Features International Sugar Journal

Disruptive technologies AI and gene drives

 The pace of scientific and technical advances driving innovation via disruptive technologies is seemingly accelerating. Advanced manufacturing is making redundant variety of jobs. “Boston Consulting Group reports that it costs barely US$8 an hour to use a robot for spot welding in the auto industry, compared to US$25 for a worker—and the gap is only going to widen.”1 After decades of research, progress in artificial intelligence (AI) is beginning to bear fruit. Recent breakthroughs in deep learning have produced AI systems that in some instances can not only match but also exceed human intelligence. Case in point is the development of a program by AlphaGo in March 2016 that defeated one of the best Go2 players of all time, South Korea’s Lee Sedol. This was a remarkable achievement as conventional programming does not lend itself to coding Go-playing program as the game is not only fairly complex but even the accomplished players are not able to say with any clarity why certain moves are good or bad.

AI-driven machine learning techniques have wide applicability in the process-intensive sugar sector as in practically any other sector. McKinsey, in its recent report3 summarised AI capabilities into the following categories: perception – “sensing the world” and describing it – supported by various tools including natural language processing, computer vision and audio processing; prediction – “using reasoning to anticipate behaviours and results” – of particular use for targeted advertising for particular customers; prescription – “what to do to achieve goals” – optimizing energy use and crystallization, controlling waste; and integrated solutions embracing “complementary technologies such as robotics”. Investment in AI currently runs into billions of dollars for applications in variety of sectors. In the US alone, Intel forecasts self-driving car sector worth US$7 trillion by 2050.While the sugar sector can do with similar levels of progress in chemical engineering, AI will doubtless be a boon for early adopters in the industry.

From the biological sciences, it is the new genetic engineering application gene drives, which has the potential to address an array of issues from agricultural problems to public health, that is provoking excitement, apprehension and trepidation.

Gene drives effectively defies normal rules of inheritance by a forcing a particular trait through a population. A specific trait ordinarily has a 50-50 chance of being passed along to the next generation. A gene drive could push that rate to nearly 100%. The effect is called “super-Mendelian” inheritance. This technology offers the prospect of engineering genes of a wild population.

Facilitated by the gene editing tool CRISPR, gene drive is inserted into an organism. “When the organism mates, its CRISPR-equipped chromosome cleaves the matching chromosome coming from the other parent. The offspring’s genetic machinery then attempts to sew up this cut. When it does, it copies over the relevant section of DNA from the first parent—the section that contains the CRISPR gene drive. In this way, the gene drive duplicates itself so that it ends up on both chromosomes, and this will occur with nearly every one of the original organism’s offspring.”4

Gene drive systems distorting the 50:50 chance of a gene copy being passed on

Two approaches that have received the most attention in exploiting gene drives in practice are replacement and suppression. “A replacement gene drive alters a specific trait. For example, an anti-malaria gene drive might change a mosquito’s genome so that the insect no longer had the ability to pick up the malaria parasite. In this situation, the new genes would quickly spread through a wild population so that none of the mosquitoes could carry the parasite, effectively stopping the spread of the disease. A suppression gene drive would wipe out an entire population. For example, a gene drive that forced all offspring to be male would make reproduction impossible.”5

To date, gene-drives have not been tested beyond the proof-of-concept in small populations. It is not clear how effective will this application be in large population in the wild where the prospect of resistance to gene drive systems is a real one. Against this backdrop, there are concerns that gene drives have the potential to cause irreversible effects on organisms and ecosystems. New Zealand is considering exploiting gene drives to help eliminate rats, mice, stoats and possums. This year the US military research agency DARPA allocated some US$75 million to develop antidotes to gene drives, should they be used as a bioweapon.

Tomorrow’s world just got a bit more fearful.

Endnotes

1 Mark Muro (2016) Manufacturing jobs aren’t coming back. MIT Technology Review, November

2 According to Wikipedia “Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent.”

3 McKinsey Global Institute (2017) Artificial intelligence: Implications for China

4 Brooke Borel (2016) When evolution fights back against genetic engineering. https://www.theatlantic.com/science/archive/2016/09/gene-drives/499574/

5. Ibid