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In today’s AI news, AI or Not is on a mission to protect individuals and organizations from the risks of generative AI misuse. AI or Not, a widely covered AI fraud detection platform, has raised $5 million in a seed funding round to accelerate its use of “AI to detect AI” in images, audio video and deepfakes to prevent fraud and misinformation.

Layoffs And, AI media tech provider Runway has announced the release of Frames, its newest text-to-image generation model, and it’s winning early praise from users for producing highly cinematic visuals — a fitting compliment given Runway is known primarily as an AI video model provider. Could Frames dethrone Midjourney as the go-to choice?

In other advancements, ChatGPT maker OpenAI has finalized a version of its new reasoning AI model o3 mini and would be launching it in a couple of weeks, CEO Sam Altman said on Friday. The Microsoft-backed company has considered user feedback and, consequently, plans to release the API and ChatGPT simultaneously, Altman wrote.

In videos, character consistency has never been easier! Now you can use just a single image to generate consistent AI videos. Hailuo (Minimax) Subject Reference is AMAZING. In this tutorial they show you how to use it, creative use cases and a honest review. Enjoy!

Then, discover how Microsoft 365 Copilot Chat enables your entire workforce from sales to field service solutions. Microsoft 365 Copilot Chat can transform business processes with free secure AI chat, agents, and enterprise data protection.

When super intelligence happens technology will greatly advance we must merge with this technology.


The Nobel Prize winning ‘Godfather of AI’ speaks to Newsnight about the potential for AI “exceeding human intelligence” and it “trying to take over.”

Geoffrey Hinton, former Vice President of Google and sometimes referred to as the ‘Godfather of AI’, has recently won the 2024 Nobel Physics Prize. He resigned from Google in 2023, and has warned about the dangers of machines that could outsmart humans.

Grok, the AI assistant integrated into the X platform, has been released as a standalone app, expanding its reach beyond the social media site. Developed by xAI, the app retains its signature conversational tone, which the company describes as “humorous and engaging.” Grok allows users to generate images, summarise text, and answer questions.

Initially launched in December 2024 for a limited set of users, the Grok app builds on X’s rollout of a free tier for the AI assistant. Previously, access to Grok was tied to an X Premium subscription, starting at $8 per month. The free tier allows 10 requests every two hours and three image analysis requests per day — restrictions that may also apply to the standalone app.

Users can sign in to the app using Apple, Google, or X accounts, or simply by email. It’s unclear if X Premium subscribers gain additional benefits when using the Grok app, as they do on X.

The latest AI News. Learn about LLMs, Gen AI and get ready for the rollout of AGI. Wes Roth covers the latest happenings in the world of OpenAI, Google, Anthropic, NVIDIA and Open Source AI.

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Columbia researchers created an AI model that predicts gene activity in any human cell, advancing disease research and treatment. It has already uncovered mechanisms behind pediatric leukemia and may reveal hidden genome functions.

Researchers at Columbia University.

Columbia University is a private Ivy League research university in New York City that was established in 1754. This makes it the oldest institution of higher education in New York and the fifth-oldest in the United States. It is often just referred to as Columbia, but its official name is Columbia University in the City of New York.

OpenAI says no money changed hands in the collaboration. But because the work could benefit Retro—whose biggest investor is Altman—the announcement may add to questions swirling around the OpenAI CEO’s side projects.

Last year, the Wall Street Journal said Altman’s wide-ranging investments in private tech startups amount to an “opaque investment empire” that is “creating a mounting list of potential conflicts,” since some of these companies also do business with OpenAI.

In Retro’s case, simply being associated with Altman, OpenAI, and the race toward AGI could boost its profile and increase its ability to hire staff and raise funds. Betts-Lacroix did not answer questions about whether the early-stage company is currently in fundraising mode.

Tomorrow at 1PM PT / 4PM ET, we Premiere a new episode of Robots In Space, and this is about bots, including the latest on Phoenix from Sanctuary AI, the impact of cognitive automation on jobs, the Economic Singularity, plus our proprietary Event Horizon Indicator.


Discover how robotics and AI are reshaping our economic landscape in this eye-opening analysis. As an engineer, I break down the latest developments in humanoid robots, particularly Sanctuary AI’s breakthrough in hydraulic robotics and robot dexterity. Learn about my proprietary Event Horizon Indicator that tracks our progression toward the Economic Singularity through labor force participation and unemployment trends. From warehouse robotics to manufacturing automation, understand how the robot workforce is transforming industries and what this means for the future of work. Whether you’re interested in AI economics or concerned about tech unemployment, this video provides crucial insights into the ongoing robot revolution and its impact on our economy.

Reservoir computing (RC) is a powerful machine learning module designed to handle tasks involving time-based or sequential data, such as tracking patterns over time or analyzing sequences. It is widely used in areas such as finance, robotics, speech recognition, weather forecasting, natural language processing, and predicting complex nonlinear dynamical systems. What sets RC apart is its efficiency―it delivers powerful results with much lower training costs compared to other methods.

RC uses a fixed, randomly connected network layer, known as the reservoir, to turn input data into a more complex representation. A readout layer then analyzes this representation to find patterns and connections in the data. Unlike traditional neural networks, which require extensive training across multiple network layers, RC only trains the readout layer, typically through a simple linear regression process. This drastically reduces the amount of computation needed, making RC fast and computationally efficient.

Inspired by how the brain works, RC uses a fixed network structure but learns the outputs in an adaptable way. It is especially good at predicting and can even be used on physical devices (called physical RC) for energy-efficient, high-performance computing. Nevertheless, can it be optimized further?