Aurametrix
  • About
    • Q & A
    • Blog
    • Topics
    • Studies
    • Founders
    • Pipeline

AURAMETRIX

Sorry, I didn't quite get that

0 Comments

 
Picture
Sorry, I didn't quite get that, try again. The holy grail of AI researchers is to fully understand human languages. Were are we now in 2020?

0 Comments

Technologies you wish existed: April 1 2019

0 Comments

 
Picture
Science fiction stories and April fool jokes often describe speculative technology we wish existed in our world. Here are a few such devices in today's fake news announcements.

New AI chip will feature enormous memory capacity and remarkable performance with  patent-pending OxygenBurst™ technology keeping it from overheating. The architecture for this chip was inspired by an area rug the founder of this company bought in the eighties. Early tests show that the chip could speed through a million-sized data set when training, almost like it dumps the data before training completes. When shown a picture of a girl named “Nikki”, it mistakenly labeled it “Micki”. Yet, it was able to accurately identify applesauce with cinnamon sprinkles, even though it was mixed with a pale, watery piece of turkey breast and soggy vegetables on a Styrofoam lunch tray.

Picture
Picture
For All Fool's day of 1931, The Los Angeles Times ran a front-page "exclusive" reporting that Hamburg scientist Dr. Eugene Lirpa had discovered good health to be caused by a bacteria, "Bacillus sanitatis." Sick people that were lacking this "germ of health," could be cured simply by breathing in the same air as healthy people. Now everyone is talking about good bacteria and fecal microbial transplants from healthy individuals. Perhaps one day "good" microbial transplants could be really delivered by inhalers? 

Picture
In 2004, Norway's Aftenposten posted a plan by government health authorities to implant electronic id chips under skin to better monitor peoples' medical needs. This was another April's fool, but chips like this  - e.g. to monitor brain injury  - are already being tested.  Implanted chips are used  by some employers to let their employees open doors, log in to computers and purchase food. 
In 1925, Hugo Gernsback invented the concept of the “teledactyle” that would allow doctors not only to see their patients through a view screen but also touch them from miles away with spindly robot arms.

Telemedicine use has grown in recent years, although many still don't believe in automated and delivered remotely healthcare. 
Picture
We still prefer a human connection, as health care information technology giant Epic suggested in their 2017 April fool's joke: unveiling "Epic TinDr," an app to allow patients and physicians to select one another with Tinder-style instant falling in love. And we may even prefer some of our health records to self-destruct  - as in "Snapchart" suggested by health coaching app Twine Health  “ - before they are collected by Facebook. 
On April 1 2019, healthcare blog announced that Facebook formally entered the EMR business. But it's not a joke  - according to some sources, Facebook planned to collect information about age, diseases, prescribed medications and visits to the hospitals. This data could be combined with all health-related information that Facebook already captured about its users.

Picture
Broom, mop and cloth. Cleaning tools every home needs. Of course, you can make cleaning easier with a microfiber cloth and delegate floor cleaning to a robot mop, but you'll still have to clean small objects and patterned carpets manually with more conventional tools. This April, Google offers a new smartphone screen cleaning app. With just a push of a button, this tool will wash away all smudges on the mobile phone screen and freshen it up with a pineapple scent. This feature uses a Smudge Detector API utilizing "geometric dirt models" and "haptic micromovement generator"  to clean and form a "long-lasting" dirt shield around the phone afterwards.  
Science fiction authors were describing future without tedious tasks such as cleaning since 19th century.  WAL-E (2008) - microbe obliterator (in the picture) was identifying dirty areas on its own, scrubbing anything and everything until it was sparkling clean. On April 1st of 2004, BMW announced a new self-cleaning car with "microscopic blowholes" clearing dust and insects. Neal Stephenson 1995 book The Diamond Age described gloves "constructed of infinitesimal fabricules that knew how to eject dirt". Not really a science fiction now, as prototype nano-enhanced textiles were able to clean themselves with light back in 2016.

Humans were always dreaming of better transportation.  In 2016, Tesla announced its first flying car Tesla​ shocks the world by announcing its first flying car, the Model F. EDIT. In 2012, Google announced Click-to-Teleport technology allowing potential customers to instantly teleport to the business location directly from a search ad in a matter of seconds. But it's hard to keep up with imagination of science fiction writers and producers of Hollywood movies. And each year technology jokes seem to get feebler and less exciting. Maybe next year? 
Picture

​
REFERENCES

Samuel R. Anderson et al. Robust Nanostructured Silver and Copper Fabrics with Localized Surface Plasmon Resonance Property for Effective Visible Light Induced Reductive Catalysis, Advanced Materials Interfaces (2016). DOI: 10.1002/admi.201500632

Karim et al Nanostructured silver fabric as a free-standing NanoZyme for colorimetric detection of glucose in urine
https://www.sciencedirect.com/science/article/pii/S0956566318301970

Photocatalysis and self-cleaning from g-C3N4 coated cotton fabrics under sunlight irradiation Y Fan, J Zhou, J Zhang, Y Lou, Z Huang, Y Ye… - Chemical Physics …, 2018 - Elsevier
0 Comments

Technology 2018

1 Comment

 
2018 should be very exciting for science and technology. ​As seen in the diagram below, recent years were filled with groundbreaking projects and the stars may be aligning for something really big, driven by advances in software and hardware.
Picture
Last year, artificial Intelligence software hit the mainstream. In 2018 it will be more prolific, more creative and invade every corner of our life. The current, second, wave of AI  is not quite ready to break and deep learning will continue to dominate this year . ​​

​Deep learning is driving the future of autonomous vehicles. Fully automated self-driving cars (level-5,  as defined by the Society of Automotive Engineers and depicted in the figure below) won't take over the roads this year or next, but we'll see plenty of highly automated vehicles in 2018. Models of ownership will be also changing -  perhaps to a monthly subscription service vs lease or traditional ownership. 
Picture
Picture
Machines will be taking on more responsibilities in education, getting more heavily involved in the evaluation and counselling of students. Massive open online courses, or MOOCS, did not meet the great expectations everybody had. But e-learning learned to overcome the challenges, smaller private online courses started to gain more popularity and many traditional universities are already at risk to become obsolete. Seven years ago, in his book The Innovative University, world-renowned innovation expert Clayton Christensen predicted that as many as half of American universities would close or go bankrupt within 10 to 15 years. This year AICTE (All India Council for Technical Education) will be shutting down around 800 engineering colleges as students can get comparable or better educations over the internet. Only the most technology-advanced Universities will evolve, find ways to reduce the cost of education, perhaps even build programs for specific companies, and survive. 

Picture
MOOCs is an example of mass collaboration - as is Wikipedia or Citizen Science. The model has many other flavors and names - the sharing economy, the gig economy, the peer, platform or on-demand economy. It enabled brands like Uber and Airbnb to become world giants. 

Virtually any industry could be disrupted by Uberisation, through sharing of assets and human resources. And labor market is quietly transforming traditional job structures into on-call microjobs without benefits. 

The gig economy is lonely and it often turns over-educated individuals into low skilled workers. Could we develop a better platform to utilize complex technical skills, social abilities and cultural competences? Could we create a new social safety net where the new age workers can rely on each other?
Picture
In 2018, Amazon and robots will be moving into health and wellbeing, and telemedicine will become mainstream. ​

Fitbits did not take over the world, but an "immersive fitness" trend is paving the way to the new gym-less future of fitness. Small accessories such as AliveCor's Kardiaband EKG reader detecting atrial fibrillation are already recognized as medical devices. Omron's HeartGuide smartwatch can measure blood pressure on the go and will seek FDA approval later this year. AI has shown its ability to screen for eye diseases and skin cancer. 
​
The cost of genome sequencing - touted as the future of healthcare - was rapidly plummeting until we realized that the quality remains a problem and much remains to be learned. In 2018, the ambitious 100K Genomes project will be completed and the 100K Foodborne Pathogen Genome project will release more data. Technological advances made it possible to sequence pathogen genomes rapidly with portable devices such as MinION. As two landmark technologies of genome editing and immune engineering - CRISPR/Cas9 and CAR-T -  had critical milestones, there will be new and exciting advances in gene therapy. 

The health industry is poised to enter the next phase of digitization: a phase of hyper-personalization. Hyper-personalization already infiltrated social networks contributing to political polarization. It might lead to mixed results in retail - that hopes to use ur biometric data for better sales. But the more personalized and precise healthcare is, the better it is for all of us. 

Picture
Virtual and digital worlds will continue to blur leading to marketplace consolidation. And there will be also an increase in consolidation in healthcare, life insurance, gig economy and science.

