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Economists use the term hysteresis to describe the phenomenonthat, when conditions in an economy change, the effects of that change often remain even after the conditions return to normal.

COVID and its impact on the workforce may provide a good example of hysteresis, said HAI Distinguished Fellow and MIT professor Erik Brynjolfsson, who will join Stanford faculty in July 2020 as the director of the new Digital Economy Lab.

To keep workers safe and continue functioning, companies have ramped up remote work and are aggressively automating someoperations and exploring machine learning.

Some of these changes are going to be permanent, he said during Stanford HAIs recent online conference COVID+AI: The Road Ahead. The question is, what parts of the economy are going to be most affected by the adoption of these technologies, andwhich parts will be less affected?

Brynjolfsson worked with Carnegie Mellon professor Tom Mitchell, MIT postdoc Daniel Rock, and others on a series of papers identifying the tasks most suitable for machine learning (ML). They applied this rubric to 950 occupations and 18,000 specific occupation tasks.

More tasks in lower-wage jobs could be replaced by machine learning applications, they found. For example, ML today can better recognize a cucumber or a banana and handle some cashier skills. But some high-paid jobs can also be impacted, such as airline pilots. No occupation is completely immune, Brynjolfsson said.

Certain industries are also more impacted than others, he noted. Manufacturing, retailing, transportation, and accommodation and food services have many tasks suitable for machine learning.

Additionally, different areas of the country will be affected unevenly. The kinds of work that people do in Wyoming are very different from what they do in Manhattan or Miami, he said.

The researchers data also allowed them to examine ML impact on individual occupations. Roles like tellers, executive assistants, and personal bankers have a large percentage of tasks that are suitable for ML.

Our tool gives them a way to have a path for what to do next, Brynjolfsson said. Personal bankers could develop more skills not subject to machine learning, like leadership, product development, or customer relations, and move away from the skills more suitable to ML like credit authorization. Another option: Find new roles with similar skill sets. A personal banker might transition to business analyst or mortgage loan officer, roles ML is less likely to disrupt.

With a little bit of training, they're in a position to be much less vulnerable to the machine learning revolution, he noted.

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How Work Will Change Following the Pandemic - Stanford University News

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Chapter 10 Continue.

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Best Report Machine Learning For Managing Diabetes Market (COVID 19 Updated) Climbs on Positive Outlook of Excellent Growth by 2027: Allscripts...

GPUs became the hardware of choice for deep learning largely by coincidence. The chips were initially designed to quickly render graphics in applications such as video games. Unlike CPUs, which have four to eight complex cores for doing a variety of computation, GPUs have hundreds of simple cores that can perform only specific operationsbut the cores can tackle their operations at the same time rather than one after another, shrinking the time it takes to complete an intensive computation.

It didnt take long for the AI research community to realize that this massive parallelization also makes GPUs great for deep learning. Like graphics-rendering, deep learning involves simple mathematical calculations performed hundreds of thousands of times. In 2011, in a collaboration with chipmaker Nvidia, Google found that a computer vision model it had trained on 2,000 CPUs to distinguish cats from people could achieve the same performance when trained on only 12 GPUs. GPUs became the de facto chip for model training and inferencingthe computational process that happens when a trained model is used for the tasks it was trained for.

But GPUs also arent perfect for deep learning. For one thing, they cannot function as a standalone chip. Because they are limited in the types of operations they can perform, they must be attached to CPUs for handling everything else. GPUs also have a limited amount of cache memory, the data storage area nearest a chips processors. This means the bulk of the data is stored off-chip and must be retrieved when it is time for processing. The back-and-forth data flow ends up being a bottleneck for computation, capping the speed at which GPUs can run deep-learning algorithms.

NEURAL MAGIC

In recent years, dozens of companies have cropped up to design AI chips that circumvent these problems. The trouble is, the more specialized the hardware, the more expensive it becomes.

So Neural Magic intends to buck this trend. Instead of tinkering with the hardware, the company modified the software. It redesigned deep-learning algorithms to run more efficiently on a CPU by utilizing the chips large available memory and complex cores. While the approach loses the speed achieved through a GPUs parallelization, it reportedly gains back about the same amount of time by eliminating the need to ferry data on and off the chip. The algorithms can run on CPUs at GPU speeds, the company saysbut at a fraction of the cost. It sounds like what they have done is figured out a way to take advantage of the memory of the CPU in a way that people havent before, Thompson says.

