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U.S. Treasury: Regulators should back off FinTech, allow innovation

The Treasury Department recently released a report urging state and federal regulators to revamp outdated statutes and support technological innovations such as AI, machine learning and blockchain that could make the U.S. financial system more nimble and competitive.
The 222-page report, commissioned by the Trump Administration 18 months ago, focused on non-bank financial services firms, such as credit lending, servicing organizations and payments networks, as well as fintech companies – tech vendors focused on innovation.
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U.S. Treasury: Regulators should back off FinTech, allow innovation

The Treasury Department recently released a report urging state and federal regulators to revamp outdated statutes and support technological innovations such as AI, machine learning and blockchain that could make the U.S. financial system more nimble and competitive.
The 222-page report, commissioned by the Trump Administration 18 months ago, focused on non-bank financial services firms, such as credit lending, servicing organizations and payments networks, as well as fintech companies – tech vendors focused on innovation.
To read this article in full, please click here

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Oracle offers GraphPipe spec for machine learning data transmission

Oracle has developed an open source specification for transmitting tensor data, which the company wants to become a standard for machine learning.
Called GraphPipe, the specification provides a protocol for network data transmission. GraphPipe is intended to bring the efficiency of a binary, memory-mapped format while being simple and light on dependencies. There also are clients and servers for deploying and querying machine learning models from any framework.
It includes:
A set of flatbuffer definitions. Flatbuffers are similar to Google protocol buffers, with an additional benefit of avoiding memory copy during deserialization. Flatbuffer definitions provide a request message that includes input, tensors, input names, and output names. Guidelines for serving models. Examples of serving models from various machine learning frameworks. Client libraries for querying models served through GraphPipe. Clients are available for Python, Go, and Java. There’s a plugin for Google’s TensorFlo..

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Oracle offers GraphPipe spec for machine learning data transmission

Oracle has developed an open source specification for transmitting tensor data, which the company wants to become a standard for machine learning.
Called GraphPipe, the specification provides a protocol for network data transmission. GraphPipe is intended to bring the efficiency of a binary, memory-mapped format while being simple and light on dependencies. There also are clients and servers for deploying and querying machine learning models from any framework.
It includes:
A set of flatbuffer definitions. Flatbuffers are similar to Google protocol buffers, with an additional benefit of avoiding memory copy during deserialization. Flatbuffer definitions provide a request message that includes input, tensors, input names, and output names. Guidelines for serving models. Examples of serving models from various machine learning frameworks. Client libraries for querying models served through GraphPipe. Clients are available for Python, Go, and Java. There’s a plugin for Google’s TensorFlo..

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Patch Tuesday fallout: Bad docs, but so far no major problems

Microsoft may have fixed July’s horrible, no good, very bad patches. Although the initial documentation for this month’s patches included warnings about many of the bugs that persisted from July, it ends up that the docs were wrong, and most of the known problems seem to be fixed.
As of early Reboot Wednesday morning, the patches seem to be behaving themselves. Of course, it frequently takes days or even weeks for bugs to appear, so you’d be well advised to avoid jumping into the unpaid battle zone for now.
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How Apple could redesign money

Like most big corporations, Apple’s financial statements always warn that currency fluctuation poses risks to its business performance — but it could mitigate such risk, perhaps with its own virtual money.
More stable than currency The problem with currency decline is that it impacts prices and revenues and can impact a company’s projected performance.
A Decluttr survey last year revealed that iPhones hold value longer than other smartphone brands. (At a stretch, you could argue that this makes them a more stable financial investment than some currencies.)
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