In this paper we investigate the vulnerability that facial recognition systems present to adversarial examples by introducing a new methodology from the attacker perspective. The technique is based on the use of the autoencoder latent space, organized with principal component analysis. We intend to analyze the potential to craft adversarial examples suitable for both dodging and impersonation attacks, against state-of-the-art systems. Our initial hypothesis, which was not strongly favoured by the results, stated that it would be possible to separate between the "identity" and "facial expression" features to produce high-quality examples. Despite the findings not supporting it, the results sparked insights into adversarial examples generation and opened new research avenues in the area.

Question: How does it fare against caricature artwork? Thought the service/model concluded that they are not the same person, the above two images returned a similarity score of 70.6%. Question: How does it fare against edited pictures? At my side of the world, the word "Meitu" is a name of a mobile app from China that has transcended into being a verb for "picture enhancement" in our pop culture. The above is a screengrab from the famous South China Morning Post's video on Youtube on Chinese live streamers.

Lauren Rhue researches the fast-paced world of artificial intelligence and machine learning technology. But she wants everyone in it to slow down. Rhue, an assistant professor of information systems at the University of Maryland Robert H. Smith School of Business, recently audited emotion recognition technology within three facial recognition services: Amazon Rekognition, Face and Microsoft. Her research revealed what Rhue called "really stark" racial disparities. Amazon Rekognition is offered for use to other companies.

Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3]. Furthermore, histological assessment is a time-consuming process that requires highly trained professionals. With significant technological advances in the last decade, techniques such as whole slide imaging (WSI) and deep learning (DL) are now widely available.

As we all know that technology is replacing everything, we've widgets and machines that do our work like mobile phones and dishwashers. Technology is expanding day by day and replaced several jobs which ran to competition in the request. Now, the coming step is to replace creativity also. Animators are on the stage where they might lose their jobs. Therefore, advanced technology has formerly taken away the jobs of several people.

What is Amazon Web Services? Amazon Web Services or AWS is world's broadly adopted cloud platform . AWS provides with a number of useful cloud computing services that are very much reliable, scalable and cost efficient as they say. AWS provides services like storage, networking, remote computing, servers, email, mobile development and security . So now coming to Amazon machine learning, frankly means leveraging ML algorithms on cloud platforms like AWS .

Recently, there have been breakthroughs in computer vision ("CV") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLIP and highlight some of the challenges such models pose. CLIP reduces the need for task specific training data, potentially opening up many niche tasks to automation. CLIP also allows its users to flexibly specify image classification classes in natural language, which we find can shift how biases manifest. Additionally, through some preliminary probes we find that CLIP can inherit biases found in prior computer vision systems. Given the wide and unpredictable domain of uses for such models, this raises questions regarding what sufficiently safe behaviour for such systems may look like. These results add evidence to the growing body of work calling for a change in the notion of a 'better' model--to move beyond simply looking at higher accuracy at task-oriented capability evaluations, and towards a broader 'better' that takes into account deployment-critical features such as different use contexts, and people who interact with the model when thinking about model deployment.

Every machine learning (ML) model demands data to train it. If your model isn't predicting Titanic survival or iris species, then acquiring a dataset might be one of the most time-consuming parts of your model-building process--second only to data cleaning. What data cleaning looks like varies from dataset to dataset. For example, the following is a set of images tagged robin that you might want to use to train an image recognition model on bird species. That nest might count as dirty data, and some model applications may make it inappropriate to include American and European robins in the same category, but this seems pretty good so far.

As AI technologies become more advanced, previously cutting-edge -- but generic -- AI models are becoming commonplace, such as Google Cloud's Vision AI or Amazon Rekognition. While effective in some use cases, these solutions do not suit industry-specific needs right out of the box. Organizations that seek the most accurate results from their AI projects will simply have to turn to industry-specific models. There are a few ways that companies can generate industry-specific results. One would be to adopt a hybrid approach -- taking an open-source generic AI model and training it further to align with the business's specific needs.

Creating a movie trailer takes time, and most broadcasters and streaming platforms don't have enough resources to do it. Their creative team, responsible for putting together promotional material for digital and social media, spends very little time being creative. To produce a 30" rough edit for a 10-movie stunt is about 5 days of viewing and logging. They also use and manage external agencies, leading to bottlenecks where one department's output is heavily prioritized over another's. The whole process is onerous, time consuming, and inefficient. PromoMii, a UK startup, solves this problem with a unique blend of domain expertise and machine learning (ML). Their product Nova provides functionality to search for scenes or specific dialogues across their library. Productivity is supercharged with template queries, enabling creatives to finish their spot in minutes in terms of days. Nordic Entertainment Group, one of PromoMii's customers, found that it was 10 times cheaper and 20 times faster to create trailers with Nova. A promotion which would usually take two days to produce was completed within 2 hours. This blog post is the first in a series of startup ML stories, where we tell stories like PromoMii's in terms of three crucial ingredients to building a successful business with ML – team, product, and partnership. PromoMii was founded by two Danes from Copenhagen to help large broadcasters promote their shows. Over time and working backwards from their customers, the company pivoted toward using Artificial Intelligence (AI) to enable creatives to be creative. The technological challenge inspired Tigran Mnatskanyan, CTO, to join PromoMii with a mission of building a great engineering team and crafting the content creation platform of the future. In terms of domain expertise, PromoMii's Chairman is Lester Mordue, an award-winning creative director bringing experience from MTV, Sky, Disney, and Discovery. As a creative himself, Lester immediately saw the benefits of Nova and is in a unique position to open doors for the business and provide guidance on product-market fit. "In my career, I've sat in boardrooms looking at tech and marketing ROI as well as sitting in edit suites looking for inspiration and story hooks," said Lester. "Viewers enjoy on-demand services and streaming platforms, and so too should marketeers who help make viewing decisions.