August 28, 2022
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Analytics teams thrive in dynamic environments that reward curiosity, encourage innovation, and set high expectations. Building and reinforcing this type of culture can help put organizations on a path to earning impressive returns from analytics investments. An active analytics culture thrives when CXOs reward curiosity over perfection. Encourage analysts to challenge convention and ask questions as a method to improve quality and reduce risks. This thinking goes hand in hand with a test-and-learn mentality, where pushing boundaries through proactive experimentation helps identify what works, and optimize accordingly. It’s also important to create a culture where failure and success are celebrated equally. Giving airtime to what went wrong allows the team to more effectively learn from their mistakes and see that perfection is an unhealthy pipe dream. This encourages an environment that holds analysts accountable for delivering quality processes and results, further helping to mitigate risk and improve marketing programs.
This approach will allow SSE to experiment with reducing risks to migrating birds. For example, they can determine an optimum blade speed that will allow flocks to pass safely while still generating power. By understanding the environment around the turbines, it will be possible to control them more effectively and with significantly less environmental impact. Simon Turner, chief technology officer for data and AI at Avanade, described this approach as “an autonomic business.” Here, data and AI work together to deliver a system that is effectively self-operating, one he described as using AI to “look after certain things that you understood that could guide the system to make decisions on your behalf.” Key to this approach is extending the idea of a digital twin with machine learning and large-scale data. ... As Turner notes, this approach can be extended to more than wind farms, using it to model any complex system where adding new elements could have a significant effect, such as understanding how water catchment areas work or how hydroelectric systems can be tuned to let salmon pass unharmed on their way to traditional breeding grounds, while still generating power.
Roger Roberts, partner at McKinsey and one of the report’s coauthors, said of applied AI, which is defined “quite broadly” in the report, “We see things moving from advanced analytics towards… putting machine learning to work on large-scale datasets in service of solving a persistent problem in a novel way,” he said. That move is reflected in an explosion of publication around AI, not just because AI scientists are publishing more, but because people in a range of domains are using AI in their research and pushing the application of AI forward, he explained. ... According to the McKinsey report, industrializing machine learning (ML) “involves creating an interoperable stack of technical tools for automating ML and scaling up its use so that organizations can realize its full potential.” The report noted that McKinsey expects industrializing ML to spread as more companies seek to use AI for a growing number of applications. “It does encompass MLops, but it extends more fully to include the way to think of the technology stack that supports scaling, which can get down to innovations at the microprocessor level,” said Roberts.?
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The main negative implication of this quantum computing concerns the cryptography of secrets, a fundamental element of information security. Cryptographic schemes that are today considered secure will be cracked in mere seconds by quantum computers, leaving persons, companies, and entire countries powerless against the computing supremacy of their adversaries. “When quantum computers reach higher levels of computing power and speed, they will be capable of breaking public key cryptography, threatening the security of business transactions, secure communications, digital signatures, and customer information,” explains CISA. This could threaten data in transit relating to top-secret communications, banking operations, military operations, government meetings, critical industrial processes, and more. Yesterday, China's Baidu introduced “Qian Shi,” an industry-level quantum supercomputer capable of achieving stable performance at 10 quantum bits of power.
Business intelligence (BI) describes the procedures and tools that assist in getting helpful information and intelligence that can be used from data. A company’s data is accessed by business intelligence tools, which then display analytics and insights as reports, dashboards, graphs, summaries, and charts. Business intelligence has advanced significantly from its theoretical inception in the 1950s, and you must realize that it is not just a tool for big businesses. Most BI providers are tailoring their software to users’ needs because they recognize that our current era is considerably more oriented toward small structures like start-ups. SaaS, or software-as-a-service, vendors are incredibly guilty of this. Another issue is that it’s a more straightforward tool than it once was. It is still a professional tool; managing data is not simple, even with the most powerful technology. Nevertheless, BI has developed into something more accessible than local software, which used to require installation on every computer in the organization and may represent a sizable expenditure with the emergence of the Cloud and SaaS in the early 21st century.
It’s unclear why Dr. Gourianov would leave big tech out of the argument entirely. There are dozens upon dozens of papers from Google and IBM alone demonstrating breakthrough after breakthrough in the field. Gourianov’s primary argument against quantum computing appears, inexplicably, to be that they won’t be very useful for cracking quantum-resistant encryption. With respect, that’s like saying we shouldn’t develop surgical scalpels because they’re practically useless against chain mail armor. Per Gourianov’s article: Shor’s algorithm has been a godsend to the quantum industry, leading to untold amounts of funding from government security agencies all over the world. However, the commonly forgotten caveat here is that there are many alternative cryptographic schemes that are not vulnerable to quantum computers. It would be far from impossible to simply replace these vulnerable schemes with so-called “quantum-secure” ones. This appears to suggest that Gourianov believes at least some physicists have pulled a bait-and-switch on governments and investors by convincing everyone that we need quantum computers for security.