The researchers created a conceptual model to systematically identify the pathways through which AVs can affect public health. The proposed model summarizes the potential changes in transportation after AV implementation into seven points of impact: transportation infrastructure; land use and the built environment; traffic flow; transportation mode choice; transportation equity; and jobs related to transportation and traffic safety. The changes in transportation are then attributed to potential health impacts. In optimistic views, AVs are expected to prevent 94% of traffic crashes by eliminating driver error, but AVs’ operation introduces new safety issues such as the potential of malfunctioning sensors in detecting objects, misinterpretation of data, and poorly executed responses, which can jeopardize the reliability of AVs and cause serious safety consequences in an automated environment. Another possible safety consideration is the riskier behavior of users because of their overreliance on AVs—for example, neglecting the use of seatbelts due to an increased false sense of safety. AVs have the potential to shift people from public transportation and active transportation such as walking and biking to private vehicles in urban areas, which can result in more air pollution and greenhouse gas emissions and create the potential loss of driving jobs for those in the public transit or freight transport industries.
For most financial institutions, the strategic planning process for 2021 is far different than any in the past. As opposed to an iterative adjustment to plans from the previous year, this year’s planning must take into account a level of change in technology, competition, consumer behaviors, society and many other areas that is far less defined than before. The uncertainty about the future requires a combination of a solid strategic foundation with sensing capabilities and the ability to respond to threats and opportunities as quickly as possible. For many banks and credit unions, this will require organizational restructuring, the reallocation of resources, revamping processes, finding new outside partners and a culture that will support flexibility in plans that never was required before. There is also the need to build a marketplace sensing capability across the entire organization and from a broader array of sources. This includes customers, internal staff (especially customer-facing employees), suppliers, strategic partners, research organizations, boards of directors and even competition. Gathering the insights is only half the battle. There must also be a centralized location to gather and analyze the insights collected.
Cybercriminals have been actively taking advantage of the global pandemic, with an increase in cyberattacks, phishing, spear-phishing, and business email compromise (BEC) attempts. And on the healthcare side of things, NSCA Executive Director, Kelvin Coleman, said it’s not a huge surprise. Even in the early 1900s during the Spanish flu pandemic, folks would put articles in newspapers to take advantage of the crisis with hoaxes and scams, Coleman explained. “Bad actors take advantage of crises,” he said. “Hackers are being aggressive, leveraging targeted emails and phishing attempts. Josh Corman, cofounder of IAmTheCalvary.org and DHS CISA Visiting Researcher, stressed that when a provider is forced into EHR downtime and to divert patient care, it’s even more nightmarish during a pandemic. In Germany, a patient died earlier this month after a ransomware attack shut down operations at a hospital, and she was diverted to another hospital. These are criminals without scruples, Corman explained. The attacks were happening before the pandemic, but there’s been no cease- fire amid the crisis. In healthcare, hackers continue to rely on previously successful attack methods – especially phishing. It continues to be a successful attack method.
US officials identified the Russian hacker group as Energetic Bear, a codename used by the cybersecurity industry. Other names for the same group also include TEMP.Isotope, Berserk Bear, TeamSpy, Dragonfly, Havex, Crouching Yeti, and Koala. Officials said the group has been targeting dozens of US state, local, territorial, and tribal (SLTT) government networks since at least February 2020. Companies in the aviation industry were also targeted, CISA and FBI said. The two agencies said Energetic Bear "successfully compromised network infrastructure, and as of October 1, 2020, exfiltrated data from at least two victim servers." The intrusions detailed in today's CISA and FBI advisory are a continuation of attacks detailed in a previous CISA and FBI joint alert, dated October 9. The previous advisory described how hackers had breached US government networks by combining VPN appliances and Windows bugs. Today's advisory attributes those intrusions to the Russian hacker group but also provides additional details about Energetic Bear's tactics. According to the technical advisory, Russian hackers used publicly known vulnerabilities to breach networking gear, pivot to internal networks, elevate privileges, and steal sensitive data.
