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Rhode Islanders can access the portal at BackToWorkRI.com.

The initiative, which was launched in July 2020 by Governor Gina M. Raimondo, was meant to be a public-private partnership designed to train, support, and hire Rhode Islanders who have been displaced by the pandemic.

Since last March, thousands of Rhode Islanders have lost their jobs, leaving them no other choice but to file for unemployment. Sarah Blusiewicz, assistant director of workforce development for the Rhode Island Department of Labor and Training, said she hopes that after filing for unemployment, Rhode Islanders will be using this career center as a tool to get back to work.

COVID-19 has left thousands of Rhode Islanders unemployed and searching for new, sustainable careers, said Blusiewicz. Our collaboration with Google Cloud has married accessible technology with government innovation to train and connect workers with the resources they need to access in-demand jobs.

The career center uses artificial intelligence and machine learning to connect the states workforce with new career opportunities while using familiar productivity tools within Google Workspace. Some of the features include the AI-powered CareerCompass Rhode Island bot, named Skipper, which uses data and machine learning to connect residents with potential new career paths and proven reskilling opportunities.

The center also integrates video calling via Google Meet, so job seekers can meet with career coaches and use screen-sharing to review and edit resumes and cover letters in real time. Coaches and job seekers are paired based on the coachs areas of expertise and language fluency. The center is available in English and Spanish now, but can support multiple languages.

Rhode Island is the first state in the country to use AI/ML and Google Workspace to deploy a job search platform at this scale, said Mike Daniels, vice president of Global Public Sector at Google Cloud. We hope that the integrated experience provided by our technology will help job seekers hit the ground running.

Here are answers to common questions about the platform:

How does applying for a job on this platform help eliminate the implicit bias issues that resume readers have?

According to Blusiewicz, the platform will help push forward resumes for skills-based hiring, unlike other platforms that use technology that eliminates job seekers who dont use the correct keywords. In addition, she said since job seekers can book video conferences with employers and recruiters, the portal encourages hiring managers to go above the resume reader noise.

In conversations with local employers, Blusiewicz said, For years, the theme has been, We have great job seekers that need jobs. Great jobs are out there, she said, but traditional job boards with resume readers that searched only for certain terms, coupled with information overload, eliminated good candidates.

How does the platform ensure that Rhode Islanders are using the center, and not people from other states?

Users are not required to plug in their address, Blusiewicz said, but the idea is to have this portal connected to Rhode Islands unemployment insurance system.

As Rhode Islanders are filling out those unemployment claims, we can then direct them to this site as the next step, she said.

The job I had before the pandemic is probably not going to come back. How will this portal help me?

The machine learning aspect of the center will help someone transition into another field. It will scan your profile and the work history that you upload, and pull administrative data on people who have skills similar to yours. Based on your existing skills, it will help match you to other sectors and positions that are available.

Will similar platforms be available in other states?

Daniels said Google Cloud is in conversations with a number of states about developing a similar career center, but he said he believes that this site will pave the way for neighboring states.

I think weve really thought deeply in terms of how we could incorporate the human element and how to make this a different feeling for someone, by curating it for them, instead of them simply going out and pounding the job boards, said Daniels. This is coming at exactly the right time.

Alexa Gagosz can be reached at alexa.gagosz@globe.com. Follow her on Twitter @alexagagosz.

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R.I., Google Cloud unveil first-in-the-nation virtual career center powered by artificial intelligence - The Boston Globe

On this fifth episode from the Blueprints for American Renewal and Prosperity project, two Brookings experts discuss their blueprints for strengthening governance to meet key international challenges in the technology arena. Senior Fellow Landry Sign is co-author with Stephan Almond of A blueprint for technology governance in the post-pandemic world, and Senior Fellow Joshua Meltzer is co-author with Cameron Kerry of Strengthening international cooperation on artificial intelligence.

Also on this episode, Senior Fellow David Wessel, director of the Hutchins Center on Fiscal and Monetary Policy at Brookings, looks at the politics and the economics around raising the federal minimum wage to $15 an hour. Listen to this segment on Soundcloud.

See below for excerpts from the transcript.

Subscribe to Brookings podcastshereor oniTunes, send feedback email toBCP@Brookings.edu, and follow us and tweet us at@policypodcastson Twitter.

The Brookings Cafeteria is part of theBrookings Podcast Network.

EXCERPTS FROM THE DISCUSSION

MELTZER: So this is the paper coauthored with Cameron Kerry, and it focuses on strengthening international cooperation on artificial intelligence. And the basic approach of the paper is to identify what the existing approaches to AI policy development, both at the domestic level but also whats happening in various international and other multilateral forums to look at some of the challenges that are arising that essentially drive the need for international cooperation on AI, to look at the limitations of the current sort of mechanisms for international cooperation. And then we propose a range of policy recommendations for this administration to take forward to really build a more systemic approach to AI cooperation internationally.

SIGN: The paper, A Blueprint for Technology Governance in the Post Pandemic World, was coauthored with Stephen Almond. As a matter of fact, too often regulations struggle to keep pace with innovation, whether we speak about new ideas, products, or business models, they are hampered while citizens are so often left without options. So as government seeks to build back better in the context of the COVID-19 pandemic, a more agile, innovative, enabling approach to regulation is needed. So, our paper presents a blueprint for regulatory reforms offices to introduce a more innovative enabling approach to regulation across government and to seize the opportunities of technological change. So, I think we really try to ensure that on the one hand, the fast pace of technological innovation can continue. And on the other hand, the ability of governments to regulate those innovations such so that they serve the greater good is also enabled.

