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To put the forecasted demand into context, consider this: A recent MIT study found that a single data center consumes electricity equivalent to 50,000 homes. Estimates indicate that Microsoft, Amazon, and Google operate about 600 data centers in the U.S. today…
Arguments exist that by 2030, 80% of renewable power sources will fulfill electricity demand. For reference, the U.S. generated roughly 240 billion kilowatt hours of solar and 425 billion kilowatt hours of wind, totaling 665 billion kilowatt hours in 2023. Assuming a 50/50 split between wind and solar, that scenario implies that, to satisfy the U.S. electricity demand that adequately facilitates AI competitiveness, wind and solar will have to generate approximately 3.4 trillion kilowatt hours of electricity each. That is more than a ten-fold increase over the next five years. The EIA highlights that the U.S. planned utility-scale electric-generating capacity addition in 2023 included 29 million kilowatts of solar (54% of the total) and 6 million kilowatts of wind (11% of the total), which pales in comparison to the estimated amount required.
It just doesn’t get much more clear; renewables cannot begin to supply the energy needed for AI data centers. Only fossil fuels and nuclear can get the job done. It’s that simple. AI is not something global elites are going to let slide by. They’ve had a good run with the Big Green Grift but those days are going to gradually (maybe not so gradually) come to an end, as the demand for energy to power AI forces a reconsideration.
Can you pass me the whatchamacallit? It’s right over there next to the thingamajig.
Many of us will experience “lethologica”, or difficulty finding words, in everyday life. And it usually becomes more prominent with age.
Continue reading “Scientists Identify a Speech Trait That Foreshadows Cognitive Decline” »
Scientists from Trinity College Dublin have created a computer program that “paints” the structure of molecules in the style of famous Dutch artist, Piet Mondrian, whose beautiful artworks will be instantly recognizable to many.
The Belle II experiment is a large research effort aimed at precisely measuring weak-interaction parameters, studying exotic hadrons (i.e., a class of subatomic particles) and searching for new physical phenomena. This effort primarily relies on the analysis of data collected by the Belle II detector (i.e., a general purpose spectrometer) and delivered by the SuperKEKB, a particle collider, both located at the High Energy Accelerator Research Organization (KEK) in Tsukuba, Japan.
Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have published their new method in Nature Physics, demonstrating a method that is much simpler than previous approaches.
CSIRO research, published as a letter in Physical Review Research journal, found for the first time that AI could help process and resolve quantum errors known as qubit noise, which are generated by the nature of quantum physics.
Overcoming these errors is widely considered the largest barrier to advanced quantum computers moving from experiment to tool.
In conventional computers, information is stored and processed in “bits,” which work on the principles of binary numbers. Each bit can represent either 0 or 1. But quantum computing devices are made up of quantum bits, or “qubits.”
Entanglement, Einstein’s “spooky action at a distance,” today is THE tool of quantum information science. It is the essential resource for quantum computers and used to transmit quantum information in a future quantum network. But it is highly sensitive. It is therefore an enormous challenge to entangle resting quantum bits (qubits) with flying qubits in the form of photons “at the push of a button.”
Join the late, renowned philosopher and cognitive scientist Daniel C Dennett on a captivating journey of intellectual exploration through his own life.
Sign up as a member to watch the Q\&A here: • Q\&A: A life of the mind — Daniel Dennett.
Buy Daniel’s book here: https://geni.us/K3Ja.
Continue reading “A life of the mind — with Daniel Dennett” »
Life’s evolutionary timescale is typically calibrated to the oldest fossil occurrences. However, the veracity of fossil discoveries from the early Archaean period has been contested11,12. Relaxed Bayesian node-calibrated molecular clock approaches provide a means of integrating the sparse fossil and geochemical record of early life with the information provided by molecular data; however, constraining LUCA’s age is challenging due to limited prokaryote fossil calibrations and the uncertainty in their placement on the phylogeny. Molecular clock estimates of LUCA13,14,15 have relied on conserved universal single-copy marker genes within phylogenies for which LUCA represented the root. Dating the root of a tree is difficult because errors propagate from the tips to the root of the dated phylogeny and information is not available to estimate the rate of evolution for the branch incident on the root node. Therefore, we analysed genes that duplicated before LUCA with two (or more) copies in LUCA’s genome16. The root in these gene trees represents this duplication preceding LUCA, whereas LUCA is represented by two descendant nodes. Use of these universal paralogues also has the advantage that the same calibrations can be applied at least twice. After duplication, the same species divergences are represented on both sides of the gene tree17,18 and thus can be assumed to have the same age. This considerably reduces the uncertainty when genetic distance (branch length) is resolved into absolute time and rate. When a shared node is assigned a fossil calibration, such cross-bracing also serves to double the number of calibrations on the phylogeny, improving divergence time estimates. We calibrated our molecular clock analyses using 13 calibrations (see ‘Fossil calibrations’ in Supplementary Information). The calibration on the root of the tree of life is of particular importance. Some previous studies have placed a younger maximum constraint on the age of LUCA based on the assumption that life could not have survived Late Heavy Bombardment (LHB) (~3.7–3.9 billion years ago (Ga))19. However, the LHB hypothesis is extrapolated and scaled from the Moon’s impact record, the interpretation of which has been questioned in terms of the intensity, duration and even the veracity of an LHB episode20,21,22,23. Thus, the LHB hypothesis should not be considered a credible maximum constraint on the age of LUCA. We used soft-uniform bounds, with the maximum-age bound based on the time of the Moon-forming impact (4,510 million years ago (Ma) ± 10 Myr), which would have effectively sterilized Earth’s precursors, Tellus and Theia13. Our minimum bound on the age of LUCA is based on low δ98 Mo isotope values indicative of Mn oxidation compatible with oxygenic photosynthesis and, therefore, total-group Oxyphotobacteria in the Mozaan Group, Pongola Supergroup, South Africa24,25, dated minimally to 2,954 Ma ± 9 Myr (ref. 26).
Our estimates for the age of LUCA are inferred with a concatenated and a partitioned dataset, both consisting of five pre-LUCA paralogues: catalytic and non-catalytic subunits from ATP synthases, elongation factor Tu and G, signal recognition protein and signal recognition particle receptor, tyrosyl-tRNA and tryptophanyl-tRNA synthetases, and leucyl-and valyl-tRNA synthetases27. Marginal densities (commonly referred to as effective priors) fall within calibration densities (that is, user-specified priors) when topologically adjacent calibrations do not overlap temporally, but may differ when they overlap, to ensure the relative age relationships between ancestor-descendant nodes. We consider the marginal densities a reasonable interpretation of the calibration evidence given the phylogeny; we are not attempting to test the hypothesis that the fossil record is an accurate temporal archive of evolutionary history because it is not28.
