Category Archives: Machine Learning
Google adds Machine Learning to power up the Chrome URL bar – Chrome Unboxed
The Chrome URL bar, also known as the Omnibox, is an absolute centerpiece of most peoples web browsing experience. Used quite literally billions billions of times a day, Chromes URL bar helps users quickly find tabs, bookmarks, revisit websites, and discover new information. With the latest release of Chrome (M124), Google has integrated machine learning (ML) models to make the Omnibox even more helpful, delivering precise and relevant web page suggestions. Soon, these same models will enhance the relevance of search suggestions too.
In a recent post on the Chromium Blog, the engineering lead for the Chrome Omnibox team shared some insider perspectives on the project. For years, the team wanted to improve the Omniboxs scoring system the mechanism that ranks suggested websites. While the Omnibox often seemed to magically know what users wanted, its underlying system was a bit rigid. Hand-crafted formulas made it difficult to improve or adapt to new usage patterns.
Machine learning promised a better way, but integrating it into such a core, heavily-used feature was obviously a complex task. The team faced numerous challenges, yet their belief in the potential benefits for users kept them driven.
Machine learning models analyze data at a scale humans simply cant. This led to some unexpected discoveries during the project. One key signal the model analyzes is the time since a user last visited a particular website. The assumption was: the more recent the visit, the more likely the user wants to go there again.
While this proved generally true, the model also detected a surprising pattern. When the time since navigation was extremely short (think seconds), the relevance score decreased. The model was essentially learning that users sometimes immediately revisit the omnibox after going to the wrong page, indicating the first suggestion wasnt what they intended. This insight, while obvious in hindsight, wasnt something the team had considered before.
With ML models now in place, Chrome can better understand user behavior and deliver increasingly tailored suggestions as time goes on for users. Google plans to explore specialized models for different use contexts, such as mobile browsing or enterprise environments, too.
Most importantly, the new system allows for constant evolution. As peoples browsing habits change, Google can retrain the models on fresh data, ensuring the Omnibox remains as helpful and intuitive as possible moving forward. Its a big step up from the earlier, rigid models used before, and it will be increasingly interesting to keep an eye on the new suggestions and tricks that well see in the Omnibox as these ML models find their stride.
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Google adds Machine Learning to power up the Chrome URL bar - Chrome Unboxed
Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning … – Nature.com
In the present study we developed multiple machine learning models to predict major adverse cardiac events and acute coronary artery occlusion with preclinically obtained data. We then compared the performance of these models to a modified established risk score. We found that all of the ML models were superior to the TropOut score with the LR and the VC demonstrating the best performance in identifying MACE (AUROC 0.78). For ACAO the VC also comprised the best performance (AUROC 0.81). This is not surprising since it combines and weights the output of multiple models to optimize the predictive performance.
Quick decision-making is of utmost importance in preclinical diagnostic and treatment of patients with suspected ACS. Not every medical facility is equipped with a 24-h catheter laboratory. Therefore, a qualified assessment of early need for coronary revascularization is important in order to decide which hospital to admit the patient to and thereby guarantee optimal patient care and improve prognosis2,3,4.
Several studies have been undertaken to evaluate the predictive value of the established HEART score in an emergency setting5,13. Sagel et al. 6 even modified the score to predict MACE in a preclinical setting, thus creating the preHEART score. However, one of the HEART score components is the analysis of troponin levels. Even though the authors of the preHEART score used rapid, visual point of care immunoassays, these are unfortunately not available for emergency care providers in most areas. In order to test the performance of this score without troponin, we retrospectively calculated the TropOut score, a version of the preHEART score comprising medical history, ECG, age and risk factors but without troponin analysis. Unfortunately, this the TropOut score showed poor discriminatory power to identify MACE and ACAO in preclinical patients with chest pain within our study cohort.
With the use of ML algorithms, we were able to create models with vastly improved performance. As mentioned above, the VC model showed an AUROC value of 0.78 for prediction of MACE and 0.81 for ACAO. Even though this performance cannot quite hold up to the original preHEART score (AUROC=0.85) for predicting MACE, the performance is remarkable, especially when considering that the driving key biomarker troponin was excluded in our proposed model. Since cardiac troponin has a high sensitivity for myocardial cell loss, it is very likely that its addition would have also significantly improved our models performance. Therefore, the addition of troponin essays in the preclinical setting would likely help identifying patients with ACAO or at risk for MACE even further.
We noted a significantly higher specificity compared to sensitivity for predicting both MACE and ACAO. Apparently, the model makes very reliable predictions the majority of the time but there seem to be cases which are wrongly classified as non-MACE and non-ACAO. This might be due to unspecific symptoms or atypical ECG findings which do not meet the established STEMI criteria14,15.
Multiple authors have used ML models for risk stratification in cardiology9,16. ML has been shown to identify and safely rule-out MI in an inner clinical cohort suspected of NSTEMI using multiple variables including cardiac troponin17,18,19. However, ML algorithms display limited ability to predict mortality in patients with MI20. To our knowledge, there have been two studies which used machine learning models to predict ACS in a purely preclinical setting. Al-Zaiti et al. tried to predict ACS only using data from a preclinical 12-lead ECG whereas Takeda et al. used vital signs, history and a 3-lead ECG to predict ACS and myocardial infarction21,22. Our approach is novel and different in that we chose a different secondary endpoint. MACE was chosen in order to directly compare our model to established, non-ML scores. For the preclinical management, our secondary endpoint, acute coronary artery occlusion, could be even more relevant. Myocardial infarction can be caused by different underlying pathophysiologies. Myocardial cell loss secondary to a demandsupply mismatch in oxygen not related to atherosclerotic plaque instability is known as a type II myocardial infarction3. However, those patients do not necessarily need immediate interventional revascularization and the broad definition of myocardial infarction therefore might be an improper endpoint. In the 2022 Expert Consensus Decision Pathway on the Evaluation and Disposition of Acute Chest Pain, the American College of Cardiology also notes that up to 40% of patients with ACAO are not correctly identified by using the STEMI criteria14,23. Therefore, ACAO could be a superior parameter to help decide on where to admit the patient to and whether or not to preclinically administer antiplatelet drugs. Patients with NSTEMI but especially with acute coronary artery occlusion without ST elevations on ECG have been shown to receive delayed PCI when compared to patients suffering from ST-elevation myocardial infarction and have worse outcomes24,25. As mentioned above, our model showed especially good predictive capabilities for ACAO.
