Artificial intelligence (AI) is the most transformative technology of our generation. If we are going to unlock the full potential of AI to tackle the worlds most challenging problems, we need to make AI education accessible to anyone with a desire to learn.

Thats why Amazon is announcing AI Ready, a new commitment designed to provide free AI skills training to 2 million people globally by 2025. To achieve this goal, were launching new initiatives for adults and young learners, and scaling our existing free AI training programsremoving cost as a barrier to accessing these critical skills.

The three new initiatives are:

The need for an AI-savvy workforce has never been greater. A new study by AWS and research firm Access Partnership found the following:

Amazon is launching AI Ready to help those with a desire to learn about AI and benefit from the tremendous opportunity ahead. The following initiatives are designed to open opportunities to those in the workforce today as well as the future generation.

To support professionals in the workplace, were announcing eight new, free AI and generative AI courses open to anyone and aligned to in-demand jobs. There is something for everyone with courses ranging from foundational to advanced and for business leaders as well as technologists. These courses augment the 80+ free and low-cost AI and generative AI courses and resources provided through AWS.

Through the AWS Generative AI Scholarship, AWS will provide Udacity scholarships, valued at more than $12 million, to more than 50,000 high school and university students from underserved and underrepresented communities globally.

We want to help as many students as possible. Eligible students can take the new Udacity course Introducing Generative AI with AWS for free. The course, which was designed by AI experts at AWS, introduces students to foundational generative AI concepts and guides them through a hands-on project. Upon successful course completion, students earn a certificate from Udacity to showcase their knowledge to future employers.

Amazon is kicking off a new collaboration between Amazon Future Engineer and Code.org to launch Hour of Code Dance Party: AI Edition. During this hour-long introduction to coding and AI, students will create their own virtual music video set to hit songs from artists including Miley Cyrus, Harry Styles, and more.

Students will code their virtual dancers choreography and use emojis as AI prompts to generate animated backgrounds. The activity will give participants an introduction to generative AI, including learning about large language models and how they are used to power the predictive analytics responsible for creating new images, text, and more.

Hour of Code will take place globally during Computer Science Education Week, December 410, engaging students and teachers in kindergarten through 12th grade. Additionally, AWS is providing up to $8 million in AWS Cloud computing credits to Code.org, which runs on AWS, to further support Hour of Code.

Amazons new AI Ready commitment is in addition to AWSs commitment to invest hundreds of millions of dollars to provide free cloud computing skills training to 29 million people by 2025, which has already trained more than 21 million people.

Excerpt from:
New Amazon AI initiative includes scholarships, free AI courses - About Amazon

Your decision to embark on a new AI journey is commendable, and at this stage, I assume youve already solidified your commitment, driven by a compelling reason. The crucial first step is to anchor yourself to this motivation let it serve as the key to unlocking your untapped potential and surmounting any challenges ahead.

Now that youve set your course, you venture online in search of valuable resources. However, what you likely didnt anticipate was navigating through a myriad of courses that often exhibit substantial topic overlap. Its a common challenge faced by many beginners, and truth be told, even seasoned professionals can find themselves entangled in the complexity of choices at times.

Luckily, I am here to help. However, there are a few things that you need to remember.

Embarking on the path of learning is akin to setting out on a journey, not a sprint. Frequently, I receive inquiries such as, How soon until I secure my first job? or What projects should I showcase on my Resume for an internship? I empathize with these queries, having navigated those uncertainties myself. However, to truly comprehend AI and harness its potential for your aspirations, impatience is not your ally. While it might be tempting to expedite the process by combining various tools to achieve quick results, the essence of learning AI lies in embracing the journey. Along this path, challenges will arise that demand a profound understanding of AI principles for long-term success.

Broadening your understanding of machine learning requires a multi-faceted approach. Delve into the intricacies of AI topics by tapping into a variety of resources. Whether its diverse instructors, a range of courses, insightful books, research papers, or thought-provoking blogs immersing yourself in multiple perspectives is the key. This comprehensive exploration not only deepens your insights but also equips you with a well-rounded understanding of artificial intelligence.

Resist the urge to dive into a multitude of courses or topics simultaneously. Opt for a focused approach master one concept thoroughly before venturing into the next. This sequential immersion ensures a solid foundation and a more effective learning experience.

