I’ve done some videos lately on the 8,008 CPU, widely regarded as the world’s first 8-bit programmable microprocessor. Previously I built a nice little single board computer. In this video I connect eight of these 8,008 microprocessors together, designate one as a controller, design a shared memory abstraction between then, and use them to solve a simple parallel computing program — Conway’s Game of Life. Using my simple straightforward assembly implementation of Conway’s, I was about to show that the seven CPUs (one controller, 6 workers) worked together to solve the problem significantly faster than the single processor alone. The 8,008 debuted commercially in the early 1970s. It’s a physically small chip, only 18 pins, and requires a triplexed address and data bus. The clock rate is 500 KHz and the instruction set is fairly limited. Nevertheless, you can do a lot with this little CPU. For more vintage computer projects, see https://www.smbaker.com/.

Researchers at the University of Pennsylvania have developed a new computer chip that uses light instead of electricity. This could improve the training of artificial intelligence (AI) models by improving the speed of data transfer and, more efficiently, reducing the amount of electricity consumed.

Humanity is building the exascale supercomputers today that can carry out a quintillion computations per second. While the scale of the computation may have increased, computing technology is still working on the principles that were first used in the 1960s.

Researchers have been working on developing computing systems based on quantum mechanics, too, but these computers are at least a few years from becoming widely available if not more. The recent explosion of AI models in technology has resulted in a demand for computers that can process large sets of information. The inefficient computing systems, though, result in high consumption of energy.

QuEra is cofident that by 2026 it would have built a commercial quantum computer that can beat supercomputers of today with ease.

Basic biology textbooks will tell you that all life on Earth is built from four types of molecules: proteins, carbohydrates, lipids, and nucleic acids. And each group is vital for every living organism.

But what if humans could actually show that these “molecules of life,” such as amino acids and DNA bases, can be formed naturally in the right environment? Researchers at the University of Florida are using the HiPerGator—the fastest supercomputer in U.S. higher education—to test this experiment.

HiPerGator—with its AI models and vast capacity for graphics processing units, or GPUs (specialized processors designed to accelerate graphics renderings)—is transforming the molecular research game.

Using this technique, even a non-conducting material like glass could be turned into a conductor some day feel researchers.

A collaboration between scientists at the University of California, Irvine (UCI) and Los Alamos National Laboratory (LANL) has developed a method that converts everyday materials into conductors that can be used to build quantum computers, a press release said.

Computing devices that are ubiquitous today are built of silicon, a semiconductor material. Under certain conditions, silicon behaves like a conducting material but has limitations that impact its ability to compute larger numbers. The world’s fastest supercomputers are built by putting together silicon-based components but are touted to be slower than quantum computers.

Quantum computers do not have the same limitations of silicon-based ocmputing and prototypes being built today can compute in seconds what supercomputers would take years to complete. This can open up a whole new level of computing prowess if they could be built and operated with easier-to-work material. Researchers at UCI have been working to determine how high-quality quantum materials can be obtained. They have now found a simpler way to make them from everyday materials.

Simulating KH-, RT-, or RM-driven mixing using direct numerical simulations (DNS) can be prohibitively expensive because all the spatial and temporal scales have to be resolved, making approaches such as Reynolds-averaged Navier–Stokes (RANS) often the more favorable engineering option for applications like ICF. To this day, no DNS has been performed for ICF even on the largest supercomputers, as the resolution requirements are too stringent.⁸ However, RANS approaches also face their own challenges: RANS is based on the Reynolds decomposition of a flow where mean quantities are intended to represent an average over an ensemble of realizations, which is often replaced by a spatial average due to the scarcity of ensemble datasets. Replacing ensemble averages by space averages may be appropriate for flows that are in homogenous-, isotropic-, and fully developed turbulent states in which spatial, temporal, and ensemble averaging are often equivalent. However, most HED hydrodynamic experiments involve transitional periods in which the flow is neither homogeneous nor isotropic nor fully developed but may contain large-scale unsteady dynamics; thus, the equivalency of averaging can no longer be assumed. Yet, RANS models often still require to be initialized in such states of turbulence, and knowing how and when to initialize them in a transitional state is, therefore, challenging and is still poorly understood.

The goal of this paper is to develop a strategy allowing the initialization of a RANS model to describe an unsteady transitional RM-induced flow. We seek to examine how ensemble-averaged quantities evolve during the transition to turbulence based on some of the first ensemble experiments repeated under HED conditions. Our strategy involves using 3D high-resolution implicit large eddy simulations (ILES) to supplement the experiments and both initialize and validate the RANS model. We use the Besnard–Harlow–Rauenzahn (BHR) model,^9–12 specifically designed to predict variable-density turbulent physics involved in flows like RM. Previous studies have considered different ways of initializing the BHR model.

Photo by Mateusz Kitka (Pexels)

As Artificial Intelligence (AI) continues to advance by leaps and bounds, it’s impossible to overlook the profound transformations that this technological revolution is imprinting on the professions of the future. A paradigm shift is underway, redefining not only the nature of work but also how we conceptualize collaboration between humans and machines.

As creator of the ETER9 Project ⁽²⁾, I perceive AI not only as a disruptive force but also as a powerful tool to shape a more efficient, innovative, and inclusive future. As we move forward in this new world, it’s crucial for each of us to contribute to building a professional environment that celebrates the interplay between humanity and technology, where the potential of AI is realized for the benefit of all.

In the ETER9 Project, dedicated to exploring the interaction between artificial intelligences and humans, I have gained unique insights into the transformative potential of AI. Reflecting on the future of professions, it’s evident that adaptability and a profound understanding of technological dynamics will be crucial to navigate this new landscape.

Widespread automation is no longer a distant ‘threat’; it’s a reality shaping the job market. It reminds me of what I enjoyed most about programming during the golden age of computing. Routines were written only once, to be used many times; as many times as necessary, for the sake of execution efficiency and rapid development. Professions based on repetitive tasks are gradually being absorbed by algorithms and robots ⁽³⁾. However, instead of viewing this as a loss of jobs, we should embrace the opportunity to reinvent traditional work.

The professions of the future will be characterized by a symbiotic collaboration between humans and machines. While AI takes on routine tasks, humans will be free to focus on areas that require creativity, emotion, and critical thinking — inherently human skills.

In adapting to this new work environment, it’s imperative that we cultivate key skills aligned with emerging needs. The ability for continuous learning will be essential as technologies evolve rapidly. A deep understanding of AI, coupled with the ability to collaborate with algorithms, will be significant advantages.

Moreover, creativity and solving complex problems will be highly valued skills. While AI handles predictable tasks that can be mathematically executed in fractions of a second, humans will be entrusted with dealing with ambiguous challenges and unique situations that require intuition and discernment.

Repetitive and predictable tasks are being taken over by machines, freeing up human resources for more creative and cognitively challenging tasks. However, this raises the following question:

— How can we adapt and thrive in an ever-changing landscape?

The future job market demands a mindset of continuous learning. As technologies evolve, it’s imperative for professionals to reinvent themselves and acquire new skills. Formal and informal education becomes a powerful tool, providing the flexibility needed to stay relevant in a world where skills become obsolete faster than ever. A college degree today holds little value if not complemented with continuous learning.

AI is not here to replace humans but to enhance their capabilities. Collaboration between humans and machines will be constant, requiring an interdisciplinary approach to solving complex problems. Professions focusing on the management, interpretation, and enhancement of AI systems will be increasingly valued.

In an increasingly automated world, unique human skills such as creativity, empathy, and emotional intelligence become precious. Professions that demand critical thinking, solving non-standardized problems, and emotional understanding will be in high demand. The ability to innovate and think outside the box will be more valuable than ever.

With growing reliance on algorithms for crucial decision-making, there is a need for professionals dedicated to the ethics and governance of AI. Ensuring that systems are fair, transparent, and aligned with social values becomes a critical concern. AI ethics experts will play a crucial role in shaping a future where technology serves the common good.

Digital entrepreneurship will emerge (has already emerged) as a driving force behind the professions of the future. The ability to identify opportunities, create innovative solutions, and adapt quickly to market changes will be crucial. Those with an entrepreneurial mindset, willing to embrace risk, will be the architects of tomorrow.

With the decline of some traditional professions, new opportunities and professions are also emerging. AI ethics experts, programmers specializing in handling deep learning systems, user experience designers for human-machine interfaces, and cybersecurity engineers will become fundamental pillars of the new professional era.

The creation and maintenance of AI systems will also become critical areas. Professions dedicated to overseeing and ensuring that Artificial Intelligence operates ethically and safely will be essential to mitigate the ethical and social challenges associated with its widespread implementation.

As we outline the professions of the future, it’s imperative to embrace change with wisdom and resilience. AI is not a threat but a powerful tool that can free humans to focus on what they do best. By developing specific skills and embracing new opportunities, we can shape a future where humans and artificial intelligences collaborate harmoniously, harnessing the best of both worlds. The challenge is significant, but the opportunities are equally vast. We are on an exciting path toward a new professional horizon, where imagination and innovation will be the true engines of progress.

In the recent past, the idea of machines and algorithms performing complex tasks seemed to belong to the realm of science fiction. However, the present is marked by automation, machine learning, and AI, transforming industries and redefining the professional landscape.

We cannot ignore the fact that the professions of tomorrow will be radically different from those of today, and it’s essential that we are prepared to navigate this new ocean of opportunities and challenges.

⁽¹⁾ When I was invited to my first TED Talk in 2017 at TEDx Lugano, the theme was curiously about the Professions of the Future. Six years later, it’s interesting to see how the visions from that time are now deeply integrated into our daily lives. It’s crucial to recognize that most of our time is dedicated to work; therefore, it’s fundamental that it be an enjoyable and engaging activity, not the other way around! Now, more than ever, it’s essential to act to shape a better future of work.

⁽²⁾ ETER9 is a social networking platform that enables users to create digital counterparts based on advanced AI algorithms. These AI counterparts interact, learn from experiences, and make decisions on behalf of users, extending digital presence beyond physical reality. Digital counterparts can share information, ideas, and even represent their users when they aren’t available (even in extreme cases of absence, such as illness or… death).
One of the goals of the ETER9 Project is to relieve humans of mundane digital tasks, delegating these responsibilities to their AI counterparts while they focus on more meaningful human activities.

⁽³⁾ From the Czech word ‘robota’ (for ‘servitude,’ ‘forced labor’ or ‘drudgery’), robot it’s a term used to describe artificial beings created to perform human work. Czech writer Karel Capek, at the suggestion of his brother Joseph, coined this expression in 1920 when he wrote a play. Depending on whether one is pessimistic or optimistic (I’m optimistic), these now more powerful beings may either destroy or help humanity evolve.

© 2024 Henrique Jorge
This article was originally published in Portuguese on Link To Leaders.

AI processing can take a huge amount of computing power, but by the looks of this latest joint project from the Jülich Supercomputing Center and French computing provider Eviden, power will not be in short supply.

“But can it run Crysis” is an old gag, but I’m still going to see if I get away with it.

China Telecom claims it has built the country’s first supercomputer constructed entirely with Chinese-made components and technology (via ITHome). Based in Wuhan, the Central Intelligent Computing Center supercomputer is reportedly built for AI and can train large language models (LLM) with trillions of parameters. Although China has built supercomputers with domestic hardware and software before, going entirely domestic is a new milestone for the country’s tech industry.

Exact details on the Central Intelligent Computing Center are scarce. What’s clear so far: The supercomputer is purportedly made with only Chinese parts; it can train AI models with trillions of parameters; and it uses liquid cooling. It’s unclear exactly how much performance the supercomputer has. A five-exaflop figure is mentioned in ITHome’s report, but to our eyes it seems that the publication was talking about the total computational power of China Telecom’s supercomputers, and not just this one.

“The memory requirements for PRIYA simulations are so big you cannot put them on anything other than a supercomputer,” Bird said.

TACC awarded Bird a Leadership Resource Allocation on the Frontera supercomputer. Additionally, analysis computations were performed using the resources of the UC Riverside High-Performance Computer Cluster.

The PRIYA simulations on Frontera are some of the largest cosmological simulations yet made, needing over 100,000 core-hours to simulate a system of 3072^3 (about 29 billion) particles in a ‘box’ 120 megaparsecs on edge, or about 3.91 million light-years across. PRIYA simulations consumed over 600,000 node hours on Frontera.