1.119.886 TB

Juli 2018

690 TB

Juli 2001

Third parties are allowed to use the AMS-IX statistics that are published on the website. Upon doing so, please make sure to mention that AMS-IX holds copyright on this information and to

accompany the figures with a link directing to the figures on our website; https://ams-ix.net/technical/statistics/historical-traffic-data

1.119.886 Terabyte

1.119.886.000 Gigabyte

1.119.886.000.000 Megabyte

1.119.886.000.000.000 Kilobyte

17.498.218.750.000 x Commodore 64

To Intelligent Health: a continuous, collaborative approach

that enables preventative care

Personalized

Treatment

Connected

Customers

Effective

Clinical Trials

Collaborative

Research

Remote

Monitoring

Connected

Devices

Go to

hospital

From a reactive, disconnectedand cyclical process

Feel

symptoms

Give

information

Receive

treatment

Recover

See

doctor

Doctor

https://www.nextrembrandt.com/

https://azure.microsoft.com/en-us/services/cognitive-services/emotion/

microsoft.com/ai

Studio is a powerfully simple

browser-based, visual drag-and-

drop authoring environment

where no coding is necessary.

Go from idea to deployment in a

matter of clicks.

Available

https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/

Available

https://azure.microsoft.com/en-us/services/cognitive-services/language-understanding-intelligent-service/

A machine learning-based service to build

natural language understanding into apps,

bots, and IoT devices. Quickly create

enterprise-ready, custom models that

continuously improve.

Intents

An intent represents actions the user wants to

perform.

Utterances

An utterance is text input from the user that your app

needs to understand.

Entities

An entity represents detailed information that is

relevant in the utterance.

Book me a flight to Amsterdam

{

“query”: “Book me a flight to Amsterdam”,

“topScoringIntent”:

{

“intent”: “BookFlight”,

“score”: 0.9887482

},

“intents”: [

{

“intent”: “BookFlight”,

“score”: 0.9887482

},

{

…

Available

Break down language barriers with your friends, family and

colleagues.

Our online translator can help you communicate more clearly.

Our voice translator currently works in 8 languages,

Arabic, Arabic (Levantine), Chinese (Mandarin), English,

French, German, Hindi, Italian, Japanese, Portuguese (Brazil),

Russian, Spanish.

and our text translator is available in more than 50 languages for

instant messaging.

Skype Translator uses machine learning. So the more you use it,

the better it gets.

https://www.skype.com/en/features/skype-translator/

microsoft.com/ai

There are clear opportunities to use AI to make health services both more

accessible and more effective. By making data collection and triage more

efficient, AI can reduce the costs of care, making services more affordable for

patients.

Collecting more and better data could see services tailored to people’s needs,

leading to better health outcomes and better-performing health systems.

It could also help us predict the risk of future health events from routinely

collected data—for example, the onset of a heart attack in a patient with high

blood pressure.

But the benefits of artificial intelligence are not only a hope for the future.

There are many examples of how artificial intelligence is already advancing

health.

For example, AI is being used to give paraplegic patients improved mobility;

to make diagnosis faster and more efficient;

to scan the news for emerging and re-emerging disease threats;

to manage road traffic, reducing crashes and increasing road safety;

and to develop new medicines and vaccines.

And there are numerous other ways.

Of course, with every new technology, there are always risks of abuse.

Even as we enjoy the benefits of artificial intelligence, we must not lose sight of

human rights.

We must ensure that national governments have the appropriate guardrails in

place.

WHO stands ready to support all countries both to realize the promise of

artificial intelligence, and to ensure the appropriate safeguards are in place. AI

is the future of health, but safeguards are important too. At the Artificial Intelligence for Good global summit

Source available here: http://www.who.int/dg/speeches/2017/artificial-intelligence-summit/en/

[2017] Source available here: https://www.microsoft.com/en-us/research/publication/cardiolens-remote-physiological-monitoring-mixed-reality-environment/

There are ethical and privacy questions related to a device that

can measure and visualize the physiological responses of another

person. We hope that this demo also spurs debate about these

issues.

Numerous vital signs can be captured through the measurement of

bloodflow; however, these signals are not visible to the unaided eye

and measurement traditionally requires customized contact

sensors. We present Cardiolens - a mixed reality application that

enables real-time hands-free measurement and visualization of

bloodflow and vital signs. The system combines a front-facing

camera, remote imaging photoplethysmography software and a

headsup display allowing users to view the physiological state of a

person simply by looking at them. Cardiolens provides the wearer

with a new way to understand physiology and has applications in

healthcare and affective computing.

