I’m sick of the AI hype and scaremongering. The idea that AI could take over the world is laughable.

Blair Fix

Download: PDF | EPUB  In the last few months, there have been a string of open letters from tech leaders warning that artificial intelligence could lead to a ‘profound change in the history of life on Earth’. According to some insiders, AI poses an extinction risk, on the same level as pandemics and nuclear war. I think this scaremongering is nonsense. It plays into popular science fiction tropes about machines conquering humanity.

No, AI Does Not Pose an Existential Risk to Humanity

Adam Day

There's a quote attributed to Ernest Rutherford: "That which is not
measurable is not science. That which is not physics is stamp
collecting". I think his point was that a lot of scientific work is just
*documenting* things. In Rutherford's day, there was a lot of exciting
new *creative* work happening in physics, so perhaps physics seemed
special to him. On the other hand, it was a mean-spirited thing to say
about other fields of science and I'm sure he felt very silly when he
was handed the Nobel prize in chemistry.

Generative AI is all the rage right now isn't it? Essentially, we can
now use AI models to do *creative* tasks, like painting a picture.

Do you remember that famous series of paintings that Claud Monet did
called "computer nerds hard at work"?

<figure>
<img
src="https://cdn-images-1.medium.com/max/512/1*ag7wUHny722vK6beW0UdfQ.png" />
</figure>

<figure>
<img
src="https://cdn-images-1.medium.com/max/512/1*GVoYZVm-u9uQuxJvjhWKIg.png" />
</figure>

<figure>
<img
src="https://cdn-images-1.medium.com/max/512/1*U9V_-9NXaDl0O3CbDU0dwQ.png" />
</figure>

<figure>
<img
src="https://cdn-images-1.medium.com/max/512/1*T5AMW56GTO2GSe6eW5I1VA.png" />
</figure>

No? Wikipedia says that Monet passed away in 1926. That means he was
dead at the time these computers were invented and can't possibly have
painted them, but they certainly look like Monet's work, don't they? If
I had wanted to fake images like this 12 months ago, I would have needed
canvas, oils and a lot of talent with a brush. Today, I just prompted
Stable Diffusion to do it for me. No coding skills required. It
took seconds.

Speaking of coding, I was looking for a very specific piece of code
recently. After half an hour searching on Google and StackOverflow I was
ready to give up searching and write it myself, but then I asked ChatGPT
to do it for me. ChatGPT wrote exactly what I needed. Again, it
took seconds.

ChatGPT is a chatbot based on a *Large Language Model* called GPT-3. You
can ask ChatGPT to write just about anything. Poems, stories, homework
solutions, research papers... anything!

The value of new AI models for **creative** tasks like this is immense.
Images and text and even video can be generated rapidly with
increasingly authentic quality standards.

I put it to you that when you write a research paper, that *isn't*
creative work. I mean, it obviously is to an extent, but the purpose of
a research paper is to document something. It's the stamp collecting
part of science and that's an important distinction.

If I ask ChatGPT for text for an original research paper, it will give
it to me. But the text won't be based on any real novel piece of
research, it will be made up and it will be either derivative, false, or
both. It will look real and [a recent study by Catherine Gao at
Northwestern University in Chicago,
Illinois](https://www.nature.com/articles/d41586-023-00056-7) showed
that scientists were often unable to distinguish real scientific
abstracts from those written by ChatGPT.

By the way, if I prompt Stable Diffusion for fake scientific images,
it'll cough those up too.

So, AI can already produce believable scientific fakes that are hard to
detect. Worse, the people building these AI applications might not even
be aware of the extent and dangers of [organised
research fraud](https://medium.com/@publisherad/how-papermills-work-generally-c080bb9fff43).

Through my company, Clear Skies, I've already been working on a couple
of methods that should be able to detect fraudulent ChatGPT-generated
content. But I'm in 2 minds as to whether it's a good idea to develop
this capability because there's no good reason that AI should be
producing this kind of content in the first place.

- AI shouldn't be trained to generate documentary material at all.
- APIs for these tools should also classify prompts and output to
 recognise when they are being abused for fraudulent or harmful
 purposes (and then decline to give output to a user).

I hope that doesn't sound like wishful thinking. Controls like these
actually do exist on both ChatGPT and Stable Diffusion. So I am
optimistic that, as the people building these applications receive
feedback, they make adaptations like those above to avoid creating
harmful outputs.

