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Meeting Topics for our 25th Anniversary Meeting on May 10th general meeting

PLUG - Mon, 2018/05/07 - 14:48

We'll have 3 presentations at this month's meeting, starting off which what we hope to be an infrastructure series, and what is more basic to our infrastructure than the power itself, so Barbara Cenalmor of SRP will be telling us about the nitty gritty of how SRP makes sure that you have all the power you need at the moment you need it.  Then der.hans will tell us about better protecting ourselves on the web with uMatrix.  Last, but not least Aaron Jones will show us how to better manage our digital fingerprints and to live safer in this world filled with social media .

Barbara Cenalmor: SRP Resource Planning
 

Description: 
It’s really easy to take electricity for granted.  You hit the ON button, and the computers instantly turn on.  However, there are a lot of things that go on behind the scenes to get that electricity to you, the main user. 

To keep up with the demand and makes sure everybody can turn on their computers, lights and appliances, utilities use resource plans. 

This presentation explains what resource plans are, why they are important and needed, what resource options are available to utilities, what the trade-offs are between different resources and what considerations are taken into account in the resource strategy.

Biography:
Barbara has 15+ years of experience working for regulatory agencies around Arizona in Air Quality issues. A few years ago she started working at Salt River Project (SRP), working to ensure that the company and its power plants were in compliance with all air quality regulations, and that the company's goals focused on reducing emissions. To get a better insight on how a large utility plans for future generation and more customers, she slightly changed careers and went to work for SRP's Resource Planning group where she participates in siting or acquiring new generation (all types).
In her spare time Barbara likes to hang out with her family, listen to her husband ramble about all Linux things (most of which she doesn't understand), reading, and traveling.


der.hans: uMatrix for web browser protection

Description:
uMatrix is a webextension security and privacy add on for Firefox.

Via a simple interface uMatrix allows personal rules for website cookies, JavaScript, CSS, pictures and video.

uMatrix uses and maintains per site allowlists and blocklists. For example, you can allow cookies, JavaScript, CSS and images for www.example.com and only CSS and images from cdb.example.com while blocking everything from spies.exampleduplicate.com.

Attendees will know what the web components are that uMatrix controls, how to use the uMatrix GUI interface and save rules.

Biography:
der.hans is a Free Software community veteran, presenter and author. He is the founder of the Free Software Stammtisch, BoF organizer for the Southern California Linux Expo (SCaLE) and chairman of the Phoenix Linux User Group (PLUG).

As a technology and entrepreneurial veteran, roles have included director of engineering, engineering manager, IS manager, system administrator, community college instructor, developer and DBA.

He presents regularly at large community-led conferences (SCaLE, SeaGL, LibrePlanet, LFNW) and many local groups.


Aaron Jones: Social Media Safety


Description:
How to manage your digital foot print to enhance security while also bringing awareness to some of the issues that social media can exacerbate.

About Aaron:
Aaron is an experienced Linux user with several years of teaching experience. He works in the industry as a software developer while also providing consultancy on cyber security related topics. His discussions are AZ Post certified for training credit for law enforcement and he prides himself on providing quality educational material that is relevant and topical. He has a Masters Degree in Intelligence Analysis with a focus in Cyber Security, is a life long learner, and prides himself on staying up to date with the ever changing field of cyber security.

3 strategies to change user behavior

O'Reilly Radar - Fri, 2018/05/04 - 10:05

There’s no (ethical) way to avoid having the user consciously choose whether or not to act, but products can change the nature of that choice.

A Decision or a Reaction: Three Strategies to Change Behavior

How can a product help its users pass all the way through the Action Funnel and actually take action? There are three big strategies that a company can choose from, to change behavior and help users take action. Two of them come straight from the research literature and from the difference between deliberative and intuitive actions. The third is less obvious, but immensely powerful—it’s called cheating.

The conscious, deliberative route is the one that most of us are familiar with already—it entails encouraging people to take action, and them consciously deciding to do it. Users have to pass through all five stages of the Action Funnel, and often spend considerable time on the conscious evaluation stage.

Continue reading 3 strategies to change user behavior.

