Tag: AI

Microsoft Goes All In on Industry Cloud and AI with $20 Billion Nuance Deal

Yesterday’s announcement of Microsoft’s acquisition of Nuance Communications signifies the big tech company’s serious intentions in the US healthcare market.

We’ve been writing about industry cloud and verticalization plays of big technology companies (nicknamed BigTech) for a while now. With the planned acquisition of Nuance Communications for US$19.7 billion, Microsoft has made its most definitive step in the healthcare and verticalization journey.

At a base level, what matters to Microsoft is that Nuance focuses on conversational AI. Over the years, it has become quite the phenomenon among physicians and healthcare providers – 77 percent of US hospitals are Nuance clients. Also, it is not just a healthcare standout – Nuance counts 85 percent of Fortune 100 organizations as customers. Among Nuance’s claims to fame in conversational AI is the fact that it powered the speech recognition engine in Apple’s Siri.

Why Did Microsoft Acquire Nuance?

The acquisition is attractive to Microsoft for the following reasons:

  1. Buy versus build: If Microsoft (under Satya Nadella) can trust itself to build a capability swiftly, it will never buy. Last year, when we wrote about Salesforce’s acquisition of Slack, we highlighted how Microsoft pulled out of its intent to acquire Slack in 2016 and launched Teams within a year. Could Microsoft have built and scaled a speech recognition AI offering?
  2. Conversational AI: Microsoft’s big three competitors – Amazon, Apple, and Google – have a significant head start in speech recognition, the only form of AI that has gone mainstream and is likely to be a US$30 billion market by 2025. Clearly, with mature competition, this was not going to be as easy as “Alexa! Cut slack, build Teams” for Nadella
  3. Healthcare: This is another battleground for which Microsoft has been building up an arsenal. As the US continues to expand on its $3 trillion spend on healthcare, Microsoft wants a share of this sizeable market. That is why it makes sense to peel the healthcare onion a bit more

 

What Role Does Microsoft Want to Play in Healthcare?

While other competitors (read Amazon, Salesforce, and Google) were busy launching healthcare-focused offerings in 2020, Microsoft was already helping healthcare providers use Microsoft Teams for virtual physician visits. Also, Microsoft and Nuance are not strangers, having partnered in 2019, to enable ambient listening capabilities for physician to EHR record keeping. Microsoft sees a clear opportunity in the US healthcare industry.

  • Everest Group estimates that technology services spending in US healthcare will grow at a CAGR of 7.5% for the next five years, adding an incremental US$25 billion to an already whopping $56 billion
  • The focus of Microsoft and its competitors is to disrupt the multi-billion ($40 billion by 2025) healthcare data (Electronic Medical Record) industry
  • Erstwhile EMR has been a major reason for physician burnout, which the likes of Nuance aim to solve
  • Cloud-driven offerings such as Canvas Medical and Amazon Comprehend Medical are already making Epic Systems and Cerner sit up and take notice

It is not without reason that Microsoft launched its cloud for healthcare last year and has followed it up by acquiring Nuance.

What Does it Mean for Healthcare Enterprises?

Under Nadella, Microsoft has developed a sophisticated sales model that takes a portfolio approach to clients. This has helped Microsoft build a strong positioning beyond its Office and Windows offerings even in healthcare. Most clients in healthcare are already exposed to its Power Apps portfolio and Intelligent Cloud (including Azure and cloud for healthcare) in some form. It is only a question of time (if the acquisition closes without issues) until Nuance becomes part of its suite of offerings for healthcare.

What Does it Mean for Service Providers?

As a rejoinder to our earlier point about head starts, this is where Microsoft has a lead over competitors. Our recent research with System Integrators (SI) ecosystem indicates that Microsoft is head and shoulders above its nearest competitors when it comes to leveraging the SI partnership channel to bring its offerings to enterprises. This can act as a significant differentiator when it comes to taking Nuance to healthcare customers as SI partners can expect favorable terms of engagement.

Partners' Perceptions

Lastly, this is not just about healthcare

While augmenting healthcare capabilities and clients is the primary trigger for this purchase, we believe Microsoft aims to go beyond healthcare to achieve the following objectives:

  • Take conversational AI to other industries: Clearly, healthcare is not the only industry warming up to conversational AI. Retail, financial services, and many other industries have scaled usage. Hence, it is not without reason that Mark Benjamin (Nuance’s CEO) will report to Scott Guthrie (Executive Vice President of Cloud & AI at Microsoft) and not Gregory Moore (Microsoft’s Corporate Vice President, Microsoft Health), indicating a broader push
  • Make cloud more intelligent: As mentioned above, Microsoft will pursue full-stack opportunities by combining Nuance’s offerings with its Power Apps and Intelligent Cloud suites. As a matter of fact, it plans to report Nuance’s performance as part of its Intelligent Cloud segment

Microsoft: $2 Trillion and Beyond

This announcement comes against the background of BigTech and platform companies making significant moves to industry-specific use cases, which will drive the next wave of client adoption and competitive differentiation. Microsoft’s turnaround and acceleration since Nadella took over as CEO in 2014 are commendable (see the image below). It is on the verge of becoming only the second company to achieve $2 trillion in market capitalization. This move is a bet on its journey beyond the $2 trillion.

Picture2 5

What do you make of its move? Please feel free to reach out to [email protected] and [email protected] to share your opinion.

Synthetic Data – Catalyst for AI innovation | Blog

With a connected world and connected humans, we are on track for a huge uptick in new data creation at an unprecedented level. IoT, digitization, and cloud have brought on the generation and storage of ZBs of data created each day. Data has become the new oil but with some caveats. The tap of this oil is controlled by a few organizations globally, making this data asset scarce and expensive. However, enterprises in their pursuit of digital transformation require this data to get insights for better decision-making.

Shortcut to access data

The next logical question arises as to how we can get hold of this data, which, if utilized to its full potential, has the power to transform enterprises. This is where synthetic data comes to play. It is the form of data that is created inorganically rather than being generated through actual interactions or events. It is usually formed by studying the characteristics and relations between different variables. A total of three types of synthetic data exist, which are shown below.

Picture1 1

Exhibit 1: Types of synthetic data

Why is it required now?

With the cultural shift towards insights-based decision making from gut-based decision making and the onset of data literacy initiatives, enterprises require apt insights, which further require the generation of huge amounts of data. There are a few instances highlighted below which make a strong case for synthetic data.

  1. GDPR mandates stringent regulations for data access which stipulates if a company can utilize it with user content. This makes it extremely difficult to share data creating hurdles to solve business problems
  2. AI models and algorithms require extensive labeled data for training purposes. In the case of self-driving cars, it needs to clock in millions of miles to test computer vision algorithms. This delays the go-to-market for such products
  3. New product development usually requires a lot of data testing before it is introduced in the market. Innovation becomes scarce if quality data from the field is not there

 Techniques to generate synthetic data

There are usually three strategies to generate synthetic data. These include some simplistic techniques as well as methods infused heavily with AI.

Picture2

Exhibit 2: Techniques to generate synthetic data

Sampling from distribution is simply drawing a lot of random numbers from a normal distribution. Agent-based modeling understands the behavior of the original data. Once the characteristics are defined, it creates new data keeping the behavior constraints in place. Generative Adversarial Network (GAN) models are synthetic data generation techniques usually used for creating image data. These networks have two DL models, one is a generator, and the other is known as a discriminator. For example, GAN can take random noises as its input. Then the generator generates output images, whereas the discriminator tries to find whether the output is fake or real. The more the image is closer to the real one, the output can be considered as real.

Applications across enterprises

An infinite source of data that mimics the real dataset can provide innumerable opportunities to create test scenarios during development.

Synthetic data acts as a beneficiary for enterprises across domains and industries, with some examples shown below.

  1. “Customer is king”: a tag line commonly used in the current environment wherein organizations strive to provide hyper-personalization to customers for better customer retention and to create upsell and cross-sell opportunities. Synthetic data helps enterprises get detailed analysis of each customer without worrying about the consent through GDPR. This data would have properties of real data and can be used for simulations
  1. Agile development and DevOps: Software testing and quality assurance often involve a long waiting period to get access to ‘real’ data. Artificially generated data can assist in eliminating this waiting period leading to reduced testing time and increased agility during development
  2. Research and product development: Synthetic data can be used to create an understanding of the format of real data that does not exist yet and build algorithms and preliminary models on top of it. It can also be used as a baseline for product development and reduce time to market
  3. Robotics: Companies often struggle to obtain quality real-life data sets to execute testing. Synthetic data helps in running thousands of simulations, thereby improving the robots and complementing expensive real-life testing
  4. Financial services: Important elements for any financial service enterprise are fraud protection and detection methods, which can be tested and evaluated for their effectiveness using synthetic data

Limitations of synthetic data

However, the use of synthetic data does not come without its own set of limitations.

  1. At its best, synthetic data imitates the real-life data sets but is not an exact replica. This can result in certain data points that are deviations or exceptions to the overall set, leading to skewed modeling outputs
  2. It is also not an easy task to assess the quality of the synthetic data set generated as it often depends on the complexity of the original data. As a result, the quality assessment parameters need to change in accordance with the variation in the original data point, meaning there can’t be a standard framework to be followed for each synthetic data set
  3. It is difficult for business users to trust the credibility of the synthetic data generated due to a lack of technological understanding leading to slow uptake. This is more so in certain industries such as the healthcare and food industry, where there are direct repercussions to human life

Way ahead

Despite these limitations, enterprises should be keen to adopt synthetic data as they have an opportunity to disrupt the business landscape by utilizing data and its benefits to full potential. It can prove to be the push that was required for AI/ML to penetrate across enterprises and gain more traction.

If you’ve utilized synthetic data in your enterprise or know about more areas where synthetic data can be advantageous and disadvantageous, please write to us at [email protected] and [email protected]. We’d love to hear your experiences and ideas!

The Evolution from Robotic Surgery to Digital Surgery | Blog

The robotic surgery market has surged over the last decade. According to an article published by the JAMA Network Open in early January 2020, robot-assisted surgical procedures accounted for 15.1 percent of all general surgeries in 2018, up from 1.8 percent in 2012. And the market has grown even more since 2018. For example, the utilization rate of Intuitive Surgical’s da Vinci robot in US hospitals has grown more than 400 percent in the last three years.

To capture their piece of the robotic surgery market pie, other MedTech giants, including Johnson & Johnson (J&J), Medtronic, Stryker, and Zimmer Biomet have turned to acquisitions and strategic partnerships. Stryker paid a whopping US$1.65 billion in 2013 to acquire Mako Surgical Corp. Zimmer Biomet bought Medtech for its Rosa Surgical Robot in 2016 for US$132 million. J&J and Medtronic acquired Orthotaxy and Mazor Robotics, respectively, in 2018. And J&J subsequently bought Auris Health and Verb Surgical in 2019.

With all this money being spent on robotic surgery company acquisitions, it is clear that the MedTech giants intended to fight head-on with one another to build the best surgical robot.

But building the best surgical robot does not assure market leadership.  Indeed, robotics is only one aspect of the digital surgery ecosystem. In order to excel in the robotic surgery space, companies need to build solutions that complement their surgical robots with digital technology tools and capabilities.

Transforming from robotic surgery to digital surgery

As you see in the following image, the digital surgery ecosystem consists of imaging, visualization, analytics, and interoperability technologies that enhance the capabilities of surgical robots, enabling companies to unlock the full array of potential benefits robotic surgery has to offer – better precision and control, enhanced surgical visibility, remote surgery, better clinician and patient experiences, etc.

Let’s take a quick look at the value each of the digital technologies can bring to robotic surgery.

  • AI/ML and data analytics will help the robots learn from past procedures and ensure better surgery planning and reasoning. They will also help support cognitive functions such as decision making, problem solving, and speech recognition. One real-world example of AI/ML is Stryker’s Mako robot, which learns from past procedures to ensure better positioning of surgical implants for stability
  • Strong network and connectivity will enable real-time data sharing of patient outcomes, best practices, and support remote surgery at a global level
  • Augmented Reality/Virtual Reality (AR/VR) and advanced visualization technologies enhance surgical visibility beyond the naked eye and improve anatomical education and surgeon training modalities through interactive simulations
  • Remote monitoring, sensors, and wearables can assist in intra-operative and post-operative surgical care through real-time data exchange for better clinical outcomes and reduced care costs

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Realizing the benefits of digital technologies, MedTech companies are starting to make investments in them to augment their surgical robots. For example, Medtronic in 2020 acquired Digital Surgery, a leader in surgical AI, data and analytics, and digital education and training to strengthen its robotic-assisted surgery platform. Similarly, in 2021, Stryker acquired Orthosensor to enhance its Mako surgical robotics systems with smart sensor technologies and wearables, and Zimmer partnered with Canary Medical to develop smart knee implants. MedTech companies are also starting to change their branding to reflect their move to digital. For example, J&J is positioning its new offerings as digital surgery platforms instead of robotic surgery platforms.

Building a single, connected next-generation digital surgery platform

Building specialized robots for different surgical procedures requires either a huge capital investment to acquire such individualized capabilities or extensive resources and time to develop them in-house. So, it’s neither feasible nor cost-effective to do so. Therefore, it would be ideal for MedTech organizations to focus on developing one robot that supports the entire breadth of surgical procedures.

With their history of robotic acquisitions over the last three years, MedTech giants should be looking at integrating multiple point solutions to build a single, connected next-generation digital surgery platform. The following image depicts our vision of a truly connected digital surgery ecosystem built around a digital surgery platform. It ensures interoperability among all types of surgical robots so they can continually learn and evolve by sharing best practices, surgical procedures, and associated patient data.

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J&J has already shared its vision and roadmap for building a next-generation digital surgery platform. It brings together robotics, visualization, advanced instrumentation, connectivity, and data analytics to enable its digital surgery platform to improve outcomes across a broad range of disease states. It has announced its plans to integrate its recently unveiled Ottava platform with the Monarch platform it gained from its 2019 acquisition of Auris Health to build a strong position in the digital surgery market.

With MedTech giants in the initial phase of building their next-generation connected digital surgery ecosystem, they will need to have the right fit of complementary digital technologies to truly scale their impact – alleviating surgeon workloads, driving productivity, enabling personalization, and better clinical outcomes. Service providers that bring niche talent and a balanced portfolio of engineering and digital services will be a partner of choice for MedTech giants in this journey.

Please share your views on robotic surgery and the digital surgery ecosystem with us at [email protected] and [email protected].

Digital Levers for Successful Category Management: AI, Automation and Analytics | Webinar

Join Shirley Hung, Vice President at Everest Group, and Paul Blake, Director of Product Marketing at GEP, as they discuss the advantages of digital category management adoption. They will explore:

  • Adoption rates for AI/ML, automation, analytics — and how they are being used
  • The “nice-to-have” capabilities that are now “must-have” tools and methods
  • Strategies to obtain deeper, more detailed supplier intelligence, instantly
  • Ways to easily isolate and track non-compliant maverick spend
  • How to improve pricing strategies and boost your negotiation leverage with smarter data
  • The advantages of low- or no-code digital platforms for business users and IT teams

Register for the webinar

When

Thursday, March 11, 2021, at 10 am CST, 11 am EST, 4 pm GMT, 9:30 pm IST

Where

Live, virtual event

Presenters

Shirley Hung
Vice President
Everest Group

Paul Blake
Director of Product Marketing
GEP

Register for the webinar

Leap towards General AI with Generative Adversarial Networks | Blog

AI adoption is on the rise among enterprises. In fact, the research we conducted for our AI Services State of the Market Report 2021 found that as of 2019, 72% of enterprises had embarked on their AI journey. And they’re investing in various key AI domains, including computer vision, conversational intelligence, content intelligence, and various decision support systems.

However, the machine intelligence that surrounds us today belongs to the Narrow AI domain. That means it’s equipped to tackle only a specified task. For example, Google Assistant is trained to respond to queries, while a facial recognition system is trained to recognize faces. Even seemingly complex applications of AI – like self-driving cars – fall under the Narrow AI domain.

Where Narrow AI falters

Narrow AI can process a vast array of data and complete the given task more efficiently; however, it can’t replicate human intelligence, their ability to reason, humans’ ability to make judgments, or be context aware.

