AI＆機械学習のリサーチサービス – 年間サービス

• Machine learning
• Artificial intelligence
• Augmented Intelligence
• Deep Learning
• Data analytics
• Predictive analytics
• Prescriptive analytics
• Algorithms and hardware technologies segmentation
• Analysis of AI Tools and SDKs
• AI and ML hot technology innovators
• Edge AI and ML
• Market segmentation and taxonomy of AI and ML use cases and applications
• Different implementation approaches of AI and ML
• AI and ML business models
• AI and ML use cases in the telecoms industry
• In-building systems including DAS (Distributed Antenna Systems)

• RF power semiconductors for pulsed applications
• Detailed spectrum analysis for 5G networks, sub-6G to mmWave, including regional disparities
• AI and ML use cases in the manufacturing industry
• AI and ML use cases in the consumer market
• AI and ML use cases in the IoT market
• The role of open source in shaping new applications and business models
• Emerging trends in speech and image recognition, machine vision, natural language processing, touch/haptics, Generative and Creative Adversarial Networks, automated reasoning and security applications
• Analysis of edge AI versus cloud AI

Technology Suppliers:

• How are the different ML hardware and algorithms are mapped against requirements of the different use cases addressed?
• What are the key verticals that will drive AI and ML applications?
• Should I create my own AI frameworks and solutions or should I adopt existing open frameworks?
• Who are competitors I should watch and who are those I should partner with?
• What emerging verticals should my organization target?
• How big is the revenue opportunity?
• What major challenges will the industry face when managing a myriad of data generated by billions of connected devices?
• Who are the companies and organizations my company should partner with to create adequate solutions for the verticals are targeting?
• Where does my company fit in the AI/ML competitive landscape?
• How can my organization productize open source code?
• How can we stream value from it?
• What are the most successful open-source communities and frameworks for my company to rely on?
• Do I have any benefit from contributing and using open source and what are the risks?
  What are the most invested in AI R&D projects and frameworks?
  What impact will the move from cloud-based to edge based have on the market dynamics and supplier positioning?

Implementers & End Users:

• How can I implement AI in my current business activities?
  How will AI create new market opportunities in my sector?
  What is the realistic time to maturity of different AI components?
  What is the best approach for integrating AI into my company’s ecosystem?
  What criteria should I consider when choosing an AI partner?
  What advanced analytics techniques should my company consider adopting?
  What are the main types of algorithms used in ML today and how this is going to evolve in the future?
  How can my company utilize AI to simplify our business and operation processes?
  What is the difference between predictive and prescriptive analytics, and what is the best course of action for my company to take to effectively keep tabs on all our generated data?
  What can my company discern from our generated data through advanced analytics?
  Are there any security concerns my company should be made aware of when relying on advanced analytics?
  How can my company protect our data and our customers’ data? • What is the value of edge computing versus cloud computing?
  Should I be using an open-source AI framework to develop models, and which one would suit my needs?