[2018] Source available here: https://www.microsoft.com/en-us/research/uploads/prod/2018/03/CHI2018-conflicts-final.pdf

In this paper, we presented a subtle approach for regulating

emotions during interpersonal conflicts, which consists in

changing how people perceive their own voice.

(…) It is important to note that the method of altering user’s voice

self-perception could be applied in other contexts besides

interpersonal conflicts. One interesting possibility is regulating the

emotions of individuals with mental health conditions. Previous

research shows that individuals with mood disorders such as social

anxiety, bipolar disorder, and schizophrenia, are more sensitive to

the way they perceive their own behavior and bodily signals.

(…) In both studies, participants were able to focus on their

conversations without drifting their attention away to any emotion

regulation technology, showing that the intervention does not

require attention or effort to be effective. These findings offer

promising opportunities for the design of technologies for emotion

regulation.

Autonomous weapons select and engage targets

without human intervention.

Artificial Intelligence (AI) technology has reached a

point where the deployment of such systems is —

practically if not legally — feasible within years, not

decades.

SAE International is a global association of more than 128,000 engineers and related technical experts in the aerospace, automotive and commercial-vehicle industries.

Source is available here: https://www.sae.org/standards/content/j3016_201401/

Level 0: No Automation

Level 1: Driver Assistance

Level 2: Partial Automation

Level 3: Conditional Automation

Level 4: High Automation

Level 5: Full Automation

The full time performance by an automated driving system of all aspects of the dynamic driving task

under all roadway and environmental conditions that can be managed by a human driver

The driving mode-specific performance of an automated driving system of all aspects of the dynamic

driving task, even if a human driver does not respond appropriately to a request to intervene.

The driving mode-specific performance by an automated driving system of all aspects of the dynamic

driving task with the expectation that the human driver will respond appropriately to a request to

intervene.

The driving mode-specific execution by one or more driver assistance systems of both steering and

acceleration/deceleration using information about the driving environment and with the expectation

that the human driver perform all remaining aspects of the dynamic driving task.

The driving mode-specific execution by a driver assistance system of either steering or

acceleration/deceleration using information about the driving environment and with the expectation

that the human driver perform all remaining aspects of the dynamic driving task.

The full time performance by an automated driving system of all aspects of the dynamic driving task

under all roadway and environmental conditions that can be managed by a human driver

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Source available here: http://europepmc.org/articles/PMC4430591

But if fully autonomous cars are going to drive on our roads, it must be

decided who is to be held responsible in case of accidents. This involves not

only legal questions, but also moral ones.

Assuming the implementation of autonomous cars would save lives, this by

itself constitutes a powerful moral reason to limit the possible responsibilities

of manufacturers to a point where it does not render the development of

such cars too risky for the companies involved. Of course, manufacturers

should not be freed of their liability in cases like the Ford Pinto, in which the

manufacturers put the car on the market fully knowing that it had major

safety defects, but considered rectifying those flaws too expensive. Also, a

certain amount of responsibility for accidents not is only morally desirable in

itself but also an important incentive for the continuous development and

improvement of such cars.

9. Bei unausweichlichen Unfallsituationen ist jede Qualifizierung

nach persönlichen Merkmalen (Alter, Geschlecht, körperliche

oder geistige Konstitution) strikt untersagt. Eine Aufrechnung

von Opfern ist untersagt. Eine allgemeine Programmierung auf

eine Minderung der Zahl von Personenschäden kann vertretbar

sein. Die an der Erzeugung von Mobilitätsrisiken Beteiligten

dürfen Unbeteiligte nicht opfern.

Source available online: https://www.bmvi.de/SharedDocs/DE/Publikationen/DG/bericht-der-ethik-kommission.pdf?__blob=publicationFile

Capability

Security

Trust

Ethics

The ability to perform or achieve certain actions or outcomes.

Security mostly refers to protection from hostile forces, but it has a wide range of other senses: for

example, as the absence of harm; as the presence of an essential good; as resilience against

potential damage or harm; as secrecy; as containment; and as a state of mind.

Definitions of trust typically refer to a situation characterized by the following aspects: One party

(trustor) is willing to rely on the actions of another party (trustee); the situation is directed to the

future.

Ethics seeks to resolve questions of human morality by defining concepts such as good and evil,

right and wrong, virtue and vice, justice and crime.

Descriptions are based on Wikipedia.

Capability

Security

Trust

Ethics

“A PC on Every Desk and in Every Home”.

