HUMBL, Inc.

San Diego, California, March 28, 2023 (GLOBE NEWSWIRE) -- HUMBL, Inc. (OTCQB: HMBL) HUMBL announced today the launch of its Artificial Intelligence (AI) and Automated Machine Learning initiatives across its consumer, commercial and Latin America business units.

On the commercial side, HUMBL kicked off its AI / Automated Machine Learning initiatives with the announcement of its first commercial sales contract in its HUMBL Latin America subsidiary, with the sale of AI / Automated Machine Learning services for a leading IT / Telecommunications provider in the Latin America region in the form of a $60,000 (USD) contract for initial deliverables and a total contract value of $195,000 (USD) over three years, pending the achievement of milestones by HUMBL Latin America.

Artificial Intelligence is an accelerant to the principles of web3, said Brian Foote, CEO of HUMBL. The use of public data sets to create more autonomous, intelligent outcomes for consumers, as well as the corporations and governments that serve them, is an excellent use of automated machine learning technologies, continued Foote. The use of AI can help our clients model for more predictive outcomes around things like credit scoring, default rates, churn rates, healthcare patterns and more; driving more tailored experiences for consumers, while driving revenues and improved efficiencies for corporations and governments.

HUMBL has also moved into internal testing on its consumer AI initiatives and its planned Hey BLUE virtual assistant, which builds on the companys signature mascot, a Bored Ape Yacht Club NFT of the same name (BLUE). The company intends to scale up its consumer AI product lines across the HUMBL Platform - in particular around its planned HUMBL Pro subscription services - which will be available across key touch points throughout the HUMBL ecosystem.

About HUMBL

HUMBL is a Web 3 platform with product lines including the HUMBL Wallet, HUMBL Search Engine, HUMBL Social, HUMBL Tickets, HUMBL Marketplace and HUMBL Authentics. The company also has a commercial blockchain services unit called HUMBL Blockchain Services (HBS) for private and public sector clients.

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Safe Harbor Statement

This release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. You can identify these statements by the use of the words "may," "will," "should," "plans," "expects," "anticipates," "continue," "estimates," "projects," "intends," and similar expressions. Forward-looking statements involve risks and uncertainties that could cause results to differ materially from those projected or anticipated. These risks and uncertainties include, but are not limited to, the Company's ability to successfully execute its expanded business strategy, including by entering into definitive agreements with suppliers, commercial partners and customers; general economic and business conditions, effects of continued geopolitical unrest and regional conflicts, competition, changes in technology and methods of marketing, delays in completing various engineering and manufacturing programs, changes in customer order patterns, changes in product mix, continued success in technical advances and delivering technological innovations, shortages in components, production delays due to performance quality issues with outsourced components, regulatory requirements and the ability to meet them, government agency rules and changes, and various other factors beyond the Company's control. Except as may be required by law, HUMBL undertakes no obligation, and does not intend, to update these forward-looking statements after the date of this release.

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HUMBL, Inc.PR@HUMBL.com

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HUMBL Launches Artificial Intelligence and Automated Machine Learning Initiatives Across Consumer, Commercial and Latin America - Yahoo Finance

In medicine, notes Tingting Zhao, Ph.D., theres no such thing as too much information. When it comes to making the best decisions for their patients, doctors always want to know more, she says. An assistant professor of Information Systems and Analytics and a faculty fellow with Bryants Center of Health and Behavioral Sciences, Zhao is an emerging leader in healthcare informatics, a field that provides new insights into keeping us healthier and better informed.

Zhao, an accomplished researcher, studies a range of topics at the intersection of data, technology, and medical knowledge. That fusion, she says, is an important and exciting frontier with limitless potential. By combining all three of them, we can make better decisions in terms of public policy, disease control, and disease prevention, says Zhao, who will be teaching in Bryants new Healthcare Informatics graduate program this fall.

As technology evolves, it produces more information than ever before. Some estimates suggest that the healthcare industry generates as much as 30 percent of the worlds data, a number that could rise to 36 percent by 2025, according to RBC Capital Markets. Healthcare informatics, which integrates healthcare sciences, computer science, information science, and cognitive science, helps practitioners figure out new ways to use data to enhance delivery of care, improve patient education, and inform public health policy.

It is now more crucial than ever that professionals have the skills and knowledge to understand, use, and innovate with data, says Zhao, pointing to the rise of precision medicine, an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Its a competitive and exciting area, she says one fueled by a desire to create new ways to treat patients. We can develop more efficient systems to not just record that data but to use it to make informed decisions as well.

The whole world is realizing we should put more resources into developing new technologies to help us provide better healthcare and better understand what makes us sick."

Personal health devices such as Apple Watches and Fitbits are other examples of the health information revolution, states Zhao. We are connecting with and developing new pieces of component technology and new techniques that we can use as analytical tools to analyze patient behaviors, she says.

The COVID-19 pandemic, Zhao says, drastically raised awareness of the value of the field and led to a massive influx of funding for data-related healthcare projects on the national level, taking it from roughly $23 billion to nearly $96 billion. The whole world is realizing we should put more resources into developing new technologies to help us provide better healthcare and better understand what makes us sick, she states.

An expert in machine learning, a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way humans learn, Zhao developed her passion for combining data science with healthcare as a doctoral student, when she discovered the thrill of knowing that something she worked on could one day help real life patients. It was exciting to know this was the future, she says.

Zhao has since contributed to a variety of projects, including a National Institutes of Health-funded collaboration with Harvard Medical School and Northeastern Group to develop algorithms that can identify Chronic Obstructive Pulmonary Disease (COPD) and make predictions regarding its progress. Her most recent research study, which involved developing and honing algorithms to identify which genes respond to specific perturbation stressors, which can provide a better understanding of the underlying mechanisms of disease and advance the identification of new drug targets, was published in the prestigious journal Briefings in Bioinformatics.

"This is an opportunity to do meaningful work that benefits the entire community."

Students in the universitys Healthcare Informatics graduate program, Zhao says, will gain exposure to research conducted by their professors, including opportunities to join them in scholarly work. When students come here, they can learn what truly goes on in the field, says Zhao, who cites working with junior partners on research projects as one of her favorite parts of being a professor.

The program aligns well with Bryants other new graduate programs in Data Science and Business Analytics, notes Zhao, who is teaching courses in both. This provides opportunities for ideas to cross-pollinate and lead to new insights and innovations. The healthcare informatics field, specifically, is ideal for curious people who want to explore and to find ways to use their talents and their education to help others.

This is an opportunity to do meaningful work that benefits the entire community, says Zhao.

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Machine learning expert: Health information revolution is underway - Bryant University

Data breaches have become a major concern for companies in recent years, as they can result in significant financial and reputational damage. A study by IBM found that the average cost of a data breach is $3.86 million, highlighting the importance of developing effective strategies to prevent them. Dr. Aashis Luitels research provides a comprehensive approach to analyzing data breach risks using machine learning models. The study emphasizes the need to conduct a detailed analysis of publicly available data breach records to identify trends in data breach characteristics and sources of geographical heterogeneity. Dr. Luitel is a Technical Program Manager at Microsofts Cloud and Artificial Intelligence and a Cybersecurity Professorial lecturer at various US universities. He earned a Doctorate from the George Washington University.

Dr. Luitels research involves developing a series of supervised machine-learning models to predict the probability of data breach incidence, size, and timing. The methodology uses tree-based supervised machine learning methods adapted to high-dimensional sparse panel data and nonparametric and parametric survival analysis techniques. The study results indicate that the proposed modeling framework provides a promising toolbox that directly addresses the timing of repeat data breaches. Analyzing feature importance, partial dependence, and hazard ratios revealed early warning signals of data breach incidence, size, and timing for US organizations.

