Monthly Archives: April 2020

Humans have often sought the fantasy of having someone who understands them. Be it a fellow companion, a pet or even a machine. No doubt man is a social animal. Yet, this may not be the exact case in case of a man engineered machine or system. Although, machines are now equipped with AI that helps them beat us by sifting through scores of data and analyze them, provide a logical solution when it comes to emotional IQ this is where man and the machine draw the line. Before you get excited or feel low, AI is now in a race to integrate the emotional aspect of intelligence in its system. Now the question is, Is it worth the hype?

We are aware of the fact that facial expressions need not be the same as what one feels inside. There is always a possibility of disconnect by a huge margin. Assuming that AI can recognize these cues by observing and comparing it with existing data input is a grave simplification of a process that is subjective, intricate, and defies quantification. For example, a smile is different from a smug, smirk.

A smile can mean genuine happiness, enthusiasm, trying to put a brave face even when hurt or an assassin plotting his next murder. This confusion exists even in gestures too. Fingers continuously folding inwards the palm can mean Come here at some places while at other places it means Go away. This brings another major issue in light: cross-cultural and ethnic references. An expression can hold a different meaning in different countries. Like thumbs-up gesture is typically considered as well done or to wish Good Luck or to show agreement. In Germany and Hungary, the upright thumb means the number 1. But, it represents the number 5 in Japan. Whereas in places like the Middle East, thumbs-up is a highly offensive thumbs-down. The horn fingers gestures can mean to rock and roll at an Elvis Presley themed or heavy metal concert. But in Spain, it means el cornudo which means translates as your spouse is cheating on you. Not only that pop culture symbols like the Vulcan salute from Star Trek may not be known to people who have not seen the series.

Not only that, but it is also found that AI tends to assign negative emotions to people of color even when they are smiling. This racial bias can cause severe consequences in the workplace hampering their career progression. In recruitments where AI is trained on analyzing male behavior patterns and features is prone to make faulty decisions and flawed role allocation in female employees. Furthermore, people show different emotional range as they grow up. A child may be more emotionally engaging than an adult who is reserved about expressing them. This can be a major glitch in automatic driving cars or AI which specifically studies the drowsiness of the driver. Elderly and sick people may give the impression of being tired and sick in comparison to a standardized healthy guy.

If we must opt for upgrading AI with emotional intelligence and unassailable, we must consider the exclusivity of the focus groups who are used to train the system. AI has to understand rather than be superficially emotional. Hence the AI has to be consumer adaptive just like humans. We need to bring out the heterogeneous interpretation in the way humans express their emotions. At the office, we have to understand how emotionally engaged the employees are. Whether it is the subjective nature of emotions or discrepancies in emotions, it is clear that detecting emotions is no easy task. Some technologies are better than others at tracking certain emotions, so combining these technologies could help to mitigate bias. Only then it can become immune to unforgiving criticisms.

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Can Emotional AI Supersede Humans or Is It Another Urban Hype? - Analytics Insight

5. Descriptive phrases that suggested at least comparable (or better) diagnostic performance of an algorithm to a clinician were found in most abstracts, despite studies having overt limitations in design, reporting, transparency and risk of bias. Qualifying statements about the need for further prospective testing were rarely offered in study abstractsand werent mentioned at all in some 23 studies that claimed superior performance to a clinician, the authors report. Accepting that abstracts are usually word limited, even in the discussion sections of the main text, nearly two thirds of studies failed to make an explicit recommendation for further prospective studies or trials, the authors write. Although it is clearly beyond the power of authors to control how the media and public interpret their findings, judicious and responsible use of language in studies and press releases that factor in the strength and quality of the evidence can help.

Expounding on the latter point in their concluding section, Nagendran et al. reiterate that using overpromising language in studies involving AI-human comparisons might inadvertently mislead the media and the public, and potentially lead to the provision of inappropriate care that does not align with patients best interests.

The development of a higher quality and more transparently reported evidence base moving forward, they add, will help to avoid hype, diminish research waste and protect patients.

The study is available in full for free.

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5 findings that could spur imaging AI researchers to 'avoid hype, diminish waste and protect patients' - Health Imaging

In a global crisis, the use of technology to gain insights into socio-economic threats is indispensable. In the current situation where the entire world faces the global pandemic of Covid-19, finding a cure and distributing it is a difficult task. Fortunately, today we have new and advanced technologies like AI, automation, analytics and more that can perform a better job. While AI is boon in the technological world, it has the potential to orchestrate troves of data to discover connections in the process to determine what kinds of treatments could work and which experiments to follow next.

Across the world, governments and health authorities are now exploring distinct ways to contain the spread of Covid-19 as the virus has already dispersed across 196 countries in a short time. According to a professor of epidemiology and biostatistics at George Washington University and SAS analytics manager for infectious diseases epidemiology and biostatistics, data, analytics, AI and other technology can play a significant role in helping identify, understand and assist in predicting disease spread and progression.

In its response to the virus, China, where the first case of coronavirus reported in late December 2019, started utilizing its sturdy tech sector. The country has specifically deployed AI, data science, and automation technology to track, monitor and defeat the pandemic. Also, tech players in China, such as Alibaba, Baidu, Huawei, among others expedited their companys healthcare initiatives in their contribution to combat Covid-19.

In an effort to vanquish Covid-19, the Stanford Institute for Human-Centered Artificial Intelligence (HAI), earlier this month, conducted a virtual COVID-19 and AI Conference, to discuss how best to approach the pandemic using technology, AI, and analytics.

Since late 2019, several groups have been monitoring the spread of the virus, as a Harvard pediatrics professor John Brownstein said. He says, it takes a small army of people and highlighting efforts by universities and other organizations to use data-mining and other tools to track early signs of the outbreak online, such as through Chinas WeChat app, and understand effects of the intervention.

In the time of crisis, AI is proving its promising capabilities through diagnosing risks, doubt-clearing, delivering services and assisting in drug discovery to tackle the outbreak. AI-driven companies like Infervision brought an AI solution for coronavirus that assists front-line healthcare workers to spot and monitor the disease efficiently. Conversely, a start-up in the AI space CoRover that has earlier developed chatbots for railways ticketing platform, has built a video-bot, in collaboration with a doctor from Fortis Healthcare. Using the platform, a doctor can take questions from people about Covid-19.

Moreover, researchers in Australia have created and are testing a Covid-19 vaccine candidate to fight against the SARS-CoV-2 coronavirus. Researchers from Flinders University, working with Oracle cloud technology and vaccine technology developed by Vaxine, assessed the Covid-19 virus and used this information to design the vaccine candidate. According to Professor Nikolai Petrovsky at Flinders University and Research Director at Vaxine, the vaccine has progressed into animal testing in the US and once they confirm it is safe and effective, then only it will be advanced into human trials.

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Combating Covid-19 with the Help of AI, Analytics and Automation - Analytics Insight

The coronavirus pandemic forced students and researchers at Carnegie Mellon University in March to abruptly stop testing an adaptive learning software tool that uses artificial intelligence to expand tutors ability to deliver personalized education. But researchers said the tool could help students get back up to speed on their learning when in-person instruction resumes.

The software, which was being tested in the Pittsburgh Public School District before the coronavirus outbreak began closing universities, relies on AI to identifystudents learning successes and challenges, giving educators a clear picture of how to personalize their education plans, said Lee Branstetter, professor of economics and public policy at Carnegie Mellon University.

When students work through their assignments, the AI captures everything students do,Branstetter told EdScoop. The data is then organized into a statistical map, which allows teachers to easily keep track of each students personal learning needs.

So the idea is that a tutor doesnt have to be standing behind the same student for hours to know where they are, he said. The system can help bring [educators] up to speed, but then the tutor can provide that human relationship and that accountability and that encouragement that we know is really important. Weve known since the early 1980s that personalized instruction can make a huge difference in learning outcomes, especially in students who arent necessarily the top learners in a classroom setting.

But with the learning technology of the 80s, there was no way to deliver personalized instruction at an acceptable cost.

In the decades since, artificial intelligence come a long way, Branstetter said. What were trying to do in the context of our study is to take this learning software and pair it with human tutors because an important part of the learning process is the relationship between instructors and students. We realize that software can never replicate the ability of human instructor to inspire, to encourage and to hold students accountable.

Although testing on the new tool was cut short when schools ceased in-person instruction, Branstetter said the disruption could actually be a good testing environment for the tool, and hopes toresume testing once schools reopen to help students recover lessons lost as a result of the pandemic.

I think whats almost certain to emerge is that theyre going to be students that are able to continue their education and students that are not, and the students that were already behind are going to fall further behind, he said. And so we really feel that the kind of personalized instruction that we can provide in the program will be more important and necessary than ever.

