AI Can Help You—And Your Boss—Maximize Your Potential. Will You Trust It?
First, in identifying which employees have high potential to be great performers or strong leaders. The company tells Yva which individuals it currently considers as best performers; Yva’s neural network identifies which behaviors are characteristic of these top performers, and then finds other employees who exhibit some if not all of the same traits. It can tell you who has the potential to become a top salesperson, or an extremely effective leader; and it can tell you which characteristics they already possess and which ones they need to develop. Second, Yva helps minimize “regrettable attrition” by identifying employees who are a high resignation risk. A decision to resign never comes out of the blue. First the employee will feel increasingly frustrated or burnt out; then she will become more open to consider other opportunities; then she will actively seek another job. Each stage carries subtle changes in our behavior: maybe how early we send out our first email in the morning, or how quickly we respond, or something in the tone of our messages. We can’t detect these changes, but Yva can.
7 Basic Tools of Quality using R
With the goal of satisfying customers’ needs while making profit out of it, companies and organizations have developed methodologies, frameworks and tools for addressing issues that attempt their products and services’ quality. However, there are some commonly used tools that can be applied across any industry–from the product development phase until its delivery–for solving critical quality related issues. Also referred as the 7 QC tools, the seven basic tools for quality were first conceptualized by Kaoru Ishikawa, an engineering professor at the University of Tokyo, influenced by a series of lectures given by W. Edwards Deming in the 1950’s. Ishikawa’s seven basic tools for quality correspond to a fixed set of graphical and statistical techniques helpful in solving critical quality related issues. They are widely used to fine-tune processes as part of an overall quality assurance effort. In the same way, they are considered as basic since they can be easily implemented by any person with very basic training in statistics.
How cybersecurity accelerates business growth
Simply focusing on security for PCs is outdated in the world of increased mobility. The mobile workforce is the new norm, enabling higher levels of productivity while geographically expanding your workforce— which is essential for innovation and growth. To empower their mobile workforce, organizations face new challenges such as properly vetting mobile users, deploying digital certificates to these users, and implementing the right mobile security technologies, tools and policies. Innovation requires flexibility, flexibility requires mobility, and having strong cybersecurity behind your mobile infrastructure is key to averting risk while enabling and accelerating growth. Increasingly, companies are migrating to the cloud to support enterprise and mobile workers and to enable stronger and seamless collaboration. Compared to on-premises solutions, hosted cloud environments offer significant advantages for employee collaboration and access to data from any location, which in turn, helps organizations innovate and grow.
Spending on security hardware, software, and services continues to increase
“The market for cybersecurity products continues to grow, growth that is renewed and reinvigorated by a C-level focus on trust. Today’s new trust environment introduces new variables that go beyond the traditional ideas of security, risk and compliance, introducing concepts of privacy and ethical business operations. “Trust is addressed in consideration of the relationships (B2B, B2C, B2E and G2C) and the attributes of interaction (people, technology, organization, culture and process). Given the complexity of implementing Trust, cybersecurity vendors are the clear beneficiaries,” said Frank Dickson, program vice president, Cybersecurity Products at IDC. Services will receive the largest share of security spending in 2019 with more than $47 billion going toward managed security services, integration services, consulting services, and IT education and training. Services will also have the fastest spending growth with a five-year CAGR of 11.2%.
Edge computing: a game changer for service providers?
Thanks to its distributed nature, edge computing can empower service providers to offer new solutions and services that simultaneously increase revenue streams and reduce network transport costs. Consider applications that require ultra-low latency (self-driving cars) or high bandwidth (video surveillance). By leveraging edge computing, service providers can choose to bring these services to market via infrastructure-as-a-service (IaaS) or platform-as-a-service (PaaS) options – all depending on how deep they want to be in the value chain. Services of this nature cannot be offered via traditional public cloud. Although we’re still in the early stages of edge computing’s evolution, we can confidently expect a host of influential IoT use cases to break into the mainstream in the coming years. For example, the development of Augmented Reality (AR), Virtual Reality (VR) and mobile gaming applications are already enthusiastically incorporating edge computing capabilities, increasingly reaping the benefits of rapid responsiveness in the face of high-bandwidth usage.
