When Molly Crabapple touched down in Italy last year for the International Journalism Festival, she expected the usual. The annual conference bills itself as Europe's largest media event, and Crabapple had planned to give a talk about her career as an artist and writer reporting from the front lines of conflict zones. But as she took in some of the panels, she felt herself growing uneasy. Sprinkled among the journalists discussing topics such as the war in Ukraine and the state of podcasting, some of the speakers were promoting the use of generative AI. She overheard someone say that journalists write too much, that much of their work could be automated.

The financial services sector has long been criticized as being insulated, elitist, and discriminatory. Will artificial intelligence finally open up and "democratize" this industry? It's likely, and will happen a number of ways – by empowering customers, by opening up services to underserved communities, and by increasing the breadth of capabilities companies can offer. While still in the minority, a growing number of financial services executives are bringing in AI as a part of their customer experiences and operations. About half of 500 executives (48%) responding to a survey conducted by Economist Impact and SAS in March 2022 identified advanced data analytics as among the most important technologies to harness, and 34% specifically cited AI and machine learning as their paths to the future.

The financial services sector has long been criticized as being insulated, elitist, and discriminatory. Will artificial intelligence finally open up and "democratize" this industry? It's likely, and will happen a number of ways – by empowering customers, by opening up services to underserved communities, and by increasing the breadth of capabilities companies can offer. While still in the minority, a growing number of financial services executives are bringing in AI as a part of their customer experiences and operations. About half of 500 executives (48%) responding to a survey conducted by Economist Impact and SAS in March 2022 identified advanced data analytics as among the most important technologies to harness, and 34% specifically cited AI and machine learning as their paths to the future.

DALL-E 2, OpenAI's powerful text-to-image AI system, can create photos in the style of cartoonists, 19th century daguerreotypists, stop-motion animators and more. But it has an important, artificial limitation: a filter that prevents it from creating images depicting public figures and content deemed too toxic. Now an open source alternative to DALL-E 2 is on the cusp of being released, and it'll have no such filter. London- and Los Altos-based startup Stability AI this week announced the release of a DALL-E 2-like system, Stable Diffusion, to just over a thousand researchers ahead of a public launch in the coming weeks. A collaboration between Stability AI, media creation company RunwayML, Heidelberg University researchers and the research groups EleutherAI and LAION, Stable Diffusion is designed to run on most high-end consumer hardware, generating 512 512-pixel images in just a few seconds given any text prompt. "Stable Diffusion will allow both researchers and soon the public to run this under a range of conditions, democratizing image generation," Stability AI CEO and founder Emad Mostaque wrote in a blog post.

Artificial Intelligence (AI) in drug discovery is now on a steep rise. A growing number of companies compete to develop new drugs faster, cheaper, and with a much higher success rate by using AI tools at all crucial stages of the drug discovery pipeline. Most of the players in this quickly expanding market are oriented towards big pharma, which is routinely investing billions into drug development. Such a partnership is tempting not only for startup companies but also for established leaders in the field of AI-based drug development because it provides stable multi-year contracts backed up by the financial resources and infrastructure of the pharmaceutical giants. As a result, end-to-end AI-based drug discovery services are tailored for large corporate customers.

When company leaders talk about democratizing artificial intelligence (AI), it's easy to imagine what they have in mind. The more people with access to the raw materials of the knowledge, tools, and data required to build an AI system, the more innovations are bound to emerge. Efficiency improves and engagement increases. Faced with a shortage of technical talent? Microsoft, Amazon, and Google have all released premade drag-and-drop or no-code AI tools that allow people to integrate AI into applications without needing to know how to build machine learning (ML) models.

The so-called'no code' revolution in programming seeks to make the automation industry more autonomous and to democratize the use of robotics at the shop floor level. Factories of the future will undoubtedly become increasingly agile and autonomous. If manufacturers want to keep up with the latest requirements, their robots need to also become faster and easier to implement. The so-called'no code' revolution in programming seeks to make the automation industry more autonomous and to democratize the use of robotics at the shop floor level. The no-code revolution consists of a series of tools that have been created to democratize access to technology that was formerly inaccessible to most industrial plants.

This is the web version of Eye on A.I., Fortune's weekly newsletter covering artificial intelligence and business. To get it delivered weekly to your in-box, sign up here. Everyone can become a data scientist. That's the somewhat radical view of Alan Jacobson, the chief data and analytic officer at Alteryx, a company that sells data analytics software to many of the Fortune 500. Jacobson says that while he frequently hears executives complain about being unable to hire people with data science experience, let alone machine-learning skills, these executives are ignoring the amazing human resource already sitting inside their own organizations.

Father Paolo Benanti is an expert in ethics, digital ethics, and technology. He is a Franciscan monk and Professor of Moral Theology, Bioethics, and Neuroethics at the Gregorian Pontifical University in Rome. I discuss with Father Benanti the controversial aspects of AI in healthcare and how the digital transformation changes us – human beings. Father Benanti, two years ago, there was a morally ambiguous case in the USA – a doctor used a virtual presence system to tell a patient he would die. With the broad adoption of telemedicine and medical workforce shortages, this practice may become an everyday reality. From the beginning of human history, we have understood medicine as a scientific discipline. There was a time when a priest and doctor was the same person. We've always picked up someone special from the human community to hold the position of a doctor.

Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks. Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises.