“What we focus on is augmented intelligence for humans to take action [on],” says Radtke when I raise this concern. “We are not prescribing the action to be taken based on the insights that we get – we're trying to make sure that the human has all the necessary intelligence to drive the behavior that they need to drive. We're reporting facts back – this actually happened here, this is what has happened in the past – and you can take action based on that. It's all about driving improved safety for everyone in that area.” When I press him on the possible human rights concern and the inevitable pushback that will arise if AI is routinely used to pre-emptively police areas deemed as problematic, he answers: “I think that with every technology that's ever been out there in history there is always a way to use it for non-good. I think you have to focus on the good that it can provide and make sure that you police the non-good behavior that could happen from it.” This will entail some sort of oversight. “There are consortiums out there to help drive the ethical adoption of AI throughout the industry – we definitely keep aware of those.
Where BPA and RPA overlap, according to Mullakara, is the goal of eliminating human intervention in order to process multiple automation. “The whole idea of BPA was to remove people from the process and that's kind of what RPA is also aiming for. In the sense of the simple workflow automation, both can do it. RPA does it through a UI integration whereas BPA does it mostly with APIs. And you know, automating the workflow with the systems by invoking the systems,” he tells us. However, Taulli explains that automation really won’t get rid of people at this point and it will be the usual suspects that will, such as recessions. Mullakara agrees that this messaging for BPA and RPA is a common misconception and has earned both technologies quite a bad rap. “So, what you actually automate with RPA for example is tasks – it's not jobs. It's not an entire job even if it's a process. It’s not jobs, so we still need people,” he says.
Many organizations have different teams and services dispersed across different networks and regions of a given cloud. Many also have services deployed across multiple cloud environments. Securely connecting these services across different cloud networks is a highly desirable function that typically requires significant effort by network teams. In addition, limitations that require non-overlapping Classless Inter-Domain Routing (CIDR) ranges between subnets can prevent network connectivity between virtual private clouds (VPCs) and virtual networks (VNETs). Service mesh products can securely connect services running on different cloud networks without requiring the same level of effort. HashiCorp Consul, for example, supports a multidata center topology that uses mesh gateways to establish secure connections between multiple Consul deployments running in different networks across clouds. Team A can deploy a Consul cluster on EKS. Team B can deploy a separate Consul cluster on AKS. Team C can deploy a Consul cluster on virtual machines in a private on-premises data center.
The proliferation of ransomware targeting ESXi systems poses a major threat to organizations using the technology, security experts have noted. An attacker that gains access to an EXSi host system can infect all virtual machines running on it and the host itself. If the host is part of a larger cluster with shared storage volumes, an attacker can infect all VMs in the cluster as well, causing widespread damage. "If a VMware guest server is encrypted at the operating system level, recovery from VMware backups or snapshots can be fairly easy," McGuffin says. '[But] if the VMware server itself is used to encrypt the guests, those backups and snapshots are likely encrypted as well." Recovering from such an attack would require first recovering the infrastructure and then the virtual machines. "Organizations should consider truly offline storage for backups where they will be unavailable for attackers to encrypt," McGuffin adds. Vulnerabilities are another factor that is likely fueling attacker interest in ESXi. VMware has disclosed multiple vulnerabilities in recent months.
Tree-based models don’t need data normalization as feature raw values are not used as multipliers and outliers don’t impact them. Neural Networks might not need the explicit normalization as well — for example, if the network already contains the layer handling normalization inside (e.g. BatchNormalization of Keras library). And in some cases, even Linear Regression might not need data normalization. This is when all the features are already in similar value ranges and have the same meaning. For example, if the model is applied for the time-series data and all the features are the historical values of the same parameter. In practice, applying unneeded data normalization won’t necessarily hurt the model. Mostly, the results in these cases will be very similar to skipped normalization. However, having additional unnecessary data transformation will complicate the solution and will increase the risk of introducing some bugs.
