Deep learning, the spearhead of artificial intelligence, is perhaps one of the most exciting technologies of the decade. There’s no doubt that machine learning and deep learning are super-efficient for many tasks. Deep learning is often compared to the mechanisms that underlie the human mind. However, they’re not a silver bullet that will solve all problems and override all previous technologies. Beyond the hype surrounding deep learning, in many cases, its distinct limits and challenges prevent it from competing with the mind of a human child.
Today’s marketers and salespeople are harnessing technology advancements to unearth insights in real-time, which was unimaginable few years ago. However, the key question is – are we looking at the right data? If our intent is to collect and understand customer feedback, we are looking at only partial information. A lot of information is usually in hidden in what is usually referred to as “dark data” or in simple terms unstructured data. You need to go beyond structured data and demystify the dark data that can give you relevant information.
The advent of IIoT (Industrial IoT) has completely changed the global IoT scenario. On one hand, IIoT drives the fourth wave of industrial revolution, and on the other hand, it contributes significantly to the unprecedented surge in the number of connected devices worldwide. Companies have started utilizing the IIoT to collect, aggregate, and analyze data to maximize efficiency and enhance productivity. The IIoT not only bring automation through machine learning but also establishes better synergy among machines to optimize output. However, implementation of IoT at your workplace requires utmost care and precision.
Product management has a major identify crisis. Product management is both art and science. You can teach it. You can learn it. But you also need to experience it. Product management seems to be an amorphous, fuzzy thing. Buzzword, acronyms and new memes seem to appear daily. Agile, lean, design thinking are popular terms. But how do they relate to product management? They all attempt to improve delivery quality and reduce delivery time. In the end, product management ensures the delivery and launch based on the requirements. If they add great value, why is their role so misunderstood?
Nearly half of cloud services in the enterprise are outside corporate IT’s domain, while around 47 percent of corporate data stored in cloud environments are not managed by the IT department. Cloud computing is attractive to enterprises for cost efficiency as well as its flexibility in allowing employees and customers 24/7 access to information and services. However, the security challenges can be significant. IT managers are often uncertain of which measures are meant to secure what data. Effective security for cloud data demands a holistic approach and recognizes that not all data is vital.
Today, all organizations are digital by default. Digital business inherently means utilizing new technology, connecting devices and operating platforms, embracing different ways of working, building large-scale data silos, and so on. The convergence of Internet of Things networks with what were once separate and self-contained — and therefore more manageable — systems represents a fundamental change. Coping with digital challenges and mitigating risks still represents a major burden for organizations across the board. The World Economic Forum now rates a large-scale cybersecurity breach as one of the five most serious risks facing the world today.
When it comes to emerging technologies, many talk about the importance of incorporating them in day to day processes - especially when it comes to businesses. But there only a few that have hands on experience with them. So we decided to interview some experts who have spent years working on different technologies, experimenting with their use cases and guiding others who wanted to grow in that field.
Behind the revolution in digital assistants and other conversational interfaces are natural language processing and generation (NLP/NLG), two branches of machine learning that involve converting human language to computer commands and vice versa. NLP and NLG have removed many of the barriers between humans and computers, not only enabling them to understand and interact with each other, but also creating new opportunities to augment human intelligence and accomplish tasks that were impossible before. Maybe NLP and NLG will remain focused on fulfilling more and more utilitarian use cases.
Machine learning is known for its difficulties with interpretability, or rather its absence. This is an issue if your users have to work with the numeric output, like in the systems used in sales, trading or marketing. If the user’s interpretation of the ML output is wrong the actual metrics won’t matter and you end up with the bad user experience. The problem is even bigger if you try switching users from an old transparent algorithm to ML. Here I outline the recipes for overcoming the user’s push-back once you start switching your system to ML.
Data is one of the most important assets that any company has Today, there are new and changing uses of data in the digital economy. The big questions however are, who is winning with data, where is this data being kept, what makes new data different, when should data be kept, moved, deleted or transformed, how should data be valued, and why data is so much more important than it used to be? Once we accept the premise that data value should be measured, what would we do with this measure?
Joining IoT and healthcare and leveraging connectivity to deliver care is not as easy as it seems. How can we ensure the successful marriage of IoT and healthcare? Let’s expand the notion of “pathways” to appreciate how IoT can be leveraged in healthcare delivery. The expansion includes the notion that data needs to flow across the players involved in care delivery and money needs to flow to compensate the right parties. We are on our way to professionally managed healthcare using IoT, not just consumer-grade wearables for the fitness-conscious.
The views on AI software development are polarized and controversial. Still, many experts see AI as an opportunity rather than a threat. AI technology may act as a method of augmenting human workforce and enabling us to work in newer and smarter environments rather than disrupting every single aspect of our lives. But like with any powerful technology, we need to use it prudently. Here are the most common illusions and myths about AI software development and shed some light on the possibilities of this disruptive technology.
