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There is a high demand for machine learning engineer jobs, but the hiring process is tough to crack. Companies want to hire professionals with experience in dealing with various machine learning problems.

For a newbie or fresh graduate, there are only a few ways to showcase skills and experience. They can either get an internship, work on open source projects, volunteer in NGO projects, or work on portfolio projects.

In this post, we will be focusing on machine learning portfolio projects that will boost your resume and help you during the recruitment process. Working solo on the project also makes you better at problem-solving.

mRNA Degradation project is a complex regression problem. The challenge in this project is to predict degradation rates that can help scientists design more stable vaccines in the future.

The project is 2 years old, but you will learn a lot about solving regression problems using complex 3D data manipulation and deep learning GRU models. Furthermore, we will be predicting 5 targets: reactivity, deg_Mg_pH10, deg_Mg_50C, deg_pH10, deg_50C.

Automatic Image Captioning is the must-have project in your resume. You will learn about computer vision, CNN pre-trained models, and LSTM for natural language processing.

In the end, you will build the application on Streamlit or Gradio to showcase your results. The image caption generator will generate a simple text describing the image.

You can find multiple similar projects online and even create your deep learning architecture to predict captions in different languages.

The primary purpose of the portfolio project is to work on a unique problem. It can be the same model architecture but a different dataset. Working with various data types will improve your chance of getting hired.

Forecasting using Deep Learning is a popular project idea, and you will learn many things about time series data analysis, data handling, pre-processing, and neural networks for time-series problems.

The time series forecasting is not simple. You need to understand seasonality, holiday seasons, trends, and daily fluctuation. Most of the time, you dont even require neural networks, and simple linear regression can provide you with the best-performing model. But in the stock market, where the risk is high, even a one percent difference means millions of dollars in profit for the company.

Having a Reinforcement Learning project on your resume gives you an edge during the hiring process. The recruiter will assume that you are good at problem-solving and you are eager to expand your boundaries to learn about complex machine learning tasks.

In the Self-Driving car project, you will train the Proximal Policy Optimization (PPO) model in the OpenAI Gym environment (CarRacing-v0).

Before you start the project, you need to learn the fundamentals of Reinforcement Learning as it is quite different from other machine learning tasks. During the project, you will experiment with various types of models and methodologies to improve agent performance.

Conversational AI is a fun project. You will learn about Hugging Face Transformers, Facebook Blender Bot, handling conversational data, and creating chatbot interfaces (API or Web App).

Due to the huge library of datasets and pre-trained models available on Hugging Face, you can basically finetune the model on a new dataset. It can be Rick and Morty conversation, your favorite film character, or any celebrity that you love.

Apart from that you can improve the chatbot for your specific use case. In case of medical application. The chatbot needs technical knowledge and understands the patient's sentiment.

Automatic Speech Recognition is my favorite project ever. I have learned everything about transformers, handling audio data, and improving the model performance. It took me 2 months to understand the fundamentals and another two to create the architecture that will work on top of the Wave2Vec2 model.

You can improve the model performance by boosting Wav2Vec2 with n-grams and text pre-processing. I have even pre-processed the audio data to improve the sound quality.

The fun part is that you can fine-tune the Wav2Vec2 model on any type of language.

End-to-end machine learning project experience is a must. Without it, your chance of getting hired is pretty slim.

You will learn:

The main purpose of this project is not about building the best model or learning new deep learning architecture. The main goal is to familiarize the industry standards and techniques for building, deploying, and monitoring machine learning applications. You will learn a lot about development operations and how you can create a fully automated system.

After working on a few projects, I will highly recommend you create a profile on GitHub or any code-sharing site where you can share your project findings and documentation.

The principal purpose of working on a project is to improve your odds of getting hired. Showcasing the projects and presenting yourself in front of a potential recruiter is a skill.

So, after working on a project, start promoting it on social media, create a fun web app using Gradio or Streamlit, and write an engaging blog. Dont think about what people are going to say. Just keep working on a project and keep sharing. And I am sure in no time multiple recruiters will approach you for the job.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

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7 Machine Learning Portfolio Projects to Boost the Resume - KDnuggets

ARLINGTON, Va. U.S. military researchers are asking industry to develop computers able not only to analyze large amounts of data automatically, but also communicate and cooperate with humans to resolve ambiguities and improve performance over time.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001122S0052) on Thursday for the Environment-driven Conceptual Learning (ECOLE) project.

From industry, the DARPA ECOLE project seeks proposals in five areas: human language technology; computer vision; artificial intelligence (AI); reasoning; and human-computer interaction.

ECOLE will create AI agents able to learn from linguistic and visual input to enable humans and computers to work together to analyze image, video, and multimedia documents quickly in missions where reliability and robustness are essential.

Related: Military researchers to apply artificial intelligence (AI) and machine learning to combat medical triage

ECOLE will develop algorithms that can identify, represent, and ground the attributes that form the symbolic and contextual model for a particular object or activity through interactive machine learning with a human analyst. Knowledge of attributes and affordances, learned dynamically from data encountered within an analytic workflow, will enable joint reasoning with a human partner.

This acquired knowledge also will enable the machine to recognize never-before-seen objects and activities without misclassifying them as a member of a previously learned class, detect changes in known objects, and report these changes when they are significant.

System interaction with human intelligence analysts is expected to be symbiotic, with the systems augmenting human cognitive capabilities while simultaneously seeking instruction and correction to achieve accuracy.

Industry proposals should specify how symbolic knowledge representations will be acquired from unlabeled data, including the specifics of the learning mechanism; how these representations will be associated and reasoned within a growing body of knowledge; how the representations will be applied to human-interpretable object and activity recognition; and how the framework will permit collaboration with several analysts to resolve ambiguity, extend the set of known representations, and provide greater recognitional accuracy and coverage.

Related: Artificial intelligence (AI) to enable manned and unmanned vehicles adapt to unforeseen events like damage

The four-year ECOLE project with three phases; this solicitation concerns only the first and second phases. The first phase will create prototype agents that can pull relevant information out of unlabeled multimedia data, supplemented with human interaction.

