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How exercise may improve the immune system’s ability to fight cancer
Jack Dorsey: Square, Cryptocurrency, and Artificial Intelligence | AI Podcast #91
The Question I Almost Didn't Ask And How It Changed My Life
Face Masks for All - #masks4all | Slowing Spread of Coronavirus Infection with Homemade DIY Masks
MIT’s deep learning found an antibiotic for a germ nothing else could kill

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An industry first! A self-driving truck delivered butter from California to Pennsylvania in three days



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A Silicon Valley startup has completed what appears to be the first commercial freight cross-country trip by an autonomous truck, which finished a 2,800-mile-run from Tulare, California to Quakertown, Pennsylvania for Land O’Lakes in under three days. The trip was smooth like butter, 40,000 pounds of it.

Plus.ai, a 3-year-old company in Cupertino, announced the milestone Tuesday. A safety driver was aboard the autonomous semi, ready to take the wheel if needed, along with a safety engineer who observed how things were going.

Seven Jobs That AI Could Replace by 2030


  
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In a recent speech, Forrester vice president and principal consultant Huard Smith said that the human aspect of many professions would be “all gone” by 2030 due to advances in AI and ML technology.

In this piece, I’ll look at seven of the industries or positions that are currently most likely to decline over the next decade. Believe me; number seven will surprise you.

1. Telemarketers
The chances of this particular role becoming fully computerized are as high as 99.9%. This is mainly because telemarketing conversion rates are relatively low.

Bjarne Stroustrup: C++ | Artificial Intelligence (AI) Podcast


(To watch the podcast, please click on the image below)

















What Is Federated Learning?


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The key to becoming a medical specialist, in any discipline, is experience.

Knowing how to interpret symptoms, which move to make next in critical situations, and which treatment to provide — it all comes down to the training you’ve had and the opportunities you’ve had to apply it.

For AI algorithms, experience comes in the form of large, varied, high-quality datasets. But such datasets have traditionally proved hard to come by, especially in the area of healthcare.

Medical institutions have had to rely on their own data sources, which can be biased by, for example, patient demographics, the instruments used or clinical specializations.

How AI will transform healthcare (and can it fix the US healthcare system?)




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For those who are new to AI, Machine Learning, and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy.
The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare.

Yann LeCun: Deep Learning, Convolutional Neural Networks, and Self-Supervised Learning | AI Podcast


(To watch the podcast, please click on the image below)




















How to beat facial recognition systems with Face Anonymization



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The year 2019 has shown itself to be unprecedented for consumer privacy concerns, and there are no signs of this trend slowing down. Via a number of avenues, such as Netflix’s new series ‘The Great Hack’, consumers are being exposed to a suffocating reality, that the idea of privacy is quickly vanishing.

There have been various natural responses to this growing concern, one of which is the introduction of theGeneral Data Protection Regulation, or GDPR for short. This new regulation aims to empower consumers (EU Citizens and Businesses), to retain more control as to how, where, and to whom their data is distributed.

The case for optimism on climate change

(To watch the TED talk, please click on the image below)






















Machine Learning Is The Future Of Cancer Prediction



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Every year, Pathologists diagnose 14 million new patients with cancer around the world. That’s millions of people who’ll face years of uncertainty.
Pathologists have been performing cancer diagnoses and prognoses for decades. Most pathologists have a 96–98% success rate for diagnosing cancer. They’re pretty good at that part.
The problem comes in the next part. According to the Oslo University Hospital, the accuracy of prognoses is only 60% for pathologists. A prognosis is the part of a biopsy that comes after cancer has been diagnosed, it is predicting the development of the disease.

How is machine learning affecting genetic testing?

 
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Machine learning is being applied to genetic testing in many different ways.
The applications are nearly endless. Machine learning is helping scientists to analyze DNA, decode the human genome, assess disease phenotypes, understand gene expression, and even participate in a process called gene editing, where DNA is actually “spliced” into an organism’s genetic code...
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(To read the entire article, please click on the image below)