By analyzing and comparing the examples, neural networks create complex mathematical functions with thousands of parameters, which can make statistical predictions and classify new data. Well-trained neural networks can produce very accurate results, sometimes even better than humans. But the problem is we don’t know how they work. Even the engineers who build deep learning models often can’t make sense of the logic behind thousands and millions of parameters that constitute the neural networks.
IoT security issues arise from ill-advised prioritization and the inherently short-term culture of the tech world. Security should be seen as a fundamental requirement for any IoT product—even MVPs. As the attitude of consumers and regulators shifts around those matters, it's becoming a simple matter of good business. Frankly, given the virulence and widespread nature of cyber threats, the need to take security seriously and embed it natively into IoT products should be seen as a simple matter of common sense for product developers and investors.
Adopting IoT tools for the retail environment allows customers to interact both directly and indirectly with everything in the store. This presents incredible business opportunities. It can be daunting trying to step into the IoT space, though, and that fact keeps many businesses from embracing a lucrative transformation. Delivering a personalized, unique experience with IoT devices presents more than a simple opportunity to boost sales. You also establish a closer relationship with buyers and potential buyers, generating customer affinity and increasing brand equity.
Today's world revolves around digital technologies. But what if all our apps suddenly stopped working? It is, therefore, imperative that developers deliver continuous quality throughout the entire software development lifecycle. While automation is a key factor in the DevOps lifecycle and makes continuous testing a reality, there are hurdles that deter development teams from embracing an earnest automation initiative. It's time to make the software development lifecycle continuous. Let's break down four challenges teams face with AI, open source and continuous testing in the DevOps lifecycle.
Equipment deployed in construction, agriculture, and healthcare can benefit from IoT-enabled predictive maintenance and asset tracking. There’s also a developing opportunity to use IoT to enable new business models for providers of such assets that can shift to delivering them on an “as-a-service” basis. Cloud-based IoT connectivity management platforms can support delivery of such applications on a flexible and cost-effective basis, even enabling dynamic pricing changes. Connectivity among people, things, and businesses is increasing exponentially. Old business models and processes are being rethought and new ones are emerging.
Within the field of computer networking, there are examples of solutions that have been implemented to understand infrastructure dependencies. As an example, Voice over Internet Protocol (VoIP) phones and network switches can determine the precise point of connectivity using a protocol known as Link Layer Discovery Protocol. In the Internet of Things (IoT), such a nifty standardized mechanism to determine dependencies does not exist. Here we explore examples of where sensors with this embedded ability would be useful and would contribute to the overall better experience and greater reliability.
In this article you’ll learn how to speed up your Docker build cycles and create lightweight images. One of Docker’s strengths is that it provides caching to help you more quickly iterate your image builds. When building an image, Docker steps through the instructions in your Dockerfile, executing each in order. As each instruction is examined, Docker looks for an existing intermediate image in its cache that it can reuse instead of creating a new (duplicate) intermediate image.
With more businesses adopting devops—and even more experiencing a need for faster development cycles—it’s imperative that executives and devops practitioners work together to ensure success. Despite devops gaining momentum, teams are still struggling to transform their current stack to better accommodate an accelerating pipeline. Upon closer examination, part of the problem is that these two groups are on very different pages when it comes to strategy customer experience and progress. CIOs have an optimistic view of the state of devops. Now’s the time to align CIO goals and devops practitioners’ realities.
Is your organization data-driven? Then you have probably wrestled with the issue of creating a data-driven culture. Having a data-driven culture means that data is the fundamental building block of your team. It means that every team member has a data-driven mindset. It means that every single decision maker uses data as their main evaluation asset. It means that every project uses, generates and pivots on data. It means that your team is constantly leveraging data as a strategic asset. But how do you get there?
In this post, you will learn about the mathematical objects of Linear Algebra that are used in Machine Learning. You will also learn how to multiply, divide, add and subtract these mathematical objects. You will also learn about the most important properties of Matrices and why they enable us to make more efficient computations. On top of that, you will learn what inverse- and transpose Matrices are and what you can do with it. Although there are also other parts of Linear Algebra used in Machine Learning, this post gives you a proper introduction to the most important concepts.
Machine learning methods are used in a wide range of areas. In this post, I will discuss a few of the ways that machine learning is used within the education space – specifically K-12. Machine learning has the potential of making a significant impact in this space, so let’s look at some of those areas.
IoT-enabled services are being adopted to provide user-friendly and aesthetic ways to address retail loss prevention. However, certain post-deployment observations have revealed some issues with IoT, relating to customer experience and the technology's ineffectiveness in addressing the real reasons for which it was originally deployed. IoT technology alone may not be the answer to solve certain business problems. As we have seen with the retail article tethering case, sometimes traditional and technological solutions may be complementary and have to co-exist, unless the technology is robust enough to overcome all possible user experience issues associated with its adoption.
