Encountering altered videos and photoshopped images is almost a rite of passage on the internet. It's rare these days that you'd visit social media and not come across some form of edited content -- whether that be a simple selfie with a filter, a highly embellished meme or a video edited to add a soundtrack or enhance certain elements. But while some forms of media are obviously edited, other alterations may be harder to spot. You may have heard the term "deepfake" in recent years -- it first came about in 2017 to describe videos and images that implement deep learning algorithms to create videos and images that look real. For example, take the moon disaster speech given by former president Richard Nixon when the Apollo 11 team crashed into the lunar surface.

Let's begin with what is machine learning? Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Popular uses of Machine learning are Play Store and App Store recommendations, google maps, email filtering, google translate, google search and so on and so forth. Let's look at 5 machine learning use case in details: They tried 3 different solution which involved using LSTM (Long short-term memory) which gained notable accuracy gains but led to serving delay since LSTM are computationally extortionate. The second solution was to replace LSTM with a Transformer model which is used for sequence-to-sequence prediction and has produced a significant result in NLP.

When it comes to online returns, the stats aren't pretty. Consumers return 30% of ecommerce purchases and a study from Shopify reveals that 40% of consumers buy variations of a product online intending to send back most of the order. In many cases, returned items cannot be put back on shelves due to product obsolescence (this is especially true with fast fashion and technology items) or slight wear and tear. This ever-increasing number of online returns is causing profits to take a massive hit. While 2019 saw a rise in retailers cracking down on serial returners and implementing a more efficient and sustainable approach to the handling of returned and excess stock, the issue isn't being solved at a fast-enough rate.

"This is the single most important problem to solve in AI today," says Yann LeCun, chief artificial intelligence scientist at Facebook Inc. It is a Manhattan Project-like effort that will go on for years, if not decades. At Facebook, Alphabet Inc.'s Google and other companies and universities around the world, scientists are working to create better AI that learns through self-supervision, teaching itself about the world the way people do. The immediate goal is broader AI that can perform multiple tasks, but that could one day lead to artificial general intelligence, or machines with humanlike thinking. A look at how innovation and technology are transforming the way we live, work and play.

Keras is a simple-to-use but powerful deep learning library for Python. In this post, we'll build a simple Recurrent Neural Network (RNN) and train it to solve a real problem with Keras. This post is intended for complete beginners to Keras but does assume a basic background knowledge of RNNs. My introduction to Recurrent Neural Networks covers everything you need to know (and more) for this post - read that first if necessary. The full source code is at the end.

Inspired by Google DeepMind's team, Shakir Mohamed, William Isaac, and Implikit's founder Marie-Therese Png article, Decolonial AI, my experience with Data Science and readings, I'll try to propose a production strategy that compensates the lack of scalable ethics in Data Science Systems and make it embedded since the beginning of the development, saving the cost of change later. The main problem that I'll approach might be kind of obvious. Data Science does not implement efficient and scalable Ethical guidelines. Data Science is not Customer Centric, yet. The reason, I'll detail along the article is: Implicitly, our work might be motivated by solely on optimizing revenue, costs, human and non-human operational resources under the facade of enriching Customer Experience when we are launching Data-based products. This is as complicated to say as is it to tackle.

Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior (AP) and lateral hip radiographs. Patients and methods -- 1,703 plain hip AP radiographs and 1,220 plain hip lateral radiographs were included in the total dataset. The CNN made the diagnosis based on: (1) AP radiographs alone, (2) lateral radiographs alone, or (3) both AP and lateral radiographs combined. The diagnostic performance of the CNN was measured by the accuracy, recall, precision, and F1 score.

Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable results, they can sometimes be fooled into misclassifying data. An adversarial attack is a type of cyberattack that specifically targets deep neural networks, tricking them into misclassifying data. It does this by creating adversarial data that closely resembles and yet differs from the data typically analyzed by a deep neural network, prompting the network to make incorrect predictions, failing to recognize the slight differences between real and adversarial data. In recent years, this type of attack has become increasingly common, highlighting the vulnerabilities and flaws of many deep neural networks.

Creativity is a deeply debated topic, as this concept is arguably quintessential to our humanity. Across different epochs, it has been infused with an extensive variety of meanings relevant to that era. Along these, the evolution of technology have provided a plurality of novel tools for creative purposes. Recently, the advent of Artificial Intelligence (AI), through deep learning approaches, have seen proficient successes across various applications. The use of such technologies for creativity appear in a natural continuity to the artistic trend of this century. However, the aura of a technological artefact labeled as intelligent has unleashed passionate and somewhat unhinged debates on its implication for creative endeavors. In this paper, we aim to provide a new perspective on the question of creativity at the era of AI, by blurring the frontier between social and computational sciences. To do so, we rely on reflections from social science studies of creativity to view how current AI would be considered through this lens. As creativity is a highly context-prone concept, we underline the limits and deficiencies of current AI, requiring to move towards artificial creativity. We argue that the objective of trying to purely mimic human creative traits towards a self-contained ex-nihilo generative machine would be highly counterproductive, putting us at risk of not harnessing the almost unlimited possibilities offered by the sheer computational power of artificial agents.

Classifying images taken from indoor scenes is an important area of research. The development of an accurate indoor scene classifier has the potential to improve indoor localization and decision-making for domestic robots, offer new applications for wearable computer users, and generally result in better vision-based situation awareness thus impacting a wide variety of applications. The introduction of deep learning methods, the creation of numerous large-scale datasets, and the development of specialized computing hardware have all contributed to the rapid improvement in image classification performance. One reason for deep learning's success has been the ability to learn multiple layers of generic image features that can then be used on other related computer vision problems. For instance, features from object trained image classifiers have been used to train indoor scene classifiers [27]. Yet, indoor scene classification is a challenging problem on its own.