As AI tools offer companies a range of new features, the use of artificial intelligence also raises ethical questions, as AI systems, for better or worse, will reinforce what they have already learned. This is problematic, because the machine-learning algorithms that underlie many of the most advanced AI tools depend on the data they receive for training. As with all data used to train an AI program, potential machine-learning biases are inherent and need to be carefully monitored. Sources: 4
Anyone who wants to use machines – learn in parts of the real world – must incorporate ethics into their AI decisions and strive to avoid prejudice. As algorithm-driven artificial intelligence (AI) technology continues to spread, people will be better off than they are today. Sources: 0, 4
We answered these questions as part of an expert survey conducted in the summer of 2018 by the Center for the Study of Artificial Intelligence (CSAI) at the University of California, Berkeley. Sources: 0
Experts predict that networked artificial intelligence will increase human effectiveness, but also threaten human autonomy, agency, and ability. Simply put, AI can be understood as a process of developing machines and enabling them to perform tasks that traditionally require human intelligence. We talked to experts about the wide-ranging possibilities of developing computers that can match or surpass the capabilities of human intelligence. Sources: 0, 3
Here at OpenGov, we lead the effort to transform urban spaces through the deployment of smart buildings, smart cities, and smart infrastructure. Today, I am secretly upset about self-driving cars, which transport me on long, arduous journeys. But when it comes to face or voice recognition on smart devices, you can excuse me for a moment, but I am sure there will be applications for artificial intelligence in the future. Sources: 3
First, Ravi highlighted the fact that much data is now digital, and in the first phase, which Davenport calls assisted intelligence, companies use scientific approaches – based approaches to making data – to make business decisions. Sources: 1, 3
Today, companies at the forefront of the AI revolution are already working on machine learning (ML), which is being strewn across existing information management systems to expand human analytical capabilities. According to Davenport, in the coming years more companies will push the digitization and automation of processes to the point where machines, bots and systems can have a direct impact on the intelligence derived from these processes. This is a growing trend that is turning companies into AI-based organizations. Sources: 1
For example, intelligent energy management systems collect data from sensors attached to various systems such as wind turbines and solar panels. This amount of data is then contextualized by machine learning algorithms and made available to human decision makers to better understand energy consumption, maintenance, and demand. These features allow artificial intelligence not only to help visitors and employees make informed decisions about the best use of their time and resources, but also to perform more complex tasks, such as monitoring a wind turbine to predict when it needs to be repaired. Sources: 5
Artificial intelligence (AI) stands out as a transformational technology in the digital age and is growing rapidly. Husain says artificial intelligence is even an indispensable ally when it comes to finding holes in computer networks and defence systems. Sources: 2, 5
We also see that technology, with its continuing limitations and obstacles, is progressing, but we are recording problems that can be solved. Based on our applied experience, we have studied the impact of deep learning on a wide range of areas such as health, education and security. Our findings underscore the potential of the use of profound learning techniques in the case of the economy. Sources: 2
Our interactive data visualization demonstrates the potential value of artificial intelligence and advanced analytics across 19 industries and nine business functions. On average, these use cases suggest that modern deep learning and AI techniques are capable of providing added value over traditional analytical techniques, which range from 30% to 128%, depending on the industry. In many use cases, however, traditional analytical and machine learning techniques continue to underpin industries such as insurance, pharmaceuticals, medical devices, and telecommunications, limiting the potential for artificial intelligence in certain contexts. Visualization of potential impacts of AI and advanced analytics: an analysis of data from the US Bureau of Labor Statistics. Sources: 2
This is partly due to the way in which the data is used by industry, the nature of its use cases and the complexity of the data. Sources: 2
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