Can artificial intelligence really help us talk to the animals? www.mgireservationsandbookings.co.uk

Can artificial intelligence really help us talk to the animals?

www.mgireservationsandbookings.co.uk

A California-based organisation wants to harness the power of machine learning to decode communication across the entire animal kingdom. But the project has its doubters

A dolphin handler makes the signal for “together” with her hands, followed by “create”. The two trained dolphins disappear underwater, exchange sounds and then emerge, flip on to their backs and lift their tails. They have devised a new trick of their own and performed it in tandem, just as requested. “It doesn’t prove that there’s language,” says Aza Raskin. “But it certainly makes a lot of sense that, if they had access to a rich, symbolic way of communicating, that would make this task much easier.”

Raskin is the co-founder and president of Earth Species Project (ESP), a California non-profit group with a bold ambition: to decode non-human communication using a form of artificial intelligence (AI) called machine learning, and make all the knowhow publicly available, thereby deepening our connection with other living species and helping to protect them. A 1970 album of whale song galvanised the movement that led to commercial whaling being banned. What could a Google Translate for the animal kingdom spawn?

The organisation, founded in 2017 with the help of major donors such as LinkedIn co-founder Reid Hoffman, published its first scientific paper last December. The goal is to unlock communication within our lifetimes. “The end we are working towards is, can we decode animal communication, discover non-human language,” says Raskin. “Along the way and equally important is that we are developing technology that supports biologists and conservation now.”

Understanding animal vocalisations has long been the subject of human fascination and study. Various primates give alarm calls that differ according to predator; dolphins address one another with signature whistles; and some songbirds can take elements of their calls and rearrange them to communicate different messages. But most experts stop short of calling it a language, as no animal communication meets all the criteria.

Until recently, decoding has mostly relied on painstaking observation. But interest has burgeoned in applying machine learning to deal with the huge amounts of data that can now be collected by modern animal-borne sensors. “People are starting to use it,” says Elodie Briefer, an associate professor at the University of Copenhagen who studies vocal communication in mammals and birds. “But we don’t really understand yet how much we can do.”

Briefer co-developed an algorithm that analyses pig grunts to tell whether the animal is experiencing a positive or negative emotion. Another, called DeepSqueak, judges whether rodents are in a stressed state based on their ultrasonic calls. A further initiative – Project CETI (which stands for the Cetacean Translation Initiative) – plans to use machine learning to translate the communication of sperm whales.

tamworth piglets in a pen in st austell, cornwall

Earlier this year, Elodie Briefer and colleagues published a study of pigs’ emotions based on their vocalisations. 7,414 sounds were collected from 411 pigs in a variety of scenarios. Photograph: Matt Cardy/Getty Images

Yet ESP says its approach is different, because it is not focused on decoding the communication of one species, but all of them. While Raskin acknowledges there will be a higher likelihood of rich, symbolic communication among social animals – for example primates, whales and dolphins – the goal is to develop tools that could be applied to the entire animal kingdom. “We’re species agnostic,” says Raskin. “The tools we develop… can work across all of biology, from worms to whales.”

The “motivating intuition” for ESP, says Raskin, is work that has shown that machine learning can be used to translate between different, sometimes distant human languages – without the need for any prior knowledge.

This process starts with the development of an algorithm to represent words in a physical space. In this many-dimensional geometric representation, the distance and direction between points (words) describes how they meaningfully relate to each other (their semantic relationship). For example, “king” has a relationship to “man” with the same distance and direction that “woman’ has to “queen”. (The mapping is not done by knowing what the words mean but by looking, for example, at how often they occur near each other.)

It was later noticed that these “shapes” are similar for different languages. And then, in 2017, two groups of researchers working independently found a technique that made it possible to achieve translation by aligning the shapes. To get from English to Urdu, align their shapes and find the point in Urdu closest to the word’s point in English. “You can translate most words decently well,” says Raskin.