Augmenting the Modern Workforce with Computer Vision
With the arrival of 5G and the FCC’s recently announced rural broadband initiative, there will be an increased need for in the field training and guidance for technicians that are working in rural areas that may not have the infrastructure and resources those in urban areas typically have access to. With that in mind, telecom companies can utilize computer vision technology to provide real-time training and feedback in the field for their technicians. Additionally, computer vision technologies enable companies to automate the quality control of installations to cut down on technician re-interventions and improve the customer experience. Finally, those within the QSR space can leverage computer vision to provide automated checkout solutions. These intelligent cash registers can reduce the time spent at the check-out to a few seconds. These smart checkouts are easy-to-use cash register terminals equipped with a camera and trained neural networks that are able to recognize more than 10,000 products and/or recipes. By removing the constraints of traditional checkout, the smart checkout can improve the customer experience within quick serve restaurants by reducing queues to fewer than 10 seconds per customer, in some cases.
AI and ML will become important for how organizations run their digital systems
IDC predicts that by 2020, 30 percent of G2000 companies will have allocated capital budget equal to at least 10 percent of revenue to fuel their digital strategies. This shift toward increased funding is an important one as business executives come to recognize digital transformation as a long-term commitment. With billions of dollars invested in digital transformation initiatives, executives are now exploring the impact of their investments and asking, “What’s next?” “The next phase of digital transformation will focus on making sense of all the data so that organizations can move faster, make better decisions, and create best-in-class digital experiences,” said Buddy Brewer, GVP and GM Client Side Monitoring, New Relic. “As indicated in our research, observing and acting on insights from data collected will play a critical role in helping digitally transformed organizations truly scale and realize the benefits of modern technological advances.” Global organizations claim to be significantly progressing their digital transformation projects, with 39 percent of global respondents saying these are completed or close to completion.
Trust, control and personalization through human-centric AI
The premise of human-centric AI is a strong conviction that artificial intelligence should serve humanity and the common good while enforcing fundamental rights such as privacy, equality, fairness and democracy. However, people can only reap the full benefits of AI if they can confidently trust that the algorithm was taught to serve their interests as opposed to those of a third-party institution or corporation. For example, instead of optimizing a recommendation engine to maximize the number of impressions, one might choose to maximize the quality of impressions. The reward to be optimized should be aligned with the user’s goals. For example, a user might be highly likely to click on a message that promotes fast food, when presented. Yet, a human-centric AI engine should take into account the user’s goals related to weight-loss or health before recommending that message to the user merely for the sake of increasing click-through rate.
The four broad types of ecosystem are classified by the operating state (open or closed) and the complexity of interconnected devices (simple or hyper-connected). While all four are distinct, each one has its own business value. Not all data can be considered open and suitable for public consumption, however. While sharing global temperature sensor information is in the public interest, a vehicle’s specific location from ANPR cameras is perhaps not. One organisation that encourages and promotes the safe, open, exchange of information in communities of interest is The Open Data Institute (ODI), set up by Sir Tim Berners-Lee in 2012. This independent, not-for-profit organisation works with companies and governments to build an open, trustworthy data ecosystem where people can make better decisions using data. Currently, there is a rise in the number of organisations collaborating to provide new services to consumers, such as showing available electric car charging points on a map to make their lives easier. There are numerous new commercial ventures for aggregating and analysing large volumes of data ranging across healthcare, smart cities, agriculture, logistics, utilities and smart buildings, to name a few.
Deepfakes and voice as the next data breach
We’ve seen how quickly deepfake videos can catch on, with tools like social media allowing them to spread like wildfire. Recent examples have included an altered video of House Speaker Nancy Pelosi slurring her words, as well as footage of Facebook’s Mark Zuckerberg giving a speech on the power of big data, actor Bill Hader doing an impression of Tom Cruise, and actress Jennifer Lawrence giving a speech with Steve Buscemi’s face. Not all of these deepfake videos had malicious intent, but show how prevalent and mainstream deepfakes are becoming, and the potential for bad actors to leverage the technology to perpetrate crimes. Prominent figures like these are easy to target because they have so much public content available online that can be repurposed for deepfakes, but as this technology continues to advance, it won’t be long before criminals have the tools to expand their targets beyond world leaders and celebrities.
Quote for the day:
"Leadership without mutual trust is a contradiction in terms." -- Warren Bennis
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