In the meantime, we are already seeing policies on artificially
generated content. StackOverflow [has temporarily banned AI-generated
content until such time as it can determine suitable guidelines
for use](https://meta.stackoverflow.com/questions/421831/temporary-policy-chatgpt-is-banned).

The future of this problem for Schol-Comms is complex. There is a lot to
consider:

- Should publishers ban AI-generated content like StackOverflow
 has done?
- Or should guidelines be more nuanced --- like should AI-generated
 content be treated like quotations where the non-original content in a
 paper is clearly set apart from the rest of the text? What about AI
 autocomplete? Should that be banned or quoted, or is that different?
- Do the copyright licenses on scientific papers allow them to be used
 for training generative AI? (And what should those licenses allow?
 It's not like they were written with this use case in mind.)

It's a tough one and I'm sure it'll take time to figure out the best way
to handle this. What is clear, though, is that there is plenty
to discuss!

There are a couple of addenda worth making here. There are other AI
use-cases that can be trained on research papers. E.g.
question-answering. That's where we can give an open-ended question like
"what's the best treatment for COVID-19" and then an AI model can find
all the best papers on that topic and generate a response to the
question based on those. That's a brilliant thing to do and so there's
definitely a good case for training models for some tasks.

It's also worth mentioning Meta's recently released Galactica model.
Galactica was supposed to enable tasks like those described above.
However: there was clear potential to use the model for research fraud
and it had a few other issues (e.g. it produced nonsense from time to
time --- if we want scientific answers, that's not very helpful). The
dangers were clear and [I was relieved to see Meta kill Galactica just
days after its
release](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science/).
There's still potential for a project like that, but it's great to see
decisive action from Meta to address the risk.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=72ab43bd217){width="1"
height="1"}

There’s a quote attributed to Ernest Rutherford: “That which is not measurable is not science. That which is not physics is stamp collecting”. I think his point was that a lot of scientific work is just

 documenting

things. In Rutherford’s day, there was a lot of exciting new

 creative

work happening in physics, so perhaps physics seemed special to him.

We need a chat about ChatGPT

Chris Armit

> ***"This vast number of records allows utilisation of Machine
> Learning/Artificial Intelligence algorithms."***

A study published in *GigaScience* provides an enormous amount of
behavioural science data, presented in a detailed videographic virtual
library, that was used to explore [Obsessive-Compulsive Disorder
(OCD)](https://www.nimh.nih.gov/health/topics/obsessive-compulsive-disorder-ocd){rel="noreferrer noopener"
target="_blank"} in an animal model. The [virtual
library](http://dx.doi.org/10.5524/102314) comprises 11.1 TB of
videography behavioural science data from a 15-year study and includes
over 2 years of continuous recording (see [EurekAlert Press
Release](https://www.eurekalert.org/news-releases/968068){rel="noreferrer noopener"
target="_blank"}).

For a [GigaBlog
*Q&A*](http://gigasciencejournal.com/blog/tag/qa/){rel="noreferrer noopener"
target="_blank"}, I discussed with principal investigator [Henry
Szechtman](https://experts.mcmaster.ca/display/szechtma){rel="noreferrer noopener"
target="_blank"} (McMaster University) his core interest in the project,
and the immense reuse potential of [these
data](http://dx.doi.org/10.5524/102314).

### **What inspired your interest in Obsessive-Compulsive Disorder?** {#what-inspired-your-interest-in-obsessive-compulsive-disorder .wp-block-heading}

**"**Actually, I was led to the study of Obsessive-Compulsive Disorder
by the results of my research. Since graduate studies at the University
of Pittsburgh I was interested in the role of
[dopamine](https://en.wikipedia.org/wiki/Dopamine){rel="noreferrer noopener"
target="_blank"} systems in normal and abnormal behaviour, which led me
to analyse the activity of rats administered dopamine stimulant drugs. 
In one such study, we analysed the activity of rats injected with the
dopamine receptors stimulant drug,
[apomorphine](https://en.wikipedia.org/wiki/Apomorphine){rel="noreferrer noopener"
target="_blank"}. I begun this study in Israel, where I was a Weizmann
Fellow, and was fortunate to team up with Drs Ilan Golani (Tel-Aviv
University) and Philip Teitelbaum (University of Illinois) who were
collaborating and applying the [Eshkol-Wachman Movement
Notation](https://noaeshkol.org/about-eshkol-wachman-movement-notation/){rel="noreferrer noopener"
target="_blank"} to analyse behaviour of rats with lesions to the
lateral hypothalamus."

"I was interested to learn and apply the same technology to study
apomorphine-induced behaviour, and fortunately for me Drs Golani and
Teitelbaum found such a project of interest too.  This project
introduced me not only to a method of behavioural analysis but also to
the importance of having a visual record of behaviour as rat activity
was being analysed frame-by-frame from 16 mm films (video recording
technology became available later)."

"How did I go from apomorphine-induced behaviour to an interest and
research of OCD?  Our analysis of apomorphine-induced behaviour was in
the context of investigating the then popular dopamine hypothesis of
schizophrenia. However, when we switched to
[quinpirole](https://en.wikipedia.org/wiki/Quinpirole){rel="noreferrer noopener"
target="_blank"}, a drug that was selective to only a subtype of
dopamine receptors stimulated by apomorphine, our behavioural analysis
did not show behaviour that was disorganised, as expected from an animal
model of schizophrenia."

> ***"Obsessive-Compulsive Disorder is a disturbance of a vital Security
> Motivation System that manages the behavioural response to
> biologically relevant potential threats"***

"A young psychiatrist at a departmental seminar I presented suggested
that quinpirole-induced behaviour was reminiscent of OCD, a psychiatric
condition I had not heard of before but that was just becoming familiar
to the general public.  Indeed, as David Eilam (Tel-Aviv University),
Bill Sulis (McMaster University) and I examined the behaviour of
quinpirole-induced behaviour in detail, we were able to propose that the
transformation in behaviour induced by quinpirole had the attributes of
an animal model of OCD and of [compulsive
checking](https://en.wikipedia.org/wiki/Compulsive_behavior){target="_blank"
rel="noreferrer noopener"} in particular."

"The work on OCD led Erik Woody (University of Waterloo) and me to
propose in 2004 a relatively new theory that [OCD is a disturbance of a
vital Security Motivation
System](http://doi.org/10.1037/0033-295X.111.1.111){target="_blank"
rel="noreferrer noopener"} that manages the behavioural response to
biologically relevant potential threats."

### **In the *GigaScience* manuscript, you highlight the reuse potential of the videography Virtual Library data *"as inputs for new experiments or for yet unknown types of analysis of behaviour"*. Can you expand on how one should address this challenge?** {#in-the-gigascience-manuscript-you-highlight-the-reuse-potential-of-the-videography-virtual-library-data-as-inputs-for-new-experiments-or-for-yet-unknown-types-of-analysis-of-behaviour.-can-you-expand-on-how-one-should-address-this-challenge .wp-block-heading}

**"**The [Virtual
Library](http://dx.doi.org/10.5524/102261){rel="noreferrer noopener"
target="_blank"} can serve as input to new experiments because of the
annotation schema, which specifies for each trial a comprehensive list
of independent variables and thereby renders each open field test as an
independent event. Hence, choosing from the list of independent
variables a judicious set for the experimental and control groups, one
constructs in essence a new experimental design, outside any published
study performed by the original investigators."

"To give a simple example: If the experimental question were whether
compulsive checking (or any other dependent variable) is more intense
after 8 injections of quinpirole or
[8OH-DPAT](https://en.wikipedia.org/wiki/8-OH-DPAT){rel="noreferrer noopener"
target="_blank"} or
[mCPP](https://en.wikipedia.org/wiki/Meta-Chlorophenylpiperazine){rel="noreferrer noopener"
target="_blank"}, then one would select from the pool of 19,978 trials
all the tests in the open field where a rat without lesion received 8
injections of quinpirole or 8OH-DPAT or mCPP or saline (the control
injection) and compared their response. However, because in the Virtual
Library each of those drugs was administered at several doses, selection
on an additional criterion is necessary, namely, equivalent doses across
drugs. If enough trials meeting those criteria are present in the
Virtual Library, then those are the inputs for a new experiment."

<figure class="wp-block-image size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2022/10/Videography.png"
class="wp-image-4610" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2022/10/Videography.png 865w, http://gigasciencejournal.com/blog/wp-content/uploads/2022/10/Videography-300x161.png 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2022/10/Videography-768x413.png 768w"
sizes="(max-width: 649px) 100vw, 649px" width="649" height="349"
alt="Obsessive-Compulsive Disorder data" />
<figcaption>In this model of Obsessive-Compulsive Disorder, a rat is
treated chronically with quinpirole, which is a dopamine agonist. As
this Path Plot demonstrates, the quinpirole-treated rat repetitively
explores 4 objects on a table (right), and the repetitive behaviour is
striking when compared to a control saline-treated rat (left). This
repetitive behaviour is a model of Obsessive-Compulsive Disorder.
Through 43 experiments that utilise this model, the effect of various
drug treatments and brain lesions in attenuating Obsessive-Compulsive
Disorder is explored.</figcaption>
</figure>

"The re-use potential of the Virtual Library is even greater because of
videography: the Virtual Library contains not only the digitised
coordinates of locomotion in the open field (from which measures of
compulsive checking and the amount of activity can be derived) but also
the video record of rat's activity in the open field. These video
recordings constitute the full documentation of the animal behaviour in
the predefined environment. As such, they have information beyond the
paths of locomotion that were of interest in our experiments."

> ***"Generally, the re-use potential of videography experiments is not
> fully appreciated."***

"For instance, the videos contain information as to what other movements
(aside from locomotion) the rat performs, what are their
characteristics, and what are the salient features of ritualistic
behaviour, etc.  Moreover, the viewer, by watching the rat's behaviour
in the videos, may formulate questions/hypotheses regarding the rat's
activity that did not occur to the experimenter prior to doing the
study.  Similarly, having video recordings of the experiment may allow
the investigator to perform in the future analyses of behaviour that
were not available at the time of the study."

"Generally, the re-use potential of videography experiments is not fully
appreciated. Many commercial software tracking systems can track the
animal directly in real time, rather than offline from video records. 
Although a video of the trial can be acquired concurrently, most
investigators do not use this option. Regrettably, that is a missed
opportunity to contribute to building a virtual library of open linked
datasets of behavioural performance in standard conditions."

### **Are there any specific questions that utilise Machine Learning / Artificial Intelligence that should be explored on your videography Virtual Library?** {#are-there-any-specific-questions-that-utilise-machine-learning-artificial-intelligence-that-should-be-explored-on-your-videography-virtual-library .wp-block-heading}

**"**The library contains videos and quantitative recordings of the
animal locations in time. The recordings of locations can be processed
by the user to allow extraction of various information based on the
timestamps and the coordinates of the animal. Each 55-minute session
yields a sequence of more than 95,000 records of locations of the
animal, and each session can be examined either separately from the
others or as a part of a sequence of sessions the animal was exposed
to."

"This vast number of records allows utilisation of [Machine
Learning/Artificial Intelligence
algorithms](https://academic.oup.com/gigascience/pages/gigascience-highlights-research-in-artificial-intelligence-machine-learning).
For example, these algorithms can be utilised to predict the escalation
of compulsive checking in animals treated with different drugs, and the
decline in exploratory behaviour of control animals, as well as to
identify the range of unique patterns of escalation and decline across
individual rats and types of treatments."

"Other questions can involve examination and prediction of activity
patterns; evolution of the places of focus in the environment during
development of compulsive checking or as a function of different
treatments; identification of outlier cases and predictor variables."

> ***"...by learning from an animal model of OCD, a Machine
> Learning/Artificial Intelligence algorithm would become a model that
> behaves like an organism with OCD."***

"Moreover, Machine Learning/Artificial Intelligence algorithms can be
used to identify measures of behaviour with long-term dependencies
across years or with seasonal periodicity.  Importantly, one could
analyse patterns of recurrence in behaviour and teach algorithms to
capture that intrinsic variability so that they can then become virtual
generative processes of behaviour. In other words, by learning from an
animal model of OCD, a Machine Learning/Artificial Intelligence
algorithm would become a model that behaves like an organism with OCD."

****Read the *GigaScience* Article:****

Henry Szechtman, Anna Dvorkin-Gheva, Alex Gomez-Marin, A virtual library
for behavioral performance in standard conditions---rodent spontaneous
activity in an open field during repeated testing and after treatment
with drugs or brain lesions, *GigaScience*, Volume 11, 2022, giac092,
[https://doi.org/10.1093/gigascience/giac092](https://doi.org/10.1093/gigascience/giac092){target="_blank"
rel="noreferrer noopener"}

The post [AI Opportunities for Obsessive-Compulsive Disorder. Q&A with
Henry
Szechtman](http://gigasciencejournal.com/blog/obsessive-compulsive-disorder-ai-opportunities/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.

A study published in

 GigaScience

provides an enormous amount of behavioural science data, presented in a detailed videographic virtual library, that was used to explore Obsessive-Compulsive Disorder (OCD) in an animal model.

AI Opportunities for Obsessive-Compulsive Disorder. Q&amp;A with Henry Szechtman

Hans Zauner

**A new article [published today in
*GigaScience*](https://academic.oup.com/gigascience/article/10/10/giab071/6396189?searchresult=1)
demonstrates that machine learning can yield "proxy measures" for
brain-related health issues from large population data, without the need
for a specialist's assessment. To develop AI models sensitive to mental
health, [Denis Engemann](https://denis-engemann.de/) and his team at
[Inria (Saclay --
Île-de-France)](https://www.inria.fr/en/centre-inria-saclay-ile-de-france)
combined questionnaires and brain imaging data from the UK Biobank (read
the press release
[here](https://www.eurekalert.org/news-releases/931340)).**

Mental health issues have been increasing worldwide, with the WHO
determining that there has been a 13% increase in mental health
conditions and substance abuse disorders between 2007 and 2017. We
interviewed Dr. Engemann via email about his research and the emerging
role of machine learning for mental health assessment:

***You are using brain signals and questionnaire data to derive measures
of mental health, using machine learning. Could you explain in simple
terms what the goal of this research is?***

::: {.wp-block-image}
<figure class="alignleft size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/EngemannBlog.jpg"
class="wp-image-4172" loading="lazy" decoding="async" width="323"
height="371" />
</figure>
:::

Mental health is a construct that is difficult to quantify and relates
to human reality at multiple levels of description: It is like a
kaleidoscope.

Research on its biological causes focuses on multiple levels such as
alterations in neurotransmitter concentrations, large-scale patterns of
neuronal activity or brain anatomy.

Yet, when it comes to inferring the mental health status, practitioners
rely on overt behavior and speech, potentially revealing individual
life-style patterns, but also the social environment. In this research,
we want to build richer measures of mental health by benefiting from
heterogeneous data describing individuals from categorically different
points of view.

***Without going into too much technical detail, how does the Machine
Learning method derive "proxy measures" of psychological concepts from
questionnaire data?***

In its essence, we see machine learning as a statistical tool for
estimating functions that robustly map outcomes of interest (like
results of cognitive tests or self-reported mood) to a broad range of
input data; even if the precise data generating mechanisms are unknown.

For instance, we can try to explain classic assessments of mood, based
on questionnaires, with brain imaging. Given the brain image of a
person, the resulting model will provide a prediction by returning the
most probable questionnaire result by extrapolating from people whose
brains "looked" similar. Thus, the predicted questionnaire result can
become a proxy for the construct measured by the questionnaire. This
reflects the statistical link between questionnaire data and brain
images and, therefore, can enrich the original questionnaire measure.

::: {.wp-block-image}
<figure class="alignleft size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures.jpg"
class="wp-image-4169" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures.jpg 826w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures-300x285.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/proxymeasures-768x729.jpg 768w"
sizes="(max-width: 298px) 100vw, 298px" width="298" height="283"
alt="proxy measures for mental health " />
<figcaption>Adapted from Fig.1 in <a
href="http://https//doi.org/10.1093/gigascience/giab071">Dadi et
al.</a></figcaption>
</figure>
:::

Once the model is built, this proxy can be obtained even if the
questionnaire of interest has never been assessed. This is a promising
method for finding large-scale statistical patterns of health within the
general population to then enhance smaller clinical studies in which
machine learning may not be directly applicable, due to limited training
data.

In the past decade, "brain age", based on this proxy measure
methodology, has led to promising studies in clinical neurology. In this
work, we generalized this methodology in two ways:

First, we showed that, beyond biological aging, the same proxy-measure
framework is applicable to constructs more directly related to mental
health. Second, we showed that useful proxy measures can be derived from
other inputs than brain images such as sociodemographic and behavioral
data.

