Революция разума: на подступах к Сингулярности. Как технологии изменят общество и сознание - Рэймонд Курцвейл Страница 119

Administration, September 17, 2019, https://www.fda.gov/vaccines-blood-biologics/science-research-biologics/tcpro-simulates-immune-system-response-biotherapeutic-drugs; Tina Morrison, “How Simulation Can Transform Regulatory Pathways”, US Food and Drug Administration, August 14, 2018, https://www.fda.gov/science-research/about-science-research-fda/how-simulation-can-transform-regulatory-pathways; Anna Edney, “Computer-Simulated Tests Eyed at FDA to Cut Drug Approval Costs”, Bloomberg, July 7, 2017, https://www.bloomberg.com/news/articles/2017-07-07/drug-agency-looks-to-computer-simulations-to-cut-testing-costs; “Virtual Bodies for Real Drugs: In Silico Clinical Trials Are the Future”, The Medical Futurist, August 10, 2019, https://medicalfuturist.com/in-silico-trials-are-the-future; Pratik Shah et al., “Artificial Intelligence and Machine Learning in Clinical Development: A Translational Perspective”, NPJ Digital Medicine 2, no. 69 (July 26, 2019), https://doi.org/10.1038/s41746-019-0148-3; Neil Savage, “Tapping into the Drug Discovery Potential of AI”, Biopharma Dealmakers 15, no. 2 (May 27, 2021), https://doi.org/10.1038/d43747-021-00045-7.

26. Ray Kurzweil, “AI-Powered Biotech Can Help Deploy a Vaccine in Record Time”, Wired, May 19, 2020, https://www.wired.com/story/opinion-ai-powered-biotech-can-help-deploy-a-vaccine-in-record-time; Aaron Dubrow, “AI Fast-Tracks Drug Discovery to Fight COVID-19”, Texas Advanced Computing Center, April 22, 2020, https://www.tacc.utexas.edu/-/ai-fast-tracks-drug-discovery-to-fight-covid-19; Thompson, “How Long Will a Vaccine Really Take?”; Tina Morrison et al., “Advancing Regulatory Science with Computational Modeling for Medical Devices at the FDA ’s Office of Science and Engineering Laboratories”, Frontiers in Medicine 5, article 241 (September 25, 2018), https://doi.org/10.3389/fmed.2018.00241.

27. “The Drug Development Process: Step 3: Clinical Resarch”, US Food and Drug Administration.

28. Daniel Bastardo Blanco, “Our Cells Are Filled with ‘Junk DNA’—Here’s Why We Need It”, Discover, August 13, 2019, https://www.discovermagazine.com/health/our-cells-are-filled-with-junk-dna-heres-why-we-need-it.

29. Jian Zhou et al., “Whole-Genome Deep-Learning Analysis Identifies Contribution of Noncoding Mutations to Autism Risk”, Nature Genetics 51, no. 6 (May 27, 2019): 973–80, https://doi.org/10.1038/s41588-019-0420-0; Thomas Sumner, “New Causes of Autism Found in ‘Junk’ DNA”, Simons Foundation, May 27, 2019, https://www.simonsfounda tion.org/2019/05/27/autism-noncoding-mutations.

30. Sumner, “New Causes of Autism Found in ‘Junk’ DNA”.

31. Nancy Fliesler, “Using Multiple Data Streams and Artificial Intelligence to ‘Nowcast’ Local Flu Outbreaks”, Vector, Boston Children’s Hospital, January 14, 2019, https://web.archive.org/web/20210121214157/https://vector.childrenshospital.org/2019/01/local-fluprediction-argonet.

32. Fliesler, “Using Multiple Data Streams and Artificial Intelligence to ‘Nowcast’ Local Flu Outbreaks”.

33. Fliesler, “Using Multiple Data Streams and Artificial Intelligence to ‘Nowcast’ Local Flu Outbreaks”.

34. Fliesler, “Using Multiple Data Streams and Artificial Intelligence to ‘Nowcast’ Local Flu Outbreaks”; Fred S. Lu et al., “Improved State-Level Influenza Nowcasting in the United States Leveraging Internet-Based Data and Network Approaches”, Nature Communications 10, article 147 (January 11, 2019), https://doi.org/10.1038/s41467-018-08082-0.

35. Подробнее о CheXNet и ее преемнике CheXpert: “CheXNet and Beyond”, Matthew Lungren, YouTube video, November 10, 2018, https://www.youtube.com/watch?v=JqYte9UMJCg; Pranav Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning”, Stanford Machine Learning Group working paper, November 14, 2017, https://arxiv.org/pdf/1711.05225v1.pdf; Jeremy Irvin et al., “CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison”, Proceedings of the AAAI Conference on Artificial Intelligence 33, no. 1 (July 17, 2019): AAAI-10, IAAI-19, EAAI-20, https://www.aaai.org/ojs/index.php/AAAI/article/view/3834.

36. Huiying Liang et al., “Evaluation and Accurate Diagnoses of Pediatric Diseases Using Artificial Intelligence”, Nature Medicine 25, no. 3 (February 11, 2019): 433–38, https://doi.org/10.1038/s41591-018-0335-9.

37. Dimitrios Mathios et al., “Detection and Characterization of Lung Cancer Using Cell-Free DNA Fragmentomes”, Nature Communications 12, article 5060 (August 20, 2021), https://doi.org/10.1038/s41467-021-24994-w.

