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For scientists, faking or making up data has obvious connotations and, thanks to some high-profile cases of scientific misconduct, theyre generally not positive ones. Chemists may, for example, be aware of a 2022 case in which a respected journal retracted two papers by a Japanese chemistry group that were found to contain manipulated or fabricated data. Or the case of Beng Sezen, the Columbia University chemist who, during the 2000s, falsified, fabricated and plagiarised data to get her work on chemical bonding published including fixing her NMR spectra with correcting fluid.

Synthetic data, unlike dishonestly made-up data, is created in a systematic way for legitimate reasons, however, usually by a machine and for a variety of reasons. Synthetic data is familiar to machine learning experts, and increasingly to computational chemists, but relatively unknown to the wider chemistry community, as Keith Butler, a materials researcher who works with machine learning methods at University College London in the UK, acknowledges.

I dont think very many people at all, in chemistry, would refer to synthetic data, he says, adding that its probably a confusing term for chemists not just because of the apparent associations with scientific misconduct, but because synthetic in chemistry has another important meaning, relating to the way chemical compounds are made. But why are chemists making up their data, how is it any different to the simulations theyve been making for decades and what are the implications?

In areas like health and finance, synthetic data is often used to replace real data from real people due to privacy concerns, or to deal with issues of imbalance, such as when people from certain ethnic groups arent well-represented. While researchers might like to use peoples personal medical records, for example, to inform their understanding of a new disease, that data is difficult to access in the quantities or levels of detail that would be most useful. Here, the creation of synthetic data, which mirrors the statistical features of the real-world data, offers a solution. Youre not taking a whole dataset and just masking it, explains Benjamin Jacobsen, a sociologist at the University of York in the UK, whose work focuses partly on the use of synthetic data. The promise of synthetic data is that you can train a model to understand the overall distribution of the particular dataset. In this way, synthetic data draws from real-world sources, but cant be traced back to real individuals. In the chemical sciences, though, synthetic data relates more to the behaviour of molecules than people and so as Butler notes, its used for a very different reason.

Synthetic data is generated by algorithms and for algorithms

In some ways, legitimately made-up data is nothing very new in chemistry. Areas such as materials and drug discovery, for example, have long used whats referred to as simulated or calculated data to expand the chemical space for exploration the data might describe predicted properties of new materials or potential drug compounds. Whats different now is that made-up data, whether its considered synthetic, simulated or calculated, is being used in combination with machine learning models. These AI models are algorithms capable of making sense of vast quantities of data, which could be from real experiments or made up (or a combination of both). They learn patterns in the data and use them to make classifications and predictions to provide valuable insights to humans whether or not its clear how the machines have delivered them.

Synthetic data is not just an input for AI models its an output from them too. Jacobsen, in fact, previously defined synthetic data as data generated by algorithms and for algorithms, although chemists may not be concerned with having such a strict definition. Some of the techniques commonly used to create it are related to those used in making deepfakes. In the same way that deepfakers might ask their machines to generate realistic-looking faces and speech, chemists might prompt theirs to generate realistic-looking chemical structures.

Another option for generating synthetic data is large language modelling the basis of generative AI tools like ChatGPT. This is an approach Butlers team recently used to build an app that can produce a hypothetical crystal structure for a compound based on a chemical formula, which is typed in as a prompt (like a question in a chat). More advanced versions of such tools, which would know more of the rules of chemistry, could prove invaluable in the search for new materials. If you could prompt [it] by saying produce me a plausible chemical structure that absorbs light well and is made only from Earth-abundant elements, thats actually interesting, says Butler. The problem being that you want to make sure that its reasonable and viable so that you dont start telling people things that are impossible to make.

Applications for synthetic data in the chemical sciences abound. However, for those who are less well-acquainted with machine learning, it can be hard to get a grip on what synthetic data is and how it can be so useful to chemists when its not actually real. Here, an example may help.