Even though ML algorithms clearly have high potential to support decision making, our model heavily relies on medical expertise by healthcare providers. As seen in Fig.5, the feature ST-Elevation as assessed by the emergency physician still is paramount for predicting both endpoints in our models. Not surprisingly, similar findings have been reported by Takeda et al.21.
SHAP analyses provides interesting insights into predictive value of symptoms, patient history and vital signs. While some features like ECG changes, age, sex and risk factor are easy to interpret, others seem more complex. In our model, diaphoresis was associated with both high and low risk for MACE and ACAO. This might be in part explained by our retrospective study design. Even though notes from the emergency protocol provide clear, dichotomous information, we cannot say if the treating physician associated the symptom diaphoresis with an ACS since the symptom can have a vastly different Clinical Gestalt. This could explain that our model performed worse when compared to Takeda et al. An alternative, provocative explanation could be a higher diagnostic skill level (like ECG interpretation and history taking) of paramedics when compared to physicians in a preclinical setting. Also, the patient collective could be different since the study by Takeda et al. was carried out in Japan.
Sensitivities for our model ranged from 0.70 to 0.77 for predicting MACE and 0.760.88 for predicting ACAO. In comparison, a meta analyzes including over 44,000 patients demonstrated a sensitivity of 0.96 for predication of MACE when a cutoff of4 points of the heart score was used. As expected, this resulted in a rather poor specificity of 0.45%26.
The ideal model would demonstrate both high sensitivities and specificities. Unfortunately, in a condition like ACS and a setting were laboratory diagnostics like troponin is not available, this seems difficult to achieve. However, we have to admitted that in a life-threatening condition like ACS, false positives (i.e. poor sensitivity) are more acceptable then false negatives (i.e. poor specificity). In our models, patients were classified as positive if the predicted probability was great or equal to 0.5, and negative if otherwise. In order to enhance sensitivity, programming of our models could be adapted. Naturally, this would result in a decline in specificity. Most importantly, clinicians using tools like the one developed in our study need to be aware of the models strengths and limitations. As of right now, our model is not suitable for excluding ACAO or patients at risk of MACE in a preclinical collective suspected of ACS. However, it could increase emergency physicians confidence in preclinically activating the coronary catheter laboratory for suspected ACAO.
In our district, preclinical documentation is carried out digitally with the use of tablets. Since patient history, vitals and ECG interpretation need to be inputted for documentation anyways, it would be feasible to integrate ML models. This way, the software could automatically calculate variables like sensitivities and specificities for endpoints like ACAO and MACE. Furthermore, ML has been used in ECG interpretation in a preclinical setting22,27. Combining those ML algorithms could potentially show a better performance and present a powerful tool in aiding preclinical health care providers on site even further.
Even in the absence of direct integration of our models into preclinical ACS diagnostics, our study has important clinical implications. Unsupervised analyses show that preclinical ACS patients are a heterogenous collective and desired endpoints are not easily identified. Even when using supervised machine learning, a high level of diagnostic skill will always be necessary since the models rely on high quality data. As mentioned before, SHAP analyses shows that out of all investigated parameters, ST-elevation is still the most important marker for properly identify ACAO and patients at risk of MACE. This highlights the necessity for a high clinical expertise and ECG interpretation skills in professionals diagnosing and treating patients with suspected ACS in a preclinical setting.
Our study has several limitations. For ECG interpretation, we had to rely on the emergency physicians documentation and were not able to manually interpret the preclinical 12-lead ECG ourselves. Therefore, the quality and accuracy of this documentation might vary. Our study design relied on retrospective data collection. A predetermined questionnaire would likely improve the quality of the data and also the models predictive power.
Since patients could present to the emergency department on their own or in rare cases might be transferred by other providers than the cooperating rescue stations, we cannot exclude missing some cases of ACS in our study. Therefore, selection bias cannot be fully excluded.
In line with common machine learning methodology, we did validate our findings on the validation cohort. However, our algorithm has not yet been validated on external data. Especially the lack of a prospective validation cohort is the biggest limitation of our study and further analysis is needed. To our knowledge, the only comparable study which used prospectively recorded data was carried out by Takeda et al. and achieved slightly better AUROC for the endpoint ACS then our study did for MACE and ACAO (0.86 versus 0.78 and 0.81 respectively)21. However, because of the different preclinical emergency systems in Japan and Germany (paramedics versus emergency medicine physician), the studies are only partially comparable. Since most countries rely on paramedics for preclinical emergency medicine, our findings might not be directly transferable to other settings. At the moment, our study can only be viewed as hypothesis generating until the algorithms are prospectively validated on another patient cohort.
Improving inclusion and accessibility through automated document translation with an open source app using Amazon … – AWS Blog
Organizations often offer support in multiple languages, saying contact us for translations. However, customers who dont speak the predominant language often dont know that translations are available or how to request them. This can lead to poor customer experience and lost business. A better approach is proactively providing information in multiple languages so customers can access it directly. This leads to more informed, satisfied, and included customers.
In this post, we share how we identified these challenges and overcame them through our work with Swindon Borough Council. We developed the Document Translation app, which uses Amazon Translate, to address these issues. The app is a business user app for self-serve translations. The app is created in partnership with Swindon Council and released as open source code freely available for your organization to use.
We identified three key challenges:
Translation accuracy and quality are critical, because the results must be accurate and understood. As quoted in the Swindon Borough Council case study:
The council ran small-scale trials with the main digital translation providers that can support the different languages spoken by Swindons citizens. It recruited local bilingual volunteers to assess the quality of the machine translations against their first languages, and Amazon Translate came out on top.
