AI in Fintech Market Forecast to Reach $11.2 Billion by 2026 An Impressive Growth Is Expected in the Fintech Industry With the Development in Other Technologies, Such as Blockchain and Cybersecurity Along With AI.
BriefingWire.com, 4/21/2021 - The market for AI in Fintech is forecast to reach $11.2 bilion by 2026, growing at a CAGR of 29.75% from 2021 to 2026. Artificial Intelligence (AI) has taken the tech world by storm, allowing companies to automate their high value and complicated processes. The reason to make a shift towards machine learning is also motivated by reduction in cost, increasing efficiency, reducing error and better customer experience. The AI in fintech is majorly driven by credit card fraud detection. With the help of Generative Adversarial Network, AI can spot the difference between real data and hacked data in each transaction and send alert to banks. The biggest challenge with AI is the sensitive issue of
Abstract
The neural architecture search (NAS) method provides a new approach to the design of generative adversarial network (GAN)’s architecture. The existing state-of-the-art algorithm, AutoGAN, is an example in discovering GAN’s architecture using reinforcement learning (RL)-based NAS. However, performance differences between the candidate architectures are not taken into consideration. In this paper, one new ImprovedAutoGAN algorithm based on AutoGAN is proposed. We found that which candidate architectures to use can affect the performance of the final network. So in the process of architecture search, compared with AutoGAN, in which candidate architectures are selected randomly, our method uses gradient bandit algorithm to increase the probability of selecting networks with better performance. This paper also introduces a temperature coefficient in the algorithm to prevent the search results from getting trapped in the local optimum. The GANs are searched using the same sea
If you’re new to transformers, it’s difficult to find a better explanation of the matrix mechanics powering the architecture than this one written by Jay Alammar. Ezra’s Mary Fallah has also written…
Volunteers looked at artificially generated faces while wearing a brain monitor
Researchers created an AI to read this brain data and understand the patterns
It was then able to automatically generate artificially produced human faces
These faces were tailed to the non-verbal preferences of the volunteers based on the data coming from their brain waves as they looked at human faces
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