Now that I've detailed an issue that has grounds for actual concern for many men (and women) in this world, let's switch gears and build something simple and fun! Here's what the finished product will look like. If you want a more in-depth view of this project, or if you want to add to the code, check out the GitHub repository. All of the necessary components to run this project are on the GitHub repository. Feel free to fork the repository and clone it to your local machine.

Just in time to get you into the holiday spirit, we are now proud to release digiKam 7.0.0 This version is a result of a long development that started one year ago and in which we have introduced new features and plenty of fixes. Check out some of the highlights listed below and discover all the changes in detail. For many years, digiKam has provided an important feature dedicated to detecting and recognizing faces in photos. The algorithms used in the background (not based on deep learning) were old and had been unchanged since the first revision that included this feature (digiKam 2.0.0). It had the peoblem of not being powerful enough to facilitate the faces-management workflow automatically.

New scholarships offer students from underrepresented backgrounds the chance of postgraduate study in AI and machine learning. Interdisciplinary artificial intelligence group DeepMind has renewed its philanthropic commitment to postgraduate students from underrepresented backgrounds who wish to study AI and machine learning at Imperial's Department of Computing. A gift to the Department from DeepMind will support six new postgraduate scholarships in AI and machine learning over the next few academic years. DeepMind's gift provides funding for four Master's-level scholarships and two PhD scholarships, all with a focus on artificial intelligence and machine learning. The scholarships are targeted at groups who are currently underrepresented in computing, particularly women and those from black and minority ethnic backgrounds.

Deep learning, a subset of artificial intelligence, is already making its way into day-to-day aspects of life and business. A few years back, the technology was touted to be the futuristic concept as it differs from traditional machine learning systems. Today, deep learning is capable of self-learning and improving as it assesses large data sets. It has a large number of business applications and has the potential to revolutionize industries, emerging as the next big disruption of AI. Deep learning is typically designed to imitate the way the human brain processes data. It re-creates the patterns found in the brain's decision-making process.

Machine learning frameworks such as Google use a TensorFlow that ease the process of acquiring data, training models, serving prediction, and refining future results. Tensorflow bundles together the machine learning and deep learning models and algorithms and makes them useful by way of common metaphor. Google uses machine learning in all of us products to improve search engine, translation, image captioning, and ordinary recommendation. To give you a concrete example, Google uses and experiences faster and modifying research with artificial intelligence. You all know if we type a keyword in Google search bar, Google provides a recommendation "what would be the next to search with".

Deploying deep learning models in production can be challenging, as it is far beyond training models with good performance. This post aims to be an engineering guideline for building production-level deep learning systems which will be deployed in real world applications. The material presented here is borrowed from Full Stack Deep Learning Bootcamp (by Pieter Abbeel at UC Berkeley, Josh Tobin at OpenAI, and Sergey Karayev at Turnitin), TFX workshop by Robert Crowe, and Pipeline.ai's Fun fact: 85% of AI projects fail. In the following, we will go through each module and recommend toolsets and frameworks as well as best practices from practitioners that fit each component.

Having covered some of our favourite AI books and AI podcasts in previous lists, this time we wanted to focus on audio books. Whilst a good book can't be beaten, many prefer to digest their information differently, seen by an increase in audio book sales in recent years. Algorithms to live by is an exploration into how computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind. In this book Brian explains the problems we face in every day life which could be solved through leveraging AI, machine processes and algorithms. This audiobook aims to teach its listeners a concept which they can, eventually after repetition, learn by heart, then allowing them to brainstorm the various opportunities for python and deep learning application.

Face recognition is one of the most convenient biometric for access control. The wide popularity of face recognition can be attributed to ease of acquisition with cheap sensors, contact-less nature, high accuracy of algorithms, and so on. While everything is well and good there, the vulnerability to presentation attacks limits its use in safety-critical situations. Imagine your phone locked with face recognition being unlocked by someone simply showing a photo or video of you in front of the phone. There you have it, its called a presentation attack (also known as spoofing attacks).

We have mastered our brains to determine objects quickly. Now, how does it happens? There are certain features that our brain relates an object to. Like for illustration, you were given an orange and an orange-colored ball. Both are identical, but still, the brain can accurately distinguish them, as we have trained it that surface of ball might be smooth but that of orange fruit is little rough.

Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.