(1980) Microsoft mission statement

“However, even more important than any of these new capabilities is the fact that it is designed

from the ground up to deliver Trustworthy Computing. What I mean by this is that customers will

always be able to rely on these systems to be available and to secure their information.

Trustworthy Computing is computing that is as available, reliable and secure as electricity, water

services and telephony.”

(2002) Bill Gates, memo on Trustworthy Computing

“Business and users are going to embrace technology only if they can trust it.”

(2010) Microsoft Trusted Cloud

The AI and Ethics for Engineering and Research (Aether) board was established in 2017 to discuss

and recommend programs, policies, and procedures, and best practices on issues of AI safety,

fairness, transparency, and ethics, and questions and challenges arising more broadly at the

intersection of AI, people, and society as AI moves into the open world.

(2017) Microsoft Aether Board

[2016] Source available here: https://www.microsoft.com/en-us/research/publication/intelligible-models-healthcare-predicting-pneumonia-risk-hospital-30-day-readmission/

A recorded talk by the author is available here: https://www.microsoft.com/en-us/research/video/intelligible-machine-learning-models-for-healthcare/

Although the neural nets were the most accurate models, after

careful consideration they were considered too risky for use on

real patients and logistic regression was used instead.

In machine learning often a tradeoff must be made between

accuracy and intelligibility. More accurate models such as boosted

trees, random forests, and neural nets usually are not intelligible,

but more intelligible models such as logistic regression, naive-

Bayes, and single decision trees often have significantly worse

accuracy. This tradeoff sometimes limits the accuracy of models

that can be applied in mission-critical applications such as

healthcare where being able to understand, validate, edit, and

trust a learned model is important.

[2017] Source available here: https://ainowinstitute.org/

When bias is embedded in AI health applications, it can have an

incredibly high cost. Worryingly, data sets used to train health-

related AI often rely on clinical trial data, which are historically

skewed toward white men, even when the health conditions

studied primarily affect people of color or women.

Even without AI amplifying such biases, African Americans with

sickle cell anemia are over-diagnosed and unnecessarily treated

for diabetes based on insights from studies that excluded them.

The prevalence of biases when combined with opacity and

inscrutability leads to a lack of trust in AI currently being

developed for neuroscience and mental health applications. The

prospect of misdiagnosis or improper treatment leading to

patient death motivates some to avoid AI systems entirely in the

health context.

[2017] Source available here: https://ainowinstitute.org/

When bias is embedded in AI health applications, it can have an

incredibly high cost. Worryingly, data sets used to train health-

related AI often rely on clinical trial data, which are historically

skewed toward white men, even when the health conditions

studied primarily affect people of color or women.

Even without AI amplifying such biases, African Americans with

sickle cell anemia are over-diagnosed and unnecessarily treated

for diabetes based on insights from studies that excluded them.

The prevalence of biases when combined with opacity and

inscrutability leads to a lack of trust in AI currently being

developed for neuroscience and mental health applications. The

prospect of misdiagnosis or improper treatment leading to

patient death motivates some to avoid AI systems entirely in the

health context.

Engage your patients Empower your research

teams and employees

Optimize your clinical and

operational effectiveness

Transform health

Transform • Medical home

• Precision medicine

• Patient wearables

• Virtual care

• Digital outreach

• Mixed reality

• Population management

• Artificial intelligence

• Conversation computing

• Care coordination

• Remote monitoring

• Genomics

Grow • Patient information

• Patient health records

• Gamified health

• Clinical education

• Social enterprise

• Knowledge

management

• Readmission management

• Fraud detection/ prevention

• Condition registries

• Flexible workstyles

• Cross-

organizational trust

• Predictive asset

management

Start • Kiosk check-in

• Meal ordering

• Customer portals

• Unified

communications

• Clinical information

systems

• Clinical mobility

• Performance dashboard

• Patient journey-board

• Clinical outcomes research

• Consolidate data

sources

• Modernize

infrastructure

• Hybrid cloud

Unmatched threat visibilityDeep security investment and expertise Industry-leading scale and speed

All enabled by a secure, compliant Microsoft cloud

To Intelligent Health: a continuous, collaborative approach

that enables preventative care

Personalized

Treatment

Connected

Customers

Effective

Clinical Trials

Collaborative

Research

Remote

Monitoring

Connected

Devices

Go to

hospital

From a reactive, disconnectedand cyclical process

Feel

symptoms

Give

information

Receive

treatment

Recover

See

doctor

Doctor