These prototypes will demonstrate not only the ability to learn new concepts, but also to recombine previously learned attributes to recognize never-before-seen objects and activities. Systems also will be able to reason over similarities and differences in objects and activities.

The second phase of the ECOLE project will scale-up the framework to include several AI agents and human analysts to help deal with uncertain or contradictory information.

Computer interaction with human analysts will enable the system to learn to name and describe objects, actions, and properties to verify and augment their representations, and to acquire complex knowledge quickly and accurately from potentially sparse observations.

Related: Wanted: artificial intelligence (AI) and machine autonomy algorithms for military command and control

Humans and computers will work together primarily through the English language -- including words with several different meanings -- in a way that is readily understandable. The ECOLE project also will have two technical areas: distributed curriculum learning; and human-machine collaborative analysis.

Distributed curriculum learning involves multimedia data, and will use human partners provide feedback on the learning process. human-machine collaborative analysis will involve a human-machine interface (HMI) to improve ECOLE representations and analyze data such as multimedia and social media.

Companies interested should upload abstracts no later than 29 Sept. 2022, and full proposals by 14 Nov. 2022 to the DARPA BAA website at https://baa.darpa.mil.

Email questions or concerns to DARPA at ECOLE@darpa.mil. More information is online at https://sam.gov/opp/fd50cb65daf5493d886fa1ddc2c0dd77/view.

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Wanted: artificial intelligence (AI) and machine learning to help humans and computers work together - Military & Aerospace Electronics

Researchers found bronchopulmonary dysplasia (BPD) was one of the premature births most common and significant consequences. It was critical to make a timely diagnosis by utilizing prediction techniques so that action could be taken quickly to mitigate any negative impacts. The study aimed to use machine learning and the idea that BPD has a developmental genesis to create a tool for predicting whether or not a person would acquire BPD. Preliminary model development made use of datasets including prenatal variables and early postnatal respiratory assistance; subsequent model combinations made use of logistic regression to yield an ensemble model. The simulation of medical situations was carried out. Results from 689 newborns were included. For model building, investigators randomly chose data from 80% of newborns, while data from 20% was used for validation. Receiver operating characteristic curves used to evaluate the final models performance yielded values of 0.921 (95% CI: 0.899-0.943) for the training dataset and 0.899 (95% CI]: 0.848-0.949) for the validation dataset. Compared to NIPPV, extubation to CPAP appears to improve BPD-free survival in simulations. Successful extubation may also be defined as the absence of the need for reintubation within 9 days of the initial extubation. Clinical utility of machine learning-based BPD prediction using perinatal characteristics and respiratory data may exist to facilitate early targeted intervention in high-risk infants.

Source: bmcpediatr.biomedcentral.com/articles/10.1186/s12887-022-03602-w

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The Prediction of Bronchopulmonary Dysplasia Free Survival in Very Preterm Infants Using Machine Learning - Physician's Weekly

SAMSHA. Key Substance Use and Mental Health Indicators in the United States: Results from the 2018 National Survey on Drug Use and Health 82 (2018).

Hasin, D. S. et al. Prevalence of marijuana use disorders in the United States between 20012002 and 20122013. JAMA Psychiat. 72(12), 12351242 (2015).

Article Google Scholar

Chapman, C. et al. Evidence for sex convergence in prevalence of cannabis use: A systematic review and meta-regression. J. Stud. Alcohol Drugs. 78(3), 344352 (2017).

PubMed PubMed Central Article Google Scholar

Nia, A. B., Mann, C., Kaur, H. & Ranganathan, M. Cannabis use: Neurobiological, behavioral, and sex/gender considerations. Curr. Behav. Neurosci. Rep. 5(4), 271280 (2018).

PubMed PubMed Central Article Google Scholar

Substance Abuse and Mental Health Services Administration. Results from the 2006 National Survey on Drug Use and Health: National Findings 282 (2007).

Center for Behavioral Health Statistics and Quality. 2017 National Survey on Drug Use and Health: Detailed Tables 2871 (Substance Abuse and Mental Health Services Administration, 2017).

Google Scholar

Khan, S. S. et al. Gender differences in cannabis use disorders: Results from the national epidemiologic survey of alcohol and related conditions. Drug Alcohol Depend. 130, 101108 (2013).

PubMed Article Google Scholar

Hernandez-Avila, C. A., Rounsaville, B. J. & Kranzler, H. R. Opioid-, cannabis- and alcohol-dependent women show more rapid progression to substance abuse treatment. Drug Alcohol Depend. 74(3), 265272 (2004).

CAS PubMed Article Google Scholar

Greaves, L. & Hemsing, N. Sex and gender interactions on the use and impact of recreational cannabis. Int. J. Environ. Res. Public Health. 17(2), E509 (2020).

PubMed Article CAS Google Scholar

Spechler, P. A. et al. The initiation of cannabis use in adolescence is predicted by sex-specific psychosocial and neurobiological features. Eur. J. Neurosci. 50(3), 23462356 (2019).

PubMed Article Google Scholar

Becker, J. B., McClellan, M. L. & Reed, B. G. Sex differences, gender and addiction. J. Neurosci. Res. 95(12), 136147 (2017).

CAS PubMed PubMed Central Article Google Scholar

Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 47654774 (2017).

Google Scholar

Koob, G. F. & Volkow, N. D. Neurobiology of addiction: A neurocircuitry analysis. Lancet Psychiatry 3(8), 760773 (2016).

PubMed PubMed Central Article Google Scholar

Bickel, W. K. et al. 21st century neurobehavioral theories of decision making in addiction: Review and evaluation. Pharmacol. Biochem. Behav. 164, 421 (2018).

CAS PubMed Article Google Scholar

Ycel, M. et al. A transdiagnostic dimensional approach towards a neuropsychological assessment for addiction: An international Delphi consensus study. Addiction 114(6), 10951109 (2019).

