Taking on the challenges head-on that stand in the way of their low-code platforms growing, Microsoft's series of new product announcements this week at Build 2022 gives organizations new options for achieving low-code development goals. Microsoft's series of low-code announcements made this week include Power Pages, the latest Microsoft Power Platform addition for creating integrated, scalable and secure websites. Lured by the promises of democratizing app development with visual, declarative, drag and drop interfaces often bundled with enterprise-wide platforms like Microsoft, Salesforce, ServiceNow and others, enterprises have been quick to jump in and experiment. They're learning that support for a low-code platform can get expensive fast once app development moves from small department coding projects to larger-scale, enterprise-wide apps. Low-code platforms' hidden costs include limited process workflow support that further adds to the challenge of scaling them enterprise-wide.

Using machine learning along with data from wave tank experiments, MIT engineers have found a way to model how waves break. "With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors," says Themis Sapsis. The new model's predictions should help researchers improve ocean climate simulations and hone the design of offshore structures. Waves break once they swell to a critical height, before cresting and crashing into a shower of droplets and bubbles. These waves can be as big as a surfer's point break and as small as a gentle ripple rolling to shore.

In this technologically advanced era, the demand for machine learning experts is growing rapidly as industrialists have already started using this technology for different purposes. As there is a skill shortage in this field, several job opportunities exist. It is a complex technical process that teaches computers to learn from data without being explicitly programmed. This technology also teaches computers to analyze data and get the work done without any human involvement. Though the technology is not new, people are adopting this technology nowadays. Let's know about modern-day applications of machine learning.

Researchers from Swinburne University of Technology have developed a robotic prototype designed to help staff automate the processing of blood donation packs. Currently, processing blood donation is a largely manual task, according to Swinburne. The university explained the process involves separating whole blood donations into its cellular components via centrifugation, a mechanical method that involves spinning each blood pack in a solution in a centrifuge rotor at high speed. To do this, the blood pack needs to be folded in a particular way to ensure there is no bacterial contamination, which can still carry risk due to human error. "Damaged or torn packs not only lead to the loss of a precious donation, but also disrupt production and expose staff to potentially hazardous biological materials. Even subtle non-conformities can occur and build over time, leading to quality deviations," the university said.

Amazon's household robot is exactly what I expected, but it's not what I wanted and it definitely isn't what anyone asked for. Instead of a multitasking mimicry of me that can empty the dishwasher, pick up my kids' shoes, feed the dog, and clean the house, Amazon's first attempt at a home bot is simply a souped-up Echo Show on wheels. It also has two cameras that it uses to find people and places in your home to deliver items, reminders, or timers. It can act as a security guard and patrol your home when paired with a Ring subscription, and it can fart and burp. In short, the Astro does everything Amazon's smart home products and services already do -- only on wheels. But the Astro is a robot. And that part is really cool.

Today, cybersecurity is in a state of continuous growth and improvement. In this on-demand webinar, learn how two organizations use a continuous AI feedback loop to identify vulnerabilities, harden defenses and improve the outcomes of their cybersecurity programs. The security risk landscape is in tremendous flux, and the traditional on-premises approach to cybersecurity is no longer enough. Remote work has become the norm, and outside the office walls, employees are letting down their personal security defenses. Cyber risks introduced by the supply chain via third parties are still a major vulnerability, so organizations need to think about not only their defenses but those of their suppliers to protect their priority assets and information from infiltration and exploitation.

We have developed a new embodied AI platform, called MyoSuite, that applies machine learning (ML) to biomechanical control problems by unifying motor and neural intelligence.

The Sensors Division focuses on advanced sensor system technology, from airborne and surface-based radar and electronic warfare to underwater acoustics, EO/IR and hyperspectral imaging. This position is with the Electronic Warfare and Novel Capabilities Group in the STR Sensors Division. We focus on technology development for advanced sensor systems, in the areas of airborne/surface-based radar, electronic warfare, data communications, and hyperspectral imaging. We develop algorithmic and hardware components, conduct experiment campaigns, and prototype systems. Design, build, and test roles within the Group include RF analog/digital hardware, advanced electronic warfare algorithms and techniques, signal processing and machine learning algorithms, cognitive electronic warfare applications, tracking/fusion, and real-time embedded processor implementation.

This program uses artificial intelligence and data science to help develop … A lot of the machine learning algorithms that we use today were …

That says that anything you can do as a human being we can augment the intelligence positions through machine learning and machine processes."