2019/9/19 – CRI Report https://www.cri-report.com market research vender! Sat, 12 Sep 2020 05:18:08 +0000 en-US hourly 1 https://www.cri-report.com/wp-content/uploads/2021/01/cropped-shcrilogo-c.jpg 2019/9/19 – CRI Report https://www.cri-report.com 32 32 Reinforcement Learning – Startup Ecosystem Analysis https://www.cri-report.com/reinforcement-learning-startup-ecosystem-analysis/ https://www.cri-report.com/reinforcement-learning-startup-ecosystem-analysis/#respond Sat, 12 Sep 2020 05:18:08 +0000 https://www.cri-report.com/product/reinforcement-learning-startup-ecosystem-analysis/ Reinforcement learning is the next revolution in artificial intelligence (AI). As a feedback-driven and agent-based learning technology stack that is suitable for dynamic environments, reinforcement learning methodologies leverage self-learning capabilities and multi-agent potential to address issues that are unaddressed by other AI techniques. In contrast, other machine learning, AI techniques like supervised learning and unsupervised learning are limited to handling one task at a given time. With the advent of Artificial General Intelligence (AGI), reinforcement learning becomes important in addressing other challenges like multi-tasking of intelligent applications across different ecosystems.…

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]]> Reinforcement learning is the next revolution in artificial intelligence (AI). As a feedback-driven and agent-based learning technology stack that is suitable for dynamic environments, reinforcement learning methodologies leverage self-learning capabilities and multi-agent potential to address issues that are unaddressed by other AI techniques. In contrast, other machine learning, AI techniques like supervised learning and unsupervised learning are limited to handling one task at a given time.

With the advent of Artificial General Intelligence (AGI), reinforcement learning becomes important in addressing other challenges like multi-tasking of intelligent applications across different ecosystems. The technology appears set to drive the adoption of AGI technologies, with companies futureproofing their AGI roadmaps by leveraging reinforcement learning techniques.

This report provides an analysis of the startups focused on reinforcement learning techniques across industries.
Startup Ecosystem
We describe the importance of agent-based learning methods and the core concepts related to reinforcement learning. The section includes a comparison of machine learning techniques, and an overview of topics like imitation learning, AGI, and model-based and model-free reinforcement learning algorithms. Further, we outline how reinforcement learning techniques can help address the current challenges of various industries in developing next-generation solutions.

The report includes an analysis of startups leveraging reinforcement learning algorithms in key sectors like automotive, retail, industrial, financial services, robotics, healthcare, IoT, food industry, and several others. We cover 39 startups, out of which some prominent startups like Osaro, Kindred, Micropsi Industries, Wayve, Cerebri AI, and OpenAI have been reported in detail.

Analysis of these startups has been undertaken basis their technologies, offerings, patenting activities, and future outlooks. Additionally, an overview of other startups including Latent Logic, NeuDax, Nnaisense PerimeterX, Deeplite, and Context Scout, which are leveraging reinforcement learning algorithms, has been covered in the ambit of the report.

Startups covered in the report
1. Osaro
2. OpenAI
3. Acutronic Robotics
4. Wayve
5. Kindred
6. Prowler.io
7. Cerebri AI
8. Micropsi Industries
9. CogitAI
10. InstaDeep
11. Latent Logic
12. NeuDax
13. Ascent Robotics
14. Context Scout
15. Wasteless
16. Insilico Medicine
17. ProteinQure
18. Evo.Do
19. Remi
20. Optimal
21. AiGent-Tech
22. Learnable AI
23. HiHedge
24. Covariant.AI
25. AIdentify
26. Nnaisense
27. PerimeterX
28. Incelligent
29. Cogent Labs
30. Pricemoov
31. Dataone
32. Intelligent Layer
33. Kinta AI
34. Omina Technologies
35. Deeplite
36. Free Machines
37. Dorabot
38. Applied Brain Research
39. Neurocat

Key insights:
– Reinforcement learning is a prime technology for the of future self-learning, self-optimizing and other self-driving abilities needed in autonomous applications across industries.
– Automotive, retail, ecommerce, and robotics is crowded with startups developing reinforcement learning techniques.
– Reinforcement learning can address the requirements related to dynamic decision-making in autonomous vehicles targeting level 5 autonomy.
– Other sectors exploring reinforcement learning are healthcare, financial services, food industry, manufacturing, education and telecom.
– Startups that are offering reinforcement learning techniques in robotic solutions are focusing on robots as a service business model.

Key questions addressed:
– What are the advantages offered by reinforcement learning algorithms in comparison with the other machine learning models?
– What are the basic concepts in reinforcement learning?
– How can reinforcement learning address the technical requirements of different sectors and emerging applications?
– What is nature and size of the startup ecosystem for reinforcement learning and how is it changing the landscape of traditional artificial intelligence?
– What are the target industries and application areas for the new companies?
– What are the technologies, future roadmap, partnership activities, limitations, and offerings of the reinforcement learning- focused startups across industries?
– What are the potential collaboration opportunities for different industry segments to leverage reinforcement learning solutions for their businesses?

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