The 315 5G chipset offers up to a 1.54Gbps data rate for 5G (3GPP Rel 15), while the 4G mode goes to 400Mbps. Other features include antenna tuning support and a dual-frequency GNSS location capability. The combination of 7nm technology, Cortex-A7, and an efficient RF front-end design enables up to 50 percent smaller modules than existing models, claims Qualcomm. Vanghi also touted the chipset for its low power consumption and extended life maintenance through 2028 to 2030. The Qualcomm 315 5G IoT Modem-RF “can be easily fitted onto industrial machines,” said Vanghi. “You can bolt it directly onto the chassis using existing holes.” The small size will also make it easy for wireless module manufacturers to upgrade existing 4G modules, said Vanghi, mentioning support for 35 x 40mm module footprints. “The 315 is a pin-to-pin compatible solution for LTE legacy modules,” he added. Vanghi noted that the chipset has all the security features of Qualcomm’s premium 5G chipsets for smartphones, which would include the Snapdragon X55 5G Modem-RF System. Security features include hardware-based cryptography, TrustZone, Qualcomm TEE, secure boot, secure storage and key provisioning, and debug security.
When users can accurately predict their efficiency with a tool, even when that tool itself is inefficient, they can strongly resist a change. I worked on an inflight commerce system, and our solution required a series of reconciliation steps to be taken at the end of a flight. The crew instinctively know how long this process takes through repetition of the process, and they set aside that time - at what is generally a very stressful point in the flight. Coming in to land is when everyone suddenly wants to be out of their seat! Changes to the software (and hence the process) around reconciliation were always difficult to achieve buy-in for, because the nervousness around trying something new at such a critical point in the crew's operational life was always a tough sell. Our software was one of the multiple tasks taking place at that time, and a change to one can lead to underperformance in any of the others. Nobody wants distracted staff on a plane. Changes to software or processes mean a risk to their ability to deliver predictably to the business, and that could have catastrophic consequences for a user's role.
When the two technical co-founders looked to expand their startup, they tapped a collection of their newly minted PhD students who had the expertise of developing wireless system-on-chip technologies in the lab. Today, Everactive has expanded into a team of nearly 90 industry veterans and technical experts, including talented minds like Alice Wang, who joined up with Calhoun and Wentzloff in 2018 after successful stints with industry giants Texas Instruments and MediaTek. Another MIT alum, she now serves as VP of hardware for Everactive, directing both silicon and hardware systems design. “We’re exceptionally proud of the team that we’ve developed,” says Wentzloff. “I think a large part of why we continue to succeed is that we’ve done a great job of surrounding our core technology students with a broad set of talented industry leaders.” Thanks to their advances in ultra-low-power circuits and wireless communication, Everactive sells full-stack industrial IoT solutions powered by their always-on Eversensors, harvesting energy exclusively from the surrounding environment. The sensors can be deployed at a larger scale than battery-powered devices, and they cost less to operate.
Sowder said that many times, “the challenge with these IoT devices is the limited compute capability that they have on them. An IP camera can’t run a full IPS protection suite against traffic to it. It has a job to record video and send it upstream.” He pointed to the potential solution of nanotechnology: Specifically, the concept of a nanoagent on each IoT node that inspects firmware code to determine if it’s engaged in malicious behavior, such as memory corruption. If so, the nanoagent can block it in real-time. The challenge is how to do it with a small footprint, Sowder said: “A lot of devices don’t have a lot of compute. Sticking a firewall in front of every IP camera simply isn’t feasible. The solution is a very, very slight agent. It phones home to get a device signature, including what kind of device it is and what can run on it.” Nanoagents don’t put a lot of overhead on these devices, so the devices’ performance isn’t slowed down, Sowder noted: “There’s no overhead to prevent them from performing their functions.” Check Point has been working on a lightweight agent that relies on a cloud instance to pull down specific protection details related to that device.
Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL). “It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley. The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images. Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.
Part of the problem stems from unprecedented demand for IoT devices. There are already more connected things than people in the world, and the trend isn't showing any sign of slowing down. In fact, it's quite the contrary: tech analyst company IDC recently estimated that there will be a total 41.6 billion connected devices by 2025. Consumers are particularly interested in using smart products in their homes – think connected plugs, lightbulbs, thermostats and even fridges. Forrester forecast that by 2025, the average US household will have 20 internet-connected devices. In this context, it won't be enough for manufacturers to produce more of the same old things. Buyers' expectations are growing: they want easy-to-use devices with new, exclusive features, which will be continuously improved; and crucially, consumers expect that their connected products work together across different platforms and operating systems. More than eight in ten respondents to Forrester's survey said that they need to rapidly manufacture new smart products and services to maintain or grow their market position – meaning, in most cases, that a new cycle of research and development is necessary.
Application development security is analyzing vulnerabilities in the app, developing and adding security features to protect it from hackers. As the field of modern software development catch up speed, more threat actors exploit the rapid production of application as a chance to attack vulnerabilities in your code. Fortunately, there are application development security experts to protect your data and digital assets from a hacker. Application security is no more an afterthought. To build a secure application, one must integrate security measures in all software development life cycle parts. Burning glass report makes this evident with demand in Application development security skills to increase 164%, topping the list among other cybersecurity skills. ... Cloud security refers to all the measures, policies, and rules implemented to protect the data in the cloud from hackers. On account of businesses making a shift to the cloud, robust cloud security is necessary. Security threat is continually evolving and becoming more complex, which means cloud computing is at no less danger than the on-premises environment.
Model unification can be useful for many types of machine learning problems. Our experience with predictive models, which are widely used by organizations across industries, has shown three important conditions that should be met for taking a unified modeling approach: A prediction is needed for the same target variable across a large number of related entities, or partitions; Each partition uses the same set of features; The models need to be refreshed on a frequent basis. ... With unified models, teams lose some flexibility for addressing problems since it is not possible to pick and choose individual partitions to roll back (or roll forward). A team can address this issue by retraining the unified model outside of the regular refresh cycle. Alternatively, if necessary, the model can be reverted for all partitions at once, across the board. For example, if you’ve created a unified model to predict demand for the full range or a set of your company’s products, you may find, after deploying the model, issues with the results for one product. You will then need to either roll back or retrain the full model.
Companies that have adopted intelligent operations within their processes are viewed as moving up their operational maturity level from “stable” to “efficient”. Once operational efficiency is achieved, the next step is to include data-driven insights into the decision-making process, putting the companies at the “predictive” maturity level. Companies that go beyond this stage are called “future-ready”. In these companies, artificial intelligence, blockchain, cloud and various forms of intelligent operations are used to drive and grow the company. According to the report, only 7% of organisations globally fall into the “future-ready” category, and these are mostly in the insurance and high-tech sectors. On average, “future-ready” organisations showed a 2.8 times boost in corporate profitability and 1.7 times increase in operational efficiency compared with companies in the lower maturity levels. Accenture Operations associate director Pankaj Jain says when new technologies are introduced, the way a company runs its operations changes dramatically.
Quote for the day:
"What lies behind us & what lies before us are tiny matters compared to what lies within us." -- Ralph Waldo Emerson