The AI sector is booming. Thanks to several leaps that have been made, we are closer than ever before to developing an AI that acts and reacts as a real human would do. Opportunities in this sector are flourishing, and there is always a way for you to get involved.

Photo by Annie Spratt.

Employees: If you are searching for a job in the tech sector, one of the most rewarding you could find is working with AI. It is a mistake to assume that all AI development is focussed on developing android technologies. There are many other applications for AI and each one needs experts at the helm to help bring it to fruition.

Whether you are a graduate, or you are looking for a change in careers, there is always a job opening that you could look into. Even if you dont have a background in this tech, there are many other ways you could get involved, whether you are working on an AIs cognitive abilities or even just testing out the product. Whatever your background and skillset might be, there is always a way for you to get involved.

Investors: AI development is incredibly costly. While many of the smaller developers may have a great idea that could be world-changing if they bring it to fruition. However, they often lack the finances to be able to do so. This is where investors can come in.

Investors like Tej Kohli, James Wise, or Jonathan Goodwin may have little expertise in these areas from their own personal experience, but they know how to recognise a viable idea when presented with one. Whether you are looking to get into venture investment yourself or you are a tech company looking for financial backing, their activities should give you some idea about the paths you need to follow.

Photo, Bence Boros.

Consumers: The world of AI isnt just open to investors and tech gurus. There is now a vast range of AI-driven tech emerging onto the market. You, as a consumer, get to be an instrumental part of driving this new tech forward as it means that the developers gain some insight into what features are popular and which arent.

Just look at the boom in home assistants that has erupted in the past few years. We are now able to live in fully functioning smart homes with music playing and lights turning off with a simple voice command. By exploring what AI has to offer through the role of the consumer, this all feeds back to the developers and helps them create the next generation of products.

No matter how interested you are in this sector, there is always going to be something you can pursue that will help to develop AI overall. This is an incredibly exciting era to live in, and AI is just one of the pieces of tech that could transform the world as we know it. Take a look at some of the roles and opportunities and see where you could jump in today.

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What Opportunities are Appearing Thanks to AI, Artificial Intelligence? - We Heart

ROCHESTER, Minn. - Artificial intelligence has a vital role in helping researchers in their efforts to fight COVID-19 and is an important tool in the work being done at Mayo Clinic.

Dr. Andrew Badley is an infectious diseases specialist and leads Mayo Clinics COVID-19 Research Task Force. He explains thatthey created a real-time tracking platform to measure the rate of positive cases throughout all counties in Minnesota.

"When we did that, we noticed that there was an outlier which occurred in Martin County. The rate of a positive test in Martin County was approaching ten percent, whereas the rate of positive testing for most of the other counties was in the neighborhood of one or two percent. Based on that, we said were probably not doing enough testing in Martin County. We redeployed tests to that area. Weve deployed personal protective equipment to the healthcare workers in that area who were doing the tests. Quite rapidly we investigated, we identified a significant number of additional cases. After we identified those cases, we counseled on self-quarantining and therapy as indicated. And wed like to think that doing that activity has helped to prevent new transmission," said Dr. Badley.

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Mayo Clinic is using artificial intelligence in its COVID-19 research - KIMT 3

The COVID-19 outbreak has highlighted the importance of working on public health and technology together in order to fight the crisis. Countries across the world are opting for different measures where several technologies are at play to tap the positive COVID-19 cases to stop the further spread of the virus.

China was the first country to report COVID-19 cases and is now witnessing the return of normalcy, but it also had to resort to technology to contain the spread. China used technologies such as smart imaging, drones and mobile apps totrace virus-carrying individuals.

The US and Europe, however, took a slightly different approach, using data derived via artificial intelligence to stop the spread of the virus. One such data provider is US-based Mobilewall, which serves countries with data to serve public health.

In an interview with Sputnik,Anind Datta, the CEO and chairman ofMobilewall, a consumer intelligence platform that is working with US task forces and other municipalities to fight the coronavirus, reflects on the importance of the use of artificial intelligence technologies to deal with the present-day crisis, especially in highly densely populated regions like South Asia.

Question: Where has Mobilewall successfully carried out data distribution?

Anind Datta:Mobilewall data is being used by health services organizations and governmental entities around the world to better predict the spread of the Novel Coronavirus at both the macro (city/county/state/country) and micro (predicting patients at a hospital) level. Mobilewall is working with various businesses and municipalities, providing data around individual mobility that acts as a proxy for social distancing. We can provide both a social isolation score and separate data attributes, features that can be used to build a custom score. Such data includes individual mobility metrics (indicating the daily distance traveled and unique locations), cluster identification (gatherings of a high number of devices) and individual device data at both the micro and macro levels. These are all foundational inputs that can be used in COVID-19 prediction models.

Question: In a country where a huge population resides in rural areas, how can AI be implemented?

Anind Datta:The purpose ofAI is to support decision makingby revealing patterns that emerge from large amounts of data. AI is particularly useful in scenarios where (a) data can be collected at a scale allowing reliable patterns to emerge, and (b) where manual efforts to both collect and analyse data do not work well.

