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This paper is under CC 4.0 license. available on arxiv Authors: (1) Zhe Liu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (2) Chunyang Chen, Monash University, Melbourne, Australia; (3) Junjie Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author; (4) Mengzhuo Chen, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (5) Boyu Wu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (6) Zhilin Tian, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (7) Yuekai Huang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (8) Jun Hu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (9) Qing Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author. Table of Links Abstract and Introduction Motivational Study and Background Approach Experiment Design Results and Analysis Discussion and Threats to Validity Related Work Conclusion and References 8 CONCLUSION Automated testing is crucial for helping improve app quality. Despite the dozens of mobile app GUI testing techniques, how to automatically generate the diversified unusual text inputs for fully testing mobile apps remains a challenge. This paper proposes InputBlaster which leverages the LLM to produce the unusual inputs together with the mutation rules which serve as the reasoning chains. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. The evaluation is conducted for both effectiveness and usefulness, with 136% higher bug detection rate than the best baselines, and uncovering 37 new crashes. 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Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Conclusion and References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 4. FUTURE PROSPECTS The search for transiting planets around small stars has been motivated in large part by their potential for atmospheric characterization. Owing to the size and proximity of its host, K2-315b is thus one of the few known terrestrial exoplanets possibly amenable for atmospheric characterization in the next two decades. In order to quantify and contextualize its prospects for atmospheric study, we followed the same approach as for TRAPPIST-1 in Gillon et al. (2016) (see de Wit & Seager 2013), focusing here on all known terrestrial planets. We selected terrestrial planets as planets with a reported radius below 1.6 R⊕ in the NASA Exoplanet Archive[4] (Rogers 2015; Fulton et al. 2017). We thus derive the amplitude of the planets’ signals in transmission as: where Rp is the planetary radius, R∗ is the stellar radius, and heff is the effective atmospheric height, µ is the atmospheric mean molecular mass, T is the atmospheric temperature and g is the local gravity. We assume heff to cover seven atmospheric scale heights, µ the atmospheric mean molecular mass to be 20 amu, and the atmospheric temperature to be the equilibrium temperature for a Bond albedo of 0. For the planets with missing masses, we estimated g using the model of Chen & Kipping (2017). The signal amplitudes are reported in Figure 7 together with the SNR relative to TRAPPIST-1 b’s, calculated by scaling the signal amplitude with the hosts’ brightness in J band. We find that K2-315b fares closely to the outer planets of TRAPPIST-1 in terms of potential for atmospheric exploration with JWST— its warmer and thus larger atmosphere compensating for its larger star. In fact, its relative SNR for transmission spectroscopy is half those of TRAPPIST-1 f–h, meaning that assessing the presence of a µ ∼ 20 atmosphere around the planet would require of the order of 40 transits—four times the ∼10 transits required for a similar assessment for TRAPPIST-1 f–h (Lustig-Yaeger et al. 2019). K2-315b is thus at the very edge of JWST’s capability for atmospheric characterization, mostly due to its “large” host star. The derivation above allows us to rank planets in terms of relative potential for atmospheric characterization, assuming a similar atmospheric scenario. In practice, a significant difference in atmospheric mean molecular mass, surface pressure, and/or cloud/haze altitude will strongly affect the actual potential of a planet for characterization (Lustig-Yaeger et al. 2019). For example, in some cases clouds could render the characterization of the favorable TRAPPIST-1 e difficult even with the generation of instruments following the JWST (i.e., LUVOIR, Pidhorodetska et al. 2020). With an estimated radial velocity semi-amplitude of 1.3 m s−1 (assuming a mass comparable to that of Earth), the planet could be accessible for mass measurements using modern ultra-precise radial velocity instruments. Such possibilities and a ranking amongst the 10 best-suited Earth-sized planets for atmospheric study, EPIC 249631677 b will therefore play an important role in the upcoming era of comparative exoplanetology for terrestrial worlds. It will surely be a prime target for the generation of observatories to follow JWST and bring the field fully into this new era. This project makes use of publicly available K2 data. P.N. would like to acknowledge funding for Kerr Fellowship and Elliot Fellowship at MIT. J.d.W. and MIT gratefully acknowledge financial support from the Heising-Simons Foundation, Dr. and Mrs. Colin Masson and Dr. Peter A. Gilman for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain. B.V.R. thanks the Heising-Simons Foundation for support. V.V.G. is a F.R.S.-FNRS Research Associate. M.G. and E.J. are F.R.S.-FNRS Senior Research Associates. This work was also partially supported by a grant from the Simons Foundation (PI Queloz, grant number 327127), and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement Number 336480, from the ARC grant for Concerted Research Actions financed by the Wallonia-Brussels Federation, from the Balzan Prize Foundation, from F.R.S-FNRS (Research Project ID T010920F), from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement n◦ 803193/BEBOP), and from a Leverhulme Trust Research Project Grant n◦ RPG-2018-418. 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π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Future Prospects & References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 3. ANALYSIS AND VALIDATION 3.1. Stellar Host Characterization 3.1.1. Semi-empirical Stellar Parameters We constructed the spectral energy distribution (SED) of EPIC 249631677 using photometric magnitudes from Gaia (GBP and GRP ; Gaia Collaboration et al. 2018) and the AllWISE source catalog (J, H, Ks, W1, W2, and W3; Cutri et al. 2013). The corresponding fluxes for these magnitudes are tabulated on VizieR (Ochsenbein et al. 2000) and shown in Figure 5 and Table 1. The parallax of EPIC 249631677 is π = 17.61 ± 0.09 mas, which yields a distance of 56.8 ± 0.3 pc (Gaia Collaboration et al. 2018; Stassun & Torres 2018). We then derived the stellar luminosity L∗ by integrating over the SED, which yielded L∗ = 0.0041 ± 0.0001L . Two independent methods were applied to obtain stellar mass. First, we used the empirical M∗−MKs relation (applying the metallicity obtained in Section 3.1.2) from Mann et al. (2019) to obtain M∗ = 0.1721 ± 0.0044M . We also applied stellar evolution modeling, using the models presented in Fernandes et al. (2019), using as a constraint the luminosity inferred above and the metallicity derived in Sect. 3.1.2). We considered the stellar age to be >1 Gyr in the absence of signs of youth, such as presence of prominent flares (Ilin et al. 2019, see Section 3.1.3). We obtained with this method M∗ = 0.176 ± 0.004 M . This uncertainty reflects the error propagation on the stellar luminosity and metallicity, but also the uncertainty associated with the input physics of the stellar models. We combined these two mass estimates as in Van Grootel et al. (2018) to obtain M∗ = 0.174±0.004M as our best estimate for the stellar mass of EPIC 249631677. Given its proximity, we expect minimal extinction (Av) for the target; the SED fitting analysis described in Section 3.2.1 similarly constrains it to be 0.02 at 3σ confidence and we adopt that upper limit here. Finally, we note that given its luminosity, mass, and Gaia colors this star is likely to be fully convective (Jao et al. 2018; Rabus et al. 2019). Due to the absence of a strong constraint on the stellar density from the transits, we further obtained stellar radius, surface gravity, effective temperature and density from our evolutionary models. Table 1 summarizes the results from this analysis, along with other properties of the star. Our values are consistent with those listed in the TESS Input Catalog (Stassun et al. 2019), and we adopt them for the remainder of this analysis. 