Machine learning in science does present problems in academia due to the lack of reproducibility of results. However, scientists are aware of these problems and a push toward more reproducible and interpretable machine learning models is underway. The real breakthrough will be once this has been completed for neural networks. The scientific community must make a concerted effort in order to understand how these algorithms work and how best to use them to ensure reliable, reproducible, and scientifically valid conclusions are made using data-driven methods.
Cloud computing has changed the way it’s working today. The advantage is huge because your data is available wherever you are and you can access it from all types of devices. This is a positive thing about the advantages of using this technology because of the possibility of great savings on IT costs for the company because someone else takes care of your data, and users can use resources from cheaper devices. But how safe is your data in the Cloud environment? This article explains a few things about the functioning of the Cloud itself and the security risks that appear in this environment.
Early Internet of Things (IoT) products created were based on the first 16-bit home computer processor. There are four fundamental factors that allowed IoT to change from very limited use-cases to the broader consumption that we have today. There’s no doubt that we will continue to see more and more devices connected to the Internet. Just because we may have more devices than IPv6 addresses, it doesn’t mean this will change our lives for the better.
Confidence Intervals are always a headache to explain even to someone who knows about them, let alone someone who doesn’t understand statistics. In statistics, a confidence interval (CI) is a type of estimate computed from the statistics of the observed data. This proposes a range of plausible values for an unknown parameter. The interval has an associated confidence level that the true parameter is in the proposed range. This post is about explaining confidence intervals in an easy to understand way without all that pretentiousness.
Unlike market studies, which take weeks to present their findings, IoT offers real-time insight. This means banks can continually evolve and personalize their products instead of maintaining a static portfolio. As financial ecosystems connect among themselves and with other ecosystems on the IoT, banks can facilitate customer journeys around products from end to end and thus intensify engagement. What will be the landscape of IoT in the future? How will banks, marketers, service providers, enablers, aggregators, and users interact with this intricate web of devices and connectivity?
While the number of regression tests will grow as new features add up, the testing debt must be regularly repaid. With manual testing, this is typically accomplished by throwing more people at the job. But if you don't have extra resources to spare, your team will have a hard time managing the test coverage and missed deadlines will become a sad reality. The smart way to reduce your team's workload is to use automation testing.
Experts worry that individuals are ceding control over many important decisions to AI-based technologies and tools. People do so because of the perceived advantages gained by the use of these powerful tools, - e.g., efficiency, convenience, search capabilities. But, human autonomy and agency may be at risk, sacrificing, to varying degrees, our independence, privacy, and decision-making power. These concerns will only increase as AI advances continue to permeate just about every aspect of the economy, society and our personal lives.
Leadership, creativity, and collaboration are some of the skills that students need to have to survive in today’s competitive industries. Thanks to technology, it’s now easier for teachers to provide them with hands-on experience, enhancing skill learning. This model doesn’t place teachers at the centre of attention. Instead, they’re there to observe how students approach a specific problem and whether they communicate with their peers to solve the issue at hand. These and other educational methods will help educators instil leadership skills in students and ensure that they can thrive in today’s fast-paced society.
Crypto currencies can have a considerable impact on developing countries, by increasing financial inclusion of individuals and companies. In particular, by reducing the transaction fees and time, cross-border payments can be improved (Scott, 2016). This is beneficial for remittance payments, peer-to-peer lending and international trade. The underlying technology also supports the fight against corruption by having a more transparent tracking system for the use of funds.Currently, crypto currencies support the growth process of the developing countries in very limited ways.
It is not a one-time decision to choose from cloud vs. on-premise hosting; that’s a question that developers and business administrators can ask themselves at multiple stages during the life cycle of the AI application. There might arise a need to switch from on-premise to cloud or vice-versa. If a business is in the early stages of digital transformation, then the cloud will be the best option to test AI applications with low cost to experiment. And then, by evaluating the requirement of the applications, businesses can adapt, change, or scale the hosting to on-premise if need be.
The new book “Cyber Minds” offers insights on cybersecurity perspectives and recommendations across the cloud, data, AI, blockchain and IoT. The interviews and insights offered in this book make it an excellent choice for technology, security and business leaders to learn the latest approaches and thinking on cybersecurity on a range of hot tech topics. The insights and interviews discussing topics like blockchain and cloud computing are very insightful and different.
Thanks to the Internet of things, the world has become a place where everything is connected through small devices, which makes information accessible everywhere. To better understand what advantages IoT can bring to mobile apps, we need to know how they work in combination. There is an ever-increasing demand for business mobile app development, devices connected to the Internet, and mobile applications that allow you to work with them. The increasing deployment of novice technologies obliges mobile app developers to move in this direction.
Data analysts turn data into information. They play a vital role by making data actionable for decision makers. Data analysts often take data provided by data engineers, analyze it, and make recommendations. They create visualizations to display their findings in dashboards and presentation. Unlike data scientists, data analysts don’t usually create predictive models based on machine learning algorithms.If you want to become a data analyst or make yourself more marketable, this article suggests you learn the certain technologies, in order of priority.
Machine learning comes in many different flavors. In this post, we will explore supervised and unsupervised learning, the two main categories of machine learning algorithms. Each subset is composed of many different algorithms that are suitable for various tasks. One of the benefits of unsupervised learning is that it doesn’t require the laborious data labeling process that supervised learning must go through. However, the tradeoff is that evaluating the effectiveness of its performance is also very difficult.
Internet of Things doesn’t really have any meaning. What things? And what’s “Internet” about them? Fortunately, there’s a growing movement to change the definition of the tech term, to give it more meaning and set us up for the next decade of innovation. The last decade was about connectivity, and we describe that dynamic with the Internet of Things, This decade is really about adding intelligence to different devices, services, etc. We’re confronted with a new IoT: The intelligence of things. Here’s what that shift will bring – and how everything’s going to change in the decade ahead.
ML models interpretability can be seen as the ability to explain or to present in understandable terms to a human. Regardless of the simple definition, technical challenges and the needs of different user communities have made interpretability a subjective and complicated subject. To make it more objective, a taxonomy was adopted that describes models in terms of their complexity, and categorizes interpretability techniques by the global or local scope of explanations they generate, the family of algorithms to which they can be applied, and their ability to promote trust and understanding.
Compared to the five JEPs in Java 13, the new version of Java 14 contains 16 major enhancements, also called JEPs (Java Enhancement Proposals).The updates touch various areas. Most likely, the most interesting updates for Java developers are going to be the new switch expressions and the enhanced NullPointerExceptions. Don’t forget to try out the new language preview features and provide your feedback to the JDK developers. Enjoy the new Java 14!
Culture and governance are key to drive change around cyber security behaviours, but too many awareness programmes focus simply on superficial technical gimmicks. Stay clear of empirical and ready-made solutions: Start with focus groups, questionnaires, and interviews and measure upfront levels of staff security maturity and engagement with corporate values. There are 3 clichés that have been dominating the security awareness arena for the past decade. And here are 5 key points to build a successful cyber security culture change programme.
The Internet of Things is a flourishing field for innovations and it’s primarily designed to make our life easier. Yet, there are spheres which are only discovering IoT benefits. The financial industry is one of them. You cannot expect finances and banking to stay the same as they have been. Our reliance on the bank as a building, the bank as a place, has become less important because now we can bank 24/7. We start rethinking the way financial services should work.
AI made us think again about the ethics and politics of computerized systems. While computerized systems have been around and influential in our lives for half a century at least, their increased use of our data and increased power to make decisions indeed justifies to think again about their ethics and politics. And, actually, it is the ingredients of “data” and “decision” in this newly reborn notion of “algorithm” that explains why there is reason for ethical concerns and political debate, and hence for the call for regulation.
Although a wide range of traditional optimization methods are available for inventory and price management applications, deep reinforcement learning has the potential to substantially improve the optimization capabilities for these and other types of enterprise operations due to impressive recent advances in the development of generic self-learning algorithms for optimal control. In this article, we explore how deep reinforcement learning methods can be applied in several basic supply chain and price management scenarios. This article is structured as a hands-on tutorial that describes how to develop, debug, and evaluate reinforcement learning optimizers using PyTorch and RLlib.
Cold chain logistics have additional challenges when compared to traditional logistics systems. IoT is already transforming traditional logistics and supply chain systems and can bring the same revolution for cold chain systems too. With cut-throat competition and obstacles in the logistics industry, businesses can’t ignore the benefits of IoT for cold chain logistics systems. While IoT devices do involve additional investment, the benefits and savings provided by them are huge in the long run.
The term Internet of Everything is a fairly new term, and there is a difference between the Internet of Everything (IoE) and the Internet of Things. The Internet of Everything (IoE) ” is bringing together people, process, data, and things to make networked connections more relevant and valuable than ever before-turning information into actions. The Internet of Things (#IoT) is the network of physical objects accessed through the Internet.
Data quality is critical because data is used for decision making and powering AI models. Models and decisions are only as good as the data behind them, so any lack of confidence in the data means they are less useful in predicting and providing insights, slowing down, and undermining fast decision making. Trust in data is hard to get and easy to lose, so data quality must be maintained for models and dashboards to be useful at all times.