The Document Translation app uses Amazon Translate for performing translations. Amazon Translate provides high-quality document translations for contextual, accurate, and fluent translations. It supports many languages and dialects, providing broad coverage for customers worldwide. Custom terminology, a feature of Amazon Translate,is dynamically utilized by the app workflow when a language has matching custom terminology available.
High costs of manual translation can prohibit organizations from supporting multiple languages, straining already tight budgets. Balancing language inclusivity and budget limitations poses a significant challenge when relying solely on traditional translation methods.
Swindon Borough Council paid around 159.81 ($194.32 USD) per single-page document, limiting them to providing translation only where legally required. As discussed in the case study, Swindon Borough Council slashed 99.96% of translation costs using Amazon Translate:
Such dramatic savings mean that its no longer limited to translating only documents it is legally required to provideit can offer citizens wider access to content for minimal extra cost.
Customers report third-party translation services fees as a major cost. The neural machine translation technology of Amazon Translate dramatically lowers these costs.
Following the Cost Optimization pillar of the AWS Well-Architected Framework further led to implementing an AWS Graviton architecture using AWS Lambda and an infrequently accessed Amazon DynamoDB table class. With no server management overhead or continually running systems, this helps keep costs low.
Manual translation delays that lower customer satisfaction also include internal processes, approvals, and logistics arrangements in place to control costs and protect sensitive and private content. Swindon Borough Council stated that turnaround times could take up to 17 days:
First, it was slow. The internal process required manual inputs from many different people. On average, that process took up to 12 days, and the time required by the translation agency was 35 days. That meant total translation time for a document was up to 17 days.
This app offers a business user self-serve portal for document translations. Users can upload documents and download translations for sharing without slow manual intervention. Amazon Translate can perform translations in about 10 minutes.
The apps business user portal is a browser-based UI that has been translated into all languages and dialects supported by Amazon Translate. The dynamic React UI doesnt require server software. To accelerate development, UI components such as buttons and input boxes come from the AWS Cloudscape Design library. For interacting with AWS services, the AWS Amplify JS library for React simplifies the authentication, security, and API requests.
The backend uses several serverless and event-driven AWS services, including AWS Step Functions for low-code workflows, AWS AppSync for a GraphQL API, and Amazon Translate. This architecture enables fast development and reduces ongoing management overhead, as shown in the following diagram.
The app is built with Infrastructure as Code (IaC) using the AWS Cloud Development Kit (AWS CDK). The AWS CDK is an open source software development framework used to model and provision cloud applications. Using the Typescript CDK provides a reliable, repeatable, and extensible foundation for deployments. Paired with a consistent continuous integration and delivery (CI/CD) pipeline, deployments are predictable. Reusable components are extracted into constructs and imported where needed, providing consistency and best practices such as AWS Identity and Access Management (IAM) roles, Amazon CloudWatch logging, and AWS X-Ray tracing for all Lambda functions.
The app is effortless to deploy using the AWS CDK. The AWS CDK allows modeling of the entire stack, including frontend React code, backend functions and workflows, and cloud infrastructure definitions packaged together.
Before deployment, review any prerequisites you may want to use, such as connecting this to your organizations single sign-on with the SAML provider.
The installation wizard provides the necessary commands. AWS CloudShell allows you to run these commands without installing anything locally. The app documentation covers all advanced options available. Installation takes 3060 minutes and is monitored from AWS CodePipeline.
A self-paced Immersion Day is available for your technical teams to get hands-on experience with the services and build core components. Alternatively, your AWS account team can provide personalized guidance through the workshop.
This app is designed with multiple features (as of this writing, Document Translation and Simply Readable). Simply Readable enables you to create Easy Read documents with generative artificial intelligence (AI) using Amazon Bedrock. The app can be installed with or without this feature.
The Document Translation app provides translations in your customers native languages. Amazon Translate enables accurate translation at scale. Communicating in customers languages shows respect, improves understanding, and builds trust.
Translation capabilities should be core to any growth strategy, building loyalty and revenue through superior localized experiences.
Business leaders should evaluate solutions like Amazon Translate to overcome language barriers and share their brand. Enabling multilingual communication conveys We value you, we hear you, and we want your experience with us to be positive.
To learn more about the app, see the FAQ.
Philip Whiteside is a Solutions Architect (SA) at Amazon Web Services. Philip is passionate about overcoming barriers by utilizing technology.
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Improving inclusion and accessibility through automated document translation with an open source app using Amazon ... - AWS Blog
Chrome’s Omnibox address bar is now powered by machine learning – 9to5Google
With Chrome 124 on Mac, Windows, and ChromeOS, Google has updated the address bar, or Omnibox, with ML models to offer better suggestions.
Previously, Chrome leveraged a set of hand-built and hand-tuned formulas that were difficult to improve or to adapt to new scenarios. For example, one signal is time since last navigation:
The expectation with this signal is that the smaller it is (the more recently youve navigated to a particular URL), the bigger the contribution that signal should make towards a higher relevance score.
Google says the scoring system responsible for showing/ranking URLs and suggested queries went largely untouched for a long time.
For most of that time, an ML-trained scoring model was the obvious path forward. But it took many false starts to finally get here. Our inability to tackle this challenge for so long was due to the difficulty of replacing the core mechanism of a feature used literally billions of times every day.
This new ML system should result in the Chrome address bar returning page suggestions that are more precise and relevant to you. It will allow Google to collect fresher signals, re-train, evaluate, and deploy new models periodically over time. One improvement the model made with time since last navigation was:
when the time since navigation was very low (seconds instead of hours, days or weeks), the model was decreasing the relevance score. It turns out that the training data reflected a pattern where users sometimes navigate to a URL that was not what they really wanted and then immediately return to the Chrome omnibox and try again. In that case, the URL they just navigated to is almost certainly not what they want, so it should receive a low relevance score during this second attempt.
Looking ahead, Google is looking at incorporating new signals, like differentiating between time of the day to improve relevance.
The team is also exploring training specialized versions of the model for particular environments. This new approach is currently live on desktop, but future iterations could target mobile, enterprise, and education usage.
