One of the special applications of AI poses estimation, a computer vision approach that aids in determining the position and orientation of the human body from an image of a person. It can be utilized, for instance, in markerless motion capture, worker position analysis, and avatar animation for virtual reality. It is required to take numerous pictures of the human actor and its surrounding environment to properly analyze posture. The joints of the human actor are then identified in these photos using a trained convolutional neural network. AI-based fitness apps typically take advantage of the camera on the device to record films up to 720p and 60fps to capture more frames while an exercise is being performed. The issue is that when utilizing a method like a posture estimation, computer vision experts require enormous volumes of visual data to train AI for fitness assessments. Data involving humans engaging in many types of exercise and interacting with several items is quite complicated. To prevent bias, the data must also have high variance and be sufficiently broad.
As leaders, we owe it to our teams to admit when we make a mistake, but it takes vulnerability to admit that we can be wrong. For example, imagine someone recommended a change that I turned it down but later recognized as the right move. There is value in providing an explanation of what made me go in that direction, but ultimately, I need to take responsibility for being wrong. People respect it when others, especially those in leadership, demonstrate the vulnerability it takes to acknowledge they, too, are only human. Leadership vulnerability drives the courage to innovate and trust among team members, with benefits that ripple into their engagement, satisfaction and retention. Mistakes happen, but a leader who pretends to be perfect and expects perfection ends up with a team too frightened to come clean about their mistakes. They either avoid admitting when they make them or avoid the risk of making them altogether, holding back creativity, innovation and new ideas.
“Publicizing details on an actively exploited zero-day vulnerability just as a patch becomes available could have dire consequences, because it takes time to roll out security updates to vulnerable systems and attackers are champing at the bit to exploit these types of flaws,” observed Satnam Narang, senior staff research engineer at cybersecurity firm Tenable, in an email to Threatpost. Holding back info is also sound given that other Linux distributions and browsers, such as Microsoft Edge, also include code based on Google’s Chromium Project. These all could be affected if an exploit for a vulnerability is released, he said. “It is extremely valuable for defenders to have that buffer,” Narang added. While the majority of the fixes in the update are for vulnerabilities rated as high or medium risk, Google did patch a critical bug tracked as CVE-2022-2852, a use-after-free issue in FedCM reported by Sergei Glazunov of Google Project Zero on Aug. 8. FedCM—short for the Federated Credential Management API–provides a use-case-specific abstraction for federated identity flows on the web, according to Google.
“We have a huge amount of momentum with our partners around SaaS,” Moore said in an interview with CRN, a week after CyberArk announced impressive second-quarter general revenues and subscription revenues tied to its new products and SasS strategies. CyberArk, with headquarters in Newton, Mass. and Petach Tikva, Israel, is now about halfway through its 36-month-long global channel transformation that includes a new emphasis on SaaS and subscriptions, said Moore, who joined CyberArk two years ago as its senior vice president of global channels. “Our channel partners love SaaS and love subscriptions, for all the reasons we love SaaS and subscriptions,” he said. ... In particular, he said he likes the fact that CyberArk is now providing earlier access to its new technologies and resources, giving his firm more time to convince customers about the pluses of CyberArk’s offerings. “It’s been nothing but positive,” he said of Optiv’s partnership with CyberArk.
Machine learning is a subset of data science that applies algorithms to make predictions about future events from data. Data scientists use machine learning to find patterns in data, make predictions, and improve the accuracy of future predictions. Data science is a broader field that includes techniques like predictive modeling, feature engineering, and data analysis. It involves understanding how data can be used to improve business outcomes. Data scientists use machine learning to analyze and understand data sets, making predictions about the relationships between variables. Some key differences between the two fields include: Machine learning is a probabilistic approach that uses algorithms to learn from data; Data science is focused on understanding and extracting knowledge from data. Machine learning is focused on making automated decisions using data; Machine learning is often used to solve problems where there is a lot of historical data, while data science is used more for situations where there is not as much historical data; and Data scientists often profoundly understand the problem they are trying to solve and use that understanding to develop machine learning models.
