How Hospitals are using Responsible AI to battle COVID-I9

Medical workers are our heroes as the COVID-19 outbreak continues with deaths and confirmed cases rising. A CNN survey found that healthcare systems are coming under strain because of the increasing number of patients infected by the coronavirus.

Partners Healthcare is supporting patients in the COVID-19 outbreak by using AI to detect those infected and those showing signs of the virus. Healthcare workers at Partners Healthcare understand the current crisis because of high patient numbers experienced and are working round the clock.

One question that comes to mind as COVID-19 pandemic continues is: How responsible is the current use of AI to combat the outbreak?

China has been criticized for using extreme measures to combat COVID-19 including strict lockdowns and despite this, their AI thermal cameras are detecting fevers and tracking movements of people.

Apple is a leading tech company acquiring AI start-ups. From acquisitions of Voysis, Turi, Xnor.ai, and Laserlike, Apple is accelerating the AI industry by picking the best innovators. Is this not good news for artificial intelligence?

Hospitals are using AI to battle COVID-I9

Partners HealthCare went live with a hotline for patients, clinicians, and anyone else with questions and concerns about COVID-19 in an effort to address soaring patient demand in Boston.

The goals are to identify and reassure the vast majority of callers who do not need additional care, 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. Many callers gave up before they could speak with the expert team as the hotline was overwhelmed, the average wait time peaked at 30 minutes.

The Partners team, led by Lee Schwamm, Haipeng Zhang, and Adam Landman, started considering interactive voice response systems & chatbots options to address the growing need for patient self-triage.

Partners HealthCare connected with Providence St. Joseph Health System in Seattle, which served some of the country’s first COVID-19 patients in early March.

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 collaboration with Microsoft.

AI is being used Responsibly to fight Coronavirus Pandemic

The disease caused by the virus COVID-19 has reached every corner of the world with the number of cases exceeding millions. The emergence of the novel coronavirus has left the world in turmoil that will affect us all in one way or another.

The imposition of lockdowns, limitations of movement, and those security services tasked with protecting the public from harm has suddenly become ever more complex. Many agencies and entities are turning to AI and related technologies to support unique and innovative ways to enhance surveillance, monitoring, and detection capabilities.

The authorities in China relied on facial recognition cameras to track a man from Hangzhou who had traveled in an affected area. Police in China and Spain have also started to use technology to enforce quarantine, with drones being used to patrol and broadcast audio messages to the public, encouraging them to stay at home.

People at Hong Kong airport receive monitoring bracelets that alert the authorities if they breach the quarantine by leaving their home. A surveillance company announced that its AI-enhanced thermal cameras can detect fevers in the United States.

How DeepMotion is Leveraging AI

Some of the DeepMotion’s products include Motion Brain, Body Tracking, and Virtual Reality Tracking. Motion Brain uses animate characters that can interact with users in believable ways using machine learning algorithms. It captures movements in the real world via a camera and reconstructs it in the digital world. Virtual Reality Tracking helps to create full-body animations for VR.

DeepMotion leverages AI to do some of the heavy liftings. The long-term vision of the company is to treat the real world as a resource that we can use to train AI. As AI can now observe how humans do everyday things, and the AI will learn how to do the movements itself in the TV series West world.

DeepMotion is also working on a Cloud Animation Service-a cloud version of its Body Tracking solution. It’s available now in an invite-only alpha testing phase which allows you to easily convert reference clips in various formats (like MP4 and AVI) into FBX animations.

To create infinite AI-crafted motions that look like real humans DeepMotion is using Intel’s 192-core SDP server to train its generative Motion Brain models. The company can decrease the costs and development time with additional computing power from Intel’s hardware.

Interesting Medical Research

Medical research and other tons of interesting stories out there that shouldn’t be lost in the furious activity of coronavirus coverage.

Arrhythmia is a condition in which the heart beats at an abnormal rate, causing a variety of effects, including, potentially, death. Electrocardiogram detects arrhythmia that relies heavily on an expert interpreting the signal, and the expert doesn’t give a good idea of what the issue looks like in that particular heart.

Ultrasound and AI promises a better diagnosis of arrhythmia. A form of ultrasound monitoring called Electro-mechanical Wave Imaging used by Researchers at Columbia University creates 3D animations of the patient’s heart as it beat, which helped specialists predict 96% of arrhythmia locations compared with 71% when using the ECG. It could be used together to provide a more accurate picture of the heart’s condition before undergoing treatment.

Stanford applies deep learning techniques to ultrasound imagery and shows that an AI agent can recognize the parts of the heart and record the efficiency with which it is moving blood with accuracy comparable to experts. AI isn’t about replacing a doctor but augmenting them with an automated system can help triage and prioritize effectively.

Energy efficiency and Location Triangulation by AI

Costas Spanos, a wiry, intense electrical engineering and computer science professor at UC Berkeley who thinks he can cut office energy use by half with the help of artificial intelligence. He also devised a way to use Wi-Fi to triangulate people’s locations by detecting their phones as they move through space.

The theory: Armed with that anonymized data, the system would learn the workers’ movements, schedules, and preferences and tweak their environment to suit.

The AI would adapt to creating microclimates to reflect their feedback if the workers tap an app to say they are feeling too hot or too cold. Spanos expected, the workers wouldn’t bother as the goal is to make the system forgettable.

Lights will turn on as workers come and go; screens will flicker to life as they settle at their desks, and the system will nudge for even more energy savings. The lights might get a bit dimmer, the room a little warmer, trying to fly under the radar of the workers’ awareness.

A proposal to automate lights and AC with deep learning looks rather quaint by comparison. We can try teaching people to change their behaviors. But Spanos thinks that is not going to be easy, you’re just going to have to automate around it.

AI turning Brain Activity into Text

Dr. Joseph Makin, co-author of the research from the University of California, San Francisco, and colleagues reveal how they developed their system by recruiting four participants who had electrode arrays implanted in their brains to monitor epileptic seizures.

50 sets of sentences including “Tina Turner is a pop singer”, and “Those thieves stole 30 jewels” were asked to these participants to read aloud multiple times. Their neural activity was tracked by the team while they were speaking.

Then the data was fed into a machine learning algorithm, a type of artificial intelligence system that converted the brain activity data for each spoken sentence into a string of numbers. The system compared sounds predicted from small chunks of the brain activity data with actual recorded audio to make sure the numbers related only to aspects of speech. Then the string of numbers was fed into a second part of the system which converted it into a sequence of words.

The system spat out nonsense sentences at first and compared each sequence of words with the sentences that were actually read aloud. It improved to learn the string of numbers related to words, and the words tend to follow each other.

Building an AI Pilot Program

Artificial Intelligence isn’t in the early adoption phase anymore. 34% of companies already deploying AI and 39% ramping up new exploratory initiatives, according to a global survey of 4,500 technology decision-makers commissioned by IBM. AI is estimated to add $15.7 trillion to the global economy. AI exploration is in the form of “proof of concept” pilot programs in the current time.

But AI pilots will fail to launch and one in four companies report an almost 50% failure rate of their AI initiatives according to IDC. Why do some fledgling AI projects soar while others crash and burn? Limited AI expertise and knowledge along with increasing data complexities and siloed data are most frequently among the major roadblocks cited by executives in the #IBMsurvey.

Sam Ransbotham, professor of information systems at Boston College said: “AI project stumbles out of the gate doesn’t mean it should be deemed a “failure” and abandoned. AI pilot projects “should be a continuous process, not a one-off.

Here are three guidelines that business leaders should follow as they conceptualize their own AI pilot programs-

- Build a culture of trust

- Start small, but think big

- Involve nontechnical stakeholders

You can build a pilot program following these guidelines.

Executing AI-led operations (AIOps)

AI-led operations (AIOps) helps businesses scale by using artificial intelligence to provide automated, data-driven insights and visibility into operations.

“The use of AIOps platforms to augment IT functions such as event correlation and analysis, anomaly detection, root cause analysis, and natural language processing is growing rapidly.” It estimates the size of this market at between $300 million and $500 million annually.”- According to Gartner.

Businesses need to define the value they’re seeking for AIOps efforts to meet their potential. To ensure AIOps success they must put in place the infrastructure, people and processes. AIOps should be implemented in a phased and programmatic approach.

An organization’s implementation must match its business needs to implement AIOps including out-of-the-box solutions. AIOps lowers the noise that IT operational staff faces in addressing tickets and serve to reduce the time it takes to solve IT issues.

AI can provide a more powerful tool when the insights are connected to automation. You need to map your AI effort to specific business objectives and outcomes and clearly defined key performance indicators (KPIs). You need to have more and better operational data to put AI led operations into practice.

Apple is the top Buyer of AI Startups

Voysis, an artificial intelligence startup acquired by Apple Inc. Dublin, Ireland-based Voysis developed a platform for digital voice assistants to better understand people’s natural language.

Voysis software could respond more accurately to voice commands from users. The company’s system taps into Wavenets, an AIbased method for creating more human-like computer speech that was first developed by Google’s DeepMind in 2016.

“Once the AI is trained, the software uses as little as 25 megabytes of memory -about the same size as four Apple Music songs.”- Peter Cahill, Co-founder, Voysis.

Siri’s understanding of natural language could use the acquired technology by Apple. Apple can also offer the Voysis platform to thousands of developers that already integrate with the Apple digital assistant. Apple has been the top buyer of AI startups in recent years and the giant company has already acquired former startups including Turi, @Xnor.ai, and Laserlike.

Voysis founded in 2012 got $8 million in venture funding from Polaris Partners in 2017. Apple also bought Dark Sky, a popular weather app for iPhones and iPads.