November 2023 additions to the ASCL

Sixteen codes were added to the ASCL in November, 2023:

CosmoLattice: Lattice simulator of scalar and gauge field dynamics in an expanding universe
FASMA: Stellar spectral analysis package
FPFS: Fourier Power Function Shaplets
Hi-COLA: Cosmological large-scale structure simulator for Horndeski theories

IQRM: IQRM interference flagging algorithm for radio pulsar and transient searches
KvW: Modified Kwee–Van Woerden method for eclipse minimum timing with reliable error estimates
MONDPMesh: Particle-mesh code for Milgromian dynamics
nemiss: Neutrino emission from hydrocode data

NEOexchange: Target and Observation Manager for the Solar System
PIPPIN: Polarimetric Differential Imaging (PDI) pipeline for NACO data
pygwb: Lighweight python stochastic GWB analysis pipeline
RoSSBi3D: Finite volume code for protoplanetary disk evolution study

Special-Blurring: Compare quantum-spacetime foam models to GRB localizations
tensiometer: Test a model until it breaks
VCAL-SPHERE: Hybrid pipeline for reduction of VLT/SPHERE data
wcpy: Wavelength Calibrator

October 2023 additions to the ASCL

Twelve codes were added to the ASCL in October, 2023:

AI-Feynman: Symbolic regression algorithm
celerite2: Fast and scalable Gaussian Processes in one dimension
clfd: Clean folded data
DustPyLib: A library of DustPy extensions
GRIZZLY: 1D radiative transfer code
IQRM-APOLLO: Clean narrow-band RFI using Inter-Quartile Range Mitigation (IQRM) algorithm

lcsim: Light curve simulation code
MAGPy-RV: Gaussian Process regression pipeline with MCMC parameter searching
q3dfit: PSF decomposition and spectral analysis for JWST-IFU spectroscopy
riptide: Pulsar searching with the Fast Folding Algorithm
wwz: Weighted wavelet z-transform code
zCluster: Measure photometric redshifts for galaxy clusters

September 2023 additions to the ASCL

Twenty codes were added to the ASCL in September, 2023:

bskit: Bispectra from cosmological simulation snapshots
ChEAP: Chemical Evolution Analytic Package
CoLFI: Cosmological Likelihood-Free Inference
DeepGlow: Neural network emulator for BOXFIT
fitScalingRelation: Fit galaxy cluster scaling relations using MCMC

FRISBHEE: FRIedmann Solver for Black Hole Evaporation in the Early-universe
GWSim: Mock gravitational waves event generator
maszcal: Mass calibrations for thermal-SZ clusters
MATRIX: Multi-phAse Transits Recovery from Injected eXoplanets toolkit
PCOSTPD: Periodogram Comparison for Optimizing Small Transiting Planet Detection

PEREGRINE: Gravitational wave parameter inference with neural ration estimation
PI: Plages Identification
PlanetSlicer: Orange-slice algorithm for fitting brightness maps to phase curves
pymccorrelation: Correlation coefficients with uncertainties
pymcspearman: Monte carlo calculation of Spearman’s rank correlation coefficient with uncertainties

Sprout: Moving mesh finite volume hydro code
StarbugII: JWST PSF photometry for crowded fields
Swiftbat: Utilities for handing BAT instrument data from the Neil Gehrels Swift Observatory
TRES: TRiple Evolution Simulation package
UBHM: Uncertainty quantification of black hole mass estimation

August 2023 additions to the ASCL

Fifteen codes were added to the ASCL in August, 2023:

AstroPhot: Fitting everything everywhere all at once in astronomical images
BCMemu: Model baryonic effects in cosmological simulations
caput: Utilities for building radio astronomy data analysis pipelines
DiskMINT: Disk Model For INdividual Targets
Driftscan: Drift scan telescope analysis

FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra
FishLSS: Fisher forecasting for Large Scale Structure surveys
FLATW’RM: Finding flares in Kepler data using machine-learning tools
glmnet: Lasso and elastic-net regularized generalized linear models
KeplerFit: Keplerian velocity distribution model fitter

MOOG_SCAT: Scattering Version of the MOOG Line Transfer Code
Nemo: Millimeter-wave map filtering and Sunyaev-Zel’dovich galaxy cluster and source detection
Rapster: Rapid population synthesis for binary black hole mergers in dynamical environments
SIMBI: 3D relativistic gas dynamics code
velocileptors: Velocity-based Lagrangian and Eulerian PT expansions of redshift-space distortions

