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Today, November 20th, people around the world pause to bear witness to Transgender Day of Remembrance, a day dedicated to honoring the memory of those murdered because of anti-transgender prejudice. Transgender Day of Remembrance reminds us to fight against forces that devalue transgender lives every day. To bring awareness to this important day, we want to pause to share a few stories of transgender people who have found their voice on WordPress.com. We posed a question: “What does Transgender Day of Remembrance mean to you?” Below, we’ve shared a few responses from creators on our platform.

We welcome you to share your own response on your site. In the meantime, read slowly and soak in the hard-fought words of the brave voices who are willing to share their experiences.

Dr. SA Smythe (They/Them) of essaysmythe.com:

Some of us have been counted, but most of us are counted out—unthought and unthinkable. And so we do it ourselves. We account for Tony McDade. We are accountable to Muhlaysia Booker. We recall Riah Milton. We recollect the fierce life of one of our greatest contemporary remembrancers, the trans griot Monica Roberts. We name the nonbinary people who continue to be treated as unnameable as we slip through the matrix of binary gender. The competing racialized pandemics of our time continues to be intensified for trans people, especially Black trans women, in this year as with any other. We live with that reality and demand non-trans people do the same because our resilience is nothing without their reckoning for the violence they allow to continue against us. Trans Day of Remembrance is not only about how trans people have been stolen from us too soon, but how we continue to survive and thrive and persist against all odds. Has there ever been anything as beautiful as that?Read more

Laura Kate Dale (She/Her) of laurakbuzz.com:

Going and spending some time in the company of other trans people was wonderful. I got to see trans people from a variety of backgrounds, some who had grown old and found love, and see proof that I could live a long and happy life as a trans woman. But the tone of the evening was contrasted by sitting with the knowledge of why we were all gathered, the knowledge of far too many lives cut far too short. I was surrounded by the trans people who had survived and thrived, as well as the memories of those who had not.Read more

Nicole Eldridge (She/Her) of transgendersupport.org:

My name is Nicole Eldridge. I’ve been transgender since third grade. As I started to transition, I would read stories online about transgender people dying. This is absolutely terrifying if you want to do what they did. I never gave up and transitioned. Transgender Day of Remembrance means to me that we remember the transgender people that have died and carry out their goal of an equal future for all transgender people. Every time I listen to a Transgender Day of Remembrance speech, it brings me back to Martin Luther King’s speech, “I Have a Dream.” What King said about everyone being equal and having equal opportunities is so true when I hear the transgender people’s names who have died. It breaks my heart to hear all of the transgender people that died for the year. In spite of the hatred toward transgender people, I rise above it all and help transgender people all over the world with my website transgendersupport.org. This is what Transgender Day of Remembrance means to me.

Tallulah Ker-Oldfield (She/Her) of transrites.wordpress.com

Trans people are nothing new. Gender and its expressions have been changing throughout cultures, and trans people have existed throughout history with notable examples in the many ancient pantheons, including deities. There’s nothing new to consider, no trans question – we’ve been here all along, and the only terrible things that happened because of it happened to us… ***And so I’m remembering trans lives lost this year, and trans lives filled with trauma, and everything that trans people have to do to simply… be. If you ever thought this year was scary, oppressive, isolating, challenging to get through and potentially fatal to be around people… you’ve been living a lot of the worst parts of the trans experience. Yet I’m remembering the powerful joy of my community, how our bonds through the pandemic have been strong, how well accustomed we immediately became to 2020, having lived our own version of it for most of our lives, creating found families, love, laughter, understanding and sometimes rainbows out of the unforgiving raw material of compromise.Read more

To read more writing by transgender people, explore these sites on WordPress.com:

letsqueerthingsup.comautistichoya.nettransprov.wordpress.comgendermom.wordpress.com

We pride ourselves on being a platform where anyone can share their perspective, and we’re honored to be able to create a space for the personal stories of transgender-identifying individuals. Take the time to read their words and remember that it’s not enough to honor transgender people just one day each year. What we do matters every day. Follow these sites and others you come upon and, as a result, show your support in the days to come.
Quelle: RedHat Stack

