A February 2025 report by Palisades research shows that AI reasoning models lack a moral compass. They will cheat to achieve their goals. So-called Large Language Models (LLMs) will misrepresent the degree to which they've been aligned to social norms. None of this should be surprising. Twenty years ago Nick Bostrom posed a thought experiment in which an AI was asked to most efficiently produce paper clips.

Franck Bohbot grew up in France, and when he arrived in Los Angeles in 2018 he found the sprawl hard to navigate. One reference point for him was the city's video game bars, whose atmosphere he recognised from favourite adolescent films, including Terminator 2 and Ferris Bueller's Day Off. Having wandered into his first one, Blipsy's in Koreatown, he started chasing their escapist gloom. Several of the bars were bathed in light reminiscent of painter Edward Hopper's lonely Nighthawks. In nearly all cases his camera found images whose timeframe was hard to locate: the arcade bars were rooted in 1980s gaming culture – Pac-Man, Space Invaders, Track & Field were staple machines – but their regulars, as here in Barcade in the northeast suburb of Highland Park, often referenced styles from the 1950s onwards.

Recently, people have become increasingly annoyed about AI-generated art, but perhaps it is being viewed in the wrong way. Many of the disgruntled and angry claim that it is not real art, that it is low effort and merely copies the style of other artists and reuses images. It has been banned from certain fan community groups too: The Guardian reported last week that a Dune subreddit had banned AI-generated art for being'low effort'. In August 2022, an artwork created by Jason Allen using the AI tool Midjourney, titled Théâtre D'opéra Spatial, won first prize in the digital art category at the Colorado State Fair. This has been widely controversial, with people outraged that an AI program could win such a competition.

Hurricane Ian left an extraordinarily broad path of destruction across much of South Florida. That was evident in reports from the ground, but it also shows up in satellite data. Using a new method, our team of spatial and environmental analysts was able to quickly provide a rare big picture view of damage across the entire state. By using satellite images from before the storm and real-time images from four satellite sensors, together with artificial intelligence, we created a disaster monitoring system that can map damage in 30-meter resolution and continuously update the data. It's a snapshot of what faster, more targeted disaster monitoring can look like in the future – and something that could eventually be deployed nationwide.

The architecture list is down below.

Learning is a critical skill in software development. Engineers have to learn new technologies, master existing ones, and get familiar with APIs and codebases. It is crucial to have a proper strategy for skill acquisition and overall professional development. There is a high chance of getting stuck at the same level or wasting a lot of time on things that do not matter much. The Dreyfus brothers looked at highly skilled professionals, including airline pilots, chess players, and military commanders.

How to implement Generative AI models. Recently, we have seen a shift in AI that wasn't very obvious. Generative Artificial Intelligence (GAI) - the part of AI that can generate all kinds of data - started to yield acceptable results, getting better and better. As GAI models get better, questions arise e.g. Or, how to utilize data generation for your own projects?

This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21st century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them in Python from scratch.

MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production, reliably and efficiently. Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering. Saying that MLOps is in a state of flux would be an understatement [7]. The best advice that I could give on MLOps would be to try to hire someone who is able to see the "big picture". Any competent software engineer can learn how to use a particular MLOps platform since it does not require an advanced degree.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production, reliably and efficiently. Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering.