Pentagon seeks to test & evaluate AI products

The Pentagon is looking to the industry regarding how to better test and evaluate artificial intelligence products in the pipeline to ensure safety and effectiveness.

In a request for information this week, the Pentagon’s Joint Artificial Intelligence Center(JAIC), seeks input on cutting-edge testing and evaluation capabilities to support the “full spectrum” of the Defense Department’s emerging AI technologies including:

1. Machine learning, 
2. Deep learning & 
3. Neural networks 

Read RFI - 832016142.docx :

Stated Objectives:

• The Pentagon wants to augment the JAIC’s Test and Evaluation office, which develops standards and conducts algorithm testing, system testing and operational testing on the military’s many AI initiatives.
• The Pentagon stood up the JAIC in 2018 to centralize coordination and accelerate the adoption of AI and has been building out its ranks in recent months, hiring an official to implement its new AI ethical principles for warfare.
• The JAIC is requesting testing tools and expertise in planning, data management, and analysis of inputs and outputs associated with those tools. 

• The introduction of AI-enabled systems brings changes to the process, metrics, data, and skills necessary to produce the level of testing the military needs, and that is the reason for requesting information.
• Testing and Evaluation provides knowledge of system capabilities and limitations to the acquisition community and to the war-fighter. 
• The JAIC's T&E team will make rigorous and objective assessments of systems under operational conditions and against realistic threats, so that our war fighters ultimately trust the systems they are operating and that the risks associated with operating these systems are well-known to military acquisition decision-makers.

The solicitation indicates it plans to use feedback from the solicitation to guide how it further builds out its capabilities. 

The Pentagon is interested in tech testing tools that focus on:

• Conversational interface applications using voice to text.
• Speech-enabled products and services for DOD applications and systems.
• Image analysis, testing deep learning-based visual search and image classifier.
• Natural Language Processing-enabled products and services.
• Humans augmented by machines, to include human-machine interfaces and improved methods to measure war-fighter cognitive and physical workloads, to include augmented reality and virtual reality test services.
•  Autonomous systems.

The Pentagon also wants feedback regarding evaluation services in five mission areas: 

1. Dateset curation
2. Test harness development
3. Model output analysis
4. Test reporting 
5. Testing services

Finally the pentagon also seeks “other technologies” that  it may not be aware of that “may be beneficial” to testing and evaluation efforts.

~ Jai Krishna Ponnappan

AI app can listen to your cough & detect COVID-19

EPFL researchers have developed an artificial intelligence-based system that can listen to your cough and indicate whether you have COVID-19.

With the new Coughvid app, developed by five researchers at EPFL's Embedded Systems Laboratory (ESL), you can record your cough on a smartphone and find out whether you might have COVID-19. So how can a smartphone app detect the new coronavirus? "According to the World Health Organization, 67.7% of people who have the virus present with a dry cough—producing no mucus—as opposed to the wet cough typical of a cold or allergy," says David Atienza, a professor at EPFL's School of Engineering who is also the head of ESL and a member of the Coughvid development team. The app is still being developed and will be released in the next few weeks.

Free and anonymous

Once the app is available, users will simply need to install it and record their cough—the results will appear immediately. "We wanted to develop a reliable, easy-to-use system that could be deployed for large-scale testing," says Atienza. "It's an alternative to conventional tests." In addition to being easy to use, the app has the advantage of being non-invasive, free and anonymous. "The app has a 70% accuracy rate," he adds. "That said, people who think they may have the disease should still go see their doctor. Coughvid is not a substitute for a medical exam."

Using artificial intelligence to help patients

Coughvid uses artificial intelligence to distinguish between different types of coughs based on their sound. "The idea is not new. Doctors already listen to their patients' coughs to diagnose whooping cough, asthma and pneumonia," says Atienza.

Right now his team is collecting as much data as possible to train the app to distinguish between the coughs of people with COVID-19, healthy people, and people with other kinds of respiratory ailments. "We'll release the app once we've accumulated enough data. It could take a few more weeks," says Atienza. In the meantime, COVID-19 patients who would like to contribute to the development work can record their cough at https://coughvid.epfl.ch/ or on the Coughvid mobile app.

