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Emerging uses of artificial intelligence in deep time biodiversity research

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  • Raup, D. M. & Sepkoski, J. J. Mass extinctions in the marine fossil record. N. Ser. 215, 1501–1503 (1982).

    CAS 

    Google Scholar
     

  • Benton, M. J. Recovery of vertebrate faunas from the end-Permian mass extinction. J. Earth Sci. 21 111 (2010).


    Google Scholar
     

  • Benton, M. J. Origins of biodiversity. PLoS Biol. https://doi.org/10.1371/journal.pbio.2000724 (2016).

  • Marshall, C. R. Forty years later: the status of the ‘Big Five’ mass extinctions. Camb. Prism Extinct. 1, e5 (2023).


    Google Scholar
     

  • Casanovas-Vilar, I., van den Hoek Ostende, L. W., Janis, C. M. & Saarinen, J. eds. Evolution of Cenozoic Land Mammal Faunas and Ecosystems 25 Years of the NOW Database of Fossil Mammals Vertebrate Paleobiology and Paleoanthropology Series (Springer, 2023).

  • Uhen, M. D. et al. Paleobiology Database User Guide Version 1.0. PaleoBios 40(11) (2023).

  • Peters, S. E. & McClennen, M. The Paleobiology Database Application Programming Interface. Paleobiology 42, 1–7 (2015).


    Google Scholar
     

  • Chiappe, L. M. et al. Cretaceous bird from Brazil informs the evolution of the avian skull and brain. Nature 635, 376–381 (2024).

    CAS 

    Google Scholar
     

  • Niklas, K. J., Tiffany, B. H. & Knoll, A. H. Patterns in vascular land plant diversification. Nature 303, 1068–1070 (1983).


    Google Scholar
     

  • Niklas, K. J. Measuring the tempo of plant death and birth. N. Phytol. 207, 254–256 (2015).


    Google Scholar
     

  • Sepkoski, J. J. A factor analytic description of the Phanerozoic marine fossil record. Paleobiology 7 36–53 (1981).


    Google Scholar
     

  • Dunne, E. M., Thompson, S. E. D., Butler, R. J., Rosindell, J. & Close, R. A. Mechanistic neutral models show that sampling biases drive the apparent explosion of early tetrapod diversity. Nat. Ecol. Evol. 7, 1480–1489 (2023).


    Google Scholar
     

  • Close, R. A., Benson, R. B. J., Saupe, E. E., Clapham, M. E. & Butler, R. J. The spatial structure of Phanerozoic marine animal diversity. Science 368, 420–424 (2020).

    CAS 

    Google Scholar
     

  • Reijenga, B. R. & Close, R. A. Apparent timescaling of fossil diversification rates is caused by sampling bias. Curr. Biol. https://doi.org/10.1016/J.CUB.2024.12.038 (2025).


    Google Scholar
     

  • Marshall, C. R. et al. Quantifying the dark data in museum fossil collections as palaeontology undergoes a second digital revolution. Biol. Lett. 14, 20180431 (2018).


    Google Scholar
     

  • Adaimé, M.-É., Urban, M. A., Kong, S., Jaramillo, C. & Punyasena, S. W. Pollen morphology, deep learning, phylogenetics, and the evolution of environmental adaptations in Podocarpus. New Phytol. 247, 1460–1473 (2025).


    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    CAS 

    Google Scholar
     

  • Tropsha, A., Isayev, O., Varnek, A., Schneider, G. & Cherkasov, A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat. Rev. Drug Discov. 23, 141–155 (2023).


    Google Scholar
     

  • Müller, J. et al. Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests. Nat. Commun. 14, 6191 (2023).


    Google Scholar
     

  • Romera-Paredes, B. et al. Mathematical discoveries from program search with large language models. Nature 625, 468–475 (2023).


    Google Scholar
     

  • Thompson, T. How AI can help to save endangered species. Nature 623, 232–233 (2023).

    CAS 

    Google Scholar
     

  • Silvestro, D., Goria, S., Sterner, T. & Antonelli, A. Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5, 415–424 (2022).


    Google Scholar
     

  • Christin, S., Hervet, É. & Lecomte, N. Applications for deep learning in ecology. Methods Ecol. Evol. 10, 1632–1644 (2019).


    Google Scholar
     

  • Yu, C. et al. Artificial intelligence in paleontology. Earth Sci. Rev. 252, 104765 (2024).


    Google Scholar
     

  • He, Y. et al. Opportunities and challenges in applying AI to evolutionary morphology. Integr. Org. Biol. 6, 36 (2024).


    Google Scholar
     

  • Mimura, K. et al. Applicability of object detection to microfossil research: implications from deep learning models to detect microfossil fish teeth and denticles using YOLO-v7. Earth Space Sci. 11, e2023EA003122 (2024).


    Google Scholar
     

  • van de Kamp, T. et al. Parasitoid biology preserved in mineralized fossils. Nat. Commun. 9, 3325 (2018).


    Google Scholar
     

  • Romero, I. C. et al. Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy. Proc. Natl Acad. Sci. USA 117, 28496–28505 (2020).

    CAS 

    Google Scholar
     

  • Kopperud, B. T., Lidgard, S. & Liow, L. H. Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps. PeerJ 10, e13921 (2022).


    Google Scholar
     

  • Di Martino, E. et al. DeepBryo: a web app for AI-assisted morphometric characterization of cheilostome bryozoans. Limnol. Oceanogr. Methods 21, 542–551 (2023).


    Google Scholar
     

  • Liu, X. et al. Heterogeneous selectivity and morphological evolution of marine clades during the Permian–Triassic mass extinction. Nat. Ecol. Evol. 8, 1248–1258 (2024).


    Google Scholar
     

  • Weeks, B. C. et al. A deep neural network for high-throughput measurement of functional traits on museum skeletal specimens. Methods Ecol. Evol. 14, 347–359 (2023).


    Google Scholar
     

  • Hou, C. et al. Fossil image identification using deep learning ensembles of data augmented multiviews. Methods Ecol. Evol. 14, 3020–3034 (2023).


    Google Scholar
     

  • Foster, W. J. et al. How predictable are mass extinction events? R. Soc. Open Sci. 10, 221507 (2023).


    Google Scholar
     

  • Foster, W. J. et al. Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. Paleobiology 48, 357–371 (2022).


    Google Scholar
     

  • Finnegan, S. et al. Paleontological baselines for evaluating extinction risk in the modern oceans. Science 348, 567–570 (2015).

    CAS 

    Google Scholar
     

  • Cooper, R. B., Flannery-Sutherland, J. T. & Silvestro, D. DeepDive: estimating global biodiversity patterns through time using deep learning. Nat. Commun. 15, 4199 (2024).

    CAS 

    Google Scholar
     

  • Nickolls, J. & Dally, W. J. The GPU computing era. IEEE Micro https://doi.org/10.1109/MM.2010.41 (2010).


    Google Scholar
     

  • Vaswani, A. et al.Attention is all you need. In Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) (2017).

  • Koch, B., Denton, E., Hanna, A. & Foster, J. G. Reduced, reused and recycled: the life of a dataset in machine learning research. Preprint at https://doi.org/10.48550/arXiv.2112.01716 (2021).

  • Villalobos, P. et al. Will we run out of data? Limits of LLM scaling based on human-generated data. Preprint at https://arxiv.org/abs/2211.04325v2 (2024).

  • Sayers, E. W. et al. GenBank 2025 update. Nucleic Acids Res. 53, D56–D61 (2025).


    Google Scholar
     

  • Waller, J. Citizen Science on GBIF – 2019. GBIF Data Blog https://data-blog.gbif.org/post/citizen-science-on-gbif-2019/ (2019).

  • Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B. & Schigel, D. Data integration enables global biodiversity synthesis. Proc. Natl Acad. Sci. USA 118, e2018093118 (2021).

    CAS 

    Google Scholar
     

  • Renaudie, J., Lazarus, D. B. & Diver, P. Nsb (Neptune Sandbox Berlin): an expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy. Palaeontol. Electron. 23, 1–28 (2020).


    Google Scholar
     

  • Žliobaitė, I. et al. The NOW database of fossil mammals. Vertebr. Paleobiol. Paleoanthropol. F1250, 33–42 (2023) .


    Google Scholar
     

  • Kocsis, Á. T., Reddin, C. J., Alroy, J. & Kiessling, W. The R package divDyn for quantifying diversity dynamics using fossil sampling data. Methods Ecol. Evol. 10, 735–743 (2019).


    Google Scholar
     

  • Smith, J. et al. BioDeepTime: a database of biodiversity time series for modern and fossil assemblages. Glob. Ecol. Biogeogr. 32, 1680–1689 (2023).


    Google Scholar
     

  • Smith, J. A. et al. Increasing the equitability of data citation in paleontology: capacity building for the big data future. Paleobiology 50, 165–176 (2024).


    Google Scholar
     

  • Nicol, D. The number of living animal species likely to be fossilized. Fla. Scientist 40, 135–139 (1977).


    Google Scholar
     

  • Foote, M., Miller, A. I., Raup, D. M. & Stanley, S. M. Principles of Paleontology (Macmillan, 2007).

  • Andermann, T., Antonelli, A., Barrett, R. L. & Silvestro, D. Estimating alpha, beta, and gamma diversity through deep learning. Front. Plant Sci. 13, 839407 (2022).


    Google Scholar
     

  • Zhuang, F. et al. A comprehensive survey on transfer learning. Proc. IEEE 109, 43–76 (2021).


    Google Scholar
     

  • Kim, H. E. et al. Transfer learning for medical image classification: a literature review. BMC Med. Imaging 22, 69 (2022).


    Google Scholar
     

  • Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

    CAS 

    Google Scholar
     

  • Hauffe, T., Cantalapiedra, J. L. & Silvestro, D. Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks. Sci. Adv. 10, eadl2643 (2024).


    Google Scholar
     

  • Marjoram, P., Molitor, J., Plagnol, V. & Tavaré, S. Markov chain Monte Carlo without likelihoods. Proc. Natl Acad. Sci. USA 100, 15324–15328 (2003).

    CAS 

    Google Scholar
     

  • Silvestro, D. et al. A 450 million years long latitudinal gradient in age-dependent extinction. Ecol. Lett. 23, 439–446 (2020).


    Google Scholar
     

  • Lambert, S., Voznica, J. & Morlon, H. Deep learning from phylogenies for diversification analyses. Syst. Biol. 72, 1262–1279 (2023).


    Google Scholar
     

  • Close, R. A. et al. Diversity dynamics of Phanerozoic terrestrial tetrapods at the local-community scale. Nat. Ecol. Evol. 3, 590–597 (2019).


    Google Scholar
     

  • Ahmad, W., Ali, H., Shah, Z. & Azmat, S. A new generative adversarial network for medical images super resolution. Sci. Rep. 12, 1–20 (2022).


    Google Scholar
     

  • Nie, D. et al. Medical image synthesis with deep convolutional adversarial networks. IEEE Trans. Biomed. Eng. 65, 2720–2730 (2018).


    Google Scholar
     

  • Khosravi, B. et al. Few-shot biomedical image segmentation using diffusion models: beyond image generation. Comput. Methods Prog. Biomed. 242, 107832 (2023).


    Google Scholar
     

  • Huang, Y. et al. SmartEdit: exploring complex instruction-based image editing with multimodal large language models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), https://doi.org/10.1109/CVPR52733.2024.00799 (IEEE, 2023).

  • Rawte, V., Sheth, A. & Das, A. A survey of hallucination in large foundation models. Preprint at https://doi.org/10.48550/arXiv.2309.05922 (2023).

  • Zhang, Y. et al. Siren’s song in the AI ocean: a survey on hallucination in large language models. Comput. Linguist. https://doi.org/10.1162/coli.a.16 (2025).

  • Bai, Y. et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. Preprint at https://doi.org/10.48550/arXiv.2204.05862 (2022).

  • Bagenal, J. Generative artificial intelligence and scientific publishing: urgent questions, difficult answers. Lancet 403, 1118–1120 (2024).


    Google Scholar
     



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    StockGro launches AI stock research engine for retail investors

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    By Vriti Gothi

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    • AI
    • Cross Border Payments
    • Digital Lending

    Stockgro

    StockGro, has launched of Stoxo, an AI-powered stock-market research engine designed exclusively for retail investors to bridge the gap between sophisticated market intelligence and everyday investors.

    Stoxo harnesses advanced artificial intelligence to transform the way retail participants access, interpret, and act on market information. With its ability to analyse real-time trends, compare stocks across multiple parameters, and deliver actionable insights in an intuitive format, the platform offers retail investors a level of research capability once reserved for institutional players. Developed with an emphasis on accessibility and user-friendly design, Stoxo ensures that complex financial data is presented with clarity, empowering users to make confident, informed investment decisions.

    The introduction of Stoxo positions StockGro at the forefront of India’s rapidly evolving investment ecosystem. The platform’s AI-driven architecture is built for scalability, enabling it to adapt seamlessly to shifting market conditions while maintaining the speed and precision required in modern trading environments. For customers, the impact is immediate greater transparency, enhanced decision-making power, and the ability to participate in the markets with a degree of insight previously out of reach for many retail investors.

    Beyond individual benefit, Stoxo represents a step forward for the broader financial sector by fostering inclusivity and boosting retail participation. By providing institutional-grade research capabilities in a digital-first, user-friendly environment, StockGro is advancing financial literacy and enabling more Indians to take an active role in wealth creation.

    With the launch of Stoxo, StockGro continues to redefine the boundaries of FinTech innovation, merging advanced technology with a deep understanding of investor needs to shape a more informed, empowered, and inclusive investing future for India.

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    Did Bill Gates Predict GPT-5’s Disappointment Before Launch?

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    There had been a lot of hype and anticipation building around GPT-5 prior to its recent launch. OpenAI touted the tool as the smartest AI model while comparing it to an entire team of PhD-level experts. GPT-5 ships with a plethora of next-gen features across a wide range of categories, including coding, writing, and medicine.

