In the last few decades, machine learning has become increasingly popular as a means to support decision-making. From banks and insurance companies to internet service providers, dentists and even the supermarkets where we do our weekly shopping, this technology is ever more pervasive in our lives. Its ubiquitous presence, however, is not just limited to the private realm. Public institutions have also begun using this technology to improve a multitude of processes, for example, to prevent crime, detect tax fraud and award subsidies and grants, among others. To a large extent, machine learning's success can be explained by its promise of greater coherence and the resulting perception of greater objectivity. In this sense, claims abound about how using machine learning can help us "make better decisions".

Position is open to Remote candidates in the US or Canada. Must be able to work starting around 9am EST to connect with teammates in Oslo, Norway. Founded in 2010, Tapad cracked the code on cross-device marketing technology. Our groundbreaking, proprietary technology assimilates trillions of data points to find the relationship between smartphones, desktops, laptops, tablets, and connected TVs. Ten years later, we are processing data at petabyte scale, with an engineering team that comprises roughly half of our entire organization.

As guests arrive at eastern Australia's Warilla Hotel, a small camera equipped with facial recognition software scans their faces as part of a scheme to tackle problem gambling. The tech – which uses artificial intelligence (AI) to identify addicts who have asked to be barred from betting sites – is set to be rolled out across gambling venues in the state of New South Wales next year. Supporters say it will help curb problem gambling in a country where the addiction affects about 1 percent of the population and annual losses run to billions of dollars. But the technology is "invasive, dangerous and undermines our most basic and fundamental rights", said Samantha Floreani, programme lead at the non-profit group Digital Rights Watch. "We should be exceptionally wary of introducing it into more areas of our lives and it should not be seen as a simple quick-fix solution to complex social issues," she said.

There is a clear gap in the space between the time that a management consultancy gets the CEO of a financial institution to sign off on some ambitious multi-year plan to put some antiquated process to rest. The consultants, never wanting to dirty their hands with anything that isn't a strategy, leave about as quickly as they came, forcing the bank, and its change function, if it has one, to turn to one of the big four accounting firms. They then send in hordes of fresh university graduates who steadily proceed to chip away at the original targets of the change plan, usually leaving the bank with something utterly unrecognizable. Nischal Tanna, who previously spent years in transformation functions at major US and Singaporean banks, is trying to come up with a better way of doing things. In an interview with finews.asia, the CEO of Transform hub said he tries to join both ends of the consulting and execution functions more effectively by using AI and focusing on particular implementation phases.

At long last, DALL-E 2, OpenAI's image-generating AI system, is available as an API, meaning developers can build the system into their apps, websites and services. In a blog post today, OpenAI announced that any developer can start tapping the power of DALL-E 2 -- which more than three million people are now using to produce over four million images a day -- once they create an OpenAI API account as part of the public beta. Pricing for the DALL-E 2 API varies by resolution. For 1024 1024 images, the cost is $0.02 per image; 512 512 images are $0.018 per image; and 256 256 images are $0.016 per image. Volume discounts are available to companies working with OpenAI's enterprise team.

Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of US mainstream media and social media posts from Reddit and Twitter. We conduct in-depth analyses of news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best-performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.

Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems. While critical concepts like fairness and explainability are often addressed, the safety of systems is typically not sufficiently taken into account. By viewing data-driven decision systems as socio-technical systems, we draw on the uncertainty in ML literature to show how fairML systems can also be safeML systems. We posit that a fair model needs to be an uncertainty-aware model, e.g. by drawing on distributional regression. For fair decisions, we argue that a safe fail option should be used for individuals with uncertain categorization. We introduce semi-structured deep distributional regression as a modeling framework which addresses multiple concerns brought against standard ML models and show its use in a real-world example of algorithmic profiling of job seekers.

At the US Patent & Trademark Office in Alexandria, Virginia, artificial intelligence (AI) projects are expediting the patent classification process, helping detect fraud, and expanding examiners' searches for similar patents, enabling them to search through more documents in the same amount of time. And every project started with a pilot project. "Proofs of concept (PoCs) are a key approach we use to learn about new technologies, test business value assumptions, de-risk scale project delivery, and inform full production implementation decisions," says USPTO CIO Jamie Holcombe. Once the pilot proves out, he says, the next step is to determine if it can scale. Indian e-commerce vendor Flipkart has followed a similar process before deploying projects that allow for text and visual search through millions of items for customers who speak 11 different languages.

In recent months, many people have begun to explore a new pastime: generating their own images using several widely-distributed programs such as DALL-E, Midjourney, and Stable Diffusion. These programs offer a straightforward interface wherein nontechnical users can input a descriptive phrase and receive corresponding pictures, or at least amusingly bad approximations of the results they intended. For most users, such artificial intelligence1 (AI)-generated art is harmless fun that requires no computer graphics skills to produce and is suitable for social media posts (see Figure 1). However, AI algorithms combine aspects of existing data to generate their outputs. DALL-E, Stable Diffusion, and other popular programs pull images directly from the internet to train their algorithms. Though these images might be easily obtainable--from the huge Google Images database, for example--the creators have not always licensed their art for reuse or use in the production of derivative works.

This tutorial explains, in the form of slides with audio, the proposal for an EU AI Act, as proposed by the European Commission in the Spring of 2021. It does not discuss the subsequently proposed amendments. Key issues discussed are: (1) the overall architecture of the AI, (2) the pragmatic approach to the definition of AI systems (which is not about ‘AI’ but about ‘AI systems’), (3) the different roles, notably that of the providers of these systems, (4) the emphasis on high risk AI systems and (5) the details of the requirement that must be met by all high risk systems. It also explain what AI practices are prohibited and what transparency requirements must be met by a small set of AI systems.