The ATA Chronicle - July/August 2020 - 31

Figure 2: A truncated view of the same exponential curve (The figures on the vertical axis represent the rate of technological change
compared to a base of 1 in the 1970s.)

fiction, that can learn to perform and recursively improve at any
task, and have autonomous decision-making abilities, including,
for instance, making the decision to adopt a new goal and learn
new skills.
NMT and most AI products in use today fall into the first
category, but there are already some nascent technologies that
can improve performance autonomously, such as robots that get
better at walking as they gain experience of their environment.
Sceptics think that we'll never reach true AGI, and even strong
proponents of AGI predict that we'll need decades to produce
computerized systems that mimic and ultimately surpass a human
being's ability to assess performance, determine what needs to be
done to improve that performance, and then take the necessary
steps to do so.
If humankind ever manages to create AGI, then that invention
will have the ability to improve itself. In his 2017 book Life 3.0,
Max Tegmark presents a hierarchy of goals that futurists predict
will evolve in AGI systems, regardless of what their initial primary
goal is.4 (See Figure 3 on page 32.) These subordinate goals will
allow such a system to continually improve its capacity to achieve
its primary goal.
Goal assessment allows the system to determine whether the
primary goal is still appropriate. In the hierarchy presented in
Figure 3, goal assessment is supported by increasing the accuracy
of one's world model-or improving one's understanding of the
environment in which the primary task is being performed. As
long as the world model indicates that the primary goal has not
yet been achieved and remains valuable, then the system will stay
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on task. But if the world model shows that the primary goal has
been achieved or is no longer needed, then the goal assessment
functionality could shut down the system or select a new goal.
Capacity enhancement ensures that the system will be in
a steady state of trying to improve its ability to achieve the
overarching goal. Capacity enhancement is supported by the
autonomous improvement of the system's hardware and software,
both of which are also informed by the improving world model.
With these sub-goals in place, the AGI system will become
increasingly efficient at achieving its primary goal until that goal is
deemed no longer necessary, and then it can move on to another
goal. In other words, an AGI system will not worry about making
itself redundant because it has a disciplined approach to honing
skills and finding problems to apply them to.

THE EVOLUTION OF YOU
Technical advancement is accelerating all around us. It will
have a dramatic impact not only on your work but also on the
needs of your customers. And while we can get sucked into
thinking that we are or will soon be competing with increasingly
intelligent systems for translation work, that's a self-defeating
attitude that ignores potential new opportunities for fulfilling
the changing needs for language services. After all, the fact
that a $20 pocket calculator can do higher mathematics has
not reduced the value of understanding mathematics for an
engineer. But engineers spend much less time doing math these
days than they did back in the age of the slide-rule.
American Translators Association

31


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The ATA Chronicle - July/August 2020

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