Creating reinforcement learning models presents several challenges. One of them is designing the right set of states, rewards, and actions, which can be very difficult in applications like robotics, where agents face a continuous environment that is affected by complicated factors such as gravity, wind, and physical interactions with other objects. This is in contrast to environments like chess and Go that have very discrete states and actions. Another challenge is gathering training data. Reinforcement learning agents need to train using data from millions of episodes of interactions with their environments. This constraint can slow robotics applications because they must gather their data from the physical world, as opposed to video and board games, which can be played in rapid succession on several computers. To overcome this barrier, AI researchers have tried to create simulated environments for reinforcement learning applications. Today, self-driving cars and robotics often use simulated environments as a major part of their training regime. “Training models using real robots can be expensive and sometimes involve safety considerations,” Chuang Gan, principal research staff member at the MIT-IBM Watson AI Lab, told TechTalks.
ONNX standard aims to bridge the gap and enable AI developers to switch between frameworks based on the project’s current stage. Currently, the models supported by ONNX are Caffe, Caffe2, Microsoft Cognitive toolkit, MXNET, PyTorch. ONNX also offers connectors for other standard libraries and frameworks. “ONNX is the first step toward an open ecosystem where AI developers can easily move between state-of-the-art tools and choose the combination that is best for them,” Facebook had said in an earlier blog. It was specifically designed for the development of machine learning and deep learning models. It includes a definition for an extensible computation graph model along with built-in operators and standard data types. ONNX is a standard format for both DNN and traditional ML models. The interoperability format of the ONNX provides data scientists with the flexibility to chose their framework and tools to accelerate the process, from the research stage to the production stage. It also allows hardware developers to optimise deep learning-focused hardware based on a standard specification compatible with different frameworks.
According to an overview compiled by the Cybersecurity and Infrastructure Security Agency, 17 of the affected product already have patches available, while the rest either have updates planned or are no longer supported by the vendor and won’t be patched. See here for a list of impacted products and patch availability. Where patching isn’t available, Microsoft advises organizations to implement network segmentation, eliminate unnecessary to operational technology control systems, use (properly configured and patched) VPNs with multifactor authentication and leverage existing automated network detection tools to monitor for signs of malicious activity. While the scope of the vulnerabilities across such a broad range of different products is noteworthy, such security holes are common with connected devices, particularly in the commercial realm. Despite billions of IoT devices flooding offices and homes over the past decade, there remains virtually no universally agreed-upon set of security standards – voluntary or otherwise – to bind manufacturers. As a result, the design and production of many IoT products end up being dictated by other pressures, such as cost and schedule.
Continuous integration is a software development principle that suggests that developers should write small chunks of code and when they push this code to their repository the code should be automatically tested by a script that runs on a remote machine, automating the process of adding new code to the code base. This automates software testing thus increasing the developers productivity and keeping their focus on writing code that passes the tests. ... If continuous integration is adding new chunks of code to the code base, then CD is about automating the building and deploying our code to the production environment, this ensures that the production environment is kept in sync with the latest features in the code base. You can read this article for more on CI/CD. I use firebase hosting, so we can define a workflow that builds and deploys our code to firebase hosting rather than having to do that ourselves. But we have one or two issues we have to deal with, normally we can deploy code to firebase from our computer because we are logged in from the terminal, but how do we authorize a remote CI server to do this? open up a terminal and run the following command firebase login:ci it will throw back a FIREBASE_TOKEN that we can use to authenticate CI servers.
Emerging technology can potentially reshape the dimensions of organizations. Augmented reality, as well as virtual reality, will play a crucial role in office design trends that have already come into being. Architecture organizations are already dedicating a space for virtual reality. This is basically an area that is equipped with all the essential requirements of virtual reality. An immense amount of businesses are prone to taking this step as more meetings are held virtually to accommodate the spread-out workforce. Organizations are currently spending a hefty amount on virtual solutions and will continue to invest in the future. Director of business development at HuddleCamHD, Paul Richards, affirmed that “Numerous meeting rooms will become more similar to TV production studios instead of collaborative spaces. Erik Narhi, an architect as well as computational design lead at the Los Angeles office of global design company Buro Happold also agreed that in this current era, it is impossible to neglect augmented reality and virtual reality.” Hybrid work for home is not going anytime soon.
