How Life Can Be Annotated Publications

[1] Dundas, J, Ling, MHT. 2011. Higher Level Intelligence in Machines. Human-Level Intelligence 2: 2. (An editorial) [PDF]

There has been a large number of studies in neurological sciences on how human brain works, especially in reading and parallel information processing. So I think this statement is really sweeping. Perhaps it is better to knowledge the abilities of human brains and to comment on the limitations of the human brain. The book “Adapt” by Tim Hartford advocates micro-step changes. An important aspect in this area is to understand the processes involved behind the scenes so that it gives us a better formulation of the creativity algorithms involved. I will try to put in some pointers on the depths to which higher level intelligence has been simulated by AI in the past years. Some of the higher level intelligence mechanisms such as creativity, dreams and logical thinking have been implemented in machines in certain ways. However, they are still not implemented in the way humans do the same. The exact mechanism in which these intelligence mechanisms are used by human brains is still way ahead of that done by computers. However, we now look at other aspects in them as well as other functions which might be explored in the years ahead in AI.

[2] Ling, MHT. 2012. Re-creating the Philosopher’s Mind: Artificial Life from Artificial Intelligence. Human-Level Intelligence 2: 1.

The ultimate goal of artificial intelligence (AI) research is to create a system with human level intelligence. This manuscript suggests that AL may be a channel towards human level intelligence, and presents an overview of how high-level intelligence can be achieved from artificial life. It will be interesting when our simulated humans (such as the characters in a future version of Diablo) start to create their own artificial intelligence.

[3] Koh, YZ, Ling, MHT. 2013. On the Liveliness of Artificial Life. Human-Level Intelligence 3: 1.

There has been on-going philosophical debate on whether artificial life models, also known as digital organisms, are truly alive. The main difficulty appears to be finding an encompassing and definite definition of life. By examining similarities and differences in recent definitions of life, we define life as “any system with a boundary to confine the system within a definite volume and protect the system from external effects, consisting of a program that is capable of improvisation, able to react and adapt to the environment, able to regenerate parts of itself or its entirety, with energy system comprises of non-interference sets of secluded reactions for self-sustenance, is considered alive or a living system. Any incomplete system containing a program and can be re-assembled into a living system; thereby, converting the re-assembled system for the purpose of the incomplete system, are also considered alive.” Using this definition, we argue that digital organisms may not be the boundary case of life even though some digital organisms are not considered alive; thereby, taking the view that some form of digital organisms can be considered alive. In addition, we present an experimental framework based on continuity of the overall system and potential discontinuity of elements within the system for testing future definitions of life.

[4] Castillo, CFG, Ling, MHT. 2014. Resistant Traits in Digital Organisms Do Not Revert Preselection Status despite Extended Deselection: Implications to Microbial Antibiotics Resistance. BioMed Research International 2014, Article ID 648389. [Full Text] [PDF]

We examined whether antibiotics resistance will decline after disuse of specific antibiotics under the assumption that there is no fitness cost for maintaining resistance. Our results show that during disuse of the specific antibiotics, a large initial loss and prolonged stabilization of resistance are observed but resistance is not lost to the stage of pre-resistance emergence. This suggests that a pool of partial resistant organisms persist long after withdrawal of selective pressure at a relatively constant proportion. Subsequent re-introduction of the same antibiotics results in rapid re-gain of resistance. Thus, our simulation results suggest that complete elimination of specific antibiotics resistance is unlikely after the disuse of antibiotics, once a resistant pool of micro-organism has been established.

[5] Ling, MHT. 2014. Applications of Artificial Life and Digital Organisms in the Study of Genetic Evolution. Advances in Computer Science: an International Journal 3(4): 107-112.

Testing evolutionary hypothesis in experimental setting is expensive, time consuming, and unlikely to recapitulate evolutionary history if evolution is repeated. Computer simulations of virtual organisms, also known as artificial life or digital organisms (DOs) can be used for in silico study of evolutionary processes. This mini-review focuses on the use of DOs in the study of genetic evolution. The three main areas focused in this review are (1) emergence of specialized cells, (2) chemical and environmental resistance, and (3) genetic adaptability. This review concludes with a discussion on the limitations on using DOs as a tool for studying genetic evolution.

[6] Castillo, CFG, Chay ZE, Ling, MHT. 2015. Resistance Maintained in Digital Organisms Despite Guanine/Cytosine-Based Fitness Cost and Extended De-Selection: Implications to Microbial Antibiotics Resistance. MOJ Proteomics & Bioinformatics 2(2): 00039.

Antibiotics resistance has caused much complication in the treatment of diseases, where the pathogen is no longer susceptible to specific antibiotics and the use of such antibiotics are no longer effective for treatment. A recent study that utilizes digital organisms suggests that complete elimination of specific antibiotic resistance is unlikely after the disuse of antibiotics, assuming that there are no fitness costs for maintaining resistance once resistance are established. Fitness cost are referred to as reaction to change in environment, where organism improves its’ abilities in one area at the expense of the other. Our goal in this study is to use digital organisms to examine the rate of gain and loss of resistance where fitness costs have incurred in maintaining resistance. Our results showed that GC-content based fitness cost during de-selection by removal of antibiotic-induced selective pressure portrayed similar trends in resistance compared to that of no fitness cost, at all stages of initial selection, repeated de-selection and re-introduction of selective pressure. Paired t-tests suggested that prolonged stabilization of resistance after initial loss is not statistically significant for its difference to that of no fitness cost. This suggests that complete elimination of specific antibiotics resistance is unlikely after the disuse of antibiotics despite presence of fitness cost in maintaining antibiotic resistance during the disuse of antibiotics, once a resistant pool of micro-organism has been established.

[7] Ling, MHT. 2016. Of (Biological) Models and Simulations. MOJ Proteomics & Bioinformatics 3(4): 00093.

Modeling and simulation are recognized as important aspects of the scientific method for more than 70 years but its adoption in biology has been slow. Debates on its representativeness, usefulness, and whether the effort spent on such endeavors is worthwhile, exist to this day. Here, I argue that most of learning is modeling; hence, arriving at a contradiction if models are not useful. Representing biological systems through mathematical models can be difficult but the modeling procedure is a process in itself that follows a semi-formal set of rules. Although seldom reported, failure in modeling is not a rare event but I argue that this is usually a result of erroneous underlying knowledge or mis-application of a model beyond its intended purpose. I argue that in many biological studies, simulation is the only experimental tool. In others, simulation is a means of reducing possible combinations of experimental work; thereby, presenting an economical case for simulation; thus, worthwhile to engage in this endeavor. The representativeness of simulation depends on the validation, verification, assumptions, and limitations of the underlying model. This will be illustrated using the inter-relationship between population, samples, probability theory, and statistics.