ESP’s aspiration is to create these kinds of representations of animal communication – working on both individual species and many species at once – and then explore questions such as whether there is overlap with the universal human shape. We don’t know how animals experience the world, says Raskin, but there are emotions, for example grief and joy, it seems some share with us and may well communicate about with others in their species. “I don’t know which will be the more incredible – the parts where the shapes overlap and we can directly communicate or translate, or the parts where we can’t.”

two dolphins in a pool

Dolphins use clicks, whistles and other sounds to communicate. But what are they saying? Photograph: ALesik/Getty Images/iStockphoto

He adds that animals don’t only communicate vocally. Bees, for example, let others know of a flower’s location via a “waggle dance”. There will be a need to translate across different modes of communication too.

The goal is “like going to the moon”, acknowledges Raskin, but the idea also isn’t to get there all at once. Rather, ESP’s roadmap involves solving a series of smaller problems necessary for the bigger picture to be realised. This should see the development of general tools that can help researchers trying to apply AI to unlock the secrets of species under study.

For example, ESP recently published a paper (and shared its code) on the so called “cocktail party problem” in animal communication, in which it is difficult to discern which individual in a group of the same animals is vocalising in a noisy social environment.

“To our knowledge, no one has done this end-to-end detangling [of animal sound] before,” says Raskin. The AI-based model developed by ESP, which was tried on dolphin signature whistles, macaque coo calls and bat vocalisations, worked best when the calls came from individuals that the model had been trained on; but with larger datasets it was able to disentangle mixtures of calls from animals not in the training cohort.

???It could produce a step change in our ability to help the Hawaiian crow come back from the brink

Another project involves using AI to generate novel animal calls, with humpback whales as a test species. The novel calls – made by splitting vocalisations into micro-phonemes (distinct units of sound lasting a hundredth of a second) and using a language model to “speak” something whale-like – can then be played back to the animals to see how they respond. If the AI can identify what makes a random change versus a semantically meaningful one, it brings us closer to meaningful communication, explains Raskin. “It is having the AI speak the language, even though we don’t know what it means yet.”

a hawaiian crow using a twig to hook grubs from a tree branch

Hawaiian crows are well known for their use of tools but are also believed to have a particularly complex set of vocalisations. Photograph: Minden Pictures/Alamy

A further project aims to develop an algorithm that ascertains how many call types a species has at its command by applying self-supervised machine learning, which does not require any labelling of data by human experts to learn patterns. In an early test case, it will mine audio recordings made by a team led by Christian Rutz, a professor of biology at the University of St Andrews, to produce an inventory of the vocal repertoire of the Hawaiian crow – a species that, Rutz discovered, has the ability to make and use tools for foraging and is believed to have a significantly more complex set of vocalisations than other crow species.

Rutz is particularly excited about the project’s conservation value. The Hawaiian crow is critically endangered and only exists in captivity, where it is being bred for reintroduction to the wild. It is hoped that, by taking recordings made at different times, it will be possible to track whether the species’s call repertoire is being eroded in captivity – specific alarm calls may have been lost, for example – which could have consequences for its reintroduction; that loss might be addressed with intervention. “It could produce a step change in our ability to help these birds come back from the brink,” says Rutz, adding that detecting and classifying the calls manually would be labour intensive and error prone.

Meanwhile, another project seeks to understand automatically the functional meanings of vocalisations. It is being pursued with the laboratory of Ari Friedlaender, a professor of ocean sciences at the University of California, Santa Cruz. The lab studies how wild marine mammals, which are difficult to observe directly, behave underwater and runs one of the world’s largest tagging programmes. Small electronic “biologging” devices attached to the animals capture their location, type of motion and even what they see (the devices can incorporate video cameras). The lab also has data from strategically placed sound recorders in the ocean.

ESP aims to first apply self-supervised machine learning to the tag data to automatically gauge what an animal is doing (for example whether it is feeding, resting, travelling or socialising) and then add the audio data to see whether functional meaning can be given to calls tied to that behaviour. (Playback experiments could then be used to validate any findings, along with calls that have been decoded previously.) This technique will be applied to humpback whale data initially – the lab has tagged several animals in the same group so it is possible to see how signals are given and received. Friedlaender says he was “hitting the ceiling” in terms of what currently available tools could tease out of the data. “Our hope is that the work ESP can do will provide new insights,” he says.

But not everyone is as gung ho about the power of AI to achieve such grand aims. Robert Seyfarth is a professor emeritus of psychology at University of Pennsylvania who has studied social behaviour and vocal communication in primates in their natural habitat for more than 40 years. While he believes machine learning can be useful for some problems, such as identifying an animal’s vocal repertoire, there are other areas, including the discovery of the meaning and function of vocalisations, where he is sceptical it will add much.

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The problem, he explains, is that while many animals can have sophisticated, complex societies, they have a much smaller repertoire of sounds than humans. The result is that the exact same sound can be used to mean different things in different contexts and it is only by studying the context – who the individual calling is, how are they related to others, where they fall in the hierarchy, who they have interacted with – that meaning can hope to be established. “I just think these AI methods are insufficient,” says Seyfarth. “You’ve got to go out there and watch the animals.”

a honey bee on a dog rose flower

A map of animal communication will need to incorporate non-vocal phenomena such as the “waggle dances” of honey bees. Photograph: Ben Birchall/PA

There is also doubt about the concept – that the shape of animal communication will overlap in a meaningful way with human communication. Applying computer-based analyses to human language, with which we are so intimately familiar, is one thing, says Seyfarth. But it can be “quite different” doing it to other species. “It is an exciting idea, but it is a big stretch,” says Kevin Coffey, a neuroscientist at the University of Washington who co-created the DeepSqueak algorithm.

Raskin acknowledges that AI alone may not be enough to unlock communication with other species.

Ever since the world has stepped forward towards the age of digitalization, things have never been the same. From the introduction of the internet to the expansion of the mobile-first concept and innovations like artificial intelligence and machine learning, people have experienced the highest exposure to technology ever.

Amidst all this development and expansion, one thing that has scaled dynamically is Artificial Intelligence. From the expansion of neural networks to energy use, data sets, and the prevalence of society, the growth of AI has made way for significant ethical concerns.

Before we jump on unraveling the most common ethical issues surrounding artificial intelligence, let us begin with developing an understanding of what ethical AI is.

What is ethical AI?

When it comes to “Ethics in AI”, the term means to investigate and constantly question the technologies that can hamper human life. Be it replacing humans with smart machines or concerns related to sharing personal information on AI-powered systems, the concept of ethical AI has gained all the pace due to the rapid scaling of AI technologies.

From computing power to data fed, AI systems have grown tremendously big in the past few years. Moreover, the rapid growth of AI has even dwarfed the potential of computing that was carried back from the era of the internet and PCs.

The extensive scale of deployment and responsibilities given to AI has now even involved other aspects of technology in the picture. Be it deep learning or scaling of any other advanced technologies that involve the use of AI, the situation has escaped the comprehension capabilities of even the most proficient practitioners.

And therefore, ethical AI brings some really interesting and important factors to the light that need immediate consideration in order to overcome ethical concerns surrounding AI technology:

1. Biases

From the training artificial intelligence algorithms to removing the bias involved, a huge amount of data is needed. Consider an example of any application made to allow editing of pictures. These applications are made to use AI to beautify the pictures and therefore contain a vast amount of data that has more white faces over non-white faces.

Therefore, it is necessary that AI algorithms must be trained to recognize and process non-white faces as efficiently as it does for white faces. The process requires feeding the right balance of faces to the database in order to ensure the algorithm works well to cut the built-in bias for beauty apps.

In other words, eliminating bias is extremely necessary when we need to create technology that can reflect our society with greater precision. Such actions thus require identifying all the potential areas of bias and fixing the AI solutions with the right approach.

2. Infusing Morality, Loss of Control

With more and more use of artificial intelligence, machines are capable of making important decisions. Be it the use of drones for delivery by carrier services or building autonomous rockets/missiles that can potentially kill a banned object. However, there is still a need for human involvement in such decision-making that can work on any rules and regulations that can impact humanity in any form.

The concern here is actually allowing AI to work on quick decisions. However, in operations like financial trading where it is essential to make split-second decisions, giving control to humans leaves no chance to make the right move at the right time.

Another example of the same is autonomous cars as they are made to make immediate reactions to take control of situations. The problem with all these scenarios is the ethical challenge of establishing a balance between control and AI.

3. Privacy

One of the most significant ethical concerns that have been long associated with AI is Privacy. There are many ethical concerns, from training AIs to the use of data and its source. Oftentimes, it is assumed that the data is coming from adults with high mental capabilities making the data used for creating AI that can work on choices. However, the situation is not always the same.

A quick example of the same can be the use of AI-powered toys that are designed to converse with children. Here the ethical concerns are about the algorithms collecting data from those conversations and making way for queries like where and how this data is being used.

The ethical concerns with such conversations grow even bigger when it comes to companies collecting that data and selling it to other companies. There is a need for rules that can justify data collection.

Moreover, there must be strict legislation made to protect the user’s privacy as an object that can collect data from conversations with children could potentially be used for taping the conversations of adults within the same environment.

4. Power Balance

The next significant ethical issue that comes with AI is giants like Amazon and Google using technology to dominate their competitors. More importantly, there are countries like China and Russia competing in the AI landscape, and here the question arises of the power balance.

From equal wealth generation to balancing monopolies, it is very likely that countries that are ahead of AI development and implementation are likely to race ahead of others.

For instance, countries with better access to resources that can develop and implement AI could utilize its power to innovate their war strategies, finance building, and more. Thus, AI creates some serious gaps around the subject of power balance.

5. Ownership

At number five, we have another big ethical challenge that needs to identify people or organizations that can be held accountable for things that AI creates. As Artificial Intelligence has all the potential to develop texts, bots, and video content, it is likely to create things that are misleading. Such material could trigger any violent circumstances for a particular community, ethnicity, or belief and therefore it becomes necessary to understand who could take ownership of the content.

Another example of the same could be AIs that are used to create music pieces or art. Thus, it is necessary that any new piece of content developed with AI that reaches some audience must have some ownership or could have intellectual property rights.

6. Environmental Concerns

Most of the time, the companies working on AI are not so concerned about how AI could impact the environment. Developers working on AI assume that they are using data on the cloud to work on their algorithm and then the data works on, say creating suggestions, recommendations, or making automated decisions. Though the systems are running efficiently and effectively, the computers that are keeping up the AI and cloud infrastructure require immense power.

A quick example of the impact that AI could create on the environment is the fact that training in AI could create 17 times more carbon emissions than an average American does in a year. Therefore, it is important that developers find ways to use this energy for other productive purposes and get over one of the most pressing problems of declining energy resources.

7. Humanity

Last but not least, it is the challenge of how humans feel in the presence of AI. Especially, when AI has been developed to be so powerful and efficient, it triggers the challenge for humans missing the feeling of how it actually feels to be human. As AI is designed and created to work on precision, it diminishes the human morale built through making errors and learning from it.

Especially, when AI has automated jobs for so long, it often leads to the question that what contributions human beings could make to the technology landscape. Though it is not possible for AI to replace humans for all jobs, only the idea of augmenting AI possesses some serious challenges.

To conclude

Humans need to get better when working along with smart machines in order to align with the tech transition. Besides, it is extremely necessary for the people to sustain their dignity and have respect for technology. Therefore, it is necessary that all the ethical challenges surrounding AI must be understood.

Especially, when AI is seen as a technology that has all the capability to create user-oriented and sustainable IT solutions, creating ethical AI could help empower digitalization. Be it advancing the process through AI improved Quality Assurance and software testing or using AI itself to create unbiased technology for users across the world.

More importantly, it is crucial that engineers working on AI technology should always have consideration for the human aspect of using AI. Be it the use of AI machines or software, it is vital that transparency should be maintained with respect to user data consumption and human involvement in decision making, the privacy of data, no biases, and the power balance.

Even if the thought of AI systems surpassing human intelligence may appear scary, the key is to have an early vision of all the ethical issues surrounding AI adoption. It not only needs humans to keep on learning but stay informed of the impact that any potential implementations related to AI could have on society.