Category Archives: Artificial Intelligence

The Pentagon is figuring ways to harness artificial intelligence (AI) for advantages as farflung as battlespace autonomy, intelligence analysis, record tracking, predictivemaintenance and military medicine. AI is a key growth investment area for DoD, withnearly $1 billion allocated in the 2020 budget. The Defense Departments Joint ArtificialIntelligence Center (JAIC) will see its budget double to over $208 million, with significantincreases likely in 2021 and beyond.

JAIC seeks to coordinate all military service anddefense agency artificial intelligence activity over a $15 million benchmark. The military is currently seeking to integrate AI into weapon systems development, augment human operatorswith AI-driven robotic maneuver on the battlefield and enhance the precision of militaryfires.

The rapid advancement and proliferation of new technologies is changing the character ofwar.

To prevent the erosion of the U.S. competitive military advantage, DOD is investing in newtechnologies to compete, deter, and if necessary, fight and win the wars of the future.

White House Fiscal Year 2020 Federal Budget

DoDs investment in AI is crucial to its continuing military advantage,ensuring the U.S. military does not lag behind rival world powers. BreakingDefense has prepared a special E-Book on Artificial Intelligence in defense, about the promise,cautionary points and future development.

Download the special Breaking Defense E-Book, Artificial Intelligence: The Frontlineof a New Age in Defense. Its free, and provides ideas and insights on the emergence of AI as a key factorin national security.

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DoD Growth In Artificial Intelligence: The Frontline Of A New Age In Defense - Breaking Defense

According to a new report from the European Space Agency (ESA), artificial intelligence and machine learning may be the key to accurately extracting and processing satellite data. For humans, it can be very challenging to locate the most relevant information in these massive datasets, which are transmitted from over 700 Earth observation satellites.

The need for reliable information about Earths climate system is more urgent now than ever before. For example, the ESA Climate Change Initiative (CCI) provides critical feedback to the UN Framework Convention on Climate Change. Teams of scientists have been formed to produce precise details on specific environmental processes.

The datasets used for the CCI include 21 essential climate variables, such as greenhouse gas concentrations, sea-level rise, and the state of the worlds polar ice sheets. These records, which cover four decades, are the foundation for the global climate models used to predict future changes.

Dr. Carsten Brockmann, who works on the CCI Ocean Colour science team, believes artificial intelligence has the power to address pressing challenges that are faced by climate researchers.

In machine learning, computer algorithms are trained to split, sort, and transform data. This can dramatically improve detection rates in Earth observation, as these algorithms can automatically make statistical connections within datasets for classification, prediction, or pattern discovery.

Connections between different variables in a dataset are caused by the underlying physics or chemistry, but if you tried to invert the mathematics, often too much is unknown, and so unsolvable, said Dr. Brockman. For humans its often hard to find connections or make predictions from these complex and nonlinear climate data.

Scientists involved in the CCI Aerosol project need to pinpoint changes in reflected sunlight caused by the presence of dust, smoke, and pollution in the atmosphere. Project leader Thomas Popp wants to use artificial intelligence to retrieve additional aerosol parameters from several sensors at once.

I want to combine several different satellite instruments and do one retrieval. This would mean gathering aerosol measurements across the visible, thermal and ultraviolet spectral range, from sensors with different viewing angles, said Popp. He said that approaching this as one big data problem could make these data automatically fit together and be consistent.

Explainable artificial intelligence is another evolving area that could help unveil the physics or chemistry behind the data, said Dr. Brockmann.

In artificial intelligence, computer algorithms learn to deal with an input dataset to generate an output, but we dont understand the hidden layers and connections in neural networks: the so-called black box.

We cant see whats inside this black box, and even if we could, it wouldnt tell us anything. In explainable artificial intelligence, techniques are being developed to shine a light into this black box to understand the physical connections.

By Chrissy Sexton, Earth.com Staff Writer

Image Credit: Shutterstock/NicoElNino

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Artificial intelligence could help to translate critical Earth observation data Earth.com - Earth.com

Representational image. Image Credit: Pixabay

New Delhi: Artificial Intelligence (AI) and cross-industry collaborations are creating new avenues for data collection and offering personalised services to users this year, according to a report.

Among other technology trends that are picking up this year are the convergence of the smart home and healthcare, autonomous vehicles coming for last-mile delivery and data becoming a hot-button geopolitical issue, according to the report titled "14 Trends Shaping Tech" from CB Insights.

"As a more tech-savvy generation ages up, we'll see the smart home begin acting as a kind of in-home health aide, monitoring senior citizens' health and well being. We'll see logistics players experiment with finally moving beyond a human driver," said the report.

"And we'll see cross-industry collaborations, whether via ancestry-informed Spotify playlists or limited edition Fortnite game skins," it added.

In September 2018, Spotify partnered with Ancestry.com to utilise DNA data to create unique playlists for individuals.

Playlists reflect music linked to different ethnicities and regions. A person with ancestral roots in Bengaluru, for example, might see Carnatic violinists and Kannada film songs on their playlists.

DNA data is also informing how we eat. GenoPalate, for example, collects DNA info through saliva samples and analyses physiological components like an individual's ability to absorb certain vitamins or how fast they can metabolize nutrients.

From there, it matches this information to nutrition analyses that it has conducted on a wide range of food and suggests a personalised diet. It also sells its own meal kits that use this information to map out menus.

"We'll also see technology brands expand beyond their core products and turn themselves into a lifestyle," said the report.

For example, as electric vehicle users need to wait for their batteries to charge for anywhere from 30 minutes to two hours, the makers of these vehicles are trying to turn this idle time into an asset.

China's NioHouse couples charging stations with a host of activities. At the NioHouse, a user can visit the library, drop children off at daycare, co-work, and even visit a nap pod to rest while charging.

Nio has also partnered with fashion designer Hussein Chalayan to launch and sell a fashion line, Nio Extreme.

Tech companies today are also attempting to bridge the gap between academia and the career market.

Companies like the Lambda School and Flatiron School offer courses to train students on exactly the skills they will need to get a job, said the report.