3.1.2. Reconaissance Spectroscopy To confirm EPIC 249631677’s stellar properties and better characterize the system, we acquired an optical spectrum using Keck/HIRES (Vogt et al. 1994) on UT 30 May 2020. The observation took place in 0.6 00 effective seeing and using the C2 decker without the HIRES iodine gas cell, giving an effective resolution of λ/∆λ ≈ 55, 000 from 3600 ˚A to 7990 ˚A. We exposed for 1800 s and obtained SNR of roughly 23 per pixel. Data reduction followed the standard approach of the California Planet Search consortium (Howard et al. 2010). We used our Keck/HIRES radial velocity and Gaia DR2 data to estimate the 3D galactic (UV W) space velocity using the online kinematics calculator[2] of Rodriguez (2016). Following Chubak et al. (2012), our Keck/HIRES spectrum gives a barycentric radial velocity of 6.25±0.17 km s−1 . With the Gaia-derived coordinates, proper motion, and distance listed in Table 1, we find (U, V, W) values of (−17.02, −9.06, +33.66) km s−1 , indicating a likely membership in the Milky Way’s thin disk (Bensby et al. 2014). Using the SpecMatch-Empirical algorithm (Yee et al. 2017), we derive from our HIRES spectrum stellar parameters of Teff = 3195 ± 70 K, R∗ = 0.23 ± 0.10R , and [Fe/H]= −0.24 ± 0.09, consistent with the values tabulated in Table 1. The three best-matching stars in the SpecMatch-Empirical template library are GJ 15B, GJ 447, and GJ 725B, which have spectral types of M3.5V, M4V, and M3.5V, respectively. Given the close match between the spectra of these stars and our target (see Fig. 4), we therefore classify EPIC 249631677 as an M dwarf with subclass 3.5±0.5. We see no evidence of emission line cores at Hα, consistent with our determination that our target is not a young star. We see no evidence of spectral broadening compared to these three stars (which all have v sin i < 2.5 km s−1 ; Reiners et al. 2012), so we set an upper limit on EPIC 249631677’s projected rotational velocity of < 5 km s−1 , comparable to the spectral resolution of HIRES. 3.1.3. Stellar Variability The long-term variations apparent in the everest light curve (Figure 1) are not evident in light curves from other reduction pipelines (e.g., K2SFF). These variations likely arise from systematics and are not reliable for estimating the stellar rotation period (Esselstein et al. 2018). Similarly, no flares are apparent either in the K2 or SPECULOOS data. Flare rates peak for ∼M3.5 stars in TESS data (G¨unther et al. 2020). However, given the long integration time of 29.4 minutes as well as a need for data processing which corrects for the saw-tooth pattern, flare signals, unless very prominent, are expected to be difficult to detect in K2 long cadence data. 3.2. Vetting In order to produce a transit depth on the level of 0.2% in the light curve of the primary target, a background eclipsing binary producing eclipses with depths of 25% to 50% would have to be 5.25 to 6.0 mag fainter than the target, respectively. Qualitatively, the odds of EPIC 249631677 hosting a planet are higher than the odds of such magnitude contrast eclipsing binary being present within the SPECULOOS aperture, given occurrence rates of M-dwarf planets (Dressing & Charbonneau 2013; Mulders et al. 2015; Hardegree-Ullman et al. 2019). A stringent quantitative constraint can be placed using the ingress/egress duration (T12/T34) compared to the total transit duration (T14) (Seager & Mall´enOrnelas 2003, Equation 21). Such a test yields an upper limit on the relative radius of the transiting body. By assuming equal effective surface temperatures, the lower magnitude limit ∆m (corresponding to a flux difference ∆F) for a blended binary mimicking a signal of depth δ is given by: Using the posterior for the transit fit (See Section 3.3), we find for EPIC 249631677 that such a background object can be fainter at most by 1.73 mag at the 3σ level. Fortunately, EPIC 249631677 has a significant proper motion, ∼140 mas yr−1 , which allows us to investigate the presence of background sources at its current sky position. We looked at archival imaging of EPIC 246331677 going back to 1953[3] . A POSS I plate from 1953 is the publicly available oldest image of EPIC 249631677, and it does not show any background source at the current position of the target as shown in Figure 6. The plate is sensitive to objects at least 3.5 magnitudes fainter than the target. Similarly, the Hubble Guide Star Catalogue (GSC), with a limiting magnitude of 20 (Lasker et al. 1990), does not show any background source. While POSS II would go the deepest in terms of limiting magnitude (20.8, Reid et al. 1991), the star has moved appreciably closer to its current location, precluding a definitive measurement from this image. Overall, using archival images we can rule out the possibility of the transit signal originating from background star at a high level of confidence. 3.2.1. Binarity of the Host Star Despite the lack of background sources, the host star could produce a false-positive transit signal if it were a grazing eclipsing binary or a hierarchical eclipsing binary. We investigated the evidence for host star binarity using the isochrones software package (Morton 2015), which performs isochrone fitting in the context of the MESA (Paxton et al. 2011, 2013, 2015) Isochrones and Stellar Tracks database (Dotter 2016; Choi et al. 2016). Single-star and binary evolutionary models are available within isochrones, and the inference is performed via the nested sampling algorithm MULTINEST (Feroz et al. 2009) (as implemented in the PyMultiNest software package (Buchner et al. 2014)), which allows for direct comparisons of the Bayesian evidence ln Z. We tested both single-star and binary models using the priors on photometric magnitudes and stellar distance described in Section 3.1.1. The inferred properties from the single-star model fit are consistent with those given in Table 1 at the 2σ level. The ln Z for the single-star model is −213.86±0.04, whereas the ln Z for the binary model is −229.6 ± 0.2. According to Kass & Raftery (1995), the corresponding Bayes factor of 16 indicates “decisive” evidence in favor of the single-star model. We also examined our Keck/HIRES spectrum for secondary lines that would indicate the presence of another star following the approach of Kolbl et al. (2015). We found no evidence of additional lines down to the method’s standard sensitivity limit of ∆V = 5 mag for ∆v > 10 km s−1 , consistent with EPIC 249631677 being a single, isolated star. We therefore conclude that the available data strongly support EPIC 246331677 being a single star. 3.2.2. Photometric Tests We performed a series of tests on the photometric data to rule out false-positive scenarios. First, we performed an even-odd test on the target using K2 photometry. The even and odd transits are consistent with one another in transit depth to within 1σ. We also looked for secondary eclipses in the phase-folded light curve and found none to be present. Note that since we observe consistent signals in both the K2 and SPECULOOS data sets, we can rule out the signal originating from systematics. The transit depth in SPECULOOS observations with I+z filter, which is redder than Kepler bandpass, are consistent to K2 transit depths within 1σ level, keeping up with the expectation of the achromatic nature of planetary transit. Furthermore, a massive companion, such as a faint white dwarf, can be ruled out using the ellipsoidal variation, which puts a 3σ upper limit on the mass of any companions at the given orbital period of the transit signal as ∼100 MJup (Morris 1985; Niraula et al. 2018). From the transit fit, we can rule out a grazing eclipse originating from a larger transiting object (i.e. ≥ 2R⊕) at >3σ confidence. Together, these tests rule out the object at 3.14 days being a massive companion. 3.3. Transit Fitting We used the refined estimates of the host properties together with a joint analysis of the K2 and SPECULOOS light curves to derive the planetary properties. In order to calculate the transit model, we used batman (Kreidberg 2015). We simultaneously model both the K2 observation as well as the ground-based observations with 21 parameters in a Monte Carlo Markov Chain (MCMC) framework using the emcee package (ForemanMackey et al. 2013). We use a Gaussian prior on the scaled semi-major axis of the orbit a/R∗ of N (25.72, 0.27), derived using Eq. 30 from Winn (2010) along with the stellar density of 32.6 ± 1.0 g cm−3 (see Section 3.1.1) and orbital period of 3.1443 days from the TLS search. As for the limb darkening, we use the non-informative q1, q2 parameterization of the quadratic limb-darkening law as suggested by Kipping (2013). We fixed the eccentricity to 0, given that the expected time of circularization is 50 Myr (assuming a quality factor Qp ∼ 500, Goldreich & Soter 1966; Patra et al. 2017), which is at least an order of magnitude smaller than the estimated age of the system. For K2 data, we supersample the transits by a factor of 15 in batman to take into account the effect of non-negligible integration time. As for the ground-based data, we use second-order polynomials to detrend against the observables airmass and FWHM. We ran the MCMC for 50,000 steps with 150 walkers performing a combined fit of K2 and SPECULOOS — (1) This work. (2) Huber et al. (2016). (3) Stassun et al. (2019). (4) Cutri et al. (2003). (5) Gaia Collaboration et al. (2018). (6) Cutri et al. (2013). (7) This work, evolutionary model analysis. (8) This work, Keck/HIRES analysis. (9) This work, SED analysis. References data, and use the last half of the run to build the parameter posteriors. We assessed the convergence of walkers using the suggested autocorrelation test for emcee. The resulting median values from the fit with 1σ deviation are reported in Table 2, while the best-fit transit models are shown in Figure 2 and Figure 3. [2] http://kinematics.bdnyc.org/query [3] http://stdatu.stsci.edu/cgi-bin/dss form

π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Analysis and Validation

This paper is available on arxiv under CC 4.0 license. . Authors: Gemini Team, Google Table of Links Abstract and Introduction Model Architecture Training Infrastructure Training Dataset Evaluation Responsible Deployment Discussion and Conclusion, References Contributions and Acknowledgments Appendix 8. Contributions and Acknowledgments Leads Rohan Anil, Co-Lead, Text Sebastian Borgeaud, Co-Lead, Text Yonghui Wu, Co-Lead, Text Jean-Baptiste Alayrac, Co-Lead, MM Vision Jiahui Yu, Co-Lead, MM Vision Radu Soricut*, Co-Lead, MM Vision* Johan Schalkwyk, Lead, MM Audio Andrew M. Dai, Co-Lead, Data Anja Hauth*, Co-Lead, Data* Katie Millican, Co-Lead, Data David Silver, Co-Lead, Fine-Tuning Slav Petrov, Co-Lead, Fine-Tuning Melvin Johnson, Lead, Instruction Tuning Ioannis Antonoglou*, Co-Lead, RL Techniques* Julian Schrittwieser, Co-Lead, RL Techniques Amelia Glaese, Lead, Human Data Jilin Chen, Lead, Safety Emily Pitler, Co-Lead, Tool Use Timothy Lillicrap, Co-Lead, Tool Use Angeliki Lazaridou, Co-Lead, Eval Orhan Firat, Co-Lead, Eval James Molloy, Co-Lead, Infra Michael Isard, Co-Lead, Infra Paul R. Barham, Co-Lead, Infra Tom Hennigan, Co-Lead, Infra Benjamin Lee, Co-Lead, Codebase & Parallelism Fabio Viola, Co-Lead, Codebase & Parallelism Malcolm Reynolds, Co-Lead, Codebase & Parallelism Yuanzhong Xu, Co-Lead, Codebase & Parallelism Ryan Doherty, Lead, Ecosystem Eli Collins, Lead, Product Clemens Meyer, Co-Lead, Operations Eliza Rutherford, Co-Lead, Operations Erica Moreira, Co-Lead, Operations Kareem Ayoub, Co-Lead, Operations Megha Goel, Co-Lead, Operations Core Contributors George Tucker Enrique Piqueras Maxim Krikun Iain Barr Nikolay Savinov Ivo Danihelka Becca Roelofs Core Contributors Anaïs White Anders Andreassen Tamara von Glehn Lakshman Yagati Mehran Kazemi Lucas Gonzalez Misha Khalman Jakub Sygnowski Alexandre Frechette Charlotte Smith Laura Culp Lev Proleev Yi Luan Xi Chen James Lottes Nathan Schucher Federico Lebron Alban Rrustemi Natalie Clay Phil Crone Tomas Kocisky Jeffrey Zhao Bartek Perz Dian Yu Heidi Howard Adam Bloniarz Jack W. Rae Han Lu Laurent Sifre Marcello Maggioni Fred Alcober Dan Garrette Megan Barnes Shantanu Thakoor Jacob Austin Gabriel Barth-Maron William Wong Rishabh Joshi Rahma Chaabouni Deeni Fatiha Arun Ahuja Ruibo Liu Yunxuan Li Sarah Cogan Jeremy Chen Core Contributors Chao Jia Chenjie Gu Qiao Zhang Jordan Grimstad Ale Jakse Hartman Martin Chadwick Gaurav Singh Tomar Xavier Garcia Evan Senter Emanuel Taropa Thanumalayan Sankaranarayana Pillai Jacob Devlin Michael Laskin Diego de Las Casas Dasha Valter Connie Tao Lorenzo Blanco Adrià Puigdomènech Badia David Reitter Mianna Chen Jenny Brennan Clara Rivera Sergey Brin Shariq Iqbal Gabriela Surita Jane Labanowski Abhi Rao Stephanie Winkler Emilio Parisotto Yiming Gu Kate Olszewska Yujing Zhang Ravi Addanki Antoine Miech Annie Louis Laurent El Shafey Denis Teplyashin Geoff Brown Elliot Catt Nithya Attaluri Jan Balaguer Jackie Xiang Pidong Wang Zoe Ashwood Anton Briukhov Albert Webson Sanjay Ganapathy Smit Sanghavi Core Contributors Ajay Kannan Ming-Wei Chang Axel Stjerngren Josip Djolonga Yuting Sun Ankur Bapna Matthew Aitchison Pedram Pejman Henryk Michalewski Tianhe Yu Cindy Wang Juliette Love Junwhan Ahn Dawn Bloxwich Kehang Han Peter Humphreys Thibault Sellam James Bradbury Varun Godbole Sina Samangooei Bogdan Damoc Alex Kaskasoli Sébastien M. R. Arnold Vijay Vasudevan Shubham Agrawal Jason Riesa Dmitry Lepikhin Richard Tanburn Srivatsan Srinivasan Hyeontaek Lim Sarah Hodkinson Pranav Shyam Johan Ferret Steven Hand Ankush Garg Tom Le Paine Jian Li Yujia Li Minh Giang Alexander Neitz Zaheer Abbas Sarah York Machel Reid Elizabeth Cole Aakanksha Chowdhery Dipanjan Das Dominika Rogozińska Vitaly Nikolaev Core Contributors Pablo Sprechmann Zachary Nado Lukas Zilka Flavien Prost Luheng He Marianne Monteiro Gaurav Mishra Chris Welty Josh Newlan Dawei Jia Miltiadis Allamanis Clara Huiyi Hu Raoul de Liedekerke Justin Gilmer Carl Saroufim Shruti Rijhwani Shaobo Hou Disha Shrivastava Anirudh Baddepudi Alex Goldin Adnan Ozturel Albin Cassirer Yunhan Xu Daniel Sohn Devendra Sachan Reinald Kim Amplayo Craig Swanson Dessie Petrova Shashi Narayan Arthur Guez Siddhartha Brahma Jessica Landon Miteyan Patel Ruizhe Zhao Kevin Villela Luyu Wang Wenhao Jia Matthew Rahtz Mai Giménez Legg Yeung Hanzhao Lin James Keeling Petko Georgiev Diana Mincu Boxi Wu Salem Haykal Rachel Saputro Kiran Vodrahalli Core Contributors James Qin Zeynep Cankara Abhanshu Sharma Nick Fernando Will Hawkins Behnam Neyshabur Solomon Kim Adrian Hutter Priyanka Agrawal Alex Castro-Ros George van den Driessche Tao Wang Fan Yang Shuo-yiin Chang Paul Komarek Ross McIlroy Mario Lučić Guodong Zhang Wael Farhan Michael Sharman Paul Natsev Paul Michel Yong Cheng Yamini Bansal Siyuan Qiao Kris Cao Siamak Shakeri Christina Butterfield Justin Chung Paul Kishan Rubenstein Shivani Agrawal Arthur Mensch Kedar Soparkar Karel Lenc Timothy Chung Aedan Pope Loren Maggiore Jackie Kay Priya Jhakra Shibo Wang Joshua Maynez Mary Phuong Taylor Tobin Andrea Tacchetti Maja Trebacz Kevin Robinson Yash Katariya Sebastian Riedel Core Contributors Paige Bailey Kefan Xiao Nimesh Ghelani Lora Aroyo Ambrose Slone Neil Houlsby Xuehan Xiong Zhen Yang Elena Gribovskaya Jonas Adler Mateo Wirth Lisa Lee Music Li Thais Kagohara Jay Pavagadhi Sophie Bridgers Anna Bortsova Sanjay Ghemawat Zafarali Ahmed Tianqi Liu Richard Powell Vijay Bolina Mariko Iinuma Polina Zablotskaia James Besley Da-Woon Chung Timothy Dozat Ramona Comanescu Xiance Si Jeremy Greer Guolong Su Martin Polacek Raphaël Lopez Kaufman Simon Tokumine Hexiang Hu Elena Buchatskaya Yingjie Miao Mohamed Elhawaty Aditya Siddhant Nenad Tomasev Jinwei Xing Christina Greer Helen Miller Shereen Ashraf Aurko Roy Zizhao Zhang Ada Ma Angelos Filos Core Contributors Milos Besta Rory Blevins Ted Klimenko Chih-Kuan Yeh Soravit Changpinyo Jiaqi Mu Oscar Chang Mantas Pajarskas Carrie Muir Vered Cohen Charline Le Lan Krishna Haridasan Amit Marathe Steven Hansen Sholto Douglas Rajkumar Samuel Mingqiu Wang Sophia Austin Chang Lan Jiepu Jiang Justin Chiu Jaime Alonso Lorenzo Lars Lowe Sjösund Sébastien Cevey Zach Gleicher Thi Avrahami Anudhyan Boral Hansa Srinivasan Vittorio Selo Rhys May Konstantinos Aisopos Léonard Hussenot Livio Baldini Soares Kate Baumli Michael B. Chang Adrià Recasens Ben Caine Alexander Pritzel Filip Pavetic Fabio Pardo Anita Gergely Justin Frye Vinay Ramasesh Dan Horgan Kartikeya Badola Nora Kassner Subhrajit Roy Ethan Dyer Core Contributors Víctor Campos Alex Tomala Yunhao Tang Dalia El Badawy Elspeth White Basil Mustafa Oran Lang Abhishek Jindal Sharad Vikram Zhitao Gong Sergi Caelles Ross Hemsley Gregory Thornton Fangxiaoyu Feng Wojciech Stokowiec Ce Zheng Phoebe Thacker Çağlar Ünlü Zhishuai Zhang Mohammad Saleh James Svensson Max Bileschi Piyush Patil Ankesh Anand Roman Ring Katerina Tsihlas Arpi Vezer Marco Selvi Toby Shevlane Mikel Rodriguez Tom Kwiatkowski Samira Daruki Keran Rong Allan Dafoe Nicholas FitzGerald Keren Gu-Lemberg Mina Khan Lisa Anne Hendricks Marie Pellat Vladimir Feinberg James Cobon-Kerr Tara Sainath Maribeth Rauh Sayed Hadi Hashemi Richard Ives Yana Hasson YaGuang Li Eric Noland Core Contributors Yuan Cao Nathan Byrd Le Hou Qingze Wang Thibault Sottiaux Michela Paganini Jean-Baptiste Lespiau Alexandre Moufarek Samer Hassan Kaushik Shivakumar Joost van Amersfoort Amol Mandhane Pratik Joshi Anirudh Goyal Matthew Tung Andrew Brock Hannah Sheahan Vedant Misra Cheng Li Nemanja Rakićević Mostafa Dehghani Fangyu Liu Sid Mittal Junhyuk Oh Seb Noury Eren Sezener Fantine Huot Matthew Lamm Nicola De Cao Charlie Chen Contributors Gamaleldin Elsayed Ed Chi Mahdis Mahdieh Ian Tenney Nan Hua Ivan Petrychenko Patrick Kane Dylan Scandinaro Rishub Jain Jonathan Uesato Romina Datta Adam Sadovsky Oskar Bunyan Dominik Rabiej Shimu Wu John Zhang Contributors Gautam Vasudevan Edouard Leurent Mahmoud Alnahlawi Ionut Georgescu Nan Wei Ivy Zheng Betty Chan Pam G Rabinovitch Piotr Stanczyk Ye Zhang David Steiner Subhajit Naskar Michael Azzam Matthew Johnson Adam Paszke Chung-Cheng Chiu Jaume Sanchez Elias Afroz Mohiuddin Faizan Muhammad Jin Miao Andrew Lee Nino Vieillard Sahitya Potluri Jane Park Elnaz Davoodi Jiageng Zhang Jeff Stanway Drew Garmon Abhijit Karmarkar Zhe Dong Jong Lee Aviral Kumar Luowei Zhou Jonathan Evens William Isaac Zhe Chen Johnson Jia Anselm Levskaya Zhenkai Zhu Chris Gorgolewski Peter Grabowski Yu Mao Alberto Magni Kaisheng Yao Javier Snaider Norman Casagrande Paul Suganthan Evan Palmer Contributors Geoffrey Irving Edward Loper Manaal Faruqui Isha Arkatkar Nanxin Chen Izhak Shafran Michael Fink Alfonso Castaño Irene Giannoumis Wooyeol Kim Mikołaj Rybiński Ashwin Sreevatsa Jennifer Prendki David Soergel Adrian Goedeckemeyer Willi Gierke Mohsen Jafari Meenu Gaba Jeremy Wiesner Diana Gage Wright Yawen Wei Harsha Vashisht Yana Kulizhskaya Jay Hoover Maigo Le Lu Li Chimezie Iwuanyanwu Lu Liu Kevin Ramirez Andrey Khorlin Albert Cui Tian LIN Marin Georgiev Marcus Wu Ricardo Aguilar Keith Pallo Abhishek Chakladar Alena Repina Xihui Wu Tom van der Weide Priya Ponnapalli Caroline Kaplan Jiri Simsa Shuangfeng Li Olivier Dousse Fan Yang Jeff Piper Nathan Ie Contributors Minnie Lui Rama Pasumarthi Nathan Lintz Anitha Vijayakumar Lam Nguyen Thiet Daniel Andor Pedro Valenzuela Cosmin Paduraru Daiyi Peng Katherine Lee Shuyuan Zhang Somer Greene Duc Dung Nguyen Paula Kurylowicz Sarmishta Velury Sebastian Krause Cassidy Hardin Lucas Dixon Lili Janzer Kiam Choo Ziqiang Feng Biao Zhang Achintya Singhal Tejasi Latkar Mingyang Zhang Quoc Le Elena Allica Abellan Dayou Du Dan McKinnon Natasha Antropova Tolga Bolukbasi Orgad Keller David Reid Daniel Finchelstein Maria Abi Raad Remi Crocker Peter Hawkins Robert Dadashi Colin Gaffney Sid Lall Ken Franko Egor Filonov Anna Bulanova Rémi Leblond Vikas Yadav Shirley Chung Harry Askham Luis C. Cobo Contributors Kelvin Xu Felix Fischer Jun Xu Christina Sorokin Chris Alberti Chu-Cheng Lin Colin Evans Hao Zhou Alek Dimitriev Hannah Forbes Dylan Banarse Zora Tung Jeremiah Liu Mark Omernick Colton Bishop Chintu Kumar Rachel Sterneck Ryan Foley Rohan Jain Swaroop Mishra Jiawei Xia Taylor Bos Geoffrey Cideron Ehsan Amid Francesco Piccinno Xingyu Wang Praseem Banzal Petru Gurita Hila Noga Premal Shah Daniel J. Mankowitz Alex Polozov Nate Kushman Victoria Krakovna Sasha Brown MohammadHossein Bateni Dennis Duan Vlad Firoiu Meghana Thotakuri Tom Natan Anhad Mohananey Matthieu Geist Sidharth Mudgal Sertan Girgin Hui Li Jiayu Ye Ofir Roval Reiko Tojo Contributors Michael Kwong James Lee-Thorp Christopher Yew Quan Yuan Sumit Bagri Danila Sinopalnikov Sabela Ramos John Mellor Abhishek Sharma Aliaksei Severyn Jonathan Lai Kathy Wu Heng-Tze Cheng David Miller Nicolas Sonnerat Denis Vnukov Rory Greig Jennifer Beattie Emily Caveness Libin Bai Julian Eisenschlos Alex Korchemniy Tomy Tsai Mimi Jasarevic Weize Kong Phuong Dao Zeyu Zheng Frederick Liu Fan Yang Rui Zhu Mark Geller Tian Huey Teh Jason Sanmiya Evgeny Gladchenko Nejc Trdin Andrei Sozanschi Daniel Toyama Evan Rosen Sasan Tavakkol Linting Xue Chen Elkind Oliver Woodman John Carpenter George Papamakarios Rupert Kemp Sushant Kafle Tanya Grunina Rishika Sinha Contributors Alice Talbert Abhimanyu Goyal Diane Wu Denese Owusu-Afriyie Cosmo Du Chloe Thornton Jordi Pont-Tuset Pradyumna Narayana Jing Li Saaber Fatehi John Wieting Omar Ajmeri Benigno Uria Tao Zhu Yeongil Ko Laura Knight Amélie Héliou Ning Niu Shane Gu Chenxi Pang Dustin Tran Yeqing Li Nir Levine Ariel Stolovich Norbert Kalb Rebeca Santamaria-Fernandez Sonam Goenka Wenny Yustalim Robin Strudel Ali Elqursh Balaji Lakshminarayanan Charlie Deck Shyam Upadhyay Hyo Lee Mike Dusenberry Zonglin Li Xuezhi Wang Kyle Levin Raphael Hoffmann Dan Holtmann-Rice Olivier Bachem Summer Yue Sho Arora Eric Malmi Daniil Mirylenka Qijun Tan Christy Koh Soheil Hassas Yeganeh Contributors Siim Põder Steven Zheng Francesco Pongetti Mukarram Tariq Yanhua Sun Lucian Ionita Mojtaba Seyedhosseini Pouya Tafti Ragha Kotikalapudi Zhiyu Liu Anmol Gulati Jasmine Liu Xinyu Ye Bart Chrzaszcz Lily Wang Nikhil Sethi Tianrun Li Ben Brown Shreya Singh Wei Fan Aaron Parisi Joe Stanton Chenkai Kuang Vinod Koverkathu Christopher A. Choquette-Choo Yunjie Li TJ Lu Abe Ittycheriah Prakash Shroff Pei Sun Mani Varadarajan Sanaz Bahargam Rob Willoughby David Gaddy Ishita Dasgupta Guillaume Desjardins Marco Cornero Brona Robenek Bhavishya Mittal Ben Albrecht Ashish Shenoy Fedor Moiseev Henrik Jacobsson Alireza Ghaffarkhah Morgane Rivière Alanna Walton Clément Crepy Alicia Parrish Contributors Yuan Liu Zongwei Zhou Clement Farabet Carey Radebaugh Praveen Srinivasan Claudia van der Salm Andreas Fidjeland Salvatore Scellato Eri Latorre-Chimoto Hanna Klimczak-Plucińska David Bridson Dario de Cesare Tom Hudson Piermaria Mendolicchio Lexi Walker Alex Morris Ivo Penchev Matthew Mauger Alexey Guseynov Alison Reid Seth Odoom Lucia Loher Victor Cotruta Madhavi Yenugula Dominik Grewe Anastasia Petrushkina Tom Duerig Antonio Sanchez Steve Yadlowsky Amy Shen Amir Globerson Adam Kurzrok Lynette Webb Sahil Dua Dong Li Preethi Lahoti Surya Bhupatiraju Dan Hurt Haroon Qureshi Ananth Agarwal Tomer Shani Matan Eyal Anuj Khare Shreyas Rammohan Belle Lei Wang Chetan Tekur Mihir Sanjay Kale Jinliang Wei Contributors Ruoxin Sang Brennan Saeta Tyler Liechty Yi Sun Yao Zhao Stephan Lee Pandu Nayak Doug Fritz Manish Reddy Vuyyuru John Aslanides Nidhi Vyas Martin Wicke Xiao Ma Taylan Bilal Evgenii Eltyshev Daniel Balle Nina Martin Hardie Cate James Manyika Keyvan Amiri Yelin Kim Xi Xiong Kai Kang Florian Luisier Nilesh Tripuraneni David Madras Mandy Guo Austin Waters Oliver Wang Joshua Ainslie Jason Baldridge Han Zhang Garima Pruthi Jakob Bauer Feng Yang Riham Mansour Jason Gelman Yang Xu George Polovets Ji Liu Honglong Cai Warren Chen XiangHai Sheng Emily Xue Sherjil Ozair Adams Yu Christof Angermueller Contributors Xiaowei Li Weiren Wang Julia Wiesinger Emmanouil Koukoumidis Yuan Tian Anand Iyer Madhu Gurumurthy Mark Goldenson Parashar Shah MK Blake Hongkun Yu Anthony Urbanowicz Jennimaria Palomaki Chrisantha Fernando Kevin Brooks Ken Durden Harsh Mehta Nikola Momchev Elahe Rahimtoroghi Maria Georgaki Amit Raul Sebastian Ruder Morgan Redshaw Jinhyuk Lee Komal Jalan Dinghua Li Ginger Perng Blake Hechtman Parker Schuh Milad Nasr Mia Chen Kieran Milan Vladimir Mikulik Trevor Strohman Juliana Franco Tim Green Program Leads Demis Hassabis Koray Kavukcuoglu Overall Technical Leads (equal contribution) Jeffrey Dean Oriol Vinyals The roles are defined as below: • : Individual(s) responsible for the sub-team throughout the project. Lead Individual that had significant impact throughout the project. • Core Contributor: : Individual that had contributions to the project and was partially involved with the effort. • Contributor Responsible for the organizational aspects of the Gemini effort • Program Lead: Responsible for the technical direction of the overall Gemini effort • Overall Technical Lead: Within each role, contributions are equal, and are listed in a randomized order. Ordering within each role does not indicate ordering of the contributions. Gemini is a cross-Google effort, with members from Google DeepMind (GDM), Google Research (GR), Knowledge and Information (K&I), Core ML, Cloud, Labs, and more. We thank our reviewers and colleagues for their valuable discussions and feedback on the report— Alexandra Belias, Arielle Bier, Eleanor Tomlinson, Emily Hossellman, Gaby Pearl, Helen King, Hollie Dobson, Jaclyn Konzelmann, Jennifer Beroshi, Joel Moss, Jon Small, Jonathan Fildes, Oli Gaymond, Rebecca Bland, Reena Jana, and Tom Lue. Our work is made possible by the dedication and efforts of numerous teams at Google. We would like to acknowledge the support from Abhi Mohan, Adekunle Bello, Aishwarya Nagarajan, Alaa Saade, Alejandro Lince, Alexander Chen, Alexander Kolbasov, Alexander Schiffhauer, Amar Subramanya, Ameya Shringi, Amin Vahdat, Anda Rabatić, Anthonie Gross, Antoine Yang, Anthony Green, Anton Ruddock, Art Khurshudov, Artemis Chen, Arthur Argenson, Avinatan Hassidim, Beiye Liu, Benjamin Schroeder, Bin Ni, Brett Daw, Bryan Chiang, Burak Gokturk, Carl Crous, Carrie Grimes Bostock, Charbel Kaed, Charlotte Banks, Che Diaz, Chris Larkin, Christy Lian, Claire Cui, Clare Bycroft, Corentin Tallec, Daniel Herndon, Dave Burke, David Battle, David Engel, Dipannita Shaw, Donghyun Koo, Doug Ritchie, Dragos Stefanescu, Elissa Wolf, Emre Sargin, Eran Ofek, Eric Herren, Estella King, Fatema Alkhanaizi, Felix Gimeno, Fernando Pereira, Florent Altché, Gabriel Carvajal, Gaurav Gandhi, George Powell, Goran Pavičić, Harry Richardson, Hassan Wassel, Hongji Li, Idan Szpektor, Igor Ivanisevic, Ivan Jambrešić, Ivan Jurin, Jade Fowler, James Assiene, Jay Yagnik, Jeanbastien Grill, Jeff Seibert, Jenna LaPlante, Jessica Austin, Jianxing Lu, Jim O’Keeffe, Jin Huang, Joe Heyward, Johannes Welbl, John Jumper, Jonathan Caton, Josh Woodward, Joshua Foster, Kathryn Tunyasuvunakool, Katrina Wong, Kavya Kopparapu, Kelvin Nguyen, Kira Yin, Konstantin Sharlaimov, Kun Li, Lee Hong, Lilly Taylor, Longfei Shen, Luc Mercier, Maciej Mikuła, Mania Abdi, Manuel Sanchez, Maria Ines Aranguren, Mario Carlos Cortes III, Matthew Tait, Matthias Lochbrunner, Mehdi Ghissassi, Micah Mosley, Michael Bendersky, Michael Figurnov, Michael Harris, Michael Mathieu, Michael O’Neill, Michael Vorburger, Mihir Paradkar, Nandita Dukkipati, Nathan Carter, Nathan Watson, Neil Rabinowitz, Nikhil Dandekar, Nishant Ranka, Obaid Sarvana, Olcan Sercinoglu, Olivier Lacombe, Ottavia Bertolli, Paul Caron, Pranesh Srinivasan, Praveen Kumar, Rahul Sukthankar, Raia Hadsell, Rajagopal Ananthanarayanan, Roberto Lupi, Rosie Zou, Sachin Menezes, Sadegh Jazayeri, Sam Cheung, Sameer Bidichandani, Sania Alex, Sanjiv Kumar, Sara Wiltberger, Sarah Fitzgerald, Saz Basu, Sebastian Nowozin, Shannon Hepburn, Shayne Cardwell, Sissie Hsiao, Srinivasan Venkatachary, Sugato Basu, Sundar Pichai, Sundeep Tirumalareddy, Susannah Young, Swetha Vijayaraghavan, Tania Bedrax-Weiss, Taylor Applebaum, Teiva Harsanyi, Terry Chen, Tim Blyth, Ting Liu, Tom Cobley, Tomas Izo, Trystan Upstill, Varun Singhai, Vedrana Klarić Trupčević, Victor Cai, Vladimir Pudovkin, Vu Dang, Wenbo Zhao, Wesley Crow, Wesley Szeng, Xiaodan Song, Yazhou Zu, Ye Tian, Yicong Wang, Yixing Wang, Yossi Matias, Yunlong Jiao, Zachary Jessup, Zhenchuan Pang, Žiga Avsec, Zimeng Yang, and Zoubin Ghahramani. We’d also like to recognize the AlphaCode team, the Borg Scheduling team, the Facilities team, the Gemini Demo Team, the Global Server Ops (GSO) team, the JAX team, the the Legal team, ML SRE team, the ML Supercomputer (MLSC) team, the PartIR team, the Platforms Infrastructure Engineering (PIE) team, and the XLA Compiler team. We thank everyone at Google not explicitly mentioned above, who have shared excitement, given feedback on early Gemini models or created interesting demo uses of Gemini, and worked with or supported the core Gemini team on many aspects of this project.

Gemini -  A Family of Highly Capable Multimodal Models: Contributions and Acknowledgments

THE STORM BURSTS.

A CHANCE MEETING

THE DAWN OF A GALA DAY.

The Underground City; Or, The Black Indies by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . NELL ADOPTED here CHAPTER XII. NELL ADOPTED A couple of hours later, Harry still unconscious, and the child in a very feeble state, were brought to the cottage by Jack Ryan and his companions. The old overman listened to the account of their adventures, while Madge attended with the utmost care to the wants of her son, and of the poor creature whom he had rescued from the pit. Harry imagined her a mere child, but she was a maiden of the age of fifteen or sixteen years. She gazed at them with vague and wondering eyes; and the thin face, drawn by suffering, the pallid complexion, which light could never have tinged, and the fragile, slender figure, gave her an appearance at once singular and attractive. Jack Ryan declared that she seemed to him to be an uncommonly interesting kind of ghost. It must have been due to the strange and peculiar circumstances under which her life hitherto had been led, that she scarcely seemed to belong to the human race. Her countenance was of a very uncommon cast, and her eyes, hardly able to bear the lamp-light in the cottage, glanced around in a confused and puzzled way, as if all were new to them. As this singular being reclined on Madge’s bed and awoke to consciousness, as from a long sleep, the old Scotchwoman began to question her a little. “What do they call you, my dear?” said she. “Nell,” replied the girl. “Do you feel anything the matter with you, Nell?” “I am hungry. I have eaten nothing since—since—” Nell uttered these few words like one unused to speak much. They were in the Gaelic language, which was often spoken by Simon and his family. Madge immediately brought her some food; she was evidently famished. It was impossible to say how long she might have been in that pit. “How many days had you been down there, dearie?” inquired Madge. Nell made no answer; she seemed not to understand the question. “How many days, do you think?” “Days?” repeated Nell, as though the word had no meaning for her, and she shook her head to signify entire want of comprehension. Madge took her hand, and stroked it caressingly. “How old are you, my lassie?” she asked, smiling kindly at her. Nell shook her head again. “Yes, yes,” continued Madge, “how many years old?” “Years?” replied Nell. She seemed to understand that word no better than days! Simon, Harry, Jack, and the rest, looked on with an air of mingled compassion, wonder, and sympathy. The state of this poor thing, clothed in a miserable garment of coarse woolen stuff, seemed to impress them painfully. Harry, more than all the rest, seemed attracted by the very peculiarity of this poor stranger. He drew near, took Nell’s hand from his mother, and looked directly at her, while something like a smile curved her lip. “Nell,” he said, “Nell, away down there—in the mine—were you all alone?” “Alone! alone!” cried the girl, raising herself hastily. Her features expressed terror; her eyes, which had appeared to soften as Harry looked at her, became quite wild again. “Alone!” repeated she, “alone!”—and she fell back on the bed, as though deprived of all strength. “The poor bairn is too weak to speak to us,” said Madge, when she had adjusted the pillows. “After a good rest, and a little more food, she will be stronger. Come away, Simon and Harry, and all the rest of you, and let her go to sleep.” So Nell was left alone, and in a very few minutes slept profoundly. This event caused a great sensation, not only in the coal mines, but in Stirlingshire, and ultimately throughout the kingdom. The strangeness of the story was exaggerated; the affair could not have made more commotion had they found the girl enclosed in the solid rock, like one of those antediluvian creatures who have occasionally been released by a stroke of the pickax from their stony prison. Nell became a fashionable wonder without knowing it. Superstitious folks made her story a new subject for legendary marvels, and were inclined to think, as Jack Ryan told Harry, that Nell was the spirit of the mines. “Be it so, Jack,” said the young man; “but at any rate she is the good spirit. It can have been none but she who brought us bread and water when we were shut up down there; and as to the bad spirit, who must still be in the mine, we’ll catch him some day.” Of course James Starr had been at once informed of all this, and came, as soon as the young girl had sufficiently recovered her strength, to see her, and endeavor to question her carefully. She appeared ignorant of nearly everything relating to life, and, although evidently intelligent, was wanting in many elementary ideas, such as time, for instance. She had never been used to its division, and the words signifying hours, days, months, and years were unknown to her. Her eyes, accustomed to the night, were pained by the glare of the electric discs; but in the dark her sight was wonderfully keen, the pupil dilated in a remarkable manner, and she could see where to others there appeared profound obscurity. It was certain that her brain had never received any impression of the outer world, that her eyes had never looked beyond the mine, and that these somber depths had been all the world to her. The poor girl probably knew not that there were a sun and stars, towns and counties, a mighty universe composed of myriads of worlds. But until she comprehended the significance of words at present conveying no precise meaning to her, it was impossible to ascertain what she knew. As to whether or not Nell had lived alone in the recesses of New Aberfoyle, James Starr was obliged to remain uncertain; indeed, any allusion to the subject excited evident alarm in the mind of this strange girl. Either Nell could not or would not reply to questions, but that some secret existed in connection with the place, which she could have explained, was manifest. “Should you like to stay with us? Should you like to go back to where we found you?” asked James Starr. “Oh, yes!” exclaimed the maiden, in answer to his first question; but a cry of terror was all she seemed able to say to the second. James Starr, as well as Simon and Harry Ford, could not help feeling a certain amount of uneasiness with regard to this persistent silence. They found it impossible to forget all that had appeared so inexplicable at the time they made the discovery of the coal mine; and although that was three years ago, and nothing new had happened, they always expected some fresh attack on the part of the invisible enemy. They resolved to explore the mysterious well, and did so, well armed and in considerable numbers. But nothing suspicious was to be seen; the shaft communicated with lower stages of the crypt, hollowed out in the carboniferous bed. Many a time did James Starr, Simon, and Harry talk over these things. If one or more malevolent beings were concealed in the coal-pit, and there concocted mischief, Nell surely could have warned them of it, yet she said nothing. The slightest allusion to her past life brought on such fits of violent emotion, that it was judged best to avoid the subject for the present. Her secret would certainly escape her by-and-by. By the time Nell had been a fortnight in the cottage, she had become a most intelligent and zealous assistant to old Madge. It was clear that she instinctively felt she should remain in the dwelling where she had been so charitably received, and perhaps never dreamt of quitting it. This family was all in all to her, and to the good folks themselves Nell had seemed an adopted child from the moment when she first came beneath their roof. Nell was in truth a charming creature; her new mode of existence added to her beauty, for these were no doubt the first happy days of her life, and her heart was full of gratitude towards those to whom she owed them. Madge felt towards her as a mother would; the old woman doted upon her; in short, she was beloved by everybody. Jack Ryan only regretted one thing, which was that he had not saved her himself. Friend Jack often came to the cottage. He sang, and Nell, who had never heard singing before, admired it greatly; but anyone might see that she preferred to Jack’s songs the graver conversation of Harry, from whom by degrees she learnt truths concerning the outer world, of which hitherto she had known nothing. It must be said that, since Nell had appeared in her own person, Jack Ryan had been obliged to admit that his belief in hobgoblins was in a measure weakened. A couple of months later his credulity experienced a further shock. About that time Harry unexpectedly made a discovery which, in part at least, accounted for the apparition of the fire-maidens among the ruins of Dundonald Castle at Irvine. During several days he had been engaged in exploring the remote galleries of the prodigious excavation towards the south. At last he scrambled with difficulty up a narrow passage which branched off through the upper rock. To his great astonishment, he suddenly found himself in the open air. The passage, after ascending obliquely to the surface of the ground, led out directly among the ruins of Dundonald Castle. There was, therefore, a communication between New Aberfoyle and the hills crowned by this ancient castle. The upper entrance to this gallery, being completely concealed by stones and brushwood, was invisible from without; at the time of their search, therefore, the magistrates had been able to discover nothing. A few days afterwards, James Starr, guided by Harry, came himself to inspect this curious natural opening into the coal mine. “Well,” said he, “here is enough to convince the most superstitious among us. Farewell to all their brownies, goblins, and fire-maidens now!” “I hardly think, Mr. Starr, we ought to congratulate ourselves,” replied Harry. “Whatever it is we have instead of these things, it can’t be better, and may be worse than they are.” “That’s true, Harry,” said the engineer; “but what’s to be done? It is plain that, whatever the beings are who hide in the mine, they reach the surface of the earth by this passage. No doubt it was the light of torches waved by them during that dark and stormy night which attracted the towards the rocky coast, and like the wreckers of former days, they would have plundered the unfortunate vessel, had it not been for Jack Ryan and his friends. Anyhow, so far it is evident, and here is the mouth of the den. As to its occupants, the question is—Are they here still?” Motala “I say yes; because Nell trembles when we mention them—yes, because Nell will not, or dare not, speak about them,” answered Harry in a tone of decision. Harry was surely in the right. Had these mysterious denizens of the pit abandoned it, or ceased to visit the spot, what reason could the girl have had for keeping silence? James Starr could not rest till he had penetrated this mystery. He foresaw that the whole future of the new excavations must depend upon it. Renewed and strict precautions were therefore taken. The authorities were informed of the discovery of the entrance. Watchers were placed among the ruins of the castle. Harry himself lay hid for several nights in the thickets of brushwood which clothed the hill-side. Nothing was discovered—no human being emerged from the opening. So most people came to the conclusion that the villains had been finally dislodged from the mine, and that, as to Nell, they must suppose her to be dead at the bottom of the shaft where they had left her. While it remained unworked, the mine had been a safe enough place of refuge, secure from all search or pursuit. But now, circumstances being altered, it became difficult to conceal this lurking-place, and it might reasonably be hoped they were gone, and that nothing for the future was to be dreaded from them. James Starr, however, could not feel sure about it; neither could Harry be satisfied on the subject, often repeating, “Nell has clearly been mixed up with all this secret business. If she had nothing more to fear, why should she keep silence? It cannot be doubted that she is happy with us. She likes us all—she adores my mother. Her absolute silence as to her former life, when by speaking out she might benefit us, proves to me that some awful secret, which she dares not reveal, weighs on her mind. It may also be that she believes it better for us, as well as for herself, that she should remain mute in a way otherwise so unaccountable.” In consequence of these opinions, it was agreed by common consent to avoid all allusion to the maiden’s former mode of life. One day, however, Harry was led to make known to Nell what James Starr, his father, mother, and himself believed they owed to her interference. It was a fête-day. The miners made holiday on the surface of the county of Stirling as well as in its subterraneous domains. Parties of holiday-makers were moving about in all directions. Songs resounded in many places beneath the sonorous vaults of New Aberfoyle. Harry and Nell left the cottage, and slowly walked along the left bank of Loch Malcolm. Then the electric brilliance darted less vividly, and the rays were interrupted with fantastic effect by the sharp angles of the picturesque rocks which supported the dome. This imperfect light suited Nell, to whose eyes a glare was very unpleasant. “Nell,” said Harry, “your eyes are not fit for daylight yet, and could not bear the brightness of the sun.” “Indeed they could not,” replied the girl; “if the sun is such as you describe it to me, Harry.” “I cannot by any words, Nell, give you an idea either of his splendor or of the beauty of that universe which your eyes have never beheld. But tell me, is it really possible that, since the day when you were born in the depths of the coal mine, you never once have been up to the surface of the earth?” “Never once, Harry,” said she; “I do not believe that, even as an infant, my father or mother ever carried me thither. I am sure I should have retained some impression of the open air if they had.” “I believe you would,” answered Harry. “Long ago, Nell, many children used to live altogether in the mine; communication was then difficult, and I have met with more than one young person, quite as ignorant as you are of things above-ground. But now the railway through our great tunnel takes us in a few minutes to the upper regions of our country. I long, Nell, to hear you say, ‘Come, Harry, my eyes can bear daylight, and I want to see the sun! I want to look upon the works of the Almighty.’” “I shall soon say so, Harry, I hope,” replied the girl; “I shall soon go with you to the world above; and yet—” “What are you going to say, Nell?” hastily cried Harry; “can you possibly regret having quitted that gloomy abyss in which you spent your early years, and whence we drew you half dead?” “No, Harry,” answered Nell; “I was only thinking that darkness is beautiful as well as light. If you but knew what eyes accustomed to its depth can see! Shades flit by, which one longs to follow; circles mingle and intertwine, and one could gaze on them forever; black hollows, full of indefinite gleams of radiance, lie deep at the bottom of the mine. And then the voice-like sounds! Ah, Harry! one must have lived down there to understand what I feel, what I can never express.” “And were you not afraid, Nell, all alone there?” “It was just when I was alone that I was not afraid.” Nell’s voice altered slightly as she said these words; however, Harry thought he might press the subject a little further, so he said, “But one might be easily lost in these great galleries, Nell. Were you not afraid of losing your way?” “Oh, no, Harry; for a long time I had known every turn of the new mine.” “Did you never leave it?” “Yes, now and then,” answered the girl with a little hesitation; “sometimes I have been as far as the old mine of Aberfoyle.” “So you knew our old cottage?” “The cottage! oh, yes; but the people who lived there I only saw at a great distance.” “They were my father and mother,” said Harry; “and I was there too; we have always lived there—we never would give up the old dwelling.” “Perhaps it would have been better for you if you had,” murmured the maiden. “Why so, Nell? Was it not just because we were obstinately resolved to remain that we ended by discovering the new vein of coal? And did not that discovery lead to the happy result of providing work for a large population, and restoring them to ease and comfort? and did it not enable us to find you, Nell, to save your life, and give you the love of all our hearts?” “Ah, yes, for me indeed it is well, whatever may happen,” replied Nell earnestly; “for others—who can tell?” “What do you mean?” “Oh, nothing—nothing. But it used to be very dangerous at that time to go into the new cutting—yes, very dangerous indeed, Harry! Once some rash people made their way into these chasms. They got a long, long way; they were lost!” “They were lost?” said Harry, looking at her. “Yes, lost!” repeated Nell in a trembling voice. “They could not find their way out.” “And there,” cried Harry, “they were imprisoned during eight long days! They were at the point of death, Nell; and, but for a kind and charitable being—an angel perhaps—sent by God to help them, who secretly brought them a little food; but for a mysterious guide, who afterwards led to them their deliverers, they never would have escaped from that living tomb!” “And how do you know about that?” demanded the girl. “Because those men were James Starr, my father, and myself, Nell!” Nell looked up hastily, seized the young man’s hand, and gazed so fixedly into his eyes that his feelings were stirred to their depths. “You were there?” at last she uttered. “I was indeed,” said Harry, after a pause, “and she to whom we owe our lives can have been none other than yourself, Nell!” Nell hid her face in her hands without speaking. Harry had never seen her so much affected. “Those who saved your life, Nell,” added he in a voice tremulous with emotion, “already owed theirs to you; do you think they will ever forget it?” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (1998). The Underground City; Or, The Black Indies. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/1353/pg1353-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

