So far, AI has shown some impressive results in narrow application areas only, like chess-playing computers beating world chess champions and supercomputers beating human Jeopardy champions. However, these are computers programmed to solve one specific problem and cannot interpret more complex and multilayered challenges beyond the given task. This is exactly what Moravec's paradox states; though it may be easy to get computers to beat human chess champions, it may be difficult to give them the skills of a toddler when it comes to perception and mobility. While AI has not reached human performance, it brings valuable solutions to many real-world problems quickly and effectively. From enhanced healthcare, innovations in banking and improved environmental protection to self-driving vehicles, automated transportation, smart homes and chatbots, AI can offer simpler and more intelligent ways of accomplishing many of our daily tasks. But how far can AI go? Will it ever be able to function autonomously and mimic cognitive human actions? We cannot envision how AI will end up evolving in the far-off future, but at this point, humans remain smarter than any type of AI.
The Data Monetization Roadmap provides both a benchmark and a guide to help organizations with their data monetization journey. To successfully navigate the roadmap, organizations must be prepared to traverse two critical inflection points: Inflection Point #1 is where organizations transition from data as a cost to be minimized, to data as an economic asset to be monetized; the “Prove and Expand Value” inflection point; Inflection Point #2 is where organizations master the economics of data and analytics by creating composable, reusable, and continuously refining digital assets that can scale the organization’s data monetization capabilities; the “Scale Value” inflection point. Carefully navigate these two inflection points enables organizations to fully exploit the game-changing economic characteristics of data and analytics assets – assets that never deplete, never wear out, can be used across an unlimited number of use cases at zero marginal cost, and can continuously-learn, adapt, and refine, resulting in assets that actually appreciate in value the more that they are used.
One of Google’s newest ASR NLPs is seeking to change the way we interact with others around us, broadening the scope of where — and with whom — we can communicate. The Google Interpreter Mode uses ASR to identify what you are saying, and spits out an exact translation into another language, effectively creating a conversation between foreign individuals and knocking down language barriers. Similar instant-translate tech has also been used by SayHi, which allows users to control how quickly or slowly the translation is spoken. There are still a few issues in the ASR system. Often called the AI accent gap, machines sometimes have difficulty understanding individuals with strong accents or dialects. Right now, this is being tackled on a case-by-case basis: scientists tend to use a “single accent” model, in which different algorithms are designed for different dialects or accents. For example, some companies have been experimenting with using separate ASR systems for recognizing Mexican dialects of Spanish versus Spanish dialects of Spanish. Ultimately, many of these ASR systems reflect a degree of implicit bias. In the United States, African-American Vernacular English ...
Over one quarter of employees admit they made cybersecurity mistakes — some of which compromised company security — while working from home that they say no one will ever know about. 27% say they failed to report cybersecurity mistakes because they feared facing disciplinary action or further required security training. In addition, just half of employees say they always report to IT when they receive or click on a phishing email. ... As lockdown restrictions are lifted, six in 10 IT leaders think the return to business travel will pose greater cybersecurity challenges and risks for their company. These risks could include a rise in phishing attacks whereby threat actors impersonate airlines, booking operators, hotels or even senior executives supposedly on a business trip. There is also the risk that employees accidentally leave devices on public transport or expose company data in public places. ... As cybersecurity will be mission-critical in the new work environment, it’s encouraging that 67% of surveyed IT decision makers report that they have a seat at the table when it comes to office reopening plans in their organizations.
Today, ReFirm needs you to provide the firmware files, but Microsoft plans to create a database of device information, Weston says. "You plug in CyberX and it discovers the devices, it monitors them and it asks ReFirm 'do you know anything about IoT device X or Y'. Hopefully we've pre-scanned most of those devices and we can propagate the information -- and for anything we don't have, there's the drag-and-drop interface to do a custom analysis." Having that visibility of what's on your network and whether it's safe to have on your network is a good first step. The Azure Device Updates service can already push IoT firmware updates out through Windows Update. Microsoft's bigger vision is to create a service based on Windows Update that can handle a much wider range of third-party devices, says Weston. "We're going to take Windows Update, which people already at least know and trust on Patch Tuesdays, and we want to push the IoT and edge devices into that model. Microsoft's update system is a pretty known commodity -- just about every government regulator out there looked at it in one form or another -- and so we feel good about being able to move customers towards it."
