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Interesting AI/ML Articles You Should Read This Week (July 4)

#artificialintelligence

Would you let a machine learning model that has a failure rate of 98% and a false positive rate of 81% into production? Well, these claimed performance figures are from a facial recognition system that is in use by the policing force in South Wales and other parts of the United Kingdom. Dave Gershgorn article starts with a description akin to the setting of a dystopian future where an overseeing governing system monitors everyone; which is hysterically a foreshadowing of a foreseeable future. South Wales Police have been using facial recognition systems since 2017 and have done this in no secrecy from the public. They've made arrests as a result of the facial recognition system.


Why IBM Decided to Halt all Facial Recognition Development

#artificialintelligence

In a letter to congress sent on June 8th, IBM's CEO Arvind Krishna made a bold statement regarding the company's policy toward facial recognition. "IBM no longer offers general purpose IBM facial recognition or analysis software," says Krishna. "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency." The company has halted all facial recognition development and disapproves or any technology that could lead to racial profiling. The ethics of face recognition technology have been in question for years. However, there has been little to no movement in the enactment of official laws barring the technology.


Detroit police chief cops to 96-percent facial recognition error rate

#artificialintelligence

Detroit's police chief admitted on Monday that facial recognition technology used by the department misidentifies suspects about 96 percent of the time. It's an eye-opening admission given that the Detroit Police Department is facing criticism for arresting a man based on a bogus match from facial recognition software. Last week, the ACLU filed a complaint with the Detroit Police Department on behalf of Robert Williams, a Black man who was wrongfully arrested for stealing five watches worth $3,800 from a luxury retail store. Investigators first identified Williams by doing a facial recognition search with software from a company called DataWorks Plus. Under police questioning, Williams pointed out that the grainy surveillance footage obtained by police didn't actually look like him.


Hawaii Is Finally Making It Easier for Tourists to Visit. Is That Smart?

Slate

Hawaii is ready for its midpandemic tourism boom. Starting on Aug. 1, tourists looking to visit Hawaii will be able to bypass the state's two-week quarantine requirement for arrivals by getting a negative COVID-19 test within 72 hours before landing in the state. Visitors can also have their quarantines cut short if they receive negative test results during those two weeks. The same rules will also apply to residents returning to the islands. Hawaii won't pay for the tests; travelers will have to handle that themselves before departure, though screeners will still administer temperature checks at airports.


Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention

Science

Cancers diagnosed early are often more responsive to treatment. Blood tests that detect molecular markers of cancer have successfully identified individuals already known to have the disease. Lennon et al. conducted an exploratory study that more closely reflects the way in which such blood tests would be used in the future. They evaluated the feasibility and safety of incorporating a multicancer blood test into the routine clinical care of 10,000 women with no history of cancer. Over a 12-month period, the blood test detected 26 cancers of different types. A combination of the blood test and positron emission tomography–computed tomography (PET-CT) imaging led to surgical removal of nine of these cancers. Use of the blood test did not result in a large number of futile follow-up procedures. Science , this issue p. [eabb9601][1] ### INTRODUCTION The goal of earlier cancer detection is to identify the disease at a stage when it can be effectively treated, thereby offering the patient a better chance of long-term survival. Adherence to screening modalities known to decrease cancer mortality such as colonoscopy, mammography, low-dose computed tomography, and Pap smears varies widely. Moreover, the majority of cancer types are diagnosed only when symptoms occur. Multicancer blood tests offer the exciting possibility of detecting many cancer types at a relatively early stage and in a minimally invasive manner. ### RATIONALE Evaluation of the feasibility and safety of multicancer blood testing requires prospective interventional studies. We designed such a study to answer four critical questions: (i) Can a multicancer blood test detect cancers not previously detected by other means? (ii) Can a positive test result lead to surgical intervention with curative intent? (iii) Can testing be incorporated into routine clinical care and not discourage patients from undergoing recommended screening tests such as mammography? (iv) Can testing be performed safely, without incurring a large number of unnecessary, invasive follow-up tests? ### RESULTS We evaluated a blood test that detects DNA mutations and protein biomarkers of cancer in a prospective, interventional study of 10,006 women who were 65 to 75 years old and who had no prior history of cancer. Positive blood tests were followed by diagnostic positron emission tomography–computed tomography (PET-CT), which served to independently confirm and precisely localize the site and extent of disease if present. The study design incorporated several features to maximize the safety of testing to the participants. Of the 10,006 enrollees, 9911 (99.1%) could be assessed with respect to the four questions posed above. (i) Detection: Of 96 cancers incident during the study period, 26 were first detected by blood testing and 24 additional cancers by conventional screening. Fifteen of the 26 patients in whom cancer was first detected by blood testing underwent PET-CT imaging, and 11 patients developed signs or symptoms of cancer after the blood test that led to imaging procedures other than PET-CT. The specificity and positive predictive value (PPV) of blood testing alone were 98.9% and 19.4%, respectively, and combined with PET-CT, the specificity and PPV increased to 99.6% and 28.3%. The blood test first detected 14 of 45 cancers (31%) in seven organs for which no standard-of-care screening test is available. (ii) Intervention: Of the 26 cancers first detected by blood testing, 17 (65%) had localized or regional disease. Of the 15 participants with positive blood tests as well as positive PET-CT scans, 9 (60%) underwent surgery with curative intent. (iii) Incorporation into clinical care: Blood testing could be combined with conventional screening, leading to detection of more than half of the total incident cancers observed during the study period. Blood testing did not deter participants from undergoing mammography, and surveys revealed that 99% of participants would join a similar, subsequent study if offered. (iv) Safety: 99% of participants did not require any follow-up of blood testing results, and only 0.22% underwent an unnecessary invasive diagnostic procedure as a result of a false-positive blood test. ### CONCLUSION A minimally invasive blood test in combination with PET-CT can safely detect and precisely localize several types of cancers in individuals not previously known to have cancer, in some cases enabling treatment with intent to cure. Further studies will be required to assess the clinical utility, risk-benefit ratio, and cost-effectiveness of such testing. ![Figure][2] Overview of cancers detected by blood testing. Twenty-six cancers (blue dots) in 10 organs were first detected by blood testing. The blue dots with the red halo represent 12 of the 26 cancers that were surgically treated with intent to cure. Nine of these 12 were detected by the combination of the blood test and PET-CT, with the remaining three identified by the blood test combined with another imaging modality. Cancer treatments are often more successful when the disease is detected early. We evaluated the feasibility and safety of multicancer blood testing coupled with positron emission tomography–computed tomography (PET-CT) imaging to detect cancer in a prospective, interventional study of 10,006 women not previously known to have cancer. Positive blood tests were independently confirmed by a diagnostic PET-CT, which also localized the cancer. Twenty-six cancers were detected by blood testing. Of these, 15 underwent PET-CT imaging and nine (60%) were surgically excised. Twenty-four additional cancers were detected by standard-of-care screening and 46 by neither approach. One percent of participants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multicancer blood testing combined with PET-CT can be safely incorporated into routine clinical care, in some cases leading to surgery with intent to cure. [1]: /lookup/doi/10.1126/science.abb9601 [2]: pending:yes


Measuring the performance of a Classification problem

#artificialintelligence

It is often convenient to combine precision and recall into a single metric called the F1 score, in particular, if you need a simple way to compare two classifiers. The F1 score is the harmonic mean of precision and recall. The F1 score favors classifiers that have similar precision and recall. This is not always what you want: in some contexts, you mostly care about precision, and in other contexts, you really care about the recall. For example, if you trained a classifier to detect videos that are safe for kids, you would probably prefer a classifier that rejects many good videos (low recall) but keeps only safe ones (high precision), rather than a classifier that has a much higher recall but lets a few really bad videos show up in your product (in such cases, you may even want to add a human pipeline to check the classifier's video selection). On the other hand, suppose you train a classifier to detect shoplifters on surveillance images: it is probably fine if your classifier has only 30% precision as long as it has 99% recall (sure, the security guards will get a few false alerts, but almost all shoplifters will get caught).


A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

#artificialintelligence

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.


Arena and the disappearing art of bootstrapping startups

ZDNet

Silicon Valley headlines often report on the size of venture capital raised by a startup -- the bigger the funding, the bigger the story. But this is a poor way to understand the startup community. Startup success isn't determined by how much you raise; it's about how much you keep. Arena.im is a great example. It recently raised a seed round of $2.3 million -- a tiny amount by local standards.


Data Science questions for interview prep (Machine Learning Concepts) -Part I

#artificialintelligence

I recently finished watching this Machine Learning playlist (StatQuest by Josh Starmer) on Youtube and thought of summarizing each concept into a Q/A. As I prepare for more data science interviews, I thought it would be a good exercise to make sure that I am communicating my thoughts clearly and concisely during an interview. Let me know in the comments, if I am not doing a good job in explaining any of the concepts. NOTE: This article is not aimed for teaching a concept to beginners. It assumes that the reader has sufficient background in data science concepts.


Abolish the #TechToPrisonPipeline

#artificialintelligence

The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release.[38] At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.