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) …
Earlier this week, the team behind Google's advanced DeepMind neural network unveiled a new ability dubbed Transframer, which allows AI to generate 30-second videos from a single image input. It's a nifty little trick at first glance, but the implications are much larger than an interesting .GIF file. Transframer is a general-purpose generative framework that can handle many image and video tasks in a probabilistic setting. New work shows it excels in video prediction and view synthesis, and can generate 30s videos from a single image: https://t.co/wX3nrrYEEa "Transframer is state-of-the-art on a variety of video generation benchmarks, and… can generate coherent 30 second videos from a single image without any explicit geometric information," the DeepMind research team explains.
Recent years have witnessed tremendous developments in the financial sector. Financial technology or FinTech, has been playing a critical role in providing next-level customer service to users via the usage of AI-powered Chatbots. Intended to assist customers with their requests in the most dynamic way possible, Chatbots today, also act as a guiding channel that can help businesses better understand the needs of their customers. According to a Juniper Study, the usage of chatbots will end up saving banks up to $7.3 billion worldwide by 2023, which represents a time saving of 862 million hours, or almost half a million years of work. To understand the Role of an AI Chatbot in the FinTech sector better, let's take a look at a few important use cases: As Artificial Intelligence, continues to create capabilities for the FinTech industry, it is obvious that customer expectations will follow suit.
Today, we will learn about Multi-Task Learning and HydraNets. This is a Deep Learning technique I first introduced back in mid-2020 in an email I sent to exactly 653 people. The responses to this email were so high (engineers from everywhere around the planet told me they loved it and wanted to apply it to their company) I had to create an entire HydraNet section in my course catalog. You can learn more by visiting https://www.thinkautonomous.ai/ Not only is this technique new and exciting for the Deep Learning field, but it's also accessible to many Computer Vision Engineers.
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Digital Twins are Virtually a Reality // eeNews Europe Newsletter 220729 Digital twins are becoming more and more reality. The ultimate digital twin is of course a virtual replica of the human being - the robot. That robot is'under development', but look at where we are! We see stumbling, crawling, dancing, fighting, cooking, steel muscle constructions that require massif battery packs and a lot of remote control - not exactly a'twin' that can do your work or can do your cleaning or shopping. Digital twins in manufacturing are doing much better.
Imitation is the highest form of flattery, often known as biomimicry in the engineering community. Mosquitoes, birds, fireflies, mussels etc. inspired surgical needles, airplanes, LED light bulbs, adhesives respectively (more); Nature has inspired numerous other innovations. Therefore, it makes sense to model an intelligent computer after the architecture of the human brain. Deep Learning Network or Artificial Neural Networks is a type of machine learning model that draws inspiration from the organic network formed by neurons in human brains. Note: Via this and related articles in this series, I hope to record and share my learning (obtained recently during and after pursuing Data Science with Business Programme at the University of Exeter, UK) in a simplified manner so that you could use this resource to quickly refer to or grasp concepts of varying complexity.
This requires a delicate balance between engaging smart, predictive technologies, such as ML and AI, and honoring the ethical standards that build trust between brands and customers. The trend toward collecting and cultivating zero- and first-party data through an open and transparent value exchange with consumers has become table stakes for creating personalized customer experiences that result in long-term loyal relationships. But using only historical data to build black-box algorithms that continuously learn upon themselves can perpetuate intrinsic biases with regard to such characteristics as gender, race, ethnicity and economic status. Ethical AI offers a unique opportunity to establish trust with consumers, who I've found are increasingly seeking humanized communication from the brands with which they engage. The question of ethics in marketing and AI has been a hot topic for some time, and many brands have worked hard to ensure their targeting algorithms aren't biased.