U.S. President Donald Trump launched a major government-wide initiative known as the Genesis Mission. This effort, signed via executive order, aims to build an integrated artificial intelligence platform utilizing federal scientific datasets.

The primary goal of the Genesis Mission is to transform scientific research and dramatically accelerate scientific discoveries. The plan involves harnessing massive government data to train scientific foundation models and create AI agents capable of testing new hypotheses, automating research workflows, and shortening discovery timelines. This groundbreaking system is designed to achieve breakthroughs that currently take years, compressing that time frame down to mere days or even hours.

To achieve this, President Trump directed the U.S. Energy Department (DOE) and U.S. National Laboratories to unite their intellectual resources, powerful computers, and vast scientific data into a single cooperative system for research. The DOE is tasked with creating a closed-loop AI experimentation platform. This platform will integrate U.S. supercomputers and datasets to generate foundation models and power robotic laboratories.

Leaders anticipate that the Genesis Mission will "massively accelerate the rate of scientific breakthrough". The AI will automate experiment design, speed up simulation, and generate predictive models crucial for complex areas such as protein folding and fusion plasma dynamics. This endeavor holds significant implications for U.S. national, economic, and health security, focusing on critical fields like biotechnology, critical materials, nuclear energy, space exploration, quantum information science, semiconductors, and microelectronics. The launch of the Genesis Mission underscores President Trump’s prioritization of winning the global AI race.

This episode dives into "How Ford Is Embracing AI To Drive Innovation In The Automotive Industry," as featured on Forbes on Nov 23, 2025.

Explore how the legendary automaker Ford Motor Company, with annual revenues of $185 billion, is adopting artificial intelligence to optimize its business operations for the next generation of customers.

We hear from Franziska (Fran) Bell, Ford's Chief Data, AI, and Analytics Officer (CDAAO). Bell explains that her primary role is partnering with the business to drive improved user experiences and deliver tangible business value at scale, ensuring data is treated as a strategic asset.

Inside Ford's AI Strategy:Ford views AI as a core capability across the enterprise, leveraging its unique and vast data sets—spanning decades of design, engineering, safety, connected vehicle, and manufacturing data. The goal is to weave AI into the fabric of core processes, from initial design concepts through manufacturing and the customer ownership experience. This strategy centers on building powerful 'human-machine teams' where AI augments human ingenuity by automating repetitive tasks and running complex simulations, freeing employees for strategic thinking and creative problem-solving.

Ford’s Transformative 'Big Bet' AI Projects Include:

• Accelerated Vehicle Design: Designers use generative AI to render hundreds of high-fidelity images and 3D models almost instantly from a single sketch.
• Virtual Wind Tunnel: Proprietary AI models reduce the time for complex aerodynamic simulations from 15 hours to approximately 10 seconds, achieving a 5000x speed increase.
• AI-Powered Customer Support: An AI agent on the Ford site acts as a single front door for customer questions, querying multiple knowledge bases and synthesizing clear, referenced answers.
• AI-Supercharged Code Reviews: An AI-assisted "code reviews-as-a-service" capability has shown a 3x reduction in cycle time for software engineers.

Cultivating an AI Culture:Ford is focused on building an AI-powered workforce through continuous training, reaching 10,000 employees in the first half of 2025. They are democratizing access to AI tools like FordLLM, their internal platform for large language models, which currently sees about 50,000 weekly active users. Trust is paramount, with Ford championing a ‘human-in-the-loop’ model and using an AI Technology Council to guide responsible innovation and ensure data privacy.

Ford measures the business value of these investments through a framework focused on accelerated delivery (reducing cycle times), improved quality and reduced costs (e.g., in manufacturing defect detection), and enhanced agility. Ultimately, this comprehensive strategy positions AI as a fundamental part of how Ford will lead, compete, and redefine automotive excellence.

The expansion of artificial intelligence (AI) is accompanied by a massive environmental cost, as the millions of computers housed in data centers consume staggering amounts of electricity and water for cooling. Since most of this power is generated by fossil fuel-burning plants, AI contributes directly to air pollution and climate change.