PubMed Article Google Scholar

Zilverstand, A. & Goldstein, R. Z. Chapter 3Dual models of drug addiction: the impaired response inhibition and salience attribution model. In Cognition and Addiction (ed. Verdejo-Garcia, A.) 1723 (Academic Press, 2020).

Chapter Google Scholar

Redish, A. D., Jensen, S. & Johnson, A. A unified framework for addiction: Vulnerabilities in the decision process. Behav. Brain Sci. 31(4), 415487 (2008).

PubMed PubMed Central Article Google Scholar

Rawls, E., Kummerfeld, E. & Zilverstand, A. An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis. Commun. Biol. 4(1), 112 (2021).

Article CAS Google Scholar

Meier, M. H. et al. Which adolescents develop persistent substance dependence in adulthood? Using population-representative longitudinal data to inform universal risk assessment. Psychol. Med. 46(4), 877889 (2016).

CAS PubMed Article Google Scholar

Khurana, A., Romer, D., Betancourt, L. M. & Hurt, H. Working memory ability and early drug use progression as predictors of adolescent substance use disorders. Addict. Abingt. Engl. 112(7), 12201228 (2017).

Article Google Scholar

Wilson, S., Malone, S. M., Venables, N. C., McGue, M. & Iacono, W. G. Multimodal indicators of risk for and consequences of substance use disorders: Executive functions and trait disconstraint assessed from preadolescence into early adulthood. Int. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol. https://doi.org/10.1016/j.ijpsycho.2019.12.007 (2019).

Article Google Scholar

Meier, M. H. et al. Associations between adolescent cannabis use and neuropsychological decline: A longitudinal co-twin control study. Addict. Abingt. Engl. 113(2), 257265 (2018).

Article Google Scholar

Schlossarek, S., Kempkensteffen, J., Reimer, J. & Verthein, U. Psychosocial determinants of cannabis dependence: A systematic review of the literature. Eur. Addict. Res. 22(3), 131144 (2016).

PubMed Article Google Scholar

Defoe, I. N., Khurana, A., Betancourt, L., Hurt, H. & Romer, D. Disentangling longitudinal relations between youth cannabis use, peer cannabis use, and conduct problems: Developmental cascading links to cannabis use disorder. Addiction 114(3), 485493 (2019).

PubMed Article Google Scholar

Pingault, J. B. et al. Childhood trajectories of inattention, hyperactivity and oppositional behaviors and prediction of substance abuse/dependence: A 15-year longitudinal population-based study. Mol. Psychiatry. 18(7), 806812 (2013).

PubMed Article Google Scholar

Oshri, A., Rogosch, F. A., Burnette, M. L. & Cicchetti, D. Developmental pathways to adolescent cannabis abuse and dependence: Child maltreatment, emerging personality, and internalizing versus externalizing psychopathology. Psychol. Addict. Behav. 25(4), 634644 (2011).

PubMed PubMed Central Article Google Scholar

Griffith-Lendering, M. F. H., Huijbregts, S. C. J., Mooijaart, A., Vollebergh, W. A. M. & Swaab, H. Cannabis use and development of externalizing and internalizing behaviour problems in early adolescence: A TRAILS study. Drug Alcohol Depend. 116(1), 1117 (2011).

CAS PubMed Article Google Scholar

Farmer, R. F. et al. Internalizing and externalizing psychopathology as predictors of cannabis use disorder onset during adolescence and early adulthood. Psychol. Addict. Behav. 29(3), 541 (2015).

PubMed PubMed Central Article Google Scholar

Proctor, L. J. et al. Child maltreatment and age of alcohol and marijuana initiation in high-risk youth. Addict. Behav. 75, 6469 (2017).

PubMed PubMed Central Article Google Scholar

Mills, R., Kisely, S., Alati, R., Strathearn, L. & Najman, J. M. Child maltreatment and cannabis use in young adulthood: A birth cohort study. Addiction 112(3), 494501 (2017).

PubMed Article Google Scholar

Fridberg, D. J., Vollmer, J. M., ODonnell, B. F. & Skosnik, P. D. Cannabis users differ from non-users on measures of personality and schizotypy. Psychiatry Res. 186(1), 4652 (2011).

PubMed PubMed Central Article Google Scholar

Ketcherside, A., Jeon-Slaughter, H., Baine, J. L. & Filbey, F. M. Discriminability of personality profiles in isolated and co-morbid marijuana and nicotine users. Psychiatry Res. 238, 356362 (2016).

PubMed PubMed Central Article Google Scholar

Terracciano, A., Lckenhoff, C. E., Crum, R. M., Bienvenu, O. J. & Costa, P. T. Five-Factor Model personality profiles of drug users. BMC Psychiatry 8(1), 22 (2008).

PubMed PubMed Central Article Google Scholar

Creemers, H. E. et al. Predicting onset of cannabis use in early adolescence: The interrelation between high-intensity pleasure and disruptive behavior. The TRAILS Study. J. Stud. Alcohol Drugs. 70(6), 850858 (2009).

PubMed Article Google Scholar

Amlung, M., Vedelago, L., Acker, J., Balodis, I. & MacKillop, J. Steep delay discounting and addictive behavior: a meta-analysis of continuous associations. Addict. Abingt. Engl. 112(1), 5162 (2017).

Article Google Scholar

Strickland, J. C., Lee, D. C., Vandrey, R. & Johnson, M. W. A systematic review and meta-analysis of delay discounting and cannabis use. Exp. Clin. Psychopharmacol. https://doi.org/10.1037/pha0000378 (2020).

Article PubMed PubMed Central Google Scholar

Meier, M. H. et al. Persistent cannabis users show neuropsychological decline from childhood to midlife. Proc. Natl. Acad. Sci. 109(40), E2657E2664 (2012).

CAS PubMed PubMed Central Article Google Scholar

Gonzalez, R., Pacheco-Coln, I., Duperrouzel, J. C. & Hawes, S. W. Does cannabis use cause declines in neuropsychological functioning? A review of longitudinal studies. J. Int. Neuropsychol. Soc. JINS 23(910), 893902 (2017).

PubMed Article Google Scholar

Zilverstand, A., Huang, A. S., Alia-Klein, N. & Goldstein, R. Z. Neuroimaging impaired response inhibition and salience attribution in human drug addiction: A systematic review. Neuron 98(5), 886903 (2018).

CAS PubMed PubMed Central Article Google Scholar

Lorenzetti, V., Chye, Y., Silva, P., Solowij, N. & Roberts, C. A. Does regular cannabis use affect neuroanatomy? An updated systematic review and meta-analysis of structural neuroimaging studies. Eur. Arch. Psychiatry Clin. Neurosci. 269(1), 5971 (2019).

PubMed Article Google Scholar

Batalla, A. et al. Structural and functional imaging studies in chronic cannabis users: A systematic review of adolescent and adult findings. PLoS ONE 8(2), e55821 (2013).

ADS CAS PubMed PubMed Central Article Google Scholar

Maggs, J. L. et al. Predicting young adult degree attainment by late adolescent marijuana use. J. Adolesc. Health Off. Publ. Soc. Adolesc. Med. 57(2), 205211 (2015).

Article Google Scholar

Danielsson, A. K., Falkstedt, D., Hemmingsson, T., Allebeck, P. & Agardh, E. Cannabis use among Swedish men in adolescence and the risk of adverse life course outcomes: Results from a 20 year-follow-up study. Addict. Abingt. Engl. 110(11), 17941802 (2015).

Article Google Scholar

Green, K. M., Doherty, E. E. & Ensminger, M. E. Long-term consequences of adolescent cannabis use: Examining intermediary processes. Am. J. Drug Alcohol Abuse. 43(5), 567575 (2017).

PubMed Article Google Scholar

Verweij, K. J. H., Huizink, A. C., Agrawal, A., Martin, N. G. & Lynskey, M. T. Is the relationship between early-onset cannabis use and educational attainment causal or due to common liability?. Drug Alcohol Depend. 133(2), 580586 (2013).

PubMed Article Google Scholar

Wiley, J. L. & Burston, J. J. Sex differences in 9-tetrahydrocannabinol metabolism and in vivo pharmacology following acute and repeated dosing in adolescent rats. Neurosci. Lett. 576, 5155 (2014).

CAS PubMed PubMed Central Article Google Scholar

Narimatsu, S., Watanabe, K., Yamamoto, I. & Yoshimura, H. Sex difference in the oxidative metabolism of delta 9-tetrahydrocannabinol in the rat. Biochem. Pharmacol. 41(8), 11871194 (1991).

CAS PubMed Article Google Scholar

Harte-Hargrove, L. C. & Dow-Edwards, D. L. Withdrawal from THC during adolescence: Sex differences in locomotor activity and anxiety. Behav. Brain Res. 231(1), 4859 (2012).

CAS PubMed PubMed Central Article Google Scholar

Fattore, L., Spano, M., Altea, S., Fadda, P. & Fratta, W. Drug- and cue-induced reinstatement of cannabinoid-seeking behaviour in male and female rats: Influence of ovarian hormones. Br. J. Pharmacol. 160(3), 724735 (2010).

CAS PubMed PubMed Central Article Google Scholar

Fattore, L. et al. Cannabinoid self-administration in rats: Sex differences and the influence of ovarian function. Br. J. Pharmacol. 152(5), 795804 (2007).

CAS PubMed PubMed Central Article Google Scholar

Hill, M. N. et al. Endogenous cannabinoid signaling is essential for stress adaptation. Proc. Natl. Acad. Sci. U.S.A. 107(20), 94069411 (2010).

ADS CAS PubMed PubMed Central Article Google Scholar

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Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder |...

The module, aimed entirely at professional investors, consists of six chapters and is valid for one hour of training for re-certification from EFPA Spain or 1 CPD credit from CFA Society Spain.

When people talk about data intelligence, they often use the terms machine learning (ML), artificial intelligence (AI), natural language processing (NLP), and deep learning (DL) interchangeably. But what is the difference between these methods? Why is it increasing in importance in asset management? How does quantitative investing apply to the investment process? Many doubts that we will solve in the six chapters in which the module is structured Machine learning and quantitative investing: how to incorporate them into portfolio buildingWhich is supported by Goldman Sachs AM.

You already know the system of adding continuing education hours. Once the test is completed and answered, module can be validated as 1 cpd credit CFA Society Spain How One Hour Recertification Training EIA, EIP, EFA and EFP D EFPA Spain, provided you successfully complete the knowledge test at the end of this module.

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New FundsPeople Learning Module! Machine Learning And Quantitative Investing: How To Incorporate Them Into A Portfolio With Goldman Sachs AM - World...

Google-Powered Julian Teaches and Learns at Walden University

Steven Tom, chief customer officer atAdtalem Global Education, was at a conference several years ago and saw a demonstration of an AI tutor that left him thinking bigger. The tool he saw took student questions and answered them according to what he called a script, following a preset path programmed by a human on the back end that, the idea was, eventually took the student to the right answer. Itsounded a lot like adaptive learning, a concept that has been around for decades and has failed to take off, in part because it takes a lot of effort to program and is fairly inflexible in how it responds.

Somebody had to spend hours and hours and days and days creating the questions, scripting how the tutor was going to interact with the student, Tom says. And then the AI portion of it was like when you read a news article and give it a thumbs-up, thumbs-down the kind of learning based on whether you liked the question or not. In that sense, it really wasnt dynamic, and it really wasnt scalable.

That experience set Tom on a mission to solve for both of those weaknesses.

His first step was to reach out to the teams atGoogleworking in AI and higher education. Toms team atWalden University, an online university recently acquired by Adtalem, worked with Google to build the AI tutor that wouldbe introduced to students and faculty as Julianin the spring of 2021.

We wanted to see if we could tackle this challenge of creating a truly dynamic, truly unscripted AI tutor that could essentially ingest content on its own, make sense of it, pull the key concepts out of it and then foster with the student a real tutoring session where it can generate its own questions and assess the students answers completely on its own, Tom says. At the time, it was kind of a pipe dream.

