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In-home saliva test detects cancer with 90% accuracy

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An AI-based home screening test to detect oral and throat cancers from saliva samples is now available in the United States with the hope of transforming oral and throat cancer detection. Based on a technology approved by the US Food and Drug Administration (FDA) as a "breakthrough device," the saliva test can detect early symptoms of oral and throat cancer with more than 90 percent accuracy. Due to a lack of effective diagnostic tools, these cancers often go undiagnosed until they have reached an advanced stage, resulting in low survival rates. In a previous study, Maria Soledad Sosa from the Icahn School of Medicine at Mount Sinai and Julio A. Aguirre-Ghiso, now at Albert Einstein College of Medicine, discovered that the ability of cancer cells to remain dormant is controlled by a protein called NR2F1. This receptor protein can enter the cell nucleus and turn numerous genes on or off to activate a program that prevents the cancer cells from proliferating.


Actionable Auditing Revisited

Communications of the ACM

Non-target corporations Kairos and Amazon have overall error rates of 6.60% and 8.66%, respectively. These are the worst current performances of the companies analyzed in the follow-up audit. Nonetheless, when comparing to the previous May 2017 performance of target corporations, the Kairos and Amazon error rates are lower than the former error rates of IBM (12.1%) and Face (9.9%) and only slightly higher than Microsoft's performance (6.2%) from the initial study.


Current Insights on AI, Breast Cancer Screening and the FDA

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Is there enough scrutiny of artificial intelligence (AI) software prior to clearance by the Food and Drug Administration (FDA) for adjunctive use in breast cancer screening? Despite the FDA clearance in recent years of several AI products to help identify suspicious breast lesions and facilitate mammography triage, researchers suggested in a recent review, published in JAMA Internal Medicine, that questions remain about data sources, clinical outcome measures and external validation. Here are a few takeaways from their review of the research leading to FDA clearance for nine AI-related products for breast cancer screening between January 1, 2017 and December 31, 2021. All of the clearances for the AI products were based on retrospective analysis of previously existing databases. Only six of the nine products had multicenter studies to support their use and research for four of the AI products lacked information about external validation, according to the review.


FDA Publishes Updated List With 521 Authorized AI/ML Enabled Devices

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Since 1995, the FDA has authorized more than 500 AI/ML-enabled medical devices via 510(k) clearance, granted De Novo request, or approved PMA. This week the FDA published an updated list with 178 new devices that were authorized through July 2022. According to the FDA, their list is based on publicly available information and is not a comprehensive resource of FDA approved AI/ML-enabled medical devices. In today's DeepTech newsletter I'm sharing a high level analysis of the 521 devices on the list, charts to visualize the data, and a summary of milestones. Note: According to the FDA their list is based on publicly available information and is not a comprehensive resource of approved AI/ML-enabled medical devices.


Computer Vision - Richard Szeliski

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As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).


The Low Threshold for Face Recognition in New Delhi

WIRED

Indian law enforcement is starting to place huge importance on facial recognition technology. Delhi police, looking into identifying people involved in civil unrest in northern India in the past few years, said that they would consider 80 percent accuracy and above as a "positive" match, according to documents obtained by the Internet Freedom Foundation through a public records request. Facial recognition's arrival in India's capital region marks the expansion of Indian law enforcement officials using facial recognition data as evidence for potential prosecution, ringing alarm bells among privacy and civil liberties experts. There are also concerns about the 80 percent accuracy threshold, which critics say is arbitrary and far too low, given the potential consequences for those marked as a match. India's lack of a comprehensive data protection law makes matters even more concerning.


AI accelerates AML processes across financial services

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Financial regulators across Europe continue to levy steep enforcement fines against banks for failures to comply with know-your-customer (KYC) and anti-money laundering (AML) regulations. At the end of 2021, the Financial Conduct Authority (FCA) fined two of the UK's largest banks, HSBC and NatWest, a total of £328.95 million ($436.1 million) for failings in their money laundering processes. Meanwhile, members of the European Parliament are calling for cryptocurrencies to be governed by the European Commission's Anti-Money Laundering Authority, as illicit organisations continue to find new methods for laundering money through the financial system. Money laundering is a process that criminals use to hide the illegal source of their funds. By passing money through multiple, sometimes complex, transfers and transactions, the money is "cleaned" of its illegitimate origin and made to appear as legitimate business profits.


US appeals court says artificial intelligence can't be patent inventor - forbque

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The Patent Act requires an "inventor" to be a natural person, the US Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The US Patent and Trademark Office declined to comment on the decision.


U.S. appeals court says artificial intelligence can't be patent inventor

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Thaler had asked for patents on behalf of his AI system Court affirms ruling that patent'inventor' must be human being Court affirms ruling that patent'inventor' must be human being The Patent Act requires an "inventor" to be a natural person, the U.S. Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The U.S. Patent and Trademark Office declined to comment on the decision.


Cyber Criminals vs Robots

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What happens when cyber criminals face robots? What happens when they use robots? How will offensive and defensive strategies of cybersecurity evolve as artificial intelligence continues to grow? Both artificial intelligence and cybersecurity have consistently landed in the top charts of fastest growing industries year after year¹². The 2 fields overlap in many areas and will undoubtedly continue to do so for years to come. For this article, I have narrowed my scope to a specific use case, intrusion detection. An Intrusion Detection System (IDS) is software that monitors a company's network for malicious activity. I dive into AI's role in Intrusion Detection Systems, code my own IDS using machine learning, and further demonstrate how it can be used to assist threat hunters.