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Chrome's Omnibox address bar is now powered by machine learning - 9to5Google
Optimized model architectures for deep learning on genomic data | Communications Biology – Nature.com
Hyperparameter search space
The hyperparameter space used for optimization is listed in Table1 and described in more detail here.
The first part of the model constructed by GenomeNet-Architect consists of a sequence of convolutional blocks (Fig.1), each of which consists of convolutional layers. The number of blocks (Ncb) and the number of layers in each block (scb) is determined by the HPs ncb and nc in the following way: Ncb is directly set to ncb unless nc (which relates to the total number of convolutional layers) is less than that. Their relation is therefore
$${N}_{{cb}}=left{begin{array}{cc}{n}_{c},&{{if} , {n}_{c}le {n}_{{cb}}}\ {n}_{{cb}}, &{otherwise}end{array}right.$$
scb is calculated by rounding the ratio of the nc hyperparameter to the actual number of convolutional blocks Ncb:
$${s}_{{cb}}={round}left(frac{{n}_{c}}{{N}_{{cb}}}right).$$
This results in nc determining the approximate total number of convolutional layers while satisfying the constraint that each convolutional block has the same (integer) number of layers. The total number of convolutional layers is then given by
$${N}_{c}={N}_{{cb}}times {s}_{{cb}}.$$
f0 and fend determine the number of filters in the first or last convolutional layers, respectively. The number of filters in intermediate layers is interpolated exponentially. If residual blocks are used, the number of filters within each convolutional block needs to be the same, in which case the number of filters changes block-wise. Otherwise, each convolutional layer can have a different number of filters. If there is only one convolutional layer, f0 is used as the number of filters in this layer. Thus, the number of filters for the ith convolutional layer is:
$${f}_{i}=leftlceil {f}_{0}times {left(frac{{f}_{{end}}}{{f}_{0}}right)}^{jleft(iright)}rightrceil,,jleft(iright)=left{begin{array}{cc}leftlfloor frac{i}{{s}_{{cb}}}rightrfloor times frac{1}{{N}_{{cb}}-1}, & {if} , res_block\ frac{i}{{N}_{c}-1}, & {otherwise}end{array}right..$$
The kernel size of the convolutional layers is also exponentially interpolated between k0 and kend. If the model has only one convolutional layer, the kernel size is set to k0. The kernel size of the convolutional layer i is:
$${k}_{i}=leftlceil{k}_{0}times {left(frac{{k}_{{end}}}{{k}_{0}}right)}^{frac{i}{{N}_{c}-1}}rightrceil.$$
The convolutional layers can use dilated convolutions, where the dilation factor increases exponentially from 1 to dend within each convolutional block. Using rem as the remainder operation, the dilation factor for each layer is then:
$${d}_{i}=leftlceil{d}_{{end}}^{,left(leftlfloor i,{{{{{boldsymbol{rem}}}}}},{s}_{{cb}}rightrfloor right)/left({s}_{{cb}}-1right)}rightrceil.$$
We apply max-pooling after convolutional layers, depending on the total max-pooling factor pend. Max pooling layers of stride and a kernel size of 2 or the power of 2 are inserted between convolutional layers so that the sequence length is reduced exponentially along the model. pend represents the approximate value of total reduction in the sequence length before the output of the convolutional part is fed into the last GAP layer or into the RNN layers depending on the model type.
For CNN-GAP, outputs from multiple convolutional blocks can be pooled, concatenated, and fed into a fully connected network. Out of Ncb outputs, the last min(1, (1 rs) Ncb) of them are fed into global average pooling layers, where rs is the skip ratio hyperparameter.
GenomeNet-Architect uses the mlrMBO software38 with a Gaussian process model from the DiceKriging R package39 configured with a Matrn-3/2 kernel40 for optimization. It uses the UCB31 infill criterion, sampling from an exponential distribution as a batch proposal method32. In our experiment, we proposed three different configurations simultaneously in each iteration.
For both tasks, we trained the proposed model configurations for a given amount of time and then evaluated them afterwards on the validation set. For each architecture (CNN-GAP and CNN-RNN) and for each sequence length of the viral classification task (150nt and 10,000nt), the best-performing model configuration found within the optimization setting (2h, 6h) was saved and considered for further evaluation. For the pathogenicity detection task, we only evaluated the 2h optimization. For each task and sequence length value, the first t = t1 (2h) optimization evaluated a total of 788 configurations, parallelized on 24 GPUs, and ran for 2.8 days (wall time). For the viral classification task, the warm-started t = t2 (6h) optimization evaluated 408 more configurations and ran for 7.0 days for each sequence length value.
During HPO, the number of samples between model validation evaluations was set dynamically, depending on the time taken for a single model training step. It was chosen so that approximately 20 validation evaluations were performed for each model in the first phase (t = 2h), and approximately 100 validation evaluations were performed in the second phase (t = 6 hours). In the first phase, the highest validation accuracy found during model training was used as the objective value to be optimized. In the second phase, the second-highest validation accuracy found in the last 20 validation evaluations was used as the objective value. This was done to avoid rewarding models with a very noisy training process with performance outliers.
The batch size of each model architecture is chosen to be as large as possible while still fitting into GPU memory. To do this, GenomeNet-Architect performs a binary search to find the largest model that still fits in the GPU and subtracts a 10% safety margin to avoid potential training failures.
For the viral classification task, the training and validation samples are generated by randomly sampling FASTA genome files and splitting them into disjoint consecutive subsequences from a random starting point. A batch size that is a multiple of 3 (the number of target classes) is used, and each batch contains the same number of samples from each class. Since we work with datasets that have different quantities of data for each class, this effectively oversamples the minor classes compared to the largest class. The validation set performance was evaluated at regular intervals after training on a predetermined number of samples, set to 6,000,000 for the 150 nt models and 600,000 for the 10,000 nt models. The evaluation used a subsample of the validation set equal to 50% of the training samples seen between each validation. During the model training, the typical batch size was 1200 for the 150 nt models, and either 120, 60, or 30 for the 10,000 nt models. Unlike during training and validation, the test set samples were not randomly generated by selecting random FASTA files. Instead, test samples were generated by iterating through all individual files, and using consecutive subsequences starting from the first position. For the pathogenicity detection task, the validation performance was evaluated at regular intervals on the complete set, specifically once after training on 5,000,000 samples. The batch size of 1000 was used for all models, except for GAP-RNN, as it was not possible with the memory of our GPU. For this model, a batch size of 500 was used.
For both tasks, we chose a learning rate schedule that automatically reduced the learning rate by half if the balanced accuracy did not increase for 3 consecutive evaluations on the validation set. We stopped the training when the balanced accuracy did not increase for 10 consecutive evaluations. This typically corresponds to stopping the training after 40/50 evaluations for the 150 nt models, 25/35 evaluations for the 10,000 nt models for the viral classification tasks, and 5/15 evaluations for the pathogenicity detection task.
To evaluate the performance of the architectures and HP configurations, the models proposed by GenomeNet-Architect were trained until convergence on the training set; convergence was checked on the validation set. The resulting models were then evaluated on a test set that was not seen during optimization.
For the viral classification task, we downloaded all complete bacterial and viral genomes from GeneBank and RefSeq using the genome updater script (https://github.com/pirovc/genome_updater) on 04-11-2020 with the arguments -d genbank,refseq -g bacteria/viral -c all and -l Complete Genome. To filter out possible contamination consisting of plasmids and bacteriophages, we removed all genomes from the bacteria set with more than one chromosome. Filtering out plasmids due to their inconsistent and poor annotations in databases avoids introducing substantial noise in sequence and annotation since they can be incorrectly included or excluded in genomes. We used the taxonomic metadata to split the viral set into eukaryotic or prokaryotic viruses. Overall this resulted in three subgroups: bacteria, prokaryotic bacteriophages, and eukaryotic viruses (referred to as non-phage viruses, Table2). To assess the models generalization performance, we subset the genomes into training, validation, and test subsets. We used the date of publishing metadata to split the data by publication time, with the training data consisting mostly of genomes published before 2020, and the validation and test data consisting of more recently published genomes. Thus, when applied to newly sequenced DNA, the classification performance of the models on yet unknown data is estimated. For smaller datasets, using average nucleotide identity information (ANI) generated with tools such as Mashtree41 to perform the splits can alternatively be used to avoid overlap between training and test data.
The training data was used for model fitting, the validation data was used to estimate generalization performance during HPO and to check for convergence during final model training, and the test data was used to compare final model performance and draw conclusions. The test data was not seen by the optimization process. The training, validation and test sets represent approximately 70%, 20%, and 10% of the total data, respectively.
The number of FASTA files in the sets and the number of non-overlapping samples in sets of the viral classification task are listed in Table2. Listed is the number of different non-overlapping sequences that could theoretically be extracted from the datasets, were they split into consecutive subsequences. However, whenever the training process reads a file again, e.g. in a different epoch, the starting point of the sequence to be sampled is randomized, resulting in a much larger number of possible distinct (though overlapping) samples. Because the size of the test set is imbalanced, we report class-balanced measures, i.e. measures calculated for each class individually and then averaged over all classes.
For the pathogenicity classification task, we downloaded the dataset from https://zenodo.org/records/367856313. Specifically, the used training files are nonpathogenic_train.fasta.gz, pathogenic_train.fasta.gz, the used validation files are pathogenic_val.fasta.gz, nonpathogenic_val.fasta.gz, and the used test files are nonpathogenic_test_1.fasta.gz, nonpathogenic_test_2.fasta.gz, pathogenic_test_1.fasta.gz, pathogenic_test_2.fasta.gz.
Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.
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Optimized model architectures for deep learning on genomic data | Communications Biology - Nature.com
Agora Introduces Adaptive Video Optimization Technology | TV Tech – TV Technology
SANTA CLARA, Calif.Agora today unveiled its Adaptive Video Optimization (AVO) technology that uses machine learning to adjust parameters dynamically at every step from capture to playback for the delivery of an enhanced live video streaming experience.
AVO, which includes support for the AV1 video codec, uses a series of advanced machine learning algorithms to address common issues, such as unstable network conditions, packet loss and limited bandwidth, and their impact on streaming video like freezes, stutters, dropped connections and grainy images, the company said.
By optimizing video quality in real time based on network conditions, device capabilities and available bandwidth, AVO ensures the highest video quality possible, it said.
"Reliable and high-quality live streaming is essential in todays video-dominated media landscape," stated Tony Zhao, CEO and co-founder of Agora. "Our Adaptive Video Optimization technology enables smooth delivery of every call and livestreamdespite network variability, users location or device. These improvements increase engagement and empower Agoras customers to provide the highest-quality live video user experience.
Machine learning is used to ensure video streaming is optimized from pre-processing, encoding, and transmission to decoding and post-processing, the company said.
AVO supports advanced video codecs like AV1 and VP9 and dynamically switches to the codec most suitable for an exceptional video experience despite device limitations or streaming constraints. By employing advanced techniques and compression methods, the technology adapts, configuring parameters dynamically to ensure crisp visuals, efficient bandwidth use and a consistent, high-quality experience, it said.
More information is available on the companys website.
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Agora Introduces Adaptive Video Optimization Technology | TV Tech - TV Technology
Bridging the Gap: Keys to Embracing AI in 2024 – AiThority
The emergence of ChatGPT in November of 2022 caused a sonic boom across every tech industry in the world. Since then, companies of all sizes have explored use cases for implementing AI into their operations. However, some sectors have been slower with adoption, namely compliance.
Recommended; Googles New Courses in GenAI, Cybersecurity, and Data Analytics
In fact, a Moodys report that surveyed compliance and risk leaders and professionals shows that only9%of firms actively use AI, and 21% arent considering using it at all.
This isnt shocking news. A recentE&Yreport said, Historically, compliance professionals have treated technological innovation with skepticism. Its easy to understand why an industry rooted in transparency, honesty, and ethics is reluctant to adopt an early-stage technology that is particularly riddled with biases andethical dilemmas. Plus, theregulatory and legaloutlook for AI is still quite murky, making it harder to implement it confidently.
AI is uncharted territory for most companies, and some in compliance might prefer to avoid it while the technology is still new. However, the more it evolves, the more it becomes clear that AI adds critical business value when used responsibly for certain tasks especially within professional services firms that have struggled to implement digital transformation and continue to rely on manual, outdated processes.
Lets explore how auditors can embrace AI, enabling them to create process efficiencies and free up time to add value to customers.
Several compliance practices can be streamlined with AI. Whether auditors are using compliance software that has recently rolled out AI functionalities or general tools like ChatGPT, theres value in automating certain processes.
For starters, theres surface-level automation like virtual meeting summaries and transcription on demand. This is a simple yet significant first step for firms to dip their toes into AI without fearing biased outcomes. Tools like Fathom AI Notetaker simply process the information and present it in a summarized and organized way. They can do this accurately and quickly, with a built-in human approval layer to eliminate the occasional mistranslation, and the details are searchable to find the exact moment in a call you want to remember.
Dropping the need to feverishly take notes (and the accompanying anxiety of forgetting a follow-up) clears your head to identify opportunities to add value and build stronger relationships with your clients.
Many of the mundane and frustrating aspects of compliance, such as performing vendor reviews, inspecting policies, and completing security questionnaires, are coming into the AI fold to speed up and simplify these processes. These tasks may give security professionals nightmares, but theyre not the risks that keep the CISO up at night.
By taking advantage of AI tools, companies are able to get rid of repetitive checklist procedures and focus on the areas of their security program that truly matter.
Top News: Aligneds AI Co-pilot Helps B2B Sales Teams Close Deals Even in Their Sleep
When its done right, the time saved from this technology is re-invested in adding value and strengthening other areas of the business. Speed and turnaround time are the obvious benefits, but firms can also drive improvements in quality and general engagement amongst their team. The energy wasted trying to write that perfect paragraph in a client deliverable that may or may not get read at all is suddenly accessible to deploy toward your passions.
Advocating for AI in the industry doesnt mean blindly trusting it.
Firms must carefully vet the tools they use and those of their business partners so they are comfortable with their safety and accuracy. The technology is breathtaking, but it still makes many mistakes, meaning auditors still need to participate in the inputs and outputs.
Moodys report also revealed that 9 out of 10 companies using AI for compliance have seen a positive impact on risk management. In todays digital age, staying competitive is made possible through emerging technologies.
Besides saving time by streamlining paperwork, chatting with a bot to answer compliance questions, and automating document classification, AI tools make it easy to delegate client-facing tasks like simpler form completion. Time and accuracy are of the essence. So, its evident why clients would prefer a firm that uses and values these new technologies instead of one with more traditional approaches.
Moreover, the key for firms in adopting AI and other new technologies is education. Employees need to understand the why and how behind each technology, how to use it, and the expected results. Once stakeholders decide AI is right for them, they can roll out dedicated training sessions for employees to get acquainted with the specific tools they need and maximize their daily operations.
Human input is critical to quality AI output, so this step cant be overlooked.
Compliance software companies are well aware of the high level of transparency their industry requires. As a result, they build trust with their customers by addressing data privacy concerns and being open about the inner workings of their AI tools avoiding the infamousblack-boxaspect of many AI-powered services.
The compliance management platforms that have implemented AI tools have delivered comprehensive information explaining their AI philosophy in tandem with the release of their AI tools. They discuss processes like encryption to ensure sensitive data stays secure, monitoring outcomes to reduce biases, and extensive testing to provide clients with a reliable and secure tool.
The keys to taking the leap into AI include understanding the technology behind each tool, how and where the information comes from, and how it is protected. If a software company isnt transparent about this information, it should be a red flag. Making informed decisions on which tools to implement will set companies up for success they must know theyre not cutting corners with this technology but rather adopting more efficient, agile, and precise methodologies into their compliance examinations.
Feeling comfortable experimenting with new technologies gives compliance firms a competitive advantage in an industry that is slow to change. AI tools arent the immediate end-all-be-all for the profession, but it wouldnt be surprising to see use cases expand in the near future. Given the current trajectory, this technology may assist in report writing and evidence review in the same way a calculatorassistsin preparing a tax return.
From what weve seen with AI in the past year, its safe to say it is an everchanging technology that will probably never be fully developed; it will take on new forms as technology advances. Those who choose to stay on the sidelines, waiting for innovations to develop into their final form, might never find the right time to engage with AI tools. The truth is, the time is now.
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Bridging the Gap: Keys to Embracing AI in 2024 - AiThority
Chrome’s address bar adds machine learning to deliver better suggestions – Android Authority
Edgar Cervantes / Android Authority
TL;DR
The address bar in the Chrome browser just got a big update. Google says this update should help the address bar provide web page suggestions that are more precise and relevant than before.
In a blog post, the Mountain View-based firm announced that the latest version of Chrome (M124) will bring a big improvement to the address bar, also known as the omnibox. Specifically, Google has integrated machine learning (ML) models into the omnibox, which will provide suggestions that more accurately align with what youre looking for.
As the company explains, the tool previously relied on hand-built and hand-tuned formulas to offer suggested URLs. The problem, however, is that these formulas werent flexible enough to be improved or adapt to different situations. Google says with these new ML models, it can collect fresher signals, re-train, evaluate, and deploy new models over time. Since these formulas have remained largely untouched for years, this update is kind of a big deal.
Something the ML models will be able to take into account before suggesting a web page is the time since you last visited a URL. For example, if you navigated away from a page in the last few seconds or minutes, the model will give that URL a lower relevancy score as it was likely not the site you were looking for.
Going forward, the tech giant says it plans to explore training specialized versions of the model for particular environments: for example, mobile, enterprise or academic users, or perhaps different locales.
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Chrome's address bar adds machine learning to deliver better suggestions - Android Authority
Zimbabwean native graduates from Thunderbird at ASU with passion for big data and machine learning | ASU News – ASU News Now
Editors note:This story is part of a series of profiles ofnotable spring 2024 graduates.
Ngonidzashe Marvin Kanjere is originally from Harare, Zimbabwe, and has a background in chemical engineering. He earned a master's degree in financial engineering from the National University of Science and Technology in Zimbabwe.
Kanjere's lineage traces back to Malawi, with his great-grandparents settling in Zimbabwe in the early 1900s. Enrolling at Thunderbird School of Global Management at Arizona State University, his goal was to secure a globally recognized management degree, enticed by the school's diverse concentrations.
Graduating with a Master of Global Managementspecializing in data science, Kanjere expresses enthusiasm for applying his analytical skills to roles in data or business analytics. Fluent in both English and Shona, he commends Thunderbird for its cultural breadth, describing it as a unique experience.
"The diversity of cultures on campus makes being a T-bird a very enriching experience. I have learned to greet in four different languages and approach ideas from different perspectives. I also appreciate the school's rich history and the supportive network of alumni," he said.
Passionate about education, Kanjere has dedicated over five years to tutoring math and science, aiming to uplift underrepresented communities. He also envisions a world where there are greater educational resources for developing countries.
As he graduates from Thunderbird, Kanjere is excited to use his degree and his passion for education to make a positive impact in the world.
He is also one of the spring 2024 recipients of Thunderbirds Barton Kyle Yount Award. This award is presented to a student who best represents the values and standards envisioned by the founders of Thunderbird in 1946, and it is the schools highest student honor.
Question: Which professor taught you the most important lesson while at Thunderbird?
Answer: In my Responsible Investing class, Professor Pecherot taught me a lesson that being a good manager requires a broad base of knowledge, saying, "You have to know a little bit about everything." With my background in engineering, finance and data analytics, this advice resonates deeply with me.
Q: What advice would you give to a student just starting a program at Thunderbird?
A: As a new student, dive into school life by joining clubs and participating in volunteer opportunities. Additionally, make the most of the Thunderbird alumni network and the Career Management Center resources for both personal and professional development.
Q: For what in your life do you feel most grateful?
A: I am grateful for all the experiences that have challenged me to grow personally and professionally, and the family and friends that have supported me through the journey.
Original post:
Zimbabwean native graduates from Thunderbird at ASU with passion for big data and machine learning | ASU News - ASU News Now
Build private and secure enterprise generative AI apps with Amazon Q Business and AWS IAM Identity Center … – AWS Blog
As of April 30, 2024 Amazon Q Business is generally available. Amazon Q Business is a conversational assistant powered by generative artificial intelligence (AI) that enhances workforce productivity by answering questions and completing tasks based on information in your enterprise systems. Your employees can access enterprise content securely and privately using web applications built with Amazon Q Business. The success of these applications depends on two key factors: first, that an end-user of the application is only able to see responses generated from documents they have been granted access to, and second, that each users conversation history is private, secure, and accessible only to the user.
Amazon Q Business operationalizes this by validating the identity of the user every time they access the application so that the application can use the end-users identity to restrict tasks and answers to documents that the user has access to. This outcome is achieved with a combination of AWS IAM Identity Center and Amazon Q Business. IAM Identity Center stores the user identity, is the authoritative source of identity information for Amazon Q Business applications, and validates the users identity when they access an Amazon Q Business application. You can configure IAM Identity Center to use your enterprise identity provider (IdP)such as Okta or Microsoft Entra IDas the identity source. Amazon Q Business makes sure that access control lists (ACLs) for enterprise documents being indexed are matched to the user identities provided by IAM Identity Center, and that these ACLs are honored every time the application calls Amazon Q Business APIs to respond to user queries.
In this post, we show how IAM Identity Center acts as a gateway to steer user identities created by your enterprise IdP as the identity source, for Amazon Q Business, and how Amazon Q Business uses these identities to respond securely and confidentially to user queries. We use an example of a generative AI employee assistant built with Amazon Q Business, demonstrate how to set it up to only respond using enterprise content that each employee has permissions to access, and show how employees are able to converse securely and privately with this assistant.
The following diagram shows a high-level architecture of how the enterprise IdP, IAM Identity Center instance, and Amazon Q Business application interact with each other to enable an authenticated user to securely and privately interact with an Amazon Q Business application using an Amazon Q Business web experience from their web browser.
When using an external IdP such as Okta, users and groups are first provisioned in the IdP and then automatically synchronized with the IAM Identity Center instance using the SCIM protocol. When a user starts the Amazon Q Business web experience, they are authenticated with their IdP using single sign-on, and the tokens obtained from the IdP are used by Amazon Q Business to validate the user with IAM Identity Center. After validation, a chat session is started with the user.
The sample use case in this post uses an IAM Identity Center account instance with its identity source configured as Okta, which is used as the IdP. Then we ingest content from Atlassian Confluence. The Amazon Q Business built-in connector for Confluence ingests the local users and groups configured in Confluence, as well as ACLs for the spaces and documents, to the Amazon Q Business application index. These users from the data source are matched with the users configured in the IAM Identity Center instance, and aliases are created in Amazon Q Business User Store for correct ACL enforcement.
To implement this solution for the sample use case of this post, you need an IAM Identity Center instance and Okta identity provider as identity source. We provide more information about these resources in this section.
An Amazon Q Business application requires an IAM Identity Center instance to be associated with it. There are two types of IAM Identity Center instances: an organization instance and an account instance. Amazon Q Business applications can work with either type of instance. These instances store the user identities that are created by an IdP, as well as the groups to which the users belong.
For production use cases, an IAM Identity Center organization instance is recommended. The advantage of an organization instance is that it can be used by an Amazon Q Business application in any AWS account in AWS Organizations, and you only pay once for a user in your company, if you have multiple Amazon Q Business applications spread across several AWS accounts and you use organization instance. Many AWS enterprise customers use Organizations, and have IAM Identity Center organization instances associated with them.
For proof of concept and departmental use cases, or in situations when an AWS account is not part of an AWS Organization and you dont want to create a new AWS organization, you can use an IAM Identity Center account instance to enable an Amazon Q Business application. In this case, only the Amazon Q Business application configured in the AWS account in which the account instance is created will be able to use that instance.