Neural Magic believes there may be a few reasons why no one took this approach previously. First, its counterintuitive. The idea that deep learning needs specialized hardware is so entrenched that other approaches may easily be overlooked. Second, applying AI in industry is still relatively new, and companies are just beginning to look for easier ways to deploy deep-learning algorithms. But whether the demand is deep enough for Neural Magic to take off is still unclear. The firm has been beta-testing its product with around 10 companiesonly a sliver of the broader AI industry.

We want to improve not just neural networks but also computing overall.

Neural Magic currently offers its technique for inferencing tasks in computer vision. Clients must still train their models on specialized hardware but can then use Neural Magics software to convert the trained model into a CPU-compatible format. One client, a big manufacturer of microscopy equipment, is now trialing this approach for adding on-device AI capabilities to its microscopes, says Shavit. Because the microscopes already come with a CPU, they wont need any additional hardware. By contrast, using a GPU-based deep-learning model would require the equipment to be bulkier and more power hungry.

Another client wants to use Neural Magic to process security camera footage. That would enable it to monitor the traffic in and out of a building using computers already available on site; otherwise it might have to send the footage to the cloud, which could introduce privacy issues, or acquire special hardware for every building it monitors.

Shavit says inferencing is also only the beginning. Neural Magic plans to expand its offerings in the future to help companies train their AI models on CPUs as well. We believe 10 to 20 years from now, CPUs will be the actual fabric for running machine-learning algorithms, he says.

Thompson isnt so sure. The economics have really changed around chip production, and that is going to lead to a lot more specialization, he says. Additionally, while Neural Magics technique gets more performance out of existing hardware, fundamental hardware advancements will still be the only way to continue driving computing forward. This sounds like a really good way to improve performance in neural networks, he says. But we want to improve not just neural networks but also computing overall.

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The startup making deep learning possible without specialized hardware - MIT Technology Review

What opportunities have we missed by not having these procedures in place?

Its easy to overhype whats possible, and AI was probably never going to play a huge role in this crisis. Machine-learning systems are not mature enough.

But there are a handful of cases in which AI is being tested for medical diagnosis or for resource allocation across hospitals. We might have been able to use those sorts of systems more widely, reducing some of the load on health care, had they been designed from the start with ethics in mind.

With resource allocation in particular, you are deciding which patients are highest priority. You need an ethical framework built in before you use AI to help with those kinds of decisions.

So is ethics for urgency simply a call to make existing AI ethics better?

Thats part of it. The fact that we dont have robust, practical processes for AI ethics makes things more difficult in a crisis scenario. But in times like this you also have greater need for transparency. People talk a lot about the lack of transparency with machine-learning systems as black boxes. But there is another kind of transparency, concerning how the systems are used.

This is especially important in a crisis, when governments and organizations are making urgent decisions that involve trade-offs. Whose health do you prioritize? How do you save lives without destroying the economy? If an AI is being used in public decision-making, transparency is more important than ever.

What needs to change?

We need to think about ethics differently. It shouldnt be something that happens on the side or afterwardssomething that slows you down. It should simply be part of how we build these systems in the first place: ethics by design.

I sometimes feel ethics is the wrong word. What were saying is that machine-learning researchers and engineers need to be trained to think through the implications of what theyre building, whether theyre doing fundamental research like designing a new reinforcement-learning algorithm or something more practical like developing a health-care application. If their work finds its way into real-world products and services, what might that look like? What kinds of issues might it raise?

Some of this has started already. We are working with some early-career AI researchers, talking to them about how to bring this way of thinking to their work. Its a bit of an experiment, to see what happens. But even NeurIPS [a leading AI conference] now asks researchers to include a statement at the end of their papers outlining potential societal impacts of their work.

Youve said that we need people with technical expertise at all levels of AI design and use. Why is that?

Im not saying that technical expertise is the be-all and end-all of ethics, but its a perspective that needs to be represented. And I dont want to sound like Im saying all the responsibility is on researchers, because a lot of the important decisions about how AI gets used are made further up the chain, by industry or by governments.

But I worry that the people who are making those decisions dont always fully understand the ways it might go wrong. So you need to involve people with technical expertise. Our intuitions about what AI can and cant do are not very reliable.

