The immediate fallouts of algorithmic risks can include inappropriate and potential illegal decisions. And they can affect a range of functions, such as finance, sales and marketing, operations, risk management, information technology, and human resources. Algorithms operate at faster speeds in fully automated environments, and they become increasingly volatile as algorithms interact with other algorithms or social media platforms. Therefore, algorithmic risks can quickly get out of hand. Algorithmic risks can also carry broader and long-term implications across a range of risks, including reputation, financial, operational, regulatory, technology, and strategic risks. Given the potential for such long-term negative implications, it’s imperative that algorithmic risks be appropriately and effectively managed. ... A good starting point for implementing an algorithmic risk management framework is to ask important questions about the preparedness of your organization to manage algorithmic risks. For example: Does your organization have a good handle on where algorithms are deployed?; Have you evaluated the potential impact should those algorithms function improperly?; Does senior management within your organization understand the need to manage algorithmic risks?
The human is better at telling them that the customers aren't happy. And the fact that the customer is unhappy is a crucial determinant in how the decision should be made. So instead of ignoring it in the automation, and throwing up an answer, and then having the person go, "Well that was a stupid answer because this customer is unhappy." Go ahead and ask the person, is the customer unhappy, and if they say yes or no, then use that as part of the decision making. So we find that there's often a role for humans in decision making, but it's often not this supervisory, "You make a suggestion, I'll override it if I feel like it kind of thing." And so we find you have to really understand the structure of your decision making before you can make those judgments. So we encourage people, when we're working with them—Look, let's understand the decision making first and let's understand all of it, automated pieces and the manual pieces. And once we understand all of it, then we can draw a suitable automation boundary to figure out which pieces to digitize, which technologies to use, and make it an integrated whole.
Even in seemingly non-tech companies (if there is such a thing in the future), the employee experience will change dramatically. For one thing, robots and cobots will have an increasing presence in many workplaces, particularly in manufacturing and warehousing environments. But even in office environments, workers will have to get used to AI tools as “co-workers.” From how people are recruited, to how they learn and develop in the job, to their everyday working activities, AI technology and smart machines will play an increasingly prominent role in the average person's working life. Just as we've all got used to tools like email, we'll also get used to routinely using tools that monitor workflows and processes and make intelligent suggestions about how things could be done more efficiently. Tools will emerge to carry out more and more repetitive admin tasks, such as arranging meetings and managing a diary. And, very likely, new tools will monitor how employees are working and flag up when someone is having trouble with a task or not following procedures correctly. On top of this, workforces will become decentralized – which means the workers of the future can choose to live anywhere, rather than going where the work is.
While most static analyzers look for a wide range of bugs, Pysa was specifically developed to look for security-related issues. More particularly, Pysa tracks "flows of data through a program." How data flows through a program's code is very important. Most security exploits today take advantage of unfiltered or uncontrolled data flows. For example, a remote code execution (RCE), one of today's worst types of bugs, when stripped down, is basically a user input that reaches unwanted portions of a codebase. Under the hood, Pysa aims to bring some insight into how data travels across codebases, and especially large codebases made up of hundreds of thousands or millions of lines of code. This concept isn't new and is something that Facebook has already perfected with Zoncolan, a static analyzer that Facebook released in August 2019 for Hack -- the PHP-like language variation that Facebook uses for the main Facebook app's codebase. Both Pysa and Zoncolan look for "sources" (where data enters a codebase) and "sinks" (where data ends up). Both tools track how data moves across a codebase, and find dangerous "sinks," such as functions that can execute code or retrieve sensitive user data.
TF-Coder uses two ML models in order to predict the needed operations from features of the input/output tensors and a natural language description of the task. These predictions are then combined within a general framework to modify the weights to customise the search process for the given task. The researchers introduced three key ideas in the synthesis algorithm. Firstly, they introduced per-operation weights to the prior algorithm, allowing TF-Coder to enumerate over TensorFlow expressions in order of increasing complexity. Secondly, they introduced a novel, flexible, and efficient type- and value-based filtering system that handles arbitrary constraints imposed by the TensorFlow library, such as “the two tensor arguments must have broadcastable shapes.” Finally, they developed a framework to combine predictions from multiple independent machine learning models that choose operations to prioritise during the search, conditioned on features of the input and output tensors and a natural language description of the task. The researchers evaluated TF-Coder on 70 real-world tensor transformation tasks from StackOverflow and from an industrial setting.
