Skip directly to search

Skip directly to content

 

Cognitive Computing Using Cloud-Based Resources

 
 

AI | Radu Orghidan |
17 September 2019

ABSTRACT

Each cloud service provider currently offers their customers similar computational services and upsell their offering through unique and compelling AI functionality. Due to the complexity of the available services, companies often lack a set of criteria to clearly distinguish between providers, platforms, or product instances in order to make the best decision for their needs.

The main goal of this paper is to offer a structured view of the commercial AI cloud solutions currently on the market. It proposes a trifold perspective according to: the business focus areas, the technologies driving AI and the start-up situation in 2019.

Additionally, performing predictive analysis in a real-time scenario is not a simple task. Besides the technical and administrative aspects that need to be considered, it requires a strong commitment from the main stakeholders. The secondary objective of the paper is to offer a set of guidelines for a successful cognitive computing approach.


This paper consists of four sections and will be shared over two parts:

Part one:
Introduction: Distinguishing between Narrow AI and Artificial General Intelligence (AGI) and how Cognitive Computing Systems are a step in the right direction toward achieving AGI.

Chapter 1: which tackles the trends in the enterprise cloud market. It covers issues related to operational costs, main players, market share and growth forecast. It sets the scene for understanding how the complexity of the ML domain and the speed at which these services are evolving makes it difficult to find a reliable, up to date, comparison between the available services.

Chapter 2: This chapter approaches this endeavour by looking at the AI domain from three different perspectives. First, we examine the key initiatives that drive companies to use AI: Insights, User Experience and Process Automation. Second, we analyse the classification of services proposed by the public cloud vendors and examine the position of the AI services among them. Finally, the 2019 start-ups that use the publicly available services are presented and the relation between the unicorns and their preferred technologies is highlighted.

Part two, which will be published in the first week of October:
Chapter 3: This chapter focuses on the AI functionalities, grouped around the three key initiatives. The services offered by the main players in the cloud market are referred according to their commercial names and compared at a high level.

Conclusion: The paper ends with a set of general recommendations and an Annex which presents a summary of the different concepts offered as a service.

INTRODUCTION

Artificial Intelligence is a concept that is often incorrectly used when talking about machine learning. This association is misleading, and even damaging for those working in the machine learning field, because it sets the wrong kind of expectations for algorithms that are designed to solve only narrow problems, such as playing a game of chess or image recognition. This kind of intelligence is, thus, called Narrow AI. The aspiration of AI enthusiasts is to reach Artificial General Intelligence (AGI) which is able to perform human-like tasks. A step in this direction is made by Cognitive Computing Systems (CCS). CCS are designed to simulate human brain processes and to make sense of complex situations by running data gathered from various sensors through machine learning algorithms that deal with computer vision, natural language processing, sentiment analysis etc. CCS are adaptive, interactive, stateful and contextual.

The main cloud providers offer AI functionalities that can be used as the basic blocks for building complex CCS. In spite of the novelty of the domain, the current, ferocious competition creates a red ocean in which the customer has a tough time finding the most suitable tools for a given problem.

1. CLOUD USAGE AND TRENDS

Cloud computing has become one of the most important enablers of business transformation in an increasing number of industries. Businesses migrate applications to the cloud for infrastructure-related cost savings or to improve their operational agility.

Cloud service providers, such as Microsoft, Amazon and Google approach customers in a similar manner by offering them computational power, cloud storage and serverless functions. Subsequently, they upsell through compelling AI functions. Other companies, such as IBM, use multiple clouds to manage AI and subsequent services.

The AI race offers interesting opportunities for machine learning algorithms and new players are still emerging. For instance, IBM offers its Watson suite, which has gained a fair amount of market share during the past year. IBM’s computing platform’s top ranking customers are located in the US, UK and India, according to the marketing intelligence study published by iDatalabs.

It has become clear by now that the cloud market is not a zero-sum game, because the output of one functionality from provider A is used as an input for another functionality from provider B. The result is that cloud expansion leads to higher IT spending, as illustrated in Figure 1. In fact, a hybrid approach is more likely to be the trend. The same company will consume services from several providers at once. The IT integrators and public cloud customers are driving a new kind of cross-vendor innovation by combining the available tools in unique ways. Moreover, a multi-cloud strategy is enabled by the surge of container solutions such as Kubernetes or Docker Swarm.