In remote rural areas, manual data collection is challenging, and even if possible, such data is reliability-challenged due to the social barriers against honest disclosures of questions perceived as personal. In the current COVID-19 crisis, where data collection involves gathering information about personal habits and symptoms related to infection, these impediments only increase. Yet, a lot of this information can be gathered from behaviour exhibited on mobile phones, which have spread well into India's rural areas. Mobile data, accumulated at a scale, can allow for inferences to be made to help critical decision-making both in urban and rural areas.

Sputnik: Please, describe the ways in which AI and data can be used to battle COVID-19.

Anind Datta:In the context of COVID-19, data and AI technologies are being used in new ways, particularly in countries that adopt a scientific approach to public health. Data scientists are creating machine learning models to predict infection and mortality rates and to determine resource needs and allocation based on these predictions.

AI can be used to power two key tasks of pandemic mitigation: infection tracking and infection spread prediction. If done correctly, AI can help uncover three foundational pieces of information, crucial to tracking and predicting the spread: measuring social isolation by observing individual mobility, identifying clusters of more than a certain number of individuals and identifying the corresponding locations; and risk assessment of individuals and locations, at scale, by understanding the movement of infected individuals.

Question: Do you have some suggestion for the government regarding use of AI in slums and high density population?

Anind Datta:AI is particularly suited for analysing large amounts of data collected via machines. In slums and other high density areas, in context of the COVID-19 crisis, it is difficult to both maintain and track social distancing. For this reason, these regions can be triggers of infection waves that could provide deadly for the entire country. AI offers a mechanism to both collect and track behavioural signals from this area, which can then inform early-warning and alert systems that can drive tactical pandemic management activities.

AI, particularly,big data and machine learning techniquescan be used to identify the infection risk of individuals, which can then be projected to those individuals and others in the geographic locations they have visited. Data scientists are creating models to track the spread of the virus and to determine resource needs and allocation based on the prediction of hard-hit areas. AI is an enabler; it identifies patterns and provides insights at speeds well beyond what humans can do manually.

But, the key to the successful use of AI relies on the data that is being fed into the models. If this data is inaccurate or lacks scale the ability of the model to predict outcomes will be impacted in a negative way. Data can be obtained in various ways, either by requesting information directly from individuals (such as what populous countries is attempting to do with the Arogya Setu app or by seeking data from other available sources.

Question: Government's have been advocating app's which is also a mobile platform to fight against COVID-19. How useful is app in terms of contact tracing?

Anindya Datta:Arogya Setu app is a worthy effort and could serve as a useful consumer tool to minimise risky behaviour and receive current COVID-19 information. However, it is important to understand that the app by itself is simply a front end to information delivery. The effectiveness of the app is only as good as the information it has access to, but the app itself is not producing that information.

The quality of the risk information and therefore, the usefulness of the app, depend on a number of variables outside of the control of the app, including the magnitude of infection detection, which depends on testing. It is easy to see that less the testing, lower the value of the information disseminated via the app. What also matters is the risk models that are being used to build risk scores for geographies and sub-geographies. If the risk models are ineffective, even with adequate testing, the information delivered will be of little value.

In South Asia, where social stigma still plays a key part in social interaction, one might question the likelihood of truthful disclosures at scale.

Another, perhaps more reliable option, is to use other available data sources that can model the activities of the population at scale. In many cases location data and behavioural data can be used as inputs to COVID-19 predictive models.

Question: Certain groups have been opposing the medics. Can AI help medics find ways to track them without going to the location?

Anind Datta:Yes, location data of these groups can help doctors to track them. Location-based data can be used to track individual mobility without in-person engagement. Depending on the source of the data, it is also possible to use this data to communicate risk of infection in an anonymous manner using digital identification or communication through mobile devices.

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Artificial Intelligence breaks barriers where policymakers may go wrong - The Nation

New DCU led research into the accuracy of artificial intelligence when it comes to reading emotions on our faces has shown that it still lags behind human observers when it comes to being able to tell whether were happy or sad. The difference was particularly pronounced when it came to spontaneous displays of emotion.

The recently published study, A performance comparison of eight commercially available automatic classifiers for facial affect recognition, looked at eight out of the box automatic classifiers for facial affect recognition (artificial intelligence that can identify human emotions on faces) and compared their emotion recognition performance to that of human observers.

It found that the human recognition accuracy of emotions was 72% whereas among the artificial intelligence tested, the researchers observed a large variance in recognition accuracy, ranging from 48% to 62%.

The work was conducted by Dr. Damien Dupr from Dublin City Universitys Business School, Dr. Eva Krumhuber from the Department of Experimental Psychology at UCL, Dr. Dennis Kster from the Cognitive Systems Lab, University of Bremenand Dr. Gary J. McKeown from the Department of Psychology at Queens University Belfast.

Key data points

How the study was done

Two well-known dynamic facial expression databases were chosen: BU-4DFE from Binghamton University in New York and the other from The University of Texas in Dallas.