July 2023 additions to the ASCL

Sixty-two codes were added to the ASCL in July, 2023:

21cmvFAST: Adding dark matter-baryon relative velocities to 21cmFAST
adiabatic-tides: Tidal stripping of dark matter (sub)haloes
AGNvar: Model spectral timing properties in active galactic nuclei
ALF: Absorption line fitter
AmpF: Amplification factor for solar lensing

APOLLO: Radiative transfer and atmosphere spectroscopic retrieval for exoplanets
axionHMcode: Non-linear power spectrum calculator
baccoemu: Cosmological emulators for large-scale structure statistics
BE-HaPPY: Bias emulator for halo power spectrum
binary_c-python: Stellar population synthesis tool and interface to binary_c

binary_c: Stellar population synthesis software framework
BOWIE: Gravitational wave binary signal analysis
connect: COsmological Neural Network Emulator of CLASS using TensorFlow
CosmicFish: Cosmology forecasting tool
DataComb: Combining data for better images

DiscVerSt: Vertical structure calculator for accretion discs around neutron stars and black holes
EAGLES: Estimating AGes from Lithium Equivalent widthS
EFTCAMB: Effective Field Theory with CAMB
EVo: Thermodynamic magma degassing model
EVolve: Growth and evolution of volcanically-derived atmospheres

FABADA: Non-parametric noise reduction using Bayesian inference
FGBuster: Parametric component separation for Cosmic Microwave Background observations
Guacho: 3D uniform mesh parallel HD/MHD code for astrophysics
GWDALI: Gravitational wave parameter estimation
gyro-interp: Gyrochronology via interpolation of open cluster rotation sequences

HAYASHI: Halo-level AnalYsis of the Absorption Signal in HI
HELA: Random Forest retrieval for exoplanet atmospheres
HilalPy: Analysis tool for lunar crescent visibility criterion
Imber: Doppler imaging tool for modeling stellar and substellar surfaces
IMRIpy: Intermediate Mass Ratio Inspirals simulator

IMRPhenomD: Phenomenological waveform model
Jdaviz: JWST astronomical data analysis tools in the Jupyter platform
LEFTfield: Forward modeling of cosmological density fields
LIMpy: Line Intensity Mapping in Python
MBASC: Multi-Band AGN-SFG Classifier

mnms: Map-based Noise ModelS
NaMaster: Unified pseudo-Cl framework
NAVanalysis: Normalized Additional Velocity analysis
orbitN: Symplectic integrator for near-Keplerian planetary systems
plan-net: Bayesian neural networks for exoplanetary atmospheric retrieval

pnautilus: Three-phase chemical code
PolyBin: Binned polyspectrum estimation on the full sky
pycrires: Data reduction pipeline for VLT/CRIRES+
pyhalomodel: Halo-model implementation for power spectra
PyIMRPhenomD: Stellar origin black hole binaries population estimator

pyPplusS: Modeling exoplanets with rings
RelicFast: Fast scale-dependent halo bias
reMASTERed: Calculate contributions to pseudo-Cl for maps with correlated masks
RUBIS: Fast centrifugal deformation program for stellar and planetary models
SAMUS: Simulator of Asteroid Malformation Under Stress

SHARK: Gas and dust hydrodynamics with dust coagulation/fragmentation
SIMPLE: Intensity map generator
SIRENA: Energy reconstruction of X-ray photons for Athena X-IFU
species: Atmospheric characterization of directly imaged exoplanets
Synthetic LISA: Simulator for LISA-like gravitational-wave observatories

TidalPy: Moon and exoplanet tidal heating and dynamics estimator
TOAST: Time Ordered Astrophysics Scalable Tools
Veusz: Scientific plotting package
WarpX: Time-based electromagnetic and electrostatic Particle-In-Cell code
WDMWaveletTransforms: Fast forward and inverse WDM wavelet transforms

WeakLensingQML: Quadratic Maximum Likelihood estimator applied to Weak Lensing
νHawkHunter: Forecasting of PBH neutrinos

June 2023 additions to the ASCL

Sixty codes were added to the ASCL in June, 2023:

AIOLOS: Planetary atmosphere accretion and escape simulations
Albatross: Stellar stream parameter inference with neural ratio estimation
ALminer: ALMA archive mining and visualization toolkit
apollinaire: Helioseismic and asteroseismic peakbagging frameworks
ARPACK-NG: Large scale eigenvalue problem solver

Beta-SGP: Scaled Gradient Projection algorithm using β-divergence
BOXFIT: Gamma-ray burst afterglow light curve generator
Butterpy: Stellar butterfly diagram and rotational light curve simulator
CADET: CAvity DEtection Tool
CHIPS: Circumstellar matter and light curves of interaction-powered transients simulator

COFFE: COrrelation Function Full-sky Estimator
COLASolver: Particle-Mesh N-body code
COLT: Monte Carlo radiative transfer and simulation analysis toolkit
CONCEPT: COsmological N-body CodE in PyThon
CONDUCT: Electron transport coefficients of magnetized stellar plasmas

COpops: Compute CO sizes and fluxes
CosmoGraphNet: Cosmological parameters and galaxy power spectrum from galaxy catalogs
Delight: Photometric redshift via Gaussian processes with physical kernels
ECLIPSE: Efficient Cmb poLarization and Intensity Power Spectra Estimator
ESSENCE: Evaluate spatially correlated noise in interferometric images

FacetClumps: Molecular clump detection algorithm based on Facet model
FRB: Fast Radio Burst calculations, estimations, and analysis
GER: Global Extinction Reduction
GRChombo: Numerical relativity simulator
HAFFET: Supernovae photometric and spectroscopic data analyzer

Hitomi: Cosmological analysis of anisotropic galaxy distributions
IDEFIX: Astrophysical fluid dynamics
kilopop: Binary neutron star population of optical kilonovae
lasso_spectra: Predict properties from galaxy spectra using Lasso regression
Mangrove: Infer galaxy properties using dark matter merger trees

margarine: Posterior sampling and marginal Bayesian statistics
MG-PICOLA: Simulating cosmological structure formation
Mixclask: Mixing Cloudy and SKIRT
MOBSE: Massive Objects in Binary Stellar Evolution
mockFRBhosts: Limiting the visibility and follow-up of FRB host galaxies

nuPyProp: Propagate neutrinos through the earth
nuSpaceSim: Cosmic neutrino simulation
Parthenon: Portable block-structured adaptive mesh refinement framework
PEP: Planetary Ephemeris Program
PEPITA: Prediction of Exoplanet Precisions using Information in Transit Analysis

PhotoParallax: Data-driven photometric parallaxes built with Gaia and 2MASS
pipes_vis: Interactive GUI and visualizer tool for SPS spectra
PSFMachine: Toolkit for doing PSF photometry
pybranch: Calculate experimental branching fractions and transition probabilities from atomic spectra
realfast: Real-time interferometric data analysis for the VLA

RELAGN: AGN SEDs with full GR ray tracing
rfast: Planetary spectral forward and inverse modeling tool
SAVED21cm: Global 21cm signal extraction pipeline
sbi: Simulation-based inference toolkit
SCF-FDPS: Disk-halo systems simulator

SCONCE-SCMS: Spherical and conic cosmic web finders with extended SCMS algorithms
SHERLOCK: Explore Kepler, K2, and TESS data
sstrax: Fast stellar stream modelling in JAX
SubgridClumping: Clumping factor for large low-resolution N-body simulations
SuperRad: Black hole superradiance gravitational waveform modeler

threepoint: Covariance of third-order aperture statistics
TiDE: Light curves and spectra of tidal disruption events
TIDYMESS: TIdal DYnamics of Multi-body ExtraSolar Systems
Zeus21: Simulations of 21-cm at cosmic dawn
ZodiPy: Zodiacal emission simulations in timestreams or HEALPix for solar system observers

May 2023 additions to the ASCL

Twenty-five codes were added to the ASCL in May, 2023:

aartfaac2ms: Aartfaac datasets converter
breizorro: Image masking tool
CELEBI: Precision localizations and polarimetric data for fast radio bursts
COLIBRI: Cosmological libraries in Python
DarkMappy: Mapping the dark universe

DDFacet: Facet-based radio imaging package
DP3: Streaming processing pipeline for radio interferometric data
EIDOS: Modeling primary beams of radio astronomy antennas
extrapops: Fast simulation and analysis of extra-galactic binary GW sources
FLAGLET: Fast and exact wavelet transform on the ball