The brainchild of IBM, Machine Learning for Kids is a free, web-based tool to introduce children to machine learning systems and applications of AI in the real world. Machine Learning for Kids is built by Dale Lane using APIs from IBM Watson. It provides hands-on experiments to train ML systems that recognise texts, images, sounds, and numbers. It leverages platforms such as Scratch and App Inventor to create interesting projects and games. It is also being used in schools as a significant resource to teach AI and ML to students. Teachers can also form their own admin page to manage their access to students. A product from the MIT Media Lab, Cognimates is an open-source AI learning platform for young children starting from age 7. Children can learn how to build games, robots, and train their own AI modes. Like Machine Learning for Kids, Cognimates is also based on Scratch programming language. It provides a library of tools and activities for learning AI. This platform even allows children to program intelligent devices such as Alexa. Another offering from Google in order to make learning AI fun and engaging is AIY. The name is an intelligent wordplay with AI and do-it-yourself (DIY).
Enterprises are working to digitally transform core business processes to enable greater automation of backend processes and to encourage more seamless customer experiences and self-service at the frontend. We are seeing banks, insurers, retailers, energy providers and telcos working to develop their own digital assistants with a growing number of skills, while still providing a consistent brand experience. Developing bots doesn’t have to be complex. It is more important to carefully identify the right use cases where these technologies will deliver clear ROI with the least amount of effort. Whether an enterprise is applying RPA or conversational AI, or both, it’s important to first understand the business problem that needs to be solved, and then identify where bots will make an immediate difference. Then consider the investment required, barriers to successful implementation, and the expected business outcomes. It’s better to start small with a narrowly focused use case and achievable KPIs, rather than trying to do too much at once. Conversational AI and RPA are very powerful automation technologies. When designed well, a chatbot can automate up to 80% of routine queries that come into a customer service centre or IT helpdesk, saving an organisation time and money and enabling it to scale its operations.
Testing – it’s an important part of a developer’s day-to-day, but it’s also crucial to the operations engineer. In a world where DevOps is more than just a buzzword, where it’s become accepted as a mindset shift and culture change, we all need to consider running quality tests. Traditional testing may include UI testing, integration testing, code coverage checks, and so forth, but at some point, we still need eyeballs on a physical page. How many times have we seen a funny looking page because of CSS errors? Or worse yet, an important button like say, “Buy now” “missing” because someone changed the CSS and now the button blends in with the background? Logically, the page still works, and even from a traditional test perspective, the button can be clicked, and the DOM (used in UI Test verification) is perfect. Visually, however, the page is broken; this is where visual testing comes into play. Visual testing allows us to use automated UI testing with the power of AI to help us determine if a page “looks right” aside from just “functions right.” Earlier this year, I partnered with Angie Jones from Applitools in a joint webinar where we talked about best practices as it pertains to both Visual Testing and also CI/CD. This blog post is a summary of that webinar and how to handle visual testing in CI/CD.
Every design has a pattern and everything has a template, whether it be a cup, house, or dress. No one would consider attaching a cup’s handle to the inside – apart from novelty item manufacturers. It has simply been proven that these components should be attached to the outside for practical purposes. If you are taking a pottery class and want to make a pot with handles, you already know what the basic shape should be. It is stored in your head as a design pattern, in a manner of speaking. The same general idea applies to computer programming. Certain procedures are repeated frequently, so it was no great leap to think of creating something like pattern templates. In our guide, we will show you how these design patterns can simplify programming. The term “design pattern” was originally coined by the American architect Christopher Alexander who created a collection of reusable patterns. His plan was to involve future users of the structures in the design process. This idea was then adopted by a number of computer scientists. Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides (sometimes referred to as the Gang of Four or GoF) helped software patterns break through and gain acceptance with their book “Design Patterns – Elements of Reusable Object-Oriented Software” in 1994.
Public blockchain is the model of Bitcoin, Ethereum, and Litecoin and is essentially considered to be the original distributed ledger structure. This type of blockchain is completely open and anyone can join and participate in the network. It can receive and send transactions from anybody in the world, and can also be audited by anyone who is in the system. Each node (a computer connected to the network) has as much transmission and power as any other, making public blockchains not only decentralized, but fully distributed, as well. ... Private blockchains, on the other hand, are essentially forks of the originator but are deployed in what is called a permissioned manner. In order to gain access to a private blockchain network, one must be invited and then validated by either the network starter or by specific rules that were put into place by the network starter. Once the invitation is accepted, the new entity can contribute to the maintenance of the blockchain in the customary manner. Due to the fact that the blockchain is on a closed network, it offers the benefits of the technology but not necessarily the distributed characteristics of the public blockchain.
Quote for the day:
"Every moment is a golden one for those who have the vision to recognize it as such." -- Henry Miller