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Optimizing Machines Is Perilous. Consider ‘Creatively Adequate’ AI.

Optimizing Machines Is Perilous. Consider ‘Creatively Adequate’ AI.

This incessant surveillance is antidemocratic, and it’s also a loser’s game. The price of accurate intel increases asymptotically; there’s no way to know everything about natural systems, forcing guesses and assumptions; and just when a complete picture is starting to coalesce, some new player intrudes and changes the situational dynamic. Then the AI breaks. The near-perfect intelligence veers into psychosis, labeling dogs as pineapples, treating innocents as wanted fugitives, and barreling eighteen-wheelers into kindergarten busses that it sees as highway overpasses.

The dangerous fragility inherent to optimization is why the human brain did not, itself, evolve to be an optimizer. The human brain is data-light: It draws hypotheses from a few data points. And it never strives for 100 percent accuracy. It’s content to muck along at the threshold of functionality. If it can survive by being right 1 percent of the time, that’s all the accuracy it needs.

The brain’s strategy of minimal viability is a notorious source of cognitive biases that can have damaging consequences: close-mindedness, conclusion jumping, recklessness, fatalism, panic. Which is why AI’s rigorously data-driven method can help illuminate our blindspots and debunk our prejudices. But in counterbalancing our brain’s computational shortcomings, we don’t want to stray into the greater problem of overcorrection. There can be enormous practical upside to a good enough mentality: It wards off perfectionism’s destructive mental effects, including stress, worry, intolerance, envy, dissatisfaction, exhaustion, and self-judgment. A less-neurotic brain has helped our species thrive in life’s punch and wobble, which demands workable plans that can be flexed, via feedback, on the fly.

These antifragile neural benefits can all be translated into AI. Instead of pursuing faster machine-learners that crunch ever-vaster piles of data, we can focus on making AI more tolerant of bad information, user variance, and environmental turmoil. That AI would exchange near-perfection for consistent adequacy, upping reliability and operational range while sacrificing nothing essential. It would suck less energy, haywire less randomly, and place less psychological burdens on its mortal users. It would, in short, possess more of the earthly virtue known as common sense.

Here’s three specs for how.

Building AI to Brave Ambiguity

Five hundred years ago, Niccolò Machiavelli, the guru of practicality, pointed out that worldly success requires a counterintuitive kind of courage: the heart to venture beyond what we know with certainty. Life, after all, is too fickle to permit total knowledge, and the more that we obsess over ideal answers, the more that we hamper ourselves with lost initiative. So, the smarter strategy is to concentrate on intel that can be rapidly acquired—and to advance boldly in the absence of the rest. Much of that absent knowledge will prove unnecessary, anyway; life will bend in a different direction than we anticipate, resolving our ignorance by rendering it irrelevant.

We can teach AI to operate this same way by flipping our current approach to ambiguity. Right now, when a Natural Language Processor encounters a word—suit—that could denote multiple things—an article of clothing or a legal action—it devotes itself to analyzing ever greater chunks of correlated information in an effort to pinpoint the word’s exact meaning.

Biden’s ‘Antitrust Revolution’ Overlooks AI—at Americans’ Peril

Biden’s ‘Antitrust Revolution’ Overlooks AI—at Americans’ Peril

Despite the executive orders and congressional hearings of the “Biden antitrust revolution,” the most profound anti-competitive shift is happening under policymakers’ noses: the cornering of artificial intelligence and automation by a handful of tech companies. This needs to change.

There is little doubt that the impact of AI will be widely felt. It is shaping product innovations, creating new research, discovery, and development pathways, and reinventing business models. AI is making inroads in the development of autonomous vehicles, which may eventually improve road safety, reduce urban congestion, and help drivers make better use of their time. AI recently predicted the molecular structure of almost every protein in the human body, and it helped develop and roll out a Covid vaccine in record time. The pandemic itself may have accelerated AI’s incursion—in emergency rooms for triage; in airports, where robots spray disinfecting chemicals; in increasingly automated warehouses and meatpacking plants; and in our remote workdays, with the growing presence of chatbots, speech recognition, and email systems that get better at completing our sentences.

Exactly how AI will affect the future of human work, wages, or productivity overall remains unclear. Though service and blue-collar wages have lately been on the rise, they’ve stagnated for three decades. According to MIT’s Daron Acemoglu and Boston University’s Pascual Restrepo, 50 to 70 percent of this languishing can be attributed to the loss of mostly routine jobs to automation. White-collar occupations are also at risk as machine learning and smart technologies take on complex functions. According to McKinsey, while only about 10 percent of these jobs could disappear altogether, 60 percent of them may see at least a third of their tasks subsumed by machines and algorithms. Some researchers argue that while AI’s overall productivity impact has been so far disappointing, it will improve; others are less sanguine. Despite these uncertainties, most experts agree that on net, AI will “become more of a challenge to the workforce,” and we should anticipate a flat to slightly negative impact on jobs by 2030.

Without intervention, AI could also help undermine democracy–through amplifying misinformation or enabling mass surveillance. The past year and a half has also underscored the impact of algorithmically powered social media, not just on the health of democracy, but on health care itself.

The overall direction and net impact of AI sits on a knife’s edge, unless AI R&D and applications are appropriately channeled with wider societal and economic benefits in mind. How can we ensure that?

A handful of US tech companies, including Amazon, Alibaba, Alphabet, Facebook, and Netflix, along with Chinese mega-players such as Baidu, are responsible for $2 of every $3 spent globally on AI. They’re also among the top AI patent holders. Not only do their outsize budgets for AI dwarf others’, including the federal government’s, they also emphasize building internally rather than buying AI. Even though they buy comparatively little, they’ve still cornered the AI startup acquisition market. Many of these are early-stage acquisitions, meaning the tech giants integrate the products from these companies into their own portfolios or take IP off the market if it doesn’t suit their strategic purposes and redeploy the talent. According to research from my Digital Planet team, US AI talent is intensely concentrated. The median number of AI employees in the field’s top five employers—Amazon, Google, Microsoft, Facebook, and Apple—is some 18,000, while the median for companies six to 24 is about 2,500—and it drops significantly from there. Moreover, these companies have near-monopolies of data on key behavioral areas. And they are setting the stage to become the primary suppliers of AI-based products and services to the rest of the world.

Each key player has areas of focus consistent with its business interests: Google/Alphabet spends disproportionately on natural language and image processing and on optical character, speech, and facial recognition. Amazon does the same on supply chain management and logistics, robotics, and speech recognition. Many of these investments will yield socially beneficial applications, while others, such as IBM’s Watson—which aspired to become the go-to digital decision tool in fields as diverse as health care, law, and climate action—may not deliver on initial promises, or may fail altogether. Moonshot projects, such as level 4 driverless cars, may have an excessive amount of investment put against them simply because the Big Tech players choose to champion them. Failures, disappointments, and pivots are natural to developing any new technology. We should, however, worry about the concentration of investments in a technology so fundamental and ask how investments are being allocated overall. AI, arguably, could have more profound impact than social media, online retail, or app stores—the current targets of antitrust. Google CEO Sundar Pichai may have been a tad overdramatic when he declared that AI will have more impact on humanity than fire, but that alone ought to light a fire under the policy establishment to pay closer attention.

AI Could Soon Write Code Based on Ordinary Language

AI Could Soon Write Code Based on Ordinary Language

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.