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UM-Dearborn Faculty, staff and students have had to reinvest a lot of their energy over the past nine months coping with a global pandemic. And rightly so. But its important to note that the redistribution of effort hasnt completely halted progress on other important work at the university. UM-Dearborns new artificial intelligence center is a great case in point. The Dearborn Artificial Intelligence Research (DAIR) Center was founded less than a year ago. But organizers have already made some important progress toward their goal of making AI one of the universitys topline areas for research, education and industry collaboration.

The DAIR Centers Founding Director and Associate Professor of Computer and Information Science Marouane Kessentini says one of the main reasons for creating the center was to ignite more collaboration among the many faculty and students whose work involves AI. He says thats imperative because todays most interesting and relevant AI problems are often so complex, no one researcher can tackle them alone. The deep projects require people from computer science, people who have core expertise in statistics and business, and even people who have an understanding of ethics and human behavior, Kessentini says. So the goal is to bring us all together to enable much larger-scale research than weve done before and which is required by todays industry.

Over the past year, the DAIR Center has organized a constant stream of activities to nurture that culture. One of the most productive, he says, has been weekly brainstorming sessions, which are often centered on broad funding opportunities rather than specific AI research areas that might attract some folks and not others. Through those sessions, DAIR Center teams have already brainstormed, vetted and submitted numerous proposals, including a smart cities-focused project designed to support the Urban Futures component of UM-Dearborns new strategic plan. The approach seems to be working, too: Several of the DAIR Center team projects have already won funding. And the center also recently organized a joint training with IBM on AI for smart manufacturing that attracted more than 150 participants from industry and academia.

Kessentini says this same philosophy could also reshape the student experience of AI, with a more interdisciplinary curriculum and increased opportunities to work closely with business leaders. In their conversations with industry, one of the themes they heard again and again was a huge need for talent that transcends mere technical expertise. When we talk about building real-world AI systems, it goes far beyond just knowing algorithms and the basics of computer science. Theyre looking for people who can actually understand the ethics part of AI, the biases in the data, and build systems that account for all of that. To that end, Kessentini says UM-Dearborn and the DAIR Center hope theyll soon be launching an interdisciplinary masters degree in artificial intelligence the first program of its kind in Michigan.

The mantra for building the new center is think big, start small, and scale fast, and even less than a year in, it looks like theyre starting to eye that third step. Already, the DAIR Centers ranks include more than 40 faculty from CECS, COB and CASL, 30 doctoral students, and dozens of alumni and industry partners, including big names like IBM, eBay and Sumitomo. Theyre hopeful theyll attract even more interest through a big AI symposium later this month. That will feature five days of keynotes and panels, and top execs from Google, IBM, Ford, Oracle, GM, Intel and more.

There will also be multiple activities where students and faculty can lay the ground for future collaborations. For example, to simulate the quintessential meet-and-greet in-person mixer, theyre trying out something called mystery speed networking. Basically, the platform will select two random people and connect them together for two minutes, Kessentini says. So you have time to introduce yourself, and then you can push a button to exchange business cards. At the end of two minutes, you get connected to another person. Because the process is completely random, he says its totally possible UM-Dearborn students could end up scoring some facetime with a VP at eBay, the head of analytics at Google, or the chief data officer at IBM.

Source: UM-Dearborn

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UM-Dearborn's new artificial intelligence center is thriving - Dearborn Press and Guide

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Wanted: Human Assistant to the Artificial Intelligence

We are seeking junior and mid-level human applicants to serve as data science assistants to our departmental artificial intelligence (AI) in charge of data analytics. Responsibilities include reviewing, interpreting, and providing feedback about analytics results to the AI, and writing summary reports of AI results for human communication. Requires ability to interact with vendors and information technology staff to provide hardware support for the AI. Experience collaborating with computer-based staff a plus. Must have good human-computer interaction skills. Formal training in the ethical treatment of computers and assessment of the fairness and bias of computer-generated results preferred.

The above is a job advertisement from the future but not that far into it. It points to where we are going, and where we could be in maybe even as few as five years if we devote the resources and resolution to do the necessary research. But our recent past has shown us that we can develop the type of machines that would soon open up a whole new field of lucrative and fulfilling work.