AI in Cloud
Figure 1. Cloud market share providers. Source: Gartner, Goldman Sachs, 2019.

2. AI TECHNOLOGIES AND BUSINESS FOCUS AREAS

This chapter examines three different viewpoints on the AI field. First, we look at the key initiatives that drive companies to use AI. Second, we show how the main public cloud vendors present their services in the AI field. Then, we analyse the way in which start-ups use the available services in 2019. Finally, the findings are represented in a graphical form, superposing the three views, for a better understanding.

KEY INITIATIVES THAT DRIVE COMPANIES TO USE AI

According to Gartner [Barot, 2019], organisations are using technology as a way to drive three key initiatives:

Insights: providing key insights that businesses are looking for. Patterns and trends are revealed using historical and current data and ML techniques. Companies can predict future trends in their business context with a span of milliseconds, hours, or even years. Reliable ways of scrutinising the future can help companies find and exploit opportunities or detect and cope with risks. For instance, retailers can forecast stocks variations and manage their logistics accordingly or adapt the design of their stores to increase sales. Similarly, airlines, hotels or restaurants analyse past trends to set their prices according to the demand forecasts and to maximise occupancy.

User experience: offering NLP techniques and video analytics that provide a rich user experience for both internal and external customers.

Nowadays, advances in speech recognition lead to conversational products such as Siri, Amazon Alexa, Cortana, or Google Home. Yet, most conversational UIs are text-based chatbots that are used to answer from web pages or applications such as Facebook or Slack. While some of the interactions are facilitated by conversational bots, a great UX needs to take into account many other factors such as the different logical paths of the users, the cultural peculiarities or even the potential security threats that can derive from the exposure of different services.

The visual part of the application and web interfaces plays an even greater role than speech in capturing users’ attention. Through image-based object detection and recognition functionalities, the user experience can be more natural. Also, applications using visual sentiment analysis can adapt the content or the conversation to the user’s mood. For instance, Netflix understood that not only the content to be presented to the user is important but also the specific design of a cover can work better with certain users than others.

Therefore, AI can be used for improving the UX of our products through natural conversations, prediction of user paths and sentiment analysis for matching users’ interest.

Process automation: uses AI and related technological advances to profoundly reshape human activities and the nature of the work itself.

One of the fastest-growing segments of the enterprise software is represented by Robotic Process Automation (RPA), also referred to as bots, that allow users to configure specific behaviours with the aim of automating repetitive or tedious tasks within a business or an IT process. RPA software revenue is expected to grow 57% year over year to more than US$2.4 billion in 2020, according to Fabrizio Biscotti, research vice president at Gartner. Among the market leaders are companies such as UiPath, with a six-fold increase in revenue between 2017 and 2018, Automation Anywhere and Blue Prism. Other big players are interested in this market and companies such as Microsoft, IBM or SAP are developing alliances with RPA software providers. The US dominates the global market with a 51% share in 2018 while Western Europe held a 23% share followed by Japan. The main adopters of RPA are banks, telcos, insurance companies and utility companies.

The increasing adoption of AI leads to a paired demand for off-the-shelf products targeted to specific business cases. Cloud vendors provide a large choice of AI technologies along with these trends. Overall, these technologies are grouped into three main clusters: Machine Learning & Deep Learning, Natural Language Processing, and Computer Vision, as shown in the left part of Figure 2.

AI in Cloud
Figure 2: AI technologies and business use cases. Source: Gartner [Barot, 2019].

COMMERCIAL CLASSIFICATION OF CLOUD SERVICES

Cloud vendors offer different classifications of their technologies and related products. One of the most granular classifications was made by Microsoft, which defined the following clusters:

Marketplace
AI and machine learning
Big data and analytics
Compute
Database
DevOps and application monitoring
Internet of Things (IoT)
Management
Messaging and eventing
Mobile services
Networking
Security, identity, and access
Storage
Web apps
Miscellaneous

The AI and machine learning category, included among the 15 classes of cloud services, will be detailed in the next chapter. However, it is important to remark here that all these services are dependent and have to be seen as part of an ecosystem, rather than stand-alone entities.

