AI in Psychology
How will AI be implemented in the field
of psychology?
This article reviews developments
in artificial intelligence (AI) technologies and their current and prospective
applications in clinical psychological practice. Some of the principal AI
assisted activities reviewed include clinical training, treatment,
psychological assessment, and clinical decision making. A concept for an
integrated AI-based clinician system is also introduced. Issues associated with
AI in the context of clinical practice, the potential for job loss among mental
health professionals, and other ramifications associated with the advancement
of AI technology are discussed. The advancement of AI technologies and their
application in psychological practice have important implications that can be
expected to transform the mental health care field. Psychologists and other
mental health care professionals have an essential part to play in the
development, evaluation, and ethical use of AI technologies.
The AI role in psychology is still underestimated by
the European psychology experts. Sometimes psychologists reject the use of
expert systems in their fields of activity because they fear that the computer
will replace them. Sometimes they do not perceive the full potential of using
IT. The same reactions have been encountered among medicine doctors when the
first automatic diagnose system was tested. The AI has not reached yet that
level of performance capable of emulating simultaneously all pieces of human
behaviour, but researchers are on the right track of getting there (Klein,
1999). Anyhow, there are many intersection points between these two domains.
One intersection is related to the cognitivist approach in psychology. Within
this domain, various programs have been developed for environment simulation,
automatic emotion recognition, the simulations of social interaction within
groups, phobias therapies, computer aided treatment in psychiatry, electronic
inquires and automatic results generation, and the list may continue. In the
UK, studies related to the efficiency in applying IT in cognitive behaviour
therapy have already been conducted (NICE, 2008) and the results are promising.
The importance of IT in psychology was recognised by the researchers’ community
by developing a new area of research – cyberpsychology. Two distinct levels of
IT use in psychotherapy have already been identified (Hovell & Muller,
2010), especially from the patient treatment point of view. Within the first
layer, we encounter the common tools developed to increase the efficiency and
performance of the therapist. Within the second level, we have the complex
systems that help both the patient and the therapist during the treatment.
There is a strong possibility that in the future low and medium complexity
problems will be handled by the expert systems. Although there are some
applications that sustain these assumptions, some controversies on the subject
still exist (Marks et al., 2007). In the second part of this chapter, a new
approach in information retrieval and testing will be presented. For the
researcher, two information flows are critical. One refers the new discoveries
regarding the global research within his area of interest. The other consists
of the experimental data needed for his research. Because psychologists measure
the thoughts, feelings and behaviour of one or more people at a time, they have
a problem in acquiring research data, especially when large numbers of subjects
are needed. At a corporate level, this problem is solved by using the
electronic version of classical inquires. Though, this solution is limited to a
medium where there are strong rules that guide employee behaviour. On the other
hand, young people are more and more adapted to the information society. As 76
Expert Systems for Human, Materials and Automation a result, the use of
cooperative layers provided by the IT permit them to interact in various spaces
- more or less virtual. The psychologists need new tools in order to gather
data not only from the point of view of social psychology, where the
information about human behaviour can be retrieved without direct interviewing,
but also from the point of view of other fields of psychology. As a result, we
need a combination between an expert system, an information retrieval system
and an intelligent interface to mediate user interaction in order to fulfill
these needs. The human computer interface will also have its role in agent
interface design.
Companies
Using AI in Psychology
Be Ready to
See Virtual Therapist Soon
Now there are a
huge number of startups aimed at creating a virtual psychologist who would work
as well as a real specialist. At the moment, all these projects have linked AI
and virtual reality to generate a virtual therapist that can communicate with
clients in real-time. Here is a simple explanation of how it works. From the
resulting database, the answers are formed depending on the question being
asked, giving the answer an emotional color. Thus, the machine is able not only
to give advice but also to express sympathy and offer several solutions to the
problem.
Here are a few
existing startups:
Quartet Health is the name of
AI-powered chatbot that puts to use machine learning powers to make diagnose
and supply a customized treatment program.
Ellie is one more good and popular AI-powered therapist.
This chatbot was developed to treat veterans suffering from Post-traumatic
stress disorder. Ellie could carefully analyze facial expressions, head
gestures, eye movements, and voice sound to recognize those indicators
typically related to depression and PTSD.
And when it comes to chatbots-psychologist, it is hard to
avoid such enterprise as X2A, which contain a
few startups at once:
1) Karim — Arabic speaking
platform to help Syrian refugees;
2) Emma is a
Dutch-language virtual psychologist created to help people with mild anxiety
and fear;
3) Nema — virtual therapist to provide pediatric diabetes care (in English).
And there is a
myriad of other similar startups. Of course, at the moment the answers of the
majority of virtual assistants are quite standard, but their text messages are
not meaningless. We should consider its imperfections only as a starting point.
After all, the system itself continues to learn, and in the future, the
researchers plan to bring the AI to a level where, in compiling the answer,
their “psychologist” will give more accurate answers.
How does AI study the brain when each of
us are unique?
The human brain — intelligent and unique
— is challenging scientists, who are determined to decode its complexity and
unlock possibilities to enhance human lives. By harnessing artificial
intelligence (AI), they have already made breakthroughs in man-machine
interactions through Watson, Siri, and more. But, for AI to have a truly transformational
impact, artificial neural networks need to be further reinforced by human
native intelligence.
Machines learn to think
Computational
neuroscience bridges the gap between human intelligence and AI by creating
theoretical models of the human brain for inter-disciplinary studies on its
functions, including vision, motion, sensory control, and learning.
Research
in human cognition is revealing a deeper understanding of our nervous system
and its complex processing capabilities. Models that offer rich insights into
memory, information processing, and speech / object recognition are
simultaneously reshaping AI.
A nuanced
understanding of the structure of the human brain can help restructure
hierarchical deep learning models. Deep learning, a branch of machine learning,
is based on a set of algorithms that attempt to model high-level abstractions
in data. It will enhance speech / image recognition programs and language
processing tools by understanding facial expressions, gestures, tone of voice,
and other abstracts. We are at the threshold of experiencing advances in speech
technology that will lead to more practical digital assistants and accurate
facial recognition that will take security systems to the next level.
However,
contemporary deep neural networks do not process information the way the human
brain does. These networks are highly data-dependent and should be trained to
accomplish even simple tasks. Complex processes require large volumes of data
to be annotated with rich descriptors and tagged accurately for the machine to
‘learn.’ Further, deep learning systems consume far more power than the human
brain (20 watts) for the same amount of work.
We need
to discover less intensive machine learning approaches to augment artificial
intelligence with native intelligence. Our world is awash with data from Internet of Things (IOT) applications. Deep neural
networks capable of consuming big data for self-learning will be immensely
useful. Just as children identify trees despite variations in size, shape, and
orientation, augmented intelligence systems should learn with less data or
independently harness knowledge from the ecosystem to accelerate learning. Such
self-learning algorithms are necessary for truly personalized products and
services.
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