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Technology Funnies and Futures: April Fool's 2018

Some April Fool's jokes come true.

Back in 2004, an electronic product review site Pocket Lint pranked about a new Apple phone add-on for the iPod. The first-generation iPhone was released in 2007.
"Ratatattle" a real-time map of rat infestations in restaurants, announced by Cnet on April 1, 2007, was actually implemented by the government of New York City in 2009.
Ultrasound-based network for unborn babies (2007) was prototyped in form of a wearable belt Kickbee in 2008. The product got a lot of press coverage but ceased to exist in 2015.

Some 1st-April jokes have the potential to be prophetic or ominous.

Like Google's Smartbox (2015) fusing physical mail with electronic kind and sorting everything into subject-specific categories. Or electro-fitness shoes (2016) that would never let you stop moving. And a full-body wearable (2017) monitoring the body and keeping you connected 24x7x365. In addition to non-wearable technologies recording all your bathroom activity.

Some April Fool's products we really wish existed.

Such as a Duolingo pillow (2016) teaching us a new language overnight. Or Ab-hancer grid (2012) quickly developing ab muscles when placed over the midsection. And a holograph-projecting drone to attend meetings instead of you.

The best April Fool's technology stories of 2018 focused on car tech, genomics, sensory perception and wearables. Toyota's luxury car division Lexus announced a new personalized car design based on 23andme data. Likelyhood to consumer higher levels of caffeine, for example, would translate into larger cupholders, susceptibility to back pain would add dozens of controls to front seats and new-car smell will be genetically personalized to match expression of olfactory receptors.

A new 4d6andMe Stat Discovery Kit will use genetic information to quantify your charisma, saving throws, ability modifiers, skill proficiencies, hit die, passive wisdom, and initiative all in one easy-to-read form - a Dungeons & Dragons 5th edition character sheet.

A taste-bud analyzing attachment to a smartphone and Artificial-Intelligence software will help to select a perfect Hummus dish for everyone - thanks to Hummus API from Google Israel.

A Czech automobile manufacturer Skoda's new QuarrelKancel technology identifies the first signs of an argument and starts to filter out backseat bickering and sibling squabbles, to let the driver concentrate on the driving.

As part of global-wide push within the motoring industry to run cars using sustainable energy sources and help reduce vehicle emissions, MG Motor UK has announced a new engine running on alpaca waste.

If you want to be even more environmentally friendly, walk instead of driving - Jabra Sneakers will also help you to develop dance skills, providing Hi-Fi audio grade quality on the go with voice activation, stereo sound, dust certification and wireless charging with unique Foot Mat.

Online tutor company Chegg is offering the Chegg Osmosis Pillow that will not only adapt to the shape of your head, but also pack your memory with actual knowledge. And you can also make money while you sleep by renting out the unused space in your bed. Mattress company Lucid just launched a cheaper version of Airbnb, the ShareBED app. There exist potential for new applications in sharing economy.

Which new technologies offer the most potential? Only time will tell.

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Crowdsourcing precision medicine

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Healthcare is evolving from one-size-fits-all to personalized, from reactive to preventive, from intuitive to data-driven, from paternalistic to participatory. Can crowdsourcing facilitate this transformation?

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Technology 2018

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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.

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.

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.

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?

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.

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.

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Fine Tuning Human Networks

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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.

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.

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.

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.

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The Evolution of Test

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“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?

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.

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.

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Technology Funnies and Futures: April Fool's 2018

Crowdsourcing precision medicine

Technology 2018

Fine Tuning Human Networks

The Evolution of Test

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AURAMETRIX

Technology Funnies and Futures: April Fool's 2018

Crowdsourcing precision medicine

Technology 2018

Fine Tuning Human Networks

The Evolution of Test

Archives

Categories

EnvironmentIrritable BowelOlfacticsTechnologies

Environment
Irritable Bowel
Olfactics
Technologies