NELL ADOPTED

A CHESS PROBLEM

Kilmeny of the Orchard by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . A SWEETER WOMAN NE’ER DREW BREATH CHAPTER XIII. A SWEETER WOMAN NE’ER DREW BREATH Thenceforward Eric Marshall was a constant visitor at the Gordon homestead. He soon became a favourite with Thomas and Janet, especially the latter. He liked them both, discovering under all their outward peculiarities sterling worth and fitness of character. Thomas Gordon was surprisingly well read and could floor Eric any time in argument, once he became sufficiently warmed up to attain fluency of words. Eric hardly recognized him the first time he saw him thus animated. His bent form straightened, his sunken eyes flashed, his face flushed, his voice rang like a trumpet, and he poured out a flood of eloquence which swept Eric’s smart, up-to-date arguments away like straws in the rush of a mountain torrent. Eric enjoyed his own defeat enormously, but Thomas Gordon was ashamed of being thus drawn out of himself, and for a week afterwards confined his remarks to “Yes” and “No,” or, at the outside, to a brief statement that a change in the weather was brewing. Janet never talked on matters of church and state; such she plainly considered to be far beyond a woman’s province. But she listened with lurking interest in her eyes while Thomas and Eric pelted on each other with facts and statistics and opinions, and on the rare occasions when Eric scored a point she permitted herself a sly little smile at her brother’s expense. Of Neil, Eric saw but little. The Italian boy avoided him, or if they chanced to meet passed him by with sullen, downcast eyes. Eric did not trouble himself greatly about Neil; but Thomas Gordon, understanding the motive which had led Neil to betray his discovery of the orchard trysts, bluntly told Kilmeny that she must not make such an equal of Neil as she had done. “You have been too kind to the lad, lassie, and he’s got presumptuous. He must be taught his place. I mistrust we have all made more of him than we should.” But most of the idyllic hours of Eric’s wooing were spent in the old orchard; the garden end of it was now a wilderness of roses—roses red as the heart of a sunset, roses pink as the early flush of dawn, roses white as the snows on mountain peaks, roses full blown, and roses in buds that were sweeter than anything on earth except Kilmeny’s face. Their petals fell in silken heaps along the old paths or clung to the lush grasses among which Eric lay and dreamed, while Kilmeny played to him on her violin. Eric promised himself that when she was his wife her wonderful gift for music should be cultivated to the utmost. Her powers of expression seemed to deepen and develop every day, growing as her soul grew, taking on new colour and richness from her ripening heart. To Eric, the days were all pages in an inspired idyl. He had never dreamed that love could be so mighty or the world so beautiful. He wondered if the universe were big enough to hold his joy or eternity long enough to live it out. His whole existence was, for the time being, bounded by that orchard where he wooed his sweetheart. All other ambitions and plans and hopes were set aside in the pursuit of this one aim, the attainment of which would enhance all others a thousand-fold, the loss of which would rob all others of their reason for existence. His own world seemed very far away and the things of that world forgotten. His father, on hearing that he had taken the Lindsay school for a year, had written him a testy, amazed letter, asking him if he were demented. “Or is there a girl in the case?” he wrote. “There must be, to tie you down to a place like Lindsay for a year. Take care, master Eric; you’ve been too sensible all your life. A man is bound to make a fool of himself at least once, and when you didn’t get through with that in your teens it may be attacking you now.” David also wrote, expostulating more gravely; but he did not express the suspicions Eric knew he must entertain. “Good old David! He is quaking with fear that I am up to something he can’t approve of, but he won’t say a word by way of attempting to force my confidence.” It could not long remain a secret in Lindsay that “the Master” was going to the Gordon place on courting thoughts intent. Mrs. Williamson kept her own and Eric’s counsel; the Gordons said nothing; but the secret leaked out and great was the surprise and gossip and wonder. One or two incautious people ventured to express their opinion of the Master’s wisdom to the Master himself; but they never repeated the experiment. Curiosity was rife. A hundred stories were circulated about Kilmeny, all greatly exaggerated in the circulation. Wise heads were shaken and the majority opined that it was a great pity. The Master was a likely young fellow; he could have his pick of almost anybody, you might think; it was too bad that he should go and take up with that queer, dumb niece of the Gordons who had been brought up in such a heathenish way. But then you never could guess what way a man’s fancy would jump when he set out to pick him a wife. They guessed Neil Gordon didn’t like it much. He seemed to have got dreadful moody and sulky of late and wouldn’t sing in the choir any more. Thus the buzz of comment and gossip ran. To those two in the old orchard it mattered not a whit. Kilmeny knew nothing of gossip. To her, Lindsay was as much of an unknown world as the city of Eric’s home. Her thoughts strayed far and wide in the realm of her fancy, but they never wandered out to the little realities that hedged her strange life around. In that life she had blossomed out, a fair, unique thing. There were times when Eric almost regretted that one day he must take her out of her white solitude to a world that, in the last analysis, was only Lindsay on a larger scale, with just the same pettiness of thought and feeling and opinion at the bottom of it. He wished he might keep her to himself for ever, in that old, spruce-hidden orchard where the roses fell. One day he indulged himself in the fulfillment of the whim he had formed when Kilmeny had told him she thought herself ugly. He went to Janet and asked her permission to bring a mirror to the house that he might have the privilege of being the first to reveal Kilmeny to herself exteriorly. Janet was somewhat dubious at first. “There hasn’t been such a thing in the house for sixteen years, Master. There never was but three—one in the spare room, and a little one in the kitchen, and Margaret’s own. She broke them all the day it first struck her that Kilmeny was going to be bonny. I might have got one after she died maybe. But I didn’t think of it; and there’s no need of lasses to be always prinking at their looking glasses.” But Eric pleaded and argued skilfully, and finally Janet said, “Well, well, have your own way. You’d have it anyway I think, lad. You are one of those men who always get their own way. But that is different from the men who TAKE their own way—and that’s a mercy,” she added under her breath. Eric went to town the next Saturday and picked out a mirror that pleased him. He had it shipped to Radnor and Thomas Gordon brought it home, not knowing what it was, for Janet had thought it just as well he should not know. “It’s a present the Master is making Kilmeny,” she told him. She sent Kilmeny off to the orchard after tea, and Eric slipped around to the house by way of the main road and lane. He and Janet together unpacked the mirror and hung it on the parlour wall. “I never saw such a big one, Master,” said Janet rather doubtfully, as if, after all, she distrusted its gleaming, pearly depth and richly ornamented frame. “I hope it won’t make her vain. She is very bonny, but it may not do her any good to know it.” “It won’t harm her,” said Eric confidently. “When a belief in her ugliness hasn’t spoiled a girl a belief in her beauty won’t.” But Janet did not understand epigrams. She carefully removed a little dust from the polished surface, and frowned meditatively at the by no means beautiful reflection she saw therein. “I cannot think what made Kilmeny suppose she was ugly, Master.” “Her mother told her she was,” said Eric, rather bitterly. “Ah!” Janet shot a quick glance at the picture of her sister. “Was that it? Margaret was a strange woman, Master. I suppose she thought her own beauty had been a snare to her. She WAS bonny. That picture doesn’t do her justice. I never liked it. It was taken before she was—before she met Ronald Fraser. We none of us thought it very like her at the time. But, Master, three years later it was like her—oh, it was like her then! That very look came in her face.” “Kilmeny doesn’t resemble her mother,” remarked Eric, glancing at the picture with the same feeling of mingled fascination and distaste with which he always regarded it. “Does she look like her father?” “No, not a great deal, though some of her ways are very like his. She looks like her grandmother—Margaret’s mother, Master. Her name was Kilmeny too, and she was a handsome, sweet woman. I was very fond of my stepmother, Master. When she died she gave her baby to me, and asked me to be a mother to it. Ah well, I tried; but I couldn’t fence the sorrow out of Margaret’s life, and it sometimes comes to my mind that maybe I’ll not be able to fence it out of Kilmeny’s either.” “That will be my task,” said Eric. “You’ll do your best, I do not doubt. But maybe it will be through you that sorrow will come to her after all.” “Not through any fault of mine, Aunt Janet.” “No, no, I’m not saying it will be your fault. But my heart misgives me at times. Oh, I dare say I am only a foolish old woman, Master. Go your ways and bring your lass here to look at your plaything when you like. I’ll not make or meddle with it.” Janet betook herself to the kitchen and Eric went to look for Kilmeny. She was not in the orchard and it was not until he had searched for some time that he found her. She was standing under a beech tree in a field beyond the orchard, leaning on the longer fence, with her hands clasped against her cheek. In them she held a white Mary-lily from the orchard. She did not run to meet him while he was crossing the pasture, as she would once have done. She waited motionless until he was close to her. Eric began, half laughingly, half tenderly, to quote some lines from her namesake ballad: “‘Kilmeny, Kilmeny, where have you been? Long hae we sought baith holt and den,— By linn, by ford, and greenwood tree! Yet you are halesome and fair to see. Where got you that joup o’ the lily sheen? That bonny snood o’ the birk sae green, And those roses, the fairest that ever was seen? Kilmeny, Kilmeny, where have you been?’ “Only it’s a lily and not a rose you are carrying. I might go on and quote the next couplet too— “‘Kilmeny looked up with a lovely grace, But there was nae smile on Kilmeny’s face.’ “Why are you looking so sober?” Kilmeny did not have her slate with her and could not answer; but Eric guessed from something in her eyes that she was bitterly contrasting the beauty of the ballad’s heroine with her own supposed ugliness. “Come down to the house, Kilmeny. I have something there to show you—something lovelier than you have ever seen before,” he said, with boyish pleasure shining in his eyes. “I want you to go and put on that muslin dress you wore last Sunday evening, and pin up your hair the same way you did then. Run along—don’t wait for me. But you are not to go into the parlour until I come. I want to pick some of those Mary-lilies up in the orchard.” When Eric returned to the house with an armful of the long stemmed, white Madonna lilies that bloomed in the orchard Kilmeny was just coming down the steep, narrow staircase with its striped carpeting of homespun drugget. Her marvelous loveliness was brought out into brilliant relief by the dark wood work and shadows of the dim old hall. She wore a trailing, clinging dress of some creamy tinted fabric that had been her mother’s. It had not been altered in any respect, for fashion held no sway at the Gordon homestead, and Kilmeny thought that the dress left nothing to be desired. Its quaint style suited her admirably; the neck was slightly cut away to show the round white throat, and the sleeves were long, full “bishops,” out of which her beautiful, slender hands slipped like flowers from their sheaths. She had crossed her long braids at the back and pinned them about her head like a coronet; a late white rose was fastened low down on the left side. “‘A man had given all other bliss And all his worldly wealth for this— To waste his whole heart in one kiss Upon her perfect lips,’” quoted Eric in a whisper as he watched her descend. Aloud he said, “Take these lilies on your arm, letting their bloom fall against your shoulder—so. Now, give me your hand and shut your eyes. Don’t open them until I say you may.” He led her into the parlour and up to the mirror. “Look,” he cried, gaily. Kilmeny opened her eyes and looked straight into the mirror where, like a lovely picture in a golden frame, she saw herself reflected. For a moment she was bewildered. Then she realized what it meant. The lilies fell from her arm to the floor and she turned pale. With a little low, involuntary cry she put her hands over her face. Eric pulled them boyishly away. “Kilmeny, do you think you are ugly now? This is a truer mirror than Aunt Janet’s silver sugar bowl! Look—look—look! Did you ever imagine anything fairer than yourself, dainty Kilmeny?” She was blushing now, and stealing shy radiant glances at the mirror. With a smile she took her slate and wrote naively, “I think I am pleasant to look upon. I cannot tell you how glad I am. It is so dreadful to believe one is ugly. You can get used to everything else, but you never get used to that. It hurts just the same every time you remember it. But why did mother tell me I was ugly? Could she really have thought so? Perhaps I have become better looking since I grew up.” “I think perhaps your mother had found that beauty is not always a blessing, Kilmeny, and thought it wiser not to let you know you possessed it. Come, let us go back to the orchard now. We mustn’t waste this rare evening in the house. There is going to be a sunset that we shall remember all our lives. The mirror will hang here. It is yours. Don’t look into it too often, though, or Aunt Janet will disapprove. She is afraid it will make you vain.” Kilmeny gave one of her rare, musical laughs, which Eric never heard without a recurrence of the old wonder that she could laugh so when she could not speak. She blew an airy little kiss at her mirrored face and turned from it, smiling happily. On their way to the orchard they met Neil. He went by them with an averted face, but Kilmeny shivered and involuntarily drew nearer to Eric. “I don’t understand Neil at all now,” she wrote nervously. “He is not nice, as he used to be, and sometimes he will not answer when I speak to him. And he looks so strangely at me, too. Besides, he is surly and impertinent to Uncle and Aunt.” “Don’t mind Neil,” said Eric lightly. “He is probably sulky because of some things I said to him when I found he had spied on us.” That night before she went up stairs Kilmeny stole into the parlour for another glimpse of herself in that wonderful mirror by the light of a dim little candle she carried. She was still lingering there dreamily when Aunt Janet’s grim face appeared in the shadows of the doorway. “Are you thinking about your own good looks, lassie? Ay, but remember that handsome is as handsome does,” she said, with grudging admiration—for the girl with her flushed cheeks and shining eyes was something that even dour Janet Gordon could not look upon unmoved. Kilmeny smiled softly. “I’ll try to remember,” she wrote, “but oh, Aunt Janet, I am so glad I am not ugly. It is not wrong to be glad of that, is it?” The older woman’s face softened. “No, I don’t suppose it is, lassie,” she conceded. “A comely face is something to be thankful for—as none know better than those who have never possessed it. I remember well when I was a girl—but that is neither here nor there. The Master thinks you are wonderful bonny, Kilmeny,” she added, looking keenly at the girl. Kilmeny started and a scarlet blush scorched her face. That, and the expression that flashed into her eyes, told Janet Gordon all she wished to know. With a stifled sigh she bade her niece good night and went away. Kilmeny ran fleetly up the stairs to her dim little room, that looked out into the spruces, and flung herself on her bed, burying her burning face in the pillow. Her aunt’s words had revealed to her the hidden secret of her heart. She knew that she loved Eric Marshall—and the knowledge brought with it a strange anguish. For was she not dumb? All night she lay staring wide-eyed through the darkness till the dawn. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2004). Kilmeny of the Orchard. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/5341/pg5341-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