A traditional Approach would be do Build a Single Solution on Visual Studio and then Seperate the Concerns via Layers. Thus you would probably have Projects like eCommerce.Core, eCommerce.DataAccess and so on. Now these seperations are just at the level of code-organization and is effecient only while developing. When you are done with the application, you will have to publish them to a single server where you can no longer see the seperation in the production environment, right? Now, this is still a cool way to build applications. But let’s take a practical scenario. Our eCommerce API has, let’s say , endpoints for customer management and product management, pretty common, yeah? Now down the road, there is a small fix / enhancement in the code related to the Customer endpoint. If you had built using the Monolith Architecture, you would have to re-deploy the entire application again and go through several tests that guarentee that the new fix / enhancement did not break anything else. A DevOps Engineer would truly understand this pain. But if you had followed a Microservice Architecture, you would have made Seperate Components for Customers, Products and so on.
In practical service application, challenges come from both the service and the technique. This section draws a detailed summary of the features of the four architectures and analyzes their key distinctions (Table 1). In terms of hierarchy, monolithic and vertical architectures centralize functional modules of each hierarchy with high coupling degree; SOA uncouples multiple functional modules of vertical and horizontal hierarchies of three or more tiers, but public modules can only be shared on horizontal hierarchies, leading to unthorough uncoupling; the fully self-service flexibility achieved by simultaneous uncoupling on vertical and horizontal hierarchies represents the main characteristic of microservice architecture; however, when putting large projects into practice, development teams cannot comply with all the features and they must consider the integration of irreplaceable systems and promote the flexibility of full uncoupling within acceptable changing rate. The core role of microservice architecture is to cope with the growing service capability within the system and the increasingly complex interaction demands between systems.
The stress and uncertainty caused by the COVID-19 crisis is creating the ideal environment for cybercriminals looking to cash in or create chaos. "Given the impact and scale of COVID-19, cyberattacks related to organizations involved in COVID-19 research or those firms providing relief services have continued to evolve, morph and expand," says Stanley Mierzwa, director of the Center for Cybersecurity at Kean University in Union, New Jersey. "Threat actors will continue to look for areas of vulnerability, and this could potentially reside in 'local' or 'satellite' offices of larger global for-profit, non-profit and non-governmental organizations that may not be utilizing centrally managed or administered systems," Mierzwa says. Craig Jones, who leads the global cybercrime program for Interpol, said in a recent interview with Information Security Media Group: "Certainly in relation to the COVID-19 pandemic, we're seeing a unique combination of events that have led to a whole range of specific criminal opportunities." Criminals haven't shied away from attempting to seize those opportunities, as demonstrated by their rush to rebrand attacks and even "fake news" campaigns to give them a COVID-19 theme, as well as unleash scams involving personal protective equipment, he told ISMG.
By removing the latency and bandwidth scaling issues of cloud-based intelligence, it paves the way for a far more natural human–machine interaction. In the smart home, for example, the AIoT brings a whole new dimension to home control. By coupling voice with human sensing technology, such as presence detection and biometrics, we can build a multi-modal interaction that delivers an energy efficient and seamless, personalised experience. The TV will know when you’re in the room and ‘wake’ to a standby mode, it will know who you are and on hearing the wake word, greet you with familiarity and deliver your preferred settings. This kind of interaction also has clear applications across smart cities. Multi-modal sensing opens the path for significant steps forward in safety, security and energy efficiency. Let’s take the humble streetlight: the inclusion of human presence detection would enable it to light up only when a pedestrian or cyclist is in the vicinity. Add in voice control and the lamppost can detect a cry for help — of even the sound of glass breaking, triggering a call to the emergency services for assistance. In offices and public buildings, we won’t need to push buttons on elevators or hunt in our bags for a lift pass, instead our biometrics will form our signature for access, enabling a secure and convenient experience.
"What does 'cyber' even mean? And where does it come from?" writes Thomas Rid in "Rise of the Machines," his book-length quest to unravel cyber's origin story. Everyone from military officers and spies, to bankers, hackers and scholars "all slapped the prefix 'cyber' in front of something else ... to make it sound more techy, more edgy, more timely, more compelling - and sometimes more ironic," writes Rid, who's a professor of political science at Johns Hopkins University. Cyber has cachet. Cyber inevitably seems to always be pointing to the future. But as Rid writes in his book, "the future of machines has a past," and cyber has long stood not just for a future, Utopian merging of humans and machines, but a potential dystopia as well. On the good side exists the potential offered by cyborg-like technologies that might one day, for example, enable humans with spinal injuries to walk again. Such technology may even facilitate the human colonization of Mars. For a view of the flip side, however, take the "Matrix's" rendering of a postapocalyptic hellhole in which humans have been made to unthinkingly serve machines.
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