Perspectives: How AI and ML can accelerate the growth of telemedicine across the globe

May 31, 2023 | by

TTA has an open invitation to industry leaders to contribute to our Perspectives non-promotional opinion area. Today’s Perspectives is from Deepak Singh, a thought leader in AI and telehealth. In his work, he builds AI-powered healthtech and telehealth solutions that can reach from big cities to remote areas of the world. With double master’s degrees in business and information systems, he has 10 years of experience in product development, management, and design ranging from telecom to multimedia and from IT solutions to enterprise healthcare platforms. This article discusses how artificial intelligence (AI) and machine learning (ML) can accelerate the global growth of telemedicine, including a consideration of risks and possible solutions.

Introduction

The ongoing technological advancements have led the way towards greater opportunities for the growth of the global health business, particularly telemedicine through increased connections via the internet, robotics, data analytics, and cloud technology that will further drive innovation over the next ten years. It is obvious that artificial intelligence (AI) usage plays a noteworthy part in the maneuvering and execution of medical technologies when considering the bulky amount of data handling needed by healthcare, the requirement for consistent accuracy in complex procedures, and the rising demand for healthcare services.

Telemedicine is the practice of performing consultations, medical tests and procedures, and remote medical professional collaborations through interactive digital communication. Telemedicine is an open science that is constantly growing as it embraces new technological developments and reacts to and adapts to the shifting social circumstances and health demands. The primary goals of telemedicine are to close the accessibility and communication gaps in four fields: teleconsultation, which is having all kinds of physical and mental health consultations without an in-person visit to a medical facility; teleradiology, which uses information and communication technologies (ICT) to transmit digital radiological images (such as X-ray images) from one place to another; telepathology, which uses ICT to transmit digitized pathological results; and teledermatology, which uses ICT to transmit medical information about skin conditions.

AI has been progressively implied in the field of telemedicine. AI deals with machine learning (ML) that discloses complex connections that are hard to figure out in an equation. In a way that is similar to the human brain and neural networks that encrypt data using an enormous number of interconnected neurons, ML systems can approach difficult problem-solving in the same way that a doctor might do by carefully analyzing the available data and drawing valid judgments.

A growing understanding of artificial intelligence and data analytics can help to broaden its reach and capabilities. Telemedicine’s goal is to boost productivity and organize experience, information, and manpower based on need and urgency and it can be augmented by the use of AI and ML.

Evolving application of AI and ML in Telemedicine

In order to enable clinicians to make more data-driven, immediate decisions that could enhance the patient experience and health outcomes, AI is being employed in telemedicine more and more. The use of AI in healthcare is a potential approach for telemedicine applications in the future.

Al and ML were able to bring about the necessary revolution in so many sectors due to their competence, increased productivity, and flawless execution of tasks. AI is now surpassing the boundaries of being a mere theory and stepping into a practical domain where the need for human supervision for the execution of jobs by machines will be minimized all due to the presence of enormous datasets along with an increment in the processing power of that data. A computer-based algorithm that uses AI has the ability to analyze any form of input data such as ‘training sets’ using pattern recognition which eventually predicts as well as categorize the output, all of that is beyond the scope of human processing or analytical powers that uses traditional statistical approaches. In the field of telemedicine, the adoption of AI and ML still has to go a long way till its vital concepts are understood and applied likewise, nevertheless, the current scenario gives a promising picture where many research projects have applied AI to predict the risk of future disease incidence, decrypting cutting-edge imaging, evaluating patient-reported results, recording value-based metrics, and improving telehealth. The perspective to mechanize tasks and improve data-driven discernments may be comprehended by profoundly improving patient care with obligation, attentiveness, and proficiency in prompting AI.

Drawbacks of artificial intelligence in telemedicine (more…)

Are AI’s unknown workings–fed by humans–creating intellectual debt we can’t pay off?

August 21, 2019 | by

Financial debt shifts control—from borrower to lender, and from future to past. Mounting intellectual debt may shift control, too. A world of knowledge without understanding becomes a world without discernible cause and effect, in which we grow dependent on our digital concierges to tell us what to do and when.

Debt theory and AI. This Editor never thought of learning exactly how something works as a kind of intellectual paydown of debt on what Donald Rumsfeld called ‘known unknowns’–we know it works, but not exactly how. It’s true of many drugs (aspirin), some medical treatments (deep brain stimulation for Parkinson’s–and the much-older electroconvulsive therapy for some psychiatric conditions), but rarely with engineering or the fuel pump on your car. 