Most students of chemistry learn, fairly early on, to sketch out a chemical structure by hand, but transferring that structure from a piece of paper to a computer could be a tedious task. It would be useful if there was a quick way of doing it say, by taking a picture of the sketch on your phone. At Stanford University in California, US, Todd Martinez and his team set about tackling this problem, their idea being to teach a machine learning model how to recognise hand-drawn chemical structures so that they could be quickly converted into virtual versions. However, to do this using real data, they would have needed to train their data-hungry model with a vast dataset of hand-drawn structures. As Martinez notes, even someone who can draw really fast is only going to be able to churn out a handful a minute. We did try, he recalls. We got 30 people together and spent hours just doing this, but we were only able to get 1000 structures or something. You need this data in the hundreds of thousands.

So, instead, they developed a process for roughing-up half a million clean, artificially generated chemical structures made with well-known software called RDKit and sticking them onto backgrounds to simulate photographs of hand-drawn structures. Having ingested this synthetic data, combined with a much smaller sample of real hand-drawn structures, their machine learning approach was able to correctly identify hand-drawn hydrocarbon structures 70% of the time compared to never when trained just with their limited hand-drawn data (and only 56% of the time with the clean RDKit structures). More recently, they developed an app that turns hand-drawn structures directly into 3D visualisations of molecules on a mobile device.

Had it been fed half a million genuine, hand-drawn structures, Martinezs model might have performed even better, but the real data just wasnt available. So in this case, using synthetic data was a solution to the problem of data sparsity a problem described as one of the main barriers to the adoption of AI in the chemical sciences. As Martinez puts it, There are lots of problems in chemistry actually, most problems, I would say where there is insufficient data to really apply machine learning methods the way that practitioners would like to. The cost savings to be made by using synthetic data, he adds, could be much larger than in his molecular recognition example, because elsewhere this made-up data wouldnt just be replacing data that it takes someone a few seconds to draw, but data from expensive, real-life experiments.

Theres certainly no shortage of such experimental data at the Rutherford Appleton Laboratory in Oxfordshire. Here, petabytes and petabytes of data are produced, Butler says, by real-life experiments in which materials are bombarded with subatomic particles in order to probe their structure. But Butlers collaborators at the facility face a different problem: a massive surplus of unlabelled data data that is effectively meaningless to many machine learning models. To understand why, think back to the previous example, where each hand-drawn chemical structure would need a label attached to teach the machine learning model how to recognise it. Without labels, its harder for the AI to learn anything. The problem is that its really expensive to label that kind of experimental data, Butler says. Synthetic data, though, can be generated ready-labelled and then used as training data to help machine learning models learn to interpret the masses of real data produced by the laboratorys neutron-scattering experiments a line of thinking Butler and co-workers explored in a 2021 study.

In the study, they trained their models with thousands of synthetic spectra images in the same format as those they got from real neutron scattering experiments, but that were created via theoretical calculations. What they realised, though, was that when it came to interpreting real spectra, the models trained on simulated data just werent very good, because they werent used to all the imperfections that exist in real-world experimental spectra. Like the fake, hand-drawn chemical structures made by Martinezs team, they needed roughing up. As a solution, Butlers team came up with a way to add experimental artefacts including noise to the clean synthetic data. He describes it as akin to a filter you might apply to selfie, except instead of giving a photo of your face the style of a Van Gogh painting, it gives a perfect, simulated spectrum the style of a messier one from a real experiment. This restyling of synthetic data could be useful more broadly than in neutron scattering experiments, according to Butler.

Another area in which synthetic data could have an important impact is in combination with AI approaches in drug discovery. Though, as in other fields, the terminology could be a little off-putting. People are a bit shy to accept synthetic data [perhaps because] it sounds like its lower quality than real data, says Ulrich Zachariae, a drugs researcher at the University of Dundee in the UK. Zachariaes recent work has focused on searching for new compounds to target antibiotic-resistant Gram-negative bacteria like Pseudomonas aeruginosa, which causes life-threatening infections in hospital. One of the issues slowing down the search is that these bugs outer shells are virtually impenetrable, and while machine learning models could help make useful predictions about which compounds might work, theres a lack of data on bacterial permeability to feed the models.

