Just because the times are fraught does not mean that leaders need to tighten control and micromanage execution. Rather the opposite. Because conditions are so difficult, frontline employees need to take on more responsibility for execution, action, and collaboration. But this isn’t always easy and requires that organizations focus on building execution muscle throughout the workforce. Leaders must assign responsibility to the line, and drive “closed-loop accountability.” That is, everyone working on a team must be clear about what needs to get done by whom, when, and why. Employees must also be equipped with the right skills and mindsets to solve problems, instead of waiting to be told what to do. And there must be disciplined follow-up to make sure actions were taken and the desired results achieved. CEOs who are serious about execution excellence are investing in helping their workforces up their execution game—through targeted programs, realigning incentives, and directing rewards and recognition to teams that execute with speed and excellence. Building execution excellence does not have to come at the expense of innovation. Quite the contrary: it can help discover powerful ideas and innovation from the frontline teams that are closest to the customer. And it can drive excitement and loyalty among the employee base.
"If you focus on results, you will never change. If you focus on change, you will get results." -- Jack Dixon
This reality is why large tech companies have extraordinary power today. Current regulatory mandates were built for corporations in the past where the market was the consideration, not forms of power. Susskind argues that we need to see technology not just as consumers, but as citizens. At the same time, social media can affect one of the most fundamental aspects of democracy, which is deliberation and the way we talk to each other. We've seen people become polarized because through their own personal choices, algorithms are making choices for them, and they are fed information that reinforces their own world view. We've seen people become more entrenched in those views because the more time you spend around people and information that agree with you, the more deeply you come to hold those views. There's also a significant problem with the spread of fake news and misinformation. In a sense, it isn't surprising that this has happened. These social media platforms have not been developed according to the principles of the forum or of healthy public debate. If that was so, they would funnel information to you that was balanced, fair, and rigorously checked or otherwise engineered to make you a better citizen.
As artificial intelligence continues to mature, we are seeing a corresponding growth in sophistication for humanoid robots and the applications for digital human beings in many aspects of modern-day life. ... Even though the earliest form of humanoid was created by Leonardo Da Vinci in 1495 (a mechanical armored suit that could sit, stand and walk), today's humanoid robots are powered by artificial intelligence and can listen, talk, move and respond. They use sensors and actuators (motors that control movement) and have features that are modeled after human parts. Whether they are structurally similar to a male (called an Android) or a female (Gynoid), it’s a challenge to create realistic robots that replicate human capabilities. The first modern-day humanoid robots were created to learn how to make better prosthetics for humans, but now they are developed to do many things to entertain us, specific jobs such as a home health worker or manufacturer, and more. Artificial intelligence makes robots human-like and helps humanoids listen, understand, and respond to their environment and interactions with humans. Here are some of the most innovative humanoid robots in development today
Cutting-edge companies are already finding patterns in user behaviour that can lead to exceptional products or features in existing products, which is giving them an extreme advantage over other businesses. Take computer vision (CV) for example. With computer vision, we can create a system that does a subset of things that the human visual system can do. In CV, a system can analyse a picture taken by a camera and understand what’s in the picture. For example, it can recognise objects like cars, streetlights and of course people. Computers can perform object recognition through a network of nodes called neural networks. An image can be fed into the network, and convolution happens at these nodes. This kind of technology can be used for various business scenarios and lead to incredible amounts of productivity and efficiency. For example, you can leverage computer vision-based licence plate recognition to run an automated car parking business. Of course, the information from registration, billing and computer vision-based license plate recognition systems would have to be integrated to automate the entire process.
The coronavirus lockdown has broken many things, including the AI algorithms that seemed to be working so smoothly before. Warehouses that depended on machine learning to keep their stock filled at all times are no longer able to predict the right items that need to be replenished. Fraud detection systems that home in on anomalous behavior are confused by new shopping and spending habits. And shopping recommendations just aren’t as good as they used to be. To better understand why unusual events confound AI algorithms, consider an example. Suppose you’re running a bottled water factory and have several vending machines in different locations. Every day, you distribute your produced water bottles between your vending machines. Your goal is to avoid a situation where one of your machines is stocked with rows of unsold water while others are empty. ... This new AI algorithm is much more flexible and more resilient to change, and it can predict sales more accurately than the simple machine learning model that was limited to date and location. With this new model, not only are you able to efficiently distribute your produced bottles across your vending machines, but you now have enough surplus to set up a new machine at the mall and another one at the cinema.
