Recently, a discussion between several top AI
minds of the world and Vietnam: professor Yoshua Bengio (Mila
Quebec AI Institute), professor Ho Tu Bao (VIASM), Dr. Truong Gia
Binh and Dr. Phong Nguyen (FPT Corporation), explored the usage of
machine learning to transform the way we make new drugs and
approached ethical AI.
The demand to find new ways to combat disease is rising by the
day. Humanity is always at risk of a new pandemic, and the mutation
of viruses creates resistance to antibiotics. According to experts,
it has caused both high fatalities and economic value losses.
“There's already 1.2 million deaths per year, and it's going to
grow to 10 million deaths per year,” said Professor Yoshua Bengio
(Mila Quebec AI Institute). “Economic costs are also rising, and
it's projected to be 100 trillion US dollars by 2050.
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the full release here:
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Tech Innovators #11 reunited Prof. Yoshua
Bengio with Dr. Truong Gia Binh and Dr. Phong Nguyen. Prof. Ho Tu
Bao, Ms. Chu Thi Thanh Ha (Chairwoman, FPT Software) joined the
event. (Photo: Business Wire)
To combat this, prof. Bengio has been looking into utilizing
Generative Flow Networks, or GFlow Nets - his team’s ML technique
for generating compositional objects at a frequency proportional to
the associated reward - to discover new drug molecules and generate
candidates. These findings were published in 3 recent papers at
renowned AI conferences.
According to prof. Bengio, one of the greatest areas of growth
is at the intersection of AI and biotechnology for the next decade,
thanks to its ability to reprogram the DNA of organisms and
synthesize new drug molecules.
However, the number of potential new drugs is vast and it takes
decades to test which drug would work in treating which subject.
Here, ML can be used to represent sample candidate experiments and
shorten the time to give an educated guess as to “what chance a
candidate drug is going to do the job”. But questions arise
regarding ML ability to acknowledge its limitations and
uncertainty, and to create diversity in candidates.
“We would like our ML system to generate candidates to be
different from each other,” said prof. Bengio. “It's important as
if one drug candidate does not work, “we still have other
candidates that are quite different. At the end of the day, we have
a greater chance of having a drug that's going to work.”
To decide which candidates to zoom in on since the number would
be too large, prof. Bengio and his team propose using generative
models, benefiting from neural nets’ ability to imagine - usually
for synthesizing new images. In this case, instead of images,
neural nets can synthesize molecules, and through training can be
used in scientific experiments. This particular ability opens many
opportunities, as it “can be applied to any kind of objects we want
to experiment over, molecules, materials, even potentially,
software.”
Stemming from these ideas, prof. Bengio and his team dug deeper
using their GFlow Nets. In his findings, GFlow Nets proved to be
working efficiently in sampling with probability and combining with
the notion of Bayesian uncertainty, finding a more diverse set of
candidate solutions compared to existing methods. Finally, in order
to “build a really good model of data that is never going to be
overconfident”, prof. Bengio looked into causality: using GFlow
Nets to generate all the causal graphs that are compatible with the
data.
Still, he noted on the challenge the current ML system faces:
“Approaches like causal machine learning are still in their
infancy,” he said, “to conceive of the learning problem not as just
learning one distribution but learning a whole family of
distributions, corresponding to different context, different
intervention, experiments and environments.”
“If we can learn a causal model well, we can generalize all of
those distributions” - the professor concluded.
Given AI's direct influence on people’s welfare, the top minds
also discussed its ethical concerns. In the Q&A, prof. Bengio
addressed several issues of the long-term effect of new drugs, the
misuse of AI to create toxins, as well as discrimination of data
provided to AI and ML. He suggested that through illustrative
research, people’s awareness of the dangers can be raised. Then,
communities & authorities can create new legislations, social
norms, guidelines and penalties to minimize the chance it
materializes.
Based on his experience in analyzing medical data research in
Japan and supporting Electrical Medical Records in Vietnam, prof.
Ho Tu Bao relaid the principles of ethical AI. As protecting the
privacy of people’s data is the top priority, prof. Bao pointed out
the First Principle of Do No Harm, the accountability and
transparency in the process of making AI, which means AI makers can
answer for their products.
From the organizational point of view, Dr. Truong Gia Binh
shared his aim to initiate immediate actions through educational
activities. He suggested raising AI literacy by bringing AI courses
to everyone, from primary to university level. His second idea is
to align the education system, first at the FPT Education, with 17
UN sustainable development goals. FPT has also tried to implement
practical projects for the pupil to learn from realistic
experience, especially in the field of social responsibilities and
AI for social good.
“AI will affect their lives later, let’s give them a chance to
use AI to increase their efficiency in their daily lives”, Dr. Binh
finished.
During the event, FPT and Mila Quebec AI Institute also
celebrated their 2-year partnership, with achievements regarding
young talents development. The representatives look forward to
continuing the partnership into the future.
The event was as part of Tech Innovators - the webinar series on
tech trends and experts insights, organized by FPT Software. For
many years, the series has acted as a forum for international top
minds to discuss the latest technology discoveries. With engaging
topics, it has attracted thousands of people tuning in on a monthly
basis.
Watch the full webinar here:
facebook.com/fptsoftware.official/videos/2297108493790947
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