Numerous industries are being transformed by artificial intelligence. Ai is transforming the way brands do business in finance, technology, and retail. It's revolutionizing the way digital marketers connect brands with their audiences.AI is now widely used in digital marketing. It works in the background to improve the effectiveness of pay-per-click advertising, personalize websites, create content, predict behavior, and more. Marketers quickly recognize the benefits of the technology, with Forbes reporting that 84 percent of marketing organizations are trying to implement or continue to expand their utilization of AI and machine learning in 2022.
Nowadays, many businesses are going through hard times with constant pandemic breakouts imposing economic, logistical, and technological challenges globally, making companies want to adapt rapidly. With face-to-face meetings being changed to video conferences to stay in touch, different cutting-edge technologies like artificial intelligence (AI) and machine learning (ML) are taking the next big step in helping humanity to augment. In fact, AI and Machine Learning are so powerful that they're projected to improve productivity by as much as 40% by 2035. Companies, big and small, strive to remain agile, experimenting with the new techs to obtain bigger ROIs. And so, this article will elaborate on what impact AI and ML make across industries and how system analysts, software engineers, and other computing professionals can integrate them to drive innovations.
In May, Google executives unveiled experimental new artificial intelligence trained with text and images they said would make internet searches more intuitive. Wednesday, Google offered a glimpse into how the tech will change the way people search the web. Starting next year, the Multitask Unified Model, or MUM, will enable Google users to combine text and image searches using Lens, a smartphone app that's also incorporated into Google search and other products. So you could, for example, take a picture of a shirt with Lens, then search for "socks with this pattern." Searching "how to fix" on an image of a bike part will surface instructional videos or blog posts.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
Zhang, Daniel, Mishra, Saurabh, Brynjolfsson, Erik, Etchemendy, John, Ganguli, Deep, Grosz, Barbara, Lyons, Terah, Manyika, James, Niebles, Juan Carlos, Sellitto, Michael, Shoham, Yoav, Clark, Jack, Perrault, Raymond
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
This has led to the development of a plethora of domain-dependent and context-specific methods for dealing with the interpretation of machine learning (ML) models and the formation of explanations for humans. Unfortunately, this trend is far from being over, with an abundance of knowledge in the field which is scattered and needs organisation. The goal of this article is to systematically review research works in the field of XAI and to try to define some boundaries in the field. From several hundreds of research articles focused on the concept of explainability, about 350 have been considered for review by using the following search methodology. In a first phase, Google Scholar was queried to find papers related to "explainable artificial intelligence", "explainable machine learning" and "interpretable machine learning". Subsequently, the bibliographic section of these articles was thoroughly examined to retrieve further relevant scientific studies. The first noticeable thing, as shown in figure 2 (a), is the distribution of the publication dates of selected research articles: sporadic in the 70s and 80s, receiving preliminary attention in the 90s, showing raising interest in 2000 and becoming a recognised body of knowledge after 2010. The first research concerned the development of an explanation-based system and its integration in a computer program designed to help doctors make diagnoses . Some of the more recent papers focus on work devoted to the clustering of methods for explainability, motivating the need for organising the XAI literature [4, 5, 6].
Last decade has seen major improvements in the performance of artificial intelligence which has driven wide-spread applications. Unforeseen effects of such mass-adoption has put the notion of AI safety into the public eye. AI safety is a relatively new field of research focused on techniques for building AI beneficial for humans. While there exist survey papers for the field of AI safety, there is a lack of a quantitative look at the research being conducted. The quantitative aspect gives a data-driven insight about the emerging trends, knowledge gaps and potential areas for future research. In this paper, bibliometric analysis of the literature finds significant increase in research activity since 2015. Also, the field is so new that most of the technical issues are open, including: explainability with its long-term utility, and value alignment which we have identified as the most important long-term research topic. Equally, there is a severe lack of research into concrete policies regarding AI. As we expect AI to be the one of the main driving forces of changes in society, AI safety is the field under which we need to decide the direction of humanity's future.
Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. Firstly, it introduces a taxonomy to analyze the field of machine ethics from an ethical, implementational, and technical perspective. Secondly, an exhaustive selection and description of relevant works is presented. Thirdly, applying the new taxonomy to the selected works, dominant research patterns and lessons for the field are identified, and future directions for research are suggested.
AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.
The unprecedented explosion in the amount of information we are generating and collecting, thanks to the arrival of the internet and the always-online society, powers all the incredible advances we see today in the field of artificial intelligence (AI) and Big Data. With this in mind, a great deal of thought and research has gone into working out the best way to store and organize information during the digital age. The relational database model was developed in the 1970s and organizes data into tables consisting of rows and columns – meaning the relationship between different data points can be determined at a glance. This worked very well in the early days of business computing, where information volumes grew slowly. For more complicated operations, however – such as establishing a relationship between data points stored in many different tables - the necessary operations quickly become complex, slow and cumbersome.