Enterprises are moving cyber security and resiliency to the top of their priority lists (if they were not there already). This is especially true for enterprises building or managing IoT applications. Attacks which used IoT devices to launch distributed denial of service attacks (DDoS) are raising both consumer and business customers’ fears regarding the security of the IoT. When it comes to cybersecurity, IoT devices come with their own unique security challenges. Here are five key best practices enterprises can adopt to significantly lower the risk of a successful attack on their IoT infrastructure.
Experienced teams know when to back up seeing a piling debt, but technical debt in machine learning piles extremely fast. You can create months worth of debt in a matter of one working day and even the most experienced teams can miss a moment when the debt is so huge that it sets them back for half a year, which is often enough to kill a fast-pacing project. You end up with the project where the metrics randomly jump up or down, do not reflect the actual quality, and you are not able to improve them.
As smaller organizations move to public cloud, the remaining private datacenters are also getting much larger. A big driver for this scale is data leading to a completely new set of storage architectures that can operate a large scale and require very little management of the data. A new class of storage vendor has emerged, whose solutions accomplish this goal through a combination of 1) software defined storage 2) commodity building block hardware componentry 3) distributed scalable storage architectures and 4) application awareness. Let’s look at each of these solution characteristics and how they make large scale datacenter operations cost effective.
As a product manager, you need to capture, understand, interpret and analyze a range of qualitative and quantitative inputs to arrive at the ideal product decisions. There is no one single source of "truth" exists when making product decisions. Some inputs are definitely more quantitative than others, but many also need a dialogue with customers as well as judgment calls, best guesses, and interpretations. There is no standard approach or single tool that meets everyone's needs. So what's the answer? Is product management more art than science?
To stay competitive and successful, both FinTech software developers and financial companies need to catch the waves of digital disruption and learn how to ride them right. To keep up with the finnovation pace, businesses are adopting the emerging technologies such as Data Science, AI, digital currency, Blockchain, Biometrics, and more. However, they may turn out to be intricate and present challenges you need to be ready to embrace. Here are 5 innovations FinTech software developers need to be ready to adopt to implement FinTech innovations with sense and caution.
In the ‘outcome economy,’ people don’t buy things. They buy outcomes. They buy the end results they are looking for. When a manufacturer sells a product-as-a-service, it is a sizeable step towards an outcome-based business model. So, what about enterprise software. Why do most enterprises still keep paying for the ‘thing’ (i.e. the software) rather than the outcome that they desire? Or to flip the question around, are there any enterprise solution providers offering outcome-based payment models? Do any enterprise software companies actually sell results rather than software?
The focus of AI implementation at present must be to minimize human involvement in the routine and non-creative tasks, so that human effort can be directed towards innovation and planning, where AI can be used for guidance. Due to its deep learning and independent decision-making capabilities, applications of AI in different business areas are seeing a steady rise in ubiquity in some industries. The concept of artificial intelligence or machines that aim to emulate human thinking is undergoing vigorous research. Here are a few application areas that you can consider for AI implementation.
Each year, Earth Day provides an opportunity for people around the world to consider how they can take action to protect our environment. For decades, it has encouraged people to undertake individual actions and advocate for policies. Digital technologies – in particular the IoT – can help us address the climate change challenge by accelerating the transition to a more sustainable, renewable-energy-powered economy. In particular, the IoT is a key enabling technology for an emerging concept called, “The Internet of Energy.”
Data is clearly not what it used to be! Organizations of all types are finding new uses for data as part of their digital transformations. New data is transactional and unstructured, publicly available and privately collected, and its value is derived from the ability to aggregate and analyze it. We can divide this new data into two categories: big data and fast data. The big data–fast data paradigm is driving a completely new architecture for data centers.I will cover each of the top five data challenges presented by new data center architectures
Most IoT ecosystem projects will involve multiple contributing application partners. They will also involve complex, evolving functional and non-functional requirements. To address these challenges and reduce complexity, IoT developers are now starting to embrace collaborative lifecycle management (CLM) technologies combined with the latest continuous engineering (CE) technologies. Organizations that want to ensure the success of complex IoT initiatives will need to mitigate the adverse potential of complexity-related development risk. Thankfully, these risks can be managed effectively using the latest CE/CLM tools to ensure that your IoT vision becomes a reality. Collaborative lifecycle management is a key to IoT success
We can see a lot of hype about AI and Machine Learning, and its potential to transform businesses. More and more companies are adopting machine learning solutions, setting up accelerators, opening R&D centers, and investing into startups. Also, there is a large number of reports with AI market estimates and forecasts. However, it’s challenging to get the right information on machine learning development that will actually work for your business. Here are our five expert tips to make machine learning development work for you.