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AWS CEO Andy Jassy, discusses a new initiative with the NFL that will transform player health and ... [+] safety using cloud computing during AWS re:Invent 2019 on Thursday, Dec. 5, 2019 in Las Vegas. (Isaac Brekken/AP Images for NFL)

Last week, I had the pleasure of attending Amazon.com AWSs re:Invent conference in Las Vegas. Re:Invent is AWSs once a year mega-event where it announces new services and holds 2,500 educational sessions for builders, CIOs, channel and ecosystem partners, customers, and of course, industry analysts like me. Its a large event at 65,000 attendees but could be much larger as it sells out after a few days. The attraction is simple. Its the most important cloud show you can attend and attendees want to get a head-start and hands-on with the latest and greatest of what AWS has to offer. AWS made hundreds of announcements and disclosures and while the Moor Insights & Strategy analyst team will be going deeper on the most impactful announcements, I wanted to make a top 5 list and why you should care.

1/ Graviton2 for EC2 M, R, and C 6th Gen instances

AWS Graviton2 instances

Based on an Arm N1 core, AWS says these new instances deliver up to 40% improved price/performance over comparable x86-based Skylake instances. In preview, AWS will make these available for Mainstream (M), memory-intensive (R) and compute intensive (C) instances.

Why this matters

You may expect that I gave the #1 spot to new chips because I can be a chip nerd. I can be, but when you think about a 40% improvement over IaaS, PaaS and SaaS services that cant easily be copied, Id say thats important. Thats not saying that advantage will last forever, but its very disruptive right now. First off, Id say that now no one can say Arm isnt ready for general purpose datacenter compute. It is, as AWS IaaS is larger than the #2-10 IaaS provider combined. I can see VMware and Oracle accelerating its offerings and maybe SAP doing anything with Arm, which they arent publicly. Finally, dont overthink this related to AMD and Intel. The market is massive, growing and I dont believe this is anti-Intel or AMD. But if a small AWS team can outperform AMD and Intel on some cloud workloads, you do have to do a pause. I wrote in-depth on all of this here.

2/ Many new hybrid offerings

Local Zones

While AWS doesnt want to use the term hybrid a lot, I think enterprises understand that it means they can extend their AWS experience to on-prem or close to on-prem compute and storage. AWS announced three capabilities here that are important, including going GA on Outposts and announcing Local Zones and Wavelength.

AWS describes it as, AWS Outposts are fully-managed and configurable racks of AWS-designed hardware that bring native AWS capabilities to on-premises locations using the familiar AWS or VMware control plane and tools. AWS Local Zones place select AWS services close to large population, industry, and IT centers in order to deliver applications with single-digit millisecond latencies, without requiring customers to build and operate datacenters or co-location facilities. AWS Wavelength enables developers to deploy AWS compute and storage at the edge of the 5G network, in order to support emerging applications like machine learning at the edge, industrial IoT, and virtual and augmented reality on mobile and edge devices.

Why this matters

AWS took the hybrid idea and doubled down on it. If youre a customer who wants a low latency experience on-prem with Outposts, lowest-latency in the public cloud with Local Zones, or in the core carrier network with Wavelength, AWS has you covered. When you add this to what AWS is doing with Snowball and where I think its going, its hard not for me to say AWS wont have the broadest and most diverse hybrid play. After our analyst fireside chat and Q&A with AWSs Matt Garman, Im convinced we will see tremendous compute and storage variability with all of AWSs offerings. It doesnt have all the blanks filled in, but I believe it will. This isnt for show; its for world domination.

AWS Wavelength

What Im most interested to see is how the economics and agility stack up compared to on-prem giants Dell Technologies, Hewlett Packard Enterprise, Cisco Systems, Lenovo and IBM.

3/ SageMaker Studio

SageMaker Studio

AWS says the Amazon SageMaker Studio is the first comprehensive IDE (integrated developer environment) for machine learning, allowing developers to build, train, explain, inspect, monitor, debug, and run their machine learning models from a single interface. Developers now have a simple way to manage the end-to-end machine learning development workflows so they can build, train, and deploy high-quality machine learning models faster and easier.

Why this matters

Machine learning is really hard without an army of data scientists and DL/ML-savvy developers. The problem is that these skills are very expensive, hard to attract and retain, not to mention the need to have very unique infrastructure like GPUs, FPGAs and ASICs. AWS did a lot with its base ML services to help solve the infrastructure and SageMaker to connect the building, training, and deploying ML at scale. But how do you connect the developer on an end to end workflow basis? Enter SageMaker Studio. Studio replaces many other components and toolsets that exist today for building, training, explaining, inspecting, monitoring, debugging, and running that may make those ISVs unhappy, but developers could be a lot happier.

Im very interested in lining this up against what both Google Cloud and Azure are doing and getting customer feedback. With SageMaker Studio, AWS is delivering what enterprises want; the only question is if its better than or a lot less expensive in what devs can put together themselves or run on another cloud.

