Head of Google AI China Center leaves for Stanford AI healthcare projects

Li Jia is now pursuing the impact of AI for good in healthcare and working full-time at Stanford University’s AIMI. Original Link

Intel to put partnership model at core of its China AI plan

Intel’s move comes as China boosts its own chip industry and competition in AI heats up. Original Link

Briefing: Peking University to build AI-centered campus to foster local talent

The university’s Party Secretary said the initiative will integrate emerging industries and attract top talent overseas. Original Link

The Future of DevOps

To understand the current and future state of DevOps, we spoke to 40 IT executives from 37 organizations. We asked them, "What’s the future of DevOps from your perspective, where do the greatest opportunities lie?" Here’s what they said:


  • Security is huge. The more we automate the more we can automate problems. How to integrate security into DevSecOps. More connected = more exposure. What happens when next wave of tools comes out. What is going to be the driving tools after containers?
  • DevSecOps – security and privacy. Don’t need to know the details. Know the kind of information that is private. Common security things and privacy things you need to worry about. As a group working with educators here’s what you need to teach on security and privacy in addition technical. Human aspect cannot be ignored. More teaching on the socio part in addition to the technical side. See more collaboration. There will be a huge backlash with more disclosures coming out. 
  • Next two to five years merging operational stuff. Public cloud is solving a lot of issues. Opportunity to get more discipline about security. Finding a way to make that easier. 
  • As DevOps grows as the standard way of working across dependent IT teams, along with its subsequent rapid increase in global adaptation, one can expect more focus on DevOps in the fields of security, IoT and cloud computing. These are relatively nascent streams of DevOps right now, but the spread of DevOps into various uncharted fields of software could mean widespread implementation into various fields of technology and operations in the future.


  • DevOps with cloud-native and microservices allows you to revolutionize the app lifecycle – testing and production are integrated, and you see problems before you go live thanks to testing and troubleshooting. Analytics across the DevOps pipeline and runtime you can do things in a much better way agility with insight and control. Apply ML to determine risk. Bandwidth use, performance, see problems able to see code and configuration changes. Connecting the two together is a big thing. Can shorten the lifecycle with higher quality. Insight and control with security. 
  • More people are taking the leap from DevOps to data ops. As streaming architectures become more popular people will want and need more data ops and data logistics in their organizations because of ML adoption. Maturation of DevOps with the data becomes more pervasive. Simplifying the toolsets to pick up GPUs and run ML is becoming more pervasive and less scary. Use cases deliver high value. This is creating the launching pad for practical applications of big data in business. 
  • I see a lot of opportunities related to artificial intelligence (AI) and machine learning (ML) as it relates to DevOps. Anticipating how changes I make to the application affect the overall user experience. What do I need to test? What is the impact of a change? There is a lot of opportunities to help companies decide what to do next with their product as well as verify the impact of those changes, so they can confidently release high-quality software targeted at their end users’ needs. For example, with AI and ML, I can analyze hundreds of thousands of test results on my platform and determine those changes to a particular portion of the code base result in, on average, a 20% failure rate. The actions I can then take care to ensure that, whenever changes occur in this part of the code, we test much more thoroughly. An even better approach would be to refactor this portion of the code base. AI and ML can allow organizations to make better decisions faster by providing unique analysis correlating, code, test results, user behavior, and production quality and performance. This type of capability can have a dramatic effect on increasing the velocity and quality of DevOps pipelines. 
  • AI integration. To get good data you have to be able to repeat the process over and over again without human intervention. Repeatable and quantifiable processes. DevOps is about scaling out the same test over and over again. With AI it’s automated and the data reports automatically. AI development with DevOps. 
  • AI/ML data-driven understanding patterns are working and automating the findings of the patterns, remediation, going into a self-driving, self-healing. DevOps has to be built into that. This is my business environment, how do I build an app to reduce friction. Infrastructure needs to be set up based on the business the developer and the user.


  • Automated cloud-based testing. Security is integrated into DevOps upfront for DevSecOps. 
  • For big companies, which is who we focus on, renew your thinking, your processes, your architecture, and your applications. All of these make up your legacy—not just the applications. All need to be modernized. By renewing your thinking, I mean, embrace that you are turning into an IT company no matter what your business is. You’re no longer just a bank or an airline. Your software is central to the services you provide your customers. Since you’re now an IT company, you need to think about how to improve your IT processes so that they’re more streamlined, efficient, and producing value. Automation of application development and provisioning is a big part of efficiency and turning out better software. Also, look to reduce your number of applications, and use principles of modern software development to rewrite them. 80% of your code is old, never used, and is full of bugs. You need to modernize it to increase quality. 
  • DevOps is one area where they are watching the market and talking to start-ups. Testing, deploy management, process, security can all benefit from automation. 
  • 1) Opportunity to pursue what DevOps is supposed to mean. Pursue automation. Opportunity to mingle changes in automation with the cultural change to maximize flow, feedback, and continuous improvement throughout the process so problems are solved in the process. 2) Think about security more heavily. 3) Making work fulfilling and rewarding. 
  • We’ve seen more standardization and common tooling. DevOps is becoming more precise and scientific practice. More reference cases. Learning and tooling will spread to the community. More understood as a practice, more standardize and more precise and scientific. Opportunity to do more with merging DevOps with security. Huge potential with automation and CI/CD with DevSecOps and the value of automation in the security area.
  • 100% automation of CI/CD, unit testing, QA. More tools and expertise get better the more quickly you can run with your plans. Fully automated to continuously release software, features and bug fixes.


