial Intelligence And Its Impact On Accounting

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AI application include expert systems, machine vision, and speech recognition. ATI Solo Travel Packages 3. AI enables humans to make predictions based on patterns and data AI helps you with mundane tasks that you need to accomplish on a daily basis Since AI helps in intensive data you get more time to focus on complex tasks AI is a good fit for those industries that require an error- free approach like in accounting industry BENEFITS OF AI 4. Accounting profession has existed since the pre-historic times.During it's long journey, it has seen many transformations as a result of the changing world and the resources available. Accounting software exhibits superior performance in comparison to traditional method of pen-paper based accounting. This evolution of technology led to the digitization of the entire accounting process. These software uses AI capabilities to automate tasks such as data entry, account payable, reconciliation, and more.


r/MachineLearning - [P] Announcing the release of StellarGraph version 0.6.0 open source machine learning library for geometric deep learning.

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StellarGraph is a Python 3 library. The StellarGraph library implements several state-of-the-art algorithms for applying machine learning methods to discover patterns and answer questions using graph-structured data. Added GraphConvolution layer, GCN class for a stack of GraphConvolution layers, and FullBatchNodeGenerator class for feeding data into GCN (from version 0.5.0) We provide examples of using StellarGraph to solve such tasks using several real-world datasets.


Artificial Intelligence Creates a New Generation of Machine Learning

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Headquartered in Silicon Valley with offices in Shanghai and Hangzhou in China, R2.ai Inc. is growing rapidly. We sat down with the company's Founder and CEO to talk about AI that creates AI, and how automation is going to affect jobs in the future. Originally a chemist, Yiwen Huang, PhD, ended up working in Artificial Intelligence (AI) and Machine Learning (ML) 23 years ago when doing research using AI to identify molecular structures in chemicals. "I found the world of machine learning and computing so fascinating that I decided to switch into computer science. Since then, I've worked for 20 years in this space with data and data management, machine learning, and enterprise software development," he tells Interesting Engineering.


6 Ways to Utilize Machine Learning with Amazon Web Services and Talend

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The world has become a global village and interactions between people from different parts of the world are increasing day-by-day. Language was one of the major roadblocks in enabling free communication between people all over the world. The natural language processing services of Amazon like Amazon Comprehend and Amazon Translate help us to understand the dominant language any given text text, translate it and perform the sentiment analysis for the incoming textual information. Talend integrates these Amazon AI services to convert end to end applications like real-time sentimental analysis dashboard and multilingual customer care system. A quick example is the sentimental analysis dashboard as shown below. Talend is integrated with Amazon's Comprehend service to identify the customer sentiments in real time and to send the sentimental analysis details to downstream system dashboards. Another example which showcases Talend's integration capabilities with Amazon Comprehend and Amazon Translate services is the creation of a multi-lingual customer care system. The incoming messages are analyzed to understand the dominant language used in it and the text is translated from non-supported languages to supported language automatically. The two Talend KB articles I would recommend getting a detailed overview and hands-on experience about Talend's integration with above two Amazon services are as shown below.