FRIDDA: Fisher foRecast code for combIned reDshift Drift and Alpha
GLASS: Cosmological simulations on the sphere
GrGadget: Evolve metric perturbations in the weak field limit
gw_pta_emulator: Gravitational Waves via Pulsar Timing Arrays
GWSurrogate: Gravitational wave surrogate models

JEDI: James’s EVE Dimming Index
katdal: MeerKAT Data Access Library
KERN: Radio telescope toolkit
killMS: Direction-dependent radio interferometric calibration package
Nextflow: DSL for data-driven computational pipelines

QuartiCal: Fast radio interferometric calibration
simple-m2m: Extensions to the standard M2M algorithm for full modeling of observational data
sterile-dm: Sterile neutrino production
Stimela: Containerized radio interferometry scripting framework
Virtual Telescope: Next-Generation Space Telescope Simulator

April 2023 additions to the ASCL

Six codes were added to the ASCL in April, 2023. What can I say? Should have been more; sorry!

ASSIST: Solar system test particles trajectories integrator
Applefy: Robust detection limits for high-contrast imaging
BatAnalysis: HEASOFT wrapper for processing Swift-BAT data
FALCO: Fast Linearized Coronagraph Optimizer in MATLAB
FALCO: Fast Linearized Coronagraph Optimizer in Python
JET: JWST Exoplanet Targeting

March 2023 additions to the ASCL

Twenty codes were added to the ASCL in March, 2023:

bajes: Bayesian Jenaer software
Blobby3D: Bayesian inference for gas kinematics
cysgp4: Wrapper for C++ SGP4 satellite library
Delphes: Fast simulation of a generic collider experiment
EvoEMD: Cosmic Evolution with an Early Matter-Dominated era

FastJet: Jet finding in pp and e+e− collisions
GPCC: Gaussian process cross-correlation for time delay estimation
HaloGraphNet: Predict halo masses from simulations
line_selections: Automatic line detection for large spectroscopic surveys
MORPHOFIT: Morphological analysis of galaxies

naif: Frequency analysis package
nd-redshift: Number Density Redshift Evolution Code
Pandora: Fast exomoon transit detection algorithm
pulsar_spectra: Pulsar flux density measurements, spectral models fitting, and catalog
PyCom: Interstellar communication

SatGen: Semi-analytical satellite galaxy and dark matter halo generator
Scri: Manipulate time-dependent functions of spin-weighted spherical harmonics
SeeKAT: Localizer for transients detected in tied-array beams
SIDM: Density profiles of self-interacting dark-matter halos with inhabitant galaxies
spinsfast: Fast and exact spin-s spherical harmonic transforms

ChatGPT and AI-generated Code: The Impact of Natural Language Models on Software Creation and Sharing

The following guest post is by John Wallin, the Director of the Computational and Data Science Ph.D. Program and Professor of Physics and Astronomy at Middle Tennessee State University

Photo of Dr. John Wallin, Director of the Computational and Data Science Ph.D. Program and Professor of Physics and Astronomy at Middle Tennessee State University

Dr. John Wallin

Since the 1960s, scientific software has undergone repeated innovation cycles in languages, hardware capabilities, and programming paradigms. We have gone from Fortran IV to C++ to Python. We moved from punch cards and video terminals to laptops and massively parallel computers with hundreds to millions of processors. Complex numerical and scientific libraries and the ability to immediately seek support for these libraries through web searches have unlocked new ways for us to do our jobs. Neural networks are commonly used to classify massive data sets in our field. All these changes have impacted the way we create software.

In the last year, large language models (LLM) have been created to respond to natural language questions. The underlying architecture of these models is complex, but the current generation is based on generative pre-trained transformers (GPT). In addition to the base architecture, they have recently incorporated supervised learning and reinforcement learning to improve their responses. These efforts resulted in a flexible artificial intelligence system that can help solve routine problems. Although the primary purpose of these large language models was to generate text, it became apparent that these models could also generate code. These models are in their infancy, but they have been very successful in helping programmers create code snippets that are useful in a wide range of applications. I wanted to focus on two applications of the transformer-based LLM  – ChatGPT by OpenAI and GitHub Copilot.

ChatGPT is perhaps the most well-known and used LLM. The underlying GPT LLM was released about a year ago, but a newer interactive version was made publicly available in November 2022. The user base exceeded a million after five days and has grown to over 100 million. Unfortunately, most of the discussion about this model has been either dismissive or apocalyptic. Some scholars have posted something similar to this:

“I wanted to see what the fuss is about this new ChatGPT thing, so I gave a problem from my advanced quantum mechanics course. It got a few concepts right, but the math was completely wrong. The fact that it can’t do a simple quantum renormalization problem is astonishing, and I am not impressed. It isn’t a very good “artificial intelligence” if it makes these sorts of mistakes!”

The other response that comes from some academics:

“I gave ChatGPT an essay problem that I typically give my college class. It wrote a PERFECT essay! All the students are going to use this to write their essays! Higher education is done for! I am going to retire this spring and move to a survival cabin in Montana to escape the cities before the machine uprising occurs.”

Jobs will change because of these technologies, and our educational system needs to adapt.Of course, neither view is entirely correct. My reaction to the first viewpoint is, “Have you met any real people?” It turns out that not every person you meet has advanced academic knowledge in your subdiscipline. ChatGPT was never designed to replace grad students. A future version of the software may be able to incorporate more profound domain-specific knowledge, but for now, think of the current generation of AIs as your cousin Alex. They took a bunch of college courses and got a solid B- in most of them. They are very employable as an administrative assistant, but you won’t see them publish any of their work in Nature in the next year or two. Hiring Alex will improve your workflow, even if they can’t do much physics.

The apocalyptic view also misses the mark, even if the survival cabin in Montana sounds nice. Higher education will need to adapt to these new technologies. We must move toward more formal proctored evaluations for many of our courses. Experiential and hands-on learning will need to be emphasized, and we will probably need to reconsider (yet again) what we expect students to take away from our classes. Jobs will change because of these technologies, and our educational system needs to adapt.

Despite these divergent and extreme views, generative AI is here to stay. Moreover, its capabilities will improve rapidly over the next few years. These changes are likely to include:

  • Access to live web data and current events. Microsoft’s Bing (currently in limited release) already has this capability. Other engines are likely to become widely available in the next few months.
  • Improved mathematical abilities via linking to other software systems like Wolfram Alpha. ChatGPT makes mathematical errors routinely because it is doing math via language processing. Connecting this to symbolic processing will be challenging, but there have already been a few preliminary attempts.
  • Increased ability to analyze graphics and diagrams. Identifying images is already routine, so moving to understand and explaining diagrams is not an impossible extension. However, this type of future expansion would impact how the system analyzes physics problems.
  • Accessing specialized datasets such as arXiv, ADS, and even astronomical data sets. It would be trivial to train GPT3.5 on these data sets and give it domain-specific knowledge.
  • Integrating the ability to create and run software tools within the environment. We already have this capability in GitHub Copilot, but the ability to read online data and immediately do customized analysis on it is not out of reach for other implementations as well.

Even without these additions, writing code with GitHub Copilot is still a fantastic experience. Based on what you are working on, your comments, and its training data, it attempts to anticipate your next line or lines of code. Sometimes, it might try to write an entire function for you based on a comment or the name of the last function. I’ve been using this for about five months, and I find it particularly useful when using library functions that are a bit unfamiliar. For example, instead of googling how to add a window with a pulldown menu in python, you would write a comment explaining what you want to do, and the code will be created below your comment. It also works exceptionally well solving simple programming tasks such as creating a Mandelbrot set or downloading and processing data. I estimate that my coding speed for solving real-world problems using this interface has tripled.

However, two key issues need to be addressed when using the code: authorship and reliability.

When you create a code using an AI, it goes through millions of lines of public domain code to find matches to your current coding. It predicts what you might be trying to do based on what others have done. For simple tasks like creating a call to a known function in a python library, this is not likely to infringe on the intellectual property of someone’s code. However, when you ask it to create functions, it is likely to find other codes that accomplish the task you want to complete. The only thing of value we produce in science is ideas. Using someone else's thoughts or ideas without attribution can cross into plagiarism, even if that action is unintentional.For example, there are perhaps thousands of examples of ODE integrators in open-source codes. Asking it to create such a routine for you will likely result in inadvertently using one of those codes without knowing its origin.

