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As if stand-alone technologies weren’t advancing fast enough, we’re in age where we must study the intersection points of these technologies. How is what’s happening in robotics influenced by what’s happening in 3D printing? What could be made possible by applying the latest advances in quantum computing to nanotechnology?
Along these lines, one crucial tech intersection is that of artificial intelligence and genomics. Each field is seeing constant progress, but Jamie Metzl believes it’s their convergence that will really push us into uncharted territory, beyond even what we’ve imagined in science fiction. “There’s going to be this push and pull, this competition between the reality of our biology with its built-in limitations and the scope of our aspirations,” he said.
Metzl is a senior fellow at the Atlantic Council and author of the upcoming book Hacking Darwin: Genetic Engineering and the Future of Humanity. At Singularity University’s Exponential Medicine conference last week, he shared his insights on genomics and AI, and where their convergence could take us.
Life As We Know It
Metzl explained how genomics as a field evolved slowly—and then quickly. In 1953, James Watson and Francis Crick identified the double helix structure of DNA, and realized that the order of the base pairs held a treasure trove of genetic information. There was such a thing as a book of life, and we’d found it.
In 2003, when the Human Genome Project was completed (after 13 years and $2.7 billion), we learned the order of the genome’s 3 billion base pairs, and the location of specific genes on our chromosomes. Not only did a book of life exist, we figured out how to read it.
Jamie Metzl at Exponential Medicine
Fifteen years after that, it’s 2018 and precision gene editing in plants, animals, and humans is changing everything, and quickly pushing us into an entirely new frontier. Forget reading the book of life—we’re now learning how to write it.
“Readable, writable, and hackable, what’s clear is that human beings are recognizing that we are another form of information technology, and just like our IT has entered this exponential curve of discovery, we will have that with ourselves,” Metzl said. “And it’s intersecting with the AI revolution.”
Learning About Life Meets Machine Learning
In 2016, DeepMind’s AlphaGo program outsmarted the world’s top Go player. In 2017 AlphaGo Zero was created: unlike AlphaGo, AlphaGo Zero wasn’t trained using previous human games of Go, but was simply given the rules of Go—and in four days it defeated the AlphaGo program.
Our own biology is, of course, vastly more complex than the game of Go, and that, Metzl said, is our starting point. “The system of our own biology that we are trying to understand is massively, but very importantly not infinitely, complex,” he added.
Getting a standardized set of rules for our biology—and, eventually, maybe even outsmarting our biology—will require genomic data. Lots of it.
Multiple countries already starting to produce this data. The UK’s National Health Service recently announced a plan to sequence the genomes of five million Britons over the next five years. In the US the All of Us Research Program will sequence a million Americans. China is the most aggressive in sequencing its population, with a goal of sequencing half of all newborns by 2020.
“We’re going to get these massive pools of sequenced genomic data,” Metzl said. “The real gold will come from comparing people’s sequenced genomes to their electronic health records, and ultimately their life records.” Getting people comfortable with allowing open access to their data will be another matter; Metzl mentioned that Luna DNA and others have strategies to help people get comfortable with giving consent to their private information. But this is where China’s lack of privacy protection could end up being a significant advantage.
To compare genotypes and phenotypes at scale—first millions, then hundreds of millions, then eventually billions, Metzl said—we’re going to need AI and big data analytic tools, and algorithms far beyond what we have now. These tools will let us move from precision medicine to predictive medicine, knowing precisely when and where different diseases are going to occur and shutting them down before they start.
But, Metzl said, “As we unlock the genetics of ourselves, it’s not going to be about just healthcare. It’s ultimately going to be about who and what we are as humans. It’s going to be about identity.”
Designer Babies, and Their Babies
In Metzl’s mind, the most serious application of our genomic knowledge will be in embryo selection.
Currently, in-vitro fertilization (IVF) procedures can extract around 15 eggs, fertilize them, then do pre-implantation genetic testing; right now what’s knowable is single-gene mutation diseases and simple traits like hair color and eye color. As we get to the millions and then billions of people with sequences, we’ll have information about how these genetics work, and we’re going to be able to make much more informed choices,” Metzl said.
Imagine going to a fertility clinic in 2023. You give a skin graft or a blood sample, and using in-vitro gametogenesis (IVG)—infertility be damned—your skin or blood cells are induced to become eggs or sperm, which are then combined to create embryos. The dozens or hundreds of embryos created from artificial gametes each have a few cells extracted from them, and these cells are sequenced. The sequences will tell you the likelihood of specific traits and disease states were that embryo to be implanted and taken to full term. “With really anything that has a genetic foundation, we’ll be able to predict with increasing levels of accuracy how that potential child will be realized as a human being,” Metzl said.
This, he added, could lead to some wild and frightening possibilities: if you have 1,000 eggs and you pick one based on its optimal genetic sequence, you could then mate your embryo with somebody else who has done the same thing in a different genetic line. “Your five-day-old embryo and their five-day-old embryo could have a child using the same IVG process,” Metzl said. “Then that child could have a child with another five-day-old embryo from another genetic line, and you could go on and on down the line.”
Sounds insane, right? But wait, there’s more: as Jason Pontin reported earlier this year in Wired, “Gene-editing technologies such as Crispr-Cas9 would make it relatively easy to repair, add, or remove genes during the IVG process, eliminating diseases or conferring advantages that would ripple through a child’s genome. This all may sound like science fiction, but to those following the research, the combination of IVG and gene editing appears highly likely, if not inevitable.”
From Crazy to Commonplace?
It’s a slippery slope from gene editing and embryo-mating to a dystopian race to build the most perfect humans possible. If somebody’s investing so much time and energy in selecting their embryo, Metzl asked, how will they think about the mating choices of their children? IVG could quickly leave the realm of healthcare and enter that of evolution.
“We all need to be part of an inclusive, integrated, global dialogue on the future of our species,” Metzl said. “Healthcare professionals are essential nodes in this.” Not least among this dialogue should be the question of access to tech like IVG; are there steps we can take to keep it from becoming a tool for a wealthy minority, and thereby perpetuating inequality and further polarizing societies?
As Pontin points out, at its inception 40 years ago IVF also sparked fear, confusion, and resistance—and now it’s as normal and common as could be, with millions of healthy babies conceived using the technology.
The disruption that genomics, AI, and IVG will bring to reproduction could follow a similar story cycle—if we’re smart about it. As Metzl put it, “This must be regulated, because it is life.”
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In the myth about the Tower of Babel, people conspired to build a city and tower that would reach heaven. Their creator observed, “And now nothing will be restrained from them, which they have imagined to do.” According to the myth, God thwarted this effort by creating diverse languages so that they could no longer collaborate.
In our modern times, we’re experiencing a state of unprecedented connectivity thanks to technology. However, we’re still living under the shadow of the Tower of Babel. Language remains a barrier in business and marketing. Even though technological devices can quickly and easily connect, humans from different parts of the world often can’t.
Translation agencies step in, making presentations, contracts, outsourcing instructions, and advertisements comprehensible to all intended recipients. Some agencies also offer “localization” expertise. For instance, if a company is marketing in Quebec, the advertisements need to be in Québécois French, not European French. Risk-averse companies may be reluctant to invest in these translations. Consequently, these ventures haven’t achieved full market penetration.
Global markets are waiting, but AI-powered language translation isn’t ready yet, despite recent advancements in natural language processing and sentiment analysis. AI still has difficulties processing requests in one language, without the additional complications of translation. In November 2016, Google added a neural network to its translation tool. However, some of its translations are still socially and grammatically odd. I spoke to technologists and a language professor to find out why.
“To Google’s credit, they made a pretty massive improvement that appeared almost overnight. You know, I don’t use it as much. I will say this. Language is hard,” said Michael Housman, chief data science officer at RapportBoost.AI and faculty member of Singularity University.