SaaS applications end the fear of delivering an unknown, showstopper bug to customers, without any way to fix it for weeks or months. The days of delivering a patch to an installed product have gone by the wayside. Instead, if a catastrophic bug does wend its way through the development pipeline and into production, you can know about it as soon as it strikes. You can take immediate action—roll back to a known good state or flip off a feature flag—practically before any of your customers even notice. Often, you can fix the bug and deploy the fix in a matter of minutes instead of months. And it’s not just bugs. You no longer have to hold new features as “inventory,” waiting for the next major release. It used to be that if you built a new feature in the first few weeks after a major release, that feature would have to wait potentially months before being made available to customers. Now, a SaaS application can deliver a new feature immediately to customers whenever the team says it is ready.
We are starting to evolve beyond classical computing into a new data era called quantum computing. It is envisioned that quantum computing will accelerate us into the future by impacting the landscape of artificial intelligence and data analytics. The quantum computing power and speed will help us solve some of the biggest and most complex challenges we face as humans. ... Science is already making great advances in brain/computer interface. This may include neuromorphic chips and brain mapping. Brain-computer interfaces are formed via emerging assistive devices that have implantable sensors that record electrical signals in the brain and use those signals to drive external devices. Eventually these nano-chips may be implanted into our brains, artificially augmenting human thought and reasoning capabilities, and we may be able to upload intelligent data and cognitive resources to our brains by 2032. ... The areas of health and medicine will witness a profound growth of technological innovation by 2032. Numerous breakthroughs in genomics anti-aging therapies will extend our longevity and quality of life.
Security researchers are urging users of Apple Mac, iPhone, and iPad devices to immediately update to newly released versions of the operating systems for each technology, to mitigate risk from two critical vulnerabilities in them that attackers are actively exploiting. The zero-day flaws allow threat actors to take complete control of affected devices. They impact users of iPhone 6s and later, all models of iPad Pro, iPod touch (7th generation), iPad Ai2 and later, iPad 5th generation and later, and iPad mini 4 and later. Also affected are users with Macs running macOS Monterey, macOS Big Sur, and macOS Catalina. Apple disclosed the vulnerabilities and the updates addressing them on Wednesday. One of the zero-days (CVE-2022-32893) exists in WebKit, Apple's browser engine for Safari and for all iOS and iPadOS Web browsers. Apple described the flaw as tied to an out-of-bounds write issue that attackers could use to remotely take control of vulnerable devices.
The majority of AI models in production today are “black box” systems that, by the very nature of their architecture, produce outputs using far too many steps of abstraction, deduction, or conflation for a human to parse. In other words, a given AI system might use billions of different parameters to produce an output. In order to understand why it produced that particular outcome instead of a different one, we’d have to review each of those parameters step-by-step so that we could come to the exact same conclusion as the machine. A solution: the EU should adopt a strict policy preventing the deployment of opaque or black box artificial intelligence systems that produce outputs that could affect human outcomes unless a designated human authority can be held fully accountable for unintended negative outcomes. ... There’s currently no political consensus as to who’s responsible when AI goes wrong. If the EU’s airport facial recognition systems, for example, mistakenly identify a passenger and the resulting inquiry causes them financial harm or unnecessary mental anguish, there’s nobody who can be held responsible for the mistake.
Organizations are sometimes tempted to do extra technical work, to modernize, or reduce their technical debt because, as they may rationalize, "we’re going to be working on that part of the application anyway, so we should clean things up while we are there." While well-intentioned, this is almost always a bad decision that results in unnecessary cost and delay because once started, it’s very hard to decide to stop. This is where the concept of the MVA pays dividends: it gives everyone a way to decide what changes must be made, and which changes should not be made, at least not yet. If a change is necessary to deliver the desired customer outcome for a release, then it’s part of the MVA, otherwise, it’s out. Sometimes, a team may look at the changes needed to an application and decide, considering the state of the code, that a complete rewrite is in order. The MVA concept, applied to legacy applications, helps to temper that by questioning whether the changes are really necessary to produce the incremental improvements in customer outcomes that are desired.
Quote for the day:
"The art of communication is the language of leadership." -- James Humes