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Hyperparameter Tuning of Decision Tree Classifier Using GridSearchCV

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The models can have many hyperparameters and finding the best combination of the parameter using grid search methods. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method.


OpenSea's new measures hope to crack down on fake NFTs

Engadget

OpenSea is putting in place a new system to spot NFT fakes and verify accounts, in an effort to cut down on the industry's growing fraud problem. In a couple of blog posts, the NFT marketplace detailed what changes users can expect, including opening up verification to more users, automated and human-assisted removal of so-called "copymints" or fake copies of authentic NFTs and changes to how collection badges -- which identify NFT collections with high sales volume or interest -- are doled out on the marketplace. First off, OpenSea will use a two-part system to detect fakes that combine both image recognition tech and human reviewers. The company says its new system will continuously scan all NFT collections (including newly minted assets) to spot any potential fakes. "Our new copymint prevention system leverages computer-vision tech to scan all NFTs on OpenSea (including new mints). The system then matches these scans against a set of authentic collections, starting with some of the most copy-minted collections -- we'll look for flips, rotations & other permutations," wrote OpenSea's Anne Fauvre-Willis in the post.


Should the "I" in "Artificial Intelligence (AI)" need a reboot?

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So, if Machine Learning is the way AI is powered to meet only the last point of acquiring knowledge and storing it for use later, then will this not be "incomplete intelligence"? At the risk of sounding like a non-conformist, Pearl argues that Artificial Intelligence is handicapped by an incomplete understanding of what intelligence really is. AI applications, as of today, can solve problems that are predictive and diagnostic in nature, without attempting to find the cause of the problem. Never denying the transformative and disruptive, complex, and non-trivial power of AI, Pearl has shared his genuine critique on the achievements of Machine Learning and Deep Learning given the relentless focus on correlation leading to pattern matching, finding anomalies, and often culminating in the function of "curve"-fitting. The significance of the "ladder of causation" i.e., progressing from association to intervention and concluding with counter factuality has been the contribution of immense consequence from Pearl. Pearl has been one of the driving forces who expects that the correlation-based reasoning should not subsume the causal reasoning and the development of causal based algorithmic tools.


An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks

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With the surge of Machine Learning (ML), An emerging amount of intelligent applications have been developed. Deep Neural Networks (DNNs) have demonstrated unprecedented performance across various fields such as medical diagnosis and autonomous driving. While DNNs are widely employed in security-sensitive fields, they are identified to be vulnerable to Neural Trojan (NT) attacks that are controlled and activated by stealthy triggers. In this paper, we target to design a robust and adaptive Trojan detection scheme that inspects whether a pre-trained model has been Trojaned before its deployment. Prior works are oblivious of the intrinsic property of trigger distribution and try to reconstruct the trigger pattern using simple heuristics, i.e., stimulating the given model to incorrect outputs.


Should the 'I' in 'Artificial Intelligence (AI)' need a reboot?

#artificialintelligence

So, if Machine Learning is the way AI is powered to meet only the last point of acquiring knowledge and storing it for use later, then will this not be "incomplete intelligence"? At the risk of sounding like a non-conformist, Pearl argues that Artificial Intelligence is handicapped by an incomplete understanding of what intelligence really is. AI applications, as of today, can solve problems that are predictive and diagnostic in nature, without attempting to find the cause of the problem. Never denying the transformative and disruptive, complex, and non-trivial power of AI, Pearl has shared his genuine critique on the achievements of Machine Learning and Deep Learning given the relentless focus on correlation leading to pattern matching, finding anomalies, and often culminating in the function of "curve"-fitting. The significance of the "ladder of causation" i.e., progressing from association to intervention and concluding with counter factuality has been the contribution of immense consequence from Pearl.


Researchers claim biometric deepfake detection method improves state-of-the-art

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Biometrics can effectively be used to detect deepfakes, according to a paper from a team of Italian and German researchers reported by Unite.AI, and could be a less "unwieldy" method of doing so than detecting synthetic artefacts and other methods. The framework for the method specifies the use of at least ten genuine videos of the subject to train the biometric model, the researchers from the University of Federico II in Naples and the Technical University of Munich write. The research into'Audio-Visual Person-of-Interest DeepFake Detection' has been posted to Arxive, and describes what the authors say is a new state-of-the-art in deepfake detection. In testing against well-known datasets, the researchers improved area under curve (AUC) scores by 3 and 10 for accuracy identifying genuine high and low-quality videos, respectively, and 7 percent for deepfake videos. Interestingly, on high-quality videos, the worst-performing system delivered deepfake detection accuracy of above 69 percent.


NYC Mayor Adams floats 'new tech,' bag checks on subway system to detect weapons

FOX News

WARNING--Graphic footage: Fox News correspondent Bryan Llenas has the latest on the investigation from Brooklyn, New York, on'Special Report.' New York City may be rolling out new technology and periodic bag checks to prevent future terrorist attacks, according to the mayor. New York City Mayor Eric Adams spoke with MSNBC's "Morning Joe" on Wednesday about the previous day's terror attack on the city's subway system. The mayor touched on the possibility of new technology on public transportation to prevent similar acts in the future. "With the gun detection devices – oftentimes when people hear of'metal detectors,' they immediately think of the airport model," Adams said.


Council Post: How Artificial Intelligence-Powered Tools Can Support Clinical Decision-Making

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At the end of a long week filled with too many deadlines and too little sleep, you wake up one morning woozy with a strange pain in your arm. You call your doctor, and she asks you a series of questions over the phone. She then tells you to go to the emergency room pronto: "I suspect you're having a heart attack." The doctor reached that conclusion not by simply making an educated guess, but by evaluating the data and using deductive reasoning. In medical school, physicians learn to estimate probabilities of disease based on symptoms, patient history, examination findings and labs or images.


All About Decision Tree

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The decision tree is one of the most powerful and important algorithms present in supervised machine learning.


Fracture Detection: Study Suggests AI Assessment May Be as Effective as Clinician Assessment

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Could artificial intelligence (AI) assessment have comparable diagnostic accuracy to clinician assessment for fracture detection? In a recently published meta-analysis of 42 studies, the study authors noted 92 percent sensitivity and 91 percent specificity for AI in comparison to 91 percent sensitivity and 92 percent specificity for clinicians based on internal validation test sets. For the external validation test sets, clinicians had 94 percent specificity and sensitivity in comparison to 91 percent specificity and sensitivity for AI, according to the study. In essence, the study authors found no statistically significant differences between AI and clinician diagnosis of fractures. "The results from this meta-analysis cautiously suggest that AI is noninferior to clinicians in terms of diagnostic performance in fracture detection, showing promise as a useful diagnostic tool," wrote Dominic Furniss, DM, MA, MBBCh, FRCS(Plast), a professor of plastic and reconstructive surgery in the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences at the Botnar Research Centre in Oxford, United Kingdom., and colleagues.