Version control systems, primarily Git, are becoming more and more prevalent outside of the realm of software development. The increase in DevOps, network automation, and infrastructure as code practices over the last decade has made it even more important to not only be familiar with Git, but proficient with it. As teams move into the realm of infrastructure as code, understanding and using Git is a key skill. ... Unlike other Version Control Systems, Git uses a snapshot method to track changes instead of a delta-based method. Every time you commit in Git, it basically takes a snapshot of those files that have been changed while simply linking unchanged files to a previous snapshot, efficiently storing the history of the files. Think of it as a series of snapshots where only the changed files are referenced in the snapshot, and unchanged files are referenced in previous snapshots. Git operations are local, for the most part, meaning it does not need to interact with a remote or central repository.
The timing couldn’t be better. The increasing availability of ransomware-as-a-service offerings, such as ransomware kits and target lists, are making it easier than ever for bad actors—even those with limited experience—to launch a ransomware attack, causing crippling damage in the very first moments of infection. Other sophisticated attackers use targeted strikes, in which the ransomware is placed inside the network to trigger on command. Another cause for concern is the increasing disappearance of an IT environment’s perimeter as cloud compute storage and resources move to the edge. Today’s organizations must secure endpoints or entry points of end-user devices, such as desktops, laptops, and mobile devices, from being exploited by malicious hackers—a challenging feat, according to Michael Suby, research vice president, security and trust, at IDC. “Attacks continue to evolve, as do the endpoints themselves and the end users who utilize their devices,” he says. “These dynamic circumstances create a trifecta for bad actors to enter and establish a presence on any endpoint and use that endpoint to stage an attack sequence.”
The neocognitron consisted of interleaved S- and C-layers of neurons (a naming convention reflecting its inspiration in the biological visual cortex); the neurons in each layer were arranged in 2D arrays following the structure of the input image (‘retinotopic’), with multiple ‘cell-planes’ (feature maps in modern terminology) per layer. The S-layers were designed to be translationally symmetric: they aggregated inputs from a local receptive field using shared learnable weights, resulting in cells in a single cell-plane have receptive fields of the same function, but at different positions. The rationale was to pick up patterns that could appear anywhere in the input. The C-layers were fixed and performed local pooling (a weighted average), affording insensitivity to the specific location of the pattern: a C-neuron would be activated if any of the neurons in its input are activated. Since the main application of the neocognitron was character recognition, translation invariance was crucial.
Most of us do not approach work (or life) with a master plan in mind, and many of the steps we take are beautiful accidents that help us become who we are. “I’m 67 years old,” Guy said, “and I think I finally found my true calling.” He was referring to his podcast, Remarkable People, where he interviews exceptional leaders and innovators (think Jane Goodall, Neil deGrasse Tyson, Steve Wozniak, and Kristi Yamaguchi) about how they got to be remarkable. “In a sense, my whole career has prepared me for this moment. I’ve had decades of experience in startups and large companies. So that gives me the data to ask great questions that my listeners really want the answers to,” Guy said. Guy is undeniably brilliant, and his success is no accident. But still, he believes that luck has played a part in his success. In his words, “Basically, I’ve come to the conclusion that it’s better to be lucky than smart.” Maybe Guy is right. Or perhaps, the smartest people know when to take advantage of luck and act on the opportunities that present themselves. Whatever the case, it’s important to take calculated risks.
With the digital transformation office comes a transformation team, who initiates organizational change. Laute says that it’s crucial that everyone inside the organization stand behind the transformation team if they truly want to see changes happening. “You need to have an environment where these people, the transformation lead and the transformation team, are allowed and are not afraid to speak up. These people shouldn't be biased, not just following what the executive board says, but really [being] able to challenge and to speak up. And they should have the freedom to call out if something is going in the wrong direction, may it be content or behavioral-wise,” she explains. And while clearly there can be technology-related challenges, Laute tells us that digital transformation is also a people problem, and calls for a change in culture and mindset in order to find success. The cultural shift, she explains, is truly where everything starts to come together in order to get the transformation going. “Digital [transformation] is not only technology. You need to change behaviors and you need to change processes. And most of the time, you change your target operating model, right?”
Quote for the day:
"Uncertainty is a permanent part of the leadership landscape. It never goes away." -- Andy Stanley