  • People on a DevOps team and looking at that as a quality of life factor when looking for a job. This will result in the coolest innovation for the companies. Focus on attracting the right talent and breaking down the silos.
  • Platforms to enable collaboration and automation. Once present in an organization enables greater success with predictability. Rather than failing fast all the time prevent failure by learning from your failures so you are failing less frequently. Tools, process, metrics, reporting take advantage of learning for continuous improvement. 10,000 releases per day at Amazon. Financial services want to reduce app dev time from 300 days to 120. Ability to articulate the value to get continued resource support. Gartner report when starting on DevOps, commit for three years. After one year, it’s worse than when started. You need to break through the wall to see the benefits. Accrue analytics necessary to learn and improve.
  • People continuing to break down silos. People are getting smarter about bringing everyone to the table. As people bring about new projects they will think about building and deploying before thinking about rearchitecting. Growing pragmatism. Ask where we get to with intelligent build automation before we go cloud or microservice.
  • Infrastructure as code. A set of tools will be used for deployment. Ultimately it will be cloud providers handling deployment effectively removing the operation side.
  • If we expand the scope of thinking a focus on user stories it’s not a question of technology it’s a question of low-level infrastructure and a full spectrum of application delivery – observability, PaaS, alerting and dashboarding. Beyond operations to entire IT organization.
  • Exploring extensively. Right now it begins when writing the code ends. Writing the code is not part of DevOps thinking. We’re changing that since code generators are becoming popular. In integration generating the code is part of the DevOps pipeline. We’re exploring extending the DevOps pipeline to generating the code as well. The industry will collapse into two or three main product ecosystems – probably a dominant in every vertical a SaaS best practice.
  • The network. Need to envision to support the application for better time to service. Hasn’t changed much in 15 years. The network has not been designed to react to the needs of the application and the change in deployment. From the operators’ perspective, there’s not much focus on day two operations and repeatable processes and the operation state of the environment at the moment.  Tooling is happening but its early in its infancy.
  • For DevOps to work, developers must be willing to expand their expertise and responsibilities. DevOps relies on a “many hands make light work” ethos which requires everyone to handle a piece of the development, operations, and security pie. This shouldn’t be viewed as a burden. DevOps is an opportunity for developers. DevOps enables companies to clearly define roles and responsibilities for developers which gives them autonomy and responsibility for the development of their project.
  • Today there are a lot of independent Lego blocks. K8s independent of cloud, CI is independent of CD, security, all are important and all are tiny dots. Will see a more holistic, integrated POV, from doing it yourself to integrated and validated as part of the flow. Simplification with alliances, acquisitions to make it easier.
  • We think the greatest opportunities lie in implementing and scaling DevOps in large enterprises. Enterprise IT is so diverse across companies and industries, so turning these horses into unicorns will have an incredible impact on the world. 
  • 1) In the future, DevOps will be popular in the Enterprise. Dev and Ops teams are increasingly moving to rapid development and reliable, high-performance services delivery. 2) Software enterprises will push towards a microservices infrastructure. Microservices allow organizations to architect an enterprise solution, independently, over a set of smaller services.
  • The biggest opportunity for DevOps is to drive into tech stacks and organizations that think that a move to DevOps requires a complete re-architecture of their application or adoption of replacement technologies. While those sorts of changes may be excellent opportunities to introduce culture change as well, teams running existing business-critical apps in “monolithic” architectures can take advantage of DevOps as well, if they choose the right tools.
  • I think containers continue to provide interesting opportunities as the tooling around them improves. Depending on how they’re used, they can confer a lot of benefits or a lot of downsides within a system. There are a lot of untapped or just-scratched ideas on how to use them to convey significant benefits, though. For a few examples of what I mean: using containers to sandbox applications for security and resource reasons. It seems basic on its face, but we mostly see containers used for stateless (and some stateful) server-side applications that are otherwise operated in traditional manners within those containers. The container is treated more like a packaging system than a runtime. What if you generated an application container per user or per user session? It provides a limitless array of opportunities for improving system security, user security, and adding additional analytics around user behavior or preventing resource contention between users of your application. As the tooling improves, containers become even lower-cost to deploy. I think there’s many yet to be tapped opportunities in how containers could be used by both technically-focused and traditional industry verticals to improve their business.
  • Platform-as-a-service is definitely a growing field. In the long-run, an application developer should be able to simply define a couple of entry points in their application package and that should be sufficient for the application to be deployed. I think this is very much aligned with what we have been doing in developing an abstraction between Applications and Technology Infrastructure. Another example is our Data Science Platform as a Service, where the DevOps of the platform itself is fully encapsulated away from the application developers who use this platform to develop machine learning powered applications.
  • Kubernetes (K8s) and Docker seem worthwhile to learn about for any DevOps team. Another one is scaling of machine learning. Not everyone is doing this and it’s new so there’s a lot to learn there. We’ve been diving into what it’s like to scale TensorFlow serving and I think that this is going to be a big part of DevOps in the future.
  • Containerization opens up a lot of possibilities in terms of revamped architecture. The surrounding architecture is isolated from everything else. This provides deployment mobility and flexibility in terms of what libraries are in use and what resources they need which is exciting. Another one is dealing with machine learning and IoT data. There’s a massive influx of data from devices and so we have to think about how we store it in a usable format and also the global distribution of it. People need to be able to get data from a variety of locations which brings politics into the mix. There are rules in development from countries all over the world that make determinations about how data is stored. It’s becoming kind of tricky to have international customers because data has to be stored differently in various physical locations. Finally, the move towards the SRE model. Operators will no longer be a separate team but work on each project with developers as they go. Part of this will be because of containers because you’ll be working together to build at the development phase. Containers couple the development process with the deployment process.
  • In today’s DevOps environments, many technology professionals have mastered operating containerized workloads at scale, and leveraging containers in production. DevOps teams that have mastered containers, and have begun deploying microservices, are swiftly moving into the future of DevOps. The future of DevOps will include increased deployment of microservices and implementation of a service mesh architecture to manage and monitor said microservices. Moving forward, tech pros will increasingly need to develop a good understanding of the breadth/depth of service mesh necessary for their environment and choose the right tool to fit their needs. The future of DevOps also lies in focusing on serverless computing and determining the use cases where serverless computing is appropriate for certain distributed environment and use cases. With this acquired skill, DevOps teams will be able to lead their organizations to strategic decision-making and ultimately into the age of digital transformation.
  • The future of DevOps is bright. I believe there are great opportunities to deliver purpose-built pipelines that would connect to common repositories and perform a set of necessary steps to reliably deliver working applications to known environments. All cloud-based of course.
  • Containers will continue to grow prevalence and influence within the modern technology landscape. DevOps has always been about automation and this technology allows for companies to more easily build and deploy infrastructure. More companies will adopt containers in an effort to improve automation capabilities and velocity. Organizations will also continue to seek out technologies that integrate into frameworks like K8s as work to further improve automation and velocity.