Theory, while essential, is only a fraction of the learning journey. The true grasp of concepts comes from hands-on implementation. Remember, knowledge solidifies when you actively engage with it. Trust the process, invest time in implementing solutions, and solve real-world problems. The insights gained from practical application far exceed those acquired through lengthy lectures, forming the bedrock of profound understanding.

The realization struck me post-industry immersion: thriving in the AI domain demands more than just technical acumen. While AI knowledge is crucial, its only the tip of the iceberg. To navigate the dynamic landscape, supplement your expertise with a toolkit that extends beyond AI algorithms embrace essential software tools like GitHub and Docker. Expand your programming language repertoire, hone your paper-reading skills, delve into cloud computing intricacies, and grasp project management essentials. You will require good writing and documentation skills. The key lies in adaptability; remaining flexible and prepared for the demands of the evolving AI industry.

The rest is here:
The last Machine Learning curriculum you will ever need! - Medium

Dataset

The dataset was originally reported by Ravel et al.16. The study was registered at clinicaltrials.gov under ID NCT00576797. The protocol was approved by the institutional review boards at Emory University School of Medicine, Grady Memorial Hospital, and the University of Maryland School of Medicine. Written informed consent was obtained by the authors of the original study.

Samples were taken from 394 asymptomatic women. 97 of these patients were categorized as positive for BV, based on Nugent score. In the preprocessing of the data, information about community group, ethnicity, and Nugent score was removed from the training and testing datasets. Ethnicity information was stored to be referenced later during the ethnicity-specific testing. 16S rRNA values were listed as a percentage of the total 16S rRNA sample, so those values were normalized by dividing by 100. pH values ranged on a scale from 1 to 14 and were normalized by dividing by 14.

Each experiment was run 10 times, with a different random seed defining the shuffle state, to gauge variance of performance.

Four supervised machine learning models were evaluated. Logistic regression (LR), support vector machine (SVM), random forest (RF), and Multi-layer Perceptron (MLP) models were implemented with the scikit-learn python library. LR fits a boundary curve to separate the data into two classes. SVM finds a hyperplane that maximizes the margin between two classes. These methods were implemented to test whether boundary-based models can perform fairly among different ethnicities. RF is a model that creates an ensemble of decision trees and was implemented to test how a decision-based model would classify each patient. MLP passes information along nodes and adjusts weights and biases for each node to optimize its classification. MLP was implemented to test how a neural network-based approach would perform fairly on the data.

Five-fold stratified cross validation was used to prevent overfitting and to ensure that each ethnicity has at least two positive cases in the test folds. Data were stratified by a combination of ethnicity and diagnosis to ensure that each fold has every representation from each group with comparable distributions.

For each supervised machine learning model, hyper parameter tuning was performed by employing a grid search methodology from the scikit-learn python library. Nested cross validation with 4 folds and 2 repeats was used as the training subset of the cross validation scheme.

For Logistic Regression, the following hyper-parameters were tested: solver (newton-cg, lbfgs, liblinear) and the inverse of regularization strength C (100, 10, 1.0, 0.1, 0.01).

For SVM, the following hyper-parameters were tested: kernel (polynomial, radial basis function, sigmoid) and the inverse regularization parameter C (10, 1.0, 0.1, 0.01).

For Random Forest, the following hyper-parameters were tested: number of estimators (10, 100, 1000) and maximum features (square root and logarithm to base 2 of the number of features).

For Multi-layer perceptron, the following hyper-parameters were tested: hidden layer size (3 hidden layers of 10,30, and 10 neurons and 1 hidden layer of 20 neurons), solver (stochastic gradient descent and Adam optimizer), regularization parameter alpha (0.0001, or .05), and learning rate (constant and adaptive).

The models were evaluated using the following metrics: balanced accuracy, average precision, false positive rate (FPR), and false negative rate (FNR). Balanced accuracy was chosen to better capture the practical performance of the models while using an unbalanced dataset. Average precision is an estimate of the area under the precision recall curve, similar to AUC which is the area under the ROC curve. The precision-recall curve is used instead of a receiver operator curve to better capture the performance of the models on an unbalanced dataset39. Previous studies with this dataset reveal particularly good AUC scores and accuracy, which is to be expected with a highly unbalanced dataset.

The precision-recall curve was generated using the true labels and predicted probabilities from every fold of every run to summarize the overall precision-recall performance for each model. Balanced accuracy and average precision were computed using the corresponding functions found in the sklearn.metrics package. FPR and FNR were calculated computed and coded using Equations below39.