Artificial intelligence (AI) and machine learning aren’t supposed to be that way. We’re supposed to be able to control the algorithms, make the rules, and understand how it works. Or so we’ve been told. Except, of course, that is not how machine learning and AI work. The crunching of massive data blocks brings about statistical correlation, which is of course a valid method of analysis. But as I learned in political science, statistics, sports, and high school physics, correlation is not causality, nor necessarily correct or predictive. What is missing are reasons why for the answers they provide–and both can be corrupted simply by feeding in bad data without judgment–or intent to defraud.

Bad or flawed data tend to accumulate and feed on itself, to the point where someone checking cannot distinguish where the logic fell off the rails, or to actually validate it. We also ascribe to AI–and to machine learning in its very name–actual learning and self-validation, which is not real. 

There are other dangers, as in image recognition (and this Editor would add, in LIDAR used in self-driving vehicles):

Intellectual debt accrued through machine learning features risks beyond the ones created through old-style trial and error. Because most machine-learning models cannot offer reasons for their ongoing judgments, there is no way to tell when they’ve misfired if one doesn’t already have an independent judgment about the answers they provide.

and

As machines make discovery faster, people may come to see theoreticians as extraneous, superfluous, and hopelessly behind the times. Knowledge about a particular area will be less treasured than expertise in the creation of machine-learning models that produce answers on that subject.

How we fix the balance sheet is not answered here, but certainly outlined well. The Hidden Costs of Automated Thinking (New Yorker)

And how that AI system actually gets those answers might give you pause. Yes, there are thousands of humans, with no special expertise or medical knowledge, being trained to feed the AI Beast all over the world. Data labeling, data annotation, or ‘Ghost Work’ from the book of the same name, is the parlance, includes medical, pornographic, commercial, and grisly crime images. Besides the mind-numbing repetitiveness, there are instances of PTSD related to the images and real concerns about the personal data being shared, stored, and used for medical diagnosis. A.I. Is Learning from Humans. Many Humans. (NY Times)

A sobering, mercifully hype-free view of AI in healthcare

October 3, 2018 | by

Way up there on the Peak of Inflated Expectations in the Gartner Hype Cycle is that two-letter creature, AI. Artificial Intelligence has been invoked in multiple tech fields, and Microsoft in the US currently is running 30 second commercials about how AI is “making tomorrow today” but without much explanation as to how.

If AI’s current puffery makes you dizzy, long-time observer of the Healthcare Scene Anne Ziegler’s article in Hospital EMR and EHR might stabilize the whirlies. In direct and brief terms, she classifies the realities of healthcare AI adoption in three areas:

  1. Lack of Transparency. How does AI reach its conclusions in making ‘good decisions’? Sometimes the logic of the conclusion is obvious, but often it is not, and what you get is physician and clinician bypass–and suspicion.
  2. That Old Monkey Wrench Tossed into Existing Processes. It’s taken a long time for organizations to fully integrate their EHR inputs and documentation. Throwing in an AI implementation even in a limited sense may require more adjustments than the outcomes are worth.
  3. It’s Too, Tooooo Much Data. Healthcare organizations do not suffer from a paucity of data. AI feeds on data. Sounds like a good match, doesn’t it. Except that a lot of this data isn’t usable without filtering and mining, and that takes a lot of processing. The future may have more advanced data processing and indexing tech to do that, but right now even natural language processing to identify useful information is rare in the field.

Widespread AI use in healthcare is, despite the IBM Watson Health hype, a long way off. In healthcare, the rubber must meet the road of patient care and clinical practicality to be useful to us with Non-Artificial Intelligence. Problems We Need To Address Before Healthcare AI Becomes A Thing

More events for your autumnal calendar, from Israel to Ireland to Santa Clara to NYC! (updated)

September 6, 2018 | by

Startup of the Year, Mediterranean Towers, Ganei Tikva, Israel, Sunday 3 September, 6-8pm (Past–but there’s a winner!)

