JMU College Republicans chairwoman Juliana McGrath shares her concerns with controversial diversity, equity and inclusion training for first-year students. James Madison University is under fire for pushing controversial rhetoric as part of its freshmen orientation training for student leaders. The PowerPoint presentation and accompanying video addressed topics like social justice, identity, power and privilege, and labeled any person who fits the parameters of White, male, straight and Christian as oppressors in a detailed chart. JMU College Republicans chairwoman Juliana McGrath shared her frustration with Fox News, saying the training at the Virginia university that's meant to bring students together will ultimately be divisive. "When you're teaching about things like this, the goal is to try to bring people together and try to get people to understand different life experiences," she said.

In 1989, Kimberlé Crenshaw, now a law professor at UCLA and the Columbia School of Law, first proposed the concept of intersectionality. In an article published in the University of Chicago Legal Forum, she critiqued the inability of the law to protect working Black women against discrimination. She discussed three cases, including one against General Motors, in which the court rejected discrimination claims with the argument that anti-discrimination law only protected single-identity categories. Black women, the court said, could not be discriminated against based on the combination of identities, in this case race and gender. Intersectionality, at its core, represents the interconnected nature of our identity.

There are many Natural Language Processing (NLP) tasks that require text to be split in chunks of varying granularity: Making a task to extract names and addresses of a person is almost impossible under these conditions – just because the data preparation stage was not up for it. Subject specific technical terms are sometimes abbreviated in a way that is otherwise, generally not used: (German Legal References: "Putzo ZPO 39. So, lets take the German legal reference example from above and apply Spark NLPs extended capabilities on a sample project (with a series of CoLab notebooks) to see how this will help us splitting text correctly into sentences. And make the first 1000 rulings available as a separate JSON file (since handling a larger data collections is otherwise difficult with a normal CoLab license). I developed a command line tool called unsplit to parse the text from the German legal court rulings to split sentences at a period, except when the period character was at one of the known abbreviations in the previously curated list (the unsplit tool is a C#/.Net command line program which I can publish on GitHub if people are interested). But honestly, I use this as hint towards the quality of a model and tend to say "the truth is in the pudding" and trust the a real world test more than any KPIs: I'll be looking forward on comments about things that could be improved in the data preparation stage of this sentence detection modelling task or other items you might find worth giving me feedback about. Making a task to extract names and addresses of a person is almost impossible under these conditions – just because the data preparation stage was not up for it. Subject specific technical terms are sometimes abbreviated in a way that is otherwise, generally not used: (German Legal References: "Putzo ZPO 39.

As artificial intelligence (AI) becomes increasingly embedded in the fabric of business and our everyday lives, both corporations and consumer-advocacy groups have lobbied for clearer rules to ensure that it is used fairly. In May, the European Union became the first governmental body in the world to issue a comprehensive response in the form of draft regulations aimed specifically at the development and use of AI. The proposed regulations would apply to any AI system used or providing outputs within the European Union, signaling implications for organizations around the world. Our research shows that many organizations still have a lot of work to do to prepare themselves for this regulation and address the risks associated with AI more broadly. In 2020, only 48 percent of organizations reported that they recognized regulatory-compliance risks, and even fewer (38 percent) reported actively working to address them.

The U.S. Securities and Exchange Commission (SEC) will seek input on whether digital customer engagement innovations used by financial firms should be governed by existing rules or may need new ones, commission chair Gary Gensler told Reuters. While the SEC's thinking on the subject is at an "early stage," its rules may need updating to account for an artificial intelligence-led revolution in predictive analytics, differential marketing and behavioral prompts designed to optimize customer engagement, he said. The SEC plans to launch a sweeping consultation in coming days that could have major ramifications for retail brokers, wealth managers and robo-advisers, which increasingly use such tools to drive customers to higher-revenue products. I really believe data analytics and AI can bring a lot of positives, but it means we should look back and think about what does this mean for user interface, user engagement, fairness and bias," said Gensler. "What does it mean about rules written ...

Artificial intelligence can generate tremendous insight and benefits, bringing efficiencies to a wide range of applications. But to maximize AI's potential, providers need to take a holistic approach, relying on the human component as much as the technology itself. Without a doubt, AI has become ubiquitous in our daily lives. AI helps curate selections for us on Netflix. It's used in facial recognition to identify customers walking into a retail store.

Would you mind sending documentation on model evaluation, carbon footprint, and discrimination prevention controls by 4 pm to the regulatory relations team?" When running an ML or AI function in a regulated industry -- that is pretty much any very soon -- such requests will become more frequent. However, it is not just regulators driving the need for a procedural framework for ML/AI operations. Process and procedures become relevant when considering building AI teams, models, and organizations and running these organizations, justifying their design. This article will shed some light on helpful practices, misleading temptations, and an outlook on emerging aspects.

There are many Natural Language Processing (NLP) tasks that require text to be split in chunks of varying granularity: 1. Document 2. Sentence 3. Token 4. etc… This post is focused on splitting text into sentences in order to facilitate later downstream tasks, such as, Named Entity Recognition (NER), Text Classification or Sentiment Analysis. Splitting a sentence correctly can be crucial for the success of the downstream task as we can see in the following example. Suppose we (wrongly) split a German legal reference like: "Schütze ZPO 4. Aufl. Now you might say this is special subject stuff and there are always exotic cases. But this issue also occurs in daily life when you want to extract common things. Consider, for example, (an invented) German address (with correct syntax for zip code and so forth): "Dr.

The rights associated with intellectual property are of immense importance to those involved in the development, exploitation and use of computer hardware and software, and information technology generally. Trademarks do not protect technology, but the names or symbols used to distinguish a product in the marketplace. This means that these intellectual property rights accord different types of legal protection on software programmes. The idea must be fixed in definite medium of expression and it must be ascertained that it's the author's own intellectual creation. There are two right or benefits that accrue to a computer programmer with respect to his software programme, which are Economic Right and Moral Right.

The Patent Office of South Africa has issued the world's first patent for an invention created by artificial intelligence. The DABUS system, which simulates human mental activity, has created a food container based on fractal geometry and has improved characteristics compared to containers of standard shapes. The application was submitted to the agency on September 17, 2019, indicating that the invention was generated by an autonomous artificial intelligence. The" author " of the invention is DABUS (Device for Autonomous Bootstrapping of Unified Sentience), an artificial intelligence system that simulates the human thought process to generate new ideas and inventions. DABUS was able to create a food container based on fractal geometry with improved structural strength and reduced heat transfer compared to conventional containers.