Today and well into the coming years, the survival of companies and the ability for humans to meet their material needs will increasingly rely on clean, well-operated, low-waste supply chains. Smart technology is now inescapable for warehouses. The benefits are just the start of what’s possible when companies take even modest steps into the future. Let’s look at some of the constituent components of a smart warehouse and find out how each one contributes to the larger goal of a more stable and efficient supply chain.
Artificial intelligence or AI, the phrase/acronym gets banded about an awful lot. In fact, many experts in AI don’t even like the term, they much prefer to use the words machine and learning, or ML. Delve deeper, and you come across many more terms, neural networks, deep learning, natural language processing and random forests. The trouble is, to a lot of people, the phrase artificial intelligence conjures up images of machines, ruling the world. The reality is very far from this vision of science fiction.
Put the finishing touches on your Data Science skill set by understanding the business perspective. This post explores the Fundamental Business Equation that each Data Scientist should understand. The content aims at educating technical Data Scientists who wish to have a tangible impact through their work. Find out which role Machine Learning can play in your company and how to find opportunities. The goal of a business is to make money, and Data Scientists are hired to help the company achieve this goal. How do you define “making money”?
As we continue to learn about the unique security threats of deep learning algorithms entail, one of the areas of focus are adversarial attacks, perturbation in input data that cause artificial intelligence algorithms to behave in unexpected (and perhaps dangerous) ways. Researchers are working on ways to build robust AI models that are more resilient against adversarial examples. Protecting deep learning algorithms against adversarial perturbation will be key to deploying AI in more sensitive settings.
Predictive analytics is one of the most important tools we have for putting humanity’s zettabytes of data to work for us. The widespread use of data in predictive analytics brings some new types of risks that should be on our radars, as well. The governments of the world are, rightfully, becoming more involved in the politics of privacy, for example. Here are four industries finding consequential ways to put this tech to good use.
Strategic investments in artificial intelligence, machine learning, and other associated technologies are becoming almost mandatory for agencies that want to stay ahead of the technological curve. It makes far more sense for the government to buy commercial technologies rather than build them. In addition, employees, both existing and potential new recruits, must factor into tech decision-making processes for the future. Here are tips from CIOs on where to use AI and emerging technologies.
Visualization tools represent an important bridge between graph data and analysts. It helps surface information and insights leading to the understanding of a situation, or the solving of a problem. Graph visualization tools turn connected data into graphical network representations that take advantage of the human brain proficiency to recognize visual patterns and more pattern variations. Graph visualization brings many advantages to the analysis of graph data. When you apply visualization methods to data analysis, you are more likely to cut the time spent looking for information.
Customers’ returning to your business is much more profitable than gaining new buyers. Always remember that if your ultimate goal is to retain customer loyalty, you need to make sure your customers enjoy interacting with your brand so much that they won’t be tempted to leave even if the competitor’s price is lower. Here are some ideas on how you can enhance customer loyalty based on the latest trends in consumer behavior.
DNA testing is evolving at an incredible pace. Scientists are using big data to develop clearer insights into the human genome and subsequently improve the quality of DNA testing. Big data is playing an important role in reducing false positives in DNA testing. It is also making the reduction of false positives more necessary. Preventing false positives with consumer DNA testing is also crucial. Millions of people are depending on DNA testing for identifying health risks and potentially identifying heirs to property. The problems associated with false positives in these instances could be just as problematic.
The truth is that every disruptive era is not only fraught with danger, but also opportunity. Every generation faces unique challenges and must find the will to solve them. Today, at the beginning of a new century, we are seeing similar shifts that are far more powerful and are moving far more quickly. Disruption is no longer seen as merely an event, but a way of life and the fissures are there for all to see. Our future will depend on our determination to solve problems faster than our proclivity to continually create them.
It’s clear the umbrella of artificial intelligence has advanced in areas like machine learning, which is already impacting areas such as image recognition, one of the biggest current viable use cases of AI-based technology. Any CTO looking at the technology will have to define a relatively narrow use case — ‘what problem will AI help me solve’, they should ask?, and focus on making sure there is enough data, and the right kind of data, to support that use case.
Automation - and specifically robotic process automation (RPA) - is indeed seeing back office processes and procedures handed over to machines (or bots). The front office of many companies, however, is often still very manual and very human. The trick to creating jobs, improving operational efficiencies and unlocking staff value lies in freeing up people in the front office so they can focus on the customer. How do you do that?