Categories: Technology

Four short links: 4 May 2018

O'Reilly Radar - Fri, 2018/05/04 - 04:05

Data Science Ethics, Networks and Markets, Chinese Sesame, and Refactoring Into Microservices

  1. Data Science Ethics Syllabus -- from Kant to A/B testing, it looks very comprehensive.
  2. Networks and the Next Economy (Tim O'Reilly) -- In 2018, we still think that we can organize our companies in old ways but do new things.
  3. China's Social Credit Mandatory in 2020 (New Republic) -- “Good” behavior is equally subjective. Sesame Credit automatically upgrades customers who purchase curtains or diapers, for example—items which suggest a certain middle-class stability. This is partly because Sesame “is designed to incentivize behaviors that drive profits for Alibaba,” explains Mark Natkin, managing director of Beijing-based Marbridge Consulting. Capitalism meets authoritarianism. "[I]nformation often includes errors like mistaken user identity, and some lenders deliberately misrepresent user information...they will actually put their favorite customers on their blacklist shared with other lenders, so that other platforms will reject the customer, allowing the original lender to have exclusive access.”
  4. Evolving Away from Entities -- Entity services are what you get when you only think about the data and not how you are going to use it. Really good case study of refactoring into microservices, part of a larger tutorial.

Continue reading Four short links: 4 May 2018.

Categories: Technology

Learn how to build better apps, systems, and teams at the Fluent and Velocity Conferences

O'Reilly Radar - Thu, 2018/05/03 - 09:40

The O’Reilly Fluent and Velocity conferences are teaming up to create a unique learning opportunity that addresses the full web experience.

This June in San Jose, CA, the O’Reilly Fluent and Velocity conferences are teaming up to create a unique learning opportunity that addresses the full web experience—from development and performance to operations and resilience. Whether you work on the systems that keep modern businesses running or the websites that attract and retain customers (or both!), you'll get the most critical, up–to–the–minute knowledge from O’Reilly’s network of experts.

With the Fluent + Velocity Super Bronze add-on pass, you will have access to both conferences—all keynotes, all sessions, all events, and unlimited networking. Regardless of your title—from SRE to JavaScript developer—you will have maximum flexibility to attend any session at either conference as you navigate the tech that you need to do your job. It’s not just about the latest framework or how fast your page loads, but how together we can build a better web and more resilient systems focused on accessibility, security, and performance.

To be an effective full-stack technologist, you’ll want to attend both Fluent and Velocity. Here are two possible “choose your own adventure” pathways for navigating both conferences: the first will appeal to front end web developers and the second is geared toward engineering team leads.

A Fluent / Velocity path for a senior front end web developer A Fluent / Velocity path for an engineering team lead

We look forward to seeing you in San Jose, CA, at Fluent and Velocity this June. You’ll leave with new ideas, proven best practices, and an expanded network of peers and innovators to help you tackle your next project and advance your career.

Continue reading Learn how to build better apps, systems, and teams at the Fluent and Velocity Conferences.

Categories: Technology

Complementary learning for AI-based predictive quality and maintenance

O'Reilly Radar - Thu, 2018/05/03 - 04:05

Using machine learning, deep learning, and cognitive computing in concert can help enterprises gain competitive edges.

The cost of unplanned downtime due to equipment failure across all industries is huge. Back in 2014, Aberdeen estimated the cost to businesses to be $164,000 an hour, on average. By 2016, that figure had skyrocketed by 60% to $260,000 an hour. Monetary losses aside, the impacts of unplanned outages for a business can be devastating—according to a 2017 study by Vanson-Bourne, 46% of businesses said they were not able to deliver services to customers and 37% said they lost production time on a critical piece of equipment or other asset.

Take the airline industry as an example. Flight delays due to mechanical problems are common. Sometimes the issue gets fixed in timely manner. But sometimes it takes hours, and, in worst-case scenarios, flights are canceled. The costs to the passengers can be high—missed connections, missed meetings, missed vacations, and more. The cost to the airline is immense: lost revenue, lost customer satisfaction, and lost brand value in addition to all the money—labor and parts—it takes to fix the problem. Consider the overtime, the shipping costs of rushed parts, and all the lost revenue, and you see how it adds up. In fact, unplanned maintenance of equipment costs three to nine times more than planned maintenance.

Clearly, a lot is at stake. Which is why many industries that focus on product quality and maintenance are turning to next-generation artificial intelligence (AI)-based predictive quality and maintenance (PQM) solutions.

In this article, I’ll go over what PQM solutions are and explain how they are being made dramatically more effective by leveraging AI. I’ll also explore complementary learning and look at how today’s leading AI-based PQM solutions incorporate it. Finally, I’ll give a real-world example of AI-based PQM in action.

What are PQM solutions?

PQM solutions, which harness data gathered by both the Internet of Things (IoT) and data from traditional legacy systems, focus on detecting and addressing quality and maintenance issues before they turn into serious problems—for example, problems that can cause unplanned downtime.

Unplanned downtime is a major cost driver in any industry that must maintain large inventories of capital assets. For an airline, for example, delaying flights due to unplanned maintenance can cost thousands of dollars each minute. Unplanned shutdowns of oil platforms can run into the millions of dollars. And in manufacturing plants, the costs of disruptions go directly to the bottom line. It is the goal of every organization to eliminate unplanned downtime in favor of planned maintenance. PQM solutions can help with planned maintenance, too, by shortening maintenance operations windows.

PQM solutions help businesses prioritize how to best allocate scarce resources, resolve issues, and plan ahead, thereby keeping—or achieving—a competitive edge. Recent research suggests that the market for PQM solutions will grow from $2.2 billion in 2017 to $10.9 billion by 2022, a 39% annual growth rate. And, of the top 10 uses predicted for AI in 2025, PQM comes in fourth place.

Artificial intelligence enters the picture

Traditionally, PQM solutions crunched numbers and came up with average statistics to predict when quality corrections or maintenance were required.

Now, with the availability of much bigger data sets and the latest developments in AI, it’s a whole new game. AI-based PQM solutions differ from traditional predictive quality and maintenance solutions because they analyze the actual condition of a product rather than just using average or expected statistics to predict when quality corrections or maintenance might be required.

AI-based PQM solutions use several technologies in concert, including machine learning, deep learning, and cognitive computing:

  • Machine learning: focuses on real-world problems by processing—and learning from—large amounts of data
  • Deep learning: uses neural networks to be able to sort through nearly unimaginable volumes of data to come to conclusions
  • Cognitive computing: a subset of AI that attempts to mimic the way humans think. And a very important subset of cognitive computing is associative-memory learning and reasoning, which mimics the way humans learn, remember, and reason by making associations
Complementary learning: The future of PQM solutions

Because each type of AI is good at solving different problems, applying them simultaneously is the key to success. Complementary learning in the context of PQM applications involves combining all these types of AI—machine learning, deep learning, and cognitive computing—to get insight into quality and maintenance issues.

In effect, a PQM solution that embraces complementary learning first uses machine learning and deep learning to answer the question, “What is the problem?” Then, cognitive computing answers such questions as: "Have I ever seen this before? What type of a problem is it? Who knows how to fix this? What caused this problem? And will it happen again?”

AI-based PQM solution in action

Accenture is a global professional services company that provides a broad range of services and solutions in business strategy, consulting, digital, technology, and operations. One of its many services focuses on software testing.

This is a rich area for AI-based PQM solutions for a couple of reasons. First, many organizations have not changed their software-testing techniques in the past 20 years. Thus, software testing often involves a significant amount of manual labor. For some mission-critical systems, test engineers spend up to 90% of their time managing test cases and documenting them rather than actually testing.

Second, the overall quality of software has traditionally been treated as just one isolated component in the software development life cycle. For example, QA used to be considered a step outside of the product development cycle—involve QA when everything is done. But testing can no longer be performed in silos outside of the development process. Now, with devices and connections getting more complicated, QA needs to be integrated as part of the software’s design and production process. If QA sees a trend that a certain part or certain function causes frequent issues, they can report such a trend to the product team to find the root cause and fix.

Accenture’s platform uses Intel Saffron AI in its software testing solution to accelerate automation, spot trends, manage risks, and continuously respond to customer feedback. According to Accenture, this approach has reduced time-to-market and cost of testing by more than 20%, and can also save 30% to 50% of time spent over-engineering.

“Testing is transforming into quality engineering, where artificial intelligence-driven analytics is at the core of driving productivity and agility,” says Kishore Durg, senior managing director and global testing lead for Accenture. “That is what the Accenture Touchless Testing Platform is designed to do.”

Looking ahead

AI, specifically in the context of complementary learning, will play a huge role in future PQM solutions that will make up for the fact that human resources simply cannot scale at the same rate as data. AI promises to extend people’s capabilities, and with a growing number of out-of-the-box vertical solutions becoming available, it’s easy to see why AI-based PQM solutions are projected to grow at such a rapid pace in the not-too-distant future.

This post is a collaboration between O'Reilly and Intel Saffron. See our statement of editorial independence.

Continue reading Complementary learning for AI-based predictive quality and maintenance.

Categories: Technology

How to build analytic products in an age when data privacy has become critical

O'Reilly Radar - Thu, 2018/05/03 - 04:00

Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products.

In this post, I share slides and notes from a talk I gave in March 2018 at the Strata Data Conference in California, offering suggestions for how companies may want to build analytic products in an age when data privacy has become critical. A lot has changed since I gave this presentation: numerous articles have been written about Facebook’s privacy policies, its CEO testified twice before the U.S. Congress, and I deactivated my mostly dormant Facebook account. The end result being that there’s even a more heightened awareness around data privacy, and people are acknowledging that problems go beyond a few companies or a few people.

Let me start by listing a few observations regarding data privacy:

Which brings me to the main topic of this presentation: how do we build analytic services and products in an age when data privacy has emerged as an important issue? Architecting and building data platforms is central to what many of us do. We have long recognized that data security and data privacy are required features for our data platforms, but how do we “lock down” analytics?

Once we have data securely in place, we proceed to utilize it in two main ways: (1) to make better decisions (BI) and (2) to enable some form of automation (ML). It turns out there are some new tools for building analytic products that preserve privacy. Let me give a quick overview of a few things you may want to try today.

Business intelligence and analytics

For most companies, BI means a SQL database. Can you run SQL queries while protecting privacy? There are already systems for doing BI on sensitive data using hardware enclaves, and there are some initial systems that let you query or work with encrypted data (a friend recently showed me HElib, an open source, fast implementation of homomorphic encryption).

Let me describe a recent collaboration between Uber and UC Berkeley’s RISE Lab.

Their joint analysis of millions of queries executed at Uber led to a system that lets analysts submit queries and get results that adhere to state-of-the-art differential privacy (a formal guarantee that provides robust privacy assurances). As I mentioned above, privacy violations can involve people who have been granted access to data. What this new Uber/RISE Lab system implies is that analysts can be granted access to a database to do their standard SQL-based analysis, while data privacy is preserved. Their system is open source and can be used with any SQL database, and it is being used in a pilot deployment within Uber (see the paper and code).

This takes care of BI for reports that rely on SQL databases. But can one build a privacy-preserving BI system that gathers real-time data from millions of users? The answer is “yes”: recent announcements from Apple and Google detail analytic tools designed to help them understand how users interact with devices. For example, Apple and Google analysts can run queries to help them gather aggregate typing statistics and browsing behavior.

Apple described their system in a detailed blog post:

Our system is designed to be opt-in and transparent. No data is recorded or transmitted before the user explicitly chooses to report usage information. Data is privatized on the user’s device using event-level differential privacy in the local model where an event might be, for example, a user typing an emoji. Additionally, we restrict the number of transmitted privatized events per use case. The transmission to the server occurs over an encrypted channel once per day, with no device identifiers. The records arrive on a restricted-access server where IP identifiers are immediately discarded, and any association between multiple records is also discarded. At this point, we cannot distinguish, for example, if an emoji record and a Safari web domain record came from the same user. The records are processed to compute statistics. These aggregate statistics are then shared internally with the relevant teams at Apple.

Other companies like Microsoft are developing similar systems involving other smart devices.

Machine learning

For machine learning, let me focus on recent work involving deep learning (currently the hottest ML method). In 2015, researchers at the University of Texas and Cornell University showed that one can “design, implement, and evaluate a practical system that enables multiple parties to jointly learn an accurate neural network model for a given objective without sharing their input data sets.” One application could be medical institutions wanting to build and learn a more accurate, joint model, without sharing data with people outside their respective organizations.

In 2016, Google took this “shared model” concept and scaled it to edge devices! They use it for products such as On-Device Smart Reply and their Mobile Vision API. This new system, which they dubbed "Federated Learning," is able to leave training data distributed on the mobile devices, while learning a shared model by aggregating locally computed updates:

The two previous examples involve learning a shared (single) model, without sharing data. There might be instances where you want a highly personalized model, or you might have natural (demographic/usage) clusters of users that would benefit from more specifically tuned models. These scenarios were the focus of recent work by researchers at Stanford, CMU, and USC: they used ideas from multi-task learning to train personalized deep learning models. In multi-task learning, the goal is to consider fitting separate but related models simultaneously.

Closing thoughts

My main message is that privacy-preserving analytics is very much possible and something you should consider today—both for BI and machine learning. It is not only the right thing to do for your users, with GDPR about to come online, privacy becomes necessary to incorporate in your data products:

At its core, privacy by design calls for the inclusion of data protection from the onset of the designing of systems, rather than an addition.

One last thing: the two technology trends I’m following very closely are automation (AI) and decentralization (blockchain, crypto, and more). There are people actively working on rebuilding key services—identity management, data storage, payments, data exchanges, social media—and moving them away from centralized systems. I believe that the data science and big data communities are well-positioned to contribute to both automation and decentralization. Our community has spent years working on productionizing important building blocks—machine learning and distributed systems—that will remain at the core of future platforms.

Related content:

Continue reading How to build analytic products in an age when data privacy has become critical.

