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The medical AI insurgency: what physicians must know about data to practice with intelligent machines
Computer scientist and AI guru Andrew Ng (of Google Brain, Baidu, and NVDIA) has offered the view that, “The measure of a good AI technology is that it does well what humans can do easily in one second”.4 While a machine that truly mimics higher cognitive function awaits human design,5 AI technologies are accelerating complex problem-solving in data-dense sectors like finance, cyber-security, social media, econometrics, computer vision, and logistics tracking (i.e., blockchain).6,7 Discriminative models for supervised machine learning (ML) are typically programmed to predict how an exploratory testing dataset relates to trusted training data.7 Preprocessing of training datasets enhances the yield of AI analytic modules for reliably selecting the most crucial features and faults, potentially rendering advanced AI modules automated (i.e., unsupervised deep learning (DL)).7 Unlike humans who can effectively transfer past experiences and expertise to new tasks, AI modules that generalize poorly to new datasets (other than those it trained on) can cause massive ML failures.8 Careful preprocessing of exploratory testing datasets before AI analytics helps to generalize knowledge in subsequent testing dataset runs.
Computer programming and DL expert François Chollet (of Google AI and Keras) attributes AI’s recent remarkable success as follows, “You can achieve a surprising amount using only a small set of very basic techniques”.9 Pure DL engines are discriminative modeling algorithms, systems of neural networks (i.e., nets) with multiple hidden layers in which neurons receive, weigh and combine inputs to produce an output that is passed to the next layer.7 Such relative simplicity implies that one need not be an AI technology expert to accomplish DL.
Very slight data matrix perturbations, introduced intentionally into discriminative neural nets by generative adversarial nets (GAN’s), can cause an AI module to become 99% certain of a predictive model output that human experts immediately recognize as 100% erroneous.10 The dual goals of purposefully pitting generative nets against discriminative nets are better discriminator object and feature identification (i.e., reinforcement learning), and better generator learning about how to deceive discriminators.
Taxonomies and ontologies help ordered proximity algorithms (i.e., k-word nearest neighbor search for text and fast nearest neighbor search for dynamic indexing) make statistical inferences and associations based on geometric distance functions (i.e., vector calculus) that reflect data similarity, or dissimilarity.16,17 More advanced analytics (like AI) compel data scientists to create tools and technologies that can wrangle an ever-expanding and more complex data universe2,3—cleaning, labeling, organizing, and integrating data from many different sources.18 Computer languages for querying databases (i.e., C++, Java, and C#) follow set data rules and programming methods.
To achieve computing efficiencies, data scientists use a wide variety of dimensionality reduction and feature selection techniques: principal component analysis (PCA),2 generalized spike models,22 robust PCA,23,24 PCA whitening,25 robust subspace tracking,24 low rank plus sparse [L + S] data decomposition26 and algorithms (i.e., t-distributed stochastic neighbor embedding).27 With proper preprocessing of dynamic datasets, AI technologies are becoming more efficient at signal processing (i.e., satellite communications and seismology), computer vision (i.e., video surveillance and traffic patterns), and network traffic analysis.
Digital medical images representing human anatomical structures and physiological functions are large (i.e., magnetic resonance imaging (MRI) = 200 MB per image), but generally clean data files.28,29 This partly explains why applying AI technologies to digital medical imaging (i.e., radiology, dermatology, histopathology, and retinal photography) datasets translates well, achieving ≥95% of human accuracy for predicting disease types and severity.6 But while digital medical imaging data quality is often superior to other medical data, it too can be messy.
