No two users see the same news feed in their social media accounts and their news curation apps. AI-driven news curation makes sure you see “relevant” results, as the curators will tell you. But in effect, these algorithms submerge you in a filter bubble, where you see too much of what you want to see and too little of what you should see. The direct result of filter bubbles is polarization of societies, amplification of biases, less tolerance for opposing views, and more vulnerability to fake news.
AI created an explosion of new jobs the likes of which the world had never seen. First came the destruction but then came the creation. New jobs we couldn’t imagine before the old ones disappeared started to flourish. People worked side by side with AI, as politicians and big companies and decentralized autonomous organizations (DAOs) learned how to balance the needs of man and machine. In just fifty years we saw the mustard seeds of the dawn of the Age of Intelligence grow into a wild and uncontrolled forest.
Technology is tucked inside every nook and corner of modern business. Smart leaders use data generated to understand what’s happening in the organization, and this analysis helps them bring agility to their business. In fact, data-driven organizations that harness insights to create a competitive advantage are growing rapidly. Data should be an influence to any major business decision. And so, data visualization is a powerful tool to bring dramatic changes to organizations. It can provide answers to many key questions, and make the data visually appealing,
Artificial Intelligence (AI) automates analytical processes much like robots and production machinery automate physical processes for companies. The misconception is robots are the front line of AI. That is simply not the case anymore especially if you expand your definition to include predictive analytics. Yes, robots involve some AI. There are many uses of AI in business processes, and in the office of distributors and manufacturers. Here are a few examples.
To refurbish what we’ve already accomplished, and to go beyond it is going to take a great effort. However, there is one kind of technology that can do all this and more, especially in the financial sector. Surprisingly, though, it isn’t AI nor Robotics, neither big data nor quantum computers. Not even, thank goodness, social media. It is called the blockchain, and it’s coming for you, it’s coming for all of us. Blockchain, which if it promises to do what the specialists in the field are telling us, will change the financial landscape of the world for all time.
There always comes a time in the business life-cycle when a business starts growing at a steady pace and so does its operations, especially the financial ones. Committing a mistake is in human nature but this can greatly be minimized and brought down to zero with the help of Point-Of-Sale Reconciliation. This monitoring of accounts during retail accounting not only saves you from the future financial problems but also provides you a platform wherein you can understand and utilize your finances in order to grow your business.
Companies regularly turn down applicants who would have been amazing technical performers. This leads to a huge number of confused, directionless aspiring data scientists. But here’s some good news: there aren’t actually that many reasons why applicants get turned down from data science roles, and there’s a lot you can do to cover those bases. And those reasons — the technical and nontechnical skills that most applicants don’t have but that companies most badly want — are what this post is all about.
AI is already radically changing the world. In the short term, the promise and peril of AI is legion. AI will deliver some of our brightest fantasies and our darkest nightmares. Why both? Because AI is a universal technology. It’s flexible enough to do whatever we want it to do. And that means it will reflect the good and evil of its creators: Us. Let’s dive in and take a look at how AI will change society in the next few years, and by the time you’re old and grey, and when you’re long since turned to dust.
Complexity can plague the success of DevOps within an organization. Complexity cannot be avoided, as DevOps is complex and will likely continue to be. However, the key to avoiding failure through your DevOps journey is to engage the complexity by using DevOps tenets to implement DevOps. Do not try to boil the ocean. Instead, at each leg in the journey, take inventory of where you are in terms of current goals, state, and best practices. Fine-tune your direction and build your solution using proven continuous delivery methods.
Since DevOps involves a collection of team members from all parts of the software delivery lifecycle (SDLC) process, the central platform needs to meet the needs of all team members. As you work to build your next test analysis toolbox, consider the five features to efficiently evaluate the data, act upon it and deliver iterations and features with confidence. Let’s explore these five essential tools that enable DevOps teams to quickly and efficiently analyze data, triage issues and act upon failures with the best possible insights.
Machine learning (ML) and artificial intelligence (AI) applications are booming in the corporation world. While algorithms aren’t always replacing humans, they are usually changing the way we work. No leader is safe from the rapid change we are seeing in the age of algorithms. Businesses are transforming at rapid pace and there is no time to dilly dally. Learn how to make use of algorithms to build on your human skills or risk being replaced entirely.
Data being the latest trend for achieving digital transformation, companies are striving to gather more and more data and also to make the most out of it. But for everything to fall in place, it is important that companies manage, organize, and also secure the data that is collected. While storing and managing a voluminous amount of data is difficult, securing the same is even more arduous. Besides, the proliferating cyberattacks and data breaches have created a sense of anxiety in companies today. Considering the promise of authenticity, integrity, and immutable storage, the disruptive and revolutionary technology, blockchain, will very well fill the gaps in traditional data management.
Industrial IoT implementation is here to stay and help businesses grow further with time. In fact, we believe that involving such an innovative technology will definitely become a driving force in the industrial revolution for the coming years. The complexity involved in adopting IIoT often scares businesses. But, the right mobile app development company can help you streamline this complexity and move beyond the above-mentioned challenges to make it work. Avoiding the change for too long, on the other hand, can dissuade your business from moving forward.
Every piece of the IT organization has their own little area to deal with. You don’t typically play outside your area and work with the other teams unless you have to. By eliminating IT silos, organizations can then enable new intelligence, more effective decision-making, and informed automation to optimize network performance management (NPM). That is to say, if siloed walls are broken down and the synergies between tasks and technologies can be better coordinated, everyone benefits. The question is how to get there without going broke or crazy.
Today’s IT landscape is dominated by cloud, edge computing, IoT, AI and other disruptive technologies, and the datacentre remains at the heart of the organisation. Its role is key to delivering IT services and providing storage and networking to an increasing number of networked devices, users, and business processes. The explosions of data, as well as businesses embracing digital transformation, are all factors that play a part in not only storage strategies, but also the evolution of the datacentre.
The mechanism that drives smart farming is Machine Learning — the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments. Machine learning is everywhere throughout the whole growing and harvesting cycle. Let’s discover how agriculture can benefit from Machine Learning at every stage.
Spark NLP library is the 7th most popular across all AI frameworks and tools. It is also by far the most widely used NLP library – twice as common as spaCy. Optimizations are done to get Apache Spark’s performance closer to bare metal, on both a single machine and cluster, meant that common NLP pipelines could run orders of magnitude faster than what the inherent design limitations of legacy libraries allowed. Being natively built on Apache Spark ML enables Spark NLP to scale on any Spark cluster, on-premise or in any cloud provider.
Debate around the advantages and disadvantages of commercial off-the-shelf (COTS) software versus one-of-a-kind, fully customised software has raged, there is a third, lesser-known, option: platform software. Think of the three software alternatives on a spectrum of development, with COTS on the left, custom-built software on the right, and the platform alternative somewhere in the middle. There are clear differences between them, with each having its own set of pros and cons. Here are five factors to consider when deciding between COTS, platform or custom-built Software.
Artificial Intelligence is changing the way traditional marketing is done. It is far more capable than humans enhancing efficiency and effectiveness of marketing. AI backed Chatbots are becoming an integral part of marketing. Such tools help businesses provide enhanced services, engaging them for a longer time and increasing the chances of conversion. Augmented or Virtual Reality is another emerging trend which is changing the way marketing is done digitally. AI can be used in marketing in all the three layers of the pyramid, bidding, targeting and messaging.
As the technology gets easier to deploy, and the Cloud Vendor data services mature, it becomes much easier to build data-centric applications and provide data and tools to the enterprise. This article is aimed at helping big data systems leaders moving from on-premise or native IaaS (compute, storage, and networking) deployments understand the current Cloud Vendor offerings. Those readers new to big data, or Cloud Vendor services, will get a high-level understanding of big data system architecture, components, and offerings.
Though immersive technologies are still in their infancy, they have come a very long way already. This gives credence to the fact that AR and VR in the finance industry will have incandescent adolescence. Looking at the current advances, AR and VR seem to be the undeniable future game changers for the financial sectors. With its immersive experiences, AR and VR in the finance industry will allow various institutions to offer the ultimate customer experience, thus enabling them to thrive amidst cutthroat competition.
There are numerous issues hindering the effective implementation of IoT affecting the role of a chief information officer (CIO). There are many networks infrastructures that are underutilized and the industry needs to realize the potential of converging and powering multiple low-voltage devices and systems on a single unified structured cabling system that supports common communication protocols and enables sharing information from one system to another. CIOs can think differently. Only those that embrace data-driven reasoning and response across a range of machines, applications and systems are truly prepared for a successful digital and business transformation.
Integration of computer vision and natural language processing (NLP) is the most actively developing machine learning research areas. Yet, until recently, they have been treated as separate areas without many ways to benefit from each other. Since the integration of vision and language is a fundamentally cognitive problem, research in this field should take account of cognitive sciences that may provide insights into how humans process visual and textual content as a whole and create stories based on them.