    The ChatGPT maker’s CEO, Sam Altman, previously claimed that something “smarter than the smartest person you know” will soon be running on a device in your pocket, potentially referring to GPT-5. However, the AI firm has received backlash from users following the model’s launch and its abrupt decision to deprecate the model’s predecessors.





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    Better Artificial Intelligence Stock: ASML vs. AMD

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    ASML and AMD are pivotal players in the booming AI market, helping both to see strong sales so far this year.

    Artificial intelligence (AI) remains a hot area to invest in, as seen in Nvidia‘s share price, which is up over 30% this year through Aug. 6. Two AI businesses to consider are ASML Holding (ASML 1.33%) and Advanced Micro Devices (AMD 0.17%), since they provide key hardware to the industry.

    The former makes cutting-edge lithography machines, which are necessary for producing the advanced microchips that power AI systems. AMD, one of Nvidia’s top competitors, sells AI chips to cloud computing companies such as Microsoft.

    ASML and AMD are both strong businesses. But determining which is a better AI investment isn’t simple. So let’s evaluate them in more detail.

    Image source: Getty Images.

    A look into ASML

    ASML’s lithography equipment is essential for manufacturing AI microchips because the technology demands immense computing power. This necessitates shrinking chip components to minuscule dimensions. For instance, a microchip the size of your fingernail contains billions of transistors. ASML’s machines support this.

    Although the Dutch company plays an important role in AI, its stock has struggled in 2025, remaining essentially flat through Aug. 6. Part of this is because management anticipates economic uncertainty ahead as a result of factors such as President Donald Trump’s aggressive tariff policies.

    Even so, ASML expects 2025 sales to rise 15% over 2024’s 28.3 billion euros ($33 billion). This is significant since 2024’s revenue represents only a 2.6% year-over-year increase. And so far this year, the company is doing well.

    Through two quarters, revenue stood at $18 billion, up from the prior year’s $13.4 billion. Operating income rose to $5.8 billion from 2024’s $3.7 billion. This robust growth resulted in net income of $5.4 billion, a strong increase over the previous year’s $3.3 billion.

    The excellent first-half results were tempered by a third-quarter revenue forecast between $8.6 billion and $9.2 billion. This outlook, when compared to the prior year’s sales of $8.9 billion, suggests the current trend of strong year-over-year growth may be slowing down, which contributed to ASML’s tepid stock performance.

    How AMD is faring

    Like rival Nvidia, AMD stock is having a stellar year. Shares are up 35% in 2025 through Aug. 6. This performance is understandable following the company’s second-quarter earnings results. The quarter’s revenue reached a record $7.7 billion, a 32% year-over-year increase.

    CEO Lisa Su said, “We are seeing robust demand across our computing and AI product portfolio and are well positioned to deliver significant growth in the second half of the year.” In that second half, AMD expects revenue of $8.7 billion, a strong increase over the previous year’s $6.8 billion.

    Despite the sales growth, AMD exited the second quarter with an operating loss of $134 million compared to operating income of $269 million in the previous year. The substantial drop was due to new U.S. government restrictions introduced earlier this year on the sale of AI chips to China. As a result, AMD could not sell chips it had intended for Chinese customers, forcing the company to write off that inventory by $800 million.

    Yet this makes its second-quarter sales growth all the more impressive. In the quarter, net income was $872 million, up 229% year over year. Consequently, diluted earnings per share soared 238% to $0.54 in a boon to shareholders.

    AMD is working to get government approval to sell AI chips to China again. When that OK is obtained, the company is in a position to deliver more outsize sales growth.

    Deciding between ASML and AMD

    AMD’s outstanding performance, its anticipated third-quarter revenue growth, and an eventual return of sales to China point to it being the superior AI stock versus ASML.

    However, an important consideration is share price valuation. The price-to-earnings ratio (P/E) tells you how much investors are willing to pay for a dollar’s worth of earnings based on the trailing 12 months.

    ASML PE Ratio Chart

    Data by YCharts.

    The top chart shows ASML’s P/E ratio has declined over the past year, indicating its stock’s valuation has improved. Compared to AMD’s recently rising earnings multiple, as seen in the bottom chart, ASML shares look like a bargain.

    ASML’s short-term sales may slow due to the current macroeconomic uncertainty, but over the long run, it’s likely to benefit from the rise of AI. The company sees the technology as a significant chance for growth in semiconductors, similar to previous opportunities like PCs, the internet, and smartphones.

    Industry forecasts support ASML’s perspective. The AI sector is projected to grow from $244 billion in 2025 to $1 trillion by 2031. While this market growth is a tailwind for both companies, ASML’s attractive valuation makes it look like the more compelling AI stock to buy right now.

    Robert Izquierdo has positions in ASML, Advanced Micro Devices, Microsoft, and Nvidia. The Motley Fool has positions in and recommends ASML, Advanced Micro Devices, Microsoft, and Nvidia. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.



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