Today, Tom says, that dream has at least partly come true.

Julian is more dynamic than most AI tutors. By design, it was rolled out in courses where its not easy for a machine to tell if the answer is right or wrong (as opposed to, say, a math course). At Walden, Julians first courses were in early childhood education and sociology.

DISCOVER:Successful AI examples in higher education that can inspire our future.

And while Julian is not exactly judging right or wrong it doesnt do any grading, for example it is ingesting information and learning as it goes. Its using the same course materials the students are given, and it directs them back to those sources in response to the questions they ask. Then it takes those questions and answers and feeds them back into itself to learn what students are asking about and grow even smarter for next time, Tom says.

Because it was developed with Google, Julian lives in theGoogle Cloud, meaning it has taken little to no infrastructure investment on Waldens part. Tom says programmers opted to use an application programming interface to drive the tool, with an eye on its future use.

Since it is API-driven, Tom says, Julian can potentially appear in many environments: While its currently a chatbot embedded in the universitys learning management system, it could easily exist as an avatar in an augmented or virtual reality environment, or as a voice on the phone, in the future.

The future is also where the innovative team at Georgia Tech is focused. The university that first brought Jill Watson to life has since rolled out two new tools related to AI tutoring:AskJillandAgent Smith. The Atlanta-based university also recently helped establish theNational AI Institute for Adult Learning and Online Education(AI-ALOE) thanks to a five-year, $20 milliongrant from the National Science Foundation.

The grant will fund large-scale investments in technology infrastructure, according to Ashok Goel, a computer science professor at Georgia Tech and the executive director of AI-ALOE. The investments will go towarddata storage, compliance andcybersecurity.

AI-ALOE is interested particularly in adult learners, as the name suggests. Goel says he anticipates millions of Americans will need to be reskilled or upskilled in the coming decade as automation changes the way we work, and those people wont be traditional college students. They will have families, jobs and other responsibilities, and they may not get to their coursework until the wee hours of the night or on the weekends, when professors, teaching assistants and human tutors are not available, increasing the need for AI to help.

READ MORE:Improve online learning and more with artificial intelligence.

As for the initiative itself, Goel wants to bring AI tutors to the world on a massive scale. Jill Watson is great, he says, but the effort it takes to create one Jill Watson makes it nearly impossible to replicate at other higher education institutions or in K12 environments. And the programs other goal improving the AI capabilities itself will go hand in hand with expanded use of a tool like Jill Watson.

Goel says Georgia Techs AI tools have learned from more than 40,000 user questions over the years. That may seem impressive at first, but Goel says 4 million questions would be a much nicer base to learn from.

Even as the AI is getting smarter, the latest version of the AI is designed to help expand its reach. Agent Smith (named after the self-cloning antagonist inThe Matrixmovies) offers the most intriguing potential for large-scale growth. Agent Smith is a cloner, capable of replicating a Jill Watson AI tutor for courses and classrooms around the country in as little as five hours. Thats still too long, Goel says, and the AI tutor interface is still a little clunky, but the team is making progress.

Why cant we offer AI tutors to every teacher and every learner and class in the world? Goel asks. If Jill Watson remains solely the provenance of 30 classes, thats interesting, but its not a game changer. It becomes a game changer only if anyone can use it.

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The Future of AI Tutors in Higher Education - EdTech Magazine: Focus on K-12

--(BUSINESS WIRE)--Astera Labs Inc. :

WHEN:

Wednesday, September 21, 2022, from 9:30 a.m.-11:30 a.m. PDT

WHERE:

Astera Labs Vancouver4370 Dominion StreetBurnaby, BC V5G 4L7Canada

WHO:

WHAT:

Astera Labs welcomes the Mayor of Burnaby and the Burnaby Board of Trade President and CEO to celebrate the grand opening of its new state-of-the-art design center and lab in the Greater Vancouver Area.

Astera Labs Vancouver will support the companys development of cutting-edge interconnect technologies for Artificial Intelligence and Machine Learning architectures in the Cloud. The rapidly growing semiconductor company chose the Vancouver area to tap into the regions rich technology talent base to drive product development, customer support and marketing. The Vancouver location increases the companys operations in Canada, which already includes the new Research and Development Design Center in Toronto, and adds to its global footprint with headquarters in Santa Clara, California and offices around the globe.

Astera Labs is actively hiring across multiple engineering and marketing disciplines to support end-to-end product and application development and overall go-to-market operations. Open positions can be found at http://www.AsteraLabs.com/Careers/.

The ribbon cutting and photo opportunity with Burnaby Officials and Astera Labs Executives will be held outdoors. Below is an overview of the event agenda:

Event Schedule

Formal Remarks

9:30 a.m. 10:00 a.m. PDT

Ribbon Cutting / Photo Op / Media Q&A

10:00 a.m. 10:30 a.m. PDT

Indoor Reception

10:30 a.m. 11:30 a.m. PDT

For onsite assistance, contact Dave Nelson at (604) 418-9930.

About Astera Labs

Astera Labs Inc. is a leader in purpose-built data and memory connectivity solutions to remove performance bottlenecks throughout the data center. With locations worldwide, the companys silicon, software, and system-level connectivity solutions help realize the vision of Artificial Intelligence and Machine Learning in the Cloud through CXL, PCIe, and Ethernet technologies. For more information about Astera Labs including open positions, visit http://www.AsteraLabs.com.

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Astera Labs to Host Mayor of Burnaby at Grand Opening Of New Vancouver Design Center and Lab Dedicated to Purpose-Built Connectivity Solutions for...

SAN JOSE, Calif., Sept. 23, 2022 /PRNewswire/ -- RealPars, the leader in cutting-edge industrial education, and Edge Impulse, the best-in-class edge machine learning platform, today announce that they are teaming up to provide new and groundbreaking content to further development of advanced manufacturing tools and techniques.