A SWEETER WOMAN NE’ER DREW BREATH

A PRISONER OF LOVE

The War of the Worlds: Chapter XIV. IN LONDON

Just glancing at the title must have raised a question in your mind! How come chess and music are interrelated? One is a board game while the other is an act of creating sounds either vocally or with the help of musical instruments. In one go, it is not easy to figure out if chess and music have anything in common. But surprisingly, people love to bring them together. There are so many significant overlaps between the two entities. François-André Danican Philidor, the very first great chess master, was also a renowned opera composer of classical music. Mark Taimanov is additionally a renowned concert pianist and the former World Champion Vassily Smyslov is an operatic singer as well. It is notable to mention that chess is itself a form of art and also closely related to what we call music. Now, if you are thinking that chess is related to music in a pretty simple form then unfortunately, you are wrong. The relation exists in such a way that music can be programmed in such a way that is very similar to the one in which we program chess. Talking about chess programs takes us back to 1987 Matthias Wüllenweber wrote the first professional chess database program. He was originally a physicist but later came up as the head of the programming department of Chess Base and also the co-founder of the company. He was also involved in development of Fritz, the popular chess program developed in back in 1991. Matthias is a prominent musician also. He is highly skilled in playing piano and does it very fluidly and contently. His pivotal focus is always on improvising in modern and as well as classical ways. He keeps on widening his domain to other instruments as well. The latest one that he is learning to master playing is the flute. This truly showcases that how deep he had dwelled in the ocean of music. A few years ago, Mattihas began using computer to work out on an experiment with music composition. Astonishingly, he did not choose pencil stub and piano as his apparatus. He started by writing algorithms and programming the code that helped in finding harmonic and rhythmic arrangements to a tune. During this work, he discovered that the techniques that could be used were very similar and in some cases same as the ones used in online chess. A few of them were cutoffs, pruning, evaluation, a brute force tree search and a lot of other algorithms that act as a host. He himself was amazed tremendously when the program began composing new music itself. It is obvious that the program was able to support the musician nonetheless. As the time flew by, there came up various personalities who sailed in both the boats of chess and music efficiently. They were basically chess players but were known better for their evident interest in music. These were Robert Schumann and Johannes Brahms. Schumann’s diary gives us the clear account of his interest in chess. It is still an interesting read in many events. He successfully created the world’s first tactics trainer application. For the same, he jotted down some of the puzzles whose sampling is present on a few German websites. Both Schumann and Brahms were together regarded as ‘doppelgangers’ owing to their similarities in age, personality, appearance, pre-eminence and nationality. Also, both of them witnessed controversies time and again. Experts say that their evolution tasted similar evolution in style from that of brilliance to absolute profundity. Once Brahms was asked what he had learnt from Schumann and he replied dryly that “nothing but chess.” In this way, Brahms’ actual relevance of chess is supposed to rest entirely on this answer. As the 19th Century and time were standing facing each other and looking right in the eye, Sergei Prokofiev came up as another great composer with much stronger credentials of the game of chess. Apart from being the first class chess player, he is among the first few to have defeated Jose Raul Capablanca. Prokofiev penned down the account of the great St. Petersburg tournament of 1914 in his diary in minute details. He happened to be present in the town at that exact time. Several fascinating and first-hand descriptions of various players have been provided by him in his diary. At a point, he wrote: ‘At eight o’clock I went to the opening of the Chess Championship and found myself translated immediately into an enchanted realm, a realm alive with the most unbelievable activity in all three rooms of the Chess Club itself and three more rooms made available by the Assembly Committee. This tournament is a top-level affair, everyone in tailcoats, and here were the masters themselves each surrounded by a crowd of admirers.’ He further talked about Lasker, Tarrasch, Rubinstein, Bernstein, Alekhine, Marshall, Yanovsky and Nimzowitsch giving interesting insights into their characters. Getting ahead in time to the 20th Century, one comes across the most remarkable duo of the musician cum chess players, Vasily Smyslov and Mark Taimanov. It is quite difficult to decide that who the better one between them was. The International Grandmaster, Taimanov was one of the great pianists of his generation and became a Word Champion candidate many a time. Smyslov, on the other hand, was world champion and a semi-professional baritone. However, the duo together was really great at that time. It is said that they used to perform together after chess tournaments. Moving further to the modern times, an end to Taimonov’s career was marked by Fischer. But unfortunately in the post-Fischer era, the number of examples of the chess players who are able to devote time to music as the second priority has gone down significantly. Still, they do exist. Calvin Blocker is one such enigmatic International Master who is also a pianist. He often succeeds in becoming the cynosure of the local media articles. Late Igor Ivanov is also a noteworthy name. At last, we have Israeli Emil Sutovsky who is well known for his brilliance and has been ranked 17th in the world as a fine bass profundo. To conclude, as seems amazing, the amalgamation of chess and music is extravagant and the aforementioned prodigies are the paragon of this. Credits Fine Art Images/Heritage Images via Getty Images/Heritage Images http://content.time.com/time/covers/0,16641,19451119,00.html https://www.chess.com/article/view/music-and-chess Previously published at https://saswath.academy/chess/chess-and-music/

Chess And Music: Not An Obvious Connection

Machines That Play (Chess — Before Deep Blue)

Machines That Play (Overview)

Red Flags in Tech Interviews

Redis search got a lot more interesting Have you heard? Redis 4.0 is out. That means . Not just in the theoretical, shouldn’t-run-in-production sense. It means primetime. Throw’em on your production server and go. modules Note: Redis (the database) and Node.js (the JS runtime) both use the term “module” for different concepts — the language herein has to be a bit laboured to be clear and differentiate. Recently, I , the /Redis search engine module. Reds is quite good — I’ve built entire web services around it and they run really quickly. However, Node.js is , and Javascript, while decently speedy, is still interpreted and is on an entirely different layer from the database (Redis). Modules, however, are built in a compiled language and without a network layer between logic and data. They have to be faster, right? Well, that was my task — how much faster will a module be over a script. Let’s find out! wrote about Reds Node.js Javascript Enter: RediSearch I’ve been maintaining Reds for years now and I’m pretty familiar with the code as well as the use of the script. I’ve also been eyeing the development of RediSearch for a few months ( got me interested). It has really evolved from something simple to something really powerful: this white paper on RediSearch RediSearch has it’s own module system for extensibility (trigger Xzibit image macro about modules inside modules) During RedisConf 17, a preview demo of a clustered implementation of RediSearch was shown that has indexed over a billion (yes, with a ) documents at 100k documents/sec. b With the exception of the metaphone indexing, has everything Reds has and a ton more. My goal was to see if I could map out the Reds functionality to RediSearch and create a (more-or-less) syntax compatible Node.js module: RedRediSearch. The syntax compatibility would allow the existing users of Reds to do a fairly painless rip-and-replace. RediSearch The small differences Unfortunately, you’re not going to be able to just point RediSearch at the Reds data and magically gain performance, you’ll need to re-index your data. RediSearch is a Redis module that uses a different (and, frankly, more efficient) custom data type, as compared to Reds that uses native zsets. Aside from that, it’s painless. And indexing is pretty quick as you’ll find out. The “natural language processing” functions can’t map over, although you will likely not need it or it can be achieved through a few tweaks to your index (I’ll cover that later). The other thing is that creating an index is not conceptually the same — in Reds only prepares Node.js for accepting data. In RediSearch, however, creating an index is an actual Redis call ( ) and consequently needs a callback. Aside from that, searching and indexing are the same syntax. Adapting a few of my scripts were as easy as just 3–4 lines of adjustment. I also added a function to confirm the existence of the module. I’m just that nice of a person. reds.createSearch FT.CREATE What do you get from RediSearch over Reds? You get better performance. Running the over the same data on the same machine, I’m getting between a 6.6x and 13x better performance out of RediSearch over Reds while indexing (depending on the size of the documents). Querying is faster too — between 1.2x and 7.4x faster. The whole benchmark is completing 12x faster. That’s a huge improvement for a drop-in replacement. Reds benchmark Yowza! Note: “ops” here are whole documents or search queries indexed per sec, not Redis operations per second! This is on a modest laptop with a stock, non-tuned install of OSS Redis. The other thing you gain by switching to RedRediSearch is a richer, more flexible query system. The other thing you gain by switching to RedRediSearch is a richer, more flexible query system. Reds had the ability to do two types of search: AND and OR. That is, you could only query a document that had one of the query keywords or all the query keywords. Useful, but not very flexible. With RediSearch you’ve got a rich query language that allows you to do all Reds can do but also exact phrase searches, negated searches, prefix searches, optional keywords and combinations. Out of the box, RedRediSearch can accommodate syntax compatible searches: //defaults to an "AND" searchsearch.query('cat dog').end(function(err, documents){/* ... */}); //using the `type` fn to get an "OR" searchsearch.query('cat dog').type('or').end(function(err, documents){/* ... */}); But you can also leverage the cool query language by specifying a direct search like this: //documents without 'dog' but with 'cat'search.query('cat -dog').type('direct').end(function(err, documents){/* ... */}); //documents with 'dog' and optionally with 'cat' but having 'cat' will boost placementsearch.query('cat ~dog').type('direct').end(function(err, documents){/* ... */}); //complex and combinations are also possiblesearch.query('(cat|dog) (felix|lassie)') //must have cat or dog AND felix or lassie.type('direct').end(function(err, documents){/* ... */}); Now, while all this is super cool and useful — it’s actually a dumbed down client for RediSearch. RediSearch can do a ton more things: filter sort index and return hashes search with related geo information search with words between (‘slop’) n search ranges of numeric fields rather than just text And probably more features that I’ve yet to fully understand or discover. You can also use RediSearch to manage suggestions (aka auto complete). The suggestions are completely divorced from the indexing process, you can add and remove items for the suggestion list then get all items that start with the same letters. You can even do “fuzzy” searching where exact matching is needed. Here is a short example: The idea with the suggestions is to maintain the suggestion list based off the queries coming into your search, adding results that aren’t search misses. When a user starts typing, the auto complete can be fetch asynchronously from the backend and presented to the user as they type. RediSearch is indeed a very full toolkit. Interacting with RediSearch Earlier, I mentioned that you can make use of more than just what is contained in the Node.js module. Node_redis has the capability to send commands directly to Redis and it’s very useful when needing some special parameters and, boy, does RediSearch have extra parameters. Reds and RedRediSearch are effectively just wrappers sending commands directly to Redis. You can absolutely use them together if a feature doesn’t exist (yet) in the Node.js module. As an example, let’s say you want to have a minimal set of “stop words” that are ignored in indexing and querying. The existing (Reds) syntax uses a callback function for stop words, but before you initially create index, you can manually call FT.CREATE with something like: client.send_command('FT.CREATE',[ myKey,'SCHEMA','payload','TEXT','STOPWORDS','2','bunny','rabbits'],function(err,response) {/* ... */}); This would create a two stop words — ‘bunny’ and ‘rabbits’ that would be ignored entirely. So, if a feature is not yet supported, it’s easy to break free from the existing confines and leverage the full power of the RediSearch module. Using the Redis module First you need to install Redis 4.0 (if you don’t already have it) and then the module. I’m using 0.19.3, but check on RediSearch.io or the github releases page for the latest version. $ wget https://github.com/RedisLabsModules/RediSearch/archive/v0.19.3.tar.gz ...Download messages... $ tar -xvzf v0.19.3.tar.gz ...decompression messages... This should yield a directory called / Switch into that directory and we’ll everything. RediSearch-0.19.3. make /RediSearch-0.19.3$ make all ...build messages... Now, at the end of your Redis 4.0+ redis.conf file , add in the module loading command: loadmodule /path/to/RediSearch-0.19.3/src/redisearch.so Your path might need to be tweaked if you decompressed the tarball in different place or used a different version, but the most relevant parts are the file ( ) and the last directory ( ). redisearch.so src At this point you should reload the and test out your module install with : redis-server redis-cli > ft.create(error) ERR wrong number of arguments for 'ft.create' command The error is good — that means your server knows the number of commands FT.CREATE needs. If the module wasn’t loaded this would happen: > ft.create(error) ERR unknown command 'ft.create' After verifying the installation of the module, all we need to do is use the Node.js module. Using the RedRediSearch module is pretty straight forward if you’re used to Reds: Should I switch from Reds to RediSearch? Generally, I would say “yes, absolutely.” However, you may need to hold off. If you’re using open-source Redis and your data fits into a single instance — go for it right now. If you’re using , you’ll need to be a little patience as they are putting the finishing touches on RediSearch + Redis Enterprise Pack, which will enable indexes that span across your entire cluster (think of the possibilities!). If you’re using , stick with Reds as the modules and (consequently) RediSearch isn’t available. However, this will be an option soon with , so stay tuned for that. In all these situations, [Red]RediSearch or Reds, you’re covered because your app-level logic will stay the same. Hopefully RedRediSearch will continue to evolve to encompass all the features of RediSearch as well as maintain backwards compatibility with the Reds syntax. You can pick up the module on or through . Redis Enterprise Pack Redis Enterprise Cloud Redis Cloud Private Github NPM

