If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The graph represents a network of 1,175 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 May 2022 at 11:21 UTC. The requested start date was Friday, 20 May 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 16-hour, 1-minute period from Tuesday, 17 May 2022 at 07:58 UTC to Friday, 20 May 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. While AI-driven solutions are quickly becoming a mainstream technology across industries, it has also become clear that deployment requires careful management to prevent unintentional damage. As is the case with most tools, AI has the potential to expose individuals and enterprises to an array of risks, risks that could have otherwise been mitigated through diligent assessment of potential consequences early on in the process. This is where "responsible AI" comes in -- that is, a governance framework that documents how a specific organization should address the ethical and legal challenges surrounding AI. A key motivation for responsible AI endeavors is resolving uncertainty about who is accountable if something goes wrong.
Tesla's founder Elon Musk said back in 2013: Self-driving cars are the natural extension of active safety and obviously something we think we should do. Fully-autonomous vehicles (AV) are no longer a technology of the future. Established and emerging manufacturers have embarked on a journey to produce the most reliable driverless cars to compete in a growing market. But people still don't trust AVs are safe, despite potential benefits of fuel efficiency, reduced emissions and improve mobility. We study the power of brands. Our research found companies can take advantage of their brand reputation to encourage consumers to adopt driverless cars.
Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer. We show that the emergent production, consumption, and pricing behaviours respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents' emergent prices for goods vary over space, reflecting the local abundance of goods.
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Essentials | 22.05.2022 | weekly digest highlighting the top articles about: - Why Artificial Intelligence Creates an Unprecedented Era of Opportunity in the Near Future. - Digital resilience: Building the economies of tomorrow on a foundation of cybersecurity. - U.S. Needs New 'Manhattan Project' to Avoid Cyber Catastrophe | Opinion. - Ransomware is already out of control. AI-powered ransomware could be 'terrifying.'. - Cybersecurity Vulnerabilities Need Addressing. - How to use responsible AI to manage risk.
Clustering of large-scale data is key to implementing segmentation-based algorithms. Segmentation can include identifying customer groups to facilitate targeted marketing, identifying prescriber groups to allow health care players to reach out to them with the right messaging, and identifying patterns or abnormal values in the data. K-Means is the most popular clustering algorithm adopted across different problem areas, mostly owing to its computational efficiency and ease of understanding the algorithm. K-Means relies on identifying cluster centers from the data. It alternates between assigning points to these cluster centers using the Euclidean distance metric and recomputes the cluster centers till a convergence criterion is achieved.
When we talk about Computer vision products, most of them have required the configuration of multiple things including the configuration of GPU and Operating System for the implementation of different problems. This sometimes causes issues for customers and even for the development team. Keeping these things in mind, Nvidia released Jetson Nano, which has its own GPU, CPU, and SDKs, that help to overcome problems like multiple framework development, and multiple configurations. Jetson Nano is good in all perspectives, except memory, because it has limited memory of 2GB/4GB, which is shared between GPU and CPU. Due to this, training of custom Computer Vision models on Jetson Nano is not possible.