The initial partnership focuses on predictive maintenance programming, leveraging RealPars' innovative training platform to show how Edge Impulse's machine learning tools can optimize maintenance cycles on industrial equipment. Predictive maintenance is quickly becoming a highly sought-after method of upkeep in industrial settings. Through the use of ML algorithms, embedded sensors, and onboard computing, the technique can be used to detect anomalies in machinery before breakdowns occur, allowing for just-in-time repair or replacement in order to maximize uptime and minimize costly shut downs. The partnership between Edge Impulse and RealPars will help build awareness and advancement of the practice.

Edge Impulse, the leading development platform for ML on edge devices, allows developers to quickly and easily create and optimize solutions with real-world data. The company's platform streamlines the entire process of collecting and structuring datasets, designing ML algorithms with ready-made building blocks, validating the models with real-time data, and deploying the fully optimized production-ready result to an edge target. The Edge Impulse development platform, already in use by thousands of companies, stands to unlock massive value across manufacturing and many other industries, with millions of developers making billions of devices smarter.

The two firms will kick off the partnership with a workshop on predictive maintenance; more information can be found on the partnership landing page, and will be announced at Imagine, Edge Impulse's ML conference, this September 28. Register at edgeimpulse.com/imagine

Edge Impulse is the leading machine learning platform, enabling all enterprises to build smarter edge products. Their technology empowers developers to bring more ML products to market faster, and helps enterprise teams rapidly develop industry-specific solutions in weeks instead of years. The Edge Impulse platform provides powerful automation and low-code capabilities to make it easier to build valuable datasets and develop advanced ML with streaming data. With over 40,000 developers, and partnerships with the top silicon vendors, Edge Impulse offers a seamless integration experience to validate and deploy with confidence across the largest hardware ecosystem. To learn more, visit edgeimpulse.com.

RealPars is the world's largest online learning platform for cutting-edge industrial technologies.

Their goal is to give anyone in the world the ability to learn new skills they need to succeed in their career as an engineer in the industrial space. RealPars is set out to create a new, easy-to-follow way of learning - making it engaging, flexible, and accessible for as many people as possible. Explaining complicated engineering concepts in an easy-to-follow format is what sets RealPars apart from any other learning platform. Since launching the platform in 2018, RealPars has helped millions of people around the globe unlock modern technical skills and reach their full potential. To learn more, head on over to realpars.com.

SOURCE Edge Impulse

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Edge Impulse and RealPars Announce Automation Technology Content Partnership - PR Newswire

Not familiar with BizarroMath? Youre in luck; Ive launched a web site for it where you can get an explanation of the numbers and browse the data.

Week 1

Week 2

Week 3

All lines courtesy of DraftKings Sportsbook as of 8:00am, Monday, September 19, 2022.

Iowa football continues to be the #1 supplier of high-quality material for the Sickos Committee.

This week, BizarroMath went 4-8 ATS and 5-7 O/U. Combined with the prior record of 11-8 and 6-13, respectively, the algorithm is 15-16 ATS and 11-20 O/U on the season after three full weeks of play. Not a great outing in the second straight strange week of Division I NCAA Football, but were still learning about these teams.

Vegas Says: MI -46.5, U/O 57.5

BizarroMath Says: MI -64.10 (MI cover), 71.66 (over)

Actual Outcome: MI 59, UCONN 0 (ATS hit, O/U hit)

One Sentence Recap: Michigan aint played nobody.

Vegas Says: OU -11.5, O/U 64.5

BizarroMath Says: OU -7.90 (NE cover), 60.03 (under)

Actual Outcome: OU 49, NE 14 (ATS miss, O/U hit)

One Sentence Recap: We should all be rooting for Mickey Josephs no-nonsense, just play the damn game style, which is a welcome departure from Scott Frosts chesty preening, but Nebraska still seems mired in a deep hole of undisciplined play and softness at the point of attack.

Vegas Says: n/a

BizarroMath Says: n/a

Actual Outcome: SILL 31, NU 24

One Sentence Recap: I watched the Salukis play many at a game at the UNI Dome in Cedar Falls over the years and I was probably less surprised than many that they pulled off this upset.

Vegas Says: Pk, O/U 58.5

BizarroMath Says: PUR -2.22 (Purdue win), O/U 47.9 (under)

Actual Outcome: SYR 32, PUR 29 (ATS miss, O/U miss)

One Sentence Recap: Much like the Penn State game, this game was there for the taking and Purdue simply refused, and I want to reiterate that Ive been skeptical since before the season began that the 2022 Edition of Purdue would be able to maintain the momentum from last year.

Vegas Says: IN -6.5, O/U 59.0

BizarroMath Says: WKY -12.90 (WKY cover), 65.99 (over)

Actual Outcome: IND 33, WKY 30 (ATS hit, O/U hit)

One Sentence Recap: BizMas prediction of a WKY upset damn near came true, but Tom Allens sweeping, must win now changing in the offseason seem to be paying dividends as the Hoosiers are figuring some things out and finding ways to win.

Vegas Says: RUT -17.5, O/U 44

BizarroMath: RUT -23.74 (RUT cover), 44.62 (over)

Actual Outcome: RUT 16, TEM 14 (ATS miss, O/U miss)

One Sentence Recap: Im pretty sure Rutgers is close to its pre-season O/U win total already, as the Scarlet Knights, like the other Eastern Red Team, keep finding ways to win.

Vegas Says: PSU -3, O/U 49

BizarroMath: PSU -2.71 (Auburn cover), 44.70 (under)

Actual Outcome: PSU 41, AUB 12 (ATS miss, O/U miss)

One Sentence Recap: It just means more.

Vegas Says: MN -27.5, O/U 46.5

BizarroMath: MN -23.95 (CO cover), 44.20 (under)

Actual Outcome: MN 49, CO 7 (ATS miss, O/U miss)

One Sentence Recap: Minnesota aint played nobody.

Vegas Says: WI -37.5, O/U 46.5

BizarroMath: WI -38.71 (WI cover), 50.1 (over)

Actual Outcome: WI 66, NMSU 7 (ATS hit, O/U hit)

One Sentence Recap: Theres nothing interesting about this game other than two observations: (1) this is the most points Wisconsin has scored in the Paul Chryst era; (2) Wisconsin has the same problem as Iowa in that Chryst has probably hit his ceiling and isnt going to elevate the program any further, but he wins too much to let him go.

