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Bitcoin Giant Bitmain Enters the High Stakes AI Race

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Artificial Intelligence

The Sophon, named for a fictional proton-sized supercomputer, could be the tool to train neural networks in data centers worldwide. It is the latest project being developed by Bitmain Technologies Ltd., the bitcoin mining giant that has carved out a dominant position in bitcoin mining.

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Such chips, called application-specific integrated circuits (ASICs), could unleash a new wave of distributed computing, according to Michael Bedford Taylor, a University of Washington professor who studies bitcoin mining and chips.

Sophon is due to debut before the end of the year.

Bitmain Has The Know-How

Bitmain has the background to play a role in the expanding artificial intelligence industry. The company designs the silicon that goes in bitcoin mining equipment, assembles the machines and sells them worldwide, in addition to its own bitcoin mining operation and the ones that it manages for other mining pools.

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Bitmain’s founders are not averse to playing a spoiler role.

Jihan Wu, the co-founder of Bitmain, supports the New York Agreement that seeks to double the bitcoin block size under the SegWit2X proposal, a move that some in the bitcoin community view as an attempt to give the miners control over bitcoin.

Some also believe Wu was behind the recent bitcoin split known as bitcoin cash, which at least one of Bitmain’s miners supported, a contention that Wu has denied. Wu points out that he was among the supporters of Bitcoin Unlimited, an earlier bitcoin scaling proposal that did not get activated.

Why Wu Supports Forks

Wu nonetheless said splits should be allowed. He said a fork is inevitable since people in the bitcoin community do not agree on how to best scale bitcoin.

Wu met Micree Zhan, Bitcoin’s co-founder, when Zhan was running DivaIP in 2010, a company that made a device that allowed a user to stream a TV show on a computer screen.

In 2011, Wu needed a chip designer to build a mining operation and approached Zhan. Zhan first designed an ASIC to run SHA-256, the cryptographic calculation used in bitcoin, at maximum efficiency. It took him six months to finish the job. His first rig, Antminer S1, was ready in November 2013.

Bitmain felt the sting of the 2014 Mt. Gox meltdown. But by 2015, bitcoin’s price bottomed out and later recovered. In the meantime, Bitmain introduced its Antminer S5.

Bitmain now employs 600 people in Beijing.

Also read: Bitmain clarifies its ‘bitcoin cash’ fork position

Ready To Take On Google

Bitmain has since developed a deep learning chip with improved efficiency. Users will be able to build their own models on the ASICs, enabling neural networks to deliver results at a faster pace. Google’s DeepMind unit used this technique to train its AlphaGo artificial intelligence.

Bitmain plans to sell the chips to any company looking to train its own neural nets, including firms like Alibaba, Tencent and Baidu. Bitmain could build its own data centers with thousands of deep learning rigs, renting out the computation power to clients the way it does with bitcoin mines.

Professor Taylor said companies like Bitmain that have excelled in bitcoin mining could take on the Googles and Nvidias since they have developed the skills to survive in an ultra-competitive and highly commoditized industry, and have the system level design expertise and the ability to reduce data center costs.

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Dutch Police Use Augmented Reality to Investigate Crime Scenes

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Dutch police are undertaking an experiment to see if augmented reality can help officers at a crime scene, according to the New Scientist.

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Using an AI system video from the body cameras worn on the officers at the scene will relay back to experts who can guide the officers by making virtual notes which the officers will be able to see via a smartphone or head-mounted device.

Dragos Datcu, principal researcher at augmented reality (AR) company Twnkls in Rotterdam, the Netherlands said:

We now have good enough software and hardware to use augmented reality at crime scenes.

What’s great about the new AI system is the fact that experts can get involved with what the crime scene investigators are doing regardless of where they are located.

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So by viewing the footage that is sent from a camera on the police vest, a chemical specialist in one location can view it while a forensic scientist in another location can too. The system is similar to the popular Pokémon Go smartphone game that has grabbed the attention of millions of people around the world.

Not Suitable for Making an Arrest

However, while the technology may prove beneficial in providing an extra pair of eyes for investigating crime scenes, when it comes to making an actual arrest the technology is not suitable for that just yet.

According to Nick Koeman, innovation adviser from the National Police of the Netherlands, the officers undertaking the AI system trial found the extra information distracting.

Of course, some may simply say that ensuring a complete team is at the scene of a crime would be more beneficial for an investigation; however, that is not always possible due to budget cuts and time constraints.

As such the use of an AI system that can cut down on the number of people involved at a crime scene without sacrificing on the required thoroughness could potentially provide the answer that many police departments are searching for.

Not only that, but by reducing the number of people at a scene it cuts the potential possibility of contaminating evidence. The use of AI gives people the chance to assess the evidence and discover additional clues without being at the crime scene.

AI could also help in court cases by helping to recreate a scene for a jury, but as Michael Buerger, professor of criminal justice at Bowling Green State University in Ohio states, legal challenges are likely to raise when augmented reality (AR) is used in the courts.

Featured image from Shutterstock.

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Apple Patent Reveals Siri-Assisted iMessage P2P Payments Platform

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Apple has filed a patent application for a “virtual assistant in a communication session.” On first glance, application 14/713,410 might scare developers of chat bots and the like, but Apple is specific in the scope of their claims – Siri, their virtual assistant platform, will be able to directly communicate with either user in an iMessage conversation and then be able to act on the instructions given. The other participant of the conversation will not see messages intended to Siri, nor messages sent from Siri to the user activating her. This is somewhat different from current implementations of chat bots and virtual assistants in chat programs, in that the classical chat bot for, say, IRC, would be accessible by most of the users of the chatroom.

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Siri Sharing Location Information Between iMessage Participants

Source: USPTO

Siri will also be able to privately give information requested of the other party, even if the other party has not provided it. In the above image, Siri lets the user know that everyone will arrive within five minutes, clearly flexing the GPS data available to her from the other user’s iPhone. One would assume a massive update to the iMessage user agreement will be necessary, and the privacy implications of voluntarily allowing an AI to be involved in every conversation are clear. However, as shown, the default is for the user to give permission for their location information to be shared. If Siri is ever compromised, so too could every conversation on the famously secure iMessage protocol.

Siri Scheduling Meetings Between Participants

Source: USPTO

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However, Siri is not involved in conversations until she is summoned. She is added as another participant when the user summons her or, presumably, adds her manually. As seen in the image above, she can schedule meetings in addition to her other functionalities. However, perhaps most interesting, and most appetizing for malicious hackers, is the prospect shown in the below image. Users who have their financial details linked to their Apple account (which is to say, most or all users) are able to send money via iMessage at the stroke of a message.

Siri Faciliating P2P Payments in iMessage

Source: USPTO

As you can see, Siri suggests giving the funds via cash, likely due to the small amount. According to the patent application, Siri first figures out what options are available to both party, and then presents options. Both users appear to use Bank of America and Paypal, but Siri recommends cash. Both the illustrations and their descriptions in the application make it unclear which option the user selects, although the logo to the left of the success message indicates that cash was, indeed, used. Also shown are some security features in the actual sending of funds, including a thumb print scan and password. One would assume that Apple Pay will play a role in all this, and speculation has abounded that the new platform is meant to rival the Venmo system.

About a year ago, reports were in circulation that Apple and banks were discussing the implementation of such a system, and indeed this patent was first filed over a year ago. Presumably it will see ratification within the next year or two, supposing that other major AI developers like Microsoft choose not to contest it on some of its broader implications.

Microsoft and Google would surely oppose paying patent licensing fees to Apple in order to instantiate their own AI peer-to-peer payment systems, and for the most part, the patent application seems to indicate that Apple would then have sole dominion over that field. Therefore, with numerous dogs in the fight including Cortana, the future of this patent and/or the platform itself (failure to acquire a patent would not prohibit Apple from creating the platform, necessarily) is still very much up in the air. More is sure to surface in the coming months, and Hacked will keep you posted.

Images from Shutterstock and USPTO.

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Nasdaq Turns to AI to Detect Market Manipulation

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Nasdaq undertakes 14 million trades a day on its exchange making it nearly impossible to detect market abuses taking place. However, with the help of artificial intelligence (AI), Nasdaq is cracking down on the bad behavior.

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There are several things that Valerie Bannert-Thurner, senior vice president and head of risk and surveillance at Nasdaq, considers when it comes to bad behavior on the exchange.

Speaking to the American Banker, she said:

If people are excessively profitable given how they trade and in comparison to everybody else trading the same instruments with similar styles, then we ask, is this luck or something else?

Yet, with an excessive amount of trades taking place each day at Nasdaq, keeping track of who’s doing what can seem a daunting task.

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In a bid to lighten the workload, the exchange is turning its attention to AI to help, and is one of the first exchanges to use AI as well.

Maintaining the Exchange’s Integrity

As Nasdaq is a well-known exchange that operates 25 exchanges in Canada, Europe, and the U.S. it needs to maintain its trustfulness or else companies will cease from using it.

According to Bannert-Thurner, Nasdaq exists because people believe in its abilities to detect market manipulation and abuse. So-much-so, that 45 outside exchanges and 13 regulators use its SMART trade surveillance platform.

Typically, Nasdaq has large teams of people using the SMART platform to detect bad behavior on the exchange. They do this while also connecting what traders are saying in emails and chats with what their actual trades are doing for them to catch them in the act.

However, it was in 2014, that the Libor scandal uncovered the manipulation of Forex markets, with U.S. regulators investigating many incriminating chats to fix rates.

To prevent situations like this taking place again, Nasdaq is employing AI software from Digital Reasoning.

AI to Detect Market Abuses

Using language processing and machine intelligence, the software can detect the language traders are using and identify any key triggers, which will alert those seeking out market abuse to look at what trades were made.

To make sure the software can detect what is going on, Nasdaq is feeding it alerts and chats from previous cases of manipulation.

Bannert-Thurner said:

We think machine learning will make a significant difference in how we detect behaviors, how we help people review and investigate.

The Digital Reasoning software will screen the threat indicators first before humans see them. It is hoped that the software will reduce the number of alerts down to only the relevant ones.

Featured image from Shutterstock.

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