Future of Artificial Intelligence

Investigating the Hardware and Machine Learning Technologies that would Realize the AI Agents of the Future

With improvements in data acquisition and computing power, artificial intelligence (AI) is becoming more of a reality each day. Companies are constantly developing self-evolving machine learning approaches and hardware enhancements to support AI applications. This study explores new innovations in machine learning and computing that will enable the application of AI in augmenting real-life scenarios and the impact this will have on the world. For businesses constantly striving to automate their services in a more intuitive and intelligent way to sustain and be competitive in tomorrow’s market, this study provides a comprehensive range of use cases across various business verticals. For companies looking to penetrate the AI industry, this study highlights the key technological directions the big movers of the industry are pursuing to mass commercialize AI solutions in an economical and utilitarian manner.
The following are key questions this study answers:
1. What are the important segments that summarize the complex AI ecosystem?
2. What are the key machine learning problems pursued by the industry and the approaches adopted by the companies to solve them?
3. What are the innovations in the hardware side that will accelerate innovation and applications in AI?
4. What are the big participants in the industry doing to enhance and mass commercialize the AI applications of the future?
5. What factors determine whether or not an organization is ready to adopt AI?
6. How will the adoption of AI affect industries, personal lives, and economies?
7. What should the actors defining employability do to avoid loss of employment because of AI adoption?
8. How will AI applications be deployed in the future?
USD 15,000

* Required Fields

USD 15,000


Be the first to review this product

Table of Contents

Executive SummaryKey FindingsResearch Scope, Objectives, and BackgroundResearch ScopeResearch MethodologyResearch BackgroundIntroductionDefining AIAI Systems OverviewEvolution of AIHuman Brain versus Machines—Comparison of LearningHuman Brain, Computers, and Supercomputers—Computing PowersDissecting AIMachine LearningKey ML ProblemsML StylesApplication of MLData Mining and MLComputer Vision and MLLearning in RobotsRL in RoboticsNLP TasksVoice Assistance System for Businesses from MindMeldMachines Learning the Brain’s Way—Deep Neural NetworksA Technical Insight into Deep Learning AlgorithmsDeep Learning ApplicationsEvolution of ML and its ApplicationsComputing in AIShortfalls of Current Computing Hardware ApproachesAnalog Replaces Digital—New Opportunities in Neuromorphic ComputingNeuromorphic Chips Modeled on NeuronsFPAA—Driving Innovation in Analog ICsPromising Memory Technologies for Neuromorphic ComputingQuantum ComputingAI Deployments—Cloud versus On-device IntelligenceIntelligence Becomes Distributed—from Cloud to Edge ComputingAI Readiness and ImplementationDigitalization—Necessary for a Successful AI StrategyAI Disrupting Business FunctionsAI Impacting Organizations and ManagementAI Readiness MapAI Champions League—Big Movers in the IndustryBig Companies Acquiring AI CapabilitiesGoogle’s AI Acquisition LibraryGoogle Brain—Helping Google Leapfrog from Search to AIAI as a Service—Evolving the Google WayMicrosoft’s Project AdamMicrosoft’s Project Adam (continued)IBM’s Massive AI System—Watson IBM Sells Intelligent Outputs, Not Intelligence—Intelligence-as-a-ServiceIBM Watson’s Cognitive API PortfolioIBM’s Neuromorphic Chip—TrueNorthQualcomm: Bringing On-device Intelligence to Mobile Low-power DevicesIndustry Collaborates at Quantum Artificial Intelligence Lab (QuAIL)Facebook Revolutionizing Social Network Using Deep LearningWhat Are the Big Participants Doing Right?Impact of AI on the EconomyTechnological Revolutions versus Job TrendsTechnology Revolutions and the EconomyAI and Automation—Job Threat MappingAI Shifting and Disrupting the Labor EcosystemTransforming the Actors Defining EmployabilityAI Disrupting the Middle ClassAre Actors Defining Employability Ready for the Future?AI Disruptions to IndustriesWhat Capabilities Does AI Bring to the Table?AI-driven Industry Transformation—AgricultureAI-driven Industry Transformation—HealthcareAI-driven Industry Transformation—ManufacturingAI-driven Industry Transformation—AutomotiveAI-driven Industry Transformation—Social MediaAI-driven Industry Transformation—Financial ServicesCloud Robotics and AIAI in Home AutomationConclusionLike Humans, Machines Need Plenty of Data to LearnHow Will AI Systems Be Realized?Future of AI—How Research Efforts Will EvolveThe Last Word—Key TakeawaysAppendixWellsprings of MLKey DNN AlgorithmsKey DNN Algorithms (continued)Key DNN Algorithms (continued)

Why Frost & Sullivan

Working with the CEO’s growth team to create a vision based on a transformation growth strategy

Creating content-based digital marketing strategies that leverage our research perspective to differentiate and “tell your story”

Tracking over 1000 emerging technologies and analyzing the impact by industry and application to reveal the companies to watch in each sector

The Frost & Sullivan team is based in our 45 global offices and have developed a powerful global understandings of how industries operate on a global level.