Today, XGBoost has grown into a production-quality software that can process huge swathes of data in a cluster. In the last few years, XGBoost has added multiple major features, such as support for NVIDIA GPUs as a hardware accelerator and distributed computing platforms including Apache Spark and Dask. However, there have been several claims recently that deep learning models outperformed XGBoost. To verify this claim, a team at Intel published a survey on how well deep learning works for tabular data and if XGBoost superiority is justified. The authors explored whether DL models should be a recommended option for tabular data by rigorously comparing the recent works on deep learning models to XGBoost on a variety of datasets. The study showed XGBoost outperformed DL models across a wide range of datasets and the former required less tuning. However, the paper also suggested that an ensemble of the deep models and XGBoost performs better on these datasets than XGBoost alone. For the experiments, the authors examined DL models such as TabNet, NODE, DNF-Net, 1D-CNN along with an ensemble that includes five different classifiers: TabNet, NODE, DNF-Net, 1D-CNN, and XGBoost.
While breaches from outside cybercriminals are becoming more complex and require more resources to combat, companies mustn’t lose sight of a data-loss cause closer to home – their employees. In their day-to-day positions, employees are entrusted with highly sensitive information, from financial and personally identifiable information (PII) to medical records or intellectual property. While employee error is a major source of security breaches, a well-trained employee who knows how to take the proper precautions is a key defense from attacks and breaches. Over the course of their daily responsibilities, employees can mistakenly share that information outside of the secure network. Often, this data loss occurs through email, such as mentioning restricted information in outside correspondence or attaching documents that may violate customer or patient privacy. For example, let’s say that an employee is working on a presentation that contains confidential data. They hit a roadblock while trying to fix a formatting issue and in their race to meet the looming deadline, they decide to reach out to a friend for help and send the presentation via email with the confidential data included.
Treating cybersecurity as a core business risk and devoting the appropriate resources to it is now essential, said Tom Kellermann, head of cybersecurity strategy at software firm VMware Inc., who also sits on the Secret Service’s Cyber Investigation Advisory Board. “Cybersecurity should no longer be viewed as an expense, but a function of conducting business,” he said. Christopher Roberti, senior vice president for cyber, intelligence and supply chain security policy at the U.S. Chamber of Commerce, which says it is the world’s largest business association, said companies don’t stand a chance against determined nation-state attacks regardless of cybersecurity investments. Partnerships between the government and the private sector are essential, he said. “Businesses must take necessary steps to ensure their cyber defenses are robust and up to date, and the U.S. government must act decisively against cyber criminals to deter future attacks. Each has a role to play and both need to work closely to do more,” Mr. Roberti said.
It is important to note that there are several functional and operational models that enterprises are adapting in regard to CoE. The change management model focuses on emphasizing the prospective innovation that artificial intelligence can provide for business stakeholders in the organization. Central to this model is education and training of executives and business units. In addition to change management, the Sandbox approach is another central model, in which the CoE acts as the company’s hub of innovation and R&D. This model emphasizes proofs of concepts and different emerging technologies. The key is alignment of business units around POCs and being accountable for the initial launch and development of per-subject use cases. Lastly, the Launchpad model for the CoE leverages and builds upon the capabilities of existing data scientists, engineers, and developers. The CoE deploys top subject-matter experts to across departments to conduct hands-on training and education and scope out early stage business solutions.
“One thing that’s better to learn earlier than later with Kubernetes is that automation and audits have an interesting relationship: automation reduces human errors, while audits allow humans to address errors made by automation,” Andrade notes. You don’t want to automate a flawed process. It’s often wise to take a layered approach to container security, including automation. Examples include automating security policies governing the use of container images stored in your private registry, as well as performing automated security testing as part of your build or continuous integration process. Check out a more detailed explanation of this approach in 10 layers of container security. Kubernetes operators are another tool for automating security needs. “The really cool thing is that you can use Kubernetes operators to manage Kubernetes itself – making it easier to deliver and automate secured deployments,” as Red Hat security strategist Kirsten Newcomer explained to us. “For example, operators can manage drift, using the declarative nature of Kubernetes to reset and unsupported configuration changes.”
Quote for the day:
"Well, I think that - I think leadership's always been about two main things: imagination and courage." -- Paul Keating