Dr. Luitel notes that his research has important implications for security engineers and developers of data security systems. By assessing an organizations susceptibility to data breach risks based on various contextual features, stakeholders can make informed decisions about protecting their organizations from data breaches. Moreover, the methodology proposed in the study can help organizations gain executive management support in implementing security systems, thereby minimizing a data breachs financial and reputational impact.

Dr. Luitels research is particularly timely given the recent surge in remote work due to the COVID-19 pandemic. The pandemic has led to an increase in cyber-attacks and data breaches, as many organizations have had to quickly shift to remote work without adequate security measures in place. Remote work has opened up new vulnerabilities and risks for organizations, such as unsecured Wi-Fi networks and personal devices for work purposes. As a result, it is more critical than ever to have effective strategies for preventing and managing data breaches.

In addition to the risks posed by remote work, organizations face a constantly evolving threat landscape, with cybercriminals using increasingly sophisticated techniques to breach networks and steal sensitive data. This makes it challenging for security professionals to keep up and identify potential threats before they cause damage.

Dr. Luitels research provides a promising solution to this challenge by using machine learning models to automate the process of identifying potential data breach risks. By analyzing large amounts of data, the models can detect patterns and trends that may be difficult for humans to discern. This can help organizations gain a more comprehensive understanding of their vulnerabilities and develop more effective security strategies.

Furthermore, the methodology proposed by Dr. Luitel can benefit organizations across a wide range of industries, including healthcare, finance, and retail. Healthcare, in particular, is a sector that is particularly vulnerable to data breaches due to the sensitive nature of patient information. With the increasing use of electronic health records and other digital tools, healthcare providers must ensure robust security measures to protect patient data.

In the finance industry, data breaches can have significant financial consequences, potentially damaging consumer trust and resulting in regulatory fines. By using machine learning models to predict the likelihood of a data breach and identify areas of vulnerability, financial institutions can develop more targeted security strategies and minimize the impact of any breaches that do occur.

In retail, data breaches can result in losing valuable customer data, including payment information and personal details. This can damage the retailers reputation and result in a loss of consumer trust. Using Dr. Luitels machine learning models, retailers can identify potential risks and develop more effective security measures to protect their customers data.

Dr. Luitels research offers a valuable contribution to the field of data security, providing a comprehensive and automated approach to identifying and mitigating data breach risks. With the ever-increasing importance of digital data and the rise of remote work, effective data security measures have become more critical than ever. By using machine learning models to analyze data breach risks, organizations can develop targeted security strategies that minimize the risk of data breaches and protect their reputation and bottom line.

Dr. Luitels research also highlights the importance of adopting a proactive approach to data security. Rather than waiting for a breach to occur, organizations can use machine learning models to predict potential breaches and implement strategies to prevent them. By analyzing patterns and trends in historical data breaches, organizations can identify potential vulnerabilities and take action to address them before cybercriminals exploit them. Moreover, the methodology proposed by Dr. Luitels research can help organizations comply with data protection regulations. The General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States require organizations to implement appropriate measures to protect personal data. Failure to comply with these regulations can result in significant fines and reputational damage.

By using machine learning models to analyze data breach risks, organizations can demonstrate their compliance with these regulations and ensure the protection of their customers personal data. In addition, Dr. Luitels research can aid in developing cyber insurance policies. Insurance companies can use the models to assess an organizations data breach risk and develop customized policies that provide appropriate coverage. By using the models to identify potential risks and vulnerabilities, insurance companies can develop policies that provide more comprehensive coverage, thereby reducing their financial risk.

According to security researchers, Dr. Luitels research contributes to data security. Organizations can develop effective strategies to prevent breaches and minimize their impact by using machine learning models to analyze data breach risks. With the increasing importance of digital data and the growing threat landscape, the need for robust data security measures has never been more critical. The models proposed in Dr. Luitels research provide a promising approach to addressing these challenges and safeguarding organizations sensitive data.

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Machine Learning Models Offer Effective Approach for Analyzing ... - The Ritz Herald

Quadratic Discriminant Analysis is a classification method in statistics and machine learning. It is similar to Linear Discriminant Analysis (LDA), but it assumes that the classes have different covariance matrices, whereas LDA assumes that the classes have the same covariance matrix. If you want to learn more about LDA, here is my previous article where I talk about it.

In QDA, the goal is to find a quadratic decision boundary that separates the classes in a given dataset. This boundary is based on the estimated means and covariance matrices of the classes. Moreover, QDA can be used for both binary and multiclass classification problems. It is often used in situations where the classes have nonlinear boundaries or where the classes have different variances.

In R, QDA can be performed using the qda() function in the MASS package. We will use it on the SMarket data, part of the ISLR2 library. The syntax is identical to that of lda(). In the context of the Smarket data, the QDA model is being used to predict whether the stock market will go up or down (represented by the Direction variable) based on the percentage returns for the previous two days (represented by the Lag1 and Lag2 variables). The QDA model estimates the covariance matrices for the up and down classes separately and uses them to calculate the probability of each observation belonging to each class. The observation is then assigned to the class with the highest probability.

train <- (Smarket$Year < 2005)Smarket.2005 <- Smarket[!train, ]Direction.2005 <- Smarket$Direction[!train]

We first load the libraries and then split the data into test and training subsets in order to avoid overfitting the model.

Then we fit a QDA model to the training data (subset = train), using the qda function.

# OUTPUT:Prior probabilities of groups:Down Up 0.491984 0.508016

Group means:Lag1 Lag2Down 0.04279022 0.03389409Up -0.03954635 -0.03132544

We only use the Lag1 and Lag2 variables because they are the ones that seem to have the highest explicative power (we discovered it in a previous article about logistic regression: basically, they are the ones with the smallest p-value). Here is the article if you want to delve deeper into the topic:

The output contains the group means. But it does not contain the coefficients of the linear discriminants, because the QDA classifier involves a quadratic, rather than a linear, function of the predictors.

Next, we make predictions on the test data using the predict function and calculate the confusion matrix and the classification accuracy.

mean(qda.pred$class == Direction.2005)# OUTPUT:[1] 0.599

The output of the table function shows the confusion matrix, and the output of the mean function shows the classification accuracy.

Interestingly, the QDA predictions are accurate almost 60% of the time, even though the 2005 data was not used to fit the model. This level of accuracy is quite impressive for stock market data, which is known to be quite hard to model accurately. This suggests that the quadratic form assumed by QDA may capture the true relationship more accurately than the linear forms assumed by LDA and logistic regression. However, I would definitely recommend evaluating this methods performance on a larger test set before betting that this approach will consistently beat the market!

We can create a scatterplot with contours to visualize the decision boundaries for the Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) models on the Smarket data.

len1<-80; len2<-80; delta<-0.1grid.X1<-seq(from=min(Smarket$Lag1)-delta,to=max(Smarket$Lag1)+delta,length=len1)grid.X2<-seq(from=min(Smarket$Lag2)-delta,to=max(Smarket$Lag2)+delta,length=len2)dataT<-expand.grid(Lag1=grid.X1,Lag2=grid.X2)

lda.pred<-predict(lda.fit,dataT)zp <- lda.pred$posterior[,2] -lda.pred$posterior[,1]contour(grid.X1, grid.X2, matrix(zp, nrow=len1),levels=0, las=1, drawlabels=FALSE, lwd=1.5, add=T, col="violet")

qda.pred<-predict(qda.fit,dataT)zp <- qda.pred$posterior[,2] -qda.pred$posterior[,1]contour(grid.X1, grid.X2, matrix(zp, nrow=len1),levels=0, las=1, drawlabels=FALSE, lwd=1.5, add=T, col="brown")

The first two lines of code create a color indicator variable for the Direction variable based on whether it is Up or Down in the training data. The plot function is then used to create a scatterplot of the Lag2 variable against the Lag1 variable, with points colored according to the color indicator variable.

The next four lines of code define a grid of points to be used for generating the contours. The expand.grid function creates a data frame with all possible combinations of Lag1 and Lag2 values within the specified grid range.

The susequent chunck of code use the predict function to generate the predicted class probabilities for each point in the grid, for both the LDA and QDA models. The contour function is then used to create a contour plot of the decision boundaries for each model, with the levels set to 0 to show the decision boundary between the two classes. The LDA contours are colored violet, while the QDA contours are colored brown.

Thank you for reading the article. If you enjoyed it, please consider following me.

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Machine Learning Prediction of S&P 500 Movements using QDA in R - DataDrivenInvestor

Keegan Hines, a former Georgetown adjunct professor and the current vice president of machine learning at Arthur AI, discussed the rapid rise in generative Artificial Intelligence (AI) programs and Georgetowns potential in adapting to software like ChatGPT.

The Master of Science in Data Science and Analytics program in the Graduate School of Arts & Sciences hosted the talk on March 17. The discussion centered on the rapid development of generative AI over the past six months.

Hines said generative AI has the capacity to radically change peoples daily lives, including how students are taught and how entertainment is consumed.

I definitely think were going to see a lot of personal tutoring technologies coming up for both little kids and college students, Hines said at the event. I have a feeling that in the next year, someone will try to make an entirely AI-generated TV show. Its not that hard to imagine an AI-generated script, animation and voice actors.

Imagine what Netflix becomes. Netflix is no longer recommend Keegan the best content; Netflix is now create something from scratch which is the perfect show Keegans ever wanted to see, Hines added.

Hines then discussed algorithms that generate text. He said the principal goal of these algorithms is to create deep learning systems that can understand complex patterns over longer time scales.

Hines said one challenge AI faces is that it can provide users with incorrect information.

These models say things and sometimes theyre just flatly wrong, Hines said. Google got really panned when they made a product announcement about Bard and then people pointed out Bard had made a mistake.

Bard, Googles AI chatbot, incorrectly answered a question about the James Webb Space Telescope in a video from the programs launch Feb. 6, raising concerns about Googles rushed rollout of Bard and the possibility for generative AIs to spread misinformation.

Hines said the potential for bias and toxicity in AI is present, as seen with Microsofts ChatGPT-powered Bing search engine, which manufactured a conspiracy theory relating Tom Hanks to the Watergate scandal.

Theres been a lot of research in AI alignment, Hines said. How do we make these systems communicate the values we have?

Teaching and learning in all levels of education will need to adapt to changes in technology, according to Hines.

One example is a high school history teacher who told students to have ChatGPT write a paper and then correct it themselves, Hines said. I think this is just the next iteration of open book, internet, ChatGPT. How do you get creative testing someones critical thinking on the material?

Hines said OpenAI, the company behind ChatGPT, noticed larger, more complex language models were more accurate than smaller models due to lower levels of test loss or errors made during training.

A small model has a high test loss whereas a really big model has a much more impressive test loss, Hines said. The big model also requires less data to reach an equivalent amount of test loss.

OpenAIs hypothesis was that the secret to unlocking rapid advancement in artificial intelligence lies in creating the largest model possible, according to Hines.

There didnt seem to be an end to this trend, Hines said. Their big hypothesis was, lets just go crazy and train the biggest model we can think of and keep going. Their big bet paid off and these strange, emergent, semi-intelligent behaviors are happening along the way.

Hines said he is optimistic about the fields future, and he predicted AI will be able to produce even more complex results, such as creating a TV show. It was really only about ten years ago that deep learning was proven to be viable. Hines said. If were going to avoid the dystopian path and go down the optimistic path, generative AI will be an assistant. It will get you 80% of the way and you do the next 20%.

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Machine Learning Executive Talks Rise, Future of Generative AI - Georgetown University The Hoya