Leaders in Pharmaceutical Business Intelligence (LPBI) Group

ArtificiaI Intelligence in Genomics & Cancer

 

• AI, NLP, ML for Text Analysis of LPBI’s four e-Books: Scientists and CS/AI Collaboration on What to look for AND setting guiding rules for algorithm searching the English Text in 

Cancer: Volume One:

Cancer: Volume Two:

Genomics: Volume One

Genomics: Volume Two

 

https://pharmaceuticalintelligence.com/2020-summer-internship/

 

PORTAL for 2020 SUMMER INTERNSHIP @LPBI on Data Curation and Data Annotation

LPBI’s 2020 Summer Interns 

LPBI’s 2020 Summer Interns in Data Science:

Daniel Menzin, BSc BioMedical Engineering, expected, May 2021, Research Assistant 4, Core Applications Developer and Acting CTO 

https://pharmaceuticalintelligence.com/contributors-biographies/research-assistants/daniel-menzin-bsc-biomedical-engineering-expected-may-2021-research-assistant-4-core-applications-developer-and-acting-cto/

Noam Steiner Tomer, Summer Internship, Research Assistant 1

https://pharmaceuticalintelligence.com/contributors-biographies/research-assistants/noam-steiner-tomer-summer-internship-research-assistant-1/

 

UPDATED on 7/14/2020

NLP Resources

• University of New South Wales NLP Dictionary

https://www.cse.unsw.edu.au/~billw/nlpdict.html

• Princeton WordNet 3.1: Thesaurus/Dictionary

http://wordnetweb.princeton.edu/perl/webwn|https://wordnet.princeton.edu/download/current-version

• Natural Language Toolkit: (NLP Python Library) https://www.nltk.org/

 

UPDATED ON 6/19/2020

From @MIT

This week we released our annual list of 35 Innovators under 35. At a time when the news has felt relentless, getting to read about so many talented, mission-driven technologists has been a sight for sore eyes. It’s especially exciting to see how many of them are working directly with AI—either to advance the field through fundamental research or by applying it responsibly across their respective industries. Here are just a few of the people that inspired me: Inioluwa Deborah Raji, 24, AI Now Institute. Inioluwa Deborah Raji was interning at machine-learning startup Clarifai when she had an experience she remembers as “horrible.” While building a computer vision model to help clients flag inappropriate images as “not safe for work,” she discovered it flagged photos of people of color much more often than those of white people. The realization led her down a path of algorithmic accountability research, where she is a leading voice today. Most recently, Amazon’s decision to implement a one-year moratorium on police use of Rekognition stemmed directly from a paper she co-authored with Joy Buolamwini (one of our innovators in 2018) to demonstrate the product’s discrimination. Read more here. Manuel Le Gallo, 34, IBM Research. Much of the energy use in modern computing comes from the fact that data needs to be constantly transferred back and forth between the memory and the processor. So Manuel Le Gallo worked with his team at IBM Research to develop a system that uses memory itself to process data. Their early work has shown they can achieve both precision and huge energy savings. They recently completed a process using just 1% as much energy as would have been needed with conventional methods. Read more here. Bo Li, 32, University of Illinois at Urbana-Champaign. A few years ago, Bo Li and her colleagues fooled an autonomous vehicle into reading a stop sign as one posting a 45 mph speed limit. All they’d done was place small black-and-white stickers on the sign in a seemingly random pattern to the human eye. But to a neural network, the graffiti-like arrangement turned into a powerful “adversarial attack”—making it see something that was not. It was one of the first times researchers had demonstrated how such vulnerabilities could translate into consequences in the physical world. Li now works on devising these subtle changes to better understand and prevent these attacks in the future. Read more here. Andrej Karpathy, 33, Tesla. As a graduate student at Stanford, Andrej Karpathy extended techniques for building what are known as convolutional neural networks (CNNs)—systems that broadly mimic the neuron structure in the visual cortex. By combining CNNs with other deep-learning approaches, he created a system that was not just better at recognizing individual items in images, but capable of seeing an entire scene full of objects and effectively building a story of what was happening in it and what might happen next. Karpathy is now applying his knowledge to Tesla, where he oversees neural networks for the cars’ Autopilot feature. Read more here. Leila Pirhaji, 34, Revivemed. Our bodies contain 100,000 metabolites, tiny molecules involved in our metabolism that show the effects of our genes and lifestyle. Such metabolites include everything from blood sugars and cholesterol to obscure molecules that appear in significant numbers only when someone is sick. The problem is that measuring and identifying these molecules is expensive and time consuming, and fewer than 5% of them in a patient can be identified using common technologies. So Leila Pirhaji developed a platform that uses machine learning to do it much more quickly. Her work could help us better detect and treat diseases. Read more here. See the full list of 35 Innovators Under 35 here.