See, over the last decade, a new computer science discipline called automated machine learning, or AutoML, has rapidly developed. AutoML grew organically in response to the many challenges of applying machine learning to the analysis of big data for the purpose of making predictions about health outcomes, economic trends, device failures, and any number of things in a wide field that are best served when rapid and comprehensive data can be analyzed.

For run-of-the-mill machine learning to work, an abundance of choices is required, ranging from the optimal method for the data being analyzed, and the parameters that should be chosen therein. For perspective, there are dozens of popular machine learning methods, each with thousands or millions of possible settings. Wading through these options can be daunting for new users and experts alike.

The promise of AutoML, then, is that the computer can find the optimal approachautomatically, significantly lowering the barrier of entry.

So how do we get to AutoML and to the job advertisement above? There are several hurdles.

The first is persistence. An artificial intelligence (AI) for AutoML must be able to analyze data continuously and without interruption. This means the AutoML AI needs to live in a robust, redundant, and reliable computing environment. This can likely be accomplished using currently available cloud computing platforms. The key advance is modifying the software to be persistent.

The second hurdle is memory and learning. An AutoML AI must have a memory of all machine learning analyses it has run and learn from that experience.PennAI, which my colleagues and I developed,is an example of an open-source AutoML tool that has both, but there arent many others. An importance would be to give AutoML the ability to learn from failure. Its current tools all learn from successes, but humans learn more from failure than success. Building this ability into AutoML AI could be quite challenging but necessary.

The third hurdle is explainability. A strength of human-based data science is our ability to ask each otherwhy. Why did you choose that algorithm? Why did you favor one result over another? Current AutoML tools do not yet allow the user to ask.

The final hurdle is human-computer interaction (HCI). What is the optimal way for a human to interact with AI doing data analytics? What is the best way for a human to give an AI feedback or provide it with knowledge? While we have made great progress in the general space of HCI, our knowledge of how to interact with AIs remains in its infancy.

It is entirely conceivable that an AI for AutoML could be built within the next few years that is persistent and can learn from experience, explain the decisions it makes as well as the results it generates, interact seamlessly with humans, and efficiently incorporate and use expert knowledge as it tries to solve a data science problem. These are all active areas of investigation and progress will depend mostly on a dedicated effort to bring these pieces together.

All that said, automated and persistent AI systems will find their place in the near future, once we make a concerted effort to thoroughly research it. We should start preparing our human-based workforce for this reality. We will need vocational programs to train humans how to interact with a persistent AI agent, in much the same way that we have programs to train others who work with and interpret specialized equipment, such as emergency room technicians. There will also need to be an educational culture shift on top of that training, as we will need to integrate AI interaction into courses covering communication, ethics, psychology, and sociology.

This technology is very much within reach. When we do reach it, well have a new, expansive field for human workers. Soon, it will be time to write a job description, but only once we figure out some crucial problems.

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Job Ads for AI Could Soon Look Like This. Are You Ready? - ExtremeTech

The Next Generation of Onit is AI

Onit Acquires McCarthyFinch and Launches Precedent, an AI Legal Platform that Reads, Writes and Reasons like a Lawyer

HOUSTON, Nov. 17, 2020 (GLOBE NEWSWIRE) -- Onit, Inc., a leading provider of enterprise workflow solutions including enterprise legal management, contract lifecycle management and workflow automation, today announced that the company has acquired McCarthyFinch and its artificial intelligence platform that accelerates contract reviews and approvals by up to 70% and increases user productivity by more than 50%.

The acquisition reinforces Onits innovation strategy to deliver powerful AI-based workflow and business process automation solutions. The company plans to further its innovation through AI by evolving its product offerings as well as the software provided by its legal operations management software subsidiary SimpleLegal.

The technology will become an integral component of Onits new artificial intelligence platform Precedent and the companys first release on the platform will be ReviewAI.

Our vision is to build AI into our workflow platform and every product across the Onit and SimpleLegal product portfolios, stated Eric M. Elfman, Onit CEO and co-founder. AI will have an active role in everything from enterprise legal management to legal spend management and contract lifecycle management, resulting in continuous efficiencies and cost savings for corporate legal departments. Historically, legal departments have been thought of as black boxes where requests go in and information, decisions or contracts come out with no real transparency. AI has the potential to enhance transparency and contribute to stronger enterprisewide business collaboration in a way that conserves a lawyers valuable time.

McCarthyFinchs breadth of AI expertise from lawyers, technologists and data scientists speaks to the ever-evolving needs of the legal profession and Onit customers.

AI is a natural extension of our evolution, continued Elfman. In addition to acquiring award-winning technology, we have gained some of the brightest minds in the AI space.

Nick Whitehouse, McCarthyFinchs CEO and co-founder, is now the general manager of the newly rebranded Onit AI Center of Excellence. He has focused on digital innovation and AI for more than 15 years and was recognized in 2019 as the IDC DX Leader of the Year for his advocacy across the legal industry and Australasia. He is joined by McCarthyFinchs vice president of legal, Jean Yang, who is now vice president of the Onit AI Center of Excellence.

McCarthyFinch has been dedicated to building world-leading AI that augments lawyers and helps automate low-value and time-intensive manual legal processes. Drafting contracts and redlining documents shouldnt take up 70% of a lawyers time, as statistics suggest. Theres a better way to work, stated Whitehouse. With AI, weve dramatically changed the contract management lifecycle and enabled businesses to move faster, provide higher-quality services and lower the cost of legal services. We are excited to join the Onit team and apply AI to Onits contract lifecycle management solution and expansive product offerings.

Onit Is AI: Introducing Precedent and ReviewAI

Onits new intelligence platform, Precedent, is uniquely positioned to complement its existing workflow automation platform, Apptitude, and drive AI and digital transformation in the legal market. The Precedent intelligence platform reads, writes and reasons like a lawyer, enabling legal and business professionals to get more work done faster. It combines machine learning and natural language processing so legal teams can automate tasks and processes to make them more efficient, cost-effective and faster.

The first release on the Precedent intelligence platform, ReviewAI, focuses on pre-signature contract review. Law departments need a rapid path through drafting and negotiation to contract closure so they can accelerate the pace of doing business, increase contract compliance and enhance employee productivity. Using ReviewAI, lawyers can streamline intelligent activities like contract creation, redlining, complex negotiations and risk rating contracts on their terms. Through Precedent, ReviewAI learns from the vast inventory of a companys contracts, leverages the companys playbook and presents the results in a Microsoft Word plug-in so the legal team can work where it is accustomed to operating. Legal and contract teams can save up to 70% on review time, increase contract compliance and lower company risk.

To learn more about the acquisition, listen to the Onit podcastfeaturing Elfman and Whitehouse and visit us online.

About Onit

Onit is a global leader of workflow and artificial intelligence platforms and solutions for legal, compliance, sales, IT, HR and finance departments. With Onit, companies can transform best practices into smarter workflows, better processes and operational efficiencies. With a focus on enterprise legal management, matter management, spend management, contract lifecycle management and legal holds, the company operates globally and helps transform the way Fortune 500 companies and billion-dollar corporate legal departments bridge the gap between systems of record and systems of engagement. Onit helps customers find gains in efficiency, reduce costs and automate transactions faster. For more information, visit http://www.onit.com or call 1-800-281-1330.

Media inquiries: Melanie BrennemanOnit(713) 294-7857Melanie.brenneman@onit.com

A video accompanying this announcement is available athttps://www.globenewswire.com/NewsRoom/AttachmentNg/445bbe2c-1b26-45b3-bcbc-75177d7e5960

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Onit Acquires New Zealand-based McCarthyFinch to Drive Innovation with Artificial Intelligence and Workflow Automation - GlobeNewswire

A computational method comprised of clinicodemographic variables with deep learning of pretreatment histology images may be able to effectively predict response to immunotherapy in patients with advanced melanoma, according to findings from a study published in Clinical Cancer Research.1

While immune checkpoint inhibitors have profoundly changed the treatment landscape in melanoma, many tumors do not respond to treatment, and many patients experience treatment-related toxicity, corresponding study author Iman Osman, MD, medical oncologist in the Departments of Dermatology and Medicine (Oncology) at New York University (NYU) Grossman School of Medicine and director of the Interdisciplinary Melanoma Program at NYU Langones Perlmutter Cancer Center, stated in a press release.2 An unmet need is the ability to accurately predict which tumors will respond to which therapy. This would enable personalized treatment strategies that maximize the potential for clinical benefit and minimize exposure to unnecessary toxicity.