What you need at all levels of AI development are people who really understand the details of machine learning to work with people who really understand ethics. Interdisciplinary collaboration is hard, however. People with different areas of expertise often talk about things in different ways. What a machine-learning researcher means by privacy may be very different from what a lawyer means by privacy, and you can end up with people talking past each other. Thats why its important for these different groups to get used to working together.

Youre pushing for a pretty big institutional and cultural overhaul. What makes you think people will want to do this rather than set up ethics boards or oversight committeeswhich always make me sigh a bit because they tend to be toothless?

Yeah, I also sigh. But I think this crisis is forcing people to see the importance of practical solutions. Maybe instead of saying, Oh, lets have this oversight board and that oversight board, people will be saying, We need to get this done, and we need to get it done properly.

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If AI is going to help us in a crisis, we need a new kind of ethics - MIT Technology Review

Minnesota hospice is recognized for their adoption and innovation in caring for patients.

ST. PAUL, Minn., June 23, 2020 /PRNewswire/ --MUSE Healthcare (www.musehc.com), the premier organization used to enhance end-of-life care with world-class analytics, has announced St. Croix Hospice as the winner of the MUSE Trailblazer Award. The award signifies the successful use of MUSE's technology and insights to achieve the highest quality of patient care. St. Croix Hospice is the first ever organization to receive the award. The award is given to one hospice provider annually that truly shows adoption and innovation in caring for patients.

Co-founder and CEO, Bryan Mosher and Co-founder and President, Jennifer Maxwell, formed a detailed selection committee to review all customers that use MUSE technologies. The selection criteria reviewed the number of patients impacted by the technology, the use of the tools to care for patients at the end of life, and feedback throughout the process. St. Croix Hospice was an early adopter and continues to provide quality feedback that will enhance the technology for years to come. Their expertise and innovative thought leader programs will truly change the way hospice patients are given care.

Executive Director of Quality at St. Croix Hospice, Ashley Arnold (RN), noted the importance of the award for the agency. "It is an honor for the staff at St. Croix Hospice to be recognized for the care we provide every day. The partnership with MUSE has been amazing. It's helped us to ensure we are there for our patients when they need us most."

Chief Clinical Officer at St. Croix Hospice, Mandy Cogswell (RN), was especially eager to share how MUSE has shaped their clinical strategy. "MUSE has helped us identify patient needs. It's not only allowed us to increase our nursing visits at the end of life, but also support provided by our chaplains, social workers and hospice aides. We only have one chance to care for these patients, enabling our clinicians to have all the tools needed to make the right decision at the right time is the St. Croix Hospice way."

About St. Croix Hospice

Founded in 2009 in Oakdale, Minnesota, St. Croix Hospice delivers expert hospice care to patient with life-limiting illnesses. St. Croix Hospice prides itself on providing personalized care to meet each patient and family's individual needs. The agency serves more than 75 percent of Minnesota, Iowa and Wisconsin; eastern Kansas and Nebraska; and western Missouri from 35 branch offices. St. Croix Hospice provides care in private residences, skilled nursing facilities and assisted living facilities. Care is available 24/7, and appointments and admits are offered via telehealth when needed. Unique services such as the North Star Dementia Program and Voyage Vigil Program offer comprehensive support and proactive symptom management to patients and their families.For more information, visit stcroixhospice.com

About MUSE Healthcare

Muse Healthcare was founded in 2019 by three leading hospice industry professionals-- Jennifer Maxwell, Tom Maxwell and Bryan Mosher. Their mission is to equip clinicians with world-class analytics to ensure every hospice patient transitions with unparalleled quality and dignity. Muse's predictive model considers hundreds of thousands of data points from millions of visits to identify which hospice patients are most likely to transition within 7-12 days. The science that powers Muse is considered a true deep learning neural network the only one of its kind in the hospice space. When hospice care providers can more accurately predict when their patients will transition, they can ensure their patients and their patients' families receive the care that matters most in the final days and final hours of a patient's life. For more information, visit musehc.com.

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SOURCE Muse Healthcare

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St. Croix Hospice Receives MUSE Trailblazer Award for Adopting Machine Learning Technology to Provide Exceptional Care - Yahoo Finance