Categories: Technology

Four short links: 3 May 2018

O'Reilly Radar - Thu, 2018/05/03 - 03:10

MySQL Migrations, 3D Faces, Economics of Privacy, and Peter Principle

  1. gh-ost -- GitHub's Online Schema Migrations for MySQL. Triggerless migrations.
  2. Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network -- In this paper, we propose an end-to-end method called "position map regression network" (PRN) to jointly predict dense alignment and reconstruct 3D face shapes. With source code. Our network is very light-weighted and spends only 9.8ms to process an image, which is much faster than previous works.
  3. The Economics of Privacy -- This page provides links to resources on the economics of privacy, financial privacy, and the economics of anonymity: papers, people, related conferences, and other links.
  4. The Cost of the Peter Principle -- The data suggest that high-performing sales representatives are indeed more likely than other workers to be promoted into management. The doubling of sales credits increases the probability that a salesperson will be promoted by 14.3% relative to the base probability of promotion. The researchers also found that pre-promotion performance data could negatively predict a new manager's value after promotion: a doubling of the new manager's pre-promotion sales was associated with a 7.5% decline in the sales performance of each new manager's subordinates.

Continue reading Four short links: 3 May 2018.

Categories: Technology

The physics of AI

O'Reilly Radar - Wed, 2018/05/02 - 13:00

Dario Gil explores state-of-the-art computing for AI as it exists today as well as an innovation that will lead us into the decades to come: quantum computing for AI.

Continue reading The physics of AI.

Categories: Technology

Serving billions of personalized news feeds with AI

O'Reilly Radar - Wed, 2018/05/02 - 13:00

Meihong Wang explains how Facebook thinks about personalization and how the company uses machine learning to provide personalized experiences.

Continue reading Serving billions of personalized news feeds with AI.

Categories: Technology

Using machine learning in workload automation

O'Reilly Radar - Wed, 2018/05/02 - 13:00

Abhijit Deshpande explains how to use machine learning to identify root causes of problems in minutes instead of hours.

Continue reading Using machine learning in workload automation .

Categories: Technology

Neural interfaces: Connecting humans and artificial intelligence

O'Reilly Radar - Wed, 2018/05/02 - 13:00

Thomas Reardon offers an overview of brain-machine interface (BMI) technology and shares CTRL-Labs’s transformative and noninvasive neural interface approach.

Continue reading Neural interfaces: Connecting humans and artificial intelligence.

Categories: Technology

Four short links: 2 May 2018

O'Reilly Radar - Wed, 2018/05/02 - 04:25

FPGA, Comics, Charts, and Learning to Code

  1. Retrospective on 10 Years of FPGA (IEEE) -- Xilinx introduced the first field programmable gate arrays (FPGAs) in 1984, though they were not called FPGAs until Actel popularized the term around 1988. Over the ensuing 30 years, the device we call an FPGA increased in capacity by more than a factor of 10,000 and increased in speed by a factor of 100. Cost and energy consumption per unit function decreased by more than a factor of 1,000.
  2. A Survey of Comics Research in Computer Science -- A large part of previous work is focusing on the low-level image analysis by using handcrafted features and knowledge-driven approaches. Recent research focuses more on deep learning and high-level image understanding. Still, many applications have been done for natural image, and the research about artworks and comics get more attention only very recently. A lot of unexplored fields remain, especially content generation and augmentation.
  3. TUI Chart -- easy way to draw various and essential charts on your web service.
  4. TIC computer -- a faux 8-bit system for learning to code, reminiscent of the PICO-8. This one is open source.

Continue reading Four short links: 2 May 2018.

Categories: Technology

200+ new live online trainings just launched on O'Reilly's learning platform

O'Reilly Radar - Wed, 2018/05/02 - 03:00

Get hands-on training in artificial intelligence, Python, Java, blockchain, security, and many other topics.

Develop and refine your skills with 200+ new live online trainings we opened up for May, June, and July on our learning platform.

Space is limited and these trainings often fill up.

Intermediate Git, May 3

Leading Change that Sticks, May 3

A Dive into Serverless Computing with Google Cloud Function, May 4

Artificial Intelligence: An Overview for Executives, May 4

How Agile and Traditional Teams Work Together, May 4

What’s New in the PMBOK® Guide, Sixth Edition?, May 7

Machine Learning with R, May 7-8

Getting Started with Python 3, May 8-9

Rich Documents with R Markdown, May 9

Introduction to Pandas, May 10-11

How to Give Great Presentations, May 11

Scala Core Programming: Sealed Traits, Collections, and Functions, May 11

Working with Engineers as a Non-Technical Product Manager, May 11

Digital Marketing, May 14

Test-Driven Development and Refactoring, May 14

Getting Started with Python 3, May 14-15

Python Beyond the Basics: Scaling Python with Generators, May 15

Mastering Data Science at Enterprise Scale, May 15-16

Getting Started with React.js, May 16

Hands-on Machine Learning with Python: Classification and Regression, May 16

Pythonic Object-Oriented Programming, May 16

Hands-on Machine Learning with Python: Clustering, Dimension Reduction, and Time Series Analysis, May 17

Java Testing with Mockito and the Hamcrest Matchers, May 17

Mastering Python’s Pytest, May 17

Python Beyond the Basics: Pythonic Design Patterns, May 17

React: Beyond the Basics, May 17

Artificial Intelligence Real-World Applications For Business, May 18

AWS: Critical Security Solutions for Developers, May 18

Test-Driven Development In Python, May 18

Beyond Python Scripts: Logging, Modules, And Dependency Management, May 21

Getting Started with Machine Learning, May 21

Jenkins 2: Up and Running, May 21

Design Patterns in Java, May 21-22

Linux Filesystem Administration, May 21-22

Beyond Python Scripts: Exceptions, Error Handling and Command-Line Interfaces, May 22

Advanced React, May 23

Python: The Next Level, May 23-24

React.js and Modern JavaScript Fundamentals, May 24-25

Pythonic Object-Oriented Programming, May 29

Beginning Data Analysis with Python and Jupyter, May 29-30

Getting Up to Speed with Vue.js, May 30

Intermediate Jupyter: Using JupyterLab, JupyterHub, Widgets, and Binder, May 30

Test-Driven Development In Python, May 30

Functional Programming in Java 8, May 30-31

High-Performance Machine Learning and Data Analysis with Julia, May 31

Apache Hadoop, Spark, and Big Data Foundations, June 1

Design Thinking: Practice and Measurement Essentials, June 1

Getting Started with Python’s Pytest, June 1

Introduction to Delegation Skills, June 1

Introduction to Digital Forensics and Incident Response (DFIR), June 1

Introduction to Time Management Skills, June 1

Cybersecurity Blue Teams vs. Red Teams, June 4

Deep Learning for Natural Language Processing (NLP), June 4

Getting Started with Pandas, June 4

High-Performance Machine Learning and Data Analysis with Julia, June 4

Node.js Beyond the Basics, June 4

Scala Fundamentals: From Core Concepts to Real Code in 5 Hours, June 4

Basic Android Development, June 4-5

Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook, June 4-5

Get Started With Kotlin, June 4-5

Getting Started with Python 3, June 4-5

Getting Started with Java: From Core Concepts to Real Code in 4 Hours, June 5

Getting Started with Node.js, June 5

Having Difficult Conversations, June 5

Implementing and Troubleshooting TCP/IP, June 5

Mastering Pandas, June 5

AWS Certified SysOps Administrator (Associate) Crash Course, June 5-6

From Monolith to Microservices, June 5-6

Deep Reinforcement Learning, June 6

SAFe 4.5 (Scaled Agile Framework) Foundations, June 6

AWS CloudFormation Deep Dive, June 6-7

Cloud Native Architecture Patterns, June 6-7

Docker: Up and Running, June 6-7

Introduction to Kubernetes, June 6-7

SQL Fundamentals for Data, June 6-7

Java 8 Generics in 3 Hours, June 7

Mastering Python’s Pytest, June 7

Amazon Web Services: Architect Associate Certification - AWS Core Architecture Concepts, June 7-8

Cyber Security Fundamentals, June 7-8

Functional Design for Java 8, June 7-8

A Dive into Serverless Computing with Google Cloud Functions, June 8

First Steps with Angular, June 8

How the Internet Really Works, June 8

Introduction to Leadership Skills, June 8

Introduction to Strategic Thinking Skills, June 8

AWS Security Fundamentals, June 11

Beginning Python Web Development with Django, June 11

Introducing Blockchain, June 11

Introduction to Encryption, June 11

Your First 30 Days as a Manager, June 11

Getting Started with Spring and Spring Boot, June 11-12

Microservices Architecture and Design, June 11-12

Scala: Beyond the Basics, June 11-12

Scalable Web Development with Angular, June 11-12

AWS: Architect Associate Certification - AWS Core Architecture Concepts, June 11 & 13

OCA Java SE 8 Programmer Certification Crash Course, June 11-13

Emotional Intelligence for Managers, June 12

Getting Started with Machine Learning, June 12

Getting Started with Product Management, June 12

Negotiation Fundamentals, June 12

Security Testing with Kali Linux, June 12

Working with Engineers as a Non-Technical Product Manager, June 12

AWS Certified Solutions Architect Associate Crash Course, June 12-13

Mastering Data Science at Enterprise Scale, June 12-13

Beginner’s Guide to Creating Prototypes in Sketch, June 13

CompTIA Security+ SY0-501 Certification Practice Questions & Exam Strategies, June 13

