Computation for AI: problems, solutions and trends by Neuromation
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Up to 70% of the total cost for AI development falls on labeling data and computing power. There are two main problems for AI - not enough labeled data to train neural networks, and expensive computational power which is a bottleneck. Neuromation aims to solve these problems by creating a marketplace platform that brings together AI practitioners and enterprise demand. It provides synthetic labeled datasets, distributed computing power, and machine learning models. Neuromation's utility token NTK will be the means of exchange on the platform.
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Computation for AI: problems, solutions and trends by Neuromation
- 1. Computation for AI Problems, Solutions, and Trends Where androids dream of electric sheep March 20, 2018
- 2. BITCOIN OR ETHER MINING AMAZON DEEP LEARNING $7-8 USD per DAY $3-4 USD HOUR Chris Up to 70% of the total cost for AI development falls on labeling data and computing power 1. NOT ENOUGH LABELED DATA 2. EXPENSIVE COMPUTATIONAL POWER THE TWO MAIN PROBLEMS OF AI PROBLEM STATEMENT
- 3. BITCOIN OR ETHER MINING AMAZON DEEP LEARNING $7-8 USD per DAY $3-4 USD HOUR Chris NOT ENOUGH LABELED DATA TO TRAIN A NEURAL NETWORK BOTTLENECK OF AUTOMATION FOR EVERY INDUSTRY: 1 BLN LABELED PHOTOS = $240 MLN if processed manually THE DATA PROBLEM
- 4. ● Labeled data with 100% accuracy ● Automated data generation with no limits ● Cheaper and faster than manual labeling SYNTHETIC DATA: BREAKTHROUGH IN DEEP LEARNING BUT SYNTHETIC DATA REQUIRES EXPENSIVE COMPUTATIONAL POWER, BOTH TO RENDER DATA AND TO TRAIN NEURAL NETWORKS THE DATA PROBLEM
- 5. BITCOIN OR ETHER MINING AMAZON DEEP LEARNING $7-8 USD per DAY $3-4 USD HOUR Chris ESTIMATED 40-50% OF ANY AI START-UP’S BUDGET IS ATTRIBUTED TO COMPUTING POWER COMPUTING POWER IS EXPENSIVE AND SCARCE BOTTLENECK FOR AI PRACTITIONERS: THE COMPUTATIONAL PROBLEM
- 6. BITCOIN OR ETHER MINING AMAZON DEEP LEARNING $7-8 USD per DAY $3-4 USD HOUR ETHER MINING AMAZON DEEP LEARNING $10-12 per DAY $6-8 per HOUR The AI industry is ready to pay miners for their computational resources - more than they can ever get from mining Ether KNOWLEDGE MINING IS MORE PROFITABLE. DEEP LEARNING NEEDS DISTRIBUTED COMPUTING POWER WE CAN BRIDGE THIS GAP GPU x6 GPU THE COMPUTATIONAL PROBLEM
- 7. BITCOIN OR ETHER MINING AMAZON DEEP LEARNING $7-8 USD per DAY $3-4 USD HOUR NEUROMATION MARKETPLACE PLATFORM — SOLVING BOTH PROBLEMS: 1. SYNTHETIC DATA 2. COMPUTING POWER + 3. MACHINE / DEEP LEARNING MODELS DISTRIBUTED COMPUTING POWER SYNTHETIC DATA SETS MACHINE LEARNING MODELS WE BRING TOGETHER AI PRACTITIONERS AND ENTERPRISE DEMAND ON ONE INTEGRATED MARKETPLACE PLATFORM NEUROMATION MARKETPLACE
- 8. Neurotoken (NTK) NTK — the utility token for an operating digital economy ● Neuromation Platform is a hub for the global AI economy ● Tokens are the unit of exchange within the Neuromation Platform ecosystem ● NTK liquidity is necessary for seamless execution of transactions by all participants NTK IS THE MEANS OF EXCHANGE ON THE NEUROPLATFORM WE HAVE SOLD OUT NTK IN 9 HOURS OF TOKEN SALE (+PRE-SALE) TOKEN ECONOMICS
- 9. THE DEEP LEARNING REVOLUTION
- 10. Chris ● Image recognition for retail: automatically recognize goods on the shelf with deep neural networks ● Central problem in many applications of neural networks: where do we get labeled data from? LOTS OF LABELED DATA REQUIRED IMAGE RECOGNITION ON THE SHELF
- 11. ● Current catalogue of goods only in Russian-speaking (ex-USSR) retail: ● Each SKU requires ● Moreover, the network needs “real-looking” photos, i.e., labeled photos ofreal shelves... 170 000 SKU 1000 - 5000 labeled photos WHERE WILL ALL THIS LABELED DATA COME FROM? IMAGE RECOGNITION ON THE SHELF
- 12. ● We create a virtual copy of the shelf (a 3D model) and place 3D objects ● Then we can generate unlimited amounts of labeled synthetic data ● We train deep neural networks on synthetic data ● Reaching good accuracy much faster and cheaper than the competition IMAGE RECOGNITION ON THE SHELF
- 13. Advantages of synthetic data ● We generate 100% accurate labeled data, with pixel-perfect labeling which is impossible to do by hand ● Increase the speed of automation by orders of magnitude ● The resulting solution is several times cheaper than hand labeling IMAGE RECOGNITION ON THE SHELF
- 14. Examples IMAGE RECOGNITION ON THE SHELF
- 15. IMAGE RECOGNITION ON THE SHELF
- 16. . Piglet’s Big Brother Piglet’s Big Brother ● Feeding is of paramount importance in pig farming ● But farmers weigh pigs only twice in their entire lives! Why?..
- 17. . Piglet’s Big Brother Piglet’s Big Brother ● Feeding is of paramount importance in pig farming ● But farmers weigh pigs only twice in their entire lives! Why?..
- 18. . Piglet’s Big Brother Piglet’s Big Brother ● We propose a non-invasive approach to weighing pigs with computer vision
- 19. . Piglet’s Big Brother Piglet’s Big Brother ● Next step: video analysis to find sick animals ● Not limited to pigs, of course
- 20. . Piglet’s Big Brother Our Team Maxim Prasolov CEO Fedor Savchenko CTO Sergey Nikolenko Chief Research Officer Denis Popov Chief Information Officer Constantine Goltsev Investor / Chairman Andrew Rabinovich Adviser Yuri Kundin COO David Orban Adviser Esther Katz VP Communication David Orban Adviser
- 21. THANK YOU FOR YOUR ATTENTION! Q&A