***What's in the UK biobank data, and how did it help you for this
project?***

The United Kingdom Biobank contains the key ingredients to address our
research question. It provides an epidemiological dataset encompassing a
large volume of heterogeneous biomedical and sociodemographic data
sampled from the general population. This dataset reaches a size that is
appropriate for a machine learning approach.

::: {.wp-block-image}
<figure class="alignright size-large is-resized">
<img
src="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-1024x499.jpg"
class="wp-image-4186" loading="lazy" decoding="async"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-1024x499.jpg 1024w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-300x146.jpg 300w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2-768x374.jpg 768w, http://gigasciencejournal.com/blog/wp-content/uploads/2021/10/fig2.jpg 1530w"
sizes="(max-width: 345px) 100vw, 345px" width="345" height="167" />
</figure>
:::

Moreover, it contains direct measures of classical psychological
constructs in academic and experimental psychology established
throughout the 20th century, including fluid intelligence (cognitive
capacity) and neuroticism (persistent negative mood and feelings). These
constructs are intimately related to mental health and have been
intensely studied in psychiatric populations.

***In your opinion, what is the most important implication of your
work?***

Our results open an entirely new set of opportunities for biomarker
development and clinical applications by providing evidence for the
generality of the proxy measure framework. Previously, we have been
actively studying brain age as an empirically derived measure of
biological aging. While we intuitively understand "brain age" as a
statistical phenomenon, we did not yet conceptualize it in more general
terms as a proxy measure.

In the present work, we have revised the framework by proposing a more
general terminology and extending the scope successfully through novel
empirical benchmarks. We not only found that the proxy could enrich the
original target, but multiple proxies of different constructs like
aging, intelligence or mood were complementary at characterizing
real-world health behavior like exercise, sleeping, or drinking habits.

***Assessing psychological constructs, such as intelligence or
neuroticism, today usually requires a human expert. Will machine
learning be able to "replace" the human expert in the future, at least
for certain questions?***

Thank you for this fascinating question. One can doubt that this type of
approach is going to replace psychologists.

Instead, the psychologist will have a richer toolkit including proxy
measures derived from large populations next to classical questionnaires
and interviews. If we think about this for a moment, already the
classical intelligence quotient, the IQ score, is obtained from
referencing the raw results of the intelligence test by comparison
against test results obtained in different age cohorts, leading to norm
tables.

Our approach generalizes this process and allows mental health
researchers and practitioners to consider additional social and
biomedical contexts for referencing individual results. Machine learning
helps here to consider a much larger and more complete volume of
observations.

***How could work such as yours change the practice of psychology and
related fields?***

In the future, psychologists and machine learning models may form a
dynamic duo for tackling mental assessments. This may lead to
increasingly fine-grained and personalized mental assessments. One can
imagine that next to completing tests, questionnaires, and interviews,
clients or patients grant a machine learning model secured access to
their social media accounts or their mobile phone data to then return
proxy measures useful to both the client and the mental health or
education expert.

However, what is not going to change is that practicing psychologists
and other mental health practitioners will need to carefully interpret
and contextualize test results on a case-by-case basis and through
social interaction; whether these results will be derived from
expert-based questionnaires or machine learning.

**Read the *GigaScience* article:**

Dadi K; Varoquaux G; Houenou J; Bzdok D; Thirion B; Engemann D (2021) 
Population modeling with machine learning can enhance measures of mental
health. *GigaScience* (October 2021).
[https//doi.org/10.1093/gigascience/giab071](https://academic.oup.com/gigascience/article/10/10/giab071/6396189?searchresult=1)

**Media coverage:**

*L'Express* (in French): *[Une intelligence artificielle pour évaluer la
santé mentale des
patients](https://www.lexpress.fr/actualite/sciences/une-intelligence-artificielle-pour-evaluer-la-sante-mentale-des-patients_2160541.html)*,
by Victor Garcia

*All GigaScience content (including articles, blogs, and peer reviews)
is open access and issued under a Commons Attribution License (CC-BY
4.0), which permits unrestricted reuse, distribution, and reproduction
in any medium, provided the original work is properly cited.*

The post [AI for mental health assessment: Author
Q&A](http://gigasciencejournal.com/blog/ai-for-mental-health/){rel="nofollow"}
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A new article published today in
 
 GigaScience
 
 demonstrates that machine learning can yield “proxy measures” for brain-related health issues from large population data, without the need for a specialist’s assessment.

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