Both are annotated in terms of emotion categories, and contain either posed or spontaneous facial expressions. All of the examined expressions were dynamic to reflect the realistic nature of human facial behavior.

To evaluate the accuracy of emotion recognition, the study compared the performance achieved by human judges with those of eight commercially available automatic classifiers.

Dr. Damien Dupr said

AI systems claiming to recognise humans emotions from their facial expressions are now very easy to develop. However, most of them are based on inconclusive scientific evidence that people are expressing emotions in the same way.

For these systems, human emotions come down to only six basic emotions, but they do not cope well with blended emotions.

Companies using such systems need to be aware that the results obtained are not a measure of the emotion felt, but merely a measure of how much ones face matches with a face supposed to correspond to one of these six emotions."

Co-author Dr. Eva Krumhuber from UCL added

AI has come a long way in identifying peoples facial expressions, but our research suggests that there is still room for improvement in recognising genuine human emotions.

Dr. Krumhuber recently led a separate study published in Emotion (also involving Dr. Kster) comparing human vs. machine recognition across fourteen different databases of dynamic facial expressions.

Researchers

Dr. Damien Dupr - Business School, Dublin City University

Dr. Eva Krumhuber - Department of Experimental Psychology, UCL

Dr. Dennis Kster - Cognitive Systems Lab, University of Bremen

Dr. Gary J. McKeown - Department of Psychology, Queens University Belfast

Photo byAndrea PiacquadiofromPexels

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New research shows Artificial Intelligence still lags behind humans when it comes to recognising emotions - Dublin City University

BRITAINS healthcare crisis has been catapulted centre stage recently as our beloved NHS warriors battle Covid-19 deprived of personal protective equipment.

Yet these indispensable heroes have long toiled exhaustinghours, struggling under a lethal concoction of heightened demand and a lack of resources,a Brexodus of staff and the coronavirus pandemic is just icing on the crumbling cake.

Vacancies are widespread. In social care, it is estimated shortages have spiralled to 122,000.

Thousands of retirees have flocked to the NHS frontlines these past few weeks, but once Britainreturns to some degree of normality, the post-Brexit immigration plan swoops in to exacerbate the vacancies once more.

Migrant healthcare staff will face exorbitant visa fees as soon as December while care workers flatly wont even qualify for a skilled worker visa.

However, the Home Office has designed a rather unorthodox and arguably unscrupulous alternative: to replace the grit and graft of flesh-and-blood migrant staff with artificial intelligence and automated robots.

Already the initiative has swallowed 284million allinall, but the government will need to pluck some more golden leaves from its magic money treeif it is to realistically transform sectors that are reliant on EU labour with automation in nine months time.

Still, the practicality of this mission when the Home Office is notorious at underdelivering and delaying projects is one thing when there is a hot debate over whether care robotsshould be wheeled in at all.

The inclusion of technology in healthcare is often framed as a step towards depravity: it invokes a consensus that society inches towards a dystopian nightmare where humans become enslaved to sentient androids, 15m jobs become sacrificed at the altar of an AI-modified world and military killer machines surpass human intelligence to bring a nuclear winter to the human race.

Stephen Hawking himself did warn us of this possibilityas have Google employees who walked out in defiance of AI warfare.

Still, job losses seem quite inevitable, but should this be an acceptable consequence of progress?

Labour MPYvette Cooperargued that a technological revolution could further entrench the stark inequalities that already exist in Britain and make extreme poverty a permanent part of our social fabric.

And she isnt wrong: Japan, at the core of technological enlightenment, has seen automation overthrowmultiple industries with robot-run hotels, restaurants and conveyor belts of food being common.

Yet others, tech giants and their allies of dreamers, envision a post-work utopia where tech bridges societies into a new world of fullyautomated luxury communismand where its gains are shared equally by all.

On this note, it is ironic that Covid-19 has pressed the Home Office to make a dramatic U-turn from its submissive acceptance of job losses.

Commiting to pay 80 per centof workers wages who are most likely to become affected by automation in the next 20 years might seem like a change of heart, but sceptics might best believe that the infrastructure for automation just simply isnt ready yet and the cogs are still needed to keep the economy oiled up until this point.

In terms of healthcare, however, there are additional concerns.

AI still bitterly lacks the empathy required for the job while algorithms are shown to absorb the darkest depths of human biases.

AI systems can only look at the world through the peripheral granted to it by its makers who, by and large, are mostly male and white.

The result has seen recognition software repeatedly misinterpret facial expressions and body language on the assumption that everyone expresses themselves in the same way as Westerners while AI favours men over women in job interviews and even prefers European-American names over African-American ones.

Not that the government pays much attention to this acute factor: its very own visa algorithm has been found to discriminate against applicants of a certain nationality.

At a very basic level, one would expect care robotsto be equipped to administer some form of care.

Yet humanoids lack the intellectual problem-solving and altruism needed to adhere to physically demanding and emotionally intuitive surroundings.

At best, they can dance, entertain, push a tray of food and deliver medication to a specified destination.

But they are defunct of tactile touch. It cannot brush hair, dry tears or offer a hand to hold with comforting words in the darkest of days.