When applied to static body imaging, the generalized spike model detects localized disease-induced anatomical variations of organ landmarks wherein the PCA eigenvectors are rendered sparse.22 Robust PCA of static brain MRI studies can separate clinically useful functional from diffusion MRI information.23 PCA preprocessing of genomic data (by creating file-backed big matrix objects for analyses using R package algorithms and statistical tools) defines relevant gene expression clusters within high-dimensional genomic data arrays, rapidly creating polygenic risk scores for conditions like celiac disease.32 Conventional PCA and hybrid ML algorithm analyses of heterogeneous high-dimensional (i.e., neuroimaging and biomarkers) and low-dimensional (i.e., medical records) data manifolds have shown comparable (83–85%) accuracy for discriminating healthy subjects from those with mild cognitive impairment and patients with documented Alzheimer’s disease.33 Applying low rank plus sparse (L + S) decomposition to contrast-enhanced digital subtraction imaging allows for automated background subtraction of S component differences (i.e., perturbations), while L images are useful for image-to-image realignment and change detection23 (Fig.
Doctors cannot ethically relate AI model results for predicting an important outcome (i.e., the genetic odds of disease32 or likelihood of inpatient death35) to their patients without also plausibly explaining how the “black box” generated those odds.6,36 While doctors might assume that the precision of an approved AI medical applications is high, doctors disintermediated from the training data cannot vouch for either the quality of the raw data or the rigor of data preprocessing.28 Thirdly, while data scientists know that data provenance—the origin of datasets in time and place—is a key determinant of the inferences to be drawn from it, most physicians do not.
Fetal ultrasound markers of Downs Syndrome derived using 1990s low-res 2-D ultrasound source data do not carry the same predictive value when remodeled with modern high-res 3-D imaging fetal ultrasound datasets.37 And while ML analytics of 3-D fetal ultrasound imaging data at scale could augment diagnostic observer reproducibility, it could also skew individual case medical decision-making as compared to human experts (i.e., the need for amniocentesis to detect the trisomy 21 chromosome).
Can artificial intelligence beat a human hacker?
A captcha, or Completely Automated Public Turing test to tell Computers and Humans Apart, is designed based on the Turing test.
Alan Turing, the founder of modern computing, built a machine that was capable of mimicking human speech in letters, so that outsiders could not distinguish between human and robotic conversations.
This machine inspired the field of artificial intelligence, bringing with it security tests to distinguish between humans and machines.
Digitalisation As manufacturing becomes more digitalised, connected machines collect real-time data that is vital in keeping facilities running at optimum capacity.
Now, cyber security companies are offering solutions to this using AI and machine learning technology to introduce more preventative security for manufacturers.
Security company, Darktrace, uses machine learning to create unique patterns of encryption for each machine and detect any abnormalities.
PrivSec Conferences will bring together leading speakers and experts from privacy and security to deliver compelling content via solo presentations, panel discussions, debates, roundtables and workshops.
Artificial Intelligence Is About to Make Ransomware Hack Attacks Even Scarier
A year ago, network security specialists spotted a worrying new trend: hackers began unleashing ransomware attacks on really big targets—America’s cities.
would later grind to a halt after devastating computer outages disrupted everything from the collection of parking tickets to the sale of new homes.
Ransomware powered by artificial intelligence, a development that could give exploits such as RobbinHood and WannaCry a potent new makeover to evade cyber defenses, burrow into computer networks and wreak mayhem.
In recent years, artificial intelligence and machine learning have been a godsend to IT security professionals, enabling them to detect malware sooner—even the moment it enters the wild—keeping networks more secure and corporate assets safer.
“Imagine getting a video call from your boss telling you she needs you to wire cash to an account for a business trip that the company will later reimburse,” the authors write.
Researchers have already demonstrated that machine learning can handily defeat the CAPTCHA security protocols that protect computer servers from certain kinds of malicious bot attacks.
Here’s how a direct offering works —4 reasons to be skeptical about Facebook’s Libra cryptocurrency —Bank of America CEO: “We want a cashless society” —Fintech startup Tally has raised $50 million to automate people’s finances —Listen to our new audio briefing, Fortune 500 Daily Follow Fortune on Flipboard to stay up-to-date on the latest news and analysis.
- On 15. januar 2021
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