Below are the equations for the metrics used to test the Supervised Machine Learning models:

$${Precision}=frac{{TP}}{{TP}+{FP}}$$

(1)

$${Recall}=frac{{TP}}{{TP}+{FN}}$$

(2)

$${Balanced},{Accuracy}=frac{1}{2}left(frac{{TP}}{{TP}+{FN}}+frac{{TN}}{{TN}+{FP}}right)$$

(3)

$${FPR}=frac{{FP}}{{FP}+{TN}}$$

(4)

$${FNR}=frac{{FN}}{{FN}+{TP}}$$

(5)

where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives.

$${Average},{Precison}=sum _{n}left({R}_{n}-{R}_{n-1}right){P}_{n}$$

(6)

where R denotes recall, and P denotes precision.

The performance of the models were tested against each other as previously stated. Once the model made a prediction, the stored ethnicity information was used to reference which ethnicity each predicted label and actual label belonged to. These subsets were then used as inputs for the metrics functions.

To see how training on data containing one ethnicity affects the performance and fairness of the model, an SVM model was trained on subsets that each contained only one ethnicity. Information on which ethnicity each datapoint belonged to was not given to the models.

To increase the performance and accuracy of the model, several feature selection methods were used to reduce the 251 features used to train the machine learning models. These sets of features were then used to achieve similar or higher accuracy with the machine learning models used. The feature selection methods used included the ANOVA F-test, two-sided T-Test, Point Biserial correlation, and the Gini impurity. The libraries used for these feature selection tests were the statistics and scikit learn packages in Python. Each feature test was performed with all ethnicities, then only the white subset, only Black, only Asian, and only Hispanic.

The ANOVA F-Test was used to select 50 features with the highest F-value. The function used calculates the ANOVA F-value between the feature and target variable using variance between groups and within the groups. The formula used to calculate this is defined as:

$$F=frac{{SSB}/(k-1)}{{SSW}/(n-k)}$$

(7)

Where k is the number of groups, n is the total sample size, SSB is the variance between groups, and SSW is the sum of variance within each group. The two-tailed T-Test was used to compare the BV negative versus BV positive groups rRNA data against each other. The two-tailed T-Test is used to compare the means of two independent groups against each other. The null hypothesis in a two-tailed T-Test is defined as the means of the two groups being equal while the alternative hypothesis is that they are not equal. The dataset was split up into samples that were BV negative and BV positive which then compared the mean of each feature against each other to find significant differences. A p-value <0.05 allows us to reject the null hypothesis that the mean between the two groups is the same, indicating there is a significant difference between the positive and negative groups for each feature. Thus, we use a p-value of less than 0.05 to select important features. The number of features selected were between 40 and 75 depending on the ethnicity group used. The formula for finding the t-value is defined as:

$$t=frac{left({bar{x}}_{1}-{bar{x}}_{2}right)}{sqrt{frac{({{s}_{1}})^{2}}{{n}_{1}}+frac{({{s}_{2}})^{2}}{{n}_{2}}}}$$

(8)

({bar{{rm{x}}}}_{1,2}) being the mean of the two groups. ({{rm{s}}}_{1,2}) as the standard deviation of the two groups. ({{rm{n}}}_{1,2}) being the number of samples in the two groups. The p-value is then found through the t-value by calculating the cumulative distribution function. This defines probability distribution of the t-distribution by the area under the curve. The degrees of freedom are also needed to calculate the p-value. They are the number of variables used to find the p-value with a higher number being more precise. The formulas are defined as:

$${rm{df}}={n}_{1}+{n}_{2}{{{-}}}2$$

(9)

$${p}=2* left(1-{rm{CDF}}left(left|tright|,{rm{df}}right)right)$$

(10)

where ({df}) denotes the degrees of freedom and ({{rm{n}}}_{1,2}) being the number of samples in the group. The Point Biserial correlation test is used to compare categorical against continuous data. For our dataset was used to compare the categorical BV negative or positive classification against the continuous rRNA bacterial data. Each feature has a p-value and correlation value associated with it which was then restricted by an alpha of 0.2 and further restricted by only correlation values >0.5 showing a strong correlation. The purpose of the alpha value is to indicate the level of confidence of a p-value being significant. An alpha of 0.2 was chosen because the Point Biserial test tends to return higher p-values. This formula is defined as:

$${{r}}_{{pb}}=frac{left({M}_{1}-{M}_{0}right)}{{rm{s}}},sqrt{{pq}}$$

(11)

where M1 is the mean of the continuous variable for the categorical variable with a value of 1; M0 is the mean of the continuous variable for the categorical variable with a value of 0; s denotes the standard deviation of the continuous variable; p is the proportion of samples with a value of 1 to the sample set; and q is the proportion of samples with a value of 0 to the sample set.