This is where General AI steps in. General AI takes the quest to replicate human intelligence meaningfully ahead by equipping machines with the ability to understand their surroundings and context.

Exhibit 1: Evolution of AI

Evolution of AI 

The pursuit of General AI

Researchers came up with Deep Neural Networks (DNN), a popular AI structure that tries to mimic the human brain. DNNs work with many labeled datasets to perform their function. For example, if you want the DNN to identify apples in an image, you need to provide it with enough apple images for it to clean the pattern to define the general characteristics of an apple. It can then identify apples in any image. But, can DNNs – or more appropriately, General AI – be imaginative?

Enter GANs

Generative Adversarial Networks (GAN) bring us close to the concept of General AI by equipping machines to be “seemingly” creative and imaginative. Let’s look at how this concept works.

Exhibit 2: GAN working block diagram

Evolution of AI

GANs work with two neural networks to create and refine data. The first neural network is placed in a generator that maps back from the output to create the input data used to create the output. A discriminator has the second network, which is a classifier. It provides a score between 0 to 1. A score of 0.4 means that the probability of the generated image being like the real image is 0.4. If the obtained score is close to zero, it goes back to the generator to create a new image, and the cycle continues until a satisfactory result is obtained.

The goal of the generator is to fool the discriminator into believing that the image being sent is indeed authentic, and the discriminator is the authority equipped to catch whether the image sent is fake or real. The discriminator acts as a teacher and guides the generator to create a more realistic generated image to pass as the real one.

Applications around GAN

The GAN concept is being touted as one of the most advanced AI/ML developments in the last 30 years. What can it help your business do, other than create an image of an apple?

  1. Creating synthetic data for scaled AI deployments: Obtaining quality data to train AI algorithms has been a key concern for AI deployments across enterprises. Even BigTech vendors such as Google, which is considered the home of data, struggle with it. So Google launched “Project Nightingale” in partnership with Ascension, which created concerns around misuse of medical data. Regulations to ensure data privacy safeguard people’s interests but create a major concern for AI. The data to train AI models vanishes. This is where a GAN shines. It can create synthetic data, which helps in training AI models
  2. Translations: Another use case where GANs are finding applications is in translations. This includes image to image translation, text to image, and semantic image to photo translation
  3. Content generation: GANs are also being used in the gaming industry to create cartoon characters and creatures. In fact, Google launched a pilot to utilize a GAN to create images; this will help gaming developers be more creative and productive

Two sides to a coin

But, GANs do come with their own set of problems:

  • A significant problem in productionizing a GAN is attaining a symphony between the generator and the discriminator. Too strong or too weak a discriminator could lead to undesirable results. If it is too weak, it will pass all generated images as authentic, which defeats the purpose of GAN. And if it is too strong, no generated image would be able to fool the discriminator
  • The amount of computing power required to run a GAN is way more significant as compared to a generic AI model, thus limiting its use by enterprises
  • GANs, specifically cyclic and pix2pix types, are known for their capabilities across face synthesis, face swap, and facial attributes and expressions. This can be utilized to create doctored images and videos (deep fakes) that usually pass as authentic have become an attractive point for malicious actors. For example, a politician expressing grief over pandemic victims could be doctored using GAN to show a sinister smile on the politician’s face during the press briefing. Just imagine the amount of backlash and public uproar that would generate. And that is just a simple example of the destructive power of GANs

Despite these problems, enterprises should be keen to adopt GANS as they have the potential to disrupt the business landscape and create immense competitive stance opportunities across various verticals. For example:

  • A GAN can help the fashion and design industry create new and unique designs for high-end luxury items. It can also create imaginary fashion models, thus making it unnecessary to hire photographers and fashion models
  • Self-driving cars need millions of miles of road to gather data to test their detection capabilities using computer vision. All the time spent gathering the road data can be cut short through synthetic data generated through GAN. That, in turn, can enable faster time to market

If you’ve utilized GANs in your enterprise or know about more use cases where GANs can be advantageous, please write to us at [email protected] and [email protected]. We’d love to hear your experiences and ideas!

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