It will be even more challenging to succeed as an entrepreneur. Big business will continue to getting bigger, swallowing up the resources, market share, and consumer support that used to be more evenly distributed among all types of companies.

But the stars may be aligning up for new breakthroughs, entrepreneurs will rise up, come up with new ingenious ideas and be the driving force behind economic growth. 

1 Comment

Fine Tuning Human Networks

0 Comments

 
Picture
Machine or artificial intelligence depends on complex architectures of neural networks that need to be properly built for every particular task. AI needs large amounts of training data to work - as machines are not yet able to contextually adapt, that is, build reliable models from sparse and noisy data, like humans do.

​But it is not just artificial neural networks that need to keep improving.

Individual intelligence depends on the complexity of neural networks in the brain. These networks consist of almost 100 billion neurons of different types and trillions of flexible connections between neurons engaged in similar tasks. With productive learning, the connections keep evolving, and patterns of electrical activity continue to tone and refine.

Picture
Picture
Every intelligent entity - whether human or machine - depends not only on the configurations of its neurons, but also connections between itself and others entities, optimized for efficient exchange of information. Hence, better human networks providing training and feedback from others will lead to both smarter humans and better AI. 

Just one example of how this could be leveraged in Healthcare.
Picture
The biggest obstacle for applying AI in Healthcare is the lack of good data available for computation. 

AI can predict heart attacks and strokes more accurately than a doctor - if there are good quality medical records. AI is better in analyzing visual information - if there are tenths of thousands of good quality images annotated for thousands of patients. But in most cases data we need for predicting outcomes is either too expensive to prepare or impossible to get - as it remains in the brains of individuals. And cages accurately monitoring food intake, activities and symptoms so far work only for mice.

Picture
In machine learning, efficiency can be improved if we pre-train algorithms on cheap and large datasets. This helps to pre-optimize the parameters for working with more expensive data. 

In the world of humans, efficiency of  medical research could be improved if cheap large datasets focusing on particular medical questions could be easily collected. If clinical trials were more engaging and convenient, generating outcomes clinically meaningful to participants; if the participants could properly design studies by themselves, guided by Software as a Medical Device (SaMD) platform and their own devices (BYOD model), we could collect enough data to get to the next level.  ​

The success of an N-of-1 trial methodology, hindered by the operational complexity, largely depends on the collaboration of  patients and knowledgeable parties. Large scale crowdsourced studies would need innovative software solutions inter-connecting participants and their treatment sequences.  And the impact of this platform would be no less important than the race to build an artificial intelligence for everything. ​
References
Geirhos R, Janssen DH, Schütt HH, Rauber J, Bethge M, Wichmann FA. Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv preprint arXiv:1706.06969. 2017 Jun 21.

Scuffham PA, Nikles J, Mitchell GK, Yelland MJ, Vine N, Poulos CJ, Pillans PI, Bashford G, Del Mar C, Schluter PJ, Glasziou P. Using N-of-1 trials to improve patient management and save costs. Journal of general internal medicine. 2010 Sep 1;25(9):906-13.


Lillie EO, Patay B, Diamant J, Issell B, Topol EJ, Schork NJ. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?. Personalized medicine. 2011 Mar;8(2):161-73.

Sackett DL. Clinician-trialist rounds: 4. Why not do an N-of-1 RCT?.

Li J, Tian J, Ma B, Yang K. N-of-1 trials in China. Complementary therapies in medicine. 2013 Jun 30;21(3):190-4.

Nyman SR, Goodwin K, Kwasnicka D, Callaway A. Increasing walking among older people: A test of behaviour change techniques using factorial randomised N-of-1 trials. Psychology & health. 2016 Mar 3;31(3):313-30.

Federman DG, Shelling ML, Kirsner RS. N-of-1 trials: not just for academics. Journal of general internal medicine. 2011 Feb 1;26(2):115-.

Duan N, Kravitz RL, Schmid CH. Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research. Journal of clinical epidemiology. 2013 Aug 31;66(8):S21-8.

Joy TR, Monjed A, Zou GY, Hegele RA, McDonald CG, Mahon JL. N-of-1 (single-patient) trials for statin-related myalgia. Annals of internal medicine. 2014 Mar 4;160(5):301-10.

