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In harm of Bidder’s Edge (an auction aggregator site) bots were sufficiently substantial to meet the requirements for trespass to chattels.

Conversely however, a Central District of California hearing of constitute trespass to chattels as “the taking of factual information from a public source was not a trespass” (Quilter, 2002).

However, the journey from Turing’s original vision to modern social bots has been circuitous; from early web crawlers, automated financial algorithms, simple bots on social networks through to the latest, emerging social bots[1], these automated technologies have rubbed against legal grey areas spanning trespass, intellectual property, impersonation and data privacy.

As early as 1964, scientists were experimenting with conversational computer algorithms. and Chatter Bot providing users with entertainment.

Finally, we will look at the legal ramifications of social bots as their technology matures by examining the effects of filter bubbles, data collection, social trust and whether bots can have intent.

Since the 1950s and Alan Turing’s infamous ‘Turing Test’, the concept of intelligent machines conversing with humans has driven academic artificial intelligence research.

This paper will examine the history of these social bots, from their humble start as web crawlers and simple, action-orientated algorithms, to the more complicated self-learning bots that roam social networks such as Twitter and Facebook.

Parallel to this, the advent of hyper-scale, cloud-based systems and data analysis, combined with advanced artificial intelligence techniques means the potential for this convergent technology to shake the foundations of our privacy and even legal frameworks needs to be considered.

The effect was stock prices such as Apple’s traded at 0,000 each (a 40,000% increase) whereas Accenture’s stock dropped from over to less than

Parallel to this, the advent of hyper-scale, cloud-based systems and data analysis, combined with advanced artificial intelligence techniques means the potential for this convergent technology to shake the foundations of our privacy and even legal frameworks needs to be considered.The effect was stock prices such as Apple’s traded at 0,000 each (a 40,000% increase) whereas Accenture’s stock dropped from over to less than [[

Parallel to this, the advent of hyper-scale, cloud-based systems and data analysis, combined with advanced artificial intelligence techniques means the potential for this convergent technology to shake the foundations of our privacy and even legal frameworks needs to be considered.

The effect was stock prices such as Apple’s traded at $100,000 each (a 40,000% increase) whereas Accenture’s stock dropped from over $40 to less than $0.01 within the space of minutes, although the market normalised shortly thereafter.

HFT algorithms traditionally used newsfeeds from curated sources that provided around 20-50,000 news items a day to base their trades upon.

These bots are design to make millions of calculations to make small profits that in aggregate add up to phenomenal sums of money.

By comparison, in the space of time it takes Usain Bolt to react to a starter pistol, an HFT system can execute 165,000 separate trades (Harford, 2012).

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Parallel to this, the advent of hyper-scale, cloud-based systems and data analysis, combined with advanced artificial intelligence techniques means the potential for this convergent technology to shake the foundations of our privacy and even legal frameworks needs to be considered.The effect was stock prices such as Apple’s traded at $100,000 each (a 40,000% increase) whereas Accenture’s stock dropped from over $40 to less than $0.01 within the space of minutes, although the market normalised shortly thereafter.HFT algorithms traditionally used newsfeeds from curated sources that provided around 20-50,000 news items a day to base their trades upon.These bots are design to make millions of calculations to make small profits that in aggregate add up to phenomenal sums of money.By comparison, in the space of time it takes Usain Bolt to react to a starter pistol, an HFT system can execute 165,000 separate trades (Harford, 2012).

]].01 within the space of minutes, although the market normalised shortly thereafter.HFT algorithms traditionally used newsfeeds from curated sources that provided around 20-50,000 news items a day to base their trades upon.These bots are design to make millions of calculations to make small profits that in aggregate add up to phenomenal sums of money.By comparison, in the space of time it takes Usain Bolt to react to a starter pistol, an HFT system can execute 165,000 separate trades (Harford, 2012).

.01 within the space of minutes, although the market normalised shortly thereafter.

HFT algorithms traditionally used newsfeeds from curated sources that provided around 20-50,000 news items a day to base their trades upon.

These bots are design to make millions of calculations to make small profits that in aggregate add up to phenomenal sums of money.

By comparison, in the space of time it takes Usain Bolt to react to a starter pistol, an HFT system can execute 165,000 separate trades (Harford, 2012).

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However, as with every new technology, there came the potential for abuse, with many realising that they could influence the market by using networks of bots to discuss certain stocks. A social media company with zero assets, one employee and no revenue saw their stock skyrocket from $0.06 to more than $21 (a 21,000% increase), giving it a market valuation in excess of $5 billion.