Risk-based vulnerability management doesn’t ask “How do we fix everything?” It merely asks, “What do we actually need to fix?” A series of research reports from the Cyentia Institute have answered that question in a number of ways, finding for example, that attackers are more likely to develop exploits for some vulnerabilities than others. Research has shown that, on average, about 5 percent of vulnerabilities actually pose a serious security risk. Common triage strategies, like patching every vulnerability with a CVSS score above 7 were, in fact, no better than chance at reducing risk. But now we can say that companies using RBVM programs are patching a higher percentage of their high-risk vulnerabilities. That means they are doing more, and there’s less wasted effort. The time it took companies to patch half of their high-risk vulnerabilities was 158 days in 2019. This year, it was 27 days. And then there is another measure of success. Companies start vulnerability management programs with massive backlogs of vulnerabilities, and the number of vulnerabilities only grows each year. Last year, about two-thirds of companies using a risk-based system reduced their vulnerability debt or were at least treading water. This year, that number rose to 71 percent.
This isn’t to suggest that RPA is without challenges. The credentials enterprises grant to RPA technology are a potential access point for hackers. When dealing with hundreds to thousands of RPA robots with IDs connected to a network, each could become an attack vessel if companies fail to apply identity-centric security practices. Part of the problem is that many RPA platforms don’t focus on solving security flaws. That’s because they’re optimized to increase productivity and because some security solutions are too costly to deploy and integrate with RPA. Of course, the first step to solving the RPA security dilemma is recognizing that there is one. Realizing RPA workers have identities gives IT and security teams a head start when it comes to securing RPA technology prior to its implementation. Organizations can extend their identity and governance administration (IGA) to focus on the “why” behind a task, rather than the “how.” Through a strong IGA process, companies adopting RPA can implement a zero trust model to manage all identities — from human to machine and application.
For CIOs determining what the next steps for quantum computing are, they must first consider the immediate use cases for their organization and how investments in quantum technology can pay dividends. For example, for an organization prioritizing accelerated or complex simulations, whether it’s for chemical or critical life sciences research like drug discovery, the increase in computing performance that quantum offers can make all the difference. For some organizations, immediate needs may not be as defined, but there could be an appetite to simply experiment with the technology. As many companies already put a lot behind R&D for other emerging technologies, this can be a great way to play around with the idea of quantum computing and what it could mean for your organization. However, like all technology, investing in something simply for the sake of investing in it will not yield results. Quantum computing efforts must map back to a critical business or technology need, not just for the short term, but also the long term as quantum computing matures. CIOs must also consider how the deployment of the technology changes existing priorities, particularly around efforts such as cybersecurity.
Many organizations that have been around for a while have established processes that are hard to change. Mitch Ashley, CEO and managing analyst at Accelerated Strategies Group, who’s helped create several DevOps organizations, shared his perspective about why changing a culture can be so difficult. “Culture is a set of behaviors and norms, and also what’s rewarded in an organization. It’s both spoken and unspoken. When you’re in an organization for a period of time, you get the vibe pretty quickly. It’s a measurement culture, or a blame culture, or a performance culture, or whatever it is. Culture has mass and momentum, and it can be very hard to move. But, you can make cultural changes with work and effort.” What Mitch is referring to, this entrenched culture that can be hard to change, is sometimes called legacy cultural debt. I loved Mitch’s story about his first foray into DevOps because it’s a great place to start if you’re dealing with a really entrenched legacy culture. He and his team started a book club, and they read The Phoenix Project. He said, “The book sparked great conversations and helped us create a shared vision and understanding about our path to DevOps. ...”
Quote for the day:
"The secret of leadership is simple: Do what you believe in. Paint a picture of the future. Go there. People will follow." -- Seth Godin