/biz/ - Business & Finance

[Anonymous No.10688460]

Deepbrainchain

1) At present, projects utilizes DBC AI training net to train Natural Language Processing, voice recognition, driverless cars, medical imaging, and more.

Trained AI model to detect defective products for certain industry, precision rate of 95%-99.9%.

Trained arrhythmia AI models for OT Medical, a portable EKG manufatucturer, with a precision rate above 97%.

2) Optimized operations on top of CUDA GPUs in order to support mainstream deep-learning frameworks, including but not limited to TensorFlow, Caffe, and CNTK.

3) Conducted efficient distributed training of complex models on 2, 4 and 8 GPUs using Horovod technology.

[Anonymous No.10688475]

Future

[Anonymous No.10688476]

I bought the dip general

[Anonymous No.10688526]

4) 4) DeepBrain Chain will invest $100m in the Silicon Valley research center in the next 3 years. After the mainnet launch, the AI team will continue to push research in distributed AI training on DBC AI clusters (16–128GPU)(Q2’19) and across DBC AI network(Q4’19).

5) Cheif AI Officer Wang Dongyan

Dr. Wang has almost 20 years of Silicon Valley experience in artificial intelligence, business intelligence and data science, leading world class, industry award winning, global high-tech organizations as senior executive for Global Fortune 500 enterprises (Cisco, NetApp, Midea Group, Samsung) and a successful startup. He has extensive experience in AI platform, AI products, AI business applications, advanced analytics, data science, big data, and a great variety of cloud and on-premise enterprise applications.

Prior to joining DeepBrain Chain, Dr. Wang was the global AI leader and VP, GM of Midea Emerging Technology Center (Silicon Valley) and AI Research Institute (Shenzhen, China) for Midea Group, a Fortune global 500 enterprise and #2 largest consumer electronics company in the world. He was responsible for the overall AI vision, strategy, architecture, roadmap & execution of AI technology and business solutions for Midea's AI products in Smart home, intelligent manufacturing, robotics and business applications.

[Anonymous No.10688530]

I bought this coin at ETH, still hodling, so i hope you’re right

[Anonymous No.10688541]

Prior to Midea, Dr. Wang was the Chief Operating Officer & SVP of Grand Intelligence, a Silicon Valley consulting firm specialized in AI+BI (a term first invented by Dongyan in 2009), deep learning, machine learning, analytics, data science and big data solutions. Before that, Dr. Wang held senior executive and manager positions in other Fortune 500 companies like NetApp, Cisco Systems and Samsung America, leading large, complex enterprise wide data analytics, machine learning, business intelligence, security and other enterprise initiatives. Dr. Wang was responsible for 350 people global organizations with annual budget of $40+ Million USD.

In addition to leading his team and winning numerous industry awards, such as #8 worldwide in AI Challenger 2017, TDWI (The Data Warehouse Institute), Intelligence Enterprise RealWare Award, Information Week 500, InfoWorld 100 Awards, and Oracle BI/BPM innovation award, Dr. Wang holds more than 10 granted US granted US patents, 10+ international patents, and 30+ pending patents mostly in AI.

[Anonymous No.10688596]

look at this DUDE OH NO NO NO HAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHAHA

[Anonymous No.10688639]

1) Artificial intelligence can diagnose eye disease as accurately as some leading experts, research suggests.

https://www.bbc.com/news/health-44924948

2) DeepMind’s AI beats world’s best Go player in latest face-off

https://www.newscientist.com/article/2132086-deepminds-ai-beats-worlds-best-go-player-in-latest-face-off/

3) The OpenAI Dota 2 bots just defeated a team of former pros

https://www.theverge.com/2018/8/6/17655086/dota2-openai-bots-professional-gaming-ai

[Anonymous No.10688983]

>>10688460
>>10688526
>>10688541
>>10688639


You're talking about things you have no idea about. Fucking typical.

[Anonymous No.10689030]

You dunces blatantly know literally nothing about AI, ML, and DL. At least watch some YouTube lectures before shitting up the board with obviously foreign shilling for a dead shitcoin.

[Anonymous No.10689187]

>>10688460
AI Datasets are, at minimum, in the terabytes. Unless cryptography magically makes those terabytes fly through a decentralized network many orders of magnitude faster than regular file transfer, DBC is ridiculous.

For example, with smart cars, one car produces about 6 terabytes in a day of driving. Instead of waiting literal days/weeks for file transfer, it's more prudent to just yank the drive and deliver it to the machine for digestion.

DBC is extremely impractical. Now for voice recognition, maybe. Natural Language Processing, less likely than voice recognition, but sure.
Machine learning on qualitative marketing data, definitely not.
Unless DBC is literally offering orders of magnitude difference in data compression and network transfer to make the network layer comparable to bare metal.

[Anonymous No.10689850]

Brain gang brain gang