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After the pandemic, AI tutoring tool could put students back on track - EdScoop News

Executive Summary

The spread of Covid-19 is stretching operational systems in health care and beyond. The reason is both simple: Our economy and health care systems are geared to handle linear, incremental demand, while the virus grows at an exponential rate. Our national health system cannot keep up with this kind of explosive demand without the rapid and large-scale adoption of digital operating models.While we race to dampen the viruss spread, we can optimize our response mechanisms, digitizing as many steps as possible. Heres how some hospitals are employing artificial intelligence to handle the surge of patients.

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On Monday March 9, in an effort to address soaring patient demand in Boston, Partners HealthCare went live with a hotline for patients, clinicians, and anyone else with questions and concerns about Covid-19. The goals are to identify and reassure the people who do not need additional care (the vast majority of callers), to direct people with less serious symptoms to relevant information and virtual care options, and to direct the smaller number of high-risk and higher-acuity patients to the most appropriate resources, including testing sites, newly created respiratory illness clinics, or in certain cases, emergency departments. As the hotline became overwhelmed, the average wait time peaked at 30 minutes. Many callers gave up before they could speak with the expert team of nurses staffing the hotline. We were missing opportunities to facilitate pre-hospital triage to get the patient to the right care setting at the right time.

The Partners team, led by Lee Schwamm, Haipeng (Mark) Zhang, and Adam Landman, began considering technology options to address the growing need for patient self-triage, including interactive voice response systems and chatbots. We connected with Providence St. Joseph Health system in Seattle, which served some of the countrys first Covid-19 patients in early March. In collaboration with Microsoft, Providence built an online screening and triage tool that could rapidly differentiate between those who might really be sick with Covid-19 and those who appear to be suffering from less threatening ailments. In its first week, Providences tool served more than 40,000 patients, delivering care at an unprecedented scale.

Our team saw potential for this type of AI-based solution and worked to make a similar tool available to our patient population. The Partners Covid-19 Screener provides a simple, straightforward chat interface, presenting patients with a series of questions based on content from the U.S. Centers for Disease Control and Prevention (CDC) and Partners HealthCare experts. In this way, it too can screen enormous numbers of people and rapidly differentiate between those who might really be sick with Covid-19 and those who are likely to be suffering from less threatening ailments. We anticipate this AI bot will alleviate high volumes of patient traffic to the hotline, and extend and stratify the systems care in ways that would have been unimaginable until recently. Development is now under way to facilitate triage of patients with symptoms to most appropriate care setting, including virtual urgent care, primary care providers, respiratory illness clinics, or the emergency department. Most importantly, the chatbot can also serve as a near instantaneous dissemination method for supporting our widely distributed providers, as we have seen the need for frequent clinical triage algorithm updates based on a rapidly changing landscape.

Similarly, at both Brigham and Womens Hospital and at Massachusetts General Hospital, physician researchers are exploring the potential use of intelligent robots developed at Boston Dynamics and MIT to deploy in Covid surge clinics and inpatient wards to perform tasks (obtaining vital signs or delivering medication) that would otherwise require human contact in an effort to mitigate disease transmission.

Several governments and hospital systems around the world have leveraged AI-powered sensors to support triage in sophisticated ways. Chinese technology company Baidu developed a no-contact infrared sensor system to quickly single out individuals with a fever, even in crowds. Beijings Qinghe railway station is equipped with this system to identify potentially contagious individuals, replacing a cumbersome manual screening process. Similarly, Floridas Tampa General Hospital deployed an AI system in collaboration with Care.ai at its entrances to intercept individuals with potential Covid-19 symptoms from visiting patients. Through cameras positioned at entrances, the technology conducts a facial thermal scan and picks up on other symptoms, including sweat and discoloration, to ward off visitors with fever.

Beyond screening, AI is being used to monitor Covid-19 symptoms, provide decision support for CT scans, and automate hospital operations. Meanwhile, Zhongnan Hospital in China uses an AI-driven CT scan interpreter that identifies Covid-19 when radiologists arent available. Chinas Wuhan Wuchang Hospital established a smart field hospital staffed largely by robots. Patient vital signs were monitored using connected thermometers and bracelet-like devices. Intelligent robots delivered medicine and food to patients, alleviating physician exposure to the virus and easing the workload of health care workers experiencing exhaustion. And in South Korea, the government released an app allowing users to self-report symptoms, alerting them if they leave a quarantine zone in order to curb the impact of super-spreaders who would otherwise go on to infect large populations.

