- Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
- Rather than train a single neural network to recognize pictures, researchers train two competing networks.
- “The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses,” explained the researchers in their paper Progressive Growing of GANs for Improved Quality, Stability and Variation.
- Since the publicly available CelebFaces Attributes (CelebA) training dataset varied in resolution and visual quality — and not sufficient enough for high output resolution — the researchers generated a higher-quality version of the dataset consisting of 30,000 images at 1024 x 1024 resolution.
- Generating convincing realistic images with GANs are within reach and the researchers plan to use TensorFlow and multi-GPUs for the next part of the work.
Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
Continue reading “Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center”
- It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car.
- Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) — even though they appear in new contexts such as IoT or machine to machine communication — still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others.
- Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon’s product listing.
- Example of deep learning algorithms for clustering
As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.
- Below are some resources to help you get started with deep learning: articles on this topic started to appear in large numbers around 2015, though many date back to before 1990.
Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make…
Continue reading “Deep Learning: Definition, Resources, Comparison with Machine Learning”
- Additionally, we announced a new promotion specifically for partners reselling our Data Security solutions.
- Informatica offers solutions to help partners help their customers with both “Detect” (Discovery and Classification) and “Protect” (Data Masking, Encryption or other 3rd party tools)
Best of all, Informatica is relying on you, our partners, to drive this strategy within our joint customer base.
- Additionally, on the May Partner Pulse Webcast, we launched our Data Security Promotion for partners!
- This includes additional front-end and back-end margin and rebates for partners who identify and close data security opportunities.
- You can listen to the replay of this webcast here and get the details of our Data Security promotion and solution portfolio on PARC, our partner portal.
Partner Opportunities in Data Security- We’ve got the enablement materials, sales tools and marketing programs to help get you started.
Continue reading “Partner Opportunities in Data Security”
- For the last years in addressing the future of work I have often focused on the human capabilities that will drive value as machines become more capable and the work landscape is transformed.
- To help define and clarify these capabilities I created a landscape on the role of Humans in the Future of Work, which I first shared publicly in my keynote yesterday.
- This framework overlaps and builds on my Future of Work Framework, specifically building out the distinctive human capabilities that will be relevant and valued as the work landscape is transformed.
- I have spoken and written before about the three fundamental human capabilities for the future of work: EXPERTISE, RELATIONSHIPS and CREATIVITY.
- Recognizing these distinctive human capabilities allows us to design work, organizations and education to use and develop these capabilities to best effect.
For the last years in addressing the future of work I have often focused on the human capabilities that will drive value as machines become more capable and the work landscape is transformed.
Continue reading “Framework: The role of Humans in the Future of Work”
- Machine learning technology can be used in automated risk assessment for lending, stock trading analysis, predictive analytics for corporate investing and day-to-day functions like improving customer service and building persuasive pitches.
- According to a white paper released by Juniper Research released this week, there is one fintech sector that may particularly benefit from the advancement in AI technology.
- Also Read: Mobile apps overtake mobile web in APAC, with Malaysia leading the way: MFI report
The UK-based research firm projected that, between 2016 and 2021, revenue growth from unsecured loans, tied directly to machine learning advancements, would jump by 960 per cent during the 5-year-period.
- For the purpose of the study, Juniper defined AI/Machine Learning as:
The reason for this revenue jump is the nature of fintech is to traffic in risk assessment, so the Juniper analysis is projecting the financial gains made from lending decisions backed by accurate, high-tech data analytics.
- Also Read: Apple growth is stagnating in China, its 2nd biggest market: Report
Finally, the report noted that US investment into AI technology has increased by 600 per cent over the last five years, signalling an expectation that machine learning will become an evermore important part of our day-to-day business lives.
The fintech industry opportunity in Asia is set to directly compete with North America and blow Europe out of the water
Continue reading “Machine learning technology set to unlock US$17B fintech opportunity: white paper”
- Back in November, Google showed off a machine learning technique that enhances low-res and blurry images.
- Apps & Updates Google Plus Machine learning
- A 100kb 1000 x 1500 image is replaced by a 25kb file that ends up having the original resolution after RAISR.
- Google Wifi Review: A great user-friendly router for your home, even if you buy just one
- 1 billion images per week have already taken advantage of RAISR, with total user bandwidth reduced by a third.
Back in November, Google showed off a machine learning technique that enhances low-res and blurry images. The RAISR technique is now being used in Google+ to display high-resolution photos while using an impressive 75% less bandwidth.
Continue reading “Google+ using machine learning to display high-resolution images w/ 75% less bandwidth”
- Google reveals RAISR: an image enhancement tech which uses machine learning
- The best way to stay connected to the Android pulse everywhere.
- Google Research Scientist Peyman Milanfar explained the technology on the Google research blog and how it differs from existing methods image enhancement methods.
- Google Photos now allows you to create animations offline
- Scott Adam Gordon is a European correspondent for Android Authority .
Google has shared details on RAISR, its new image enhancement technology which uses machine learning to produce high-quality versions of low-quality images.
Continue reading “Google reveals RAISR: an image enhancement tech which uses machine learning”