These apprenticeships mostly focus on tech skills like computer science and coding. Training comes with the explicit goal of employment and students only need to pay their tuition once they have landed a job that pays them above a certain range.

Investors are also betting on the rise of digital goods. While these goods cannot be owned in the physical world, they come with clout, and offer personalisation and in-game experiences to otherwise one-size-fits-all characters, the research showed.

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Artificial Intelligence (AI) creates new possibilities for personalisation this year - Gulf News

One of England's biggest academy chains is testing pupils' mental health using an AI (artificial intelligence) tool which can predict self-harm, drug abuse and eating disorders, Sky News can reveal.

A leading technology think tank has called the move "concerning", saying "mission creep" could mean the test is used to stream pupils and limit their educational potential.

The Academies Enterprise Trust has joined private schools such as Repton and St Paul's in using the tool, which tracks the mental health of students across an entire school and suggests interventions for teachers.

This month, 50,000 schoolchildren at 150 schools will take the online psychological test, called AS Tracking, including 10,000 Academies Enterprise Trust pupils.

Teachers say use of the tool is "snowballing" as it offers a way to ease the pressure on teenagers struggling to deal with social media scrutiny and academic stress.

The test, which is taken twice a year, asks students to imagine a space they feel comfortable in, then poses a series of abstract questions, such as "how easy is it for somebody to come into your space?"

The child can then respond by clicking a button on a scale that runs from "very easy" to "very difficult".

Dr Simon Walker, a cognitive scientist who conducted studies with 10,000 students in order to develop AS Tracking, says this allows teachers to hear pupils' "hidden voice" - in contrast to traditional surveys, which tend to ask more direct questions.

"A 13-year-old girl or boy isn't going to tell a teacher whether they're feeling popular or thinking about self harm, so getting reliable information is very difficult," he says.

Once a child has finished the questionnaire, the results are sent to STEER, the company behind AS Tracking, which compares the data with its psychological model, then flags students which need attention in its teacher dashboard.

"Our tool highlights those particular children who are struggling at this particular phase of their development and it points the teachers to how that child is thinking," says STEER co-founder Dr Jo Walker.

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Neil Woods led part of the Academies Enterprise Trust pilot of AS Tracking, in Tendring Technology College in Essex. He says that since introducing it the college has seen a 20% decrease in self harm.

"We've had a number of students where this has really significantly helped," he says.

"There is a mental health crisis, we know that. This tool is not going to solve it, but it's going to help us identify those students who may need the support."

The AS Tracking dashboard labels children red, amber or green according to their level of mental wellbeing. STEER, which provides training in the use of its tool, say this is necessary in order to make the complex data accessible for teachers.

However, technology experts warn that such rankings could be misused.

"With these types of technologies there is a concern that they are implemented for one reason and later used for other reasons," said Carly Kind, director of the Ada Lovelace Institute.

"There is the scope for mission creep, where somebody in a school says this would be a great tool to sort children into different classrooms, or decide which students should go on to university and which shouldn't."

AS Tracking costs a school with 1,200 pupils up to 25,500 a year. According to STEER's own figures, the psychological biases it tests for are linked to risks of self-harm, bullying and not coping with pressure in 82% of cases.

Once pupils have finished at school, they get their AS Tracking data in an app which they can use to see their own progress.

The National Education Union cautiously welcomed AS Tracking's growth.

"Exploring new ways for students to ask for help might be valuable, but aren't a substitute for giving teachers time to know their students and maintain supportive relationships," deputy general secretary Amanda Brown told Sky News.

Mr Wood, who also oversees art and music therapy at Tendring Technology College, agreed. "It's the wraparound interventions that you give to students that are important," he said.

"It's not just that we are looking at the data in one context, we are looking at their academic profile, we're looking at their pupil voice, we're looking at what parents are actually saying to us and AS Tracking is just another part of the puzzle."

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Artificial intelligence being used in schools to detect self-harm and bullying - Sky News

Arup's Tim Chapman points the way forward for people planning a career, which is likely to last at least 45 years, and through which they will encounter unfathomable change

Standard computer programming has been around for decades; what is new (ish) is the propensity for computers to teach themselves how to spot patterns and progressively improve.

Arups Tim Chapman points the way forward for people planning a career, which is likely to last at least 45 years, and through which they will encounter unfathomable change.

It is very easy to become fearful when reading press report after press report about the impact of artificial intelligence (AI) on our civilisation all the jobs to be lost and what will become of us? Or our children?

First, it is worthwhile being clear about what AI actually is. Definitions can all too easily become conflated with the latest scary film portraying robots with high intelligence and even human emotions great films like I Robot and Ex Machina are wonderful stories and do show what may ultimately happen but the level of technology is many decades away, if it ever happens.

What is more insidious is the progressive augmentation we get from ever more adept systems. Standard computer programming has been around for decades; what is new (ish) is the propensity for computers to teach themselves how to spot patterns and progressively improve.

And generally these algorithms are good for us, such as the ones that learn how to detect potentially cancerous moles on skin initially learning from the best doctors and thereby becoming better than any of them, then learning from historic photos with a precise diagnosis of which ones did actually turn cancerous later.

This is an application of machine learning or artificial intelligence in action the whizzy clever robots which can do anything are called artificial general intelligence (AGI).

So then what is intelligence? Einstein wrote, The true sign of intelligence is not knowledge but imagination, while Socrates wrote, I know that I am intelligent, because I know that I know nothing. It is unlikely that your HP laptop is anywhere near any thoughts that profound.

1.) Musical-rhythmic2.) Visual-spatial3.) Verbal-linguistic4.) Logical-mathematical understand principles of a causal system5.) Bodily kinaesthetic sports, dance, acting, making things6.) Interpersonal social skills7.) Intrapersonal self-reflection and aware8.) Naturalistic nurturing information to natural surroundings9.) Existential spiritual

Currently, AI is making some progress in only a small portion of these areas, fortunately. In the field of original composition, AI is making some progress in art and music writing, but mainly by averaging many prior human art.

Cloudpainter won the 2018 Robot Art Prize with a decidedly confused pastiche and the Portrait of Edmond Belamy was exhibited at Christies with an asking price of 7,000 to 9,000 it was made of an amalgamation of 15,000 portraits from the 14th to the 20th centuries so is far from original.

Every year the firm Gartner come up with its hype curve for new technologies plotting the progress of each from an innovation trigger through a peak of inflated expectations towards a trough of disillusionment, eventually into a slope of enlightenment and hopefully reaching a plateau of productivity.

Various AI technologies can be found throughout all of these zones, with AGI at the most undeveloped end.

It is worth putting AI into a context of world trends, which can combine to either thwart or reinforce existential threats so AI can be seen as either a saviour or a reinforcer for risks to humanitys future like global warming, resource depletion, destruction of our environment and deteriorating global order, alongside more usual threats like disease pandemics, which we thought we had cured but antibiotic resistance could allow to return.

Another interesting facet of this trend towards computer assisted process improvement and ever more expert systems is where does it leave the current human experts?

The professions derive their exalted position in society from the pact made at the time of the medieval guilds and it has been unchallenged until now.

Now various professions are being dumbed down by the invasion of expert systems, initially amplifying and improving expert opinions, but eventually supplanting them, apart from a small number of more complex cases.

This could easily lead to a reduction in status and salary for adherents to those professions. The recent 737 Max crashes illustrate the perils of uncontrolled trust in AI systems, but also show the zeal with which such systems are intruding into activities that we consider to be human controlled. Will truck and train drivers be needed in the long term?

AI systems can also disrupt industries in other ways by overturning standard business models. Hence Uber is the worlds biggest taxi company but owns no taxis.

Facebook is the worlds biggest media content provider but provides none of the content itself it is just a platform. Many industries are ripe for revolution in ways we cant yet imagine.

And these changes now happen quickly. It used to be that a disrupted industry had time to react to change but now it can occur in months.

This backdrop can be applied to any industry including that for infrastructure provision. In parallel, we are getting sharper about how we provide infrastructure nowadays. It is no longer the domain of nerdish engineers working in a vacuum plotting lines on maps with less consideration for the communities that will host it than they should have done.

We are much more aware of the special needs of the society for whom we provide infrastructure and which will pay for it though taxes or user charges.

We recognise that it is the outcome from the infrastructure that really matters rather than the asset themselves and we also know that the successful operation of assets is as least as noble an activity as designing new ones.

AI is intruding into all of these worlds too, and in some ways the expert systems are starting to obviate the need for high technical skills.

Equally data analytics on users of infrastructure are providing us with fascinating insights about how it can work and enable us to use these tools to design much better infrastructure that is ever more useful to the communities whose standard of living depends on its successful operation.

It is interesting to muse about whether there will be limits to the levels of intrusiveness that computers will be allowed to reach in our society. While they have the power to render many services quicker and thereby cheaper, what will happen to all the displaced humans?

Initially those that are at most risk of being displaced from the workforce are those with the lowest skills drivers being an obvious example what will the people who currently drive taxis and trucks do if that opening is no longer available to them will there be other jobs that allow them to support their families?

Presently, it seems that governments are becoming weaker and are less capable of taming the concerted global actions of the big tech organisations.

And the ambition of those large corporations to impose new technologies on us,making our lives potentially easier, but all the time minimising the tax bills that sustain our society and enable us to make our civilisation generous to everybody. Will governments eventually exert a higher level of control or will the big tech firms continue to run wild?

Before we get too worried it is worth reflecting on what computers are good for, and not so good at. We know that they are very very good at: Tasks/processes (if programmed well) Ordered memory (if designed well)

But not so good at: Curiosity Obscure disordered memory Radical rethinking Strategies Different situations Non-routine tasks

Hence an AI expert firm might thrive for two years, but would be incapable of dealing with the changes in our world, not least in the advance of technology.

It is worthwhile reflecting on the various levels at which AI might hit: Industry profound inexorable change Firm inexorable too, with winners and losers Person think of yourself or your children there will be winners and losers too; therefore we all need to make the right personal choices: staying ahead of the sorts of technology that could make us redundant. This makes it very difficult to plan for a 45-year career Society which depends on how nimble governments are and whether they stay ahead of the global tech firms?

So when AI finally hits the world of infrastructure creation and operation, it is fair to say that: Industry will become far more efficient and agile And also potentially more responsive to society And hopefully less impactful on society in terms of pollution, which can be more efficiently minimised Hopefully making construction cheaper to build lower user charges, so more affordable But blander too With fewer people employed And fewer experts needed so fewer peak salaries (some will still be needed though!).

A critical reflection is what happens to those who have no other place to go?

Author: Tim Chapman CEng FICE FIEI FREng, director and leader infrastructure London Group, Arup.

Arup's Tim Chapman points the way forward for people planning a career, which is likely to last at least 45 years, and through which they will encounter unfathomable change.It is very easy to become fearful when reading press report after press report about the impact of artificial intelligence (AI)...

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The impact on infrastructure once artificial intelligence shifts into top gear - Engineers Journal

When the Los Alamos researchers probed those inner workings of their algorithm, what they learned surprised them. The statistical feature the algorithm leaned on most heavily for its predictions was unrelated to the precursor events just before a laboratory quake. Rather, it was the variance a measure of how the signal fluctuates about the mean and it was broadcast throughout the stick-slip cycle, not just in the moments immediately before failure. The variance would start off small and then gradually climb during the run-up to a quake, presumably as the grains between the blocks increasingly jostled one another under the mounting shear stress. Just by knowing this variance, the algorithm could make a decent guess at when a slip would occur; information about precursor events helped refine those guesses.

The finding had big potential implications. For decades, would-be earthquake prognosticators had keyed in on foreshocks and other isolated seismic events. The Los Alamos result suggested that everyone had been looking in the wrong place that the key to prediction lay instead in the more subtle information broadcast during the relatively calm periods between the big seismic events.

To be sure, sliding blocks dont begin to capture the chemical, thermal and morphological complexity of true geological faults. To show that machine learning could predict real earthquakes, Johnson needed to test it out on a real fault. What better place to do that, he figured, than in the Pacific Northwest?

Most if not all of the places on Earth that can experience a magnitude 9 earthquake are subduction zones, where one tectonic plate dives beneath another. A subduction zone just east of Japan was responsible for the Tohoku earthquake and the subsequent tsunami that devastated the countrys coastline in 2011. One day, the Cascadia subduction zone, where the Juan de Fuca plate dives beneath the North American plate, will similarly devastate Puget Sound, Vancouver Island and the surrounding Pacific Northwest.

The Cascadia subduction zone stretches along roughly 1,000 kilometers of the Pacific coastline from Cape Mendocino in Northern California to Vancouver Island. The last time it breached, in January 1700, it begot a magnitude 9 temblor and a tsunami that reached the coast of Japan. Geological records suggest that throughout the Holocene, the fault has produced such megaquakes roughly once every half-millennium, give or take a few hundred years. Statistically speaking, the next big one is due any century now.

Thats one reason seismologists have paid such close attention to the regions slow slip earthquakes. The slow slips in the lower reaches of a subduction-zone fault are thought to transmit small amounts of stress to the brittle crust above, where fast, catastrophic quakes occur. With each slow slip in the Puget Sound-Vancouver Island area, the chances of a Pacific Northwest megaquake ratchet up ever so slightly. Indeed, a slow slip was observed in Japan in the month leading up to the Tohoku quake.

For Johnson, however, theres another reason to pay attention to slow slip earthquakes: They produce lots and lots of data. For comparison, there have been no major fast earthquakes on the stretch of fault between Puget Sound and Vancouver Island in the past 12 years. In the same time span, the fault has produced a dozen slow slips, each one recorded in a detailed seismic catalog.

That seismic catalog is the real-world counterpart to the acoustic recordings from Johnsons laboratory earthquake experiment. Just as they did with the acoustic recordings, Johnson and his co-workers chopped the seismic data into small segments, characterizing each segment with a suite of statistical features. They then fed that training data, along with information about the timing of past slow slip events, to their machine learning algorithm.

After being trained on data from 2007 to 2013, the algorithm was able to make predictions about slow slips that occurred between 2013 and 2018, based on the data logged in the months before each event. The key feature was the seismic energy, a quantity closely related to the variance of the acoustic signal in the laboratory experiments. Like the variance, the seismic energy climbed in a characteristic fashion in the run-up to each slow slip.

The Cascadia forecasts werent quite as accurate as the ones for laboratory quakes. The correlation coefficients characterizing how well the predictions fit observations were substantially lower in the new results than they were in the laboratory study. Still, the algorithm was able to predict all but one of the five slow slips that occurred between 2013 and 2018, pinpointing the start times, Johnson says, to within a matter of days. (A slow slip that occurred in August 2019 wasnt included in the study.)

For de Hoop, the big takeaway is that machine learning techniques have given us a corridor, an entry into searching in data to look for things that we have never identified or seen before. But he cautions that theres more work to be done. An important step has been taken an extremely important step. But it is like a tiny little step in the right direction.

The goal of earthquake forecasting has never been to predict slow slips. Rather, its to predict sudden, catastrophic quakes that pose danger to life and limb. For the machine learning approach, this presents a seeming paradox: The biggest earthquakes, the ones that seismologists would most like to be able to foretell, are also the rarest. How will a machine learning algorithm ever get enough training data to predict them with confidence?

The Los Alamos group is betting that their algorithms wont actually need to train on catastrophic earthquakes to predict them. Recent studies suggest that the seismic patterns before small earthquakes are statistically similar to those of their larger counterparts, and on any given day, dozens of small earthquakes may occur on a single fault. A computer trained on thousands of those small temblors might be versatile enough to predict the big ones. Machine learning algorithms might also be able to train on computer simulations of fast earthquakes that could one day serve as proxies for real data.

But even so, scientists will confront this sobering truth: Although the physical processes that drive a fault to the brink of an earthquake may be predictable, the actual triggering of a quake the growth of a small seismic disturbance into full-blown fault rupture is believed by most scientists to contain at least an element of randomness. Assuming thats so, no matter how well machines are trained, they may never be able to predict earthquakes as well as scientists predict other natural disasters.

We dont know what forecasting in regards to timing means yet, Johnson said. Would it be like a hurricane? No, I dont think so.

In the best-case scenario, predictions of big earthquakes will probably have time bounds of weeks, months or years. Such forecasts probably couldnt be used, say, to coordinate a mass evacuation on the eve of a temblor. But they could increase public preparedness, help public officials target their efforts to retrofit unsafe buildings, and otherwise mitigate hazards of catastrophic earthquakes.

Johnson sees that as a goal worth striving for. Ever the realist, however, he knows it will take time. Im not saying were going to predict earthquakes in my lifetime, he said, but were going to make a hell of a lot of progress.

This article was reprinted onWired.com.

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Artificial Intelligence Takes On Earthquake Prediction - Quanta Magazine

Companies have learned the hard way that their artificial intelligence tools have unforeseen outputs, like Amazons (AMZN) favoring mens resumes over womens or Ubers disabling the user accounts of transgender drivers. When not astutely overseen by human intelligence, deploying AI can often bend into an unseemly rainbow of discriminatory qualities like ageism, sexism, and racism. Thats because biases unnoticed in the input data can become amplified in the outputs.

Another underappreciated hazard is the potential for AI to cater to our established preferences. You can see that in apps that manage everything from sources of journalism to new music and prospective romance. Once an algorithm gets a sense of what you like, it delivers the tried and true, making the world around you more homogeneous than it might otherwise be without embedded artificial intelligence. Having your preferences catered to can sometimes be great. But it can also be debilitating in insidious ways, like in the search to find the right therapist.

As a psychiatrist who works on improving the design of tech-enabled health care services, I foresee how misapplied AI could create pitfalls for the growing number of online platforms that aim to make it easier for individuals seeking therapy to find a therapist.

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It is now possible to search through online listings that sort therapists by various factors. You can learn about the type of therapy a provider offers as well as his or her clinical specialization and educational background. You can sometimes see a photo of the clinician, or even watch a video interview to get a better sense of what she or he is all about.

Shared demographic features can be key factors in finding a therapist. Hearing about a few of my friends personal experiences, demographic matching was salient. One was willing to go only to a male person of color; another told me he would only feel comfortable seeking therapy with a woman or a gay man. Minority mental health care providers are demographically underrepresented in the profession, which can result in the therapeutic environment feeling like a foreign, unwelcoming space to many prospective patients.

Making health care and specifically mental health care culturally inclusive is extremely important. To get a sense of how much of an impact representation can have, look no further than the inspirational force of psychiatrist Jessica Clemons, also known by her Instagram handle as @askDrJess, who has done preeminent work flipping the script on mental health in communities of color and engaging underrepresented groups with mental health.

What often drives individuals searches for providers with shared demographic features is the belief that such providers will be able to understand them better and help them get better because the therapist shares their lived experiences. But that presumption may unintentionally set up a therapeutic environment built on a shaky foundation.

Heres what the available evidence shows happens in practice. Among racial and ethnic minorities, there tends to be a preference for providers who share their identity, or at least share a common status as a racial/ethnic minority. Although a meta-analysis showed that such individuals perceive their identity-matched providers to be more effective, measurable outcomes didnt match the perception. Ultimately, the treatment outcomes were about the same regardless of the therapists identity.

Its possible that studies like these systematically underreport hard-to-quantify factors that are part and parcel of psychotherapy, like helping an individual feel fundamentally understood and making ones personal struggles comprehensible to themselves in light of their prior experiences. And some might argue that a patients positive perception is, in fact, the best way to measure therapeutic success.

Yet a 2015 study demonstrated that individuals would knowingly seek less-effective therapy in exchange for sharing identity features with their therapist. At the very least, this shows that individuals are willing to make objective sacrifices in order to ensure that their identity is a central part of their therapy, even if that means at odds with what an economist would call rational decision-making that the therapy itself is less effective in addressing its defined purpose.

The overlooked danger in using artificial intelligence or other tools to prioritize identity features when selecting a mental health provider is that it could systematically amplify a potential confirmation bias for some individuals seeking therapy. In daily life, confirmation biases develop when individuals set up their environments often subconsciously to make new evidence confirm their preconceptions.

The challenges, for example, that a gay or black person faces often rightly justified through hard-earned if not traumatic life experiences may make it difficult to navigate social or professional interactions with others whom they believe cannot empathize or appreciate their perspectives or values. In turn, if that individual exclusively seeks a therapist with whom they readily identify, the experience can confirm and amplify the discrepancy they feel between their carefully pre-screened therapist and the rest of the outside world.

Offering the idea of establishing a relationship with a therapist who is outside a patients natural comfort zone may seem insensitive to patient autonomy. As a white man with reasonable knowledge of mental health care systems and the means to afford care, I dont face nearly any of the barriers to care that many others do. As a clinician, however, I have witnessed firsthand the unique value of therapeutic relationships that transcend demographics. When the visible identity differences melt into the background with time, the therapeutic relationship has the potential to gently challenge a patients preconceptions regarding trust, compassion, and clinical competence. Thus, the successful patient/provider relationship is more dependent on a multi-dimensional interpersonal connection than on one or a few demographic features.

Artificial intelligence and sorting algorithms invisibly embedded in mental health care navigation can have unintended consequences that become greater as technological innovation slips further into the patient-provider matching process. Therapists, entrepreneurs, and health care service designers must be collectively attuned to the potential problems posed by artificial intelligence when it is treated as a magical black box. Not paying attention to input features and catering to customer demands without foresight could systematically segregate health care provision and erect greater barriers in health care as we cluster into ever more homogenized groups. And because of the underrepresentation of minorities as mental health care providers, any homogenization of care could have the potential to significantly worsen access to care for many minority patients.

Addressing these concerns will require several steps. Solutions must be carried out on two levels: the individual provider and the health care system. Technology developers must exercise a great degree of caution in the use of automated identity matching between patient and mental health provider. For clinicians and clinical educators, its important to note that most individuals value a therapist with extensive cultural training and experience even more highly than simply sharing racial or ethnic identity with their provider. This makes patient-panel diversity and cultural competency training to be defining issues in the quality of clinical training and continuing professional education in the coming years.

Technology has the potential to readily improve individuals opportunities to seamlessly connect with high quality mental health care. However, treating artificial intelligence as if it were a black box that automatically produces an optimal result holds the potential to systematically undercut access to care when expert clinical judgement isnt closely tied to applying AI.

Scott Breitinger, M.D., is an instructor in psychiatry at the Mayo Clinic in Rochester, Minn.

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Artificial intelligence can complicate finding the right therapist - STAT

Google has reportedly achieved the famous Quantum Supremacy, as the Financial Times first reported on 20 September 2019. Indeed, the NASA/Google claim that our processor takes about 200 seconds to sample one instance of the quantum circuit 1 million times, a state-of-the-art supercomputer would require approximately 10,000 years to perform the equivalent task. This would mean indeed quantum supremacy, i.e. out-powering even the most powerful classical computer with a quantum computer for a computing task (for more explanations, see The Coming Quantum Computing Disruption, Artificial Intelligence and Geopolitics (1)).

The paper describing this achievement was, however, then removed from the NASA website, the initial publisher. We can find, of course, cached versions of the paper, for example here (Bing cache) and here (pdf on a google drive). Furthermore, Bing specified it cached the page in 2006, possibly deepening the mystery. As a result, the web is abuzz with discussions regarding the validity of the claim (e.g. Hacker News).

One way or another, this reminds us that a world with quantum computers is about to be born. All actors need to take this new future into account, in all its dimensions. This is even truer for those concerned with international security at large.

This article is the first of a new series that focuses on understanding the coming quantum-AI world. How will this future world look like? What will be the impacts on geopolitics and international security? When will these changes take place?

Previously, we highlighted how crucial it is to foresee the future quantum-AI world. First, imagining this world of tomorrow drives forward investments in quantum, thus the position in the race for quantum technologies. Second, and relatedly, displaying the right vision of the world of tomorrow will allow for readiness. In turn, that readiness will impact states relative power in the international system.

