Today, machine learning networks require the scale of computing resources found only in large data centres, but the scale of the IoT will require that these capabilities are pushed out from the cloud to the edge, and into mobile devices themselves. This will require challenging advances in energy-efficient computing, the area of business where Arm is world-leading. IoT devices will make a great deal of information available, with associated issues of data protection and cybersecurity. Where the potential exists to use the internet to disrupt the control of critical equipment, such as an autonomous vehicle, security matters must be paramount.
A lot has been said about the negative connotations of artificial intelligence but the hard evidence continues to tell us something different. For starters, the National Association of Software and Services Companies has stated that robotic process automation (RPA) can already reduce operational costs by as much as 65% - with ROI within as little as half a year. With ever more sophisticated decisioning as part of this, AI is becoming an incredible asset to the human workforce and is increasingly becoming an inevitable addition to the business process. In the area of customer service, in particular, artificial intelligence is already proving its worth in ways that cannot be ignored or played down.
Most healthcare providers are waking up to the fact that their operations need a data-driven, scientific overhaul much the same way as auto manufacturing, semiconductor manufacturing and all other asset-intensive, “flow”-based systems have experienced. The good news is that there are tools, software and resources that can be used to bring about this transformation.
Are you ready for the Internet of Things (IoT)? Despite its transformational potential, most organizations are not. In an era of rapid disruption and digital transformation, IT executives and managers must lead the charge. You must bridge the gap between technology, business, engineering and operations. You must be evangelists, teachers, facilitators and innovators. To succeed, I’ve listed six things you must do to accelerate IoT adoption within your organization.
Although the GSMA describes eSIM as being a vital enabler of the growth of the machine-to-machine (M2M) ecosystem, the applications of this nascent technology are in fact even wider-reaching than pure M2M applications and that not only opens up broad opportunities for IoT/M2M companies to define and own their category, it also creates a literal, built-in differentiator that will set them apart from competitive solutions and devices that are not eSIM-enabled.
In recent years, as the sultans of Silicon Valley have pressed both computation speeds and data storage capacities to dizzying heights, researchers and analysts working at the intersection of statistics and computer science have leveraged new tools to chase increasingly sophisticated modeling techniques. This dramatic expansion in both software tools and, especially, the quantity and quality of data available led to the emergence of data science as a discipline, and most important the assets created by a data science teams: predictive analytic models.
IoT protocols aren’t limited to internet protocols or the ones that IT domain created. A plethora of standards exist in OT and are tailored for specialized OT applications from production floor control to subsystem connectivity in cars. OT and IT protocols evolved separately with some borrowing from each other.
The number of network-connected and intercommunicating devices is expected to climb well into the tens of billions within a few years. As the world grows ever more connected, how can we trust these devices? Such a network of intercommunicating objects requires a secure and efficient way to track all interactions, transactions, and activities of every “thing” in the network. Interoperability, security, compliance, privacy, and reliability are all barriers to IoT growth, but the paramount challenge for ubiquitous connectivity, never mind device autonomy, is a lack of trust.
With all the articles being published about robots taking over repetitive tasks and transforming the operations in businesses across sectors I find it hard believe that any IT leader is unaware of the robotic process automation (RPA) technology, yet it seems to be the case.
Many of you have likely experienced the same situation that I have encountered where you have received a report and thought “what am I looking at?”, “whats the point?”. The pack is full of tables and it's difficult to see the wood for the trees! How can you, as the data expert, show your business partners that you have a clear and comprehensive understanding of their business, without burying them in data?
For years, we have been hearing about the virtues of IoT and specifically how Industrial IoT (IIoT) is the cure for inefficient machine maintenance and unplanned downtime. While all that is true if zero-downtime ("ZDT") was such a pain, why isn't everyone rushing to get connected and drive maintenance costs down and uptime up?
When evaluating innovative ideas, it is crucial to gain a good understanding of the value drivers. The value propositions for target customer groups and key partners must be clear and compelling. Given the complexity of connected solutions and the associated ecosystem, this is easier said than done. However, there are several tools that can support this process and help reduce the risks and complexity of IoT solutions. Below we present a selection of our favorites, which evolved from the IoT Business Model Builder.
IoT may be the next big thing in enterprise technology, but the various types of connected devices that seem useful for a business have the potential to cause more harm than they help. If these risks are controlled by some of the best practices, IoT can be engaged with in a healthier and safer fashion. If not, IoT has the potential to evolve into a minefield for cyber-risk in the enterprise.
Improving hospital operational efficiency through data science boils down to applying predictive analytics to improve planning and execution of key care-delivery processes, chief among them resource utilization (including infusion chairs, operating rooms, imaging equipment, and inpatient beds), staff schedules, and patient admittance and discharge. When this is done right, providers see an increase in patient access (accommodation of more patients, sooner) and revenue, lower cost, increased asset utilization, and an improved patient experience.
The field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.
Are you new to building Machine Learning Product initiatives and struggles to find where to start, and looking for tips and guides on how you can iterate and learn quickly from your customers when it comes to ML products. Here is kick-starter materials online which you can use and follow
Artificial intelligence is such a vast, nascent, complex area of computer science that even the world’s richest companies are struggling to recruit at the required pace. Within this article, we will first define what the role of AI scientist entails, before delving into the causes of — and potential solutions to — the global AI talent shortage.
Many organizations are still thinking of IoT as simple consumer toys that do not impact them, but that is not the case. Early on I circled my wagons around explaining IoT from a security perspective and describing how to better examine and address the security of an IoT products ecosystem, no matter what we determined “IoT” to be. The main focus of this concept is that the security of any part of an IoT ecosystem can, and will, affect the security of all other parts of that ecosystem.
The promise of IoT remains unchanged, from subtle efficiencies, to disrupting industries, to global economic impact, but it remains early. The technology and know how will improve and every company will need to determine how and when to engage.
Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. The role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers, Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress.
Two big trends in the customer interaction and engagement space make Robotic Automation very relevant. First, the imperative to better enable agents. In the past years, customer service departments have invested in self-service. The second trend is to improve the customer experience. It entails streamlining and digitizing customer-facing processes.
There’s a lot of talk about the applicability of artificial intelligence (AI) and deep learning to taming the vast quantities of data that modern Operations teams and their tools deal with. Analyst reports frequently tout AI capabilities, no matter how minor, as a strength of a product, and the lack of them as a weakness.
The intent of this blog was to provide some clarification of how machine learning can be readily applied to solve real world problems. This simplistic Value prediction function was just one example where machine learning can be used to avoid building a potentially rigid and complex manual model and ultimately save time and effort.
There is no one, single killer app for IoT. Instead, any company can create the right killer app that solves the need of their specific customer, in their specific industry, and vertical. In this post, I shared a handful of examples, but we are seeing applications in all industries, from Healthcare, to Energy, to Smart Agriculture, Smart Buildings, Transportation, etc. Companies in all industries are looking to adopt IoT as the catalyst to improve their existing solutions.