Category Archives: Machine Learning

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

Every day for the past few weeks, charts and graphs plotting the projected apex of Covid-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the Covid-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus.

Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology, explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).

Most models used to predict the spread of a disease follow what is known as the SEIR model, which groups people into susceptible, exposed, infected, and recovered. Dandekar and Barbastathis enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others.

The model finds that in places like South Korea, where there was immediate government intervention in implementing strong quarantine measures, the virus spread plateaued more quickly. In places that were slower to implement government interventions, like Italy and the United States, the effective reproduction number of Covid-19 remains greater than one, meaning the virus has continued to spread exponentially.

The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the United States will arrive somewhere between April 15-20. This prediction is similar to other projections like that of the Institute for Health Metrics and Evaluation.

Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one, says Barbastathis. That corresponds to the point where we can flatten the curve and start seeing fewer infections.

Quantifying the impact of quarantine

In early February, as news of the virus troubling infection rate started dominating headlines, Barbastathis proposed a project to students in class 2.168. At the end of each semester, students in the class are tasked with developing a physical model for a problem in the real world and developing a machine learning algorithm to address it. He proposed that a team of students work on mapping the spread of what was then simply known as the coronavirus.

Students jumped at the opportunity to work on the coronavirus, immediately wanting to tackle a topical problem in typical MIT fashion, adds Barbastathis.

One of those students was Dandekar. The project really interested me because I got to apply this new field of scientific machine learning to a very pressing problem, he says.

As Covid-19 started to spread across the globe, the scope of the project expanded. What had originally started as a project looking just at spread within Wuhan, China grew to also include the spread in Italy, South Korea, and the United States.

The duo started modeling the spread of the virus in each of these four regions after the 500th case was recorded. That milestone marked a clear delineation in how different governments implemented quarantine orders.

Armed with precise data from each of these countries, the research team took the standard SEIR model and augmented it with a neural network that learns how infected individuals under quarantine impact the rate of infection. They trained the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.

Using this model, the research team was able to draw a direct correlation between quarantine measures and a reduction in the effective reproduction number of the virus.

The neural network is learning what we are calling the quarantine control strength function, explains Dandekar. In South Korea, where strong measures were implemented quickly, the quarantine control strength function has been effective in reducing the number of new infections. In the United States, where quarantine measures have been slowly rolled out since mid-March, it has been more difficult to stop the spread of the virus.

Predicting the plateau

As the number of cases in a particular country decreases, the forecasting model transitions from an exponential regime to a linear one. Italy began entering this linear regime in early April, with the U.S. not far behind it.

The machine learning algorithm Dandekar and Barbastathis have developed predictedthat the United States will start to shift from an exponential regime to a linear regime in the first week of April, with a stagnation in the infected case count likely betweenApril 15 and April20. It also suggests that the infection count will reach 600,000 in the United States before the rate of infection starts to stagnate.

This is a really crucial moment of time. If we relax quarantine measures, it could lead to disaster, says Barbastathis.

According to Barbastathis, one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly. While the team didnt study Singapores Covid-19 cases in their research, the second wave of infection this country is currently experiencing reflects their models finding about the correlation between quarantine measures and infection rate.

If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic, Barbastathis adds.

The team plans to share the model with other researchers in the hopes that it can help inform Covid-19 quarantine strategies that can successfully slow the rate of infection.

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Model quantifies the impact of quarantine measures on Covid-19's spread - MIT News

Welcome back to our Windows 10 news recap, where we go over the top stories of the past week in the world of Microsofts flagship operating system.

Microsoft to introduce PowerToys launcher for Windows 10 in May

A new report suggests that a new update for PowerToys is being prepared that includes a Mac OS style Spotlight launcher, making it easier find apps and files on a Windows 10 PC.

concept design for PowerToys Launcher UX

Microsoft starts sending invites for first Halo 2 Anniversary beta on PC

Invites for the Halo 2 Anniversary beta on PC have started to be sent out this week. Members of the Halo Insider program who have opted into PC flighting will receive an email with the invite.

Microsoft is using machine learning to identify security bugs during software development

In order to help Microsoft identify security bugs and resolve them before public release of software, the company is employing machine learning to find security bugs.

Thats it for this week. Well be back next week with more Windows 10 news.

Originally posted here:
Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more - OnMSFT

Knowingly or unknowingly we are all using artificial intelligence or AI. There is a combination of always-on devices, cloud and edge computing, and APIs in our everyday lives and business practices bringing AI into practice. Here is how to create symbiotic relationships with AI in business.

Even though the relationship between humans and machines is growing ever closer, its much too early to describe many of these collaborations as symbiotic.

When humans have specific types of problems, weve built and trained machines to solve those problems.

Examples include machine learning or ML. The ML algorithms that can identify cancer in brain images. The algorithms can also determine the best placements or designs for online ads, and there are deep learning systems that can predict customer churn in business.

At the moment, we can only imagine how much more productive we will become as we form symbiotic relationships with AI. Routine tasks that currently take hours or days could be abbreviated to 10 or 15 minutes with the aid of a digital partner.

From simple exercises like finding a new restaurant to more expert tasks such as cancer detection, we will increasingly rely on machines for everyday tasks. Dependence on machines might begin as a second pair of eyes or a second opinion, but our commitment to machines (and AI) will evolve into full-on digital collaborators.

Machine learning could bring about a revolution in how we solve problems to which the principle of optimal stopping applies.

Research in mathematics and computer science regarding these problems has shown that the optimal time to stop searching and make a decision is after37% of the time has been spent, options have been reviewed, and parking spaces have been passed.

Examples of these sorts of traditions problems include hiring the right person, making the right amount of R&D investment, and buying or selling a home. Humans tend to stop searching and considering data at about 31% well before they could have found the best option.

Forming symbiotic relationships with machines will free up time for us to focus on honing soft skills such as empathy, management, and strategy. It is not unreasonable to conclude that this symbiotic relationship will even present a new factor in the simple ability to enjoy life outside of work.

Very soon, AI could help us review enough options to find the right homebuyer, apartment tenant, job applicant, and perhaps even the right spouse.

For businesses and organizations with knowledge work as their output employees will benefit in several ways by applying machine learning to their advantage. Employees will use applications that cut across a variety of industries.

Some industry-agnostic roles such as a project manager will be able to offload routine tasks.

Tech will benefit substantially. Similar to how content creators benefit from writing agents such as Grammarly, software developers will benefit from a pair programming agent. The agent will suggest not only the right code syntax, but also the most appropriate framework, library, or API.

These agents will also have the opportunity to improve code quality and user experience drastically.

For industries like construction, AI could take advantage of the increased digitization of blueprints. AI will automate tasks that are routine but critical as project estimation. Depending on the size of the project, a human estimator can take up to four weeks to estimate a project.

Effortlessly, a digital agent could determine the materials needed for the project and set the number of workers necessary to staff the project.

More dramatic still, the AI digital agent could be connected to a supply store and incorporate real-time pricing into the final quote.

Medicine is another prime exampleof an industry ripe for disruption through human-AI symbiosis.

Pharmaceutical companies are leveraging machine learning to determine the optimal levels of research and development, using factors such as projected market size, revenue, and lifetime value of potential drugs.

Many doctors and hospitals have begun to incorporate AI recommendations into their processes. Increasing successes are seen, with 35% of doctors in a 2019 survey stating they use AI in their practices.

Some approaches in medicine have leveraged AI to provide potential options to doctors. Other choices analyze a doctors recommendation to predict the probability of success.

The dynamic symbiotic relationship between doctors and AI will also likely alter how malpractice riskis assessed for insurance.

As AI becomesmore commonplace in healthcareand is proven to improve outcomes for patients and decrease costs for hospitals, malpractice insurance will evolve to see AI as a way to reduce overall risk.

Similarly, doctors and hospitals that invest in AI solutions will see an improved return on investment in the form of lower insurance costs, improved outcomes, and increased efficiency.

Organizations that want to embrace the advances in AI and ML to produce symbiotic relationships between machines and themselves can take these steps.

The first step is to assess how artificial intelligence stands to impact your business as well as your industry and value chain. Examine whether you can add AI to your services.

Will AI change your product entirely, or can AI open new possibilities for entirely new products and services?

Once you complete your assessment and identify your options, break down your potential financial value to the organization. The assessment will uncover both potential risks you could incur and opportunities for new revenue streams you could open once you achieve AI-human symbiosis.

Every organization needs to learn where its data is stored and used. Proactively make this data available across the organization for experimentation, proofs of concepts, and other innovation projects.

Gain a firm understanding of what data you have and who owns it and share the information across the organization safely and democratically. The open network and feeling you are creating with this action are crucial to enabling machines to work for you, and sowing the seeds of innovation.

Assess your workforce to determine the roles that will most likely benefit from AI and machine learning solutions. The assessments can be divided into varying styles across individual employees or teams. These assessments include:

Data-driven thinkers versus big-picture focus thinkers.

Strengths in strategy versus problem-solving strengths.

Skill sets in software development versus the risk assessment skill set.

Is the talent expertise contained in surgery versus the expertise in research and development?

Machines are forging new opportunities for human work throughout the value chain as humans and machines collaborate to create more meaningful human jobs.

An organization must align its approach to building symbiotic relationships with its overarching purpose and that begins with leadership.

Leaders must excite their workforces about the ultimate goal of integrating AI, provide a clear vision for the organizations goals, and assure their workers that machines will enhance and alter (but not replace) their roles.

Its important to create near and long-term plans and then share those timelines across the organization, and connect those benchmarks to your greater purpose.

Organizations wont be able to take advantage of the value of these symbiotic relationships without carefully appraising the opportunities and risks.

Businesses must get their data houses in order and encourage innovation that enhances their talent and their organizations purpose. Only then will humans use AI to its full potential.

Image Credit: franck-v, Unsplash

Daniel Williams is a principal with Pariveda Solutions, specializing in digital strategy, implementation, and analytics. With B.S. and M.S. degrees in Computer Science and Technology Management, he has become an expert in digital transformation and AI/ML.

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Create Symbiotic Relationships with AI in Business - ReadWrite

During the COVID19 pandemic, I decided that I wanted to use the time at home to invest in myself. One of the things I was challenged by in a recent episode of Business Casual was when Mark Cuban discussed the role of Artificial Intelligence in the future and recommended some tools to learn more. He mentioned some Coursera courses, so I am currently working my way through some of their AI training, but he also mentioned an AI-focused podcast called theTWIMLAI Podcast that I added to my podcast subscription list.

9to5Macs Podcast of the Week is a weekly recommendation of a podcast you should add to your subscription list.

TWIML (This Week in Machine Learning and AI) is a perfect way to hear from industry experts about how Machine Learning and AI will change our world. I plan to work through the back catalog soon, but the newest episodes have been informative. I particularly enjoyed this episode with Cathy Wu, Gilbert W. Winslow Career Development Assistant Professor in the Department of Civil and Environmental Engineering at MIT where they discussed simulating the future of traffic.

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. By sharing and amplifying the voices of a broad and diverse spectrum of machine learning and AI researchers, practitioners, and innovators, our programs help make ML and AI more accessible, and enhance the lives of our audience and their communities.

TWIML has its origins in This Week in Machine Learning & AI, a podcast Sam launched in mid2016 to a small but enthusiastic reception. Fast forward three years, and the TWIML AI Podcast is now a leading voice in the field, with over five million downloads and a large and engaged community following. Our offerings now include online meetups and study groups, conferences, and a variety of educational content.

Subscribe to the TWIML AI Podcast on Apple Podcasts, Spotify, Castro, Overcast, Pocket Casts, and RSS.

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Segmentation Overview:

Product Type Segmentation :

Software Tools, Cloud and Web-based Application Programming Interface (APIs), Other

Application Segmentation :

Manufacturing, Retail, Healthcare and Life Sciences, Telecom, BFSI, Other (Energy and Utilities, Education, Government)

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MagnusRT @rjparkerjr09: "Quants were too reliant on models and forecasts. They need to begin practicing nowcasting reacting to real-time data13 hours ago

Mansoor Fayyaz MianAQR's former machine-learning head says its time for quants to 'pay less attention to crystal balls' and react to re https://t.co/WrhikvFdjM17 hours ago

Jerry Parker"Quants were too reliant on models and forecasts. They need to begin practicing nowcasting reacting to real-time https://t.co/ozQlfldTdI22 hours ago

Truth 2 PowerAQR's former machine-learning head says it's time for quants to 'pay less attention to crystal balls' and react to https://t.co/i0jGvPVwBz1 day ago

JoseWorksAQR's former machine-learning head says its time for quants to 'pay less attention to crystal bal... https://t.co/PGaMlHXBS22 days ago

Manpreet SinghRT @businessinsider: AQR's former machine-learning head says its time for quants to 'pay less attention to crystal balls' and react to real2 days ago

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AQR's former machine-learning head says quant funds should start 'nowcasting' to react to real-time data instead of trying to predict the future - One...