Two feature sets were made from the Point Biserial test. One feature set included only the features that were statistically significant using a p-value of <0.2 which returned 60100 significant features depending on the ethnicity set used. The second feature set included features that were restricted by a p-value<0.2 and greater than a correlation value of 0.5. This second feature set contained 815 features depending on the ethnicity set used.

Features were also selected using Gini impurity. Gini impurity defines the impurity of the nodes which will return a binary split at a node. It will calculate the probability of misclassifying a randomly chosen data point. The Gini impurity model fitted a Random Forest model with the dataset and took the Gini scores for each feature based on the largest reduction of Gini impurity when splitting nodes. The higher the reduction of Gini value, the impurity after the split, the more important the feature is used in predicting the target variable. The Gini impurity value varies between 0 and 1. Using Gini, the total number of features were reduced to 310 features when using the ethnicity-specific sets and 20 features when using all ethnicities. The formula is defined as:

$${Gini}=1-sum {{p}_{i}}^{2}$$

(12)

where ({{rm{p}}}_{{rm{i}}}) is the proportion of each class in the node. The five sets of selected features from each of the five ethnicities were used to train a model using four supervised machine learning algorithms (LR, MLP, RF, SVM) with the full dataset using our nested cross-validation schemed as previously described. All features were selected using the training sets only, and they were applied to the test sets after being selected for testing. Five-fold stratified cross validation was used for each model to gather including means and confidence intervals.

Originally posted here:
Ethnic disparity in diagnosing asymptomatic bacterial vaginosis ... - Nature.com

If a financial institution looks beyond the hype of AI and tempers its expectations, it can use AI to deliver measurable business results. Thats been the experience of Amounts director of decision science Garrett Laird.

Given the interest in Chat GPT and related tools, the recent buzz around AI is understandable. Like many in fintech, Laird reminds the excited that AI has been around in such forms as machine learning for years. Avant has used machine learning in credit underwriting for at least a decade.

Its not a silver bullet, Laird said. It does some things really, really well. But it wont solve all your problems, especially in our space.

Financial products are highly regulated, right? These new LLMs (large language models) are entirely unexplainable; theyre pretty much true black-box models, so they limit the applications and use cases.

Laird sees clear use cases in outlier detection and unsupervised learning. He credits the current AI fervor with igniting interest in LLMs. As businesses look for ways to deploy LLMs, they are also looking at other AI types.

Regulations prevent AI from being used everywhere in financial services. Laird cited the many protected classifications that dictate how and where advertisements and solicitations can be sent. If your AI model cannot explain why one customer got an offer while another did not, youre asking for trouble.

Machine learning can be used to become more compliant because you can empirically describe why youre making the decisions youre making, Laird said. When there are humans making decisions everyone has their implicit biases, and those are hard to measure or even know what they are.

With algorithms and machine learning, you can empirically understand if a model is biased and in what ways and then you can control for that. While there are many restrictions on one side, I think many things were doing with machine learning and AI benefit consumers from a discrimination and compliance perspective.

Laird said the training models depend on what their systems are used for. Fraud models must be updated quickly and often with third-party sources, historical information and consumer data.

This is one area where machine learning helps. Machine learning operations can ensure proper validations are completed. They prevent it from picking up discriminatory data or information from protected classes.

Laird said an industry cliche is that 90% of machine learning work is data preparation. That has two parts: having relevant data and ensuring it is accessible in real time so it can make valuable business decisions.

While credit provision might not bring the same urgency as fraud, Laird also advises considering how it can benefit from AI. Credit models must have strong governance and risk management processes in place. They need good data sets. Lenders require a thorough understanding of their customers, which, in the case of mortgages, can take years.

Getting access to the right data is a huge challenge, and then making sure its the right population, Laird said. Thats a trend the industry is moving in: product-specific but also customer-base-specific modelling.

