- Every development in AI is portrayed as the forerunner to Skynet from the Terminator movies, or something from Blade Runner, Westworld or another vision of a future ruled by robot overlords.
- Paypal founder and billionaire entrepreneur Elon Musk joined in, warning that AI represented the greatest threat to mankind.
- Sadly, we’re probably going to have to get used to this for a few years yet, until AI becomes more mainstream, creates as many jobs as it eliminates, and starts to deliver huge benefits to businesses and society – much like new technologies have for the last 50 years.
- We are, in many ways, starting into an age where what was once science fiction will become a reality – but just because sci-fi writers realised that dystopian visions sell more books than utopian dreams, we’ve become culturally conditioned to the idea that too much new technology is a bad thing.
- For IT professionals, your job is to understand and explain what AI and other emerging technologies can bring to your business and your customers – and to deliver the enormous potential on offer.
Clearly we are going through the phase in the development of artificial intelligence (AI) technology where rationality and reasoned debate are replaced by science-fiction scaremongering and …
Continue reading “IT leaders’ pragmatism will be the antidote to AI scaremongering”
- Venkatesh Ramanathan is a senior data scientist at PayPal where he is working on building state-of-the-art tools for payment fraud detection.
- Venkatesh has worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.
- Data Science is an emerging field that allows businesses to effectively mine historical data and better understand consumer behavior.
- This type of scientific data management approach is critical for any business to successfully launch its products and better serve its existing markets.
Venkatesh Ramanathan presents how advanced machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention.
Continue reading “Large Scale Machine Learning for Payment Fraud Prevention Recorded at:”
- That’s the vision of Andrew Ng, a founder of the Google Brain deep learning project, and former head of AI at Baidu–a position he left in March–who is today announcing a set of five interconnected online courses on the subject.
- “Today, if you want to learn deep learning, there are lots of people searching online, reading [dozens of] research papers, reading blog posts, and watching YouTube videos,” Ng tells Fast Company.
- As Ng sees it, getting to an AI-powered economy is going to take the work of much more than any one, or even several companies.
- “I hope we can build an AI-powered future that provides everyone affordable healthcare, accessible education, inexpensive and convenient transportation, and a chance for meaningful work for every man and woman,” Ng says in his announcement, which is the first from his newly created company, deeplearning.ai.
- Ng is aware that many people are still confused by AI, often getting bogged down in the different subspecialties, and lingo that can easily be misused.
The founder of Google Brain and former head of Baidu’s AI efforts wants to train a giant new workforce to help make “AI the new electricity.”
Continue reading “AI Superstar Andrew Ng Is Democratizing Deep Learning With A New Online Course”
- Nexus CX, a pioneer in AI, is getting properly up close and personal.
- In SU’s pilot stages, men who thought they were talking to a bot responded more openly than those who were told they were speaking with a human at the other end.
- Nexus CX are working with Amazon’s Alexa, recording Trainor’s friend, documenting his memories and thoughts, helping to test a virtual counterpart and robot avatar that will speak based on collected patterns of speech.
- It’ll console people in a way that humans can’t.
- And when you think of the great technological advancements of the past decade, one creation stands head and shoulders above the rest: the iPhone.
Apparently, artificial intelligence is going to take over our lives, our jobs, our minds even and not necessarily in a good way. It’s inevitable.
Continue reading “I blame the parents – AI needs to be raised right”
- Is AI on your software development roadmap?
- With technologies like advanced machine learning, deep learning, natural language processing, and business rules, AI is poised to disrupt both how developers build applications and the nature of those applications.
- The risks—unrealistic expectations, integration with traditional applications, and more—can’t be ignored as your organization strives for the rewards of an accelerated development cycle and a new generation of self-learning applications.
- Uncover this shifting digital landscape and how your business can take advantage of it in the Forrester Research report, “How AI Will Change Software Development And Applications.”
- Fill out the form at right to read the free report.
Learn how AI is changing the application development landscape
Continue reading “Learn how AI is changing the application development landscape”
- From smart connected plants and insight-driven enterprises to Blockchain-enabled services, Indian enterprises want to take digital to the next level.
- How should they leverage the evolution of digital technology-especially in artificial intelligence-to build new business models, new products and services or enter new markets?
- This edition of the Accenture Business Journal for India reveals the secret sauce for digital success across industries-from telecom, consumer packaged goods to manufacturing.
- Take a deep dive and learn how to Lead in the New and avoid digital oblivion.
Accenture Business Journal for India – Vol. 3
Continue reading “Accenture Business Journal for India – Vol. 3”
- In this work, we propose Dense Transformer Networks to apply spatial transformation to semantic prediction tasks.
- The third and fourth rows are the segmentation results of U-Net and DTN, respectively.
- max_epoch: how many iterations or steps to train
test_step: how many steps to perform a mini test or validation
save_step: how many steps to save the model
summary_step: how many steps to save the summary
sampledir: where to store predicted samples, please add a / at the end for convinience
model_name: the name prefix of saved models
test_epoch: which step to test or predict
network_depth: how deep of the U-Net including the bottom layer
class_num: how many classes.
- We have conv2d for standard convolutional layer, and ipixel_cl for input pixel convolutional layer proposed in our paper.
- We have deconv for standard deconvolutional layer, ipixel_dcl for input pixel deconvolutional layer, and pixel_dcl for pixel deconvolutional layer proposed in our paper.
Contribute to dtn development by creating an account on GitHub.
Continue reading “GitHub”