Here’s who shared their insights with us:

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How The Cloud is Changing IT’s Role

Great having the opportunity to meet with Raj Sabhlok, President of ManageEngine at their Chicago user conference and learn more his vision for the role of IT in the cloud era. 

ManageEngine has been providing IT operations and service management since 2001. Their offerings include Active Directory management, operations management, analytics, service management, endpoint management, and security. Zoho’s operating system for business with more than 40 business apps to run entire businesses.

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DZone Research: Database Additional Considerations

To gather insights on the current and future state of the database ecosystem, we talked to IT executives from 22 companies about how their clients are using databases today and how they see use and solutions changing in the future.

We asked them, "What have I failed to ask that you think we need to cover in this research guide?" Here’s what they told us:

Original Link

DZone Research: Developers and Databases: What You Need to Know

To gather insights on the current and future state of the database ecosystem, we talked to IT executives from 22 companies about how their clients are using databases today and how they see use and solutions changing in the future.

We asked them, "What advanced database knowledge or skills do developers need?" Here’s what they told us:

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How Google’s Cloud AutoML Makes AI Accessible To Businesses

Artificial intelligence is never again going to remain the mystery sauce of giant technology companies. Google laid out how it is bringing artificial intelligence to developers, as well as integrating more AI capabilities throughout its cloud products. So developers can utilize these AI cloud services to give the best user experience.

Google I/O 2018 kicked off with a key theme of AI for everyone. In the current year’s I/O, Google exhibited that AI cannot simply help to make items more valuable, they additionally rethink to cool new user experiences. Here is a brief synopsis of Google’s featured declarations made so far on AI, that may intrigue developers and businesses.

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Who’s Calling? Model and Load a Schema Into a Knowledge Graph

In this tutorial, our aim is to write a schema and load it into our knowledge graph; phone_calls. One that describes the reality of our dataset.

The Dataset

First off, let’s look at the dataset we are going to be working with. Simply put, we’re going to have:

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Cloud Automation and Machine Learning for CloudOps [Comic]

Cloud automation has been helping DevOps and CloudOps engineers save a lot of time and effort on tasks such as taking cloud backups, monitoring resources, managing hundreds of instances, etc.

Machine Learning, on the other hand, is a step ahead of it predicting and managing the cloud operations for them.

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Future of Organizations

In my previous article, I quoted what new forms of organization in the broad sense might be. Obviously, this does not go into detail of the small and big changes, but here are some changes that might appear in my opinion.

Flattened Organizations

If you want to be Agile, you want to be able to adapt to any change. If you have to adapt to any change, you must be able to change the organization of a team at any time. If you want to change the organization of a team, the concept of hierarchy does not allow you to adapt to change, because a 40-year-old person can very well be the strong leader of the subject to be dealt with, then become a more "simple" operator, bringing less value to the work in progress. If you put a hierarchy on that, you won’t be able to reconfigure your teams. Moreover, a hierarchy pushes a good number of people to respect its hierarchy, encouraging them to be silent when an error is about to be committed. Let’s stop the hierarchy defined by the company and switch to the hierarchy of competencies. It’s simpler and more efficient.

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Not Only Cars: The Six Levels of Autonomous Testing

There is something similar about driving and testing. While testing is an exercise in creativity, parts of it are boring — just like driving is. Regression testing is tedious in that you need to do the same tests over and over again, every time a release is created, just like your daily commute. And just like during your daily commute, doing something repetitively is a recipe for mistakes, so repetitive testing, just like driving, is a dangerous activity, as can be seen from the various crash sites strewn over our commute highways. Or by the various
bugs that slipped from us during our regression testing.

Which is why we automate testing. We write code that runs our tests and run them whenever we want. But even that gets repetitive. Another day, another form field to check. Another form, another page. And one gets the feeling that writing those tests is a repetitive process. That it too can be automated.

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Cheetah Mobile discusses the future of AI and humanity

While it takes time for any superintelligence to be born, AI’s purpose, if there is any, should serve the ultimate purpose of mankind. Original Link

Briefing: Microsoft to set up Asia AI research branch in Shanghai

During the World AI Conference taking place in Shanghai, Microsoft announced they will launch Microsoft Research Asia’s Shanghai branch for AI. Original Link

AI-Powered Experience for Spotfire

Thanks to TIBCO for inviting me to TIBCO NOW 2018 where they made several announcements. In talking with Bob Eve, Senior Director of Analytics at TIBCO and Keith Woodie, System Engineer Consultant and Dan Hudson, V.P. Senior Manager Systems Integration at First Citizens Bank, they believe the DZone community will be most interested in the introduction of TIBCO Spotfire X.

Spotfire X is an AI-driven analytics experience that integrates agile data exploration with natural language processing (NLP), machine learning recommendations, and model-based authoring, adding native support for real-time streaming data. This will allow users to humanize their data and information, resulting in making better decisions, faster.

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Using AIOps for DevOps Workflows

Every DevOps support team has to deal with large amounts of monitoring data and logs in order to take care of their cloud infrastructure. AIOps is when AI is leveraged to make use of that data.

We have explained what AIOps is and the benefits it provides to any DevOps workflow. There are multiple DevOps tools used at various stages of software delivery, from iterations through code versioning, building, testing, pushing to production and monitoring the ready product performance. There are also various parameters to be taken into consideration while monitoring these software development lifecycle stages, from CPU/RAM usage to disk volume and bandwidth usage, to the numbers of app sessions, etc.

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Afrolynk: African Tech and Entrepreneurship

I attend many tech conferences around Europe (and occasionally the World), and Africa is always underrepresented. I was delighted to receive an invite to Afrolynk, an annual event that aims to bridge the European and African entrepreneurial scenes. Entering the Microsoft offices in Berlin for the event, a different crowd greeted me from typical tech and startup events, which was an incredible sight to see.

The day started with a round of drumming to bring people into the room and began a solid day of panels and keynotes. Attendees are sharply and colorfully dressed, energetic, and the variety of languages spoken around the venue highlights the multiculturalism of the African continent.

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Briefing: Uniqlo’s AI solution provider receives Pre-A funding

Uniqlo’s leading position in the retail industry provides physical opportunities for third-party service businesses. Original Link

QA for Machine Learning Models With the PDCA Cycle

The primary goal of establishing and implementing Quality Assurance (QA) practices for machine learning/data science projects or projects using machine learning models is to achieve consistent and sustained improvements in business processes, making use of underlying ML predictions. This is where the idea of the PDCA cycle (Plan-Do-Check-Act) is applied to establish a repeatable process ensuring that high-quality machine learning (ML)-based solutions are served to the clients in a consistent and sustained manner.