In the study, investigators utilized computer algorithms referred to as deep convolutional neural networks (DCCN) to examine digital images of metastatic melanoma tumors and determine patterns linked with response to treatment with immune checkpoint blockade. Using this approach, they created a response classifier which was designed to predict if a patients tumor would respond to this treatment approach or would progress after treatment.

Results demonstrated that the multivariable classifier predicted response with AUC of 0.800 on images from the slide scanner Aperio AT2 and area under the curve (AUC) of 0.805 on images from the Leica SCN400. Moreover, the classifier was found to effectively stratify patients into groups who were at high or low risk for progressive disease.

Moreover, patients who were determined to be at high risk for progression were found to have significantly worse progression-free survival (PFS) versus those who were considered to be low risk (P =.02 for the Aperio AT2 and P =.03 for the Leica SCN400).

Several recent attempts to predict immunotherapy responses do so with robust accuracy but use technologies, such as RNA sequencing, that are not readily generalizable to the clinical setting, corresponding study author Aristotelis Tsirigos, PhD, professor in the Institute for Computational Medicine at NYU Grossman School of Medicine and member of NYU Langones Perlmutter Cancer Center, stated in the release. Our approach shows that responses can be predicted using standard-of-care clinical information such as pretreatment histology images and other clinical variables.

For the study, investigators examined data from a training cohort of 121 patients with metastatic melanoma who had received treatment with immunotherapy between 2004 and 2018. All of these patients had received frontline treatment with a CTLA-4 inhibitor, a PD-1 inhibitor, or a combination of the 2 agents. The clinical outcomes for these patients included disease progression or response to treatment; those who had achieved stable disease were excluded from the analysis.

The investigators validated the DCCN response classifier in an independent cohort of 30 patients with metastatic melanoma who had received treatment at Vanderbilt-Ingram Cancer Center between 2010 and 2017. They examined the performance of this classifier by calculating the AOC, where a score of 1 correlates with perfect prediction with the model. The model was found to have an AUC of about 0.7 in the training and validation cohorts.

Multivariable logistic regressions were then conducted, which combined the DCCN prediction with conventional clinical features. To augment prediction accuracy, data regarding the DCCN prediction, ECOG performance status, and treatment regimen were all incorporated into the final model. In both cohorts examined, the multivariable classifier achieved an AUC rose by 0.1. Now, the model was able to stratify patients based on risk for progression.

Additional data revealed that most of the patients, or about 64%, in the training group had received treatment with a single-agent CTLA-4 inhibitor. A little more than half of the patients, or about 53%, in the validation group had received PD-1 inhibitors. This information indicates that some of the predictive patterns observed are not necessarily dependent on the immune checkpoint target. Moreover, data gleaned from class activation mapping showed that cell nuclei played a central role in the DCCN predictions in that more and larger nuclei was determined to be associated with disease progression.

The study was not without limitations. For one, a relatively small number of images were utilized to train the computer algorithm; only 302 images and 40 images were examined in the training and validation cohorts, respectively. The authors admit that thousands of images may be required to adequately train models for clinical-grade performance.

There is potential for using computer algorithms to analyze histology images and predict treatment response, but more work needs to be done using larger training and testing datasets, along with additional validation parameters, in order to determine whether an algorithm can be developed that achieves clinical-grade performance and is broadly generalizable, concluded Tsirigos.

References

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Artificial Intelligence Possesses Potential to Predict Response to Immunotherapy in Advanced Melanoma - OncLive

EASYpour - the concept for automatic pouring

What have we heard and read about the potential of artificial intelligence (AI)?Which areas of application and advantages arise for foundries and which references are reliable?Can competitiveness actually be increased?

PourTech AB, the specialist for automatic casting and software applications, presents its path to the development and use of artificial intelligence in foundries based on laser and visualization technology.

The concept is called EASYpour, which in the following webinar clearly explains the basics and advantages of automation.

Fluctuations in the process, quality stabilization and improvement are just as targeted as the workload of employees and theCoordination of the cycle times of the molding system with optimized casting cycles.

See how production costs are reduced and all process data are saved and evaluated in real time and assigned to the casting.

Experience, hear and see what EASYpour has in store for you and your company:

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FREE WEBINAR: Use of artificial intelligence (AI) in the foundry - once a dream, now a reality - Foundry-Planet.com