Imagine a few days before an election, a video of a candidate is released, showing them using hate speech, racial slurs, and epithets that undercut their image as pro minorities. Imagine a teenager watching embarrassingly an explicit video of themselves going viral on social media. Imagine a CEO on the road to raise money when an audio clip stating her fears and anxieties about the product is sent to the investors, ruining her chances of success. All the above scenarios are fake, made up, and not actual, but can be made real by AI-generated synthetic media, also called deepfakes[1]. The same technology that can enable a mother, losing her voice to Lou Gehrig's disease to talk to her family using a synthetic voice can also be used to generate a political candidate's fake speech to damage their reputation.

By combining purpose-built materials and neural networks, researchers at EPFL have shown that sound can be used in high-resolution imagery. Imaging allows us to depict an object through far-field analysis of the light- and sound-waves that it transmits or radiates. However, the level of detail is limited by the size of the wavelength in question--until now. Researchers at EPFL's Laboratory of Wave Engineering have successfully proven that a long, and therefore imprecise, wave (in this case a sound wave) can elicit details that are 30 times smaller than its length. Their research, which has just been published in Physical Review X, is creating exciting new possibilities, particularly in the fields of medical imaging and bioengineering.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Those are terms you hear a lot from companies developing artificial intelligence systems, whether it's facial recognition, object detection, or question answering. And to their credit, the recent years have seen many great products powered by AI algorithms, mostly thanks to advances in machine learning and deep learning. But many of these comparisons only take into account the end-result of testing the deep learning algorithms on limited data sets. This approach can create false expectations about AI systems and yield dangerous results when they are entrusted with critical tasks.

In order to create effective machine learning and deep learning models, you need copious amounts of data, a way to clean the data and perform feature engineering on it, and a way to train models on your data in a reasonable amount of time. Then you need a way to deploy your models, monitor them for drift over time, and retrain them as needed. You can do all of that on-premises if you have invested in compute resources and accelerators such as GPUs, but you may find that if your resources are adequate, they are also idle much of the time. On the other hand, it can sometimes be more cost-effective to run the entire pipeline in the cloud, using large amounts of compute resources and accelerators as needed, and then releasing them. The major cloud providers -- and a number of minor clouds too -- have put significant effort into building out their machine learning platforms to support the complete machine learning lifecycle, from planning a project to maintaining a model in production.

AI has made several breakthroughs when it comes to implementation into games. The functionalities of AI in video games include various domains such as real-time facial emotion recognition, automated difficulty adaptation, sentiment analysis, non-verbal bodily motion, lip-synchronised speech and more. This technique has been used in games to enhance graphical realism, to generate levels, sceneries and storylines, to establish player profiles, balance complexity or to add intelligent behaviours to non-playing characters. In this article, we list down the seven popular AI projects that work on gesture gaming. About: This project will help you understand how to use TensorFlow object detection API with the computer's webcam to play a snake game by using hand gestures.

The ability to train a deep learning (DL) system on large amounts of data has increased the speed of analysis and results, but the need for more and …

Artificial intelligence is right up there with robots taking over our jobs. This is the first in a series on how big tech like Facebook uses AI to manipulate you. The number of AI applications has increased rapidly. We speculate and marvel about what AIs will be able to do in the future. But what we don't realise is that AI has already had a huge impact on the goods and services we use every day.

The second experiment tested the abilities of deep learning algorithms in abstract visual reasoning.

LONDON--(BUSINESS WIRE)--Deep Instinct, the first and only cybersecurity company to apply end-to-end deep learning to predict, identify, and prevent cyberattacks, is continuing its strategic expansion into EMEA, contracting with T-Systems (Poland), one of the region's largest IT services providers, to utilize and distribute Deep Instinct's protection to its customers. Deep Instinct also signed strategic partnership agreements with Cyber Monks and Spinnakar to distribute Deep Instinct's deep learning-based solution across the region. Leading Deep Instincts' EMEA expansion is Brooks Wallace, VP Sales EMEA, a veteran cybersecurity sales leader with over 20 years of experience in building sales teams. Wallace will oversee the newly opened sales and support office in the UK and forge additional strategic partnerships with MSSPs across the region. "Our expansion into EMEA comes at a critical time for the region, and contracting with T-Systems Poland attests to the unique value of our deep learning-based cyber-attack prevention solution," said Guy Caspi, CEO and Co-founder of Deep Instinct.

Using Transformer models has never been simpler! Yes that's what Simple Transformers author Thilina Rajapakse says and I agree with him so should you. You might have seen lengthy code with hundreds of lines to implement transformers models such as BERT, RoBERTa, etc. Once you understand how to use Simple Transformers you will know how easy and simple it is to use transformer models. TheSimple Transformers library is built on top of Hugging Face Transformers library. Hugging Face Transformers provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, etc.) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) and provides more than thousand pre-trained models and covers around 100 languages.