Goto

Collaborating Authors

Social & Ethical Issues


Why are we failing at the ethics of AI? A critical review

#artificialintelligence

Anja Kaspersen and Wendell Wallach are senior fellows at Carnegie Council for Ethics in International Affairs. In November 2021, they published an article that changed the AI ethics conversation: Why Are We Failing at the Ethics of AI? Six months later, the questions the article raised are no closer to resolution. This article was a don't-hold-your-punches review on the state of AI ethics, with which I am in almost complete agreement. If we want to advance the AI conversation, this is still a good place to start. I've quoted a portion of their article, with my comments interspersed: While it is clear that AI systems offer opportunities across various areas of life, what amounts to a responsible perspective on their ethics and governance is yet to be realized.


Artificial Intelligence: The Science Behind The Good Customer Experience - Elets BFSI

#artificialintelligence

Artificial Intelligence (AI), as one of the leading technological trends, continues to grow in popularity among marketers and sales professionals, and has evolved into an essential tool for brands seeking to provide a hyper-personalized, exceptional customer experience. AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software is now available, bringing AI to the enterprise without the high costs previously associated with the technology. On the basis of exclusive interactions with leaders in the BFSI sector, Nidhi Shail Kujur of Elets News Network (ENN) explores how with constantly evolving technologies, the banking and financial services industry promises to exceed customer expectations. The banking industry is undergoing significant change, particularly with the spread of customer-centricity. We live in a world where the majority of people have access to the internet.


Steve Blank Artificial Intelligence and Machine Learning– Explained

#artificialintelligence

Hundreds of billions in public and private capital is being invested in Artificial Intelligence (AI) and Machine Learning companies. The number of patents filed in 2021 is more than 30 times higher than in 2015 as companies and countries across the world have realized that AI and Machine Learning will be a major disruptor and potentially change the balance of military power. Until recently, the hype exceeded reality. Today, however, advances in AI in several important areas (here, here, here, here and here) equal and even surpass human capabilities. If you haven't paid attention, now's the time. Artificial Intelligence and the Department of Defense (DoD) The Department of Defense has thought that Artificial Intelligence is such a foundational set of technologies that they started a dedicated organization- the JAIC – to enable and implement artificial intelligence across the Department. They provide the infrastructure, tools, and technical expertise for DoD users to successfully build and deploy their AI-accelerated projects. Some specific defense related AI applications are listed later in this document. We're in the Middle of a Revolution Imagine it's 1950, and you're a visitor who traveled back in time from today. Your job is to explain the impact computers will have on business, defense and society to people who are using manual calculators and slide rules. You succeed in convincing one company and a government to adopt computers and learn to code much faster than their competitors /adversaries. And they figure out how they could digitally enable their business – supply chain, customer interactions, etc. Think about the competitive edge they'd have by today in business or as a nation. That's where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies.


Finding the Fairness in AI

#artificialintelligence

Explains Nikola Konstantinov of Switzerland's ETH Zürich, "Fairness in AI is about ensuring that AI models don't discriminate when they're making decisions, particularly with respect to protected attributes like race, gender, or country of origin." As artificial intelligence (AI) becomes more widely used to make decisions that affect our lives, making certain it is fair is a growing concern. Algorithms can incorporate bias from several sources, from the people involved in different stages of their development to modelling choices that introduce or amplify unfairness. A machine learning system used by Amazon to pre-screen job applicants was found to display bias against women, for example, while an AI system used to analyze brain scans failed to perform equally well across people of different races. "Fairness in AI is about ensuring that AI models don't discriminate when they're making decisions, particularly with respect to protected attributes like race, gender, or country of origin," says Nikola Konstantinov, a post-doctoral fellow at the ETH AI Center of ETH Zürich, in Switzerland. Researchers typically use mathematical tools to measure the fairness of machine learning systems based on a specific definition of fairness.


Skills and security continue to cloud the promise of cloud-native platforms

ZDNet

Joe McKendrick is an author and independent analyst who tracks the impact of information technology on management and markets. As an independent analyst, he has authored numerous research reports in partnership with Forbes Insights, IDC, and Unisphere Research, a division of Information Today, Inc. The KubeCon and CloudNativeCon events just wrapped up in Europe, and one thing has become clear: the opportunities are outpacing organizations' ability to leverage its potential advantages. Keith Townsend, who attended the conference, observed in a tweet that "talent and education is the number one challenge. I currently don't see a workable way to migrate thousands of apps without loads of resources. Information technology gets more complex every day, and there is no shortage of demand for monitoring and automation capabilities the build and manage systems. Cloud-native platforms are seen as remedies for not only improved maintenance, monitoring, and automation, but also for modernizing ...


La veille de la cybersécurité

#artificialintelligence

AI applications are increasingly used to make important decisions about humans' lives with little to no oversight or accountability. This can have devastating consequences: wrongful arrests, incorrect grades for students, and even financial ruin. The European Union thinks it has a solution: the mother of all AI laws, called the AI Act. It is the first law that aims to curb these harms by regulating the whole sector. If the EU succeeds, it could set a new global standard for AI oversight around the world.


The CPSC Digs In On Artificial Intelligence - AI Summary

#artificialintelligence

On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety. The CPSC defines AI as "any method for programming computers or products to enable them to carry out tasks or behaviors that would require intelligence if performed by humans" and machine learning as "an iterative process of applying models or algorithms to data sets to learn and detect patterns and/or perform tasks, such as prediction or decision making that can approximate some aspects of intelligence."3 To inform the ongoing discussion on how to regulate AI, machine learning, and related technologies, the CPSC provides the following list of considerations: Do AI and machine learning affect consumer product safety? Do AI and machine learning affect consumer product safety? UL 4600 Standard for Safety for the Evaluation of Autonomous Products covers "fully autonomous systems that move such as self-driving cars along with applications in mining, agriculture, maintenance, and other vehicles including lightweight unmanned aerial vehicles."5


Convolutional Neural Networks

#artificialintelligence

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.


Edge AI vs. Cloud AI: Which is Best for Your Business?

#artificialintelligence

In an ideal deployment, all workloads would be centralized in the cloud to enjoy the benefits of scale and simplicity. These deployments can take on the form of edge AI and/or cloud AI, each offering their own potential unique use cases, benefits, and challenges. With this in mind, it will take careful consideration when choosing the best model for your business. Edge AI and cloud AI play a complementary role in ensuring the models serving AI deployments are continuously improving without compromising on data quality and quantity. Cloud AI complements the instant decision-making of edge AI by providing deeper insights for more longitudinal data.


Artificial intelligence at the gates of the food industry

#artificialintelligence

First of all, when growing agricultural products, which is the first step of food production, in the future, consumers are expected to grow and use plants directly for cooking without using pesticides at home. There are already many companies that have introduced growers of plants that make this possible. Samsung Electronics and LG Electronics are examples. Vegetables are automatically grown by placing the seeds in the inner shelf of the planter, which is similar in size to a household refrigerator. Temperature, humidity and nutrients are automatically controlled by AI (artificial intelligence). Heliponics, a start-up from Purdue University in the United States, has also introduced the'Gropot' indoor plant grower. Artificial intelligence automatically adjusts the temperature and humidity of agricultural products... The entire process of distribution and transportation is tracked seamlessly using blockchain technology.