Network Security Fundamentals, June 13

Reactive Python for Data Science, June 13

Testing and Validating Product Ideas with Lean, June 13

Architecture By Example, June 13-14

Blockchain Applications and Smart Contracts, June 14

Practicing Agile Data Science, June 14

Python Beyond the Basics: Scaling Python with Generators, June 14

Reactive Spring and Spring Boot, June 14

Amazon Web Services: AWS Managed Services, June 14-15

Design Patterns Boot Camp, June 14-15

Hands-On Introduction to Apache Hadoop and Spark Programming, June 14-15

Introduction to Pandas, June 14-15

Advanced Agile: Scaling in the Enterprise, June 15

AWS: Critical Security Solutions for Developers, June 15

Learn the Basics of Scala in 3 hours, June 15

Linux Performance Optimization, June 15

Linux Under the Hood, June 15

Pythonic Object-Oriented Programming, June 15

Software Architecture for Developers, June 15

Building Data APIs with GraphQL, June 18

Designing Bots and Conversational Apps for Work, June 18

Hands-on Machine Learning with Python: Classification and Regression, June 18

Introduction to Lean, June 18

Python Beyond the Basics: Pythonic Design Patterns, June 18

Beginning R Programming, June 18-19

Design Patterns Boot Camp, June 18-19

Getting Started with Python 3, June 18-19

Iot Fundamentals, June 18-19

Red Hat Certified Engineer (RHCE) Crash Course, June 18-21

Creating a Custom Skill for Amazon Alexa, June 19

Hands-On Machine Learning with Python: Clustering, Dimension Reduction, and Time Series Analysis, June 19

Next-generation Java testing with JUnit 5, June 19

Test-Driven Development In Python, June 19

Usability Testing 101, June 19

Deploying Container-Based Microservices on AWS, June 19-20

Kafka Fundamentals, June 19-20

Amazon Web Services (AWS) Security Crash Course, June 20

Democratizing Machine Learning, June 20

Introduction to Analytics for Product Managers, June 20

Java Testing with Mockito and the Hamcrest Matchers, June 20

Object-Oriented GUI Design In Java, June 20

Understanding Business Strategy, June 20

Docker: Beyond the Basics (CI/CD), June 20-21

Introduction to Tensorflow, June 20-21

Building a Cloud Roadmap, June 21

Building and Managing Kubernetes Applications, June 21

Crafting a Great Research Story, June 21

Leadership Communication Skills for Managers, June 21

Amazon Web Services: AWS Design Fundamentals, June 21-22

Google Cloud Platform Professional Cloud Architect Certification, June 21-22

Medium R Programming, June 21-22

The DevOps Toolkit, June 21-22

Acing the CCNA Exam, June 22

Design Patterns In Java GUI Development, June 22

Getting Up to Speed with Vue.js, June 22

Leading Change that Sticks, June 22

Scala Core Programming: Methods, Classes, and Traits, June 22

Bash Shell Scripting in 3 Hours, June 25

Digging Deeper with PostgreSQL, June 25

Getting Started with OpenStack, June 25

AWS: Architect Associate Certification - AWS Core Architecture Concepts, June 25 & 29

High Performance TensorFlow in Production: Hands on with GPUs and Kubernetes, June 25-26

IPv4 Subnetting, June 25-26

Microservices Architecture and Design, June 25-26

Python: The Next Level, June 25-26

Cyber Security Defense, June 26

Digging Deeper with PostgreSQL: Automation with Scripts and Functions, and Postgres Inheritance, June 26

How Agile and Traditional Teams Work Together, June 26

Introduction to Project Management, June 26

Modern JavaScript, June 26

Rethinking REST, June 26

Threat Hunting in Practice, June 26

From Monolith to Microservices, June 26-27

Advanced SQL for Data Analysis, June 27

Managing Team Conflict, June 27

Network Security Testing with Kali Linux, June 27

Node.js Advanced Topics, June 27

Architecture By Example , June 27-28

CISSP Crash Course, June 27-28

From Developer to Software Architect, June 27-28

Getting Started with Go, June 27-28

Introduction to Ethical Hacking & Penetration Testing, June 27-28

Building Distributed Pipelines for Data Science, June 27-29

Advanced React.JS, June 28

Amazon Web Services (AWS): Up and Running, June 28

How to Give Great Presentations, June 28

Unit Testing with the Spock Framework, June 28

Creating Serverless APIs with AWS Lambda and API Gateway, June 29

Design Fundamentals for Non-Designers, June 29

Jenkins 2: Up and Running, June 29

Scala Programming Fundamentals: Sealed Traits, Collections, and Functions, June 29

Troubleshooting Agile, June 29

Visualizing Software Architecture with the C4 Model, June 29

What’s New in the PMBOK® Guide, Sixth Edition?, June 29

Implementing Evolutionary Architectures, July 10-11

From Monolith to Microservices, July 11-12

From Monolith to Microservices, July 23-24

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Categories: Technology

Highlights from the Artificial Intelligence Conference in New York 2018

O'Reilly Radar - Tue, 2018/05/01 - 13:00

Watch highlights covering artificial intelligence, machine learning, automation, and more. From the Artificial Intelligence Conference in New York 2018.