That dataset provided us with enough input data that we could understand what was going on

To start tackling the permeability problem, Zachariaes team first constructed a model with what data they had data that came from existing antibiotics and used it predict whether other compounds would be good permeators, or not. This worked well, but didnt explain anything about why one compound was better than another. The researchers then wanted to probe the effects of small differences in chemical structure on permeability , but this required lots more data. So, to generate more, they used their own machine learning model to predict the properties of hundreds of thousands of (real) compounds for which there was no experimental data on permeability creating a huge new synthetic dataset. That dataset provided us with enough input data for [the additional analysis] that we could understand what was going on and why these compounds were good permeators or not, Zachariae explains. They were then able to suggest chemical features, including amine, thiophene and halide groups, that medicinal chemists should look out for in their hunt for new Gram-negative antibiotics.

For Martinez, understanding why machine learning models make the predictions they do is another key motivation for using synthetic data. The internal workings of AI models can be difficult to unravel theyre widely referred to as black boxes but Martinez says he thinks of synthetic data as a tool to sharpen a particular model that is producing that data, or to understand its essence in a simpler form. I think you can see examples of people groping towards this, but I dont know that its clearly stated, he muses. Martinezs interests lie mainly in quantum chemistry, where more traditional computational models are used to solve theoretical problems. Addressing the same problems with machine learning may be a way to get to solutions faster while also with the help of synthetic data getting to the heart of what the AI models have learned. In this way, chemists may be able to improve their more traditional models.

But what are the risks of using data that isnt real? Its hard to answer this question at this point. In other fields, the risks often relate to people whose data was absorbed to generate synthetic data, or who are affected by the decisions it is used to make. As Jacobsen notes, the risks are going to vary depending on the application area. Chemists have to delineate exactly How do we frame what risk is in this context? he says.

In the drug discovery space, Zachariae wonders if there is any more risk associated with artificially generated data than with simulated data used in the past. From a purely scientific perspective, I dont see how its any different from previous cycles of predictions, where we just used physical models, he says, adding that any hit molecule identified using AI and synthetic data would still have to go through rigorous safety testing.

The risk is that we lose credibility if our predictions dont match up with reality

Martinez, though, sees a potential problem in areas of theoretical chemistry where there is no real or experimental data on which to train machine learning models because that data can only be arrived at by computational means. Here, synthetic data may effectively be the norm, although the magic words often arent mentioned, he says, because chemists arent familiar with them. In quantum chemistry, for example, a molecules geometry can be used to compute its energy and now machine learning models are trained, based on existing theory, to take in geometries and spit out energies, just in faster and cheaper ways. However, since traditional methods are more accurate for smaller molecules than bigger ones and theres no way of experimentally checking the results, machine learning algorithms trained to spit out energies for big molecules could be doing a poor job which could be a concern if millions of data points are being generated. The interesting point about synthetic data in this context is that these issues do not seem to always be at the forefront of the communitys thinking, Martinez says. This seems to be because the synthetic nature of the data implies tight control over the training dataset and this can give a false confidence in data correctness and coverage.

In materials discovery, similar concerns surround the use of machine learning to predict stable chemical structures as Google DeepMind researchers have done in chemical spaces where the existing theory was not necessarily that accurate. The risk, says Butler, is that we lose credibility (and funding) if the properties of predicted materials dont match up with reality. So, while making up data may mean something different these days, its worth remembering that there could still be a lot at stake if its not done well.

Hayley Bennett is a science writer based in Bristol, UK

View original post here:
Why are computational chemists making up their data? - Chemistry World

The aim of this research is to develop precise and dependable machine learning models for the prediction of SW (Water Saturation) using three DL and three SL techniques: LSTM, GRU, RNN, SVM, KNN and DT. These models were trained on an extensive dataset comprising various types of log data. The findings of our investigation illustrate the efficacy of data-driven machine learning models in SW prediction, underscoring their potential for a wide range of practical applications.

When evaluating and comparing algorithms, researchers must take into account several crucial factors. Accuracy and disparities in prediction are among the most significant considerations. To evaluate these factors, researchers can utilize various criteria, including Eqs.16. The Mean Percentage Error (MPE) calculates the average difference between predicted and actual values as a percentage, while the Absolute Mean Percentage Error (AMPE) measures the absolute difference between them. Additionally, the Standard Deviation (SD) determines the variability of data points around the mean. Moreover, the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) quantify the mean and root mean squared differences between predicted and actual values, respectively. Lastly, the R2 metric assesses the fraction of diversity in the reliant variable that can be accounted for by the autonomous variable.

$$text{MPE}=frac{{{sum }_{text{i}=1}^{text{n}}(frac{{SWE}_{(text{Meas}.)}-{SWE}_{(text{Pred}.)}}{{SWE}_{(text{Meas}.)}}text{x }100)}_{text{i}}}{text{n}}$$

(1)

$$text{AMPE}=frac{{sum }_{text{i}=1}^{text{n}}left|{(frac{{SWE}_{(text{Meas}.)}-{SWE}_{(text{Pred}.)}}{{SWE}_{(text{Meas}.)}}text{x }100)}_{text{i}}right|}{text{n}}$$

(2)

$$text{SD}=sqrt{frac{{sum }_{text{i}=1}^{text{n}}{({left(frac{1}{text{n}}sum_{text{i}=1}^{text{n}}left({{SWE}_{text{Meas}.}}_{text{i}}-{{SWE}_{text{Pred}.}}_{text{i}}right)right)}_{text{i}}-(frac{1}{text{n}}sum_{text{i}=1}^{text{n}}left({{SWE}_{text{Meas}.}}_{text{i}}-{{SWE}_{text{Pred}.}}_{text{i}}right))text{imean})}^{2}}{text{n}-1}}$$

(3)

$$text{MSE}=frac{1}{text{n}}sum_{text{i}=1}^{text{n}}{left({{SWE}_{text{Meas}.}}_{text{i}}-{{SWE}_{text{Pred}.}}_{text{i}}right)}^{2}$$

(4)

$$text{RMSE}=sqrt{frac{1}{text{n}}sum_{text{i}=1}^{text{n}}{left({{SWE}_{text{Meas}.}}_{text{i}}-{{SWE}_{text{Pred}.}}_{text{i}}right)}^{2}}$$

(5)

$${text{R}}^{2}=1-frac{sum_{text{i}=1}^{text{N}}{({{SWE}_{text{Pred}.}}_{text{i}}-{{SWE}_{text{Meas}.}}_{text{i}})}^{2}}{sum_{text{i}=1}^{text{N}}{({{SWE}_{text{Pred}.}}_{text{i}}-frac{{sum }_{text{I}=1}^{text{n}}{{SWE}_{text{Meas}.}}_{text{i}}}{text{n}})}^{2}}$$

(6)

In order to forecast SW, three DL and three SL techniques: LSTM, GRU, RNN, SVM, KNN and DT, were used in this study. Each algorithm underwent individual training and testing processes, followed by independent experiments. To ensure the accuracy of the predictions, the dataset was carefully divided into three subsets. The training subset accounted for 70% of the data records, while 30% was allocated for independent testing.

Choosing the most suitable algorithm for a specific task is a crucial undertaking within the realm of data analysis and machine learning. Therefore, this research aimed to assess and compare the performance of multiple LSTM, GRU, and RNN algorithms in predicting SW. The outcomes of these algorithms, utilizing the train data values, as well as the test, have been meticulously documented and presented in Table 2. By analyzing the results, researchers can gain insights into the effectiveness of each algorithm and make informed decisions about their implementation in practical applications.

The results from the test data are presented in Table 2, highlighting the excellent performance of the RMSE, MPE and AMPE metrics for the GRU algorithm, with values of 0.0198,0.1492 and 2.0320, respectively. Similarly, for the LSTM algorithm, the corresponding values are 0.0284,0.1388 and 3.1136, while for the RNN algorithm, they are 0.0399,0.0201 and 4.0613, respectively. For SVM, KNN and DT these metrics are includes: 0.0599,0.1664 and 6.1642; 0.7873, 0.0997 and 7.4575; 0.7289,0.1758 and 8.1936. The results show the GRU model has high accuracy than other algorithms.

The R2 parameter is a crucial statistical measure for evaluating and comparing different models. It assesses the adequacy of a model by quantifying the amount of variation in the outcome variable that can be clarified by the explanatory variables. In this study, Fig.5 illustrates cross plots for predicting SW values based on the train and test data, demonstrating significantly higher prediction accuracy compared to the other evaluated models. Additionally, Fig.5 confirms that the RGU model exhibits superior prediction accuracy compared to the LSTM and RNN models.

Cross plot for predicting SW using three DL algorithms such as RGU, LSTM and RNN for test data.

To assess the precision of the GRU model, the results presented in Table 2 and Fig.5 were carefully analyzed for the train and test data. The analysis revealed that the GRU algorithm achieved low errors for SW, with RMSE values of 0.0198 and R-square values of 0.9973. The R2 values provided serve as quantitative metrics assessing the predictive prowess of ML models. The R2, denoting the coefficient of determination, gauges the extent to which the variance in the dependent variable can be foreseen from the independent variable(s), essentially showcasing how well the model aligns with observed data points. The R2 values for the GRU, LSTM, RNN, SVM, KNN and DT models stand at 0.9973, 0.9725, 0.9701, 0.8050, 0.7873 and 0.7289 respectively, reflecting their respective accuracy and reliability in predicting SW levels. Figure5 shows the cross plot for predicting SW using three DL algorithms such as RGU, LSTM, and RNN for test data. The GRU model's notably high R2 of 0.9973 underscores its exceptional correlation between predicted and observed SW values, implying that nearly 99.73% of SW data variance can be elucidated by its predictions, showcasing its precision and reliability in SW prediction tasks. Comparatively, the LSTM and RNN models, with R2 values of 0.9725 and 0.9701 respectively, also exhibit strong predictive capabilities, albeit slightly lower than the GRU model. These findings underscore the GRU model's superiority in SW prediction, attributed to its adeptness in capturing intricate temporal dependencies within SW data, thereby yielding more accurate predictions.

Figure6 provides a visual representation of the calculation error for the test data, illustrating the error distribution for predicting SW using three DL algorithms (GRU, LSTM, and RNN). The plotted coordinates in the figure depict the error range for each algorithm. For the GRU algorithm, the error range is observed to be between0.0103 and 0.0727. This indicates that the predictions made by the GRU model for the test data exhibit a relatively small deviation from the actual SW values within this range. In contrast, the LSTM algorithm demonstrates a slightly wider error range, ranging from0.146 to 0.215. This suggests that the predictions generated by the LSTM model for the test data exhibit a somewhat higher variability and may deviate from the actual SW values within this broader range.

Error points for predicting SW using three DL and three SL algorithms such as GRU, LSTM, RNN, SVM, KNN and DT for test data.

Similarly, the RNN algorithm exhibits an error range between0.222 and 0.283. This indicates that the predictions made by the RNN model for the test data show a larger spread and have the potential to deviate more significantly from the actual SW values within this range. By visually comparing the error ranges for the three DL algorithms, it becomes apparent that the GRU algorithm achieves a narrower range and thus demonstrates better precision and accuracy in predicting SW for the test data. Conversely, the LSTM and RNN algorithms exhibit broader error ranges, indicating a higher degree of variability in their predictions for the same dataset. These findings further support the conclusion that the GRU algorithm outperforms the LSTM and RNN algorithms in terms of SW prediction accuracy, as it consistently produces predictions with smaller errors and tighter error bounds.

Figure7 presents an error histogram plot, depicting the prediction errors for SW using three DL and three SL algorithms such as GRU, LSTM, RNN, SVM, KNN and DT. Each histogram represents the distribution of prediction errors for each algorithm, displaying a normal distribution centered around zero with a relatively narrow spread and no noticeable positive or negative bias. This plot enables a comprehensive analysis of the algorithms' performance and aids in determining the best algorithm with a normal error distribution. Upon careful investigation, it becomes evident that the GRU algorithm exhibits a superior normal distribution of data compared to the other algorithms. The GRU algorithm's performance is characterized by a more accurate standard deviation and a narrower spread of prediction errors. This indicates that the GRU algorithm consistently produces more precise and reliable predictions for SW. By comparing the results presented in Table 2 and analyzing the error histogram plot in Fig.7, we can conclude that the performance accuracy of the algorithms can be ranked as follows: GRU>LSTM>RNN>SVM>KNN>DT.

Histogram plot for SW prediction using three DL and three SL algorithms such as GRU, LSTM, RNN, SVM, KNN and DT.

Figure8 illustrates the error rate of the three DL and three SL algorithms such as GRU, LSTM, RNN, SVM, KNN and DT as a function of iteration for SW prediction. The findings of this study indicate that the GRU and LSTM algorithms initially exhibit higher error values that progressively decrease over time. However, this pattern is not observed in the RNN algorithm. Upon analyzing the figure, it becomes evident that the LSTM algorithm achieves higher accuracy than the other algorithms at the beginning of the iteration. At the threshold of 10 iterations, the LSTM algorithm surpasses the GRU algorithm with a lower error value. However, in the subsequent iterations, specifically at iteration 31, the GRU algorithm outperforms the LSTM algorithm with superior performance accuracy. In contrast, the RNN algorithm shows a consistent decrease in performance accuracy from the start to the end of the iterations, without displaying significant fluctuations. When focusing on the zoomed-in portion of the figure, specifically repetitions 85100, the ongoing performance trends of these algorithms become more apparent. It is evident from the analysis that the GRU algorithm consistently outperforms the other algorithms in terms of performance accuracy. The LSTM algorithm follows, with a decrease in accuracy over the iterations. On the other hand, the RNN algorithm exhibits a declining performance accuracy without any notable changes or fluctuations. These findings emphasize the superiority of the GRU algorithm in terms of performance accuracy when compared to the LSTM and RNN algorithms. The GRU algorithm consistently maintains a higher level of accuracy throughout the iterations, while the LSTM and RNN algorithms experience fluctuations and decreasing accuracy over time.

Iteration plot for SW prediction using three DL and three SL algorithms such as GRU, LSTM, RNN, SVM, KNN and DT.

Pearson's coefficient (R) is a widely used method for assessing the relative importance of input-independent variables compared to output-dependent variables, such as SWL. The coefficient ranges between1 and+1 and represents the strength and direction of the correlation. A value of+1 indicates a strong positive correlation, -1 indicates a strong negative correlation, and a value close to 0 indicates no correlation. Equation7 illustrates the calculation of Pearson's correlation coefficient, which is a statistical measure of the linear relationship between two variables. It allows researchers to quantify the extent to which changes in one variable are associated with changes in another variable. By applying Pearson's coefficient, researchers can determine the level of influence that input-independent variables have on the output-dependent variable, SWL.

$$R=frac{sum_{i=1}^{n}({Z}_{i}-overline{Z })({Q}_{i}-overline{Q })}{sqrt{{sum }_{i=1}^{n}{({Z}_{i}-overline{Z })}^{2}}sqrt{{sum }_{i=1}^{n}{({Q}_{i}-overline{Q })}^{2}}}$$

(7)

A coefficient of+1 indicates a perfect positive correlation, suggesting that the input-independent variables have the greatest positive impact on the output-dependent variable. Conversely, a coefficient of1 represents a perfect negative correlation, indicating that the input-independent variables have the greatest absolute impact on the output-dependent variable. When the coefficient is close to 0, it suggests that there is no significant correlation between the variables, indicating that changes in the input-independent variables do not have a substantial effect on the output-dependent variable. Pearson's correlation coefficient is a valuable tool for assessing the relationship between variables and understanding their impact. It provides researchers with a quantitative measure to determine the relative importance of input-independent variables compared to the output-dependent variable, SWL.

By heat map shows Fig.9, a comparison of Pearson correlation coefficients can be made to gain insights into the relationship between input variables and SW. The results reveal several significant correlations between the variables. Negative correlations are observed with URAN and DEPTH, indicating an inverse relationship with SW. This suggests that higher values of URAN and DEPTH are associated with lower SW values. On the other hand, positive correlations are observed with CGR, DT, NPHI, POTA, THOR, and PEF. These variables show a direct relationship with SW, meaning that higher values of CGR, DT, NPHI, POTA, THOR, and PEF are associated with higher SW values. The comparison of Pearson correlation coefficients provides valuable insights into the relationship between input variables and SW.

Heat map plot for SW prediction using three DL and three SL algorithms such as GRU, LSTM, RNN, SVM, KNN and DT.

These findings can be utilized to develop predictive models of SW based on the input variables. By incorporating the correlations into the models, researchers can enhance their accuracy and reliability in predicting SW values. The expression of the relationships between the input variables and SW in the form of Eq.8 allows for quantitative analysis of the data. This equation provides a mathematical representation of the correlations, enabling researchers to quantitatively evaluate the impact of the input variables on SW.

$$SWE=propto left(text{CGR},text{ DT},text{ NPHI},text{ POTA},text{ THOR},text{ PEF}right) and SWE=propto frac{1}{left(URAN, DEPTHright)}$$

(8)

Read more here:
Deep learning algorithm-enabled sediment characterization techniques to determination of water saturation for tight ... - Nature.com

Automated Machine Learning (AutoML) Market

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The AutoML market has been expanding rapidly in recent years, driven by the increasing demand for machine learning solutions across a variety of industries. AutoML tools offer a range of functionalities, such as automating feature engineering, hyperparameter tuning, model selection, and deployment. This allows data scientists, engineers, and businesses to build and deploy high-quality machine learning models much faster and with less expertise required.

Healthcare & Lifesciences to account for higher CAGR during the forecast period

The AutoML market for healthcare is categorized into various applications, such as anomaly detection, disease diagnosis, drug discovery, chatbot and virtual assistance and others (clinical trial analysis and electronic health record (EHR) analysis). In the healthcare and life sciences industry, AutoML can help automate various tasks such as disease diagnosis, drug discovery, and patient care. AutoML can be used to analyze large volumes of medical data, such as electronic health records, medical images, and genomic data, to identify patterns and make predictions. This can help healthcare professionals make more accurate diagnoses, identify potential treatments, and improve patient outcomes. AutoML can also be used in drug discovery to identify potential drug candidates and optimize drug development processes. By analyzing molecular structures, genetic data, and other factors, AutoML can help identify potential drug targets and optimize drug efficacy and safety. AutoML can also be used to monitor patient progress and adjust treatment plans as needed. The implementation of AutoML in healthcare and life sciences should be done with caution and consideration for ethical and regulatory concerns.

Services Segment to account for higher CAGR during the forecast period

The market for Automated Machine Learning is bifurcated based on offering into solution and services. The CAGR of services is estimated to be highest during the forecast period. AutoML services allow users to automate various tasks involved in building and deploying machine learning models, such as feature engineering, hyperparameter tuning, model selection, and deployment. These services are designed to make it easier for businesses and individuals to leverage the power of machine learning without requiring extensive knowledge or expertise in the field.

Asia Pacific to exhibit the highest CAGR during the forecast period

The CAGR of Asia Pacific is estimated to be highest during the forecast period. Automated machine learning is rapidly growing in Asia Pacific, which includes China, India, Japan, South Korea, ASEAN, and ANZ (Australia and New Zealand). In recent years, there has been significant growth in the adoption of both AutoML and machine learning across various industries in Asia Pacific, driven by the region's large and diverse datasets, as well as the need for faster and more efficient decision-making. Many companies in the region are also investing in the development of AutoML platforms and tools to help accelerate the adoption of AI and machine learning. To support the adoption of AutoML and machine learning, governments and organizations in the Asia Pacific region are investing in infrastructure and programs to promote innovation, education, and collaboration.

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Top Companies in Automated Machine Learning :

Major vendors in the global Automated Machine Learning market are IBM (US), Oracle (US), Microsoft (US), ServiceNow (US), Google (US), Baidu (China), AWS (US), Alteryx (US), Salesforce (US), Altair (US), Teradata (US), H2O.ai (US), DataRobot (US), BigML (US), Databricks (US), Dataiku (France), Alibaba Cloud (China), Appier (Taiwan), Squark (US), Aible (US), Datafold (US), Boost.ai (Norway), Tazi.ai (US), Akkio (US), Valohai (Finland), dotData (US), Qlik (US), Mathworks (US), HPE (US), and SparkCognition (US).

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Original post:
Automated Machine Learning (AutoML) Market Size to Exhibit a CAGR of 44.6% By 2028 - openPR

The arms race for the next AI breakthrough is upon us -- and for good reason. While the news cycles are quick to point to fears around job displacement, bias, security risks, and weaponization, there is reason to be optimistic about this technology if we proceed carefully. We are stepping into the future of an AI-enabled world, and the competitive landscape for AI innovation should have only one bias in mind: a bias towards humanity.

AI should be viewed as a tool for enhancing human capabilities and productivity rather than one that replaces them. AI has the potential to amplify creativity, solve complex problems faster, and tackle tasks that are lengthy, tedious or impossible for humans. By positioning AI as a partner in progress, we can foster an environment where technological advancement serves to elevate human potential. From the perspective of the enterprises and innovators that are building this technology, its important that these tools are built with that idea at the core.

These improvements are lifesaving in certain sectors. In healthcare, AI algorithms are deployed and being refined to allow practitioners to diagnose diseases quickly and accurately, spotting early signs of conditions like cancer, diabetic retinopathy and heart disease from imaging scans that surpass the human eye by itself. AI systems can also speed up drug discovery and development by predicting molecular behavior and identifying promising drug candidates much faster than traditional methods. That significantly reduces the time and cost to bring new medicines to market.

Related:What You Need to Know about AI as a Service

Another example is in education. AI breakthroughs are establishing a more personalized process by adapting content and pacing it to individual learners needs. Machine learning can identify areas where students struggle, provide tailored resources, and enhance engagement through interactive and immersive experiences. This can help teachers create more tailored learning processes for each of their students, including those with special needs, so no one student gets left behind.

This just scratches the surface. AI has the potential to play a role in a variety of industries from manufacturing, supply chain, entertainment, environmental conservation, and much more.

Despite fears surrounding job displacement, history has shown that technological advancements lead to more jobs, not fewer. The advent of AI is no exception. It's true that repetitive tasks that are tedious in nature and easily structured, such as data processing and extraction from documents, will slowly start being automated away. However, these systems will have to be overseen by specialists and experts to ensure everything is running in a user-friendly way.

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By automating these routine tasks, AI frees humans to engage in more creative, strategic, and interpersonal roles. This transition underscores the importance of retraining initiatives to prepare the workforce for the jobs of the future. As the workforce evolves alongside technology, AI will serve more as a job creator rather than a job destroyer, as long as companies invest in upskilling their employees with the necessary capabilities to understand, leverage, implement, and improve technology.

We're already seeing evidence of this phenomenon. Big companies are starting to create new opportunities revolving around machine-learning or Gen AI capabilities. As Aaron Mok of Business Insider points out, job postings on LinkedIn that mention GPT have grown 20-fold from 2022 to 2023. He goes on to point out how Meta, Netflix, Apple and even non-tech companies in the healthcare, education, and legal industries have posted listings for AI-related jobs. Those who learn how to operate this technology effectively will have a growing number of employment opportunities.

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Theres a Pandora's box opening up as it relates to AI as it plays an increasingly significant role in creative and inventive processes, raising questions about authorship and ownership. At what point does a user take what is being given to them from an AI algorithm without challenging it? What are the ethical considerations of a consumer or an employee taking those considerations or recommendations and then parading them as their own work versus the work that's being done by the AI engine?

Addressing these concerns requires a multi-stakeholder approach, including policymakers, legal experts, technologists, and creators, to redefine intellectual property frameworks that recognize the contributions of both humans and AI. This could involve exploring new licensing models, establishing AI as a tool under human direction, establishing regulations and laws, and ensuring that creators are fairly compensated for AI-assisted work.

A human-centric approach should guide AI in all its developments. This involves ensuring transparency, accountability, and fairness in AI systems, with a focus on minimizing biases and increasing accessibility. Its important that AI systems also consider those with disabilities or sensory impairments to make their lives easier rather than creating more walls. Addressing AI bias requires a multifaceted approach, including diversifying datasets, implementing fairness metrics, and incorporating diverse perspectives in AI development teams.

By adopting a proactive, collaborative, and regulation-informed approach, we can harness the benefits of AI while safeguarding against its risks. The arms race of AI is here; but we can ensure that practitioners and thought leaders are always within arms reach of any AI implementation introduced into our daily lives.

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Focus on Humanity in the Age of the AI Revolution - InformationWeek