When Grayson Kollins was two and a half years old--just shortly after the birth of his younger sister--his parents noticed that he had all but stopped uttering the sentences and phrases that up until then he had been using to communicate. In addition, his daycare provider mentioned that Grayson had begun repeating phrases over and over, and lacked interest in playing with other children. Grayson's father Scott Kollins, Ph.D., a clinical psychologist and professor of psychiatry and behavioral sciences in the School of Medicine at Duke, was well aware of the symptoms of autism spectrum disorder, or ASD, a neurodevelopmental disorder that affects the ability to socially interact and communicate with others. Although it usually manifests early in life, it is a lifelong condition and can have profound effects on learning, employment, and personal relationships. Prompted by these early symptoms, Grayson's parents subsequently had him assessed, and he received a clinical diagnosis of ASD.

When Grayson Kollins was two and a half years old--just shortly after the birth of his younger sister--his parents noticed that he had all but stopped uttering the sentences and phrases that up until then he had been using to communicate. In addition, his daycare provider mentioned that Grayson had begun repeating phrases over and over, and lacked interest in playing with other children. Grayson's father Scott Kollins, Ph.D., a clinical psychologist and professor of psychiatry and behavioral sciences in the School of Medicine at Duke, was well aware of the symptoms of autism spectrum disorder, or ASD, a neurodevelopmental disorder that affects the ability to socially interact and communicate with others. Although it usually manifests early in life, it is a lifelong condition and can have profound effects on learning, employment, and personal relationships. Prompted by these early symptoms, Grayson's parents subsequently had him assessed, and he received a clinical diagnosis of ASD.

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Eight University of Toronto artificial intelligence researchers – four of whom are women – have been named CIFAR AI Chairs, a recognition of pioneering work in areas that could have global societal impact. One of the new chairs is Anna Goldenberg, an associate professor of computer science in U of T's Faculty of Arts & Science and the first-ever chair in biomedical informatics and artificial intelligence at the Hospital for Sick Children. She and her colleagues, including U of T's Dr. Peter Laussen, have developed a computer model that uses signals in physiological data, such as a patient's pulse, to detect an oncoming heart attack – giving doctors and nurses vital minutes to intervene and save an infant's life. The early-warning system has been able to predict 70 per cent of heart attacks at least five minutes – and up to 15 minutes – before a patient's heart stops beating. "In machine learning and health care, the key word is prevention," says Goldenberg, whose team is on track to have the system tested in a silent trial in a clinical environment.

After a successful early career in R&D in Silicon Valley, I spent 12 years working as a carpenter. This may sound like a big U-turn. But, while I loved the intellectual piece of science, I really loved the people aspect of construction. I got to build something and turn raw materials into gratifying, highly visible results: houses that enabled life and buildings that enabled commerce. I get the same kind of rush daily as lead data-ops engineer for Life Sciences at Tamr.*

Deepfakes, or media that takes a person in an existing image, audio recording, or video and replaces them with someone else's likeness using AI algorithms, are multiplying quickly. Amsterdam-based cybersecurity startup Deeptrace found 14,698 deepfake videos on the internet during its most recent tally in June and July, up from 7,964 last December -- an 84% increase within only seven months. That's troublesome not only because deepfakes might be used to sway public opinion during, say, an election, or to implicate someone in a crime they didn't commit, but because the technology has already generated pornographic material and swindled firms out of hundreds of millions of dollars. In an effort to fight deepfakes' spread, Facebook -- along with Amazon Web Services (AWS), Microsoft, the Partnership on AI, Microsoft, and academics from Cornell Tech, MIT, University of Oxford, UC Berkeley; University of Maryland, College Park; and State University of New York at Albany -- are spearheading the Deepfake Detection Challenge, which was announced in September. It's launching globally at the NeurIPS 2019 conference in Vancouver this week, with the goal of catalyzing research to ensure the development of open source detection tools.

New research will be live in npj Digital Medicine on December 12, 2019, that will feature a machine learning app aimed at preventing patients from severe fall-related injuries and death. This AI technology was developed by Houston Methodist and tested over an eight-month period to help address the growing concern of severe patient falls with seniors and the worry it causes their care-providers and care-givers. The AI app predicts the risk of getting injured when a patient falls. Clinical parameters and patient demographics such as bone density, diagnosis, past procedures, etc. are used to populate the app so it then triggers tailored interventions to prevent these high-risk severe injury patients from falling - whether they are in the hospital setting or with home caregivers. This AI technology can be integrated into the patient's electronic medical record (EMR) and make things easier for clinicians since it will be part of the record and will automatically flag or alert the care-providers for high risk fall with harm patients when they enroll in the hospital.

Welcome to the November edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted articles about AI that can identify who wrote each scene in Shakespeare's Henry VIII, and teach non-native speakers how to pronounce English words! Let's start, as usual, with the comic of the month: In a recent article researchers describe how they trained machine-learning algorithms to predict what features in a song would impact people's emotional responses. They predicted brain and heart activities as well as physiological response using features based on music dynamics such as timbre, harmony, etc...

VANCOUVER, BC / ACCESSWIRE / December 12, 2019 / Dimension Five Technologies Inc. (CSE:DFT) (the "Company"), is pleased to announce that it has entered into a share exchange agreement dated December 11, 2019 (the "SEA") with Digital Cavalier Technology Services Inc. doing business as Youneeq ("Youneeq") to acquire all of the issued and outstanding securities of Youneeq (the "Transaction"). The Company and Youneeq have signed the SEA and signatures are being gathered from Youneeq shareholders in order to obtain a fully executed version. The proposed transaction is dependent on all Youneeq shareholders signing the SEA. Youneeq is an award-winning personalization and recommendation engine powered by artificial intelligence (AI). The company is poised to become a leading multi-channel AI personalization engine focused on the anonymous audience, the single biggest segment for marketers.

I came to a shocking conclusion while writing about diversity for my book on machine learning: diversity in Artificial Intelligence is not your problem, it's hers. I mean, of course, that the problem is with the English pronoun "hers". There is a bias against "hers" in most major AI systems today, and the source of the bias is the perfect metaphor for bias in AI more broadly. Like you might remember from high school, "hers" is a pronoun. Each word in a sentence belongs to one of a small number of categories: nouns, pronouns, adjectives, verbs, adverbs, etc. One common building block in many AI applications is to identify the right category in raw text. Today, "hers" is not recognized as a pronoun by the most widely used technologies for Natural Language Processing (NLP), including (alphabetically) Amazon Comprehend, Google Natural Language API, and the Stanford Parser. The video shows that in the sentence "the car is hers", Amazon and Google classify "hers" as a noun and the Stanford parser classifies "hers" as an adjective. They don't make the same mistake with the sentence "the car is his", correctly identifying "his" as a pronoun.