The direction were headed is like the democratization of machine learning for credit underwriting where you have models that are very catered to your very unique situation. That challenges many banks because it takes a lot of human capital. Having it takes a lot of data, and its not something you have overnight.

Also read:

AI lowers the entry barrier for fraudsters by providing sophisticated tools and allowing them to communicate in better-quality English. Combatting them also involves AI as one of many layers.

However, AI is used differently with different fraud types. First-party fraudsters can evade identity checks, which introduce friction for legitimate customers.

Third-party fraud brings challenges to supervised models. Those models are based on learnings from previous cases of such fraud. Their characteristics are identified, and models are developed. AI can help to identify those patterns quickly.

However, the process is never-ending because systems must quickly adjust as fraudsters determine how to beat mitigation challenges. Laird said he focuses on that by deploying velocity checks.

We put a lot of mental effort into identifying ways to pick up on these clusters of bad actors, Laird said. And there are many ways you can do that. A couple of the interesting ones that we employ are velocity checks. A lot of times, a fraud ring will exhibit similar behaviors. They might be applying from a certain geography, have the same bank theyre applying from, or have similar device data. They might use VOIP, any number of like attributes.

Laird said some institutions also use unsupervised learning. They might not have specific targets, but they can detect patterns using clustering algorithms. If a population starts defaulting or claiming fraud, the algorithms can identify similar behaviors that need further scrutiny.

Recent financial sector turbulence lends itself to rising deposit-related fraud. If a banks defences are sub-par, they could find themselves vulnerable to fraud that is already happening.

That is probably a problem thats already starting to rear its head and will only get worse, Laird suggested. I think with all of the movement in deposits that happened this past spring, with SVB and all the other events, there was a mad rush of deposit opening.

And with that, two things always happen. Theres an influx of volume. It makes it easier for fraudsters to slip through the cracks. Also, many banks saw that as an opportunity and probably either rushed solutions out or reduced some of their defences. We think theres probably a lot of dormant, recently opened deposit accounts that are probably in the near future going to be utilized as vehicles for bust-out fraud.

Laird returned to case-specific modelling as a significant emerging trend. FICO and Vantage are good models many use, but theyre generic for everything from mortgages to credit cards and personal loans. Casting a wide net limits accuracy, and given increased competition, more bespoke models are a must.

I can go on Credit Karma and get 20 offers with two clicks of a button, or I can go to 100 different websites and get an offer without impacting my credit, Laird observed. If youre trying to compete with that, if your pricing is just based on a FICO score or Vantage score, youre going to get that 700 FICO customer thats trending towards 650, whereas someone with a more advanced credit model is going to get that 700 thats trending towards 750.

Laird is eagerly watching developments following the Consumer Financial Protection Bureaus recent announcement on open banking. Financial institutions must make their banking data available.

Thats a modelling goldmine, Laird said. Financial institutions had an advantage in lending to their customer bases because only they can access that information. Now that its publicly available, that data can be used by all financial institutions to make underwriting decisions. Laird said its mission-critical for financial institutions to have good solutions.

Financial institutions generally take conservative approaches to AI. Most have used Generative AI for internal efficiencies, not direct customer interactions. That time will come but in limited capacities.

Laird reiterated his excitement about the renewed interest in machine learning. He believes they are well-suited to address the problems.

Im excited that theres that renewed interest in investment and an appetite for starting to leverage AI for fraud, Laird said. Its been there for a while.

I think the increased focus on credit underwriting is another one that I get really excited about because with the new open banking regulations coming out, I think financial institutions that dont embrace it are going to get left behind. Theyre going to be adversely selected; theyre not going to be able to remain competitive. It behooves everyone to start thinking about it and understanding ways to leverage that from not just the traditional fraud focuses but increasingly on the credit side.

Tony is a long-time contributor in the fintech and alt-fi spaces.A two-time LendIt Journalist of the Year nominee and winner in 2018, Tony has written more than 2,000 original articles on the blockchain, peer-to-peer lending, crowdfunding, and emerging technologies over the past seven years.He has hosted panels at LendIt, the CfPA Summit, and DECENT's Unchained, a blockchain exposition in Hong Kong. Email Tony here.

Read this article:
Looking beyond the AI hype: Delivering real value for financial ... - Fintech Nexus News