Shaffer JA, Falzon L, Cheung K, Davidson KW. N-of-1 randomized trials for psychological and health behavior outcomes: a systematic review protocol. Systematic reviews. 2015 Jun 17;4(1):87.
0 Comments

The Evolution of Test

0 Comments

 
Picture
“Everything is a test,” says Terry Pratchett in "I Shall Wear Midnight".


Test - a way to establish if something is acceptable or not - is one of the world's most commonly performed procedures. Billions of years ago, Mother Nature established agile processes to test molecules, cells and organisms while creating them. Stone age toolmakers were checking if their tool was doing what it was supposed to do before using it. Ancient humans assessed others for civil service and education. How did all those tests evolved with time?

Picture
First tests were manual. Teachers read students' essays, engineers manually debugged their code, and a bridge was tested by walking an elephant across its length.

The need to scale up led to automation. Educators got standardized tests and scanners. Software Quality Assurance professionals mastered machine-driven test cases and new 
test automation frameworks, teaming with software developers for better productivity. Medical testing evolved from tasting urine to sophisticated techniques and molecular diagnostics. At the forefront of test automation, electronic design engineers moved from asking "Does it work?" to "Are all elements present and working?" to "What could go wrong with this design?" focusing on defect-, circuit-, environment- and equipment-dependent variations. ​

Picture
What's next? 

Will artificial Intelligence take over, testing software, hardware and interviewing people for the AI-proof jobs? Will the Internet of Things and Wearables improve the assessment of people's health, educational progress and behavior? Will medical diagnostics merge with therapeutics enhancing nature's proof-reading and error correction mechanisms? ​Will crowdsourcing evolve into testsourcing with everyone everywhere being a tester of something for public good?

Perhaps. We have already started to explore AI-powered bots testing apps and see Artificial Intelligence challenging medical doctors on their home turf, so anything is possible.  
​

0 Comments
<<Previous

    RSS Feed

    Archives

    January 2023
    December 2022
    November 2022
    October 2022
    September 2022
    August 2022
    May 2022
    April 2022
    March 2022
    February 2022
    January 2022
    December 2021
    November 2020
    October 2020
    September 2020
    August 2020
    May 2020
    April 2020
    March 2020
    February 2020
    January 2020
    December 2019
    November 2019
    October 2019
    September 2019
    August 2019
    July 2019
    June 2019
    April 2019
    November 2018
    June 2018
    May 2018
    April 2018
    March 2018
    February 2018
    January 2018
    November 2017
    October 2017
    September 2017
    June 2017
    May 2017
    April 2017
    March 2017
    February 2017
    December 2016
    November 2016
    October 2016
    August 2016
    July 2016
    June 2016
    May 2016
    April 2016
    February 2016
    January 2016
    November 2015
    August 2015
    June 2015
    May 2015
    March 2015
    November 2014
    September 2014
    June 2014
    April 2014
    February 2014
    December 2013
    November 2013
    October 2013
    September 2013
    August 2013
    July 2013
    June 2013
    May 2013
    March 2013
    February 2013
    January 2013
    December 2012
    November 2012
    September 2012
    August 2012
    July 2012
    June 2012
    May 2012
    April 2012
    March 2012
    January 2012
    December 2011
    November 2011
    August 2011
    June 2011
    May 2011
    April 2011
    February 2011
    December 2010
    October 2010
    August 2010
    July 2010
    April 2010
    January 2010
    December 2009
    November 2009
    July 2009
    May 2009
    April 2009
    March 2009

    Categories

    All
    AI
    Air We Breathe
    Allergies
    Analytics
    Asthma
    Behavioral Health
    Big Data
    Chemical Sensing
    Circadian Rhythms
    COVID19
    COVID19 Vaccines
    Dermatology
    Diagnostics
    Digestion
    Elderly
    Emotions
    Environment
    Exercise
    Food We Eat
    Generations
    Genetics
    Health Management
    Hearing
    Heart Health
    Infections
    Internet Of Things
    Long COVID
    Metabolomics
    Microbiome
    Music
    Odor
    Privacy
    Resolutions
    Seasons
    Security
    Senses
    Sensors
    Technology
    Tricorder
    Vision
    Wearables
    Weather
    Weight Loss

    RSS Feed

    Environment
    Irritable Bowel
    Olfactics
    Technologies

Powered by Create your own unique website with customizable templates.
  • About
    • Q & A
    • Blog
    • Topics
    • Studies
    • Founders
    • Pipeline