Full Transcript (pdf)

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Proposals to meet global challenges in artificial intelligence and technology regulation - Brookings Institution

The goal of retinopathy of prematurity (ROP) screening is to detect the constellation of clinical signs that suggest a high risk of progression to retinal detachment so that urgent treatment can be given. At each screening episode, there are 3 considerations: whether urgent treatment is needed, whether follow-up screening is needed, and whether there is no risk of sight-threatening ROP so that the screening can stop. The decisions are based on a detailed examination of the retina focused on the severity (ie, stage), location (ie, zone), and degree of dilation and tortuosity of retinal blood vessels (ie, plus disease). If ROP is not present, the peripheral retina needs to be assessed to determine the degree of maturity of the retinal vessels; if they are mature or ROP detected earlier is definitely regressing, the screening can stop.

Telemedicine with artificial intelligence (AI) image analysis could transform ROP screening, especially in settings with an insufficient supply of ophthalmologists, which can happen either because of absolute workforce shortages

Address correspondence to Clare Gilbert, FRCOphth, MSc, MD, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom. E-mail: clare.gilbert{at}lshtm.ac.uk

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Artificial Intelligence for ROP Screening and to Assess Quality of Care: Progress and Challenges - American Academy of Pediatrics

Artificial Intelligence

Insurance works with large amounts of data, about many individuals, many instances requiring insurance, and many factors involved in solving the claims. To add to the complexity, not all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice.

Insurance is about risk. The insurance industry sets rates based on expected payouts so that, hopefully, they end up with positive revenue. Setting rates and understanding payout in order to maintain profitability is complex, and the industry hope is that ML can help in achieving that goal. Note, here, Im focusing more on ML than artificial intelligence (AI), because many of the complex statistical tools that are now considered ML can more efficiently accomplish some of the tasks than would neural networks, expert systems, or other purely AI tools.

There are multiple ways machine learning can help in the insurance industry. Let us take a look at three.

Health and life insurance are complex. There are multiple factors that go into understanding an individuals risk factors for disease, illness, and mortality. Insurance underwriters have historically used a core set of factors such as male/female, age, and smoker/non-smoker. When other factors have been used, such as zip code, the problem of red-lining has appeared in insurance as well as the more well-known area of financial red-lining. Therefore, there are regulations about how some demographic information must be used.

The need to address those legal concerns means that underwriting isnt only about individual health risks, but about legal risks as well. Analysis must be done to back out some features that could cause legal risk while still creating pools that remain profitable.

This is where machine learning comes in. The performance of modern computing can handle large amounts of data, and complex regression analysis can perform clustering that can help with analysis. Those ML techniques provide value without the need for AI. For insurance underwriting, statistical models and procedural code are providing an improvement in analysis for companies, said Paul Ford, CEO & Co-Founder, Traffk. We are working with neural network models, but the overhead for training and runtime must be balanced with the accuracy improvement necessary to make it worthwhile to roll-out those engines. While things might change, down the road, our existing ML models provide advances in analysis and profitability for our customers.

At the other end of the insurance process is the issue of claims. It is not only the insured who have problems with claims complexity. In the automotive industry, the need to understand the variety of repair options and parts available create a challenge for both service providers and the insurers.

With automotive claims, providing an estimate based on the typical costs for repair is not sufficient. Its not only that vehicle types vary, within a class of vehicle the repair costs can vary based on the insurance coverage, as well as the availability of parts in geographic regions.

Machine learning can help with claims in a number of ways. In addition, multiple ML tools can be used throughout the claims process.

Take the First Notice of Loss (FNOL), the initial notification to the insurer about the accident or damage. If theres a quick estimate of total loss, theres a different process flow that is much simpler. No ML is needed in the review of damage, but robotic process automation (RPA) might be used to simplify the claim flow to payment.

With other damage, or even to understand if there is a total loss, ML can be used. The most obvious tool is AI vision, but even this can have multiple processes. A phone app can step a customer through taking pictures that an AI system can then analyze for damage, with a backend AI system working to link to parts and estimate. A repair shop, in comparison to the insured, is more familiar with the process and can have a different front-end asking more detailed questions to more quickly get a more educated response from the repair experts.

Note that two different approaches were mentioned. It would be overly complex to have a single AI system that could support every step in the claims process. More efficiency is gained by letting separate systems process claims, identify damage and provide repair estimates, said Evan Davies, CTO, Solera. By using different approaches to machine learning through the claims process, you maximize the benefits of automation and enable skilled workers to focus on more complex cases.

One thing Evan Davies also pointed out was how the process flow can change depending on the severity of accident or the type of insurance coverage provided. Minor damage and standard coverage can be fully automated, as all parties are fairly comfortable with the process and dollar amounts. Totals, as mentioned, dont require AI. Those claims in the middle, however, can be helped with an adjuster reviewing the analysis and working with the customer, for the benefit of both short term monetary issues and long term customer relations.

Yes, we keep coming back to fraud. Sadly, it is a human condition and a risk in so many areas of business. Insurance is no exception. As Ive recently talked about fraud and ML in other business arenas, I wont go into detail here. Let it be sufficient to point out that analysis of claims doesnt stop at processing all claims as if they are proper.

Cluster analysis is used to understand, for instance, if a similar type of accident is happening in an area at above normal amounts; potentially indicating organized fraud.

In the analysis of potential fraud, multiple tools are used, some are in ML, such as statistics, rules based approached and even neural networks.

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Machine Learning Can Help The Insurance Industry Throughout The Process Lifecycle - Forbes