The following diagram represents the details:

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Top Technology Trends of 2018

2017 became the Year of Intelligence: the advance of technological achievements has triggered exciting and unexpected trends with wider impact horizons and very promising business prospects. This year we expect drastic exponential changes in every technological direction. Machine learning and artificial intelligence will transform entire industries, making way for virtual helpers and a myriad of cases for automatization. The Internet of Things (IoT) will become more intelligent, uncovering a huge potential for smart homes and smart cities. A more efficient human-machine interaction will become established with natural language replacing specific commands.

In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

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Alibaba doubles down on facial recognition

Alibaba Joins $600 Million Round for AI Startup Megvii– Bloomberg

What happened: Megvii Inc., the Chinese developer of facial recognition system Face++, reportedly raising at least $600 million from investors including Alibaba Group and Boyu Capital. Face++ offers face-scanning systems to companies including Lenovo and Ant Financial, etc.

Why it’s important: AI startups become the new darlings of Chinese investors who are ramping up their investments in deep tech companies. Megvii’s massive round comes shortly after a $620 million C round of another Alibaba-backed computer vision startup SenseTime. Its rival Yitu has just secured a $100 million funding. The emerging unicorns are competing in sectors such as retail, finance and smartphone and public security that could utilize facial recognition.

Original Link

Alibaba doubles down on facial recognition

Alibaba Joins $600 Million Round for AI Startup Megvii– Bloomberg

What happened: Megvii Inc., the Chinese developer of facial recognition system Face++, reportedly raising at least $600 million from investors including Alibaba Group and Boyu Capital. Face++ offers face-scanning systems to companies including Lenovo and Ant Financial, etc.

Why it’s important: AI startups become the new darlings of Chinese investors who are ramping up their investments in deep tech companies. Megvii’s massive round comes shortly after a $620 million C round of another Alibaba-backed computer vision startup SenseTime. Its rival Yitu has just secured a $100 million funding. The emerging unicorns are competing in sectors such as retail, finance and smartphone and public security that could utilize facial recognition.

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iQiyi: What to expect from the integration of AI and entertainment

iQiyi: What to expect from the integration of AI and entertainment · TechNode

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TouchPal’s next-generation keyboard app at TechCrunch Hangzhou

TouchPal’s next-generation keyboard app at TechCrunch Hangzhou · TechNode

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Big data is not only a boon to AI, but also a challenge: Alibaba Cloud’s chief machine intelligence scientist

Big data is not only a boon to AI, but also a challenge: Alibaba Cloud’s chief machine intelligence scientist · TechNode

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TechCrunch Hangzhou talks about China’s path to AI for healthcare

TechCrunch Hangzhou talks about China’s path to AI for healthcare · TechNode

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Highlights from the CA Agile Ops Summit 2018

On June 12th and 13th, CA hosted the fourth annual CA Agile Operations Summit at Ditton Park in the UK—an event aimed at helping ops professionals plot a course for their organizations’ IT operations in a rapidly changing tech landscape. With around 100 attendees, the exclusive event was an opportunity to exchange ideas with like-minded peers at various stages of their Agile Ops journeys, as well as meet and talk with thought leaders from CA.

Key Themes: AI, Machine Learning & Automation

Keynote speeches by CA leaders Ashok Reddy (Group General Manager of DevOps), Kaj Wierda (Senior Director of Product Management) and Ali Siddiqui (General Manager of Agile Operations) welcomed the audience on the first day of the event. These talks addressed the effects we can expect from advancements in artificial intelligence, machine learning and automation—and how to use agile operations to drive business value in the face of disruption.

“We are in a global business revolution, driven by software, artificial intelligence, and machine learning,” explained Ashok, who recently gave an interview on the same subject. “AI is driving business innovation. This summit was an opportunity to discuss applying artificial intelligence to automate automation, and how this relates to operations.”

With the exponential growth of data created by modern organizations, effective data handling is becoming increasingly necessary, with big implications for operations. Ashok discussed the importance of translating data into business insight and the value of predictive analytics and process monitoring to operations. He also described how to get closer to this goal using CA Digital Operational Intelligence, a tool that is powered by CA Jarvis and uses machine learning and analytics to bring end-to-end visibility to tool workflows: “Every data-based company needs to be a Modern Software Factory… Pay attention to disruption and learn from the data.”

Automation Drives Better Business

A Modern Software Factory can only be constructed using business automation, even though the methods of utilizing automation are changing rapidly. Ashok compared the evolution of business automation to the development of self-driving cars: Just as the technology behind autonomous cars started slowly, with features like assisted parking, business automation began with simple processes; but it has become possible to automate gradually more complex processes. Eventually, applications can be trained to become “self-driving”—a goal CA is making continuous progress towards. “Ultimately, I want our users to think of CA as the self-driving car factory,” Ashok concluded.