2020 Artificial Intelligence Industry Trends

As of date some industries have been doing better than others at implementing AI. AI is lifting efficiency and performance across many diverse industries in 2020, with an array of benefits sweeping across multiple categories. As AI matures, these 9 areas and industries look like they are poised to benefit the most:



Financial Services

Banking and financial services are among the key industries benefiting from AI. The structured nature of financial data and the industry's past experiences with analytics may have made it easier for companies in this sector to implement artificial intelligence.

Many legacy organizations [in other industries] are still learning how to move from pilot projects to full operational deployments since the underlying data and architecture is very different in a pilot versus being operationalized. Financial services and insurance firms have been better at the advanced analytics on transitions since that data is well understood and managed.

In the future, the industry could see additional gains by applying AI to other areas beyond analytics, such as customer service.



Cybersecurity

Cybersecurity is likely to see big gains from AI in 2020. Already, many vendors are adding AI capabilities to their products. In its Top 10 Strategic Technology Trends for 2020, Gartner noted, "ML-based security tools can be a powerful addition to your toolkit when aimed at a specific high-value use case such as security monitoring, malware detection or network anomaly detection."

However, enterprises are likely going to need AI-based cybersecurity in order to counter new threats, which might make use of AI and machine learning themselves. As Forrester noted in its Predictions 2020, "The unfortunate reality will come to light that evil forces can adopt technologies such as AI and machine learning faster than security leaders can."

Predictive Maintenance

Many different industries, such as manufacturing, transportation, oil and gas, utilities, and even cloud computing data centers rely on complex machinery in order to stay in business. And any downtime of critical resources quickly results in significant financial losses. As a result, many organizations are investing in a combination of IoT sensors, computer vision, and/or machine learning technology to help them improve uptime by proactively identifying potential risks and scheduling maintenance in advance.

AI is improving business performance with predictive maintenance, where deep learning analyzes large amounts of high-dimensional data to detect anomalies in everything from factory assembly lines to building HVAC systems to commercial aircraft engines.



Manufacturing

Manufacturing companies are beginning to dip their toes in the AI waters. In addition to predictive maintenance, manufacturing firms are using artificial intelligence and subsets like machine learning to better manage their supply chains, forecast demand, improve quality, deliver products, and increase customer satisfaction.

However, manufacturing firms will need to improve their underlying information architecture and data management practices if they want to be successful with their AI efforts. Even when machine learning algorithms do not need any reference architecture to function (visual identification of part defects for example), application of the results of the analysis does require that knowledge and information architecture.



Logistics and Transportation

Within the transportation industry, enterprises are using artificial intelligence to help them shave precious minutes off their delivery times and dollars off their costs. Spread across a large fleet, these small gains can result in millions of dollars of savings per year, alongside increases in customer satisfaction. In logistics and transportation, AI optimizes the routing of delivery traffic, improving fuel efficiency and reducing delivery times.

In addition, the transportation industry is also turning to AI to help with the creation of advanced safety systems, semi-autonomous vehicles, and eventually, fully autonomous vehicles. The result could be safer travel for everyone.



Travel

Like the transportation industry, the travel industry is also using machine learning (a subset of AI) to enhance logistics, which can allow them to reduce prices for customers. The travel industry is seeing positive results from AI in the area of fraud detection.

Criminals often target airlines and hotels to convert stolen credit card numbers into cash. They will book travel on the stolen card, and then attempt to get a refund back to their own personal cards, pocketing the difference. Others set up far more elaborate schemes where they book travel and then attempt to resell it on the black market. AI can help identify both kinds of fraudulent transactions, resulting in cost savings for the travel industry and their customers, as well as reducing the inconvenience for people with the stolen credit cards.



B2B

Another prime area for AI implementation right now is B2B, particularly B2B sales. B2B sales are benefiting from AI as well, with speech recognition making it possible to track and optimize every customer interaction, from research to early engagement to closing the sale.

In addition to speech recognition, AI firms are also making use of advanced machine learning and analytics for a variety of purposes. For example, within B2B, pricing often differs significantly from customer to customer, and machine learning can help them better segment their customers and price more effectively. Of course, they can also use machine learning for forecasting, supply chain management, and for uncovering other insights that can help them become more competitive.



Healthcare

With coronavirus on the news every day, everyone is interested in ways to improve healthcare, and artificial intelligence seems like one promising way to speed innovation in the field. Healthcare continues to be a prime application for AI. Algorithms can assist with tasks as diverse as analysis of scans, development of vaccines, interpreting research results, and improving patient care.