Mediterranean Towers Ventures, the investment subsidiary of the largest retirement living community in Israel, is sponsoring this competition featuring five finalists:

1. Facense – Facense Ltd. develops smartglasses with tiny thermal and CMOS sensors to measure vital signs unobtrusively and continuously, with one application being to detect a person having a stroke.
2. MyTView – My-TView’s proprietary technology enables dynamic modification and enhancement of real-time broadcasts, whilst numbing the “noise”.
3. Invisi.care – transforming existing non-medical data into an effective large-scale clinical prevention tool; a remote seamless population monitoring technology encourages independent and active lifestyle.
4. GaitBetter – A universal, VR based, expert system add-on transforming any treadmill to an operator independent motor cognitive training solution
5. TuneFork–a software audio personalization technology that gives you the optimal hearing experience anywhere you go. 

And there’s a winner–TuneFork. Their award includes free participation at the Aging2.0 Optimize Conference in San Francisco (14-15 Nov), professional mentoring by Mediterranean Towers Ventures, and the opportunity to be evaluated for investment. 

Hat tip to Dov Sugarman, co-CEO of MTV.

Health 2.0’s 12th Annual Fall Conference, Santa Clara, California, 16-18 September

This year’s conference, despite the corporate hand of HIMSS, may be as breezy as ever with a continued concentration on early stage companies and speakers like Lisa Suennen, late of GE Ventures, Sarah Krug of the Society for Participatory Medicine, and Sean Lane of Olive talking about AI. Register here, and dig deep for the ticket.

UK Health Show, ExCel, London, 25-26 September

A major and mostly free event for providers, NHS, local authorities, independent sector, and primary care that addresses system transformation and integration through digital technology, commissioning, procurement and cybersecurity. More information on their website here. Registration here (free full passes for providers and public sector, floor passes for vendors and commercial organizations)

Brain Health, Aging 2.0 Los Angeles, Thursday 27 September, 6-8pm

Not many details on this other than it will be in West LA and the topic will be Brain Health and Artificial Intelligence. The keynote speaker will be Adam C. Lichtl, Ph.D., CEO of Delta Brain, Inc. More information to come and RSVP for now on Eventbrite.

Inspiring Innovation in Digital Health: The UK Telehealthcare Marketplace Northern Ireland. La Mon Hotel and Country Club, Castlereagh, Belfast, Wednesday 3 October 10am – 3pm

UK Telehealthcare is traveling to Northern Ireland for their first event in the beautiful Lisburn & Castlereagh area, one of Northern Ireland’s fastest growing regions. It will be a showcase for digital technology to help people to stay safely and independently in their own homes for longer. A ‘don’t miss’. See the attached PDF for details including masterclass speakers and exhibitors. Free registration here. Hat tip to Gerry Allmark, UK Telehealthcare’s managing director.

Additional UK Telehealthcare events into December are listed on their main page which is linked through their advert on right or here. They are partnering with the UK Health Show (above) and exhibiting in the UK TECS Hub in the assistive technology area (blocks F98, F100, F92, F94).

MedStartr Momentum 2018, PwC Madison Avenue, NYC, Thursday-Friday 29-30 November

Put this on your calendars for after Thanksgiving. 20 startups, 9 Momentum Talks, 4 stakeholder panels, and Mainstream 2019. More here on Eventbrite and as in previous years, watch this website. TTA is a media partner and supporter of Momentum,  MedStartr and Health 2.0 NYC.

Themes and trends at Aging2.0 OPTIMIZE 2017

November 10, 2017 | by

Aging2.0 OPTIMIZE, in San Francisco on Tuesday and Wednesday 14-15 November, annually attracts the top thinkers and doers in innovation and aging services. It brings together academia, designers, developers, investors, and senior care executives from all over the world to rethink the aging experience in both immediately practical and long-term visionary ways.

Looking at OPTIMIZE’s agenda, there are major themes that are on point for major industry trends.

Reinventing aging with an AI twist

What will aging be like during the next decades of the 21st Century? What must be done to support quality of life, active lives, and more independence? From nursing homes with more home-like environments (Green House Project) to Bill Thomas’ latest project–‘tiny houses’ that support independent living (Minkas)—there are many developments which will affect the perception and reality of aging.

Designers like Yves Béhar of fuseproject are rethinking home design as a continuum that supports all ages and abilities in what they want and need. Beyond physical design, these new homes are powered by artificial intelligence (AI) and machine learning technology that support wellness, engagement, and safety. Advances that are already here include voice-activated devices such as Amazon Alexa, virtual reality (VR), and IoT-enabled remote care (telehealth and telecare).

