A California-based organization hopes to use machine learning to decipher
animal communication on a global scale. However, there are many who have
their doubts about the initiative.
A dolphin handler gives the "together" and "create" signals with her hands.
Underwater, the two trained dolphins exchange noises before emerging,
flipping around, and lifting their tails. They came up with a brand-new
trick of their own and executed it simultaneously as required. Aza Raskin
claims that "it doesn't establish that there is language." However, it
stands to reason that if they had access to a sophisticated, symbolic
language of communication, this work would be made much simpler.
Raskin is the co-founder and president of the Earth Species Project (ESP),
a non-profit organization with the audacious goal of deciphering non-human
communication using machine learning, making all the knowledge available to
the public, and strengthening our bond with other living species while also
promoting their protection. The movement that led to the outlawing of
commercial whaling was sparked by an album of whale songs released in 1970.
What could an animal version of Google Translate produce?
The organization, which was established in 2017 with the aid of significant
funders like LinkedIn co-founder Reid Hoffman, released its first academic
article in December. Within our lives, communication must be unlocked. The
goal, according to Raskin, is to decode animal communication and identify
non-human languages. The fact that we are creating technologies to assist
biologists and conservation efforts now is also crucial and is being done
along the way.
Human interest and research in animal vocalizations have existed for a very
long time. The warning sounds of various primates vary depending on the
predator, dolphins communicate with distinctive whistles, and certain
songbirds may rearrange the components of their calls to convey different
meanings. Most experts, however, refrain from referring to it as a language
because no animal communication satisfies all the requirements.
Decoding has typically relied on meticulous observation up until recently.
However, there has been a surge in interest in using machine learning to
handle the massive volumes of data that can currently be gathered by
contemporary animal-borne sensors. Elodie Briefer, an associate professor at
the University of Copenhagen who specializes in the study of vocal
communication in mammals and birds, claims that "people are starting to
utilize it." But we're still not entirely sure how much we can do.
Pig grunts may be analyzed using an algorithm that Briefer co-developed to
determine if the animal is feeling happy or sad. Another program,
DeepSqueak, analyzes rats' ultrasonic sounds to determine whether they are
under stress. Project CETI, which stands for the Cetacean Translation
Program, is another initiative that aims to interpret sperm whale
communication using machine learning.
However, ESP claims that their strategy is distinct since it focuses on
deciphering all species' communications rather than just one. Raskin agrees
that social species like primates, whales, and dolphins are more likely to
engage in complex, symbolic communication, but the ultimate objective is to
create tools that may be used across the board in the animal kingdom. Raskin
declares, "We don't care about species. The methods we create are applicable
to all of life, from worms to whales.
According to Raskin, research has proven that machine learning may be used
to translate between several, often distant human languages without the need
for any prior knowledge. This is the "motivating intuition" for ESP.
The creation of an algorithm to represent words in a physical location is
the first step in this process. The distance and direction between the
points (words) in this multidimensional geometric representation define
their meaningful relationships with one another (their semantic
relationship). For instance, the distance and direction between "king" and
"man" are the same as those between "woman" and "queen." (The mapping is not
done by understanding what the words imply but rather by examining, for
instance, how frequently they appear next to one another.)
Later, it was discovered that these "shapes" are consistent across
languages. Then, in 2017, two separate research teams separately discovered
a method that allowed for translation by aligning the forms. Align the forms
of the words to locate the Urdu point that is closest to the English word's
point. Raskin claims that most words can be properly translated.
The goal of ESP is to develop these types of animal communication
representations, focusing on both individual species and a large number of
species at simultaneously, and then investigate issues like if there is
overlap with the universal human form. According to Raskin, we don't know
how animals see the world, but it appears that some share our feelings with
us and may even talk to other members of their species about them. The
sections where the forms overlap and we can immediately converse or
translate, or the parts where we can't, I'm not sure which will be more
fantastic.
Animals may communicate nonverbally as well, he continues. Bees, for
instance, use their "waggle dance" to signal to other animals the location
of a flower. It will be necessary to translate between other communication
channels as well.
Raskin agrees that the objective is "like travelling to the moon," but the
intention is also not to arrive there all at once. Instead, ESP's roadmap
focuses on resolving a number of smaller issues that must be resolved in
order to realize the greater objective. This should lead to the creation of
broad tools that can assist researchers who are attempting to use AI to
discover the mysteries of the species they are studying.
For instance, the so-called "cocktail party dilemma" in animal
communication, where it is challenging to identify which individual within a
group of the same animals is vocalizing in a loud social situation, was the
subject of a recent work published by ESP (and shared with the
public).
Raskin claims that no one has ever completed this end-to-end detangling of
animal sound. The AI-based model created by ESP, which was tested on bat
vocalizations, macaque coo calls, and dolphin signature whistles, performed
best when the calls came from the individuals the model had been trained on;
however, with larger datasets, it was able to separate mixtures of calls
from animals that were not in the training cohort.
Another research uses humpback whales as a test species to create unique
animal noises using AI. The innovative calls may then be played back to the
animals to observe how they react. They are created by breaking
vocalizations into micro-phonemes, which are discrete units of sound lasting
one tenth of a second. Raskin claims that if AI can distinguish between
random and semantically significant changes, it will help humanity move
toward meaningful communication. Even if we don't yet understand the
language, it involves having AI speak it.
Another study intends to create an algorithm that determines the number of
call types a species may use by using self-supervised machine learning,
which does not require human specialists to categorize the data in order to
identify trends. The Hawaiian crow is a species that, according to Christian
Rutz, a professor of biology at the University of St. Andrews, has the
ability to make and use tools for foraging and is thought to have a
significantly more complex set of vocalizations than other crow species. In
an early test case, the system will mine audio recordings made by a team led
by Rutz to produce an inventory of the vocal repertoire of the Hawaiian
crow.
In particular, Rutz is enthusiastic about the project's conservation
potential. Only found in captivity, where it is being raised in preparation
for reintroduction to the wild, the Hawaiian crow is a species that is
severely endangered. It is hoped that by comparing recordings from various
times, it will be possible to determine whether the species' call repertoire
is deteriorating in captivity. For example, certain alarm calls may have
been lost, which could have an impact on its reintroduction. That loss might
be addressed with intervention. Rutz asserts that the technology "may
provide a step change in our capacity to help these birds come back from the
edge" and that manually identifying and categorizing the sounds would be
labor- and error-intensive.
Another effort aims to automatically decipher the functional significance
of vocalizations. It is being worked on in Professor Ari Friedlaender's lab
at the University of California, Santa Cruz, which specializes in ocean
sciences. One of the biggest tagging programs in the world is handled by the
lab, which also analyzes how wild marine animals interact underwater despite
being impossible to witness directly. The animals are equipped with tiny
electronic "biologging" devices that record their location, kind of
movements, and even what they observe (the devices can incorporate video
cameras). The lab also has information from underwater sound recordings that
were put deliberately.
The goal of ESP is to first use self-supervised machine learning to analyze
tag data to automatically determine what an animal is doing (such as eating,
sleeping, moving, or socializing), and then add audio data to determine
whether calls associated with that behavior can be given functional meaning.
(Following playback trials, results might be verified using calls that have
already been decoded.) This method will be used to analyze data from
humpback whales in the beginning since the lab has tagged multiple members
of the same group, making it feasible to see the transmission and reception
of signals. Friedlaender claims that he "reached the ceiling" in terms of
what the data could be extracted with the methods at hand. The researcher
said, "Our aim is that the work ESP can undertake will bring fresh
insights.
However, not everyone is as optimistic about the potential of AI to
accomplish such lofty goals. Robert Seyfarth is an emeritus psychology
professor at the University of Pennsylvania who has spent more than 40 years
researching social behavior and vocal communication in monkeys in their
natural environment. While he thinks machine learning can be helpful for
some issues, including detecting an animal's vocal repertoire, he is
skeptical that it will offer much in terms of understanding the meaning and
purpose of vocalizations.
He argues that the issue is that while many animals can have sophisticated,
complex communities, their sound repertoire is far less than that of humans.
The end result is that the same sound may be used to indicate different
things in different settings, and the only way to determine meaning is by
understanding the context — the individual's calling, their relationships
with others, their position in the hierarchy, and the people they have dealt
with. These AI techniques, in my opinion, are just insufficient, argues
Seyfarth. You must walk outside and see the wildlife.
The idea that animal communication would resemble human communication in
any significant sense is also contested. It is one thing to use
computer-based studies on human language, with which we are so accustomed,
claims Seyfarth. But applying it to other animals might often be "very
different." According to Kevin Coffey, a neurologist at the University of
Washington and co-creator of the DeepSqueak algorithm, "It is a fascinating
notion, but it is a significant reach."
Raskin recognizes that AI might not be sufficient on its own to enable
interspecies communication. However, he makes reference to studies that have
revealed that many animals interact in ways that are "more intricate than
humans have ever dreamed." Our inability to obtain enough data and analyze
it comprehensively, as well as our own restricted vision, have been the
major roadblocks. He explains, "These are the instruments that enable us to
remove the human spectacles and comprehend whole communication
networks."
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