4/ Inf1 EC2 instances with Inferentia

Inf1 Instances

Last year, AWS pre-announced Inferentia, its custom silicon for machine learning inference. This year, it announced the availability of instances based on that chip, called EC2 Inf1. AWS explains that With Amazon EC2 Inf1 instances, customers receive the highest performance and lowest cost for machine learning inference in the cloud. Amazon EC2 Inf1 instances deliver 2x higher inference throughput, and up to 66% lower cost-per-inference than the Amazon EC2 G4 instance family, which was already the fastest and lowest cost instance for machine learning inference available in the cloud.

Why this matters

Machine learning workloads in the cloud are split into training and inference. Enterprises train the workload with big data and monster GPUs and then run the model, or infer on smaller silicon close to the edge. Currently, the highest performance training and inference currently occurs on NVIDA GPUs, namely the V100 and G4. Most inference is done on a CPU for lower cost and latency purposes as described by Amazon retail gurus during the last two Xeon launches. While I am sure NVIDIA is hard at work on its next generation silicon, this is fascinating as nothing has served as a challenge even to NVIDIAs highest-performance instances. While I havent done a deep dive yet like Graviton 2 above, when I do, I will report back as will ML lead Karl Freund. Whatever the outcome, its good to see the level of competition rising in this space.

5/ No ML experience required services

AWS came out strong touting new services that dont require ML experience. Think of these as SaaS or high-order PaaS capabilities where you dont need a framework expert or even a data scientist. Amazon said

Why this matters

I will posit that theres more market opportunity for AWS in ML PaaS and SaaS if for nothing else the lack of data scientists and framework-savvy developers. If youre not a Fortune 100 company, youre at a distinct disadvantage to attract and retain those resources and I doubt they can be at the scale that you need them. Also, as AWS does most of its business in IaaS, theres just more opportunity in PaaS and SaaS.

AWS ML Stack

Kendra sound incredible and it will have an immense amount of competition from Azure and Google Cloud. Azure likely already has a lot of the enterprise data through Office 365, Teams and Skype and Google is good at search. CodeGuru sounds too good to be true but isnt, based on a few developer conversations I had at the show. The only thing limiting this service will be the cost, which I think is dense, given what it can save, but its human nature to not see the big picture. Fraud detector, like Kendra, will have a lot of competition, especially from IBM who have been doing this for decades. I love that the service is bringing its knowledge from its Amazon.com dealings and Id be surprised if the website has the highest fraud attacks given it does 40% of online etail transactions. Transcribe Medical is a dream come true for surgeons like my brother in-law and I hope AWS runs a truck through the aged transcription industry. AWS will have a lot of competition from both Azure and Google Cloud. A2I has been needed in the industry for a while as no state or federally regulated industry can deal with a black box.

Honorable mentions

There were so many good announcements to choose from I had to do an honorable mention list with my quick take.

Wrapping up

While its impossible to do justice to a huge event like AWS re:Invent in a single point, I also think its as important to point out the highlights with some honorable mentions. All in all, AWS answered the hybrid critics and raised the ante, introduced some homegrown silicon that de-commoditizes IaaS, and gave more reasons to use its databases and machine learning services from newbie to Ph.D.

Moor Insights & Strategy analysts will be diving more into AWS Outposts and Graviton2 (Matt Kimball), Braket (Paul Smith-Goodson), Inf1 (Karl Freund), and overall impressions (Rhett Dillingham).

More:

The Top Five AWS Re:Invent 2019 Announcements That Impact Your Enterprise Today - Forbes

A small number of companies have begun developing new tools to measure employee engagement without requiring workers to fill out surveys or sit through focus groups. HR professionals and engagement experts are watching to see if these tools gain traction and lead to more effective cultural and retention strategies.

Two of these companiesNetherlands-based KeenCorp and San Franciscos Cultivateglean data from day-to-day internal communications. KeenCorp analyzes patterns in an organizations (anonymized) email traffic to gauge changes in the level of tension experienced by a team, department or entire organization. Meanwhile, Cultivate analyzes manager email (and other digital communications) to provide leadership coaching.

These companies are likely to pitch to a ready audience of employers, especially in the technology space. With IT unemployment hovering around 2 percent, corporate and HR leaders cant help but be nervous about hiring and retention. When competition for talent is fierce, companies are likely to add more and more sweeteners to each offer until they reel in the candidates they want. Then theres the matter of retaining those employees in the face of equally sweet counteroffers.

Thats why businesses utilize a lot of effort and money on keeping their workers engaged. Companies spend more than $720 million annually on engagement, according to the Harvard Business Review. Yet their efforts have managed to engage just 13 percent of the workforce.

