My first encounter with Broadway actor Jonathan Groff was innocuous. Stuck in the wilds of Donegal for two weeks as part of teacher training, I listened to Broadway musicals while the rest of the lads watched the Gaelic fixtures and got drunk. I…
Milestone: Chemotherapy agent sends leukemia into remission
Date: Dec. 6, 1954
Where: Sloan Kettering Institute and Weill Cornell Medical College in New York
Who: Gertrude Elion and colleagues
In 1954, researchers described a new drug that sent…
Houses damaged by the overflowing Mahaweli River following Cyclone Ditwah, in Kandy, Sri Lanka
| Photo Credit: Reuters
Indian High…
The eighth-wicket pair put on 33 runs, with Starc taking Australia’s total past 400 with an attacking boundary against Brydon Carse in the 79th over, before Carey was out in the third over with the new ball.
Carey faced 69 deliveries and hit six…
LOS ANGELES, Dec. 5 (Xinhua) — The U.S. Centers for Disease Control and Prevention (CDC)’s Advisory Committee on Immunization Practices (ACIP) voted on Friday to end the long-standing recommendation that all newborns receive a Hepatitis B…
Le Traon, P. Y. et al. From observation to information and users: The Copernicus Marine Service perspective. Front. Mar. Sci. 6, 234 (2019).
Sun, R. et al. SKRIPS v1.0: A regional coupled ocean-atmosphere modeling framework (MITgcm-WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea. Geosci. Model Dev. 12, 4221–4244 (2019).
Sakamoto, K. et al. Development of a 2-km resolution ocean model covering the coastal seas around Japan for operational application. Ocean Dyn. 69, 1181–1202 (2019).
Ciliberti, S. A. et al. Monitoring and forecasting the ocean state and biogeochemical processes in the Black Sea: Recent developments in the Copernicus Marine Service. J. Mar. Sci. Eng. 9, 1146 (2021).
Kärnä, T. et al. Operational marine forecast model for the Baltic Sea. Nemo-Nordic 2.0. Geosci. Model Dev. 14, 5731–5749 (2021).
Zhu, X. et al. Improvements in the regional South China Sea operational oceanography forecasting system (SCSOFSv2). Geosci. Model Dev. 15, 995–1015 (2022).
Bruschi, A. et al. Indexes for the assessment of bacterial pollution in bathing waters from point sources: The northern Adriatic Sea CADEAU service. J. Environ. Manag. 293, 112878 (2021).
Liubartseva, S. et al. Modeling chronic oil pollution from ships. Mar. Pollut. Bull. (2023).
Mannarini, G., Salinas, M. L., Carelli, L., Petacco, N. & Orović, J. VISIR-2: Ship weather routing in Python. Geosci. Model Dev. 17, 4355–4382 (2024).
Coppini, G. et al. The Mediterranean forecasting system-Part 1: Evolution and performance. Ocean Sci. 19, 1483–1516 (2023).
Bi, K. et al. Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 533–538 (2023).
Nguyen, T. et al. Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. Adv. Neural Inf. Process. Syst. 37, 68740–68771 (2024).
Pathak, J. et al. FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators. arXiv preprint arXiv:2202.11214 (2022).
Keisler, R. Forecasting global weather with graph neural networks. arXiv preprint arXiv:2202.07575 (2022).
Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 382, 1416–1421 (2023).
Oskarsson, J., Landelius, T., Deisenroth, M. & Lindsten, F. Probabilistic weather forecasting with hierarchical graph neural networks. Adv. Neural Inf. Process. Syst. 37, 41577–41648 (2024).
Dheeshjith, S. et al. Samudra: An AI global ocean emulator for climate. Geophys. Res. Lett. 52(10), e2024GL114318 (2025).
Wang, C. et al. Coupled ocean-atmosphere dynamics in a machine learning Earth system model. arXiv preprint arXiv:2406.08632 (2024).
Guo, Z. et al. Data-driven global ocean modeling for seasonal to decadal prediction. arXiv preprint arXiv:2405.15412 (2024).
Wang, X. et al. XiHe: A data-driven model for global ocean eddy-resolving forecasting. arXiv preprint arXiv:2402.02995 (2024).
