Generative artificial intelligence (GAI) started making waves a few years ago with the release of systems such as ChatGPT and DALL-E. They are able to produce sophisticated and human-like text, code, or images after the models powering them are trained on large quantities of data. However, it soon became apparent that the specific phrasing of a question or statement input by a user, known as a prompt, had an impact on the quality of the resulting output. "It's a way of unlocking different capabilities from these models," says Andrei Muresanu, an AI researcher at Vector Institute in Toronto, Canada. "If you tell ChatGPT to pretend that it's a professor of mathematics, it will do better on math questions than if you just say, 'answer this question' or'pretend you're a student'." Coming up with prompts that steer a model towards a desired output has emerged as a relatively new profession, called prompt engineering, to help achieve more relevant and accurate results.

OpenAI has filed a countersuit against Elon Musk, accusing him of staging press attacks and malicious campaigns on "the social media platform he controls," as well as of making "harassing legal claims" and a "sham bid for OpenAI's assets." In its filing, courtesy of TechCrunch, the ChatGPT-maker said Musk could not tolerate seeing such "success for an enterprise he had abandoned and declared doomed" and had made it his own project to take down the organization. It also said that Musk's efforts have ramped up in recent months after it announced its plans to restructure and become a for-profit entity with a non-profit division. Last year, Musk sued OpenAI, accusing it of ditching its nonprofit mission, becoming a "closed-source de facto subsidiary" Microsoft and of violating its foundational agreement to develop generative AI "for the benefit of humanity." But Musk, OpenAI said in its new lawsuit, is only pretending to represent the public and in truth is seeking to stop it from restructuring.

It's 2025, so it should be no surprise that another organization has sold its soul (entered into a licensing deal with an AI company) for an undisclosed sum. A new partnership allows UK-based Synthesia to access Shutterstock's content library for training its latest AI model, EXPRESS-2. This deal isn't the first of its kind for Shutterstock, which previously teamed up with OpenAI to sell stock images made using AI generator DALL-E 2. Synthesia creates avatars for corporate videos about topics such as cybersecurity and good communication at work. It aims to use Shutterstock's video data to "try out new approaches that will improve the performance of EXPRESS-2, and increase the realism and expressiveness of our AI generated avatars, bringing them closer to human-like performances.," Synthesia stated in a release. Typically, Synthesia uses actors to create avatars, paying to use their likeness for three years.

A 2bn ( 1.6bn) British startup that uses artificial intelligence to generate realistic avatars has struck a licensing deal with the stock footage firm Shutterstock to help develop its technology. Synthesia will pay the US-based Shutterstock an undisclosed sum to use its library of corporate video footage to train its latest AI model. It expects that incorporating the clips into its model will produce even more realistic expressions, vocal tones and body language from the avatars. "Thanks to this partnership with Shutterstock, we hope to try out new approaches that will … increase the realism and expressiveness of our AI generated avatars, bringing them closer to human-like performances," said Synthesia. Synthesia uses human actors to generate digital avatars of people, which are then deployed by companies in corporate videos in a range of scenarios such as advising on cybersecurity, calculating water bills and how to communicate better at work.

The high efficiency and quality of artwork generated by Artificial Intelligence (AI) has created new concerns and challenges for human artists. In particular, recent improvements in generative AI have made it difficult for people to distinguish between human-generated and AI-generated art. In this research, we consider the potential utility of various types of Machine Learning (ML) and Deep Learning (DL) models in distinguishing AI-generated artwork from human-generated artwork. We focus on three challenging artistic styles, namely, baroque, cubism, and expressionism. The learning models we test are Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Our best experimental results yield a multiclass accuracy of 0.8208 over six classes, and an impressive accuracy of 0.9758 for the binary classification problem of distinguishing AI-generated from human-generated art.

For decades, Moore's Law has served as a steadfast pillar in computer architecture and system design, promoting a clear abstraction between hardware and software. This traditional Moore's computing paradigm has deepened the rift between the two, enabling software developers to achieve near-exponential performance gains often without needing to delve deeply into hardware-specific optimizations. Yet today, Moore's Law -- with its once relentless performance gains now diminished to incremental improvements -- faces inevitable physical barriers. This stagnation necessitates a reevaluation of the conventional system design philosophy. The traditional decoupled system design philosophy, which maintains strict abstractions between hardware and software, is increasingly obsolete. The once-clear boundary between software and hardware is rapidly dissolving, replaced by co-design. It is imperative for the computing community to intensify its commitment to hardware-software co-design, elevating system abstractions to first-class citizens and reimagining design principles to satisfy the insatiable appetite of modern computing. Hardware-software co-design is not a recent innovation. To illustrate its historical evolution, I classify its development into five relatively distinct ``epochs''. This post also highlights the growing influence of the architecture community in interdisciplinary teams -- particularly alongside ML researchers -- and explores why current co-design paradigms are struggling in today's computing landscape. Additionally, I will examine the concept of the ``hardware lottery'' and explore directions to mitigate its constraining influence on the next era of computing innovation.

Formulating research questions is a foundational yet challenging academic skill, one that generative AI systems often oversimplify by offering instant answers at the expense of student reflection. This protocol lays out a study grounded in constructivist learning theory to evaluate a novel AI-based Socratic Tutor, designed to foster cognitive engagement and scaffold research question development in higher education. Anchored in dialogic pedagogy, the tutor engages students through iterative, reflective questioning, aiming to promote System 2 thinking and counteract overreliance on AI-generated outputs. In a quasi-experimental design, approximately 80 German pre-service biology teacher students will be randomly assigned to one of two groups: an AI Socratic Tutor condition and an uninstructed chatbot control. Across multiple cycles, students are expected to formulate research questions based on background texts, with quality assessed through double-blind expert review. The study also examines transfer of skills to novel phenomena and captures student perceptions through mixed-methods analysis, including surveys, interviews and reflective journals. This study aims to advance the understanding of how generative AI can be pedagogically aligned to support, not replace, human cognition and offers design principles for human-AI collaboration in education.

A group of organizations, including nonprofits like LatinoProsperity and labor groups like the California Teamsters, are petitioning California Attorney General Rob Bonta to stop OpenAI from becoming a for-profit entity, The Los Angeles Times reports. OpenAI announced plans to transition to a public-benefit corporation in 2024, and reportedly has two years to pull it off or risk a large portion of the money its raised become debt. The group's primary concerns are that OpenAI "failed to protect its charitable assets" and is actively "subverting its charitable mission to advance safe artificial intelligence." OpenAI started as a nonprofit research organization studying AI, but transitioned to a for-profit company that's overseen and run by a nonprofit in 2019. That structure is legally allowed in the state of California, but the group's petition claims that OpenAI's decision to pursue a new structure is driven by a desire not to further its mission, but to provide "AI's benefits -- the potential for untold profits and control over what may become powerful world-altering technologies -- to a handful of corporate investors and high-level employees."

Anthropic is joining the ranks of OpenAI in offering a more expensive tier of its flagship chatbot. On Wednesday, the company announced Max Plan. Starting today, you can either pay 100 or 200 per month to use Claude up to 5x or 20x more than you can with Anthropic's existing Pro plan. The company told Engadget it's introducing the Max tier in response to the popularity of Claude 3.7 Sonnet. The new hybrid reasoning model, which excels at coding tasks, has been so popular with users, many are asking to use it as much as they want.

Developing new drugs to treat illnesses has typically been a slow and expensive process. However, a team of researchers at the University of Waterloo uses machine learning to speed up the development time. The Waterloo research team has created "Imagand," a generative artificial intelligence model that assesses existing information about potential drugs and then suggests their potential properties. Trained on and tested against existing drug data, Imagand successfully predicts important properties of different drugs that have already been independently verified in lab studies, demonstrating the AI's accuracy. Traditionally, bringing a successful drug candidate to market can cost between US 2 billion and US 3 billion and take over a decade to complete.