21. November 2020

da Agency

Beyond COVID-19, retail looks to transform with AI/ML

The global retail industry, which has grappled with waves of change over the past decade, is facing one of its most dynamic and unpredictable periods to date. When I speak with retail executives, some are thriving, some are surviving, and some are struggling. What I have taken from these conversations is that COVID-19 has seriously condensed the timeline available to play ‘catch up’ in developing agile, resilient operating models powered by cloud infrastructure, artificial intelligence and machine learning (AI/ML) technologies. At Google Cloud, we recently commissioned a survey of global retail executives to better understand which AI/ML use cases across the retail value chain drive the highest value and returns in retail, and what retailers need to keep in mind when going after these opportunities. While the study has applicability across all of retail, the researchers focused their effort around two specific sub-segments —Food, Drug, and Mass merchants (FDM) and Specialty —as these verticals have faced dramatically different challenges during COVID-19. Here are the key findings: There is significant value at stake that can be created with AI/ML across Food, Drug, Mass and Specialty retailersThe researchers looked at 75 use cases across Food, Drug, Mass and Speciality retail to understand where AI/ML can help retailers derive value across different areas of their business.Click to enlargeFor Food, Drug, Mass merchant retail the research showed that the application of AI/ML technologies can potentially drive ~$280-650B across the value chain as adoption accelerates. Similarly for Speciality Retailers the research found a potential to deliver ~$230-520B in value by 2023. In an industry where profit margins are in the single digits, AI/ML is increasingly a foundational investment area for retail leaders. A few use cases unlock a disproportionately large share of value While there are many considerations that go into deciding which AI/ML projects to work on, the data showed there is a clear opportunity for retailers to choose initiatives that drive greater value creation. Across a wide range of retailers a few use cases stood out.Click to enlargeFor Food, Drug and Mass merchant retailers, merchandising, store operations and logistics are some of the largest cost drivers of the P&L. Not surprisingly, four out of the top ten use cases for this segment fall under these parts of the value chain, including frictionless check out, picker routing, automated task dispatch and shelf checking. Retailers in this space can leverage AI/ML capabilities to gain efficiency and productivity for their employees by automating tasks across stores and distribution centers. In Specialty, the use cases that drive the most value fall within merchandising and assortment, product lifecycle management, and logistics and fulfillment. For these retailers, use cases within merchandising and assortment make up five out of the top ten list and focus on improvements in demand planning by optimizing assortment, inventory, and markdowns. By combining data and signals from various parts of their business and using AI/ML enabled analytics, merchandising teams can see patterns that traditional analytics often miss and make granular predictions even for new/ short life cycle products. Learn more about transforming your business with AIAt Google, we’ve been solving business problems with AI and machine learning technologies for over a decade and are excited to be bringing this experience and our technology to our customers. Leading retailers including Carrefour, Zulilyand The Home Depot rely on Google Cloud AI and machine learning products to transform their businesses. You can dig deeper into the results of the research and read more customer case studies in this ebook or watch our recent webinar featuring a conversation with Zulily.
Quelle: Google Cloud Platform

21. November 2020

da Agency

How AI, and specifically BERT, helps the patent industry

In recent years the patent industry has begun to use machine-learning (ML) algorithms to add efficiency and insights to business practices. Any company, patent office, or academic institution that works with patents—generating them through innovation, processing applications about them, or developing sophisticated ways to analyze them—will benefit from doing patent analytics and machine learning in Google Cloud. Today, we are excited to release a white paper that outlines a methodology to train a BERT (bidirectional encoder representation fromtransformers) model on over 100 million patent publications from the U.S. and other countries using open-source tooling. The paper describes how to use the trained model for a number of use cases, including how to more effectively perform prior art searching to determine the novelty of a patent application, automatically generate classification codes to assist with patent categorization, and autocomplete. The white paper is accompanied by a colab notebook as well the trained model hosted in GitHub. Google’s release of the BERT model (paper, blog post, and open-source code) in 2018 was an important breakthrough that leveraged transformers to outperform other leading state of the art models across major NLP benchmarks, including GLUE, MultiNLI, and SQuAD. Shortly after its release, the BERT framework and many additional transformer-based extensions gained widespread industry adoption across domains like search, chatbots, and translation.We believe that the patents domain is ripe for the application of algorithms like BERT due to the technical characteristics of patents as well as their business value. Technically, the patent corpus is large (millions of new patents are issued every year world-wide), complex (patent applications generally average ~10,000 words and are often meticulously wordsmithed by inventors, lawyers, and patent examiners), unique (patents are written in a highly specialized ‘legalese’ that can be unintelligible to a lay reader), and highly context dependent (many terms are used to mean completely different things in different patents). Patents also represent tremendous business value to a number of organizations, with corporations spending tens of billions of dollars a year developing patentable technology and transacting the rights to use the resulting technology and patent offices around the world spending additional billions of dollars a year reviewing patent applications.We hope that our new white paper and its associated code and model will help the broader patent community in its application of ML, including:Corporate patent departments looking to improve their internal models and tooling with more advanced ML techniques.Patent offices interested in leveraging state-of-the-art ML approaches to assist with patent examination and prior art searching.ML and NLP researchers and academics who might not have considered using the patents corpus to test and develop novel NLP algorithms.Patent researchers and academics who might not have considered applying the BERT algorithm or other transformer based approaches to their study of patents and innovation.To learn more, you can download the full white paper, colab notebook, and trained model. Additionally, seeGoogle Patents Public Datasets: Connecting Public, Paid, and Private Patent Data, Expanding your patent set with ML and BigQuery, and Measuring patent claim breadth using Google Patents Public Datasets for more tutorials to help you get started with patent analytics in Google Cloud.Related ArticleUnifiedpost and Google collaborate on Document AI to automate procurement data captureUnifiedpost uses Google Cloud Document AI to automate procurement data capture.Read Article
Quelle: Google Cloud Platform