Aouni, A. E. et al. GLONET: Mercator’s end-to-end neural forecasting system. arXiv preprint arXiv:2412.05454 (2024).
Cui, Y. et al. Forecasting the eddying ocean with a deep neural network. Nat. Commun. 16, 2268 (2025).
Andersson, T. R. et al. Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nat. Commun. 12, 5124 (2021).
Chattopadhyay, A., Gray, M., Wu, T., Lowe, A. B. & He, R. OceanNet: A principled neural operator-based digital twin for regional oceans. Sci. Rep. 14, 21181 (2024).
Subel, A. & Zanna, L. Building ocean climate emulators. arXiv preprint arXiv:2402.04342 (2024).
Sanchez-Gonzalez, A. et al. Learning to simulate complex physics with graph networks. In International Conference on Machine Learning. 8459–8468 (2020).
Marchesiello, P., McWilliams, J. C. & Shchepetkin, A. Open boundary conditions for long-term integration of regional oceanic models. Ocean Model. 3, 1–20 (2001).
Escudier, R. et al. A high resolution reanalysis for the Mediterranean Sea. Front. Earth Sci. 9, 702285 (2021).
Hersbach, H. et al. ERA5 monthly averaged data on single levels from 1940 to present. In Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2023).
Molteni, F., Buizza, R., Palmer, T. N. & Petroliagis, T. The ECMWF ensemble prediction system: Methodology and validation. Q. J. R. Meteorol. Soc. 122, 73–119 (1996).
Lang, S. et al. AIFS – ECMWF’s data-driven forecasting system. arXiv preprint arXiv:2406.01465 (2024).
ECMWF. Plans for high resolution forecast (HRES) and ensemble forecast (ENS). In focus (2024). https://www.ecmwf.int/en/about/media-centre/focus/2024/plans-high-resolution-forecast-hres-and-ensemble-forecast-ens.
Nardelli, B. B., Tronconi, C., Pisano, A. & Santoleri, R. High and ultra-high resolution processing of satellite sea surface temperature data over southern European seas in the framework of MyOcean project. Remote Sens. Environ. 129, 1–16 (2013).
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
Kochkov, D. et al. Neural general circulation models for weather and climate. Nature 632, 1060–1066 (2024).
Marshall, J. & Schott, F. Open-ocean convection: Observations, theory, and models. Rev. Geophys. 37, 1–64 (1999).
Large, W. G., McWilliams, J. C. & Doney, S. C. Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys. 32, 363–403 (1994).
Wunsch, C. & Stammer, D. Atmospheric loading and the oceanic inverted barometer effect. Rev. Geophys. 35, 79–107 (1997).
Gill, A. E. Atmosphere–Ocean Dynamics (Academic Press, 1982).
Clementi, E. et al. Coupling hydrodynamic and wave models: first step and sensitivity experiments in the Mediterranean Sea. Ocean Dyn. 67, 1293–1312 (2017).
McDonagh, B., Clementi, E., Goglio, A. C. & Pinardi, N. The characteristics of tides and their effects on the general circulation of the Mediterranean Sea. Ocean Sci. 20, 1051–1066 (2024).
Rühling Cachay, S., Zhao, B., Joren, H. & Yu, R. Dyffusion: A dynamics-informed diffusion model for spatiotemporal forecasting. Adv. Neural Inf. Process. Syst. 36, 45259–45287 (2023).
Andrae, M., Landelius, T., Oskarsson, J. & Lindsten, F. Continuous ensemble weather forecasting with diffusion models. In International Conference on Learning Representations (2025).
Nipen, T. N. et al. Regional data-driven weather modeling with a global stretched-grid. arXiv preprint arXiv:2409.02891 (2024).
Adamov, S. et al. Building machine learning limited area models: Kilometer-scale weather forecasting in realistic settings. arXiv preprint arXiv:2504.09340 (2025).
Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K. & Grover, A. ClimaX: A foundation model for weather and climate. In International Conference on Machine Learning (2023).
Bodnar, C. et al. A foundation model for the Earth system. Nature 1–8 (2025).
Price, I. et al. Probabilistic weather forecasting with machine learning. Nature 637, 84–90 (2025).
Larsson, E., Oskarsson, J., Landelius, T. & Lindsten, F. Diffusion-LAM: Probabilistic limited area weather forecasting with diffusion. arXiv preprint arXiv:2502.07532 (2025).
Fortunato, M., Pfaff, T., Wirnsberger, P., Pritzel, A. & Battaglia, P. Multiscale meshgraphnets. arXiv preprint arXiv:2210.00612 (2022).
Battaglia, P., Pascanu, R., Lai, M., Jimenez Rezende, D. et al. Interaction networks for learning about objects, relations and physics. Adv. Neural Inf. Process. Syst. 29 (2016).
Ramachandran, P., Zoph, B. & Le, Q. V. Searching for activation functions. arXiv preprint arXiv:1710.05941 (2017).
Ba, J., Kiros, J. & Hinton, G. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).
Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In International Conference on Learning Representations (2019).
Madec, G. et al. NEMO ocean engine. In Scientific Notes of Climate Modelling Center. Vol. 27 (2017).
WAVEWATCH III Development Group (WW3DG). User Manual and System Documentation of WAVEWATCH III Version 6.07. In Technical Note 333, NOAA/NWS/NCEP/MMAB, College Park, MD, USA (2019).
Dobricic, S. & Pinardi, N. An oceanographic three-dimensional variational data assimilation scheme. Ocean Model. 22, 89–105 (2008).
Weatherall, P. et al. A new digital bathymetric model of the world’s oceans. Earth Sp. Sci. 2, 331–345 (2015).
Hellerman, S. & Rosenstein, M. Normal monthly wind stress over the world ocean with error estimates. J. Phys. Oceanogr. 13, 1093–1104 (1983).
Placenta previa (PP) is a major obstetric complication associated with increased risks of maternal morbidity, hemorrhage, and perinatal mortality. PP is a condition where the placenta is positioned such that it fully or partially…
Several billionaires loaded up on Broadcom, Meta Platforms, and Microsoft stocks in Q3.
However, Alphabet and Nvidia were the most popular AI stocks with billionaire investors during the quarter.
Both stocks should have tremendous growth prospects over the next several years.
These 10 stocks could mint the next wave of millionaires ›
The phrase “follow the money” gained widespread attention thanks to the 1976 movie, All the President’s Men. While the quote and the movie were about the Watergate scandal, following the money has become a popular approach for many investors who track the stocks bought by billionaires.
As you might expect, quite a few billionaires have invested heavily in artificial intelligence (AI) stocks. But which AI stocks are they buying the most?
To answer that question, I examined the 13F filings for the third quarter of 2025 of companies, family offices, and hedge funds run by 10 prominent billionaire investors:
Bill Ackman
Warren Buffett
Chase Coleman
Stanley Druckenmiller
Israel “Izzy” Englander
Ken Griffin
Carl Icahn
Paul Tudor Jones
George Soros
David Tepper
Each of these billionaires, except for Ackman and Icahn, bought at least one AI stock in Q3. Three AI stocks didn’t rank at the top of the list, but deserve honorable mentions: Broadcom (NASDAQ: AVGO), Meta Platforms (NASDAQ: META), and Microsoft (NASDAQ: MSFT).
Jones’ Tudor Investment hedge fund initiated new positions in Broadcom and Meta in Q3. Druckenmiller’s Duquesne Family Office also initiated a new position in Meta during the quarter.
Coleman’s Tiger Global Management increased its position in Broadcom in Q3. Englander’s Millennium Management and Griffin’s Citadel Advisors each bought additional shares of Broadcom, Meta, and Microsoft. Soros Fund Management more than tripled its position in Microsoft in Q3.
However, two other AI stocks stood out as most popular with billionaire investors. Half of the billionaires on the list bought either Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL) or Nvidia (NASDAQ: NVDA) in Q3.
Buffett surprised some observers by initiating a significant new position in Alphabet for Berkshire Hathaway (NYSE: BRK.A) (NYSE: BRK.B) during the quarter. This purchase was a long time in the making. The legendary investor revealed in an interview with CNBC in 2017 that he regretted not buying shares of Google’s parent company earlier.
Druckenmiller also opened a new position in Alphabet in Q3. Meanwhile, Englander, Griffin, and Soros added to their hedge fund’s stakes in the tech giant. Griffin’s Citadel even bought more of both of Alphabet’s share classes.