This post helps you consider how to communicate Data Science through your internal events. It shares the content choices to answer challenges needed to communicate about Data Science to a non-technical audience within your business. Understanding the breadth of Big Data types and their potential relevance and challenge for legacy systems is also important. Non- technical audience need to carefully manage their relationship with the IT department and ensure they have clarity on goals to achieve, and the software/tools that Data Scientists need.
The cloud system architecture is not limited geographically. One of the main attractions to cloud computing is the ability to provide robust cloud-based services from various widely dispersed locations. In this way, if one of the server facilities goes offline for any reason the other facilities in the cloud-services network can handle the processing work until the offline system comes back up. Opportunities exist and should be explored for every part of the process that connects the front end to the back end in cloud computing.
It is now becoming crystal clear that cybersecurity – beyond good practice and good ethics – is quite simply good business. Following cybersecurity best practices is a problem. In fact, it is an important reason why the issue is still shifting in and out of most boards’ radars. Gut feeling alone does not make for a strong-enough case: Top executives are increasingly asking to show the data. Being able to show key stakeholders in business terms what exactly is the tangible value-added of cybersecurity will be key in finally anchoring the topic at the right level of organizations.
Unfortunately for consumers, many business owners still convince themselves that their businesses are “too small” to be of interest to hackers. However, Small businesses are a favorite target of cyber criminals. With this in mind, we’ve put together a list of some of the small business cyber security statistics you should know in one convenient resource. We’ll also discuss why SMBs make such attractive targets and what you can do to protect your business
A few barriers to AI adoption have slowed down the mainstream adoption and full-fledged applications of this technology. With time, investment, and continued experimentation, these obstacles will eventually be overcome, giving rise to a whole new generation of advanced AI applications. These barriers to AI adoption, mostly stemming from the rapid and the exponential growth of AI capabilities combined with the lack of preparedness of businesses as well as governments, must be addressed before we transition to an AI-driven future.
The field of graph theory has spawned multiple algorithms on which analysts can rely on to find insights hidden in graph data. This article covers the graph analytics landscape. Graph analytics, or computing, frameworks. They consist of a set of tools and methods developed to extract knowledge from data modeled as a graph. They are crucial for many applications because processing large datasets of complex connected data is computationally challenging.
Over the next three years, embedded artificial intelligence (AI) will dramatically reshape the customer experience across nearly all industries, with most major businesses providing some type of AI-centred customer experience. AI is a way of providing new, novel experiences that are useful and helpful to people. While every industry will be affected by AI, specifically this technology will drive the greatest transformation over the next three years in healthcare; travel, transportation & hospitality; and manufacturing, banking, and insurance.
While it is true that technology can do some wonderful things, the measurable impact has been relatively meagre. At the same time the power of digital technology is diminishing. Without advancement in the underlying technology, it is hard to see how digital technology will ever power another productivity boom. Perhaps the biggest reason that the digital revolution has been such a big disappointment is because we expected the technology to largely do the work for us.
Marketing has changed and the change has been so rapid, it demands that marketing people have to change the way they learn, adapt and operate in order to cope with this ever-changing landscape. Is agile a practical method for marketing? In this post, an attempt is made to capture what has accelerated the change and how the marketing professional can cope with this change. At a broad level, the changes can be captured under three headings.
Data Science methodologies is definitely still an evolving field. Data Science teams may routinely need to draw data from a variety of database structures including column and even graph databases. They may also need to use their wider data access to load data into Data Lakes and spend longer on sourcing data than previous generations. For all these reasons, it is not surprising to see an explosion of more varied options. It is impossible to be comprehensive in this post, but let me share some exemplars that typify different approaches to this challenge.
B2B companies can benefit from machine learning in numerous ways — generate more leads, gain a better understanding of customers, establish high-quality omnichannel relationships, and so much more. While the number of B2C companies using machine learning is skyrocketing, adoption by B2B companies has yet to take off. It’s time for B2B companies to get their game on. Here are some of the ways machine learning can make a significant difference for B2B companies.
The clear business benefits of a strong privileged access security programme can be realised across numerous digital transformation initiatives – from RPA and cloud to DevOps. Effectively conveying the value of privileged access security in enhancing the business will help in gaining critical executive support and obtaining necessary budget and resources. From there, executive leadership can help rally employees to make it an organisational priority, impart a sense of urgency and ownership, and prevent it from being derailed.