The Algorithms of Life

Feng Zengkun, freelance writer

For decades, scientists have tried to make robots more like human
beings by designing computer algorithms that allow them to learn and become
smarter.

In fact, similar kinds of biological algorithms might exist in
people and govern not only how we learn and act but also how our species
evolved.

That is the firm belief of Professor Leslie Valiant, whose
ground-breaking research has been fundamental to the development of machine
learning, artificial intelligence and the broader field of computer science.

“You think of an algorithm as something running on your computer,
but it could just as easily run on a biological organism,” said the 67-year-old
in an interview in January (2016) with Quanta Magazine, which reports on
developments in mathematics and the physical and life sciences.

“If one has a more high-level computational explanation of how the
brain works, one would get closer to having an explanation of human behaviour
that matches our mechanistic understanding of other physical systems.”

Teaching machines to learn

One of the speakers at the upcoming Global Young Scientists
Summit@one-north 2017 (GYSS 2017), Prof Valiant has long blurred the lines
between the computer and life sciences in his work.

In 1984, inspired by the fact that human beings appear to be able to
learn new concepts without needing to be programmed explicitly, he published a
seminal paper outlining the conditions under which a machine could also be said
to “learn”.

This paper, titled “A Theory of the Learnable”, provided both a
general framework for machine learning as well as concrete computational
models.

His approach, called the “Probably Approximately Correct” (PAC)
model, posits that a machine can create useful generalisations by examining an
array of examples.

For instance, it might be able to determine the characteristics of
human beings by analysing examples of mammals that are labelled either “a human
being” or “not a human being”.

From these examples, it might develop the classification that human
beings are “warm-blooded, have opposable thumbs and give birth to their young”.
It would then be able to use this algorithm to decide if animals it sees in the
future are human beings or not.

Of course, this algorithm is not entirely correct. Chimpanzees, too,
have those characteristics, and if the samples did not include chimpanzees, the
machine might go on to mistake chimpanzees as human beings.

There is also the minute chance that the examples labelled “a human
being” all have fair hair, thus causing the machine to add the incorrect
generalisation that human beings must have fair hair.

While the machine’s algorithms are unlikely to be always and
entirely correct, with enough examples, they are likely to be probably and
approximately correct – hence the name of the model.

The PAC model has become one of the most important contributions to
machine learning, and is the foundation of the modern field of computational
learning theory, where scientists study the design and analysis of machine
learning algorithms.

For this and other trail-blazing work, Prof Valiant was awarded the
2010 Turing Award, which is regarded as the Nobel Prize in computing.

The international Association for Computing Machinery (ACM), which
bestows the award, said in its citation that Prof Valiant “brought together
machine learning and computational complexity, leading to advances in
artificial intelligence as well as computing practices such as natural language
processing, handwriting recognition and computer vision”.

In fact, “mainstream research in artificial intelligence has
embraced his viewpoint as a critical tool in designing intelligent systems,”
the ACM added.

The algorithms to life

The PAC model’s usefulness might also not be limited to machines.
More recently, Prof Valiant, who is the T Jefferson Coolidge Professor of
Computer Science and Applied Mathematics at Harvard University in the United
States, has expanded his theory to include biological evolution.

He believes, for instance, that Darwin’s theory of evolution is
convincing but incomplete. It does not explain, among other things, the rate at
which evolution occurs.

“Amazingly, although the evidence that evolution has taken place is
overwhelming, we still do not have a quantitative explanation of how it could
have proceeded as fast as it has on Earth. Understanding the speed of evolution
is a fundamental goal,” he said.

“The PAC framework spells out what biological details need to be
understood before such an explanation might be derived, and offers methods of
analysis related to the speed once those details are known.”

Prof Valiant believes that if biologists and computer scientists
collaborated more, they might be able to unlock the algorithms used in biology
that would explain not just evolution but human behaviour as well.

In his 2013 book, also titled “Probably Approximately Correct”, he
notes that if human beings are shaped entirely by evolution before conception
and by learning afterwards, all of our characteristics, whether biological or
psychological, will have been determined by adaptive mechanisms.

He came up with the concept of an “ecorithm”, which is essentially a
learning algorithm that runs on any system that can interact with its physical
environment.

“There is a clear argument for the statement that all aspects of an
individual’s behaviour are controlled by the joint influence of the evolution
of the species and what the individual has learned. If evolution and learning
are both forms of ecorithms, then the study of ecorithms offers a unified
approach to understanding both,” he said.

“For example, the many ways in which human reasoning is prone to
mistakes, as studied by psychologists over the decades, could be reflections of
the way humans learn and represent information in the brain.”

Cracking the codes of such ecorithms could also help scientists to
develop more advanced robots that can better learn from their environment,
allowing the machines to evolve in a manner similar to people and become more
useful.

While popular culture is replete with depictions of
super-intelligent machines run amok, Prof Valiant does not believe in such
doomsday scenarios. 

“I regard intelligence as made up of tangible, mechanical and
ultimately understandable processes,” he said in the Quanta Magazine interview.

“We will understand the intelligence that we put into machines in
the same way that we understand the physics of explosives – that is, well
enough to render their behaviour predictable enough that in general they don’t
cause unintended damage.

“I’m not so concerned that artificial intelligence is different in
kind from other existing powerful technologies. It has a scientific basis like
the others.”

-END-

About
the Global Young Scientists Summit 2017

Organised by the Global Young Scientists
Summit (GYSS) @one-north (GYSS@one-north) and taking place 15 to 20 January
2017, the fifth iteration of the event will welcome over 300 of the world’s
most outstanding science graduates and post-doctoral fellows under the age of
35. Young scientists will have the privilege of attending live plenary
lectures, panel discussions and interactive group sessions with 22 highly
distinguished speakers. Highly respected in the science and technology
community, these speakers comprise Field Medal holders, Millennium Technology
Prize winners, Turing Award holders, as well as two special guest speakers.

Speakers new to GYSS@one-north in 2017
include Turing Awardees Vinton Gray Cerf (2004), Barbara Liskov (2008) and
Butler Lampson (1992. The special guest speakers are Chairman of the Lindau
Foundation and Council Member for the Lindau Novel Laureate Meetings,
Heinz-Jürgen Kluge, and Juha Ylä-Jääseki, President and CEO of Technology
Academy of Finland (TAF).

The theme for GYSS@one-north in 2017 is
“Advancing Science, Creating Technologies for a Better World”. The
summit covers a range of disciplines in science such as chemistry, physics,
medicine, mathematics, to computer science and engineering.

Learn more about GYSS 2017 at

http://www.nrf.gov.sg/gyss-one-north/gyss@one-north-2017