Elon Musk is having a terrible February. At the moment, the Tesla and SpaceX CEO is facing scrutiny from three different government agencies, an animal-cruelty scandal, a car recall, a failed satellite launch, and accusations of trivializing the Holocaust. This is the kind of pileup of scandals that it usually takes a resignation to resolve. But anyone who's followed Musk's career knows that even all these simultaneous firestorms are likely to leave the executive barely singed. On Feb. 9, the California Department of Fair Employment and Housing filed a lawsuit against Tesla over alleged racial discrimination at its Fremont factory after receiving hundreds of complaints and conducting a 32-month investigation. The suit describes abominable treatment of Black workers at the factory, including an alleged practice of segregating them into areas that other employees referred to as "the plantation" and "the slave ship."

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For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators' models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent."

In large technology companies, the requirements for managing and organizing technical documents created by engineers and managers have increased dramatically in recent years, which has led to a higher demand for more scalable, accurate, and automated document classification. Prior studies have only focused on processing text for classification, whereas technical documents often contain multimodal information. To leverage multimodal information for document classification to improve the model performance, this paper presents a novel multimodal deep learning architecture, TechDoc, which utilizes three types of information, including natural language texts and descriptive images within documents and the associations among the documents. The architecture synthesizes the convolutional neural network, recurrent neural network, and graph neural network through an integrated training process. We applied the architecture to a large multimodal technical document database and trained the model for classifying documents based on the hierarchical International Patent Classification system. Our results show that TechDoc presents a greater classification accuracy than the unimodal methods and other state-of-the-art benchmarks. The trained model can potentially be scaled to millions of real-world multimodal technical documents, which is useful for data and knowledge management in large technology companies and organizations.

Clearview AI has announced it aims to put almost every human's face in its facial recognition database, making'almost everyone in the world will be identifiable' A controversial AI company has announced it aims to put an image of nearly every human face in its facial recognition database, making it possible for'almost everyone in the world [to] be identifiable.' In its latest report in December, facial recognition firm Clearview AI told investors that the company is currently collecting 100 billion photos of human faces for the unprecedented campaign, which will be stored in its dedicated database. The collection of images - approximately 14 photos for each of the 7 billion people on the entire planet, scraped from social media and other sources - would extensively bolster the company's extensive surveillance system, already the most elaborate of its kind. The American company headquartered in Manhattan further told investors that its'index of faces' has grown from 3 billion images to more than 10 billion since the start of 2020. The firm's technology has already been used by myriad law enforcement and government agencies around the world, helping police make thousands of arrests by aiding in various criminal investigations.

The controversial facial recognition company Clearview AI reportedly told investors that it aims to collect 100 billion photos--supposedly enough to ensure that almost every human will be in its database. With $50 million from investors, the company said, it could bulk up its data collection powers to 100 billion photos, build new products, expand its international sales team and pay more toward lobbying government policymakers to "develop favorable regulation." "It limits the uses of its system to agencies engaged in lawful investigative processes directed at criminal conduct, or at preventing specific, substantial, and imminent threats to people's lives or physical safety." A federal judge "rejected Clearview's First Amendment defense, denied the company's motion to dismiss, and allowed the lawsuits to move forward," the Electronic Frontier Foundation wrote yesterday. A Vice report yesterday quoted Ton-That as saying that Airbnb, Lyft, and Uber have "expressed interest" in using Clearview facial recognition "for the purposes of consent-based identity verification, since there are a lot of issues with crimes that happen on their platforms."

The controversial facial recognition company Clearview AI reportedly told investors that it aims to collect 100 billion photos--supposedly enough to ensure that almost every human will be in its database. "Clearview AI is telling investors it is on track to have 100 billion facial photos in its database within a year, enough to ensure'almost everyone in the world will be identifiable,' according to a financial presentation from December obtained by The Washington Post," the Post reported today. There are an estimated 7.9 billion people on the planet. The December presentation was part of an effort to obtain new funding from investors, so 100 billion facial images is more of a goal than a firm plan. However, the presentation said that Clearview has already racked up 10 billion images and is adding 1.5 billion images a month, the Post wrote.

A range of regulatory changes and new hires from the Biden administration signals a more proactive stance by the federal government towards artificial intelligence (AI) regulation, which brings the U.S. closer to that of the European Union (EU). These developments are promising, as is the inclusion of AI issues in the new EU-U.S. Trade and Technology Council (TTC). But there are other steps that these leading democracies can take to build alignment on curtailing AI harms. Since 2017, at least 60 countries have adopted some form of artificial intelligence policy, a torrent of activity that nearly matches the pace of modern AI adoption. The expansion of AI governance raises concerns about looming challenges for international cooperation.

The issue of environmental sustainability is becoming a more crucial to any organization day by day as the finance world drives this change. It has been observed that all organizations be it large/medium or small tend to have a massive carbon footprint, so it is increasingly important to understand and analyze company's environmental impact first using innovative AI driven tools & technologies to drive a greener and safer planet while "meeting the needs of the present without compromising the ability of future generations to meet their own needs". High energy consumption in industrial applications is one of the key reasons for higher carbon footprints. All organizations need Artificial Intelligence (AI) as partner to plan, design, execute, and recommend the sustainability measures to ensure the future of our planet. AI tools, technologies and techniques will help us to achieve sustainability through defining right data driven climatic control measures more efficiently, effective and efficient use of natural resources, reducing and managing the waste we generate more effectively and effective use of environmental opportunities.

In the last three years, financial regulators worldwide have been actively highlighting the need for responsible use of Artificial Intelligence/ Machine Learning (AI/ML). What have they been saying? What common underlying concerns and regulatory themes are emerging? What can the industry expect in the coming years, and how can it start responding now? To date, no major financial regulator has introduced explicit regulations dedicated to the use of AI/ML.