Given the competitive advantage tech organizations enjoy when their teams are happy and productivenot to mention the money they save by keeping employees in placeengagement and retention are critical. But HR cant create and maintain an engagement strategy if it doesnt know the workforces mindset. So companies have to measure, and they measure primarily through surveys.

Today, many experts believe surveys dont provide the information employers need to understand their workforces attitudes. Traditional surveys have their place, they say, but more effective methods are needed. They see the answer, of course, in artificial intelligence (A.I.) and machine learning (ML).

One issue with surveys is they only capture a part of the information, and thats the part that the employee is willing to release, said KeenCorp co-founder Viktor Mirovic. When surveyed, respondents often hold back information, he explained, leaving unsaid data that has an effect similar to unheard data.

I could try to raise an issue that you may not be open to because you have a prejudice, Mirovic added. If tools dont account for whats left unsaid and unheard, he argued, they provide an incomplete picture.

As an analogy, Mirovic described studies of combat aircraft damaged in World War II. By identifying where the most harm occurred, designers thought they could build safer planes. However, the study relied on the wrong data, Mirovic said. Why? Because they only looked at the planes that came back. The aircraft that presumably suffered the most grievous damagethose that were shot downwerent included in the research.

None of this means traditional surveys surveys dont provide value. I think the traditional methods are still useful, said Alex Kracov, head of marketing for Lattice, a San Francisco-based workforce management platform that focuses on small and mid-market employers. Sometimes just the idea of starting to track engagement in the first place, just to get a baseline, is really useful and can be powerful.

For example, Lattice itself recently surveyed its 60 employees for the first time. It was really interesting to see all of the data available and how people were feeling about specific themes and questions, he said. Similarly, Kracov believes that newer methods such as pulse surveyswhich are brief studies conducted at regular intervalscan prove useful in monitoring employee satisfaction, productivity and overall attitude.

Whereas surveys require an employees active participation, the up-and-coming tools dont ask them to do anything more than their work. When KeenCorps technology analyzes a companys email traffic, its looking for changes in the patterns of word use and compositional style. Fluctuations in the products index signify changes in collective levels of tension. When a change is flagged, HR can investigate to determine why attitudes are in flux and then proceed accordingly, either solving a problem or learning a lesson.

When I ask you a question, you have to think about the answer, Mirovic said. Once you think about the answer, you start to include all kinds of other attributes. You know, youre my boss or youve just given me a raise or youre married to my sister. Those could all affect my response. What we try to do is go in as objectively as possible, without disturbing people as we observe them in their natural habitats.

Read more:

Measuring Employee Engagement with A.I. and Machine Learning - Dice Insights

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.

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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.

Follow this link:

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.

See the original post:

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

We found that if Caterpillars (NYSE: CAT) stock drops 10% in a week (5 trading days), there is a solid 25% chance that it will rise by 10% over the subsequent month (20 trading days).

Caterpillar stock has seen significant volatility this year. While the company is being impacted by growing headwinds to the global economy, the uncertainty surrounding the trade war between the U.S. and China, relatively mixed quarterly earnings reports, as well as slowing sales, its relatively high capital returns, and strong balance sheet have supported the stock to an extent.

Considering the recent price swings, we started with a simple question that investors could be asking about Caterpillar stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if Caterpillar stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 23%. Quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves. Given the recent volatility in the market, the mix of macroeconomic events (including the trade war with China and interest rate easing by the U.S. Fed), we think investors can prepare better.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in CAT stock become more likely after a drop?

Answer:

Consider two situations:

Case 1: CAT stock drops by 5% or more in a week

Case 2: CAT stock rises by 5% or more in a week

Is the chance of say a 5% rise in CAT stock over the subsequent month after Case 1 or Case 2 occurs much higher for one versus the other?

The answer is absolutely!

Turns out, chances of a 5% rise over the next month (20 trading days) is meaningfully more for Case 1, where the CAT has just suffered a big loss, versus Case 2.

Specifically, chances of a 5% rise in CAT stock over the next month:

= 40% after Case 1, where CAT stock drops by 5% in a week

versus,

= 32% after Case 2: where CAT stock rises by 5% in a week

Question 2: What about the other way around, does a drop in CAT stock become more likely after a rise?

Answer:

Consider, once again, two cases

Case 1: CAT stock drops by 5% in a week

Case 2: CAT stock rises by 5% in a week

Turns out the chances of a 5% drop after Case 1 or Case 2 has occurred, is actually quite similar, both pretty close to 23%.

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, absolutely!

Given a drop of 5% in CAT stock over a week (5 trading days), while there is only about 21% chance the CAT stock will gain 5% over the subsequent week, there is more than 50% chance this will happen in 6 months, and 62% chance itll gain 5% over a year (about 250 trading days).

The table below shows the trend:

Trefis

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in CAT stock are about 24% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 23% when the waiting period is a year (250 trading days).

The table below shows the trend:

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

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Machine Learning Answers: If Caterpillar Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes