...now they write books. Well, only a chapter yet, and ok,
you have to see it for yourself, the text is somewhat
hilarious, but still...it is somewhat impressing.
I knew that there are bots writing sports and financial
articles since some time, but this is a step into something
else.

http://botnik.org/content/harry-potter.html
Posted on RetroBBS II

RIP noob writers
Posted on RetroBBS II

another case in point:
https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html

Google Duplex: An AI System for Accomplishing Real-World
Tasks Over the Phone
Tuesday, May 8, 2018
Posted by Yaniv Leviathan, Principal Engineer and Yossi
Matias, Vice President, Engineering, Google

A long-standing goal of human-computer interaction has been
to enable people to have a natural conversation with
computers, as they would with each other. In recent years,
we have witnessed a revolution in the ability of computers
to understand and to generate natural speech, especially
with the application of deep neural networks (e.g., Google
voice search, WaveNet). Still, even with today's state of
the art systems, it is often frustrating having to talk to
stilted computerized voices that don't understand natural
language. In particular, automated phone systems are still
struggling to recognize simple words and commands. They
don't engage in a conversation flow and force the caller to
adjust to the system instead of the system adjusting to the
caller.

Today we announce Google Duplex, a new technology for
conducting natural conversations to carry out "real world"
tasks over the phone. The technology is directed towards
completing specific tasks, such as scheduling certain types
of appointments. For such tasks, the system makes the
conversational experience as natural as possible, allowing
people to speak normally, like they would to another person,
without having to adapt to a machine.

One of the key research insights was to constrain Duplex to
closed domains, which are narrow enough to explore
extensively. Duplex can only carry out natural conversations
after being deeply trained in such domains. It cannot carry
out general conversations.

Here are examples of Duplex making phone calls (using
different voices):
Duplex scheduling a hair salon appointment:
Duplex calling a restaurant:

While sounding natural, these and other examples are
conversations between a fully automatic computer system and
real businesses.

The Google Duplex technology is built to sound natural, to
make the conversation experience comfortable. It's important
to us that users and businesses have a good experience with
this service, and transparency is a key part of that. We
want to be clear about the intent of the call so businesses
understand the context. We'll be experimenting with the
right approach over the coming months.

Conducting Natural Conversations
There are several challenges in conducting natural
conversations: natural language is hard to understand,
natural behavior is tricky to model, latency expectations
require fast processing, and generating natural sounding
speech, with the appropriate intonations, is difficult.

When people talk to each other, they use more complex
sentences than when talking to computers. They often correct
themselves mid-sentence, are more verbose than necessary, or
omit words and rely on context instead; they also express a
wide range of intents, sometimes in the same sentence, e.g.,
"So umm Tuesday through Thursday we are open 11 to 2, and
then reopen 4 to 9, and then Friday, Saturday, Sunday we...
or Friday, Saturday we're open 11 to 9 and then Sunday we're
open 1 to 9."
Example of complex statement:

In natural spontaneous speech people talk faster and less
clearly than they do when they speak to a machine, so speech
recognition is harder and we see higher word error rates.
The problem is aggravated during phone calls, which often
have loud background noises and sound quality issues.

In longer conversations, the same sentence can have very
different meanings depending on context. For example, when
booking reservations "Ok for 4" can mean the time of the
reservation or the number of people. Often the relevant
context might be several sentences back, a problem that gets
compounded by the increased word error rate in phone calls.

Deciding what to say is a function of both the task and the
state of the conversation. In addition, there are some
common practices in natural conversations -- implicit
protocols that include elaborations ("for next Friday" "for
when?" "for Friday next week, the 18th."), syncs ("can you
hear me?"), interruptions ("the number is 212-" "sorry can
you start over?"), and pauses ("can you hold? [pause] thank
you!" different meaning for a pause of 1 second vs 2
minutes).

Enter Duplex
Google Duplex's conversations sound natural thanks to
advances in understanding, interacting, timing, and
speaking.

At the core of Duplex is a recurrent neural network (RNN)
designed to cope with these challenges, built using
TensorFlow Extended (TFX). To obtain its high precision, we
trained Duplex's RNN on a corpus of anonymized phone
conversation data. The network uses the output of Google's
automatic speech recognition (ASR) technology, as well as
features from the audio, the history of the conversation,
the parameters of the conversation (e.g. the desired service
for an appointment, or the current time of day) and more. We
trained our understanding model separately for each task,
but leveraged the shared corpus across tasks. Finally, we
used hyperparameter optimization from TFX to further improve
the model.
Incoming sound is processed through an ASR system. This
produces text that is analyzed with context data and other
inputs to produce a response text that is read aloud through
the TTS system.

Sounding Natural
We use a combination of a concatenative text to speech (TTS)
engine and a synthesis TTS engine (using Tacotron and
WaveNet) to control intonation depending on the
circumstance.

The system also sounds more natural thanks to the
incorporation of speech disfluencies (e.g. "hmm"s and
"uh"s). These are added when combining widely differing
sound units in the concatenative TTS or adding synthetic
waits, which allows the system to signal in a natural way
that it is still processing. (This is what people often do
when they are gathering their thoughts.) In user studies, we
found that conversations using these disfluencies sound more
familiar and natural.

Also, it's important for latency to match people's
expectations. For example, after people say something
simple, e.g., "hello?", they expect an instant response, and
are more sensitive to latency. When we detect that low
latency is required, we use faster, low-confidence models
(e.g. speech recognition or endpointing). In extreme cases,
we don't even wait for our RNN, and instead use faster
approximations (usually coupled with more hesitant
responses, as a person would do if they didn't fully
understand their counterpart). This allows us to have less
than 100ms of response latency in these situations.
Interestingly, in some situations, we found it was actually
helpful to introduce more latency to make the conversation
feel more natural -- for example, when replying to a really
complex sentence.

System Operation
The Google Duplex system is capable of carrying out
sophisticated conversations and it completes the majority of
its tasks fully autonomously, without human involvement. The
system has a self-monitoring capability, which allows it to
recognize the tasks it cannot complete autonomously (e.g.,
scheduling an unusually complex appointment). In these
cases, it signals to a human operator, who can complete the
task.

To train the system in a new domain, we use real-time
supervised training. This is comparable to the training
practices of many disciplines, where an instructor
supervises a student as they are doing their job, providing
guidance as needed, and making sure that the task is
performed at the instructor's level of quality. In the
Duplex system, experienced operators act as the instructors.
By monitoring the system as it makes phone calls in a new
domain, they can affect the behavior of the system in real
time as needed. This continues until the system performs at
the desired quality level, at which point the supervision
stops and the system can make calls autonomously.

Benefits for Businesses and Users
Businesses that rely on appointment bookings supported by
Duplex, and are not yet powered by online systems, can
benefit from Duplex by allowing customers to book through
the Google Assistant without having to change any day-to-day
practices or train employees. Using Duplex could also reduce
no-shows to appointments by reminding customers about their
upcoming appointments in a way that allows easy cancellation
or rescheduling.
Duplex calling a restaurant:

In another example, customers often call businesses to
inquire about information that is not available online such
as hours of operation during a holiday. Duplex can call the
business to inquire about open hours and make the
information available online with Google, reducing the
number of such calls businesses receive, while at the same
time, making the information more accessible to everyone.
Businesses can operate as they always have, there's no
learning curve or changes to make to benefit from this
technology.
Duplex asking for holiday hours: