An organization’s survival is now based upon its ability to rapidly adapt as never seen before. The story of evolution is often framed as “survival of the fittest,” but there is an equally compelling and relevant story of extinction for those species whose adaptations left them unable to compete for resources. In a rapidly changing business environment, it is impossible to predict your success. However, one can observe what traits are advantageous in the market.
Information and data are currently located on legacy systems within organizational premises, which is why accessibility and real-time insights are found lacking. The move to the cloud can address this concern thoroughly. Adapting to the constantly changing market landscape around us is key to being ready for what the market may hold in the future. AI strategies are at the forefront of all tactical organizational decisions. Your hybrid cloud system can help you gain real business value through AI by incorporating all the facets in one place.
Microservice architecture means that each application, or microservice’s code and resources are its very own and will not be shared with any other app. When two applications need to communicate, they use an application programming interface (API) — a controlled set of rules that both programs can handle. Developers can make many changes to each application as long as it plays well with the API. This idea comes in many flavors, with different shares of the monolith architecture. In this post, we are going to discuss one of such variations of microservice architecture, known as Hexagonal Architecture.
Blockchain in general provides tremendous benefits in the field of finance as it can enhance security, transparency, immutability and data privacy of transaction data. Why do organisations not fully embrace blockchain yet, as it seems? In our opinion, this is due to the high regulatory burden for financial corporations. But here, governments are changing laws to remove frictions between decades-old law frameworks and dematerialized assets and rights based on blockchain, crypto assets and Distributed ledger technology (DLT).
There are many sub fields in data science, but irrespective of that, SQL remains an important ingredient in the Data science. It will be useful to know why and how SQL holds an important stature in the field of data science. Without the essence of SQL, your entry into this field would be incomplete. Learning and implementing SQL will go a long way in helping you think of more creative ideas and turn your data into useful business use cases or insights.
Technologies such as Artificial Intelligence, Machine Learning, Neural Networks, Big Data, Blockchain, IoT, etc are trending individually or with the blend of others. The use-cases solved with these technologies make them crucial for various business practices that vary from security and pattern detection to data collection and automation. Hence improve revenue, marketing, and performance. For instance, many online businesses ensure online security by detecting fraud in the financial system using AI-powered solutions. Let's first understand the terms individually...
AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools. Fintech’s traditional tech stacks weren’t designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What’s needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. Here are ten predictions of how AI will improve FinTech in 2020.
Not sure which evaluation metric you should choose for your binary classification problem? You want to know for each metric, the definition and intuition behind it, the non-technical explanation that you can communicate to business stakeholders, how to calculate or plot it, and when you should use it. You will learn about a bunch of common and lesser-known evaluation metrics and charts to understand how to choose the model performance metric for your problem. After reading this blog post you should have a good idea.
What can AI do for Manufacturing Industry? The Manufacturing industry has always been available to embrace the innovative technologies. From machinery inspection and diagnostics to production planning, AI-powered analytics enables manufacturers with the improvements in efficiency, product quality and safety of the employees. While Sensors, IoT and connectivity can fetch you operational data, advanced AI algorithms in the form of Machine Learning and Artificial Neural Networks help you to predict the next failure of a part, machine or system.
Could 2020 be the year AI as a Service takes off? To some extent it already has. AI can lead to greater efficiencies and better customer insights for businesses in nearly every industry. In 2020, AI won’t just be nice to have, it will be a necessity. AI as a service is one of the biggest game-changers we’ll see in digital transformation. Yes, as AI as a service becomes more popular, it will also become more popular for hackers. AI as a service could definitely improve a company’s odds of experiencing success.
No doubt that all vehicles in the future will be connected. This small revolution has already begun. Its first steps are soft, but it will soon be a regular topic on many conference agendas – not only those specific for Telematics and in-vehicle information and entertainment systems. The future is in integrated, synchronized systems providing personalized user interfaces across all everyday life devices, for example, computers, tablets, smartphones and cars. Synchronized data is kept in the cloud and app UI should be customized for each device.
A significant change seen 2019 is the increase in the use of GPUs at work. While most of the data scientists still use PCs and similar models, the second-favourite product is Nvidia GeForce GTX 9 Series GPU. The number of people using it has grown from a mere 8% last year, to 28% in 2019. As the data-science tooling for GPU's gets better and the price for GPU's reduces, even older model GPU's can be used to demonstrate and get executive buy-in for a GPU based strategy going forward.
Machine learning is a statistical modelling technique, like data mining and business analytics, which finds and correlates patterns between inputs and outputs without necessarily capturing their cause-and-effect relationships. Determining causal relationships requires tried-and true scientific methods, that is, empirical and measurable evidence subject to testable explanations and predictions. And, in particular, as we’re frequently reminded, correlation does not imply causation. Here are the key benefits of AI solutions based on augmenting statistical methods with domain-based models.
The great thing about using z/OS Connect EE is that developers can access most mainframe assets from a single product. It also makes it easy for mainframes to join in the API economy. And the developers that you use, can access mainframe APIs without needing to know very much about mainframes. They can treat the mainframe like any other server. Making the best use you can of existing applications and data by making them available as APIs and then developing new applications to meet a need of customers and potential customers.
Immersive technologies deliver value by creating new digital environments. Whether viewed from a headset or a smartphone app, virtual characters and tools help people learn more, work smarter, and have fun. Businesses are racing to take advantage of this trend, which is why users are poised to receive ever more sophisticated VR and AR products and services. From medicine and art to HR and manufacturing, no industry will be left untouched by immersive solutions that change our perception of reality.
Medical culture has begun to change, but the change is by no means complete. AI developers need to tackle the issues both doctors and patients will have with the adoption of AI and build the right tools to facilitate the changing medical landscape. Before AI can truly transform the healthcare sector, the elephant in the room needs to be addressed: patient confidentiality or privacy. AI in healthcare will have a wide range of applications in everything from personal medicine to research, diagnosis and logistics.
To keep up with the modern pace of life, the tourism and travel industry needs to see how to deploy the latest technology at hand. That’s when new AI innovations come into play with their huge potential to make travel faster, more efficient or more personalized. There is no doubt that the use of AI for travel will keep shaping the way we explore the world in the future. Recommendation systems come into play.
IoT/IIoT challenges include skills shortage, standards, security, uncertain ROI, etc. Success can be achieved with top-down business solutions that involve People, IoT/IIoT connected devices, trading partners, and enterprise applications, all collaborating and orchestrating their activities The collaborations are in the context of end-to-end value streams, that are modeled, automated and monitored through Digital Process Automation (DPA) for continuous improvement. IoT/IIoT constitutes a powerful extension the business processes that are supported through DPA.
With all the hype surrounding AI we tend to overlook a very important fact. The best defense against a potential AI cyber-attack is rooted in maintaining a fundamental security posture that incorporates continuous monitoring, user education, diligent patch management and basic configuration controls to address vulnerabilities. In the security world AI has a very clear-cut potential for good. The industry is notoriously unbalanced, with the bad actors getting to pick from thousands of vulnerabilities to launch their attacks, along with deploying an ever-increasing arsenal of tools to evade detection once they have breached a system. All explained in this article.
The adoption of IoT continues to grow globally. Eighty-five percent of IT decision makers say they have at least one IoT project in the proof of concept, learning or purchase phase in their organisation. We are already seeing more involvement from senior-level executives in using IoT to transform the business; clearer, business-oriented goals for projects; and a willingness to adapt organisational structures and procedures to better support IoT and other transformational technologies. Here are some of the IoT trends to look out for in the year ahead.
When first starting out on a digital transformation journey, remember that other organizations are in the same position as you: struggling to align their culture, process and technology, or even just starting out trying to figure out their change strategy. Experimentation is a key part of starting a transformation initiative, and it requires an often painful shift in mindset, behaviors and structure. And while there is no completely prescriptive approach, there is universal belief in what needs to be addressed. The road there can be bumpy, but those who take the time will be rewarded.
As the technologies become more developed, the union of AI and intelligent personalization could lead to the emergence of companies that are predicting your dream vacation before you have any idea of your own. The futuristic chatbot will already know your preferred airport, dates, times, mode of transport and your travel companion from your emails, Facebook, Twitter and calendars — if you grant the required access, of course.
It won’t be wrong to say that the future of ERP will include big data. The incorporation of big data with ERP should make businesses capable of having a broad perspective over their business operations. However, businesses should ensure that the risks associated with big data are addressed to ensure optimum utilization of the technology. As an additional step, companies need a convergence of multiple advanced technologies with high-speed decision-making capabilities if they want to stay ahead of the competition.
Convolutional neural networks, also called ConvNets, were first introduced in the 1980s. The early version of CNNs, called LeNet could recognize handwritten digits. CNNs found a niche market in banking and postal services and banking, where they read zip codes on envelopes and digits on checks. Convolutional neural networks are composed of multiple layers of artificial neurons. One of the great challenges of developing CNNs is adjusting the weights of the individual neurons to extract the right features from images.