38. Sophie Bushwick, “Algorithm That Detects Sepsis Cut Deaths by Nearly 20 Percent”, Scientific American, August 1, 2022, https://www.scientificamerican.com/article/algorithm-that-detects-sepsis-cut-deaths-by-nearly-20-percent; Roy Adams et al., “Prospective, Multi-Site Study of Patient Outcomes After Implementation of the TREWS Machine Learning-Based Early Warning System for Sepsis”, Nature Medicine 28 (July 21, 2022): 1455–60, https://doi.org/10.1038/s41591-022-01894-0; Katharine E. Henry et al., “Factors Driving Provider Adoption of the TREWS Machine Learning-Based Early Warning System and Its Effects on Sepsis Treatment Timing”, Nature Medicine 28 (July 21, 2022), 1447–54, https://doi.org/10.1038/s41591-022-01895-z.

39. Lungren, “CheXNet and Beyond”; Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection”; Irvin et al., “CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison”, AAAI-10, IAAI-19, EAAI-20; Thomas Davenport and Ravi Kalakota, “The Potential for Artificial Intelligence in Healthcare”, Future Healthcare Journal 6, no. 2 (June 2019): 94–98, https://doi.org/10.7861/futurehosp.6-2-94.

40. Dario Amodei and Danny Hernandez, “AI and Compute”, OpenAI, May 16, 2018, https://openai.com/blog/ai-and-compute.

41. Eliza Strickland, “Autonomous Robot Surgeon Bests Humans in World First”, IEEE Spectrum, May 4, 2016, https://spectrum.ieee.org/the-human-os/robotics/medical-robots/autonomous-robot-surgeon-bests-human-surgeons-in-world-first.

42. Alice Yan, “Chinese Robot Dentist Is First to Fit Implants in Patient’s Mouth Without Any Human Involvement”, South China Morning Post, September 21, 2017, https://www.scmp.com/news/china/article/2112197/chinese-robot-dentist-first-fit-implants-patients-mouth-without-any-human.

43. Илон Маск рассказывает о технологии автоматического вживления электродов компании Neuralink: “Neuralink: Elon Musk’s Entire Brain Chip Presentation in 14 Minutes (Supercut)”, CNET, YouTube video, August 28, 2020, https://www.youtube.com/watch?v= CLUWDLKAF1M.

44. Wallace P. Ritchie Jr., Robert S. Rhodes, and Thomas W. Biester, “Work Loads and Practice Patterns of General Surgeons in the United States, 1995–1997”, Annals of Surgery 230, no. 4 (October 1999): 533–43, https://doi.org/10.1097/00000658-199910000-00009.

45. Hans Moravec, Mind Children: The Future of Robot and Human Intelligence (Cambridge, MA: Harvard University Press, 1988).

46. Peter Weibel, “Virtual Worlds: The Emperor’s New Bodies”, in Ars Electronica: Facing the Future, ed. Timothy Druckery (Cambridge, MA: MIT Press, 1999), 215, https://monoskop.org/images/4/47/Ars_Electronica_Facing_the_Future_A_Survey_of_Two_Decades_1999.pdf.

47. “Neuron Firing Rates in Humans”, AI Impacts, April 14, 2015, https://aiimpacts.org/rate-of-neuron-firing; Suzana Herculano-Houzel, “The Human Brain in Numbers: A Linearly Scaled-up Primate Brain”, Frontiers in Human Neuroscience 3, no. 31 (November 9, 2009), https://doi.org/10.3389/neuro.09.031.2009; David A. Drachman, “Do We Have Brain to Spare?”, Neurology 64, no. 12 (June 27, 2005), https://doi.org/10.1212/01.WNL.00001 66914.38327.BB; Antony Leather, “Intel Fires Back at AMD with Fastest Ever Processors: Mobile CPUs with up to 8 Cores and 5.3GHz Inbound”, Forbes, April 2, 2020, https://www.forbes.com/sites/antonyleather/2020/04/02/intel-fires-back-at-amd-with-fastest-ever-processors-mobile-cpus-with-up-to-8-cores-and-53ghz-inbound/#210c5243643d.

48. Mladen Božanić and Saurabh Sinha, “Emerging Transistor Technologies Capable of Terahertz Amplification: A Way to Re-Engineer Terahertz Radar Sensors”, Sensors 19, no. 11 (May 29, 2019), https://doi.org/10.3390/s19112454; “Intel Core i9-10900K Processor”, Intel, accessed December 23, 2022, https://ark.intel.com/content/www/us/en/ark/products/199332/intel-core-i910900k-processor-20m-cache-up-to-5-30-ghz.html.

49. Ray Kurzweil, The Singularity Is Near (New York: Viking, 2005), 125; Moravec, Mind Children, 59.

50. “June 2022”, Top500.org, accessed October 20, 2022, https://www.top500.org/lists /top500/2022/06.

51. Статья, содержащая пересказ лекции Фейнмана от 29 декабря 1959 года: Richard Feynman, “There’s Plenty of Room at the Bottom”, Engineering and Science 23, no. 5 (February 1960): 22–26, 30–36, http://calteches.library.caltech.edu/47/2/1960Bottom.pdf.

52. Feynman, “There’s Plenty of Room at the Bottom”, 22–26, 30–36.

53. John von Neumann, Theory of Self-reproducing Automata (Urbana, IL: University of Illinois Press, 1966), https://archive.org/details/theoryofselfrepr00vonn_0/mode/2up; John G. Kemeny, “Man Viewed as a Machine”, Scientific American 192, no. 4 (April 1955): 58–67, in nearly complete form at https://dijkstrascry.com/sites/default/files/papers/JohnKemenyManViewedasaMachine.pdf.

54. Von Neumann, Theory of Self-reproducing Automata, 251–96, 377.

55. For Drexler’s original books, see K. Eric Drexler, Engines of Creation: