The Hunt for Pierre Jnr Read Online Free PDF Page A

avoid directly using the Weave. Pushing them to extremes will only make them more powerful.
     
    ‘You’re an interesting man.’
     
    ‘I’m glad you think so.’
     
    In a strange parallel to Pete’s probing, Geof was mining the available data on his new colleague, scanning the official and unofficial records regarding the non-Citizen Peter Lazarus. Pete observed this process with some fascination, not only a little shocked at the speed with which Geof processed the stream, but also at the content. He’d never known how close Services had come to capturing him.
     
    Geof broke his concentration. ‘I still can’t see why you’re ranked at the threat level you are, Pete. No offence, but you’re just a reader who hasn’t even attempted any manipulation for personal ...’ Geof’s voice trailed off as he figured it out. Pete was tagged as an alpha-type, a born leader.
     
    ‘I have no intention —’
     
    ‘Of course not.’
     
    Pete smiled. ‘Isn’t there a contradiction at work when we don’t wish our enemies to share the very traits we deem valuable?’
     
    You’re not my enemy.  Geof thought these words three times to make sure Pete received the message. Out loud, he covered, ‘Let me show you what I’ve been working on.’
     
    Geof’s vision went in and out of focus as he concentrated; whenever he was immersed in the Weave, he glazed over as if probing an old memory. When talking to Pete, he flicked between watching what was before him and visualising the data flowing from his symbiot. Of course, he could still see what was before his eyes, but it was peripheral. As his gaze focused and defocused, datastreams bounced to Pete’s symb, crowding into a reading pile.
     
    ‘You see, in the Weave there are anomalies and then there are abnormalities. Anomalies we get used to, they’re patterns we see again and again. Some we can identify, others not, but we’re used to them. Abnormalities, though, are more kinky. They are sporadic and spiky. You understand me?’
     
    ‘I’m trying to.’ Pete floated over Geof’s inner eye, but only saw images of digital sunsets and wire-frame landscapes. ‘I don’t understand what I’m seeing.’
     
    ‘I’m showing you some common visualisations. You can do anything with data. Most of our information is number-based, or we can attribute values along any parameters we like and in this way create an image that we can then adjust with filters and tweaking until patterns emerge. There’s too much data to search the Weave by hand, so we have to use different techniques to get overviews that react usefully to the flow of information. You with me yet?’
     
    The look on Pete’s face was enough for him to continue.
     
    ‘Okay, graphs are a simplistic data visualisation, and that’s where it starts — ways to compare data that work with our innate bias toward visual stimuli. Like we use the analogy of the Weave for the combined Earth networks because we can only understand it that way. Strands of information that go from one point to another. They overlap. They interact. But it’s not  woven  — that implies a neatness that doesn’t exist on any level. When we add in  all  available information, it becomes a visual mess we can’t possibly interpret, but by using abstractions we can turn the data into something we  can  see. We set conditions to limit the data and calibrate different patterns into focus. That’s only part one. After you’ve found a pattern, then you have to figure out what it is that’s going on, whether it’s spending patterns, weather impacts or an amusing joke that’s being passed around. A lot of the time the patterns are unidentifiable.’
     
    ‘You’ve lost me.’
     
    ‘It’s not important. This is what I do. It’d help you to understand, but, then again, I don’t understand what you do.’
     
    ‘I feel there may be some similarities.’
     
    ‘Let’s talk about you,’ Geof suggested, while ordering