Doug Ortiz<p>🤖 AutoML: AI Without Code?</p><p>Automated Machine Learning (AutoML) is changing the game. No coding, no complex algorithms—just AI-powered insights.<br>🚀 Industries using AutoML right now:<br>✅ Retail & E-commerce<br>✅ Finance<br>✅ Healthcare<br>✅ Marketing</p><p>But AutoML isn't magic—it still requires smart data strategies.</p><p>Check out this quick video: <a href="https://link.illustris.org/automl" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">link.illustris.org/automl</span><span class="invisible"></span></a></p><p>💡 Is AutoML the future of AI?</p><p>🔗 <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/AutoML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AutoML</span></a> <a href="https://mastodon.social/tags/TechTrends" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TechTrends</span></a> <a href="https://mastodon.social/tags/AIForEveryone" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIForEveryone</span></a> <a href="https://mastodon.social/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ArtificialIntelligence</span></a></p>
#AutoML
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Our Alzheimer's knowledgebase paper is finally out in @jmirpub. We are actively working on strategies to integrate Alzkb.ai with our #AutoML methods. AlzKB is open access https://www.jmir.org/2024/1/e46777 #bioinformatics #datascience #alzheimers funded by NIH R01 AG066833
Up early to participate in the Alzheimer's Disease Sequencing Project (ADSP) consortium review meeting. We are a funded participant to develop #AutoML methods for risk prediction using genetics data. Ultimate goal is drug discovery. #alzheimers #adsp
Aliro: an automated machine learning tool leveraging large language models https://academic.oup.com/bioinformatics/article/39/10/btad606/7291858 #automl #llms #aliro #bionformatics
#cybersecurity #python #automation #AI #MachineLearning #technology
QC Evaluation of the H2O #AutoML #workflow to classify and interpret #malware in real time.
Optimized Stacked Ensemble model: F1 Score: 0.9958. SHAP & H2O Feature Engineering plots.
newdigitals.org · Effective AI-Powered Malware Detection & Interpretation – 1. H2O AutoMLThis study explores AI-powered malware detection using the H2O AutoML algorithm for effective and rapid classification of PE files. The optimized Stacked Ensemble model achieved high precision, rec…
Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors https://dl.acm.org/doi/abs/10.1145/3583133.3590584 #automl #tpot #machinelearning
ACM ConferencesExtending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors | Proceedings of the Companion Conference on Genetic and Evolutionary Computation
Braved the storm to drive to Laguna Hills to give a keynote lecture on automated machine learning for an IEEE Health AI conference. Will participate in a panel discussion after lunch and then head back into the storm. https://www.aimhc.org/ #automl
aimhcAIMHCIEEE International Conference on Artificial Intelligence for Medicine, Health, and Care (AIMHC) is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice, as well as to identify the emerging topics and define the future of how AI can assist health and care.
Google/Deepmind just published last week some kind of Evolutionary Programming approach using a LLM as a variation operator, in order to design the source code of an optimization solver.
As usual, it is oversold as hell. And its performances are VERY poorly evaluated. I don't understand how Nature can accept that, to be honest. It was probably not reviewed by AutoOptim specialists.
Anyway, the underlying approach is still interesting.
'AutoKeras: An AutoML Library for Deep Learning', by Haifeng Jin, François Chollet, Qingquan Song, Xia Hu.
http://jmlr.org/papers/v24/20-1355.html
#automl #autokeras #tensorflow
@mempko totally agree! Did you see my post yesterday about the hazards of #AutoML ? Nice HBR article just came out. Maybe people will take it seriously now (because clearly Harvard consultants are more reliable than practitioners 
) https://hbr.org/2022/12/the-risks-of-empowering-citizen-data-scientists
Harvard Business ReviewThe Risks of Empowering “Citizen Data Scientists”New tools are enabling organizations to invite and leverage non-data scientists — say, domain data experts, team members very familiar with the business processes, or heads of various business units — to propel their AI efforts. There are advantages to empowering these internal “citizen data scientists,” but also risks. Organizations considering implementing these tools should take five steps: 1) provide ongoing education, 2) provide visibility into similar use cases throughout the organization, 3) create an expert mentor program, 4) have all projects verified by AI experts, and 5) provide resources for inspiration outside your organization.
I’m not just being defensive about my job in #datascience : I really agree “there are all sorts of ways an AI can go technically or functionally sideways, and non-data scientists with #automl may run straight into those pitfalls.” #ai #datascientists #machinelearning
https://hbr.org/2022/12/the-risks-of-empowering-citizen-data-scientists
Harvard Business ReviewThe Risks of Empowering “Citizen Data Scientists”New tools are enabling organizations to invite and leverage non-data scientists — say, domain data experts, team members very familiar with the business processes, or heads of various business units — to propel their AI efforts. There are advantages to empowering these internal “citizen data scientists,” but also risks. Organizations considering implementing these tools should take five steps: 1) provide ongoing education, 2) provide visibility into similar use cases throughout the organization, 3) create an expert mentor program, 4) have all projects verified by AI experts, and 5) provide resources for inspiration outside your organization.