Solutions at Every Step

The summit also included two featured case studies, which highlighted the range of solutions CA provides and applied them to operations specifically. These solutions are aligned with the evolution of DevOps and able to integrate with a wide variety of open-source tools and public clouds.

Hands-on workshops and technical deep-dive learning experiences were available during the summit and facilitated by the experts in automation and Agile Ops at CA. Guests could attend sessions on a variety of solutions, from Application and Infrastructure Monitoring tools to the CA Automic One Automation Platform.

For a company that hopes to thrive in the face of constantly increasing consumer expectations, the right tools can be the path to success. As Ali Siddiqui put it, “All of you in operations are under pressure to do more releases, faster, and more efficiently… At CA, we will futureproof your operations.”

Streams of the summit speeches are available online; click here to watch the keynotes by Ashok Reddy and Kaj Wierda; click here for Ali Siddiqui’s; and click here to view a speech on product design by Chris Kline (VP for DevOps Strategy).

To find out more about upcoming CA events near you, check out our events calendar.

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China’s largest carmaker SAIC Motor launches its own AI lab

China’s largest carmaker SAIC Motor launches its own AI lab · TechNode

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Microsoft Azure: How To Build Smart Apps Using Cognitive Services

In today’s mobile ecosystem, you need to create more engaging, insightful, and targeted mobile app experiences. User expectations have evolved as mobile technologies have become more sophisticated, and things considered bleeding edge in the recent past are no longer up to par.

Today, Customers Expect Apps to Be Smart.

Users want minimal friction, the ability to navigate and interact effortlessly. They want catered experiences that are relevant to them. They expect that apps are intelligent enough to know their intent and desires with less input and fewer taps.

Even a year ago, this wasn’t easy to achieve. But with huge advances in artificial intelligence, big data, and machine learning, it’s rapidly becoming a reality. The combination of these three technologies enables us to make apps smart. We can make our apps learn from each customer interaction and a variety of data points, ensuring that experiences are better, more engaging, and more personalized for users – every time they use the app.

At the forefront of many of these developments is Microsoft Azure’s Cognitive Services. Cognitive Services is a suite of APIs that allow developers to add AI to their offerings. There are a wide range of APIs available for purposes ranging from natural language processing to voice recognition and beyond.

Microsoft Azure

Source: Microsoft Azure

There is huge potential to leverage Cognitive Services to improve and even revolutionize mobile app experiences. We can make our apps smarter to:

  1. Learn from data and user behavior to be more engaging and relevant
  2. Automate the way content is served to users to make experiences contextual and personal
  3. Reduce friction points while simultaneously improving security

While Cognitive Services has an impressive list of APIs in production and soon to be available, I believe the following are the most interesting in their potential to impact user experiences, customer intelligence, and engagement.

1. Custom Decision Service

This service leverages machine learning to serve contextual content based on data you provide and the behavior of your users. Using feedback (user activities and behaviors), it makes decisions on the type of content to serve to particular users, and learns to serve more targeted content as more data is gathered. The Custom Decision service is also intelligent enough to run experiments with the type of content it serves by testing content options with users.

To give an example, if a music app has a recommendation feature that makes suggestions based on listening history by genre, the service could test user reactions to music that is from a different genre than they had previously listened to. In this way, this allows the service to ensure it is not making mistakes with the type of content it is serving.

The API allows your app to automatically and rapidly learn about users in order to make the user experience more engaging, personal, and relevant. And since everything is hosted on Azure, you only have to provide the data and it learns automatically.

There are many use cases for implementing the Custom Decision service into mobile apps, particularly for things like ad targeting, news cycles, recommendation engines, push notifications, any content-focused subscription services, and more.

2. Content Moderator

Content Moderator is a service that combines machine and human-based content review to allow app owners to have greater control over any user-generated content on their mobile app. It is ideal for mobile apps that allow for user-generated content because it can moderate text, images, and video.

Text: The service is able to detect potentially offensive content in over 100 languages by matching it against custom lists, and also looks for personally identifiable information. It flags content and blocks it from being published on your app.

Images: Using custom lists, optical character recognition, and machine learning based classifiers, Content Moderator can block and flag any user-generated images.

Video: Content Moderator searches for and blocks adult video content to prevent it being published to your app.

Furthermore, you are able to add content to blacklists and the system will learn what kind of content it should be blocking based on the data you provide it. As more data is collected, the Content Moderator gets better at identifying which content needs to be flagged.

Microsoft Azure Content Moderator

Source: Microsoft Azure

Importantly, Content Moderator allows you to retain control and visibility with a human review tool. This is a great feature for content that falls into a grey area and is not easily caught algorithmically, as it flags potentially offensive content and allows a human moderator to review and either discard or approve it.

Content Moderator is ideal for mobile apps that have a lot of user-generated content. Apps that don’t have this type of service in place require a great deal of manual moderation – in other words, you have to pay somebody to monitor, review, approve and remove this kind of content from your app. Apps that allow commenting, photo and video uploading, and other forms of user-generated content can benefit tremendously from this service.

3. Speaker Recognition API

The Speaker Recognition API is the next step in authentication and security. It can be used to add voice authentication as a safeguard for users to access applications, rather than things like passwords or pins, logins, or even biometric authentication like fingerprint scans. Voice authentication has great potential to enhance the user experience by streamlining authentication, particularly for apps with username/password login or pins.

Many users opt to remain logged into apps so they don’t have to sign in every time they use them. While this is more convenient, it is a security risk. Adding voice authentication keeps the authentication process simple and fast while blocking access to unrecognized users, which is both convenient and secure. When users want to use the app, they simply say a word or phrase to gain access.

This API is especially useful for mobile apps that deal with sensitive information. Banking and financial services first come to mind, though the Speaker Recognition API can be useful for any application that has user profiles with personal data.

Closing Thoughts

The trend toward intelligent apps is quickly gaining momentum and is likely to be a huge driving force in the way mobile apps evolve in the foreseeable future. With the potential to create more targeted, contextually relevant, and personal user experiences, it’s an area that we will see a lot of focus on. Microsoft Azure’s Cognitive Services are currently a step above the rest when it comes to adding intelligent features into applications, with just a few of many APIs being discussed above.

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Yitu Technology announces $200 million funding

Yitu Technology announces $200 million funding · TechNode

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2018 Java Ecosystem Executive Insights

This article is featured in the new DZone Guide to Java: Features, Improvements, & Updates. Get your free copy for more insightful articles, industry statistics, and more!

To gather insights on the current and future state of the Java ecosystem, we talked to executives from 14 companies. 

Key Findings

While Java is still the prevalent language in enterprises and there are more Java developers than anything else, this topic does not generate the level of response of the other topics for which we produce research guides. Java content continues to outperform all other content on DZone by a 4:1 margin — but because it’s not new and “sexy,” not a lot of people want to talk about it. I’m most appreciative to the IT professionals that took the time to share their insights on the current and future state of the Java ecosystem.

  1. Java continues to be the platform of choice for a number of enterprise companies, including financial institutions, as a result of its portability and because it allows developers to write once run anywhere. There are plenty of Java developers and it is seeing a resurgence with big data.

  2. The Java Virtual Machine (JVM) was most frequently mentioned as the most important element of the Java ecosystem. The JVM is the most critical element followed by its openness, compatibility, the vastness of the libraries, and the completeness of the toolchains. The JVM enables languages other than Java to flourish. The fact that Java is open-source while championed by a large company was deemed to be important by a couple of respondents, as was the community in which no one participant is more important than the community. The maturity level of Java is high. This results in a lot of frameworks, libraries, and IDEs as well as a high-performance, consistent, compatible language that’s simple and stable.

  3. The most important player in the Java ecosystem is Oracle followed by multiple mentions of IBM, the Apache and Eclipse foundations, Red Hat, and Pivotal. Oracle was seen as the key holder but seems to be pulling back. It was interesting that financial institutions as a whole were mentioned given their use of Java and the number of developers they employ along with other companies like Twitter, Alibaba, Facebook, and Google with GCP and Kubernetes.

    The Eclipse Foundation is likely to become the most significant player with its efforts around MicroProfile and Jakarta EE.

  4. The two most significant changes to the ecosystem in the past year have been the move to semi-annual releases and Java EE moving to the Eclipse Foundation as Jakarta EE, putting the future of enterprise Java into the hands of the community. The move to halfyearly releases will boost interest in, and use of, Java by developers. The “open-sourcing” of Java EE to the Eclipse Foundation, creating EE4J and now the birth of Jakarta EE, is significant. Java has been the dominant player in enterprise applications for two decades. Jakarta EE ensures Java will continue to be the dominant player for enterprise computing for a long time.

  5. The availability of developers helps Java solve many problems in organizations. There is a shortage of security, big data, and AI/ ML/DL/NLP professionals, but there are plenty of Java developers who can get organizations’ work done. Twitter, financial institutions, automobile manufacturers, and AI/ML companies are all heavy users of Java. Java has the ability to support high-speed concurrent processing at scale, which becomes more important as the amount of data continues to grow.

  6. The people I spoke with are all Java devotees and don’t see any problems with Java. They realize that those who are not Java developers see the language as being too verbose. They believe Eclipse is a good steward of open source and, like open source, there needs to be more participation and engagement in the community.

    While Java is considered verbose by some, there are alternatives like Kotlin, Scala, and project Lombok. Java lags behind because it’s heavily used by large enterprises. With slowness comes stability. While it lacks some of the niceties of other languages, it provides quick wins with fast coding.

