Machine learning has rapidly become one of those buzzwordsembraced by companies around the world. Even if they dont fully understandwhat it means, executives think that machine learning will magically transformtheir operations and generate massive profits. Thats good news fortechnologistsprovided they actually learn the technologys fundamentals, ofcourse.

Amazon wants to help with the learning aspect of things. At this years AWS re:Invent, the company is previewing the DeepComposer, a 32-key keyboard thats designed to train you in machine learning fundamentals via the power of music.

No, seriously. AWS DeepComposer is theworlds first musical keyboard powered by machine learning to enable developersof all skill levels to learn Generative AI while creating original musicoutputs, reads Amazonsultra-helpful FAQ on the matter. DeepComposer consists of a USB keyboardthat connects to the developers computer, and the DeepComposer service,accessed through the AWS Management Console.There are tutorials andtraining data included in the package.

Generative AI, the FAQcontinues, allows computers to learn the underlying pattern of a given problemand use this knowledge to generate new content from input (such as image,music, and text). In other words, youre going to play a really simple songlike Chopsticks,and this machine-learning platform will use that seed to build a four-hourWagner-style opera. Just kidding! Or are we?

Jokes aside, the ideathat a machine-learning platform can generate lots of data based on relativelylittle input is a powerful one. Of course, Amazon isnt totally altruistic inthis endeavor; by serving as a training channel for up-and-comingtechnologists, the company obviously hopes that more people will turn to it forall of their machine learning and A.I. needs in future years. Those interestedcan sign up for the preview on adedicated site.

This isnt the first time that Amazon has plunged into machine-learning training, either. Late last year, it introduced AWS DeepRacer, a model racecar designed to teach developers the principles of reinforcement learning. And in 2017, it rolled out AWS DeepLens camera, meant to introduce the technology world to Amazons take on computer vision and deep learning.

Membership has its benefits. Sign up for a free Dice profile, add your resume, discover great career insights and set your tech career in motion. Register now

For those who master the fundamentals of machine learning, the jobs can prove quite lucrative. In September, theIEEE-USA Salary & Benefits Salarysuggested that engineers with machine-learning knowledge make an annual average of $185,000. Earlier this year, meanwhile, Indeed pegged theaverage machine learning engineer salary at $146,085, and its job growth between 2015 and 2018 at 344 percent.

If youre not interested in Amazonsversion of a machine-learning education, there are other channels. For example,OpenAI, the sorta-nonprofit foundation (yes, itsas odd as it sounds), hosts what it calls Gym, a toolkit fordeveloping and comparing reinforcement algorithms; it also has a set of modelsand tools, along with a very extensive tutorialin deepreinforcement learning.

Googlelikewise has acrash course,complete with 25 lessonsand 40+ exercises, thats a good introduction to machine learning concepts.Then theres Hacker Noon and its interesting breakdown ofmachine learning andartificial intelligence.

Onceyou have a firmer grasp on the core concepts, you can turn to BloombergsFoundations of Machine Learning,afree online coursethat teaches advanced concepts such asoptimization and kernel methods. A lotof math is involved.

Whateverlearning route you take, its clear that machine learning skills have anincredible value right now. Familiarizing yourself through thistechnologywhether via traditional lessons or a musical keyboardcan only helpyour career in tech.

See the original post:

Amazon Wants to Teach You Machine Learning Through Music? - Dice Insights

The goal of population health is to use data to identify those who will benefit from intervention sooner, typically in an effort to prevent unnecessary hospital admissions. Machine learning introduces the potential of moving population health away from one-size-fits-all risk scores and toward matching individuals to specific interventions.

The combination of the two has enormous potential. However, many of the factors that will determine success or failure have nothing to do with technology and should be considered before investing in machine learning or population health.

Population health software, with or without machine learning, only produces suggestions. Getting a team to take action, particularly if that action is different, is one of the hardest things to do in healthcare. You will not succeed without executive support. Executives will not support you without significant incentive to do so.

Here's an easy surrogate for whether there is enough of that incentive: whether those executives jobs are in jeopardy if too many people go to the hospital. If not, the likelihood that an investment will lead to measurable improvement is minimal.

If youve been ordered to "do" population health, your best bet is to install a low cost risk score or have your team write a query to identify the oldest sickest people with the most readmissions. Either will return the same results more or less and your team of care managers are used to ignoring said results without rocking the boat. If there is sufficient incentive, read on.

Henry Ford is credited with saying, "If I asked people what they wanted, they would have said faster horses." Its human nature to try to apply a new technology in an old way.

Economists have named this the IT Productivity Paradox and have studied the cost of applying new technical capabilities in old ways. There are signs that healthcare organizations are unknowingly walking this plank.

For decades, risk scores were designed to identify the costliest patients with little consideration of the types of costs, the diseases they suffer, whether or not those costs are preventable, etc.

As a result, according to a systematic review of 30 risk stratification algorithms appearing in the Journal of the American Medical Association, "most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly." A recently published study in Science also showed that prioritizing based on cost discriminates against people of color. Applying more data and better math to solve the problem in the old way is an expensive way to propagate existing shortcomings.

The opportunity now made possible is the ability to match individuals to interventions. Patients with serious mental illness that are most likely to have an inpatient psychiatric admission are very different than those with serious illnesses that might benefit from home-based palliative care. Clinicians wouldnt treat them the same, neither should our approach to prioritization.

However, you will need to design for this and clinical teams should be prepared for the repercussions. Patients identified with rising risk (as opposed to peak utilization) will not seem as sick.

Clinical teams trained to triage may feel like theyre not doing their jobs if the patients arent as obviously acute. Its important to discuss these repercussions and prepare in advance of the introduction of new technology.

Using technology to send more of the right people into a program that doesnt have an impact only adds to the cost of an already failing program. Surprisingly, very few programs have ever measured the impact of their interventions.

Those that have, often rely on measuring patients before and after they enter into care management programs which is misleading and biased on many levels.

If you are not confident that the existing program makes a difference, invest in measuring and improving the existing programs performance before investing additional resources. A good read on the pros and cons of different approaches to measuring impact is here.

Starting with a program of measurement can create a culture of measurement, improvement, and accountability - a great foundation for a pop health effort. Involving the clinical team in the definition of measures that matter will go a long way.

Another important consideration is whether your intervention is costly to deliver. The more costly it is to steer resources toward the wrong people, the more likely your program is to benefit from smarter prioritization.

For both reasons above if your program is entirely telephonic and targets older people with chronic complex diseases, you may want to invest in program design and measurement before investing in stratification technology.

Youre in great shape, and your odds of success are exponentially higher. Youre also better informed, as you and the team shift focus to decisions such as whether to build versus partner, what unique data you collect that can be used to your advantage and how youll measure algorithm and program performance.

Leonard DAvolio, PhD is an assistant professor at Harvard Medical School and Brigham and Womens Hospital, and the CEO and founder of Cyft. He shares his work on LinkedIn and Twitter.

Continue reading here:

3 questions to ask before investing in machine learning for pop health - Healthcare IT News

Robotics has already revolutionized the manufacturing industry, but it has begun to impact the healthcare industry as well. AI is already showing that it can do a lot of what humans can, only faster and cheaper.

The potential benefits of machine learning and artificial intelligence are only starting to be seen, though we can make an educated guess regarding the benefit it can have.

Lets look at the current and future impact robotics and machine learning is making on the healthcare industry.

The Automation of Hospital Support Tasks

Robots may or may not be wandering the halls dispensing hot towels and medication, but they are filling a number of critical hospital support roles. For example, theyre used to quickly and thoroughly disinfect operating rooms and scrub floors.

They dont get tired, their performance doesnt deteriorate at the end of a shift, and they cant get sick themselves. This technology is expected to reduce the rate of hospital-acquired infections that currently stands at 4 percent.

Robots are at the vanguard of minimally invasive surgery. This is why surgical robot sales are expected to hit $6.4 billion in 2020.

Were only starting to see what care robots can do. One system can draw blood and provide assistance with patient care like changing undergarments. This frees up nurses from unpleasant and physically demanding tasks.

They may one day take the next step and perform lab tests and deliver care without requiring a human present beyond the patient.

Were already seeing robots push around racks of medication, towels and laboratory specimens. This frees up hospital staff to work on more creative and demanding tasks.

The Impact of AI and Robotics in Manufacturing

Robots have been improving productivity and quality in the manufacturing sector for years. For example, these machines can manufacture complete medical device components without any further processing.

CNC Swiss machining is the next generation in manufacturing technology and could be taken to a whole new plateau thanks to AI. It can already do everything from threading to milling to slotting before delivering a high quality, completed part.

CNC machines can now be driven by advanced AI to create precise parts, and it can make a wide variety of parts, all of them made as quickly as possible.

Advances in digital design mean you can design the product, test the design in various simulations, and send the data file to a contract manufacturer who sends the completed item back. Prototyping is faster, and low rate production and custom parts are now economical.

The Arrival of Caregiving Robots

Robots will soon be able to interact with patients, call for help when required, dispense medication and remind patients of appointments.

Everything that can be done to help people age in peace saves money while improving quality of life. This same technology could be used in long-term care facilities and nursing homes to improve the quality of care at a lower cost.

Robotic medical dispenser systems can already dispense medication to patients whenever required as long as it is within predetermined limits. And pharmaceutical companies could get valuable insight into how their medications are used.

AI Promise

Artificial intelligence and robotics promise to improve quality of care, access to medical services, and support for patients and caregivers alike at a lower cost to everyone.

Promoted

You might also like

Read more from the original source:

Exploring the Present and Future Impact of Robotics and Machine Learning on the Healthcare Industry - Robotics and Automation News

Amazon Web Services debuted a keyboard called DeepComposer this week, claiming its the worlds first musical keyboard powered by generative AI. It has 32 keys, costs $99, and connects to a software interface that uses machine learning and cloud computing to generate music based on what you play.

Its been unclear who this is for, and many have latched on to the fact that the music it creates just sounds bad. It looks like a consumer product, and Amazon used an over-the-top presentation to hype it, which included what AWS claimed was the first hybrid AI human pop acoustic collaboration. (Its not.)

But actually, the keyboard is intended to be a beginning tool for developers to get into machine learning and music. The device is AWSs newest offering for developers to familiarize themselves with aspects of machine learning, following AWS DeepRacer (an RC car) and AWS DeepLens (a camera).

DeepComposer is not meant to make music for entertainment purposes or push the state of generative AI. It will never be marketed to aspiring musicians.

Even though Amazon says DeepComposer is for developers, many devs dont understand what to do with it. According to Amazon, since this is for developers, no musical knowledge is needed. But it still uses traditional music theory terms and is centered on a thing that very much requires a modicum of musical knowledge a keyboard.

The $99 physical keyboard isnt even necessary since the DeepComposer software has a virtual keyboard. And AWS didnt develop or design the keyboard. Its a MIDI controller by Taiwanese company Midiplus thats been around for years. Amazon sells it for $46.15. Theyre the same. Amazons costlier version is not powered by AI, it sends MIDI to software thats hooked up to the cloud. Any MIDI keyboard would technically work.

At the end of the day, this is not for the everyday person. AWS does not claim it is. DeepComposer is not supposed to write the next radio hit. But its also hard to see how its meaningful for its target audience.

It is compelling to see a company as large as Amazon getting into the world of democratizing music creation, even if this first iteration somewhat missed the mark. Who knows, maybe its all a long play to get more devs familiar with AWSs machine learning platform, SageMaker.

But given how simple DeepComposer is, developers could learn about the same basics of machine learning and music through any number of other interfaces, like Google Magenta, without being pitched an overpriced controller stamped with a logo.

The rest is here:

Amazons new AI keyboard is confusing everyone - The Verge