Artificial Intelligence (AI) And Security: A Match Made In The SOC
Artificial Intelligence (AI) And Security: A Match Made In The SOC ---> https://fancli.com/2t7NEg
According to research by global services firm Pwc, they are. In a survey looking at investment in security software, 23 percent of respondents said they were planning to invest in artificial intelligence and machine learning over the year.
When security analysts leverage artificial intelligence, it increases analyst productivity and streamlines threat detection and investigation processes, saving a significant amount of analyst time. AI does the leg work for analysts and helps them work smarter by taking over the most time-consuming and cumbersome parts of the threat investigation process, such as threat intelligence mapping, local data gathering, associating business context with potential security alerts, assessing high-value assets being targeted and more.
Gartner coined another new term, artificial intelligence for IT operations, or AIOPs, in 2016. This represents systems that store event information being gathered over a long period of time, perhaps years, in a database and then applying analytics to that data.
Table of ContentsI. Qualities of artificial intelligenceII. Applications in diverse sectorsIII. Policy, regulatory, and ethical issuesIV. RecommendationsV. Conclusion
Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it.1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.
The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity.50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.
According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology.55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits.56
Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.
If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.
Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.
To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.
The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.
In this Backstage Pass video, which aired Sept. 27, 2021, Motley Fool contributor John Bromels discusses how this fintech company brings artificial intelligence to the consumer credit industry and how disruptive its approach could be to traditional lending solutions.
John Bromels: Co-Founder and CEO, Dave Girouard is the former President of Enterprise Google and Co-Founder and a Councilman, the former Manager of Global Enterprise Customer Programs and Consumer Operations at Google. Why is that important? What does Upstart do? Upstart is an AI-driven lending platform. It uses artificial intelligence, big data, and predictive modeling to create an alternative to the FICO score to determine a person's creditworthiness.
CEO Dave Girouard has also expressed an interest in disrupting the payday lending industry, which as we know, is essentially designed. The payday lending industry argues that they need to charge these exorbitant and these very high fees because of the high risk of default. Well, Dave Girouard and Upstart says well, if our model can more accurately predict who's going to default and who isn't, perhaps we can be a better alternative for many consumers than a payday loan. That brings up the question of bias. The trick with artificial intelligence is quite often it's looking for the best efficiency, or the most mathematically perfect number or outcome.
Sometimes, however, that can introduce biases into the system, that can introduce things that we don't want to be there. Of course, the very famous example of this is when some researchers created an artificial intelligence, and basically used social media to allow it to examine how people talked. The algorithm picked up all objectionable content and started skewing it out because of the flawed input and had to be shut down and reconfigured to work that out. Upstart is really committed though to reducing bias in its algorithm because of course, you don't want unconscious bias to creep its way into your lending. Because not only is that illegal, it is also really unfair.
A focus on nurturing unique human skills that artificial intelligence (AI) and machines seem unable to replicate: Many of these experts discussed in their responses the human talents they believe machines and automation may not be able to duplicate, noting that these should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI. These respondents suggest that workers of the future will learn to deeply cultivate and exploit creativity, collaborative activity, abstract and systems thinking, complex communication, and the ability to thrive in diverse environments.
Google, a leader in AI and data analytics, is on a massive AI acquisition binge, having acquired a number of AI startups in the last several years. Google is deeply invested in furthering artificial intelligence capabilities. In addition to using AI to improve its services, Google Cloud sells several AI and machine learning services to businesses. It has an industry-leading software project in TensorFlow as well as its own Tensor AI chip project.
IBM is a leader in the field of artificial intelligence. Its efforts in recent years center around IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. It has been acquisitive, purchasing several startups over several years. It benefits from having a strong cloud platform.
iCarbonX is a Chinese biotech startup that uses artificial intelligence to provide personalized health analyses and health index predictions. It has formed an alliance with several technology companies from around the world that specialize in gathering different types of healthcare data and will use algorithms to analyze genomic, physiological, and behavioral data. It also works to provide customized health and medical advice.
In a world with a vast ocean of podcasts and videos to transcribe, Rev uses AI to find its market. An AI-powered, but human-assisted, transcription provider, the company also sells access to developers, so tech-savvy folks can use its speech recognition technology. But the key part here is the combination of humans with AI, which is a sweet spot in the effective use cases for artificial intelligence. With a growing need for accessibility features in audiovisual production especially, expect more AI competitors to take advantage of a similar business model in the future.
OpenAI is a nonprofit research firm that operates under an open-source type of model to allow other institutions and researchers to freely collaborate, making its patents and research open to the public. The founders say they are motivated in part by concerns about existential risk from artificial general intelligence. ChatGPT is a recent part of OpenAI that allows users to generate text from poetry to short stories. However, despite OpenAI being nonprofit, ChatGPT is now its own for-profit company. 2b1af7f3a8