He explained that the ideal scenario for machine learning and artificial intelligence is something with fixed rules and a clear-cut measure of success or failure. He named chess as an obvious example, and noted machines were able to beat the best human Go player. This happened faster than anyone anticipated because of the game’s very clear rules and limited set of moves.
Housman elaborated, “Language is almost the opposite of that. There aren’t as clearly-cut and defined rules. The conversation can go in an infinite number of different directions. And then of course, you need labeled data. You need to tell the machine to do it right or wrong.”
Housman noted that it’s inherently difficult to assign these informative labels. “Two translators won’t even agree on whether it was translated properly or not,” he said. “Language is kind of the wild west, in terms of data.”
Google’s technology is now able to consider the entirety of a sentence, as opposed to merely translating individual words. Still, the glitches linger. I asked Dr. Jorge Majfud, Associate Professor of Spanish, Latin American Literature, and International Studies at Jacksonville University, to explain why consistently accurate language translation has thus far eluded AI.
He replied, “The problem is that considering the ‘entire’ sentence is still not enough. The same way the meaning of a word depends on the rest of the sentence (more in English than in Spanish), the meaning of a sentence depends on the rest of the paragraph and the rest of the text, as the meaning of a text depends on a larger context called culture, speaker intentions, etc.”
He noted that sarcasm and irony only make sense within this widened context. Similarly, idioms can be problematic for automated translations.
“Google translation is a good tool if you use it as a tool, that is, not to substitute human learning or understanding,” he said, before offering examples of mistranslations that could occur.
“Months ago, I went to buy a drill at Home Depot and I read a sign under a machine: ‘Saw machine.’ Right below it, the Spanish translation: ‘La máquina vió,’ which means, ‘The machine did see it.’ Saw, not as a noun but as a verb in the preterit form,” he explained.
Dr. Majfud warned, “We should be aware of the fragility of their ‘interpretation.’ Because to translate is basically to interpret, not just an idea but a feeling. Human feelings and ideas that only humans can understand—and sometimes not even we, humans, understand other humans.”
He noted that cultures, gender, and even age can pose barriers to this understanding and also contended that an over-reliance on technology is leading to our cultural and political decline. Dr. Majfud mentioned that Argentinean writer Julio Cortázar used to refer to dictionaries as “cemeteries.” He suggested that automatic translators could be called “zombies.”
Erik Cambria is an academic AI researcher and assistant professor at Nanyang Technological University in Singapore. He mostly focuses on natural language processing, which is at the core of AI-powered language translation. Like Dr. Majfud, he sees the complexity and associated risks. “There are so many things that we unconsciously do when we read a piece of text,” he told me. Reading comprehension requires multiple interrelated tasks, which haven’t been accounted for in past attempts to automate translation.
Cambria continued, “The biggest issue with machine translation today is that we tend to go from the syntactic form of a sentence in the input language to the syntactic form of that sentence in the target language. That’s not what we humans do. We first decode the meaning of the sentence in the input language and then we encode that meaning into the target language.”
Additionally, there are cultural risks involved with these translations. Dr. Ramesh Srinivasan, Director of UCLA’s Digital Cultures Lab, said that new technological tools sometimes reflect underlying biases.
“There tend to be two parameters that shape how we design ‘intelligent systems.’ One is the values and you might say biases of those that create the systems. And the second is the world if you will that they learn from,” he told me. “If you build AI systems that reflect the biases of their creators and of the world more largely, you get some, occasionally, spectacular failures.”
Dr. Srinivasan said translation tools should be transparent about their capabilities and limitations. He said, “You know, the idea that a single system can take languages that I believe are very diverse semantically and syntactically from one another and claim to unite them or universalize them, or essentially make them sort of a singular entity, it’s a misnomer, right?”
Mary Cochran, co-founder of Launching Labs Marketing, sees the commercial upside. She mentioned that listings in online marketplaces such as Amazon could potentially be auto-translated and optimized for buyers in other countries.
She said, “I believe that we’re just at the tip of the iceberg, so to speak, with what AI can do with marketing. And with better translation, and more globalization around the world, AI can’t help but lead to exploding markets.”
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