As the way we work shifts, employees’ prior strengths may become liabilities, so it’s important to monitor behaviors over time and under different circumstances. Someone who excelled at building relationships through watercooler chitchat will need to find new methods when the work group goes completely virtual. Likewise, the individual who was overlooked as too socially awkward may begin to shine in a remote working environment. Employees will need feedback on how effective their behavior is in this new world so they can learn which new behaviors they may need to adopt and which may now be seen as strengths. Peer reviews help people understand better how to adjust to new technologies, even as the technology itself is becoming part of the process. For example, I recently coached a business unit chief financial officer (CFO) of a Fortune 500 company in the U.S. who had been passed over for a promotion in the middle of 2019. His 360 feedback results in 2019 made it clear he was struggling with his peer relationships. He was whip-smart, and everyone knew it — but his peers felt he was too quick to show them up in meetings with the senior leadership team.
Proactive conversational AI platforms can resolve requests before the customer even feels the need to reach out. Modern solutions integrated with various data systems can analyze large quantities of internal and external data and identify triggers to start proactive and personalized conversations through a customer’s preferred channels. For example, a leading telco was able to eliminate 50 percent of unnecessary service calls and inbound calls related to repairs by using robotics to proactively contact customers and resolve issues as soon as remote monitoring detected a malfunction. Two-thirds of customers believe service through online channels and mobile devices should be faster, more intuitive, and better able to serve their needs.1 Organizations should seize the opportunity with improved front-end robotics or “virtual agents” to handle repetitive, transactional requests as well as to guide customers through a logical menu of topics and intentions to address issues. Companies that have incorporated such technologies are seeing significant returns: in fact, effectively deploying conversational AI can create a twofold improvement in customer experience; reduce cost to serve by 15 to 20 percent; improve churn, upsell, and acquisition by 10 to 15 percent; and result in a fourfold increase in employee productivity.
Setting up HTTPS inspection can be tricky as it does require some extra effort. And if not configured correctly, this process can actually weaken the end-to-end encryption and protection provided by security gateways and products. "Some organizations are reluctant to set up HTTPS inspection due to the extra work involved, but our threat data clearly shows that a majority of malware is delivered through encrypted connections and that letting traffic go uninspected is simply no longer an option," Corey Nachreiner, chief technology officer at WatchGuard, said in a press release. "As malware continues to become more advanced and evasive, the only reliable approach to defense is implementing a set of layered security services, including advanced threat detection methods and HTTPS inspection." A report from the US Department of Homeland Security's Cybersecurity and Infrastructure Security Agency (CISA) offers some recommendations on HTTPS inspection. "Organizations using an HTTPS inspection product should verify that their product properly validates certificate chains and passes any warnings or errors to the client," CISA said.
Smith, a top lawyer at Microsoft during its war on open source, admitted earlier this month that the company was wrong but said it had now changed, pointing to its acquisition of GitHub and the company's open-source activities on the code-sharing site. Now Sinofsky, who has a new book detailing Microsoft's antitrust and security problems during his years overseeing Windows and Office, has attempted to put some context around Microsoft's new attitude and its old antagonism to open source. Microsoft today has espoused open source as its focus shifts from Windows PCs to Azure and Office in the cloud. But Sinofsky outlines reasons why Microsoft's approach at the time was understandable – and how its model was upended by software-as-a-service in 1999-2000, to which Linux was better suited than Windows, and later Google's infrastructure. Sinofsky's defense of Microsoft fleshes out Gates' explanation of GPL in 2001 that it "makes it impossible for a commercial company to use any of that work or build on any of that work". "Microsoft was founded on the principle that software was intellectual property," Sinofsky says
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