Thus, those countries that will lag behind may well pay a very high price in terms of independence, economic development and wealth, security and capability to protect themselves and their citizens from foreign aggression, etc. Companies that do not foresee the forthcoming quantum-AI world and do not embrace it adequately could similarly have to face a very high cost, become obsolete, fail and disappear.

Foreseeing the future quantum-AI world is thus a geopolitical and security imperative. Yet foreseeing the future quantum-AI world is also particularly difficult. We explain why in the first part of this article. We then present a framework to move forward with foresight in the case of the future quantum-AI, security and geopolitics. In the second part, we highlight another danger, the inability to think and imagine the world beyond its current structure. We suggest ways to avoid this failure of imagination. Finally, we present the building blocks upon which the future quantum-AI world is likely to be built. These blocks will be the starting point that will allow us to sketch the future quantum-AI world throughout the series.

The difficulty to imagine a Quantum-AI powered world results from the need to understand and foresee different layers of changes.

We need to foresee not one evolution, or a couple of dynamics and processes, but a myriad of them, as well as their interactions. Furthermore, those feedbacks will take place in and between different types of fields, and propagate differently. Indeed, quantum technologies and notably quantum computing and simulations, mixed with Artificial Intelligence (AI), or rather deep learning, will, first and foremost, be used by other sciences. As a result new discoveries and innovations will emerge in various areas. Furthermore, a resulting change in one science can be the starting point for a whole series of innovations in another scientific field. In turn, each of these evolutions will impact the world outside science in many ways. Meanwhile these consequences will feed into each other.

In other words, we must foresee quantum-AI-based changes in many different sciences, then the changes brought about by these innovations across areas and with different sequences of development.

Furthermore, as far as international security, warfare, geopolitics and governance are concerned, we cannot stop there. We also need to envision how these changes will impact all security-related areas, as well as governance and international relations.

We thus need to use a foresight approach that could look like a layer-cake model funnily enough a metaphor already used in international relations (George Modelski, Principles of World Politics, 1972; Paul James, Interview with George Modelski, 2017).

Applications and usages for quantum technologies and science are already being identified.

However, often, these are mainly the projection of already existing usages.

For example, some actors need supercomputers or High Performance Computing (HPC) systems (for an analysis of such needs in the case of AI/deep learning, see High Performance Computing Race and Power Artificial Intelligence, Computing Power and Geopolitics (3)). Thus, such actors start from their HPC needs and extend them to new, existing, or future quantum computing capacities. Airbus, for example, is using such an approach.

This is indeed a very important way to start imagining how quantum technologies could be used in the future. It is absolutely crucial to start being ready to use new computing languages, new types of algorithms, to start being acquainted with an entirely novel technology.

Yet, this is not enough.

The danger is to fail to envision surprises, be they totally novel possibilities or unexpected consequences. If we were solely extending the present, we could merely foresee a world that is quite similar to what it is nowadays. The new quantum-powered world could be faster, with some important improvements, but yet, it would be similar to what we know currently. In that case, the new quantum-AI world may well fall short of the paradigmatic revolution that is expected. Yet, it is also quite likely that what will emerge will finally be extremely new and very different. If we want to achieve actionable foresight then we must allow also for the very different to occur.

Past history can help us wondering about the types of changes we could be about to face.

Shall we see changes that are as crucial as had been, in the past, the progressive improvement of the determination of longitude at sea (e.g. The Galileo Project,Longitude at Sea)? Then, as we progressed in our capabilities to determine longitude at sea with various instruments and methodologies, societies moved from sailing only with reference to land to navigating across oceans. Societies that developed the new capabilities to navigate went from a restricted world to the capacity for expansion.

Meanwhile, maps fundamentally changed and improved with immense consequences on the modern-state system and international relations (Thongchai Winichakul,Siam Mapped: A History of the Geo-Body of a Nation, 1994; Helene Lavoix, The Power of Maps, 2012).

Shall we see a revolution as potent as what the use and spread of steamboats created in the past, notably the era known as gunboat diplomacy in the 19th century (e.g. Matthew McLin, Building Up Steam: Steamship Technology In 19th Century East Asian Colonial Warfare, 2012)?

That time, for example, meant the imposition by the West upon China of the(unequal)Treaty Port system (e.g. Albert Feuerwerker, The Foreign Presence in China, 1983, 128-207). It triggered an immense cascade of consequences through time and space, which still impacts us today (for a cursory summary and a first bibliography, see Helene Lavoix, From the Diaoyu Islands, with Warning, 2012). Indeed, for China, this time is known as the century of shame and humiliation and is part and parcel of its historically constructed worldview. It thus infuses its current decisions, including regarding the race for quantum technologies (for the theoretical underpinnings, see Helene Lavoix,Nationalism and genocide).

These are the types of very real and crucial questions to which, in terms of politics and geopolitics we must answer.

In the approach to the quantum future where we merely extend the present to integrate quantum technologies, the structure of the world does not change. For example, manufacturing planes could be done more quickly, at a lower cost, and the planes produced could be of better quality, maybe flying more quickly, maybe flying autonomously. But, fundamentally, planes will still be planes and not much will have changed. For a plane manufacturer, not developing quantum capabilities would most likely still be catastrophic compared to competition. Nonetheless, the likely changes on the world may not be fundamentally disruptive.

We tend to be locked into the world we know and can only push forward and imagine existing trends and their known and main drivers.

What the former past examples show is that we must think beyond our current world. We must be able to think todays and tomorrows version of crossing vast expanses of water, without seeing the land, of new representations of the world on new devices that will fundamentally alter polities and the international system. We need to be able to imagine future events similar to steamboats and consequent gunboat diplomacy, furthermore ran by barbarians, that will fundamentally disrupt our world.

In other words, we must make sure that we do not fall prey to a failure of imagination. Indeed, that error was identified as one of the major causes for 9/11 warning failure (The 9/11 Commission Report, pp. 339-348).

On the contrary, we must favour imagination. We need to succeed in thinking out of or beyond the structure of our known world.

To be able to do so, we shall first identify the building blocks upon which the quantum revolution is being currently built. We shall then investigate each block and look at the classical usage that is planned. We shall, however, not stop at first order effect, but also try envisioning second and third order impacts, including in terms of security, politics and geopolitics.

As we move through our building blocks we shall progress towards increasing levels of complexity. We shall try to imagine how some of these blocks are or could be combined. Finally, to try moving beyond the current structure of our world, we shall ask what if questions. We shall there suspend disbelief and favour imagination. These questions could lay the ground for future multi-disciplinary foresight work.

The corporate world and notably start-ups have started working upon the way they could create and sell application for quantum computing. In the meanwhile, they have adopted categorisation or classifications.

For example, D-Wave (a company focusing on a type of quantum computing called quantum annealing whilst most other develop gate-based quantum computers), identifies four major areas for its applications: optimization, machine learning, materials science and Monte Carlo simulations.

The start up Zapata Computing, specialised in creating quantum algorithms and developing software across quantum computing platforms, categorises its applications according to three areas: quantum chemistry, optimization and quantum machine learning, as shown on the table below:

Another startup QCware, identifies five types of use cases: chemistry simulations, optimization, machine learning, differential equations and Monte Carlo Methods.

Consulting companies, such as the Boston Consulting Group, focus on applications, but already segment them according to the sectors they use for their consulting business (PhilippGerbertandFrankRue, The Next Decade in Quantum Computingand How to Play, BCG, 15 November 2018). We thus have as users categories: High Tech, Industrial Goods, Chemistry and Pharma, Finance and Energy (see notably exhibit 9). This early categorisation, however, makes it also difficult to imagine other usages in other areas, while it does hardly consider feedbacks across industries and larger impacts on society, which will then, in turn, have consequences for all actors, including businesses.

If we synthesise these approaches, as well as others, finding out future and current application and use for quantum computing, as well as more generally quantum science, tends to follow two paths, that may then be combined.

First, and logically because we deal with new computing facilities, actors use types of algorithms as starting points and categories to envision future quantum computing applications. We thus have mainly quantum optimization algorithms and quantum machine learning. We find also simulations and notably Monte Carlo simulations/methods, as well as differential equations.

Because of Shors algorithm, quantum computing and cryptography should belong here (see The Coming Quantum Computing Disruption, Artificial Intelligence and Geopolitics 1). However, we shall also consider this section, as well as the related quantum communication field, notably because of their impacts on intelligence and counter-intelligence, as a complete layer, impacting all others (see also Quantum, AI, and Geopolitics (2): The Quantum Computing Battlefield and the Future).

Second, various scientific disciplines try to develop a quantum approach to their field and investigate if quantum mechanics can improve their scientific understanding. In that case, they benefit from the new quantum computing approaches, including the development of quantum algorithms.

We notably identified quantum chemistry and quantum new materials, quantum biology, as well as quantum physics, including quantum optics. Quantum biology, surprisingly dismissed sometimes as humbug, actually appears to be a potentially interesting scientific field, according, for example, to the very serious and scientifically recognised journal, the Royal Society Publishing Interface (Adriana Marais et al., The future of quantum biology, J R Soc Interface, 2018 Nov). Quantum sensing and metrology, a part of quantum information science (QIS) could be seen as belonging here.

With the next articles, we shall start investigating these building blocks for the future quantum-AI world.

Featured image: Image by alan9187 from Pixabay

Fairbank, John K., ed. 1983.The Cambridge History of China Vol.12: Republican China 1912-1949, Part 1. Cambridge: Cambridge University Press.

Feuerwerker, Albert, The Foreign Presence in China, in Fairbank, ed. 1983.

Gerbert, PhilippandFrankRue, The Next Decade in Quantum Computingand How to Play, BCG, 15 November 2018.

James, Paul, Interview with George Modelski, in Manfred B. Steger,Paul James, Globalization: The Career of a Concept, Routledge,Oct 2, 2017.

Lavoix, Helene, The Power of Maps, The Red (Team) Analysis Society, 2012.

Lavoix, Helene, From the Diaoyu Islands, with Warning, The Red (Team) Analysis Society, 2012.

Lavoix, Helene,Nationalism and genocide: the construction of nation-ness, authority, and opposition the case of Cambodia (1861-1979) PhD Thesis School of Oriental and African Studies (University of London), 2005. Access and download throughthe British Library Ethos.

Marais, Adriana, Betony Adams,Andrew K. Ringsmuth,Marco Ferretti,J. Michael Gruber,Ruud Hendrikx,Maria Schuld,1Samuel L. Smith,Ilya Sinayskiy,Tjaart P. J. Krger,Francesco Petruccione, andRienk van Grondelle, The future of quantum biology, J R Soc Interface, 2018 Nov; 15(148): 20180640.Published online 2018 Nov 14,doi:10.1098/rsif.2018.0640 PMCID: PMC6283985 PMID: 30429265.

McLin, Matthew, Building Up Steam: Steamship Technology In 19th Century East Asian Colonial Warfare, Masters Thesis, Florida University, 2012.

Modelski, George, Principles of World Politics, Free Press, 1972.

The 9/11 Commission Report.

Winichakul, Thongchai,Siam Mapped: A History of the Geo-Body of a Nation, Chiang Mai: Silkworm Books, 1994.

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Foreseeing the Future Quantum-Artificial Intelligence World and Geopolitics - The Red (Team) Analysis Society

Event It's barely a week till we kick open the doors at MCubed, but theres still time for you to join us and learn from a lineup of speakers whove all put the theory behind machine learning and artificial into practice.

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Likewise, well help steer you away from pitfalls you might not even have considered yet, whether its who owns the IP your systems generate, or ensuring that your pricing algorithms dont end up being accused of collusion.

These will be bookended by our superb keynote speakers, Facebooks Sebastian Riedel and veteran technologist Lorien Pratt.

And you can get even deeper, with places available on our highly practical workshops covering: developing and deploying neural nets; developing with TensorFlow 2; and getting machine learning into production using containers and DevOps.

This will all be taking place at the QE II Conference Center, London, from September 30 to October 2, and you can bag your spot right now - by heading over to the MCubed website.

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Where can you learn the limits and abilities of artificial intelligence? Try Westminster - The Register

By Our Correspondent

Zuzanna Kosowska-Stamirowska writes how data acquisition and algorithm design need to be defined - or redefined - for the existing market players to make the AI revolution work.

The logistics industry is at the brink of an artificial intelligence (AI) revolution. This sounds like a bold statement for an industry which is currently taking its first steps into digitisation, with close to half of air freight still being processed through paper waybills, shipping agents still communicating with carriers by phone and email, data entered into systems by operators with hours or days of delay, and serious operation planning often still being done in a spreadsheet. But the AI revolution is coming to logistics quickly, perhaps much sooner than expected - and if it does not come from within the industry, then it is likely to come from outside.

There are other similarly complex industries, such as the energy market, which also include network-based operations with separated supply and demand, and which are already deep into the AI revolution. So, we should rather ask: why is AI only reaching the logistics industry now? We can hazard the obvious answer: because, up to now, this was a traditional industry, with operation planning tuned to the capacity of human operators, and there was simply not enough external pressure to drive an external AI revolution. Until recently, the large players of the New Economy who might have had a skin in the game, Amazon in the West, and Alibaba group in the East, relied on more established logistics actors to grow and scale their e-commerce and distribution operations. This resulted in a complicated provider-client relationship between the incumbents and the new players, with little will to enter into direct competition. The lack of appropriate AI methods for the challenges in logistics didnt help to speed up the process.

In the B2C realm, a new standard of service and of expectations has been irrevocably set by the likes of Amazon and Uber, featuring: predictable waiting times for delivery, high service availability, online quotes, and improved visibility of the delivery process. Indeed, all of these aspects are just as pertinent in supply chain planning as they are in consumer operations. It is equally clear that the right answers come through a combination of demand-driven planning, e-platforms, and AI-powered predictive analytics.

The only question is: who will bring the new approach into the industry? Will the current air and sea carriers be those best equipped to use data to optimise and tune their operations? Will it be freight forwarders who take advantage of their existing B2B client relationships to bring them into a new digital era? Or will it be Amazon, whose 230+ billion USD revenue (2018) already tops the combined revenue of all of the major world sea freight companies, who will take a shot at redefining the logistics industry its own way? This question is becoming ever more real. A few years back, we could muse futuristically who will be launching the first commercial drone-based delivery service, Amazon, Uber, or DB Schenker? Today, we may instead ask ourselves if our next years Christmas gift will be flying to us with FedEx from Memphis, with Amazon Air from their hub in Kentucky, or perhaps with "Alibaba Air" from their new hub in Liege. The new players in the field have a clear advantage in terms of their potential for supply chain consolidation and data-driven operations planning. But, the market is not theirs yet - and the next move in the game is with the established actors in the logistics playing field.

For any supply chain actor launching into the AI revolution, the key building blocks to take into account are data acquisition and algorithm design. Both of these elements need to be defined - or redefined - for the existing market players to make the AI revolution work. Every case calls for a different, tailor-made process. Nonetheless, there are some sure ingredients of the right AI approach which repeat as a leitmotif.

First, it always pays to act globally. This is a guideline which applies at all stages of an AI rollout, to favour global data processing on the entire logistics network over pointwise operation planning. From a pure business angle, the costs of deploying an AI platform do not depend much on the scale of operations, while the benefits are incomparably higher at a larger scale. But in fact, there is also a more profound engineering explanation for such an approach. Indeed, in AI methods, performing global forecasting of demand and resource planning is simply much easier to achieve than local. New forecasting methods succeed in predicting demand for a service (such as air freight) at a hub almost perfectly if given enough data to churn - from multiple hubs, not just the single hub concerned. In these methods, machines both significantly exceed human performance, and process more data than a human operator could ever be reasonably expected to process or even digest.

The second ingredient is to always rely on your own data, while working with other actors. This is not to say that current standardisation efforts or industry cooperation in terms of data collection and sharing are not crucial for the sector. Nevertheless, collaborative efforts may come too late, and the stake in the game is too high to lose due to inertia in the sector.

In terms of data acquisition, it seems likewise prudent not to rely too much on local data integration efforts, which simply take too much manpower to put into place. It is advisable to squeeze as much value as possible from the data which is already available and move quickly on the market. As an illustrative example, the benchmark approach can be the way Google Maps addresses trip planning where all the crucial traffic data is transmitted automatically by GPS sensors of users mobile devices. Incidentally, for freight logistics as well, predictive intelligence based on IoT sensor data may be the path to follow for data acquisition. And, just as for Google Maps, sensor data acquired in such a solution benefits all clients and users of the service, providing them with accurate forecasts on demand, congestion, and delays. Not every shipment needs to have a sensor on it: there exists an optimal level of coverage of shipments by IoT devices, sufficient to ensure supply chain predictability. Guiding IoT investment, and subsequent steps such as IoT data segmentation and analysis, come as a natural part of the move towards intelligent logistics platforms.

All in all, it is up to every supply chain actor to take intelligent decisions in the face of uncertainty, to ensure robust, on-time operations, and minimise the effects of disruptions - a key point to ensure a robust supply chain for all.

We created NavAlgo with the mission of helping key players in the logistics industry to embark swiftly on the AI revolution. Our objective is to assist our clients to adapt their service offer and their data-driven operations planning to meet the needs of the logistics market of the future. We combine expertise and viewpoints of active researchers in AI and forecasting, recruited from research institutes such as Google Brain, industry practitioners for real-world logistics operations, and experts in business process and pricing design. We take a holistic approach to problems in logistics, linking forecasting with dynamic optimisation in conditions of uncertainty. In this way, we put forward a service which provides the most value to the end-client, with operations adapting to meet her expectations in an efficient way. This means meeting demand where it appears and providing enhanced supply chain visibility and predictability.

The writer is the CEO of NavAlgo, an information technology company that build integrated solutions for intelligent resource allocation in logistics and creates cutting-edge algorithms to navigate in a sea of data.

This story was originally published in Logistics Update Africa's September - October 2019 issue.

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FROM MAGAZINE: Bringing Artificial Intelligence into the supply chain: Who and How? | Technology - Logistics Update Africa