This is why, before diving into the AI field, it is interesting to superpose the two previous classifications: the key initiatives that drive companies to use AI and the services offered by the public cloud providers. Therefore, the diagram presented in Figure 3 shows a possible configuration in which these services are used to obtain applications in the fields of Insights, Process Automation, User Experience or Others.

We placed AI & Machine Learning services at the core of the four domains because of their extreme versatility. Also, DevOps and Application monitoring services gain an increasingly important role in the development of distributed cognitive solutions. There are no purely User Experience based services and, on the opposite side, there are numerous services in the Process Automation domain.

AI in Cloud
Figure 3: Gartner and Microsoft overlap on technology clusters.

START-UPS IN AI

Another way of looking at the AI scene is from a start-up perspective. The most promising 100 start-ups in 2019 developed solutions using combinations of different AI services which belong to 13 categories, according to CBInsights:

1. Agriculture
2. Auto
3. Enterprise Tech
4. Finance & Insurance
5. Government
6. Healthcare
7. Industrials
8. Legal, Compliance, & HR
9. Media
10. Real estate
11. Retail
12. Semiconductor
13. Telecom

The start-ups can be classified in relation to their focus industry, as illustrated by Figure 4:

AI in Cloud
Figure 4. Start-ups are categorised by their main focus areas. Source: CBInsights.

Amongst these start-ups, the 11 companies listed below reached a valuation of more than $1B and, thus, reached the unicorn level.

Company

Sector

Focus Area

Country

SenseTime

Government

Security

China

UiPath

Enterprise Tech

Other: RPA

United States

Automation Anywhere

Enterprise Tech

Other: RPA

United States

YITU Technology

Government

Security

China

Graphcore

Semiconductor

Data Centres

United Kingdom

C3

Industrials

IIoT

United States

Butterfly Network

Healthcare

Imaging & Diagnostics

United States

4Paradigm

Finance & Insurance

Anti-Fraud

China

Pony.ai

Auto

Autonomous Vehicles

United States

Face++

Government

Facial Recognition

China

Momenta

Auto

Perception

China

Table 1. Unicorn Start-ups in 2019. Source: CBInsights.

The current trends in the AI domain are underpinned by an analysis of the distribution of the 2019 unicorns’ solutions over the technology clusters, as presented in Figure 5. The main focus is on AI solutions that enhance the user experience. These start-ups use computer vision for text, object and face recognition such as SenseTime (sensetime.com), YITU Technology (yitutech.com), or Face++ (faceplusplus.com) and for diagnostic and therapeutic imaging such as Butterfly Network (ButterflyNetwork.com). Process automation takes the second place in the unicorns’ preferences with RPA providers such as UiPath (UiPath.com) and Automation Anywhere (AutomationAnywhere.com) or with autonomous driving solutions such as Pony.ai and Momenta (Momenta.ai). Finally, AI is used in building solutions for fraud detection by financial firms, such as the one offered by 4Paradigm (4Paradigm.com) or IoT for industrial approaches such as Graphcore (Graphcore.ai) and C3 (C3.ai).

AI in Cloud
Figure 5: Start-ups and technology clusters.

BUSINESS FOCUS AREAS AND CLOUD PROVIDERS’ PERFORMANCE

According to the previously introduced three key initiatives that drive companies, Gartner compared the cloud providers, as shown in Table 2. The colour convention sky blue, blue, navy blue stands for Low, Medium and High, respectively and represent the degree to which each company covers the given dimension. Concretely, the products that were evaluated in each case are presented in Table 2 and are explained in detail in Part 2, Chapter 4.

Assessment Criterion

AWS

Google

Microsoft

Insights

 

 

 

Machine Learning Platform

Amazon SageMaker

AI Platform and Cloud AutoML

Azure Machine Learning Service

User Experience

 

 

 

Conversational Platform

Amazon Lex

Dialogflow

Microsoft Bot Framework
+
Azure Bot Service

Text Summarization and Analytics

Amazon Comprehend
+
Amazon Textract
Cloud Natural Language (NL) API
+
AutoML Natural Language
+
Document Understanding AI

Azure Cognitive Services - Language

Image Classification

Amazon Rekognition Image

Vision API and AutoML Vision

Azure Cognitive Services - Computer Vision

Streaming Video Processing

Amazon Rekognition Video
+
Amazon Kinesis Video Streams
Cloud Video Intelligence
+
AutoML Video

Azure Media Services - Video Indexer

Process Automation

 

 

 

IoT Implementation

AWS IoT

Cloud IoT Core

Azure IoT Central

Contact Center

Amazon Connect

Contact Center AI

Dynamics 365 Virtual Agent for Customer Service

Table 2. Source: Gartner 2019.