Amazon Q Business implements a per-user subscription fee. A user is billed only one time if they are uniquely identifiable across different accounts and different Amazon Q Business applications. For example, if multiple Amazon Q Business applications are within a single AWS account, a user that is uniquely identified by an IAM Identity Center instance tied to this account will only be billed one time for using these applications. If your organization has two accounts, and you have an organization-level IAM Identity Center instance, a user who is uniquely identified in the organization-level instance will be billed only one time even though they access applications in both accounts. However, if you have two account-level IAM Identity Center instances, a user in one account cant be identified as the same user in another account because there is no central identity. This means that the same user will be billed twice. We therefore recommend using organization-level IAM Identity Center instances for production use cases to optimize costs.
In both these cases, the Amazon Q Business application needs to be in the same AWS Region as the IAM Identity Center instance.
If you already use an IdP such as Okta or Entra ID, you can continue to use your preferred IdP with Amazon Q Business applications. In this case, the IAM Identity Center instance is configured to use the IdP as its identity source. The users and user groups from the IdP can be automatically synced to the IAM Identity Center instance using SCIM. Many AWS enterprise customers already have this configured for their IAM Identity Center organization instance. For more information about all the supported IdPs, see Getting started tutorials. The process is similar for IAM Identity Center organization instances and account instances.
The following screenshot shows the IAM Identity Center application configured in Okta, and the users and groups from the Okta configuration assigned to this application.
The following screenshot shows the IAM Identity Center instance user store after configuring Okta as the identity source. Here the user and group information is automatically provisioned (synchronized) from Okta into IAM Identity Center using the System for Cross-domain Identity Management (SCIM) v2.0 protocol.
Complete the following steps to create an Amazon Q Business application and enable IAM Identity Center:
For more information about Amazon Q Business retrievers, refer to Creating and selecting a retriever for an Amazon Q Business application.
The following instructions demonstrate how to configure the Confluence data source. These may differ for other data sources.
After the application is created, you will see the application settings page, as shown in the following screenshot.
To illustrate how you can build a secure and private generative AI assistant for your employees using Amazon Q Business applications, lets take a sample use case of an employee AI assistant in an enterprise corporation. Two new employees, Mateo Jackson and Mary Major, have joined the company on two different projects, and have finished their employee orientation. They have been given corporate laptops, and their accounts are provisioned in the corporate IdP. They have been told to get help from the employee AI assistant for any questions related to their new team member activities and their benefits.
The company uses Confluence to manage their enterprise content. The sample Amazon Q application used to run the scenarios for this post is configured with a data source using the built-in connector for Confluence to index the enterprise Confluence spaces used by employees. The example uses three Confluence spaces: AnyOrgApp Project, ACME Project Space, and AJ-DEMO-HR-SPACE. The access permissions for these spaces are as follows:
Lets look at how Mateo and Mary experience their employee AI assistant.
Both are provided with the URL of the employee AI assistant web experience. They use the URL and sign in to the IdP from the browsers of their laptops. Mateo and Mary both want to know about their new team member activities and their fellow team members. They ask the same questions to the employee AI assistant but get different responses, because each has access to separate projects. In the following screenshots, the browser window on the left is for Mateo Jackson and the one on the right is for Mary Major. Mateo gets information about the AnyOrgApp project and Mary gets information about the ACME project.
Mateo chooses Sources under the question about team members to take a closer look at the team member information, and Mary choosing Sources under the question for new team member onboarding activities. The following screenshots show their updated views.
Mateo and Mary want to find out more about the benefits their new job offers and how the benefits are applicable to their personal and family situations.
The following screenshot shows that Mary asks the employee AI assistant questions about her benefits and eligibility.
Mary can also refer to the source documents.
The following screenshot shows that Mateo asks the employee AI assistant different questions about his eligibility.
Mateo looks at the following source documents.
Both Mary and Mateo first want to know their eligibility for benefits. But after that, they have different questions to ask. Even though the benefits-related documents are accessible by both Mary and Mateo, their conversations with employee AI assistant are private and personal. The assurance that their conversation history is private and cant be seen by any other user is critical for the success of a generative AI employee productivity assistant.
If you created a new Amazon Q Business application to try out the integration with IAM Identity Center, and dont plan to use it further, unsubscribe and remove assigned users from the application and delete it so that your AWS account does not accumulate costs.
To unsubscribe and remove users go to the application details page and select Manage access and subscriptions.
Select all the users, and then use the Edit button to choose Unsubscribe and remove as shown below.
Delete the application after removing the users, going back to the application details page and selecting Delete.
For enterprise generative AI assistants such as the one shown in this post to be successful, they must respect access control as well as assure the privacy and confidentiality of every employee. Amazon Q Business and IAM Identity Center provide a solution that authenticates each user and validates the user identity at each step to enforce access control along with privacy and confidentiality.
To achieve this, IAM Identity Center acts as a gateway to sync user and group identities from an IdP (such as Okta), and Amazon Q Business uses IAM Identity Center-provided identities to uniquely identify a user of an Amazon Q Business application (in this case, an employee AI assistant). Document ACLs and local users set up in the data source (such as Confluence) are matched up with the user and group identities provided by IAM Identity Center. At query time, Amazon Q Business answers questions from users utilizing only those documents that they are provided access to by the document ACLs.
If you want to know more, take a look at the Amazon Q Business launch blog post on AWS News Blog, and refer to Amazon Q Business User Guide. For more information on IAM Identity Center, refer to the AWS IAM Identity Center User Guide.
Abhinav Jawadekar is a Principal Solutions Architect in the Amazon Q Business service team at AWS. Abhinav works with AWS customers and partners to help them build generative AI solutions on AWS.
Venky Nagapudi is a Senior Manager of Product Management for Q Business, Amazon Comprehend and Amazon Translate. His focus areas on Q Business include user identity management, and using offline intelligence from documents to improve Q Business accuracy and helpfulness.
Originally posted here:
Build private and secure enterprise generative AI apps with Amazon Q Business and AWS IAM Identity Center ... - AWS Blog