From Reds to RediSearch

may not deliver the AI revolution you have been led to expect. Is this season of hype simply a repeat of that of the late ’80s, just at a much bigger scale? Perhaps Winter is coming again for the Beatles-era technology, based as it is on the Neuroscience of WWII and the Statistical Mechanics of Victorian times. Fortunately, the renewed energy, enthusiasm, and investment in Deep Learning need not go to waste, if a new approach can fuse recent knowledge from Computational Neuroscience and Applied Mathematics with the power of today’s GPUs. The is both an accurate description of how the brain really works, and a blueprint for Machine Intelligence. Combining recent discoveries in the Applied Maths of coupled, communicating, chaotic Dynamical Systems with those in Neuroscience, we formed a year ago to turn into and build a foundation for a new AI technology. Deep Learning Feynman Machine Ogma theory working software I won’t dwell too much on why I believe the Deep Learning boom of today is not the panacea it has been hyped up to appear. Many of DL’s own long-ignored and newly-lauded leaders are already doing so ( , and most prominent among them), despite their recent high-profile appointments and astronomical financial underwriting. To varying degrees, they are now looking beyond the recent successful applications of DL, and seeking new ideas which might provide the next step forward. Yoshua Bengio Yann LeCun Geoff Hinton The main weaknesses of DL (as I see them) are: reliance on the simplest possible model neurons (“cartoonish” as LeCun calls them); use of ideas from 19th century Statistical Mechanics and Statistics, which are the basis of energy functions and log-likelihood methods; and the combination of these in techniques like backprop and stochastic gradient descent, leading to a very limited regime of application (offline, mostly batched, supervised learning), requiring highly-talented practitioners (aka “ ”), large amounts of expensive labelled training data and computational power. While great for huge companies who can lure or buy the talent and deploy unlimited resources to gather data and crunch it, DL is simply neither accessible nor useful to the majority of us. Stochastic Graduate Descent The Feynman Machine, on the other hand, uses learning from entire fields of study which simply didn’t exist before the 1960s and ’70s. For the first time, they are combined in a model which describes how the neocortex processes high-velocity, streaming sensory data and produces behaviour and cognition, and is also a novel architecture for intelligent machines. I’ll briefly describe these results and how they are used in the natural and artificial Feynman Machines. The first thing to learn about is the branch of Applied Mathematics which examines the properties of Nonlinear Dynamical Systems (NDSs), known popularly as Chaos Theory. Most phenomena in science (both natural and human) can only be modelled accurately as NDSs, but this was not practical until computers became widely available in the early 1960s. The landmark by Edward Lorenz is widely considered to be the first detailed description of an entirely unknown class of behaviour: deterministic chaos. While literally impossible to deal with using traditional mathematical tools, systems involving NDSs have such rich structure, and are so pervasive in nature, that their study has dominated Applied Mathematics for over forty years. 1963 paper In 1978, discovered something unexpected about signals coming out of chaotic systems: they contain all the information needed to reconstruct the entire temporal behaviour of the chaotic system and predict its future, without of the actual mechanism driving the system. In 1981, Floris Takens on this startling fact. The following video from George Sugihara’s lab briefly describes how the Theorem works and how reconstruction can be used to examine and analyse NDSs (here the Lorenz system is used for illustration): Packard et al any knowledge whatsoever proved his famous Theorem The causality criterion described in the video has since become known as Sugihara Causality, and is much more powerful than the Granger Causality traditionally used in Statistics and , due to its use of the temporal structure in the time series signals. is one of the leading figures applying these powerful methods in areas as diverse as fishery sustainability and gene networks. machine learning George Sugihara The central idea of the Feynman Machine is that regions of the brain form a network of NDSs which communicate and cooperate by sending one another time series of nerve signals, and cognition emerges from the causal interactions of the coupled Dynamical Systems. The Applied Mathematics tells us that this is possible, but we now need to look to Neuroscience to explain how exactly this might occur in real brains. In 1986, future Palm founder Jeff Hawkins wrote a in which he describes a model of neocortex which is constantly predicting its own future evolution. Due to a lack of interest in Theoretical Neuroscience at the time, it was not until the early 2000s that Hawkins was able to return to this idea and devote his entire time to leading its development. He wrote with Sandra Blakeslee, formed the (now part of UC Berkeley), and co-founded , where his theory, now known as Hierarchical Temporal Memory, continues to be researched and developed. Hawkins (along with Numenta colleagues Subutai Ahmad and Yuwei Cui) bases all of his theory on hard Neuroscience, with nothing included unless there is strong evidence of it in the brain. In 2013, Numenta open-sourced their NuPIC HTM software, and I became part of the worldwide community which has joined Hawkins in exploring HTM as a cortical model and a promising machine learning technology. My 2015 was the first deep mathematical description of how HTM works. thesis proposal On Intelligence Redwood Center for Theoretical Neuroscience Numenta paper That paper was the first of a pair which outlined the discovery I’d made in 2015, that the mechanisms of coupled NDSs were the secret source of computational power in the mammalian brain. The insight was triggered when I saw by Melanie Mitchell of the Santa Fe Institute and took her courses on the . I was too old to have been taught any of this in University, so this was the first time I had the chance to learn about the vast world of complex systems and the power of methods developed very recently to explore and exploit them. this talk Complexity Explorer MOOC website My second paper — — outlines how the brain might act as a new kind of computing machine, using Takens’ Theorem to process streaming data, emergently self-organise cognition, and behave intelligently. HTM was used to demonstrate the direct connection between neurons in layers of neocortex and the emergent processing of networked NDSs. Since Numenta already had working software which emulates cortical computation in small regions, I proposed that intelligent machines could be feasible simply by building the right kind of network of such modules. Symphony from Synapses: Neocortex as a Universal Dynamical Systems Modeller using Hierarchical Temporal Memory We now know (h/t George Sugihara) that there is of the Feynman Machine process in real primate neocortex. Interestingly, this analysis used Takens’ Theorem and Sugihara’s methods to find the causal network across over a hundred brain regions (Tajima won for the paper). Just weeks ago, the same team between this kind of analysis and Tononi-Koch Information Integration Theory. strong evidence this prize established a link Around the same time in late 2015, Eric Laukien was developing , loosely based on ideas we’d been discussing in the HTM community. In January 2016, Eric, our friend Richard Crowder, and I (with support from Eric’s father Marc) formed to develop a new technology by fusing the theory with Eric’s experience in GPU-powered machine learning. Ogma is named after , or Ogmios, a Gaelic demigod of learning and poetry, and the reputed inventor of Ireland’s first writing system, Ogham. high-performance GPU technology Ogma Ogma mac Elathan Last September, we published on what we now call the , and released the first version of our . our first paper Feynman Machine OgmaNeo software suite As predicted by the theory, a brain region is only one example of a wide variety of adaptively-learning NDSs which, when wired together appropriately, will self-organise and emergently co-operate to form a Feynman Machine. There is no need to mimic all the details of real neurons (as seen in Human Brain Project models), nor even the simpler abstraction of neurons, columns and layers in HTM. Similarly, traditional Deep Learning gave us some ideas for modelling and implementing high-performance, adaptive data transformations in parallel, but we could avoid many of its limitations too, since each module in the Feynman Machine is a semi-independent predictive learner, and learning is online, local and unsupervised. The Feynman Machine is named in honour of our hero, Richard P. Feynman, for a number of reasons. A pioneer in computing, from Los Alamos to his involvement in early massively parallel algorithms in the 1980’s, he was a colleague of John von Neumann, now a synonym for the architecture of modern digital computers, though von Neumann’s designs were inspired by his understanding of cortical neurons. We hope that Feynman would appreciate the simplicity of these ideas, and might even have figured them out himself, had he lived to see recent progress in Neuroscience and Applied Mathematics. We recently discovered that his , herself an eminent physicist, based one of her seminal papers on Takens’ Theorem. We might justifiably have called it the Feynman-Feynman Machine, since Richard may well have had this insight, if Joan had had the chance to explain the power of NDSs. sister Joan In any case, our paper was listed as among the MIT Technology Review for the week of its publication, and we are presenting posters during March at the upcoming workshop and the , organised by MIT’s (if you’re also attending, please drop by our poster). Best of the Physics arXiv Neuro Inspired Computing Elements AAAI Spring Symposium on the Science of Intelligence Center for Brains, Minds and Machines I’ll explain the details of the Feynman Machine in my next post, but in the meantime here is a recent demo of it in action on the “Hello World” of NDSs, the Lorenz System: The “noisy” Lorenz attractor (based on ) is used here because it’s much more challenging to learn than the vanilla NDS. There are a couple of important points about this demo which might not be obvious. First, the step number at top left is the number of data points the FM has seen since it was initialised randomly, so this is online learning, completely from scratch, with no pretraining. Second, the signal the FM is seeing is just the highly noisy observations, and never the smooth Takens trajectory displayed in most of the video, so the resemblance is due to the FM finding the true signal among the noisy data. Third, this represents the entire 8-layer network running at about 60 steps per second on my Macbook Pro, which includes drawing all the 3D graphics. this recent paper Stay tuned for my second post, which will describe the inner workings of the Feynman Machine.

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