One of the big promises of IoT is understanding the physical world around us and taking action based on insights and observations. Often, every millisecond counts—especially for use cases like earthquake monitoring. We need to build real-time networks for these mission-critical scenarios that can keep up. We have sensors everywhere distributed across nature and remote areas of the world—always on and always streaming new readings as they happen. This has transformed our understanding of how we work and live because we have more up-to-the-second data and analysis than ever before.
It is very difficult for organisations to discern which tools will bring them the most benefit, and which issues they need to plan for. New technological developments provide the platform for the next generation of innovation, as we’ve seen with the evolution of ‘Big Data’ into advanced analytics, machine learning, and artificial intelligence. How can businesses navigate this increasingly-complex data landscape to make the wisest investments? Here is our guide to the top seven data trends that should be on every organisation’s radar for the year ahead.
In terms of cyber security AI is as much a tool for the implementation of security measures, and the vendors selling them, as it is for cyber attackers. Every time a new network security technology is introduced, you can be certain terms such as deep learning, machine learning, self-learning algorithms, cognitive analytics and neural networks are part of the pitch. But the industry's focus on AI as a way to boost protection against threats ignores the larger problem, which is that of 'self-learning attackers'.
Faster R-CNN, R-FCN, and SSD are three of the best and most widely used object detection models out there right now. Other popular models tend to be fairly similar to these three, all relying on deep CNN’s (read: ResNet, Inception, etc.) to do the initial heavy lifting and largely following the same proposal/classification pipeline. At this point, putting these models to use just requires knowing Tensorflow’s API. Tensorflow has a starter tutorial on using these models. Give it a try!
“Correlation does not imply causation” we all know this mantra from statistics. And we think that we fully understand it. Human (and not human) brains, being machines to find patterns, quickly understand that my coffee mug broke because it fell to the floor. One event (the falling) occurred just before the other (mug breaking) and without the first event, we would never see the second.
Containers are popular among organisations transforming their IT operations from physical, single-tenant computing resources to a more efficient service provider infrastructure model. As application deployment using container technologies grows in production environments, security processes must scale with them. Containerisation provides a number of intrinsic security benefits, such as consistent deployment models, and production container security models should take full advantage of these benefits. To get a full picture of the risks in a container cluster, organisations must automate the process of identifying, mitigating, and alerting on any risks – regardless of source or container origin.
Do you want to become a data scientist? You’re a self-motivated person who is very passionate about data science and bringing values to companies by solving complex problems. Great. But you have ZERO experience in data science and have no clue how to get started in this field. That’s why this post is dedicated to you — enthusiastic and aspiring data scientists — to answer the most common questions and challenges faced by most people.
We keep hearing about new solutions for test automation and continuous testing. There are plethora of tools evolving these days aiming to solve test authoring, analysis, and maintenance problems. While these are all awesome initiatives that will position testing higher and smarter in the overall DevOps processes, this does not translate into the extinction of the tester. Each of the tools, as well as new tools that will rise are rising to help the existing testers to become more agile, smarter, and efficient.
For decades, AI scientists and researchers have been trying to recreate the logic and functionalities of the human brain. And for decades, they have dismayed themselves and the general public. Today, we’ve reached a point where artificial intelligence algorithms can solve very complicated problems, and in many cases with speed and accuracy that is far superior to those of humans. But whether contemporary AI works likes the human mind is up for debate.
Previous industrial revolutions have always been identified by some or other major event. With the advent of steam power, mechanisation made it possible for machines to take over a lot of the heavy lifting. The second industrial revolution saw electricity, petroleum, and steel creating the mass production of goods. The invention of the microprocessor kick-started the third industrial revolution. We suddenly found ourselves with wearables on our arms. And it's this convergence of technology and humans that's not only currently driving the fourth industrial revolution but quickly barrelling us into the fifth: the era of artificial intelligence (AI).
The value of AI is the promise of AI improving your everyday work life, and hopefully improving your everyday social life. AI walks the tight rope of enabling and taking away work-life balance. The current driving force for applying Artificial Intelligence solutions, i.e. current AI value propositions – tend to solely focus on the optimization of business functions. How do I either maximize revenue or reduce costs? Or manipulate some factor or indicator that influences either one of those things? The primary driving force is an optimization function that builds upon business objectives.
To secure the value that data can offer, you must manage it in a way that aligns and unifies your disparate sources. Enter master data, the foundation of any data-driven enterprise. It serves as the fuel that flows through the entire ecosystem of your business. It breaks down data silos and allows internal systems to work together. Every type of enterprise, legacy data migration program, and enterprise data management initiative includes five common requirements.