Again, experts caution, however, that in order for AI to be effective within healthcare, organizations need to have good data, solid training models, and the right IT infrastructure in place to both conduct the analysis and secure sensitive data.

Retail

For online stores like Amazon, AI has become such an expected part of the sales process that people don't even notice it anymore. In retail sales, combining customer demographic and past transaction data with social media behavior observation helps generate individualized 'next product to buy' recommendations, which is now routine for many retailers. Through 2020, look for these recommendation engines to continue to improve and for retailers to find new ways to implement AI.

One interesting data point is how democratized AI adoption is. AI flattens the competitive landscape, empowering smaller businesses to leapfrog and outmaneuver much bigger ones. Expect enterprises in retail and other industries to attempt to use AI technology to better compete against larger firms.

Healthy skepticism of Artificial Intelligence & Coronavirus

The COVID-19 outbreak has spurred considerable news coverage about the ways artificial intelligence (AI) can combat the pandemic’s spread. Unfortunately, much of it has failed to be appropriately skeptical about the claims of AI’s value. Like many tools, AI has a role to play, but its effect on the outbreak is probably small. While this may change in the future, technologies like data reporting, telemedicine, and conventional diagnostic tools are currently far more impactful than AI.

1. LOOK TO THE SUBJECT-MATTER EXPERTS

Still, various news articles have dramatized the role AI is playing in the pandemic by overstating what tasks it can perform, inflating its effectiveness and scale, neglecting the level of human involvement, and being careless in consideration of related risks. In fact, the COVID-19 AI-hype has been diverse enough to cover the greatest hits of exaggerated claims around AI. And so, framed around examples from the COVID-19 outbreak, here are eight considerations for a skeptic’s approach to AI claims.

No matter what the topic, AI is only helpful when applied judiciously by subject-matter experts—people with long-standing experience with the problem that they are trying to solve. Despite all the talk of algorithms and big data, deciding what to predict and how to frame those predictions is frequently the most challenging aspect of applying AI. Effectively predicting a badly defined problem is worse than doing nothing at all. Likewise, it always requires subject matter expertise to know if models will continue to work in the future, be accurate on different populations, and enable meaningful interventions.

In the case of predicting the spread of COVID-19, look to the epidemiologists, who have been using statistical models to examine pandemics for a long time. Simple mathematical models of smallpox mortality date all the way back to 1766, and modern mathematical epidemiology started in the early 1900s. The field has developed extensive knowledge of its particular problems, such as how to consider community factors in the rate of disease transmission, that most computer scientists, statisticians, and machine learning engineers will not have.

“There is no value in AI without subject-matter expertise.”

It is certainly the case that some of the epidemiological models employ AI. However, this should not be confused for AI predicting the spread of COVID-19 on its own. In contrast to AI models that only learn patterns from historical data, epidemiologists are building statistical models that explicitly incorporate a century of scientific discovery. These approaches are very, very different. Journalists that breathlessly cover the “AI that predicted coronavirus” and the quants on Twitter creating their first-ever models of pandemics should take heed: There is no value in AI without subject-matter expertise.

2. AI NEEDS LOTS OF DATA

The set of algorithms that conquered Go, a strategy board game, and “Jeopardy!” have accomplishing impressive feats, but they are still just (very complex) pattern recognition. To learn how to do anything, AI needs tons of prior data with known outcomes. For instance, this might be the database of historical “Jeopardy!” questions, as well as the correct answers. Alternatively, a comprehensive computational simulation can be used to train the model, as is the case for Go and chess. Without one of these two approaches, AI cannot do much of anything. This explains why AI alone can’t predict the spread of new pandemics: There is no database of prior COVID-19 outbreaks (as there is for the flu)

To even attempt this, companies would need to collect extensive thermal imaging data from people while simultaneously taking their temperature with a conventional thermometer. In addition to attaining a sample diverse in age, gender, size, and other factors, this would also require that many of these people actually have fevers—the outcome they are trying to predict. It stretches credibility that, amid a global pandemic, companies are collecting data from significant populations of fevered persons. While there are other potential ways to attain pre-existing datasets, questioning the data sources is always a meaningful way to assess the viability of an AI system.