REFERENCES

[Barot, 2019], Soyeb Barot, Solution Comparison for Cloud-Based AI Services, ID: G00377714, Gartner, 23 May 2019

Radu Orghidan

VP Cognitive Computing

Radu is a technical business consultant with in-depth knowledge of innovation management. He leads cross-functional teams to achieve cutting edge technical objectives for our clients. His projects use data acquired from different sensors such as depth sensing cameras, mobile robots or microphones to create systems that enhance a users’ abilities to understand and interact with the environment… or sometimes to create 3D scans of his two kids. In his free time Radu loves running outdoors, skiing and being in the snow, or eating seafood while sharing a good bottle of wine.

 

Categories

 

Related Articles

  • 05 November 2019

    The Twisted Concept of Securing Kubernetes Clusters

  • 01 October 2019

    Cognitive Computing Using Cloud-Based Resources II

  • 17 September 2019

    Cognitive Computing Using Cloud-Based Resources

  • 20 August 2019

    Extracting Data from Images in Presentations

  • 30 April 2019

    Kubernetes Design Principles Part 1

 

From This Author

  • 01 October 2019

    Cognitive Computing Using Cloud-Based Resources II

  • 27 August 2019

    Taming AI in a Cognitive Driven Business World

Most Popular Articles

Kubernetes Design Principles Part 1
 

Architecture | Armen Kojekians | 30 April 2019

Kubernetes Design Principles Part 1

11 Things I wish I knew before working with Terraform – part 1
 

Architecture | Julian Alarcon | 25 June 2019

11 Things I wish I knew before working with Terraform – part 1

11 Things I wish I knew before working with Terraform – part 2
 

Architecture | Julian Alarcon | 23 July 2019

11 Things I wish I knew before working with Terraform – part 2

Infrastructure as Code with Terraform
 

Architecture | Vlad Cenan | 25 February 2019

Infrastructure as Code with Terraform

Microservices and Serverless Computing
 

Architecture | Radu Vunvulea | 30 May 2019

Microservices and Serverless Computing

The Twisted Concept of Securing Kubernetes Clusters
 

Architecture | Vlad Calmic | 05 November 2019

The Twisted Concept of Securing Kubernetes Clusters

Cognitive Computing Using Cloud-Based Resources
 

AI | Radu Orghidan | 17 September 2019

Cognitive Computing Using Cloud-Based Resources

Internet Scale Architecture
 

Architecture | Gareth Badenhorst | 28 January 2019

Internet Scale Architecture

Developing your Product Owner mindset
 

Agile | Thomas Behrens | 09 July 2019

Developing your Product Owner mindset

 

Archive

  • 05 November 2019

    The Twisted Concept of Securing Kubernetes Clusters

  • 01 October 2019

    Cognitive Computing Using Cloud-Based Resources II

  • 17 September 2019

    Cognitive Computing Using Cloud-Based Resources

  • 03 September 2019

    Creating A Visual Culture

  • 20 August 2019

    Extracting Data from Images in Presentations

  • 06 August 2019

    Evaluating the current testing trends

  • 23 July 2019

    11 Things I wish I knew before working with Terraform – part 2

  • 12 July 2019

    The Rising Cost of Poor Software Security

  • 09 July 2019

    Developing your Product Owner mindset

  • 25 June 2019

    11 Things I wish I knew before working with Terraform – part 1

  • 30 May 2019

    Microservices and Serverless Computing

  • 14 May 2019

    Edge Services

  • 30 April 2019

    Kubernetes Design Principles Part 1

  • 09 April 2019

    Keeping Up With The Norm In An Era Of Software Defined Everything

  • 25 February 2019

    Infrastructure As Code With Terraform

  • 11 February 2019

    Distributed Agile – Closing the Gap Between the Product Owner and the Team

  • 28 January 2019

    Internet Scale Architecture

We are listening

How would you rate your experience with Endava so far?

We would appreciate talking to you about your feedback. Could you share with us your contact details?

 

By using this site you agree to the use of cookies for analytics, personalized content and ads. Learn More