The spread of Covid-19 is stretching operational systems in health care and beyond. We have seen shortages of everything, from masks and gloves to ventilators, and from emergency room capacity to ICU beds to the speed and reliability of internet connectivity. The reason is both simple and terrifying: Our economy and health care systems are geared to handle linear, incremental demand, while the virus grows at an exponential rate. Our national health system cannot keep up with this kind of explosive demand without the rapid and large-scale adoption of digital operating models.

While we race to dampen the viruss spread, we can optimize our response mechanisms, digitizing as many steps as possible. This is because traditional processes those that rely on people to function in the critical path of signal processing are constrained by the rate at which we can train, organize, and deploy human labor. Moreover, traditional processes deliver decreasing returns as they scale. On the other hand, digital systems can be scaled up without such constraints, at virtually infinite rates. The only theoretical bottlenecks are computing power and storage capacity and we have plenty of both. Digital systems can keep pace with exponential growth.

Importantly, AI for health care must be balanced by the appropriate level of human clinical expertise for final decision-making to ensure we are delivering high quality, safe care. In many cases, human clinical reasoning and decision making cannot be easily replaced by AI, rather AI is a decision aid that helps human improve effectiveness and efficiency.

Digital transformation in health care has been lagging other industries. Our response to Covid today has accelerated the adoption and scaling of virtual and AI tools. From the AI bots deployed by Providence and Partners HealthCare to the Smart Field Hospital in Wuhan, rapid digital transformation is being employed to tackle the exponentially growing Covid threat. We hope and anticipate that after Covid-19 settles, we will have transformed the way we deliver health care in the future.

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How Hospitals Are Using AI to Battle Covid-19 - Harvard Business Review

A team of Microsoft and Huazhong University researchers this week open-sourced an AI object detector Fair Multi-Object Tracking (FairMOT) they claim outperforms state-of-the-art models on public data sets at 30 frames per second. If productized, it could benefit industries ranging from elder care to security, and perhaps be used to track the spread of illnesses like COVID-19.

As the team explains, most existing methods employ multiple models to track objects: (1) a detection model that localizes objects of interest and (2) an association model that extracts features used to reidentify briefly obscured objects. By contrast, FairMOT adopts an anchor-free approach to estimate object centers on a high-resolution feature map, which allows the reidentification features to better align with the centers. A parallel branch estimates the features used to predict the objects identities, while a backbone module fuses together the features to deal with objects of different scales.

The researchers tested FairMOT on a training data set compiled from six public corpora for human detection and search: ETH, CityPerson, CalTech, MOT17, CUHK-SYSU, and PRW. (Training took 30 hours on two Nvidia RTX 2080 graphics cards.) After removing duplicate clips, they tested the trained model against benchmarks that included 2DMOT15, MOT16, and MOT17. All came from the MOT Challenge, a framework for validating people-tracking algorithms that ships with data sets, an evaluation tool providing several metrics, and tests for tasks like surveillance and sports analysis.

Compared with the only two published works that jointly perform object detection and identity feature embedding TrackRCNN and JDE the team reports that FairMOT outperformed both on the MOT16 data set with an inference speed near video rate.

There has been remarkable progress on object detection and re-identification in recent years, which are the core components for multi-object tracking. However, little attention has been focused on accomplishing the two tasks in a single network to improve the inference speed. The initial attempts along this path ended up with degraded results mainly because the re-identification branch is not appropriately learned, concluded the researchers in a paper describing FairMOT. We find that the use of anchors in object detection and identity embedding is the main reason for the degraded results. In particular, multiple nearby anchors, which correspond to different parts of an object, may be responsible for estimating the same identity, which causes ambiguities for network training.

In addition to FairMOTs source code, the research team made available several pretrained models that can be run on live or recorded video.

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Researchers open-source state-of-the-art object tracking AI - VentureBeat

This week for our Vergecast interview series, Verge editor-in-chief Nilay Patel chats with Microsoft chief technology officer Kevin Scott about his new book Reprogramming the American Dream: From Rural America to Silicon ValleyMaking AI Serve Us All.

Scotts book tackles how artificial intelligence and machine learning can help rural America in a more grounding way, from employment to education to public health. In one chapter of his book, Scott focuses on how AI can assist with health care and diagnostic issues a prominent concern in the US today, especially during the COVID-19 pandemic.

In the interview, Scott refocuses the solutions he describes in the book around the current crisis, specifically supercomputers Microsoft has been using to train natural language processing now being used to search for vaccine targets and therapies for the novel coronavirus.

Below is a lightly edited excerpt of the conversation.

So lets talk about health care because its something you do focus on in the book. Its a particularly poignant time to talk about health care. How do you see AI helping broadly with health care and then more specifically with the current crisis?

I think there are a couple of things going on.

One I think is a trend that I wrote about in the book and that is just getting more obvious every day is that we need to do more. So that particular thing is that if our objective as a society is to get higher-quality, lower-cost health care to every human being who needs it, I think the only way that you can accomplish all three of those goals simultaneously is if you use some form of technological disruption.

And I think AI can be exactly that thing. And youre already seeing an enormous amount of progress on the AI-powered diagnostics front. And just going into the crisis that were in right now, one of the interesting things that a bunch of folks are doing including, I think I read a story about the Chan Zuckerberg Initiative is doing this is the idea is that if you have ubiquitous biometric sensing, like youve got a smartwatch or a fitness band or maybe something even more complicated that can sort of read off your heart-tick data, that can look at your body temperature, that can measure the oxygen saturation in your blood, that can basically get a biometric readout of how your bodys performing. And its sort of capturing that information over time. We can build diagnostic models that can look at those data and determine whether or not youre about to get sick and sort of predict with reasonable accuracy whats going on and what you should do about it.

Like you cant have a cardiologist following you around all day long. There arent enough cardiologists in the world even to give you a good cardiological exam at your annual checkup.

I think this isnt a far-fetched thing. There is a path forward here for deploying this stuff on a broader scale. And it will absolutely lower the cost of health care and help make it more widely available. So thats one bucket of things. The other bucket of things is like just some mind-blowing science that gets enabled when you intersect AI with the leading-edge stuff that people are doing in the biosciences.

Give me an example.

So, two things that we have done relatively recently at Microsoft.

One is one of the big problems in biology that weve had that that immunologists have been studying for years and years and years, is whether or not you could take a readout of your immune system by looking at the distribution of the types of T-cells that are active in your body. And from that profile, determine what illnesses that your body may be actively dealing with. What is it prepared to deal with? Like what might you have recently had?

And that has been a hard problem to figure out because, basically, youre trying to build something called a T-cell receptor antigen map. And now, with our sequencing technology, we have the ability to get the profile so you can sort of see what your immune system is doing. But we have not yet figured out how to build that mapping of the immune system profile to diseases.

Except were partnering with this company called Adaptive that is doing really great work with us, like bolting machine learning onto this problem to try to figure out what the mapping actually looks like. We are rushing right now a serologic test like a blood test that we hope well be able to sort of tell you whether or not you have had a COVID-19 infection.

So I think its mostly going to be useful for understanding the sort of spread of the disease. I dont think its going to be as good a diagnostic test as like a nasal swab and one of the sequence-based tests that are getting pushed out there. But its really interesting. And the implications are not just for COVID-19, but if you are able to better understand that immune system profile, the therapeutic benefits of that are just absolutely enormous. Weve been trying to figure this out for decades.

The other thing that were doing is when youre thinking about SARS-CoV-2 which is the virus that causes COVID-19 that is raging through the world right now we have never in human history had a better understanding of a virus and how it is attacking the body. And weve never had a better set of tools for precision engineering, potential therapies, and vaccines for this thing. And part of that engineering process is using a combination of simulation and machine learning and these cutting-edge techniques of biosciences in a way where youre sort of leveraging all three at the same time.

So weve got this work that were doing with a partner right now where I have taken a set of supercomputing clusters that we have been using to train natural language processing, deep neural networks, just massive scale. And those clusters are now being used to search for vaccine targets and therapies for SARS-CoV-2.

Were one among a huge number of people who are very quickly searching for both therapies and potential vaccines. There are reasons to be hopeful, but weve got a way to go.

But its just unbelievable to me to see how these techniques are coming together. And one of the things that Im hopeful about as we deal with this current crisis and think about what we might be able to do on the other side of it is it could very well be that this is the thing that triggers a revolution in the biological sciences and investment in innovation that has the same sort of a decades-long effect that the industrialization push around World War II had in the 40s that basically built our entire modern world.

Yeah, thats what I keep coming back to, this idea that this is a reset on a scale that very few people living today have ever experienced.

And you said out of World War II, a lot of basic technology was invented, deployed, refined. And now we kind of get to layer in things like AI in a way that is, quite frankly, remarkable. I do think, I mean, it sounds like were going to have to accept that Cortana might be a little worse at natural language processing while you search for the protein surfaces. But I think its a trade most people make.

[Laughs] I think thats the right trade-off.

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Microsofts CTO explains how AI can help health care in the US right now - The Verge