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Category: robotics/AI

The Inevitable Shift towards Machine Labor.
Impact Multiplier of Artificial Cognition and Synthetic Minds.
Economic Benefits of Cognition and Embodied Services.
Addressing Displacement with UBI Funded with Cognitive Services Impact Multipliers.

Navigating the Future with AI, Robotics, and UBI
Introduction.
In the context of the inevitable shift from human labor to machines, particularly in the realm of cognitive and physical tasks, the introduction of advanced technologies like Tesla’s Optimus robot and the development of artificial cognition and synthetic minds carry profound implications.

The Inevitable Shift towards Machine Labor.
The transition from human to machine labor in both cognitive and physical domains is becoming increasingly unavoidable. Technologies like Tesla Optimus represent a significant leap in this direction.

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A new artificial intelligence tool that interprets medical images with unprecedented clarity does so in a way that could allow time-strapped clinicians to dedicate their attention to critical aspects of disease diagnosis and image interpretation.

The tool, called iStar (Inferring Super-Resolution Tissue Architecture), was developed by researchers at the Perelman School of Medicine at the University of Pennsylvania, who believe they can help clinicians diagnose and better treat cancers that might otherwise go undetected.

The imaging technique provides both highly detailed views of individual cells and a broader look at the full spectrum of how people’s genes operate, which would allow doctors and researchers to see cancer cells that might otherwise have been virtually invisible. This tool can be used to determine whether safe margins were achieved through cancer surgeries and automatically provide annotation for microscopic images, paving the way for molecular disease diagnosis at that level.

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Manolis Kellis, an accomplished Computer Science Professor at MIT and member of the Broad Institute, is a trailblazer in computational biology. Renowned for leading the MIT Computational Biology Group, his impactful research spans disease genetics, epigenomics, and gene circuitry. With numerous cited publications and leadership in transformative genomics projects, Kellis has garnered prestigious accolades, including the PECASE and Sloan Fellowship, shaping the field with his international perspective from Greece and France to the US.

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A new, potentially revolutionary artificial intelligence framework called “Blackout Diffusion” generates images from a completely empty picture, meaning that the machine-learning algorithm, unlike other generative diffusion models, does not require initiating a “random seed” to get started. Blackout Diffusion, presented at the recent International Conference on Machine Learning (“Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces”), generates samples that are comparable to the current diffusion models such as DALL-E or Midjourney, but require fewer computational resources than these models.

“Generative modeling is bringing in the next industrial revolution with its capability to assist many tasks, such as generation of software code, legal documents and even art,” said Javier Santos, an AI researcher at Los Alamos National Laboratory and co-author of Blackout Diffusion. “Generative modeling could be leveraged for making scientific discoveries, and our team’s work laid down the foundation and practical algorithms for applying generative diffusion modeling to scientific problems that are not continuous in nature.”

A new generative AI model can create images from a blank frame. (Image: Los Alamos National Laboratory)

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Welcome to the thrilling world of autonomous fabrication, where the only constant is change, and the speed of that change is akin to a caffeinated cheetah on a treadmill.

This blog focuses on revolutionizing the iteration cycle in autonomous fabrication, emphasizing the need for rapid and efficient transitions from design to deployment.

The overarching theme is the synergy between advanced technology and a transformative mindset in manufacturing, aiming for smarter, more sustainable, and compliant operations.

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Artificial intelligence has progressed from sci-fi fantasy to mainstream reality. AI now powers online tools from search engines to voice assistants and it is used in everything from medical imaging analysis to autonomous vehicles. But the advance of AI will soon collide with another pressing issue: energy consumption.

Much like cryptocurrencies today, AI risks becoming a target for criticism and regulation based on its high electricity appetite. Partisans are forming into camps, with AI optimists extolling continued progress through more compute power, while pessimists are beginning to portray AI power usage as wasteful and even dangerous. Attacks echo those leveled at crypto mining in recent years. Undoubtedly, there will be further efforts to choke off AI innovation by cutting its energy supply.

The pessimists raise some valid points. Developing ever-more capable AI does require vast computing resources. For example, the amount of compute used to train OpenAI’s ChatGPT-3 reportedly equaled 800 petaflops of processing power—on par with the 20 most powerful supercomputers in the world combined. Similarly, ChatGPT receives somewhere on the order of hundreds of millions of queries each day. Estimates suggest that the electricity required to respond to all these queries might be around 1 GWh daily, enough to power the daily energy consumption of about 33,000 U.S. households. Demand is expected to further increase in the future.

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