Vegas Says: OSU -31.5, O/U 61

BizarroMath: OSU -28.36 (Toledo cover), 66.12 (over)

Actual Outcome: OSU 77, TOL 21 (ATS miss, O/U hit)

One Sentence Recap: OSUs opponent-adjusted yards surrendered this year is an absurd 2.28, which could be more a function of the small sample size we have for their opponents than anything, but this is why I blend data, folks.

Vegas Says: -MSU 3, O/U 57.5

BizarroMath: MSU -8.44 (MSU cover), 50.28 (under)

Actual Outcome: WA 39, MSU 28 (ATS miss, O/U miss)

One Sentence Recap: Ive told you my numbers dont like the Spartans, and Washington just showed us why.

Vegas Says: IA -23, O/U 40

BizarroMath: IA -2.48 (Nevada cover), 47.22 (over)

Actual Outcome: IA 27, NE 0 (ATS miss, O/U miss)

One Sentence Recap: Weird how when you inject a bunch of scholarship players back into your line-up, and play a defense of dubious quality, you can kind of, sort of, move the ball a little bit, even with an historically incompetent offense.

Vegas Says: MD -3.5, O/U 69.5

BizarroMath: SMU -1.31 (SMU cover), 75.22 (over)

Actual Outcome: MD 34, SMU 27 (ATS hit, O/U miss)

One Sentence Recap: First Four Games Maryland has scored 121 points through 3 games; I put the O/U on how many more games before Iowa breaks that mark at 5.5.

Now that I have the http://www.BizarroMath.com web site up and running, you can take a look at Iowas game-by-game projections and season projections yourself. Im going to not post the images this week and leave it to you to visit the site if you want to see the data. This is not a clickbait money scheme. There are no ads on that site, I wrote the HTML by hand because Im old and thats how I roll, and I make $0 off you visiting that site.

If you prefer to have the data presented in-line here, let me know, I will do that next week. Please answer the poll below to help me figure out how best to do this.

5%

21%

54%

13%

4%

1%

Also Caveat: If you come back to these links in the future, they will be updated with the results of future games, which also is a reason to post the data here for posterity, I suppose. Anyway, I may change the web site in the future to provide week-by-week updates showing the net changes. If youre interested in that, please let me know.

On to the analysis.

We finally have two FBS games worth of data on Iowa and can we start jumping to conclusions. Iowas raw PPG against D1 competition are at 17.0, which is good for #108 in the country. Iowas raw YPG are 243.50, which puts the Hawkeyes at #115. Iowas raw YPP are 4.20, ranking the Black and Old Gold at #110. The team is very slowly crawling out of the Division 1 cellar, but didnt exactly light the world on fire Saturday in a wet, frequently-interrupted outing against a Nevada team widely regarded as being Not Very Good.

We dont have enough data for opponent-adjustments for Iowa at this point (I require at least 3 adjustable games). Iowas blended data is what is used for the projections, and you can review that on the BizarroMath.com web site. Suffice it to say that Iowas outing against Nevada was similar in profile to what the team looked like last year. But, the schedule is a bit tougher this year, and Iowa needed some good fortune last year to make the Big 10 Championship game. I know nobody wants to hear it, but if this offense can climb up out of the triple digit rankings and get even to the 80th-90th range, that just might be enough to stay in the conference race.

But this season may simply boil down to schedule. Wisconsins cross-over games are @Ohio State, @MSU, and Maryland at home. Thats about as hard as it gets without playing either Michigan or Penn State. Minnesotas cross-over games are @Penn State, @Michigan State, and Rutgers. Iowas are @Rugers, Michigan, @Ohio State. From most to least difficult, Id say Iowa has the worst draw, then Wisconsin, then Minnesota. The Gophers also get Purdue and Iowa at home, and have Nebraska, Wisconsin, and Illinois on the road. The Badgers have Illinois and Purdue at home and go on the road to play Nebraska, Iowa, and (dont laugh) Northwestern. The Badgers are 1-6 at Northwestern this Century. The schedule generally favors the Gophers, and with Iowa playing Michigan and Ohio State in October, we shouldnt be surprised if the Hawkeyes are out of the division race before November.

That said, Iowas game-by-game odds are moving in the right direction. Iowa is a significant underdog vs. Michigan and Ohio State as expected, and a slight dog to Wisconsin and (stop traffic) Illinois. Perhaps most alarming is that the Hawkeyes have only a 37.92% chance to beat Minnesota. But! Recall that I am not doing opponent adjustments to the 2022 data yet for Minnesota, so their gaudy numbers are being taken at face value, and theyll drop after the Gophers play Michigan State this weekend.

To give you an idea of how that works, consider Michigan, which has played enough adjustable games that I can run opponent adjustments. Their opposition has been so terrible that BizarroMath discounts the Wolverines raw 55.33 PPG by a whopping 22.54 points. This means that this Wolverine team is expected to put up just 32.80 points against an average D1 defense, to say nothing of what they can do against a Top 5 defense, which Iowa just so happens to have (again, before opponent-adjustments). Michigans adjusted data is thus actually worse than last year, whose offense was, opponent-adjusted, worth 42.17 PPG.

Minnesotas adjustments will come soon enough, and well see them return to deep below the Earth, where filthy rodents belong. But, so, too, will Iowas, and of Iowas three adjustable opponents after this coming weekend - Rutgers, Nevada, and Iowa State - the Cyclones are by far the best team.

Iowa Season Projections

The Nevada win and swing in the statistics towards something more similar to last years putrid but still-better-than-this-crap offensive performance has brightened Iowas season outlook somewhat. Iowas most likely outcome now is 7-5 (27.13% chance), with 6-6 being more likely (25.89%) than 8-4 (17.52%). There is a 92.11% chance that Iowa doesnt reach 9.3, and a 78.42% chance that the Hawkeyes get bowl eligible this year.