  7. Serverless was mentioned by a couple of respondents as the future of Java. They believe it will lead to a major reshaping. Java is built for serverless, but it needs work. With Spring Boot, containers can be lighter-weight and great to build serverless upon. There were good changes in Java 8 and 9 for easier execution in container management, memory, and CPU. JVM-based languages and tooling will continue to evolve. The JVM enables many different types of languages to be built. Others see Java thriving in the open-source software ecosystem with innovations continuing to support Java’s ongoing success.

  8. The biggest concerns with the current state of the Java ecosystem were quality deterioration because people were not learning from their mistakes, understanding the value of the ecosystem, or taking security seriously enough. Some question the benefits of the more frequent release cycles because it may lead to release fatigue and meaningless releases that ultimately will not be adopted or supported.

  9. When working with Java, developers need to realize the depth of the ecosystem and not try to reinvent the wheel. Learn the libraries and know your code is going to be attacked — prepare accordingly. Pay attention to the Java roadmap and try new builds before they go live to be seen as an expert. Like any other language, ensure your code is well-designed, extendable, maintainable, and easily understood by others.

    JVM is the top-performing platform. Most languages live on the JVM so start with Java. Be language-agnostic. Learn domain-driven design. Read Design Patterns by the Gang of Four. There’s a good future with Java. Look for tools to help with development. Keep an eye on open source projects through good information outlets.

  10. Additional considerations regarding Java include:

  • Containers are changing how developers deploy applications and that affects Java applications.

  • Pay attention to Kotlin which runs on the JVM. Adopt new languages to help create new applications. There are a lot of really smart people in the ecosystem; pay attention to what they have to say. There’s a lot to continue learning since things will continue to change.

  • Java developers’ participation matters in the continued consistency, stability, and security of the Java ecosystem.

  • One of the strengths of Java that’s undervalued is that it’s a small language that does not offer a huge range of options for how to do things. As a result, two engineers writing the same solution for the same problem tend to end up with the same code. This is a strong advantage of Java that is not true of many other competing languages.

  • We need to recognize the importance of open source — embrace it and contribute to it where we can. If you find problems, work on fixing them. It’s a great community and it’s made better when everyone is involved and contributing.

And here’s who we spoke to:

This article is featured in the new DZone Guide to Java: Features, Improvements, & Updates. Get your free copy for more insightful articles, industry statistics, and more!

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Opinion: China isn’t the AI-powered dystopia you think it is

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Guangzhou releases autonomous vehicle road test draft proposal

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The Future of Automated Testing

Easily enforce open source policies in real time and reduce MTTRs from six weeks to six seconds with the Sonatype Nexus Platform. See for yourself – Free Vulnerability Scanner. 

Automate open source governance at scale across the entire software supply chain with the Nexus Platform. Learn more.


automated testing ,ai ,ml ,devops ,test automation

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The trends driving Chinese tech: Highlights from Mary Meeker’s 2018 Internet Trends Report

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166: Alexa gets better at business and AI at the edge

The General Data Protection Regulation took effect last week so we kick off this episode by talking about what it means for IoT devices. We then hit the Z-Wave security news and explain why it isn’t so bad, after which we indulge in some speculation on Amazon’s need to buy a security company. We also discuss a partnership between Sigfox and HERE and a new cellular module for enterprises. Also on the enterprise IoT side, we review Amazon’s new Alexa meeting scheduler feature. Then we hit on news about Arlo cameras, Philips’ lights, new gear from D-Link and Elgato’s compelling new HomeKit accessories. We also have a surprisingly useful Alexa skill for enterprise service desks.

The new Elgato Aqua is a HomeKit water controller for your spigot. It will sell for $99.95. Image courtesy of Elgato.

Our guest this week is Jesse Clayton, a product manager for Nvidia’s Jetson board. I asked Clayton to come on the show because the 10-watt Jetson board is being used in a lot of industrial IoT applications and I want to understand why. He tells me, explains how AI at the edge works and shares some cool use cases. I think you’ll learn a lot.

Hosts: Stacey Higginbotham and Kevin Tofel
Guest: Jesse Clayton of Nvidia
Sponsors: Praetorian and Bosch

  • Baby, don’t fear the GDPR
  • Here’s that list of Z-Wave certified devices
  • Amazon’s scheduling has a lot of hoops
  • A good explainer of machine learning
  • Why companies need computer vision at the edge

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Huawei joins forces with iFlytek for consumer voice recognition

Huawei joins forces with iFlytek for consumer voice recognition · TechNode

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SenseTime raises $620 million in Series C+

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If you focus, you’ll think up a hundred ways to solve a problem: Exclusive interview with Sogou CEO Wang Xiaochuan

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Alipay opens Future Pharmacy supporting facial recognition payment in Zhengzhou

Alipay opens Future Pharmacy supporting facial recognition payment in Zhengzhou · TechNode

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