SOURCE

From: The Algorithm from MIT Technology Review <newsletters@technologyreview.com>

Reply-To: The Algorithm from MIT Technology Review <newsletters@technologyreview.com>

Date: Friday, June 19, 2020 at 12:38 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Meet these brilliant young AI innovators under 35

 

Important resources

 

• Natural Language Processing (NLP) with Python: The Free eBook

https://www.kdnuggets.com/2020/06/natural-language-processing-python-free-ebook.html#.Xt4-SgW3r4w.linkedin

• Data Curation and Data Annotation 

https://www.oreilly.com/library/view/natural-language-annotation/9781449332693/

 

Breaking News May 2020

Artificial Intelligence Cannot Be Inventors, US Patent Office Rules

On Monday, the United States Patent and Trademark Office published a decision that claims artificial intelligences cannot be inventors. Only “natural persons” currently have the right to get a patent.

Last year, two relatively mundane patents—one for a shape-shifting food container and another for an emergency flashlight—posed an existential question for international patent regulations around the world: Does an inventor have to be a human?

These two inventions were the work of DABUS, an artificial intelligence system created by physicist and AI researcher Stephen Thaler. Now, the USPTO has decided that DABUS and any other AI cannot be listed as an inventor on a patent filing.

Until now, US patent law was vague about whether machines could invent, referring to “individuals” as eligible inventors. Thaler, along with a group of patent law experts, argued that because Thaler didn’t have any expertise in containers or flashlights, and didn’t help DABUS make the inventions, it wouldn’t be right for him to be listed as the inventor.

“If I teach my Ph.D. student that and they go on to make a final complex idea, that doesn’t make me an inventor on their patent, so it shouldn’t with a machine,” Ryan Abbott, a law and health-sciences professor at the University of Surrey in the UK who led the group of legal experts in the AI patent project, told the Wall Street Journal last year.

In the UK, the DABUS patents were rejected under patent laws there that forbid non-natural persons from inventing. With this week’s announcement, the US has followed suit, stating that “only natural persons may be named as an inventor in a patent application.”

The DABUS case brings up similar questions of ownership and non-human rights—and even what makes us human, and what makes other entities not—as that infamous monkey-selfie copyright case, where PETA tried to say a monkey could own the rights to a selfie. Ultimately, under regulations from the U.S. Copyright Office that only photographs taken by humans can be copyrighted, PETA’s case was dismissed.

 

 

Four of LPBI Group’s 16 volumes in Medicine deal with Genomics and

Two of LPBI Group’s 16 volumes in Medicine deal with Cancer and Oncology.

In June 2020 we are launching a new initiative on Artificial Intelligence in GENOMICS and in CANCER for pharmacological & therapeutics aims.

• Applications of NLP, ML and AI on our six books is the Goal for automation of the exploration of novel pathways and potential off-label indications for existing therapies
• For Artificial Intelligence in Medicine Search the SELECT CATEGORY field on the Home Page on the right hand side column on the Home Page
• New content is forthcoming