The Gilded Rodents flashy numbers have pulled them almost even with Wisconsin, as the Badgers and Gophers are both in the 35-40% range for a division championship. Purdues continued struggles drops the Boilermakers to the four spot, elevating hapless Iowa to the third place in the West, though the Hawkeyes chances of actually winning the damn thing drop to 8.40%, Iowas climb up the division ladder from 5th to 3rd is more a function of the poor play of the teams now ranked lower than anything Iowa is doing on the field.

Im a bit puzzled by the conference race in the East, where Ohio State shot from last weeks 21.53% to this weeks 64.18% chance, but I think its because BizMa now has the Buckeyes with a 77.74% chance of winning The Game, which is the main shift that accounts for this change. Why? Well, this week we have opponent-adjustments for both teams and OSU has played a tougher schedule, so the Buckeyes numbers are not being discounted nearly as much as Michigans.

For example, on offense, OSU is putting up a raw 8.49 YPP, which BizMa is actually adjusting up to 9.58. Michigan, by comparison, is putting up 7.97 YPP, but BizMa is adjusting it down to 6.36 YPP based on the competition. As we move into the conference slate and the quality of each teams opposition evens out, well probably see those numbers flatten out a bit.

I love week 4. Because the number of games I have to track is cut in half.

Vegas Says: n/a

BizarroMath: n/a

One Sentence Prediction: Your Fighting Illini are going to be 3-1 going into conference play, and they have been competitive, if a bit raggety.

Vegas Says: MI -17, O/U 62.5

BizarroMath: MI -3.81, O/U 58.01 (MD cover, under)

One Sentence Prediction: BizMa sees this game as being much closer than Vegas does, and I think the difference might be a function of where we are in the season, as I just dont see Marylands defense holding Michigan down, and I dont buy that under for even a second, folks.

Vegas Says: PSU -26, O/U 60.5

BizarroMath: PSU -30.47, O/U 58.97 (PSU cover, under)

One Sentence Prediction: I dont know a thing about Central Michigan this year but a final along the lines of 45-13 sounds about right.

Vegas Says: MN -2, O/U 51.0

BizarroMath: MN -8.75, O/U 45.49 (MN cover, under)

One Sentence Prediction: Well soon know if the Gilded Rodents are fools gold, but not this week, as I think Minnesota is going to put up some points here in something like a 42-23 affair.

Vegas Says: CIN -15.5, O/U 54.0

BizarroMath: CIN -25.04, O/U 53.90 (CIN cover, under)

One Sentence Prediction: The Hoosiers either crash hard back down to Terra Firma in an embarrassing road rout, or this winds up being an unexpectedly knotty game.

Vegas Says: IA -7.5, O/U 35.5

BizarroMath: IA -9.85, O/U 32.09 (IA cover, under)

One Sentence Prediction: In Assy Football, the MVP is from one of two separate, yet equally important, groups: the punt team, which establishes poor field position for the opposition; and the punt return team, who try to field the ball outside of the 15 yard line without turning it over; this is their magnum opus.

Vegas Says: OSU -17.5, O/U 56.5

Read more:
Machine Learning Week 4 - Updated Iowa Game by Game Projections, Season Record, and Championship Odds - Black Heart Gold Pants

image:The Archytas Dome region of the lunar surface is the target area for the EXPLORE Lunar Data Challenges 2022. view more

Credit: Credit: NASA/GSFC/Arizona State University/EXPLORE/Jacobs University.https://exploredatachallenges.space/wp-content/uploads/2022/09/Archytas2.png

Lunar enthusiasts of all ages are challenged to help identify features on the Moon that might pose a hazard to rovers or astronauts exploring the surface.

The2022 EXPLORE Lunar Data Challengeis focused on theArchytas Dome region, close to the Apollo 17 landing site where the last humans set foot on the Moon 50 years ago this December.

The Machine Learning Lunar Data Challenge is open to students, researchers and professionals in areas related to planetary sciences, but also to anyone with expertise in data processing. There is also a Public Lunar Data Challenge to plot the safe traverse of a lunar rover across the surface of the Moon, open to anyone who wants to have a go, as well as a Classroom Lunar Data Challenge for schools, with hands-on activities about lunar exploration and machine learning.

Announcing the EXPLORE Machine Learning Lunar Data Challenge during the Europlanet Science Congress (EPSC) 2022 in Granada, Spain, this week Giacomo Nodjoumi said: The Challenge uses data of the Archytas Dome taken by the Narrow Angle Camera (NAC) on the Lunar Reconnaissance Orbiter (LRO) mission. This area of the Moon is packed craters of different ages, boulders, mounds, and a long, sinuous depression, or rille. The wide variety of features in this zone makes it a very interesting area for exploration and the perfect scenario for this Data Challenge.

The Machine Learning Lunar Data Challenge is in three steps: firstly, participants should train and test a model capable of recognising craters and boulders on the lunar surface. Secondly, they should use their model to label craters and boulders in a set of images of the Archytas zone. Finally, they should use the outputs of their models to create a map of an optimal traverse across the lunar surface to visit defined sites of scientific interest and avoid hazards, such as heavily cratered zones.

The public and schools are also invited to use lunar images to identify features and plot a journey for a rover. Prizes for the challenges include vouchers totalling 1500 Euros, as well as pieces of real Moon rock from lunar meteorites.

The EXPLORE project, which is funded through the European Commissions Horizon 2020 Programme, gathers experts from different fields of science and technical expertise to develop new tools that will promote the exploitation of space science data.

Through the EXPLORE Data Challenges, we aim to raise awareness of the scientific tools that we are developing, improve their accuracy by bringing in expertise from other communities, and involve schools and the public in space science research, said Nick Cox, the Coordinator of the EXPLORE project.

The deadline for entries closes on 21 November 2022 and winners will be announced in mid-December on the anniversaries of the Apollo 17 mission milestones.

The 2022 EXPLORE Data Challenges can be found at:https://exploredatachallenges.space

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Join the challenge to explore the Moon! - EurekAlert