For attendees at Aging2.0, there will be substantial discussion on AI’s impact and implications, highlighted at Tuesday afternoon’s general session ‘AI-ging Into the Future’ and in Wednesday’s AI/IoT-related breakouts. AI is powering breakthroughs in social robotics and predictive health, the latter using sensor-based ADL and vital signs information for wellness, fall prevention, and dementia care. Some companies part of this conversation are CarePredict, EarlySense, SafelyYou, and Intuition Robotics.

Thriving, not surviving

Thriving in later age, not simply ‘aging in place’ or compensating for the loss of ability, must engage the community, the individual, and providers. There’s new interest in addressing interrelated social factors such as isolation, life purpose, food, healthcare quality, safety, and transportation. Business models and connected living technologies can combine to redesign post-acute care for better recovery, to prevent unnecessary readmissions, and provide more proactive care for chronic diseases as well as support wellness.

In this area, OPTIMIZE has many sessions on cities and localities reorganizing to support older adults in social determinants of health, transportation innovations, and wearables for passive communications between the older person and caregivers/providers. Some organizations and companies contributing to the conversation are grandPad, Village to Village Network, Lyft, and Milken Institute.

Technology and best practices positively affect the bottom line

How can senior housing and communities put innovation into action today? How can developers make it easier for them to adopt innovation? Innovations that ‘activate’ staff and caregivers create a multiplier for a positive effect on care. Successful rollouts create a positive impact on both the operations and financial health of senior living communities.

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AI good, AI bad. Perhaps a little of both?

August 1, 2017 | by

Everyone’s getting hot ‘n’ bothered about AI this summer. There’s a clash of giants–Elon Musk, who makes expensive, Federally subsidized electric cars which don’t sell, and Mark Zuckerberg, a social media mogul who fancies himself as a social policy guru–in a current snipe-fest about AI and the risk it presents. Musk, who is a founder of the big-name Future of Life Institute which ponders on AI safety and ethical alignment for beneficial ends, and Zuckerberg, who pooh-poohs any downside, are making their debate points and a few headlines. However, we like to get down to the concretes and here we will go to an analysis of a report by Forrester Research on AI in the workforce. No, we are not about to lose our jobs, yet, but hold on for the top six in the view of Gil Press in Forbes:

  1. Customer self-service in customer-facing physical solutions such as kiosks, interactive digital signage, and self-checkout.
  2. AI-assisted robotic process automation which automates organizational workflows and processes using software bots.
  3. Industrial robots that execute tasks in verticals with heavy, industrial-scale workloads.
  4. Retail and warehouse robots.
  5. Virtual assistants like Alexa and Siri.
  6. Sensory AI that improves computers’ recognition of human sensory faculties and emotions via image and video analysis, facial recognition, speech analytics, and/or text analytics.
[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2017/08/AI.jpg” thumb_width=”200″ /]For our area of healthcare technology, look at #5 and #6 first–virtual assistants leveraging the older adult market like 3rings‘ interface with Amazon Echo [TTA 27 June] and sensory AI for recognition tools with broad applications in everything from telehealth to sleepytime music to video cheer-up calls. Both are on a ‘significant success’ track and in line to hit the growth phase in 1-3 years (illustration at left, click to expand).

Will AI destroy a net 7 percent of US jobs by 2027? Will AI affect only narrow areas or disrupt everything? And will we adapt fast enough? 6 Hot AI Automation Technologies Destroying And Creating Jobs (Forbes)

But we can de-stress ourselves with AI-selected music now to soothe our savage interior beasts. This Editor is testing out Sync Project’s Unwind, which will help me get to sleep (20 min) and take stress breaks (5 min). Clutching my phone (not my pearls) to my chest, the app (available on the unwind.ai website) detects my heart rate (though not giving me a reading) through machine learning and gives me four options to pick on exactly how stressed I am. It then plays music with the right beat pattern to calm me down. Other Sync Project applications with custom music by the Marconi Union and a Spotify interface have worked to alleviate pain, sleep, stress, and Parkinson’s gait issues. Another approach is to apply music to memory issues around episodic memory and memory encoding of new verbal material in adults aging normally. (Zzzzzzzz…..) Apply.sci, Sync Project blog

PwC: your job at risk by robots, AI by 2030?

March 25, 2017 | by
[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2016/06/robottoy-1.jpg” thumb_width=”150″ /]PwC‘s latest study on the effect of robotics and artificial intelligence on today’s and future workforce is the subject of this BBC Business article focusing on the UK workforce. 30 percent of existing jobs in the UK were potentially at a high risk of automation by the 2030s, compared with 38 percent in the US, 35 percent in Germany and 21 percent in Japan. Most at risk are jobs in manufacturing and retail, but to quote PwC’s page on their multiple studies, robotics and AI may change how we work in a different way, an “augmented and collaborative working model alongside people – what we call the ‘blended workforce’”. Or not less work, but different types of work. But some jobs, like truck (lorry) drivers, would go away or be vastly diminished.

The effect on healthcare? The categories are very broad, but the third category of employment affected is administrative and support services at 37 percent, followed by professional, scientific and technical at 26 percent, and human health and social work at 17 percent. Will it increase productivity and thus salaries, which have languished in the past decade? Will it speed innovation and care in our area? Will it help the older population to be healthy and productive? And the societal effects will roll on, but perhaps not for some. View this wonderful exchange between Jean Harlow and Marie Dressler that closes the 1933 film Dinner at Eight. Hat tip to Guy Dewsbury @dewsbury via Twitter

Towards 2020: Big Tech developments predicted to impact healthcare delivery

February 14, 2017 | by | 2 Comments

[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2017/02/Gartner-curve.png” thumb_width=”175″ /]Healthcare doesn’t stand outside major technology trends, and two we’ve covered extensively are machine learning and artificial intelligence (AI), which this Editor observed [TTA 3 Feb] may beat out Massive Data Crunchers like IBM Watson Health for many diagnoses. What has been ‘bubbling up’ in the past year is blockchain, the technology behind bitcoin which is a ‘distributed, secure transaction ledger’ that uses a private key. Each record block has an identifying hash that links each block into a virtual chain [TTA 16 July]. Brian Ahier, Director of Standards and Government Affairs at Medicity, Aetna’s data analytics/population health subsidiary, predicts in Health Data Management that both will be the Big Trends for the next three years, with a substantial discussion behind both, particularly AI (though citing global equity funding of $1.5 bn in AI for healthcare since 2012 seems a paltry reinforcement). There are links to two blockchain studies from Deloitte and IEEE (requires subscription or purchase) for further reference. Two other bonuses: a link to New School’s Melanie Swan’s blockchain blog with four potential uses in healthcare and a Gartner hype cycle chart (left above) identifying both machine learning and blockchain (distributed ledger) near the peak of the curve. His third trend, digital transformation, is less a trend than an admonition that our present business structures need to change, that they must realize the potential of fully utilizing data, and to consider the customer experience.

AI as diagnostician in ophthalmology, dermatology. Faster adoption than IBM Watson?

February 3, 2017 | by

Three recent articles from the IEEE (formally the Institute of Electronics and Electrical Engineers) Spectrum journal are significant in pointing to advances in artificial intelligence (AI) for specific medical conditions–and which may go into use faster and more cheaply than the massive machine learning/decision support program represented by IBM Watson Health.

A Chinese team developed CC-Cruiser to diagnose congenital cataracts, which affect children and cause irreversible blindness. The program developed algorithms that used a relatively narrow database of 410 images of congenital cataracts and 476 images of normal eyes. The CC-Cruiser team from Sun Yat-Sen and Xidian Universities developed algorithms to diagnose the existence of cataracts, predict the severity of the disease, and suggest treatment decisions. The program was subjected to five tests, with most of the critical ones over 90 percent accuracy versus doctor consults. There, according to researcher and ophthalmologist Haotian Lin, is the ‘rub’–that even with more information, he cannot project the system going to 100 percent accuracy. The other factor is the human one–face to face interaction. He strongly suggests that the CC-Cruiser system is a tool to complement and confirm doctor judgment, and could be used in non-specialized medical centers to diagnose and refer patients. Ophthalmologists vs. AI: It’s a Tie (Hat tip to former TTA Ireland Editor Toni Bunting)

In the diagnosis of skin cancers, a Stanford University team used GoogleNet Inception v3 to build a deep learning algorithm. This used a huge database of 130,000 lesion images from more than 2000 diseases. Inception was successful in performing on par with 21 board-certified dermatologists in differentiating certain skin lesions, for instance, keratinocyte carcinomas from benign seborrheic keratoses. The major limitations here are the human doctor’s ability to touch and feel the skin, which is key to diagnosis, and adding the context of the patient’s history. Even with this, Inception and similar systems could help to triage patients to a doctor faster. Computer Diagnoses Skin Cancers

Contrasting this with IEEE’s writeup on the slow development of IBM Watson Health’s systems, each having to be individually developed, continually refined, using massive datasets, best summarized in Dr Robert Wachter’s remark, “But in terms of a transformative technology that is changing the world, I don’t think anyone would say Watson is doing that today.” The ‘Watson May See You Someday’ article may be from mid-2015, but it’s only this week that Watson for Oncology has announced its first implementation in a regional medical center based in Jupiter, Florida. Watson for Oncology collaborates with Memorial Sloan-Kettering in NYC (MSK) (and was tested in other major academic centers). Currently it is limited to breast, lung, colorectal, cervical, ovarian and gastric cancers, with nine additional cancer types to be added this year. Mobihealthnews

What may change the world of medicine could be AI systems using smaller, specific datasets, with Watson Health for the big and complex diagnoses needing features like natural-language processing.

Artificial intelligence with IBM Watson, robotics pondered on 60 Minutes

October 12, 2016 | by
[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2016/06/robottoy-1.jpg” thumb_width=”150″ /]This Sunday, the long-running TV magazine show 60 Minutes (CBS) had a long Charlie Rose-led segment on artificial intelligence. It concentrated mainly on the good with a little bit of ugly thrown in. The longest part of it was on IBM Watson massively crunching and applying oncology and genomics to diagnosis. In a study of 1,000 cancer patients reviewed by the University of North Carolina at Chapel Hill’s molecular tumor board, while 99 percent of the doctor diagnoses were confirmed by Watson as accurate, Watson found ‘something new’ in 30 percent. As a tool, it is still considered to be in adolescence. Watson and data analytics technology has been a $15 billion investment for IBM, which can afford it, but by licensing it and through various partnerships, IBM has been starting to recoup it. The ‘children of Watson’ are also starting to grow. Over at Carnegie Mellon, robotics is king and Google Glass is reading visual data to give clues on speeding up reaction time. At Imperial College, Maja Pantic is taking the early steps into artificial emotional intelligence with a huge database of facial expressions and interpretations. In Hong Kong, Hanson Robotics is developing humanoid robots, and that may be part of the ‘ugly’ along with the fears that AI may outsmart humans in the not-so-distant future. 60 Minutes video and transcript

Speaking of recouping, IBM Watson Health‘s latest partnership is with Siemens Healthineers to develop population health technology and services to help providers operate in value-based care. Neil Versel at MedCityNews looks at that as well as 60 Minutes. Added bonus: a few chuckles about the rebranded Siemens Healthcare’s Disney-lite rebranding.

RSM hosts digital health event 25 February

January 19, 2016 | by

[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2016/01/RSM.jpg” thumb_width=”150″ /]Recent developments in digital health 2016
Thursday 25 February 2016
Royal Society of Medicine, 1 Wimpole Street, London, W1G 0AE

Presented by the Royal Society of Medicine’s Telemedicine and eHealth Section (presided by our Editor Charles), this full day conference is open to the public and provides a global perspective from leaders within digital health. Keynoters are Mustafa Suleyman from Google’s Artificial Intelligence branch, DeepMind, and Dr Euan Ashley from Stanford University in California who leads Apple’s MyHeartCounts. Rates are reasonable: £50-115 for RSM members and £60-175 for non-members, plus 6 CPD credits. More information and registration on the RSM website here and download the flyer here.

Upcoming RSM Telemedicine events into early June:
Medical apps: Mainstreaming innovation–Thursday 7 April 2016

The future of medicine – the role of doctors in 2025–Thursday 19 May 2016

Big data 2016–Thursday 2 June 2016

Your Friday superintelligent robot fix: the disturbing consequences of ultimate AI

March 27, 2015 | by
[grow_thumb image=”https://telecareaware.com/wp-content/uploads/2014/01/Overrun-by-Robots1-183×108.jpg” thumb_width=”200″ /]Our own superintelligent humans–Elon Musk (Tesla), Steve Wozniak (Apple), Bill Gates (Microsoft) and Stephen Hawking–are converging on artificial intelligence, not just everyday, pedestrian robotics, but the kind of AI superintellect that could make pets out of people–if we are lucky. In his interview with Australian Financial Review, the Woz (now an Australian resident) quipped: ‘Will we be the gods? Will we be the family pets? Or will we be ants that get stepped on?’ (more…)