Experts from across the AI world came together in New York for the Artificial Intelligence Conference. Below you'll find links to highlights from the event.

Increasing business results through AI in the entertainment industry

Fiaz Mohammed and Justin Herz discuss how artificial intelligence can improve content discovery and monetization

Bringing AI into the wild

Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that help map wildlife and scale conservation efforts around the world.

Understanding automation

Ben Lorica and Roger Chen discuss the state of deep learning, reinforcement learning, and automation.

Autonomy and human-AI interaction

Manuela Veloso looks at the role humans can play in autonomy-based AI interactions and the underlying challenges to AI.

Using machine learning, the IoT, drones, and networking to reduce world hunger

Food production needs to double by 2050 to feed the world’s growing population. Jennifer Marsman details a solution that uses sensors in the soil, aerial imagery from drones, and machine learning.

Intel AI for the enterprise ecosystem

Fiaz Mohamed explains how Intel AI solves today’s business problems.

Fireside chat with Peter Norvig and Kavya Kopparapu

Kavya Kopparapu shares her inspiration for starting GirlsComputingLeague.

Rapid AI experimentation and innovation on Amazon Web Services

Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go.

The frontiers of machine learning and AI

Zoubin Ghahramani discusses recent advances in artificial intelligence, highlighting research in deep learning, probabilistic programming, Bayesian optimization, and AI for data science.

Machine learning just ate algorithms in one large bite

Tim Kraska explains the basic intuition behind learned data structures and outlines the potential consequences of this technology.

--> Using machine learning in workload automation

Abhijit Deshpande explains how to use machine learning to identify root causes of problems in minutes instead of hours.

AI4ALL: AI will change the world, but who will change AI?

Olga Russakovsky explains how her organization, AI4ALL, aims to increase diversity and inclusion in AI development and research.

--> The physics of AI

Dario Gil explores state-of-the-art computing for AI as it exists today as well as an innovation that will lead us into the decades to come: quantum computing for AI.

Serving billions of personalized news feeds with AI

Meihong Wang explains how Facebook thinks about personalization and how the company uses machine learning to provide personalized experiences.

Neural interfaces: Connecting humans and artificial intelligence

Thomas Reardon offers an overview of brain-machine interface (BMI) technology and shares CTRL-Labs’s transformative and noninvasive neural interface approach.

WTT: What the tensor?

Ron Bodkin explains what a tensor is and why you should care.

Hybrid bio-opto-electronics for AI

George Church discusses the IARPA MICrONS project, which aims to revolutionize machine learning by reverse-engineering the algorithms of the brain.

-->

Continue reading Highlights from the Artificial Intelligence Conference in New York 2018.

Categories: Technology

Rapid AI experimentation and innovation on Amazon Web Services

O'Reilly Radar - Tue, 2018/05/01 - 13:00

Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go.

Continue reading Rapid AI experimentation and innovation on Amazon Web Services.

Categories: Technology

Bringing AI into the wild

O'Reilly Radar - Tue, 2018/05/01 - 13:00

Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that help map wildlife and scale conservation efforts around the world.

Continue reading Bringing AI into the wild.

Categories: Technology

Understanding automation

O'Reilly Radar - Tue, 2018/05/01 - 13:00

Ben Lorica and Roger Chen discuss the state of reinforcement learning and automation.

Continue reading Understanding automation.

Categories: Technology

Intel AI for the enterprise ecosystem

O'Reilly Radar - Tue, 2018/05/01 - 13:00

Fiaz Mohamed explains how Intel AI solves today’s business problems.

Continue reading Intel AI for the enterprise ecosystem.

Categories: Technology

Fireside chat with Peter Norvig and Kavya Kopparapu

O'Reilly Radar - Tue, 2018/05/01 - 13:00

Kavya Kopparapu shares her inspiration for starting GirlsComputingLeague.

Continue reading Fireside chat with Peter Norvig and Kavya Kopparapu.

Categories: Technology

Using machine learning, the IoT, drones, and networking to reduce world hunger

O'Reilly Radar - Tue, 2018/05/01 - 13:00

Food production needs to double by 2050 to feed the world’s growing population. Jennifer Marsman details a solution that uses sensors in the soil, aerial imagery from drones, and machine learning.

Continue reading Using machine learning, the IoT, drones, and networking to reduce world hunger.

Categories: Technology

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