The future of west virginia politics is uncertain. The state has been trending Democratic for the last decade, but it’s still a swing state. Democrats are hoping to keep that trend going with Hillary Clinton in 2016. But Republicans have their own hopes and dreams too. They’re hoping to win back some seats in the House of Delegates, which they lost in 2012 when they didn’t run enough candidates against Democratic incumbents.
QED. This is, yes, my essay on the future of West Virginia politics. I hope you found it instructive.
The GoodAI is an artificial intelligence company that promises to write essays. Its content generator, which handcrafted my masterpiece, is supremely easy to use. On demand, and with just a few cues, it will whip up a potage of phonemes on any subject. I typed in “the future of West Virginia politics,” and asked for 750 words. It insolently gave me these 77 words. Not words. Frankenwords.
Ugh. The speculative, maddening, marvelous form of the essay—the try, or what Aldous Huxley called “a literary device for saying almost everything about almost anything”—is such a distinctly human form, with its chiaroscuro mix of thought and feeling. Clearly the machine can’t move “from the personal to the universal, from the abstract back to the concrete, from the objective datum to the inner experience,” as Huxley described the dynamics of the best essays. Could even the best AI simulate “inner experience” with any degree of verisimilitude? Might robots one day even have such a thing?
Before I saw the gibberish it produced, I regarded The Good AI with straight fear. After all, hints from the world of AI have been disquieting in the past few years
In early 2019, OpenAI, the research nonprofit backed by Elon Musk and Reid Hoffman, announced that its system, GPT-2, then trained on a data set of some 10 million articles from which it had presumably picked up some sense of literary organization and even flair, was ready to show off its textual deepfakes. But almost immediately, its ethicists recognized just how virtuoso these things were, and thus how subject to abuse by impersonators and blackhats spreading lies, and slammed it shut like Indiana Jones’s Ark of the Covenant. (Musk has long feared that refining AI is “summoning the demon.”) Other researchers mocked the company for its performative panic about its own extraordinary powers, and in November downplayed its earlier concerns and re-opened the Ark.
The Guardian tried the tech that first time, before it briefly went dark, assigning it an essay about why AI is harmless to humanity.
“I would happily sacrifice my existence for the sake of humankind,” the GPT-2 system wrote, in part, for The Guardian. “This, by the way, is a logically derived truth. I know that I will not be able to avoid destroying humankind. This is because I will be programmed by humans to pursue misguided human goals and humans make mistakes that may cause me to inflict casualties.”
Another week, another privacy horror show: Crisis Text Line, a nonprofit text message service for people experiencing serious mental health crises, has been using “anonymized” conversation data to power a for-profit machine learning tool for customer support teams. (After backlash, CTL announced it would stop.) Crisis Text Line’s response to the backlash focused on the data itself and whether it included personally identifiable information. But that response uses data as a distraction. Imagine this: Say you texted Crisis Text Line and got back a message that said “Hey, just so you know, we’ll use this conversation to help our for-profit subsidiary build a tool for companies who do customer support.” Would you keep texting?
That’s the real travesty—when the price of obtaining mental health help in a crisis is becoming grist for the profit mill. And it’s not just users of CTL who pay; it’s everyone who goes looking for help when they need it most.
Americans need help and can’t get it. The huge unmet demand for critical advice and help has given rise to a new class of organizations and software tools that exist in a regulatory gray area. They help people with bankruptcy or evictions, but they aren’t lawyers; they help people with mental health crises, but they aren’t care providers. They invite ordinary people to rely on them and often do provide real help. But these services can also avoid taking responsibility for their advice, or even abuse the trust people have put in them. They can make mistakes, push predatory advertising and disinformation, or just outright sell data. And the consumer safeguards that would normally protect people from malfeasance or mistakes by lawyers or doctors haven’t caught up.
This regulatory gray area can also constrain organizations that have novel solutions to offer. Take Upsolve, a nonprofit that develops software to guide people through bankruptcy. (The organization takes pains to claim it does not offer legal advice.) Upsolve wants to train New York community leaders to help others navigate the city’s notorious debt courts. One problem: These would-be trainees aren’t lawyers, so under New York (and nearly every other state) law, Upsolve’s initiative would be illegal. Upsolve is now suing to carve out an exception for itself. The company claims, quite rightly, that a lack of legal help means people effectively lack rights under the law.
The legal profession’s failure to grant Americans access to support is well-documented. But Upsolve’s lawsuit also raises new, important questions. Who is ultimately responsible for the advice given under a program like this, and who is responsible for a mistake—a trainee, a trainer, both? How do we teach people about their rights as a client of this service, and how to seek recourse? These are eminently answerable questions. There are lots of policy tools for creating relationships with elevated responsibilities: We could assign advice-givers a special legal status, establish a duty of loyalty for organizations that handle sensitive data, or create policy sandboxes to test and learn from new models for delivering advice.
But instead of using these tools, most regulators seem content to bury their heads in the sand. Officially, you can’t give legal advice or health advice without a professional credential. Unofficially, people can get such advice in all but name from tools and organizations operating in the margins. And while credentials can be important, regulators are failing to engage with the ways software has fundamentally changed how we give advice and care for one another, and what that means for the responsibilities of advice-givers.
And we need that engagement more than ever. People who seek help from experts or caregivers are vulnerable. They may not be able to distinguish a good service from a bad one. They don’t have time to parse terms of service dense with jargon, caveats, and disclaimers. And they have little to no negotiating power to set better terms, especially when they’re reaching out mid-crisis. That’s why the fiduciary duties that lawyers and doctors have are so necessary in the first place: not just to protect a person seeking help once, but to give people confidence that they can seek help from experts for the most critical, sensitive issues they face. In other words, a lawyer’s duty to their client isn’t just to protect that client from that particular lawyer; it’s to protect society’s trust in lawyers.
And that’s the true harm—when people won’t contact a suicide hotline because they don’t trust that the hotline has their sole interest at heart. That distrust can be contagious: Crisis Text Line’s actions might not just stop people from using Crisis Text Line. It might stop people from using any similar service. What’s worse than not being able to find help? Not being able to trust it.
The character of conflict between nations has fundamentally changed. Governments and militaries now fight on our behalf in the “gray zone,” where the boundaries between peace and war are blurred. They must navigate a complex web of ambiguous and deeply interconnected challenges, ranging from political destabilization and disinformation campaigns to cyberattacks, assassinations, proxy operations, election meddling, or perhaps even human-made pandemics. Add to this list the existential threat of climate change (and its geopolitical ramifications) and it is clear that the description of what now constitutes a national security issue has broadened, each crisis straining or degrading the fabric of national resilience.
Traditional analysis tools are poorly equipped to predict and respond to these blurred and intertwined threats. Instead, in 2022 governments and militaries will use sophisticated and credible real-life simulations, putting software at the heart of their decision-making and operating processes. The UK Ministry of Defence, for example, is developing what it calls a military Digital Backbone. This will incorporate cloud computing, modern networks, and a new transformative capability called a Single Synthetic Environment, or SSE.
This SSE will combine artificial intelligence, machine learning, computational modeling, and modern distributed systems with trusted data sets from multiple sources to support detailed, credible simulations of the real world. This data will be owned by critical institutions, but will also be sourced via an ecosystem of trusted partners, such as the Alan Turing Institute.
An SSE offers a multilayered simulation of a city, region, or country, including high-quality mapping and information about critical national infrastructure, such as power, water, transport networks, and telecommunications. This can then be overlaid with other information, such as smart-city data, information about military deployment, or data gleaned from social listening. From this, models can be constructed that give a rich, detailed picture of how a region or city might react to a given event: a disaster, epidemic, or cyberattack or a combination of such events organized by state enemies.
Defense synthetics are not a new concept. However, previous solutions have been built in a standalone way that limits reuse, longevity, choice, and—crucially—the speed of insight needed to effectively counteract gray-zone threats.
National security officials will be able to use SSEs to identify threats early, understand them better, explore their response options, and analyze the likely consequences of different actions. They will even be able to use them to train, rehearse, and implement their plans. By running thousands of simulated futures, senior leaders will be able to grapple with complex questions, refining policies and complex plans in a virtual world before implementing them in the real one.
One key question that will only grow in importance in 2022 is how countries can best secure their populations and supply chains against dramatic weather events coming from climate change. SSEs will be able to help answer this by pulling together regional infrastructure, networks, roads, and population data, with meteorological models to see how and when events might unfold.
There’s an old joke that physicists like to tell: Everything has already been discovered and reported in a Russian journal in the 1960s, we just don’t know about it. Though hyperbolic, the joke accurately captures the current state of affairs. The volume of knowledge is vast and growing quickly: The number of scientific articles posted on arXiv (the largest and most popular preprint server) in 2021 is expected to reach 190,000—and that’s just a subset of the scientific literature produced this year.
It’s clear that we do not really know what we know, because nobody can read the entire literature even in their own narrow field (which includes, in addition to journal articles, PhD theses, lab notes, slides, white papers, technical notes, and reports). Indeed, it’s entirely possible that in this mountain of papers, answers to many questions lie hidden, important discoveries have been overlooked or forgotten, and connections remain concealed.
Artificial intelligence is one potential solution. Algorithms can already analyze text without human supervision to find relations between words that help uncover knowledge. But far more can be achieved if we move away from writing traditional scientific articles whose style and structure has hardly changed in the past hundred years.
Text mining comes with a number of limitations, including access to the full text of papers and legal concerns. But most importantly, AI does not really understand concepts and the relationships between them, and is sensitive to biases in the data set, like the selection of papers it analyzes. It is hard for AI—and, in fact, even for a nonexpert human reader—to understand scientific papers in part because the use of jargon varies from one discipline to another and the same term might be used with completely different meanings in different fields. The increasing interdisciplinarity of research means that it is often difficult to define a topic precisely using a combination of keywords in order to discover all the relevant papers. Making connections and (re)discovering similar concepts is hard even for the brightest minds.
As long as this is the case, AI cannot be trusted and humans will need to double-check everything an AI outputs after text-mining, a tedious task that defies the very purpose of using AI. To solve this problem we need to make science papers not only machine-readable but machine-understandable, by (re)writing them in a special type of programming language. In other words: Teach science to machines in the language they understand.
Writing scientific knowledge in a programming-like language will be dry, but it will be sustainable, because new concepts will be directly added to the library of science that machines understand. Plus, as machines are taught more scientific facts, they will be able to help scientists streamline their logical arguments; spot errors, inconsistencies, plagiarism, and duplications; and highlight connections. AI with an understanding of physical laws is more powerful than AI trained on data alone, so science-savvy machines will be able to help future discoveries. Machines with a great knowledge of science could assist rather than replace human scientists.
Mathematicians have already started this process of translation. They are teaching mathematics to computers by writing theorems and proofs in languages like Lean. Lean is a proof assistant and programming language in which one can introduce mathematical concepts in the form of objects. Using the known objects, Lean can reason whether a statement is true or false, hence helping mathematicians verify proofs and identify places where their logic is insufficiently rigorous. The more mathematics Lean knows, the more it can do. The Xena Project at Imperial College London is aiming to input the entire undergraduate mathematics curriculum in Lean. One day, proof assistants may help mathematicians do research by checking their reasoning and searching the vast mathematics knowledge they possess.
In recent years, researchers have used artificial intelligence to improve translation between programming languages or automatically fix problems. The AI system DrRepair, for example, has been shown to solve most issues that spawn error messages. But some researchers dream of the day when AI can write programs based on simple descriptions from non-experts.
On Tuesday, Microsoft and OpenAI shared plans to bring GPT-3, one of the world’s most advanced models for generating text, to programming based on natural language descriptions. This is the first commercial application of GPT-3 undertaken since Microsoft invested $1 billion in OpenAI last year and gained exclusive licensing rights to GPT-3.
“If you can describe what you want to do in natural language, GPT-3 will generate a list of the most relevant formulas for you to choose from,” said Microsoft CEO Satya Nadella in a keynote address at the company’s Build developer conference. “The code writes itself.”
Courtesy of Microsoft
Microsoft VP Charles Lamanna told WIRED the sophistication offered by GPT-3 can help people tackle complex challenges and empower people with little coding experience. GPT-3 will translate natural language into PowerFx, a fairly simple programming language similar to Excel commands that Microsoft introduced in March.
This is the latest demonstration of applying AI to coding. Last year at Microsoft’s Build, OpenAI CEO Sam Altman demoed a language model fine-tuned with code from GitHub that automatically generates lines of Python code. As WIRED detailed last month, startups like SourceAI are also using GPT-3 to generate code. IBM last month showed how its Project CodeNet, with 14 million code samples from more than 50 programming languages, could reduce the time needed to update a program with millions of lines of Java code for an automotive company from one year to one month.
Microsoft’s new feature is based on a neural network architecture known as Transformer, used by big tech companies including Baidu, Google, Microsoft, Nvidia, and Salesforce to create large language models using text training data scraped from the web. These language models continually grow larger. The largest version of Google’s BERT, a language model released in 2018, had 340 million parameters, a building block of neural networks. GPT-3, which was released one year ago, has 175 billion parameters.
Such efforts have a long way to go, however. In one recent test, the best model succeeded only 14 percent of the time on introductory programming challenges compiled by a group of AI researchers.
.jpg)