3. DON’T TRUST AI’S ACCURACY

The company Alibaba claims it can use AI on CT imagery to diagnose COVID-19, and now Bloomberg is reporting that the company is offering this diagnostic software to European countries for free. There is some appeal to the idea. Currently, COVID-19 diagnosis is done through a process called polymerase chain reaction (PCR), which requires specialized equipment. Including shipping time, it can easily take several days, whereas Alibaba says its model is much faster and is 96% accurate.

However, it is not clear that this accuracy number is trustworthy. A poorly kept secret of AI practitioners is that 96% accuracy is suspiciously high for any machine learning problem. If not carefully managed, an AI algorithm will go to extraordinary lengths to find patterns in data that are associated with the outcome it is trying to predict. However, these patterns may be totally nonsensical and only appear to work during development. In fact, an inflated accuracy number can actually be an important sign that an AI model is not going to be effective out in the world. That Alibaba claims its model works that well without caveat or self-criticism is suspicious on its face.

“[A]n inflated accuracy number can actually be an important sign that an AI model is not going to be effective out in the world.”

In addition, accuracy alone does not indicate enough to evaluate the quality of predictions. Imagine if 90% of the people in the training data were healthy, and the remaining 10% had COVID-19. If the model was correctly predicting all of the healthy people, a 96% accuracy could still be true—but the model would still be missing 40% of the infected people. This is why it’s important to also know the model’s “sensitivity,” which is the percent of correct predictions for individuals who have COVID-19 (rather than for everyone). This is especially important when one type of mistaken prediction is worse than the other, which is the case now. It is far worse to mistakenly suggest that a person with COVID-19 is not sick (which might allow them to continue infecting others) than it is to suggest a healthy person has COVID-19.

Broadly, this is a task that seems like it could be done by AI, and it might be. Emerging research suggests that there is promise in this approach, but the debate is unsettled. For now, the American College of Radiology says that “the findings on chest imaging in COVID-19 are not specific, and overlap with other infections,” and that it should not be used as a “first-line test to diagnose COVID-19.” Until stronger evidence is presented and AI models are externally validated, medical providers should not consider changing their diagnostic workflows—especially not during a pandemic.

4. REAL-WORLD DEPLOYMENT DEGRADES AI PERFORMANCE

The circumstances in which an AI system is deployed can also have huge implications for how valuable it really is. When AI models leave development and start making real-world predictions, they nearly always degrade in performance. In evaluating CT scans, a model that can differentiate between healthy people and those with COVID-19 might start to fail when it encounters patients who are sick with the regular flu (and it is still flu season in the United States, after all). A drop of 10% accuracy or more during deployment would not be unusual.

In a recent paper about the diagnosis of malignant moles with AI, researchers noticed that their models had learned that rulers were frequently present in images of moles known to be malignant. So, of course, the model learned that images without rulers were more likely to be benign. This is a learning pattern that leads to the appearance of high accuracy during model development, but it causes a steep drop in performance during the actual application in a health-care setting. This is why independent validation is absolutely essential before using new and high-impact AI systems.

“When AI models leave development and start making real-world predictions, they nearly always degrade in performance.”

This should engender even more skepticism of claims that AI can be used to measure body temperature. Even if a company did invest in creating this dataset, as previously discussed, reality is far more complicated than a lab. While measuring core temperature from thermal body measurements is imperfect even in lab conditions, environmental factors make the problem much harder. The approach requires an infrared camera to get a clear and precise view of the inner face, and it is affected by humidity and the ambient temperature of the target. While it is becoming more effective, the Centers for Disease Control and Prevention still maintain that thermal imaging cannot be used on its own—a second confirmatory test with an accurate thermometer is required.

5. MOST PREDICTIONS MUST ENABLE AN INTERVENTION TO REALLY MATTER

In high-stakes applications of AI, it typically requires a prediction that isn’t just accurate, but also one that meaningfully enables an intervention by a human. This means sufficient trust in the AI system is necessary to take action, which could mean prioritizing health-care based on the CT scans or allocating emergency funding to areas where modeling shows COVID-19 spread.

With thermal imaging for fever-detection, an intervention might imply using these systems to block entry into airports, supermarkets, pharmacies, and public spaces. But evidence shows that as many as 90% of people flagged by thermal imaging can be false positives. In an environment where febrile people know that they are supposed to stay home, this ratio could be much higher. So, while preventing people with fever (and potentially COVID-19) from enabling community transmission is a meaningful goal, there must be a willingness to establish checkpoints and a confirmatory test, or risk constraining significant chunks of the population.

This should be a constant consideration for implementing AI systems, especially those used in governance. For instance, the AI fraud-detection systems used by the IRS and the Centers for Medicare and Medicaid Services do not determine wrongdoing on their own; rather, they prioritize returns and claims for auditing by investigators. Similarly, the celebrated AI model that identifies Chicago homes with lead paint does not itself make the final call, but instead flags the residence for lead paint inspectors.

6. AI IS FAR BETTER AT MINUTE DETAILS THAN BIG, RARE EVENTS

Wired ran a piece in January titled “An AI Epidemiologist Sent the First Warnings of the Wuhan Virus” about a warning issued on Dec. 31 by infectious disease surveillance company, BlueDot. One blog post even said the company predicted the outbreak “before it happened.” However, this isn’t really true. There is reporting that suggests Chinese officials knew about the coronavirus from lab testing as early as Dec. 26. Further, doctors in Wuhan were spreading concerns online (despite Chinese government censorship) and the Program for Monitoring Emerging Diseases, run by human volunteers, put out a notification on Dec. 30.

That said, the approach taken by BlueDot and similar endeavors like HealthMap at Boston Children’s Hospital aren’t unreasonable. Both teams are a mix of data scientists and epidemiologists, and they look across health-care analyses and news articles around the world and in many languages in order to find potential new infectious disease outbreaks. This is a plausible use case for machine learning and natural language processing and is a useful tool to assist human observers. So, the hype, in this case, doesn’t come from skepticism about the feasibility of the application, but rather the specific type of value it brings.

“AI is unlikely to build the contextual understanding to distinguish between a new but manageable outbreak and an emerging pandemic of global proportions.”

Even as these systems improve, AI is unlikely to build the contextual understanding to distinguish between a new but manageable outbreak and an emerging pandemic of global proportions. AI can hardly be blamed. Predicting rare events is just very hard, and AI’s reliance on historical data does it no favors here. However, AI does offer quite a bit of value at the opposite end of the spectrum—providing minute detail.

For example, just last week, California Gov. Gavin Newsom explicitly praised BlueDot’s work to model the spread of the coronavirus to specific zip codes, incorporating flight-pattern data. This enables relatively precise provisioning of funding, supplies, and medical staff based on the level of exposure in each zip code. This reveals one of the great strengths of AI: its ability to quickly make individualized predictions when it would be much harder to do so individually. Of course, individualized predictions require individualized data, which can lead to unintended consequences.

7. THERE WILL BE UNINTENDED CONSEQUENCES

AI implementations tend to have troubling second-order consequences outside of their exact purview. For instance, consolidation of market power, insecure data accumulation, and surveillance concerns are very common byproducts of AI use. In the case of AI for fighting COVID-19, the surveillance issues are pervasive. In South Korea, the neighbors of confirmed COVID-19 patients were given details of that person’s travel and commute history. Taiwan, which in many ways had a proactive response to the coronavirus, used cell phone data to monitor individuals who had been assigned to stay in their homes. Israel and Italy are moving in the same direction. Of exceptional concern is the deployed social control technology in China, which nebulously uses AI to individually approve or deny access to public space.

Government action that curtails civil liberties during an emergency (and likely afterwards) is only part of the problem. The incentives that markets create can also lead to long-term undermining of privacy. At this moment, Clearview AI and Palantir are among the companies pitching mass-scale surveillance tools to the federal government. This is the same Clearview AI that scraped the web to make an enormous (and unethical) database of faces—and it was doing so as a reaction to an existing demand in police departments for identifying suspects with AI-driven facial recognition. If governments and companies continue to signal that they would use invasive systems, ambitious and unscrupulous start-ups will find inventive new ways to collect more data than ever before to meet that demand.

8. DON’T FORGET: AI WILL BE BIASED

In new approaches to using AI in high-stakes circumstances, bias should be a serious concern. Bias in AI models results in skewed estimates across different subgroups, such as women, racial minorities, or people with disabilities. In turn, this frequently leads to discriminatory outcomes, as AI models are often seen as objective and neutral.

While investigative reporting and scientific research has raised awareness about many instances of AI bias, it is important to realize that AI bias is more systemic than anecdotal. An informed AI skeptic should hold the default assumption that AI models are biased, unless proven otherwise.

“An informed AI skeptic should hold the default assumption that AI models are biased, unless proven otherwise.”

For example, a preprint paper suggests it is possible to use biomarkers to predict mortality risk of Wuhan COVID-19 patients. This might then be used to prioritize care for those most at risk—a noble goal. However, there are myriad sources of potential bias in this type of prediction. Biological associations between race, gender, age, and these biomarkers could lead to biased estimates that don’t represent mortality risk. Unmeasured behavioral characteristics can lead to biases, too. It is reasonable to suspect that smoking history, more common among Chinese men and a risk factor for death by COVID-19, could bias the model into broadly overestimating male risk of death.

Especially for models involving humans, there are so many potential sources of bias that they cannot be dismissed without investigation. If an AI model has no documented and evaluated biases, it should increase a skeptic’s certainty that they remain hidden, unresolved, and pernicious.

THE FUTURE OF AI SYSTEMS IS MORE PROMISING

While this article takes a deliberately skeptical perspective, the future impact of AI on many of these applications is bright. For instance, while diagnosis of COVID-19 with CT scans is of questionable value right now, the impact that AI is having on medical imaging is substantial. Emerging applications can evaluate the malignancy of tissue abnormalities, study skeletal structures, and reduce the need for invasive biopsies.

Other applications show great promise, though it is too soon to tell if they will meaningfully impact this pandemic. For instance, AI-designed drugs are just now starting human trials. The use of AI to summarize thousands of research papers may also quicken medical discoveries relevant to COVID-19.

AI is a widely applicable technology, but its advantages need to be hedged in a realistic understanding of its limitations. To that end, the goal of this paper is not to broadly disparage the contributions that AI can make, but instead to encourage a critical and discerning eye for the specific circumstances in which AI can be meaningful.

Analyzing Corona Virus data with AI

Fully understanding and solving the coronavirus pandemic will be about the data. There’s no shortage of data sources that are growing hourly. Now nine organizations, business and academic, have formed a coalition to bring coronavirus data sources together, and added incentives for researchers who can apply modern data analysis and artificial intelligence to it. Leading this effort is the Silicon Valley company C3.ai

C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign (UIUC), the University of California, Berkeley, Princeton University, the University of Chicago, the Massachusetts Institute of Technology, Carnegie Mellon University, and the National Center for Supercomputing Applications at UIUC announced two major initiatives:

• C3.ai Digital Transformation Institute (C3.ai DTI), a research consortium dedicated to accelerating the application of artificial intelligence to speed the pace of digital transformation in business, government, and society. Jointly managed by UC Berkeley and UIUC, C3.ai DTI will sponsor and fund world-leading scientists in a coordinated effort to advance the digital transformation of business, government, and society.
• C3.ai DTI First Call for Research Proposals: C3.ai DTI invites scholars, developers, and researchers to embrace the challenge of abating COVID-19 and advance the knowledge, science, and technologies for mitigating future pandemics using AI. This is the first in what will be a series of bi-annual calls for Digital Transformation research proposals.

“The C3.ai Digital Transformation Institute is a consortium of leading scientists, researchers, innovators, and executives from academia and industry, joining forces to accelerate the social and economic benefits of digital transformation,” said Thomas M. Siebel, CEO of C3.ai. “We have the opportunity through public-private partnership to change the course of a global pandemic,” Siebel continued. “I cannot imagine a more important use of AI.”

Immediate Call for Proposals: AI Techniques to Mitigate Pandemic

Topics for Research Awards may include but are not limited to the following:

1. Applying machine learning and other AI methods to mitigate the spread of the COVID-19 pandemic
2. Genome-specific COVID-19 medical protocols, including precision medicine of host responses
3. Biomedical informatics methods for drug design and repurposing
4. Design and sharing of clinical trials for collecting data on medications, therapies, and interventions
5. Modeling, simulation, and prediction for understanding COVID-19 propagation and efficacy of interventions
6. Logistics and optimization analysis for design of public health strategies and interventions
7. Rigorous approaches to designing sampling and testing strategies
8. Data analytics for COVID-19 research harnessing private and sensitive data
9. Improving societal resilience in response to the spread of the COVID-19 pandemic
10. Broader efforts in biomedicine, infectious disease modeling, response logistics and optimization, public health efforts, tools, and methodologies around the containment of rising infectious diseases and response to pandemics, so as to be better prepared for future infectious diseases

The first call for proposals is open now, with a deadline of May 1, 2020. Researchers are invited to learn more about C3.ai DTI and how to submit their proposals for consideration at C3DTI.ai. Selected proposals will be announced by June 1, 2020.

Up to $5.8 million in awards will be funded from this first call, ranging from $100,000 to $500,000 each. In addition to cash awards, C3.ai DTI recipients will be provided with significant cloud computing, supercomputing, data access, and AI software resources and technical support provided by Microsoft and C3.ai. This will include unlimited use of the C3 AI Suite and access to the Microsoft Azure cloud platform and access to the Blue Waters supercomputer at the National Center for Supercomputing Applications (NCSA) at UIUC.

“We are collecting a massive amount of data about MERS, SARS, and now COVID-19,” said Condoleezza Rice, former US Secretary of State. “We have a unique opportunity before us to apply the new sciences of AI and digital transformation to learn from these data how we can better manage these phenomena and avert the worst outcomes for humanity,” Rice continued. “I can think of no work more important and no response more cogent and timely than this important public-private partnership.”

“We’re excited about the C3.ai Digital Transformation Institute and are happy to join on a shared mission to accelerate research at these eminent research institutions,” said Eric Horvitz, Chief Scientist at Microsoft and C3.ai DTI Advisory Board Member. “As we launch this exciting private-public partnership, we’re enthusiastic about aiming the broader goals of the Institute at urgent challenges with the COVID-19 pandemic, as well as on longer-term research that could help to minimize future pandemics.”

"At UC Berkeley, we are thrilled to help co-lead this important endeavor to establish and advance the science of digital transformation at the nexus of machine learning, IoT, and cloud computing,” said Carol Christ, Chancellor, UC Berkeley. “We believe this Institute has the potential to make tremendous contributions by including ethics, new business models, and public policy to the technologies for transforming societal scale systems globally."

“The C3.ai Digital Transformation Institute, with its vision of cross-institutional and multi-disciplinary collaboration, represents an exciting model to help accelerate innovation in this important new field of study,” said Robert J. Jones, Chancellor of the University of Illinois at Urbana-Champaign. “At this time of a global health crisis, the Institute’s initial research focus will be on applying AI to mitigate the COVID-19 pandemic and to learn from it how to protect the world from future pandemics. C3.ai DTI is an important addition to the world’s fight against this disease and a powerful new resource in developing solutions to all societal challenges.”

“Together with the other C3.ai Digital Transformation Institute partners, we look forward to creating a powerful ecosystem of scholars and educators committed to applying 21st century technologies to the benefit of all,” said Chris Eisgruber, President of Princeton University. “This public-private partnership with innovators like C3.ai and Microsoft, providing support to world-class researchers across a range of disciplines, promises to bring rapid innovation to an exciting new frontier.”

“By strongly supporting multidisciplinary research and multi-institution projects, the C3.ai DTI represents a new avenue to develop breakthrough scientific results with a positive impact on society at a time of great need,” said Robert Zimmer, President of the University of Chicago. “I’m very pleased that the University of Chicago is part of this formidable collaboration between academia and industry to lead crucial innovation with great purpose and urgency.”

“The vision of C3.ai DTI is driven by the recognition of digital transformation as both a science as well as a scientific imperative for this pivotal time, applicable to every sector of our economy across the public and private sectors, including in healthcare, education, and public health,” said Farnam Jahanian, President of Carnegie Mellon University. “We are excited to participate in building out the Institute’s structure, program and further alliances. This is just the beginning of an ambitious journey that can have enormous positive impact on the world.”

"At MIT, we share the commitment of C3.ai DTI to advancing the frontiers of AI, cybersecurity and related fields while building into every inquiry a deep concern for ethics, privacy, equity and the public interest,” said Rafael Reif, President of the Massachusetts Institute of Technology. “At this moment of national emergency, we are proud to be part of this intensive effort to apply these sophisticated tools to better analyze the COVID-19 epidemic and devise effective ways to stop it. We look forward to accelerating this work both by collaborating with the companies and institutions in the initiative, and by drawing on the frontline experience and clinical data of our colleagues in Boston's world-class hospitals."

Building Community

At the heart of C3.ai DTI will be the constant flow of new ideas and expertise provided by ongoing research, visiting professors and research scholars, and faculty and scholars in residence, many of whom will come from beyond the member institutions. This rich ecosystem will form the foundational structure of a new Science of Digital Transformation.

“This is about global innovation based on multinational collaboration to accelerate the positive impact of AI by providing researchers access to real world data and to massive resources,” said Jim Snabe, Chairman, Siemens. “This is exactly the kind of multinational public-private partnership that is required to address this critical issue.”

“I could not be more proud of our association with C3.ai and Microsoft,” said Lorenzo Simonelli, CEO of Baker Hughes. “This is exactly the kind of leadership that is required to bring together the best of us to address this critical need.”

“We are at war and we must win it! Using all means,” said Jacques Attali, French statesman. “This great project will organize global scientific collaboration for accelerating the social impact of AI, and help to win this war, using new weapons, for the best of mankind.”

“In these difficult times, we need – now more than ever – to join our forces with scholars, innovators, and industry experts to propose solutions to complex problems. I am convinced that digital, data science and AI are a key answer,” said Gwenaëlle Avice-Huet, Executive Vice President of ENGIE. “The C3.ai Digital Transformation Institute is a perfect example of what we can do together to make the world better.”

Establishing the New Science of Digital Transformation

C3.ai DTI will focus its research on AI, Machine Learning, IoT, Big Data Analytics, human factors, organizational behavior, ethics, and public policy. The Institute will support the development of ML algorithms, data security, and cybersecurity techniques. C3.ai DTI research will analyze new business operation models, develop methods of implementing organizational change management and protecting privacy, and amplify the dialogue around the ethics and public policy of AI.

C3.ai Digital Transformation Institute is a Research Initiative that Includes:

• Research Awards: Up to 26 cash awards annually, ranging from $100,000 to $500,000 each
• Computing Resources: Access to free Azure Cloud and C3 AI Suite resources
• Visiting Professors & Research Scientists: $750,000 per year to support C3.ai DTI Visiting Scholars
• Curriculum Development: Annual awards to faculty at member institutions to develop curricula that teach the emerging field of Digital Transformation Science
• Data Analytics Platform: C3.ai DTI will host an elastic cloud, big data, development, and operating platform, including the C3 AI Suite hosted on Microsoft Azure for the purpose of supporting C3.ai DTI research, curriculum development, and teaching.
• Educational Program: $750,000 a year to support an annual conference, annual report, newsletters, published research, and website
• Industry Alignment: C3.ai DTI Industry Partners will be established to assure the institute’s operations are aligned to the needs of the private sector.
• Open Source: C3.ai DTI will strongly favor proposals that promise to publish their research in the public domain.

To support the Institute, C3.ai will provide C3.ai DTI $57,250,000 in cash contributions over the first five years of operation. C3.ai and Microsoft will contribute an additional $310 million in-kind, including use of the C3 AI Suite and Microsoft Azure computing, storage, and technical resources to support C3.ai DTI research.

To learn more about C3.ai DTI’s program, award opportunities, and call for proposals, please visit C3DTI.ai.

About C3.ai Digital Transformation Institute

C3.ai Digital Transformation Institute represents an innovative vision to take AI, ML, IoT, and big data research in a consortium model to a level that cannot be achieved at any one institution alone. Jointly managed and hosted by the University of California, Berkeley and the University of Illinois at Urbana-Champaign, C3.ai DTI will attract the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of business, government, and society, and establish the new Science of the Digital Transformation of Societal Systems.

About C3.ai

C3.ai is a leading AI software provider for accelerating digital transformation. C3.ai delivers the C3 AI Suite for developing, deploying, and operating large-scale AI, predictive analytics, and IoT applications in addition to an increasingly broad portfolio of turn-key AI applications. The core of the C3.ai offering is a revolutionary, model-driven AI architecture that dramatically enhances data science and application development. Learn more at: www.c3.ai.