The only thing of value we produce in science is ideas. Using someone else’s thoughts or ideas without attribution can cross into plagiarism, even if that action is unintentional. Code reuse and online forums are regularly part of our programming process, but we have a higher level of awareness of what is and isn’t allowed when we are the ones googling the answer. Licensing and attribution become problematic even in a research setting. There may be problems claiming a code is our intellectual property if it uses a public code base. Major companies have banned ChatGPT from being used for this reason. At the very least, acknowledging that you used an AI to create the code seems like an appropriate response to this challenge. Only you can take responsibility for your code, but explaining how it was developed might help others understand its origin.

The second issue for the new generation of AI assistants is reliability. When I asked ChatGPT to write a short biographical sketch for “John Wallin, a professor at Middle Tennessee State University,” I found that I had received my Ph.D. from Emory University. I studied Civil War and Reconstruction era history. It confidently cited two books that I had authored about the Civil War. All of this was nonsense created by a computer creating text that it thought I wanted to read.

It is tempting to think that AI-generated code would produce correct results. However, I have regularly seen major and minor bugs within the code it has generated. Some of the mistakes can be subtle but could lead to erroneous results. Therefore, no matter how the code is generated, we must continue to use validation and verification to determine if we both have a code that correctly implements our algorithms and have the correct code to solve our scientific problem.

Both authorship and reliability will continue to be issues when we teach our students about software development in our fields. At the beginning of the semester, I had ChatGPT generate “five group coding challenges that would take about 30 minutes for graduate students in a Computational Science Capstone course.” When I gave them to my students, it took them about 30 minutes to complete. I created solutions for ALL of them using GitHub Copilot in under ten minutes. Specifying when students can and can’t use these tools is critical, along with developing appropriate metrics for evaluating their work when using these new tools. We also need to push students toward better practices in testing their software, including making testing data sets available when the code is distributed.

Sharing your software has never been more important, given these challenges. Although we can generate codes faster than ever, the reproducibility of our results matters. Your methodology’s only accurate description is the code you used to create the results. Publishing your code when you publish your results will increase the value of your work to others. As the abilities of artificial intelligence improve, the core issues of authorship and reliability still need to be verified by human intelligence.

Addendum: The Impact of GPT-4 on Coding and Domain-Specific Knowledge
Written with the help of GPT-4; added March 20, 2023

Since the publication of the original blog post, there have been significant advancements in the capabilities of AI-generated code with the introduction of GPT-4. This next-generation language model continues to build on the successes of its predecessors while addressing some of the limitations that were previously observed.

One of the areas where GPT-4 has shown promise is in its ability to better understand domain-specific knowledge. While it is true that GPT-4 doesn’t inherently have access to specialized online resources like arXiv, its advanced learning capabilities can be utilized to incorporate domain-specific knowledge more effectively when trained with a more specialized dataset.

Users can help GPT-4 better understand domain-specific knowledge by training it on a dataset that includes examples from specialized sources. For instance, if researchers collect a dataset of scientific papers, code snippets, or other relevant materials from their specific domain and train GPT-4 with that data, the AI-generated code would become more accurate and domain-specific. The responsibility lies with the users to curate and provide these specialized datasets to make the most of GPT-4’s advanced learning capabilities.

By tailoring GPT-4’s training data to be more suited to their specific needs and requirements, users can address the challenges of authorship and reliability more effectively. This, in turn, can lead to more efficient and accurate AI-generated code, which can be particularly valuable in specialized fields.

In addition to the advancements in domain-specific knowledge and coding capabilities, GPT-4 is also set to make strides in the realm of image analysis. Although not directly related to coding, these enhancements highlight the growing versatility of the AI engine. While the image analysis feature is not yet publicly available, it is expected to be released soon, allowing users to tap into a new array of functionalities. This expansion of GPT-4’s abilities will enable it to understand and interpret images, diagrams, and other visual data, which could have far-reaching implications for various industries and applications. As GPT-4 continues to evolve, it is crucial to recognize and adapt to the ever-expanding range of possibilities that these AI engines offer, ensuring that users can leverage their full potential in diverse fields.

With the rapid advancements in AI capabilities, it is essential for researchers, educators, and developers to stay informed and adapt to the changes that GPT-4 and future models bring. As AI-generated code becomes more accurate and domain-specific, the importance of understanding the potential benefits, limitations, and ethical considerations of using these tools will continue to grow.