It cannot compute the nuances of human emotion and speech. Social care was ranked as one of the least automatable jobs of all in only 2016 as a result, but others just flatly find care robotsto camp in the category of undignified.

Only 26 per centof respondents to a survey said they would feel comfortable being hoisted and attended to by a robot when in care or a hospital, and many understandably have concerns around camera-fitted and potentially hackable devices in the rise of spy campornography.

However, the coronavirus pandemic may have considerably shaken the narrative and has, by twist of fate in the governments favour, propelled the case for AI in British healthcare forward.

Technology has undoubtedly played a vital role in this unparalleled era of segregation; FaceTiming loved ones, YouTube yoga classes, Skype work conferences and live-streamed concerts and theatre shows have kept Britons indoors while still relatively connected and entertained as before.

Yet even further afield, AI has become pivotal in delaying the spread of Covid-19.

In one hospital at the heart of the outbreak in Wuhan, China, robots outnumbered doctors as they patrolled the corridors, disinfected areas and monitored patients temperature and overall wellbeing.

The CEO behind this remarkable technology argued thatrobots do not carry disease, and robots can be easily disinfected.

Other countries, such as Singapore, Iran and Israel, have resorted to far more draconian invasions on civil liberties through the use of tech.

Yetspymobile tracking apps and ramped-up surveillance haveproved paramount in curbing the death toll and a similarly designed app by the NHS may be coming to Britain in the next few weeks.

Even so, care robots overseas appear quite revolutionary.

Consider Pepper, a humanoid bot, that is able to entertain residents with knitting and exercise classes in care homes and help the staff with mundane tasks.

The therapeutic cuddly seal, Paro, has been proven to soothe Alzheimers sufferers.

Kirobo by Toyota similarly comforts childless adults; RoBear can physically lift patients from wheelchairs and Leka can break through barriers to communicate with autistic children.

Already the NHS uses digital aides which can outperform human hands and eyes in intricate surgeries and when detecting breast cancer and the early onset of Alzehimers disease.

Evidently, tech can be a force for good if executed right. The University of Oxford, McKinsey Global Institute, PwC and Shift Commission predict that although millions of jobs will fall victim to automation, social and healthcare will emerge largely unscathed.

The NHS will still be dominated by human staff, yet tech could vastly alleviate doctors attendance to paperwork by 5.7m hours, generating a saving of13 billion.

Similarly, social care could save 6bn according to surgeonLord Darzi.

However, the biggest battle for tech remains in public confidence; confidence that has waned in the government as it arbitrarily stifles migration while the frail become collateral damage.

Tech wont be able to slice through social inequalities for as long as overzealous benefit assessors penalise disabled and vulnerable people for making improvements in their lives.

This move towards automation risks exacerbating the wealth and class division system in Britain and appears little more than another hostile political ploy to warrant the governments anti-migrant agenda.

Olivia Bridge is a political correspondent for the Immigration Advice Service,an organisation of Britainand Irelandimmigration lawyers.

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The ethics of artificial intelligence and automation amid a global pandemic - Morning Star Online

The disruptive technologies of this era have brought about a term called programmable economy, created by Gartner Inc that describes the all-new smart economy which is a result of technological innovations. It is the way now goods and services are created and consumed that has enabled diverse ways of exchanging monetary and non-monetary values. The traditional ways, which appear very inefficient and non-optimal, are making it difficult for companies to get a position in this competitive world. Time-to-market has become crucial and a delay in product or service release leads to loss of money and reputation.

This wave of Millennials and now Gen Z workforce is all about passion economy which enables them to pursue what they love and make money out of it. The idea of working from home, flexible hours and new business models to support this is inundating the workspace and the job arena. This change calls for an evolution in the way recruiting is done, with the advent of artificial intelligence in this space.

Blockchain technology is an emerging technology being adopted by forward-looking companies which is all about shifting from a centralized to a decentralized, transparent, and safe way of managing data. Using this technology, the activities of all the stakeholders of a project are supported by Smart Contracts, while the adoption of sophisticated methods of Artificial Intelligence helps the stakeholders to make business-critical decisions.

In this context, working nomads, who travel and still keep in touch with their customers, is very alluring but also requires safety measures and regulation. Sensitrust aims to be that bridge between customers and professionals to define a new ecosystem of safe interactions by exploiting the peculiarities of Blockchain, Smart Contracts, and Artificial Intelligence technologies.

Blockchain technology is also referred to as DLT (Distributed Ledger Technology) and is a means to share digital assets whose integrity is preserved by maintaining a transactional ledger of all changes happening to the asset. This revolutionary technology allows a scalable and risk-free system for several uses.

How will it be if you can get the right kind of data, which is always up to date, which matches professionals to customers and gives the most appropriate advice by filtering from a large amount of data?

Instead of rummaging through a wide array of profiles, many of which are of no use to you, you can actually get a selected few which are an ideal match for your requirements. Imagine how much time you would save and also make a risk-free selection by eliminating wrong profiles.

This is where the AI technology, adopted by Sensitrust, comes in with its predictive engine. It acts like a human expert who has accumulated a huge experience analyzing historical data, collected organically in the platform, to predict the outcome of newly occurring situations.

The predictive engine of Sensitrust is capable of learning from mistakes automatically in a transparent way using deep neural networks and many other models. The many ways it helps customers and professionals are the following:

The Sensitrust native token (SETS token) will be used to access all such services at a discounted rate.

This plethora of capabilities provided by Sensitrust is backed by a team of highly informed technical wizards who make use of the latest and most sophisticated AI and Machine Learning approaches, including:

Sensitrust is a platform which helps in managing data and artefacts used for carrying out projects, by means of Smart Contracts, throughout all the phases of a project which is developed using this platform. The many applications of Sensitrust can be found in the IT industry for hiring quality professionals, in the banking domain by replacing traditional operations with Blockchain-based solutions, and also in the Academy, for the identification of expert reviewers as well as of an international team for the implementation of research projects.

Buy SETS for a price of 0.05.

Token Sale for Sensitrust is Live at https://www.sensitrust.io/

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Riding the wave of artificial intelligence with Sensitrust - Blockmanity

When you're fighting a pandemic, almost nothing matters more than speed. A little-known band of doctors and hi-tech wizards say they were able to find the vital speed needed to attack the coronavirus: the computing power of artificial intelligence. They call their new weapon "outbreak science." It could change the way we fight another contagion. Already it has led to calls for an overhaul of how the federal government does things. But first, we'll take you inside BlueDot, a small Canadian company with an algorithm that scours the world for outbreaks of infectious disease. It's a digital early warning system, and it was among the first to raise alarms about this lethal outbreak.

It was New Year's Eve when BlueDot's computer spat out an alert: a Chinese business paper had just reported 27 cases of a mysterious flu-like disease in Wuhan, a city of 11 million. The signs were ominous. Seven people were already in hospitals.

Almost all the cases came from the city's sprawling market, where live animals are packed in cages and slaughtered on-site. Medical detectives are now investigating if this is where the epidemic began, when the virus made the leap from animals to us.

Half a world away on the Toronto waterfront, BlueDot's founder and CEO, Dr. Kamran Khan, was on his way to work. An infectious disease physician, he had seen another coronavirus in 2003 SARS kill three colleagues. When we spoke with him remotely he told us this outbreak had him worried.

Dr. Kamran Khan: We did not know that this would become the next pandemic. But we did know that there were echoes of the SARS outbreak, and it was something that we really should be paying attention to.

COVID-19 soon got the world's attention. BlueDot's Toronto staff now works from home, except for Dr. Khan. But in December, the office kicked into high-gear as they rushed to verify the alert.

Chinese officials were secretive about what was happening. But BlueDot's computer doesn't rely on official statements. Their algorithm was already churning through data, including medical bulletins, even livestock reports, to predict where the virus would go next.

It was also scanning the ticket data from 4,000 airports.

BlueDot wasn't just tracking flights, but calculating the cities at greatest risk. On December 31, there were more than 800,000 travellers leaving Wuhan, some likely carrying the disease.

Dr. Kamran Khan: So these yellow lines reflect the nonstop flights going out of Wuhan. And then the blue circles reflect the final destinations of travelers. The larger the circle, the larger number of travelers who are going to that location. These were many of the first cities that actually received cases of COVID-19 as it spread out of mainland China.

Bill Whitaker: You can do that in a matter of seconds?

Dr. Kamran Khan: We can analyze and visualize all this information across the globe in just a few seconds.

The virus wasn't just spreading to east Asia. Thousands of travelers were heading to the United States too.

Dr. Kamran Khan: Most of the travel came into California and San Francisco and Los Angeles. Uh, also, into New York City. And we analyzed that way back on December 31. Our surveillance system that picked up the outbreak of Wuhan automatically talks to the system that is looking at how travelers might go to various airports around Wuhan.

Bill Whitaker: So when you see that map, you don't just see flight patterns?

Dr. Kamran Khan: If you think of an outbreak a bit like a fire and embers flying off, these are like embers flying off into different locations.

Bill Whitaker: So in this case, that ember landed in dry brush in New York and started a wildfire?

Dr. Kamran Khan: Absolutely.

Dr. Khan told us he had spent the better part of a year persuading the airlines to share their flight data for public health. Nobody had ever asked that before. But he saw it as information gold.

Dr. Kamran Khan: How is it that someone knows 16B - that seat is available, but 14A has been taken? There clearly must be some kind of information system.

Bill Whitaker: Why is that so important?

Dr. Kamran Khan: There are over 4 billion of us that board commercial flights and travel around the world every year. And so that is why understanding population movements becomes so important in anticipating how disease is spread.

The virus spread across Asia with a vengeance. BlueDot has licensed access to the anonymized location data from millions of cellphones. And with that data it identified 12 of the 20 cities that would suffer first.

Dr. Kamran Khan: What we're looking at here are mobile devices that were in Wuhan in the previous 14 days and where are they now across East Asia. Places like Tokyo have a lot of devices, Seoul in South Korea--

Bill Whitaker: So you're following those devices from Wuhan to these other cities?

Dr. Kamran Khan: That's correct. I do wanna point out these are also anonymized data. But they allow us to understand population movements. That is how we can understand how this virus will spread.

To build their algorithm, Dr. Khan told us he deliberately hired an eclectic mix: engineers, ecologists, geographers, veterinarians all under one roof. They spent a year teaching the computer to detect 150 deadly pathogens.

Dr. Kamran Khan: We can ultimately train a machine to be reading through all the text and picking out components that this is talking about an outbreak of anthrax and this is talking about the heavy metal band Anthrax. And as you do this thousands and thousands and thousands of times, the machine starts to get smarter and smarter.

Bill Whitaker: And how many different languages does the computer understand?

Dr. Kamran Khan: So it's reading this currently in 65 languages, and processing this information every 15 minutes, 24 hours a day. So it's a lotta data to go through.

Within two hours of detecting the outbreak on December 31, BlueDot had sent a warning of the potential threat to its clients: public health officials in 12 countries, airlines and frontline hospitals, like Humber River in Toronto.

Dr. Michael Gardam: We've been able to really make a lot of decisions, I think, a little bit earlier 'cause I kinda feel like we had a bit of an inside scoop here.

One of Canada's top infectious disease physicians, Dr. Michael Gardam, told us it was like getting real time intelligence.

Bill Whitaker: What did you do when you got that information from BlueDot?

Dr. Michael Gardam: Getting that intel allowed me to kinda be the canary in the coal mine, to stand up and say we need to pay attention to this. And to start thinking about it, start thinking about supplies, start thinking about how busy we might be.

Dr. Michael Gardam: Now at this point, everybody knows about CoVid-19. But it's, it's not so much now. Now you've pretty much bought whatever PPE you can buy, it's very hard to buy that anymore. It's what did you do a month and a half ago that was so important. So, none of this is any surprise to us whatsoever, and yet, you see countries around the world where this has been a surprise.

BlueDot had no clients in the U.S., so while Dr. Gardam's hospital was making plans in January, President Trump, as late as March, was still assuring Americans that everything was under control.

California wasn't so sure, and braced for the worst. In March, it became the first state in the country to lock down its cities. Mickey Mouse suddenly looked lonely, drivers had only dreamed of such empty freeways. But the lock-down bought time. Despite having its first case of COVID-19 five weeks before New York, California dodged the hurricane of infection that slammed into New York City. At his daily teleconference in Sacramento, Governor Gavin Newsom made no secret where he'd gotten his edge: outbreak science.

Gavin Newsom: It's not a gross exaggeration when I say this the old modeling is literally pento-paper in some cases. And then you put it into some modest little computer program and it spits a piece of paper out. I mean, this is a whole other level of sophistication and data collection.

With the virus spreading around the world, California enlisted the help of BlueDot, Esri, Facebook and others, using mapping technologies and cell phone data to predict which hospitals would be hit hardest, and see if Californians were really staying at home. Data became California's all-seeing crystal ball.

Gavin Newsom: We are literally seeing in to the future and predicting in real-time based on constant update of information where patterns are starting to occur before they become headlines.

Bill Whitaker: Can you just sort of like, give me an example?

Gavin Newsom: We can see in real time on a daily basis, hourly basis, moment-by-moment basis if necessary, whether or not our stay-at-home orders were working. We can truly track now by census tract, not just by county.

Here's what it looked like. BlueDot scanned anonymous cell phone data over a 24-hour period last month in Los Angeles. The blue circles indicate less movement than the week before, the red spots show where people are still gathering. It could be a hospital or a problem. That cellphone data allows public health officials to investigate. It also raises worrisome privacy issues.

Bill Whitaker: How are you able to ensure that this cell phone data will remain anonymous?Gavin Newsom: Well, I didn't want to take the companies' words for it, I say that respectfully. I have a team of folks that are privacy-first advocates in our Technology Department. And we are making sure that no individualized data is provided. If it is, we're out.

Bill Whitaker: So what's been the most frustrating part of this for you?

Gavin Newsom: It's just incumbent upon us to have a national lens. And to recognize we'remany parts but one body. And if one part suffers, we all suffer.

Bill Whitaker: From this experience, do you think the Federal Government needs to overhaul the way it tackles pandemics?

Gavin Newsom: I don't know that there's a human being out there, maybe one or two, that would suggest otherwise. No, the absolute answer is, of course, unequivocally.

Dylan George: Data technology has transformed the way we do business in many aspects of our lives. But it has not transformed the way things are done in public health.

For Dylan George that's an urgent priority. As a scientist tracking biological threats in the Bush and Obama administrations, he has seen first-hand what he calls the panic-neglect cycle.

Dylan George: Perhaps the most tragic idea in all of public health is this: in a time of an outbreak everyone lights their hair on fire and is running around trying to figure out. After it's over, everyone forgets about it

He has joined a growing number of scientists pressing to revive an old idea: an infectious disease forecasting center modeled on the National Weather Service.

Dylan George: We need to have professionals that their day job is dedicated to helping us understand how infectious diseases will-- will risk our well being economically and from a national security perspective.

Bill Whitaker: That idea has been kicking around for a while. It's never gotten the funding. Do you think things will be different this time?

Dylan George: When we see that there is $2 trillion being spent on stimulus bills to help us get out of this, to make sure that we can rebound, we need to think transformatively. We need to think broadly about how we can move these things forward. This kind of a center would help us do that.

As the coronavirus continues to upend our lives, Toronto's Dr. Michael Gardam told us he has seen the difference a digital early-warning system can make.

Dr. Michael Gardam: One of the biggest challenges in infectious diseases is you never wanna be the doctor that picks up the first case because you're probably going to miss it. And you probably weren't wearing the right gear and it's probably already spread in your hospital. And so getting the early warning that help gives you the intel to make that first call is so incredibly important.

Produced by Heather Abbott. Associate producer, David M. Levine. Broadcast associate, Emilio Almonte. Edited by Robert Zimet.

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Outbreak Science: Using artificial intelligence to track the coronavirus pandemic - 60 Minutes - CBS News

Ever since the 1930s when scientists, namely Hans Bethe, discovered that nuclear fusion was possible, researchers strived to initiate and control fusion reactions to produce useful energy on Earth. The best example of a fusion reaction is in the middle of stars like the Sun where hydrogen atoms are fused together to make helium releasing a lot of energy that powers the heat and light of the star. On Earth, scientists need to heat and control plasma, an ionised state of matter similar to gas, to cause particles to fuse and release their energy. Unfortunately, it is very difficult to start fusion reactions on Earth, as they require conditions similar to the Sun, very high temperature and pressure, and scientists have been trying to find a solution for decades.

In May 2019, a workshop detailing how fusion could be advanced using machine learning was held that was jointly supported by the Department of Energy Offices of Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR). In their report, they discuss seven 'priority research opportunities':

'Science Discovery with Machine Learning' involves bridging gaps in theoretical understanding via identification of missing effects using large datasets; the acceleration of hypothesis generation and testing and the optimisation of experimental planning. Essentially, machine learning is used to support and accelerate the scientific process itself.

'Machine Learning Boosted Diagnostics' is where machine learning methods are used to maximise the information extracted from measurements, systematically fuse multiple data sources and infer quantities that are not directly measured. Classifcation techniques, such as supervised learning, could be used on data that is extracted from the diagnostic measurements.

'Model Extraction and Reduction' includes the construction of models of fusion systems and the acceleration of computational algorithms. Effective model reduction can result in shorten computation times and mean that simulations (for the tokamak fusion reactor for example) happen faster than real-time execution.

'Control Augmentation with Machine Learning'. Three broad areas of plasma control research would benefit significantly from machine learning: control-level models, real-time data analysis algorithms; optimisation of plasma discharge trajectories for control scenarios. Using AI to improve control mathematics could manage the uncertainty in calculations and ensure better operational performance.

'Extreme Data Algorithms' involves finding methods to manage the amount and speed of data that will be generated during the fusion models.

'Data-Enhanced Prediction' will help monitor the health of the plant system and predict any faults, such as disruptions which are essential to be mitigated.

'Fusion Data Machine Learning Platform' is a system that can manage, format, curate and enable the access to experimental and simulation data from fusion models for optimal usability when used by machine learning algorithms.

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Nuclear Fusion and Artificial Intelligence: the Dream of Limitless Energy - AI Daily

Artificial Intelligence is a topic that has been getting a lot of attention, mostly because of the rapid improvement that this field has seen since the turn of the 21st century. Amazing innovations are laying the foundation for ongoing breakthrough achievements. In this article, Im going to focus on three specific topics:

In the 1950s, AI pioneers Minsky and McCarthy described artificial intelligence as any task performed by a program or a machine that, was it performed by a human, would have required that human to apply intelligence to accomplish the task.

This is a fairly broad description. Nowadays, all tasks associated with human intelligence are described as AI when performed by a computer. This includes planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, manipulation and, to a lesser extent, social intelligence and creativity.

Artificial intelligence is defined as the branch of science and technology that [is] concerned with the study of software and hardware to provide machines the ability to learn insights from data and [the] environment and the ability to adapt in changing situation[s] with high precision, accuracy and speed.Amit Ray, Compassionate Artificial Superintelligence AI 5.0AI with Blockchain, BMI, Drone, IOT and Biometric Technologies

Now that we know what AI actually means, lets find out what its used for!

While surfing the web, have you ever wondered how most ads are related to your interests? Thats a representation of AI, more specifically, machine learning. However, AI is more commonly associated with robots, such as the ability of a robot to think on its own and the potential for computer consciousness. While these would be astounding achievements, they involve highly complex algorithms which we still cant produce today.

Machine learning is a big part of AI, and it might be the key reason for this fields meteoric rise. Its based on the principle of trial and error; every time we try to solve a problem, like a maze, were going to fail at least once. However, failing is a good thing in machine learning, because it enables the program to learn new information. That information is stored as data, and each time an AI goes down a specific path, it will reference the data from prior trials to see which one will work best this time.

To expand on the above example, Im going to teach you one of the first AI algorithms (often used to solve mazes), the A* algorithm.

To understand this algorithm, lets visualize our maze as a chess board with inaccessible regions (like a maze) that well call nodes.

This is a fun example of AI in action, since flying cars would be reliant on AI to function properly. In the future, scientists believe were going to have autonomous cars that transport people to their desired destinations. This involves cars having some sort of artificial intelligence, more specifically, machine learning,because they need to always find the best possible course to the destination, not crash into buildings and respect other vehicles. A very basic implementation of this, although extremely ineffective and slow, could be the A* algorithm, where buildings represent inaccessible nodes. However, some good alternatives exist that we didnt review in detail due to their high levels of complexity:

This article was written to provide a fun introduction to AI and to show its potential for future technologies. More than ever, its crucial to know the principles of artificial intelligence since it will be so important in the future. We need to constantly be open to new ideas and approaches, such as artificial intelligence (AI), and be willing to challenge assumptions of what this technology can achieve.

Read more here:
What Is Artificial Intelligence (AI)? - IoT For All

You have probably heard that artificial intelligence could be used to do lots of impressive tasks and jobs. AI can help designers and artists make quick tweaks to visuals. AI can also help researchers identify fake images or connect touch and sense. AI is being used to program websites and apps by combining symbolic reasoning and deep learning. Basically, artificial intelligence goes beyond deep learning. Here are five reasons why AI is important to you.

It is no news that AI will replace repetitive jobs. It literally means that these kinds of jobs will be automated, like what robots are currently doing in a myriad of factories. Robots are rendering the humans that are supposed to do those tasks practically jobless.

And it goes further than that many white collar tasks in the fields of law, hospitality, marketing, healthcare, accounting, and others are adversely affected. The situation seems scary because scientists are just scratching the surface as extensive research and development of AI. AI is advancing rapidly (and it is more accessible to everybody).

Some believe that AI can create even more new jobs than ever before. According to this school of thought, AI will be the most significant job engine the world has ever seen. Artificial intelligence will eliminate low-skilled jobs and effectively create massive high-skilled job opportunities that will span all sectors of the economy.

For example, if AI becomes fully adapt to language translation, it will create a considerable demand for high-skilled human translators. If the costs of essential translations drop to nearly zero, this will encourage MORE companies that need this particular service to expand their business operations abroad.

To those who speak different languages than the community in which they reside, this help will inevitably create more work for high-skilled translators, boost more economic activities. As a result of this, and more people will be employed in these companies due to the increased workload.

Boosting international trade it one of the most significant benefits of our global times. So yes, AI will eliminate some jobs, but it will create many, many more.

AI can be used extensively in the healthcare industry. It is applicable in automated operations, predictive diagnostics, preventive interventions, precision surgery, and a host of other clinical operations. Some individuals predict that AI will completelyreshape the healthcare landscape for the better.

And here are some of the applications of artificial intelligence in healthcare:

AI is also used in the agriculture industry extensively. Robots can be used to plant seeds, fertilized crops and administer pesticides, among a lot of other uses. Farmers can use a drone to monitor the cultivation of crops and also collect data for analysis.

The value-add data will be used to increase the final output. How? The data collected is analyzed by AI on such variables as crop health and soil conditions, boosting final production, and it can also be used in harvesting, especially for crops that are difficult to gather.

AI is changing the workplace, and there are plenty of reasons to be optimistic. It is used to do lots of tedious and lengthy tasks, especially the low-skilled types of jobs that are labor-intensive. It means that employees will be retasked away from boring jobs and bring significant and positive change in the workplace.

For instance, artificial intelligence is used in the automotive industry to do repetitive tasks such as performing a routine operation in the assembly line, for example. Allowing a robot to care for well, robotic-tasks, has created a shift in the workforce.

Auto accidents are one of the most popular types of accidents that happen in America. It kills thousands of people annually. A whopping 95 percent of these accidents are caused byhuman error, meaning accidents are avoidable.

The number of accident cases will reduce as artificial intelligence is being introduced into the industry by the use of self-driving cars. On-going research in the auto industry is looking at ways AI can be used to improve traffic conditions.

Smart systems are currently in place in many cities that are used to analyze traffic lights at the intersections. Avoiding congestion leads to safer movements of vehicles, bicycles, and pedestrians.

Conclusion

Artificial intelligence is very useful in all industries as more research is being done to advance it. The advancements in this AI tech will be most useful if it is understood and trusted. An important part of it is that artificial intelligence and related technologies such as drones, robots, and autonomous vehicles can create around tens of millions of jobs over the next decade.

Having more jobs created not less will be great news for everyone. More jobs will help boost the GDP of the economy. Advancement in AI and its impressive computational power has already led to the concept of supercomputers and beyond.

Elena Randall is a Content Creator Who works for Top Software Companies, provides a top 10 list of top software development companies within the world. She is passionate about reading and writing.

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5 Reasons Why Artificial Intelligence Is Important To You