UC Riverside engineering scientists offer a blueprint for a solution called the Federated Carbon Intelligence, or FCI. This novel system outlines a method to dramatically reduce the pollution caused by AI processing in large data centers while also extending the life of the hardware doing the work. No existing system combines these two crucial goals.

The FCI system recognizes that sustainability in AI cannot be achieved by focusing on clean energy alone; the aging and heating of AI systems and their changing efficiency have a measurable carbon cost. The framework integrates environmental awareness—gauging the carbon intensity of electricity at a given time and place—with real-time assessments of the condition of the servers in use, including temperature, age, and physical wear.

By monitoring server health, FCI prevents overworking stressed machines, helping to avoid costly breakdowns, reducing the need for energy and water-intensive cooling, and keeping servers running longer. The system dynamically determines where and when to process AI workloads, using this integrated data to send the task to the server best suited to handle it with the least impact on the machine and the planet.

Simulations backing this proposal showed that FCI could reduce carbon dioxide emissions by up to 45 percent over a five-year period. Crucially, the system could also extend the operational life of a server fleet by 1.6 years. By slowing down hardware degradation, FCI addresses the complete lifecycle carbon footprint, including the embodied emissions from manufacturing new servers. Implementing this adaptive framework would not require new equipment, only smarter coordination across the systems already in place. Researchers state that frameworks like FCI show that climate-aligned computing is achievable without sacrificing performance, paving the way for NetZero-aligned AI infrastructure worldwide.

Nvidia, the undisputed bellwether of the AI market, recently surprised Wall Street with accelerating growth following its stellar third-quarter earnings and robust fourth-quarter forecast. Despite widespread talk of an AI bubble, CEO Jensen Huang firmly dismissed these concerns, stating that the company sees "something very different" from its vantage point.

The chipmaker reported that its third-quarter sales rose 62%, marking the first acceleration in seven quarters, with sales in the crucial data-center segment reaching $51.2 billion. The company is guiding for strong performance, expecting fiscal fourth-quarter sales to reach approximately $65 billion. Huang also highlighted the incredible reach of Nvidia's architecture, noting that it is in every cloud and is used everywhere from on-premise solutions to robotic systems, edge devices, and PCs.

While these results temporarily calmed investor nerves, analysts caution that the report may not be enough to quell ongoing AI bubble fears. Specific concerns revolve around the sustainability of AI infrastructure spending and the increased concentration of Nvidia's business, where four customers accounted for 61% of sales in the fiscal third quarter. We delve into worries about a "circular AI economy," as the company invests billions of dollars into firms that are often among its most significant customers.

The episode also explores constraints to future growth: While massive GPU demand continues, physical bottlenecks, such as limitations in power, land, and grid access, could cap how quickly that demand translates into revenue. Finally, we examine Nvidia's new avenue for expansion in the Middle East, specifically tapping Saudi Arabia and the United Arab Emirates, as it seeks new growth after being largely locked out of the China market due to U.S. export restrictions. The transformation of the AI industry is complex, demanding careful planning across supply chains, infrastructure, and financing.

This episode dives into a significant new investment by the U.S. Department of Health and Human Services (HHS) aimed at supporting America’s essential caregivers.

HHS, through the Administration for Community Living (ACL), has announced a $2 million Caregiver Artificial Intelligence Prize Competition. This challenge is designed to recognize and support the 1 in 4 Americans serving as caregivers for older adults and people with disabilities. These dedicated individuals—including family members, friends, and the direct care workforce—form the backbone of America’s long-term care system, enabling people to live with dignity and independence at home.

Despite their commitment, caregivers often face intense emotional, physical, and financial strain, with nearly half reporting worsening mental health. Furthermore, high turnover and staffing shortages in direct care mean family caregivers are often asked to do more with fewer resources.

To address these challenges, HHS is funding innovators developing AI tools that provide safe, person-centered care at home and aim to reduce administrative strain so caregivers can focus on their well-being and the people they care for. The competition also seeks solutions that support employers by improving efficiency, scheduling, and training within the caregiving workforce.

Health and Human Services Secretary Robert F. Kennedy, Jr., stated that this initiative advances the goals of the Make America Healthy Again Strategy Report by mobilizing innovation to lighten caregivers’ load. ACL is committed to identifying scalable, practical solutions that empower caregivers and expand access to high-quality care for the millions of Americans who give and receive care every day.

Welcome to the essential guide on automating your artificial intelligence needs, focusing on the latest advancements in Google Gemini and ChatGPT. The developers of major generative AI chatbots are continuously pushing out new features to ensure their bot is the one you turn to for assistance. One of the most powerful recent updates is the ability to set up automated, recurring tasks.

This description covers exactly what you need to know about setting up scheduled actions in Gemini and scheduled tasks in ChatGPT. Learn how the bot can carry out your commands at a specific point in the future and keep repeating them on a set schedule.

Key Concepts Covered:

• Setting up Scheduled Commands Discover how easy it is to create an automated task simply by including the scheduling details in your prompt. You can schedule actions to happen once—such as next Friday at 3 pm—or set them to run on a recurring daily, weekly, or monthly basis.
• Examples of Automation Explore the versatility of scheduling, whether you want an evening meal suggestion every evening at 7 pm, a weather and news report every morning at 7 am, or a general knowledge trivia question every evening. Creative commands can also be automated, such as asking the bot to generate an image of a cat playing with a ball of yarn every Monday, or to generate a poster for a high-concept sci-fi movie every day.
• Requirements and Management Understand the technical logistics, including the typical requirement of a paid subscription, such as Google AI Pro for Gemini or ChatGPT Plus. While individual plans typically start at $20 a month, this grants access to powerful automation. Learn the limits—you can keep track of up to 10 scheduled actions or tasks at once on either platform. Finally, we detail how tasks run regardless of whether the user has the chatbot open and how you can manage, pause, or delete these recurring actions through the platform settings.

Dive into the science and philosophy surrounding the universe's greatest enigma: consciousness. This podcast confronts the "hard problem" of experience, asking why physical processes give rise to a rich inner life, subjectivity, and the felt quality of existence—a question that reductive, functional explanations have consistently failed to answer.

The Battle for the Brain

We dissect the cutting-edge scientific debate by exploring the clash between two dominant frameworks for explaining consciousness: Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT). IIT postulates that consciousness is mathematically described by the amount of integrated information in a system's causal structure (known as the quantity of consciousness), arising when information is highly unified and irreducible. Conversely, GNWT suggests that consciousness results when information is widely broadcast across specialized neural subsystems in the brain.

We examine the results of an unprecedented, large-scale adversarial collaboration that subjected these theories to rigorous empirical testing. The results challenged both models, revealing that consciousness may be rooted more in sensory processing and perception (linked to posterior brain regions) than in the complex reasoning and planning functions associated with the frontal cortex.

Philosophical Fundamentals and the AI Challenge

Our exploration extends into metaphysics, contrasting the view that consciousness is an emergent phenomenon arising only in complex systems (Physicalism) with the radical notion of Panpsychism, which argues that consciousness is a fundamental property of reality, on par with mass or charge.

We also confront the rapidly advancing field of Artificial Consciousness (AC). Learn the crucial distinction between Weak AC, which merely simulates conscious functions (acting like a philosophical zombie), and Strong AC, which involves genuine subjective experience or synthetic phenomenology. Current efforts in AC implementation often leverage theories like the Global Workspace and the Attention Schema Theory (AST) to create systems that control and monitor information flow, enhancing their performance and coordination.

The Ethical Core of Experience

Finally, we discuss the profound ethical implications of these discoveries. We delve into the phenomenal theory of welfare subjects, which argues that phenomenal consciousness is precisely what makes an entity a welfare subject—the kind of thing that can be better or worse off and is deserving of moral status. This insight is critical for determining moral status not only for non-human animals but also for prospective conscious artificial agents.

Join us as we navigate the complex, interdisciplinary journey toward understanding how, and why, things matter.

This podcast explores a groundbreaking advancement in artificial intelligence hardware: light-based tensor computing. Researchers from Aalto University have developed a method that could usher in a new era of ultra-fast and energy-efficient AI performance.

In this episode, we dive into single-shot tensor computing, a fundamentally new approach that executes complex AI calculations—like convolutions and attention layers—within a single pass of light through an optical system. This technology performs these necessary operations, which support modern AI systems for image processing and language understanding, literally at the speed of light.

Traditional digital hardware, such as GPUs, face increasing strain in speed, energy use, and scalability as the demands of deep learning continue to grow. This breakthrough overcomes those challenges by moving beyond electronic circuits. The team encodes digital information directly into the amplitude and phase of light waves. As the light interacts, it automatically and simultaneously carries out the necessary mathematical procedures, such as matrix and tensor multiplication.

The method utilizes passive optical processing, meaning the necessary operations occur automatically as the light travels, requiring no active control or electronic switching during the computation. The ultimate objective is to integrate this framework directly onto photonic chips, enabling complex AI tasks with extremely low power consumption. This innovation promises to create a new generation of optical computing systems, significantly accelerating advanced AI tasks across many fields.

(Analogy for Clarity):

Think of current AI hardware like a long customs line where every parcel (data operation) must be individually inspected and sorted by multiple machines one step at a time. This new optical computing method is like merging all the parcels and all the inspection machines together: one pass of light instantly connects every input to its correct output, completing all inspections and sorting instantly and in parallel.

This podcast explores the next-generation HPE Cray Supercomputing portfolio, which introduces industry-leading compute density to boost AI productivity and meet the demands of converged AI and HPC workloads. The new HPE Cray Supercomputing platform is engineered for groundbreaking outcomes, providing a unified AI and HPC architecture that establishes one of the most powerful supercomputing architectures in the industry. This expansion includes three multi-partner, multi-workload compute blades that support the deployment of flagship components such as the next-generation NVIDIA Rubin platform and AMD Instinct MI430X GPUs, as well as next-generation AMD EPYC processors codenamed “Venice”. These systems are supported by 100 percent direct liquid cooling, which allows customers to maximize energy efficiency and enable the reuse of waste heat. Furthermore, we discuss the HPE Supercomputing Management Software, which delivers a secure systems management experience across the supercomputer’s life cycle, including monitoring power and integrating with power-aware schedulers. The GX5000 platform is already seeing rapid industry adoption, having been selected by the University of Stuttgart (HLRS) for the Herder supercomputer and the Leibniz Supercomputing Centre (LRZ) for their upcoming flagship system, Blue Lion.

Would you be interested in a deeper technical dive into the HPE Cray Supercomputing Storage Systems K3000, which features embedded DAOS open source software, or learning more about the enhanced security and governance features of the HPE Supercomputing Management Software?

This podcast explores the crucial intersection of Artificial Intelligence, human cognition, and consciousness. Recent advancements in Large Language Models (LLMs) have prompted new questions about the computational significance of conscious processing, especially since AI systems appear to exhibit intelligence without consciousness.

We delve into the potential domains of conscious supremacy, a concept analogous to quantum supremacy, which refers to areas of computation efficiently executed by conscious processes that systems lacking consciousness cannot perform within practical biological time limits. Consciousness itself is defined by phenomenal properties, such as qualia, intentionality, and self-awareness.

Discover the cognitive domains believed to be unique to human conscious processing, including flexible attention modulation, robust handling of new contexts, complex choice and decision making, integrated sensory information processing, and embodied cognition. We discuss how metacognitive monitoring and control are central to identifying and rectifying the limits of artificial intelligence systems.

Drawing on neuroscientific insights and adopting a theory-heavy approach, we examine the necessary structural and functional conditions for consciousness in AI. Learn how the elucidation of these unique conscious computations provides a necessary constraint for effective AI alignment strategies, advocating for a crucial division of labor where tasks belonging to conscious supremacy are better left to humans.

Would you like to explore the specifics of the "indicator properties" derived from theories like Global Workspace Theory (GWT) or Recurrent Processing Theory (RPT), or discuss the profound implications of using a "computational functionalism" hypothesis to assess AI consciousness?