The wild uncertainty that is 2020 can be psychologically paralyzing, but there are ways to better navigate a future that has never seemed less clear.

Why it matters: If this year has taught us anything, it's that anything can happen. With an uncertain pandemic, election and who knows what else coming, now is the time to prepare yourself to live through an age of anxiety.

What's happening: I mean, what isn't happening?

• The president and first lady tested positive for COVID-19, part of what appears to be a White House cluster of unknown size.
• The U.S. is staring down an election whose outcome may remain unclear for weeks or even months.
• The wobbly economic recovery appears to be losing steam, which ups uncertainty for everyone from business owners to employees.

At this point, I would not be surprised if we discovered aliens tomorrow.

• Reality check: Actually, that might have already happened.

Context: The human brain does not do well with this kind of extreme uncertainty, as my Axios colleague Alison Snyder wrote this week.

• But while we have limited control over the uncertainty we're all facing, there are psychological strategies that can be employed to toughen our resilience.

Details: One technique employed by businesses to plan for the future is strategic foresight.

• The key here is that, especially in times of significant uncertainty, we need to plan not for one possible future, but for many potential alternatives.
• Doing so "can help us better anticipate possible circumstances and — importantly — adapt when those circumstances threaten our ability to achieve our goals," writes Kristel Van der Elst, CEO of The Global Foresight Group, in MIT Tech Review.
• In the case of COVID-19, that means envisioning futures where the virus comes under control and ones where the pandemic continues affecting our lives for months or even years.

As we plan for a growing number of potential alternative futures, we can assert some control by identifying and clarifying the goals we want to achieve in the time ahead.

• Just as the captain of an airliner wouldn't put his craft on autopilot in the middle of a storm, this is not the moment to continue your old life in the blind hope that you'll simply reach your destination.
• Living through extreme uncertainty means accepting that there are very few certain answers to any question.
• For example, there are health risks to sending children to school in the middle of the pandemic, but there are also educational risks to keeping them at home. While it's vital to be as informed as possible about the potential consequences of either choice, ultimately it's up to the individual to identify what's more important and make choices accordingly.

Yes, but: In a fluid situation like a pandemic, you also need a backup plan.

• The biggest mistake you can make in a time of uncertainty is to be locked into a course of action with no willingness to alter that course when circumstances change.

Lastly, we all need to accept the fact that the months to come will be really, really hard.

• Anxiety in the face of the current reality isn't pathological. It's all too human.

The bottom line: Hope for the best, plan for the worst — all of the worsts — and as much as possible, stay focused on what's most important to you.

• And please wear a mask.

A new report shows that cripplingly high computational costs mean just a handful of big companies are able to do top-flight AI research.

Why it matters: AI will do more than any other technology to shape our future. If only the Googles and the Microsofts of the world have the resources needed to move the field forward, it will solidify their power — and possibly strangle innovation.

By the numbers: It likely cost the Microsoft-funded research group OpenAI more than $10 million to train GPT-3, its cutting-edge, new natural language processing algorithm, according to the annual State of AI Report published Thursday.

• That's because these models are created by essentially throwing ever-increasing amounts of data at the thorny problems of AI. Processing all of that data takes lots of computational power — and compute costs money.
• What this means "is that a handful of well-capitalized entities are now in control of artificial general intelligence research," says Ian Hogarth, a visiting professor at University College of London and one of the co-authors of the report.

Of note: OpenAI was originally founded as a nonprofit with the purpose of pursuing AI research for the benefit of all humanity.

• But last year it set up a for-profit arm and accepted a billion-dollar investment from Microsoft.
• Last month Microsoft announced it would be exclusively licensing GPT-3.

What they're saying: "This is a direct reflection of the cost of doing frontier research in compute and talent," says Hogarth.

Context: In the future the costs of developing these massive models may become prohibitive even for the richest tech companies.

• The report found that without major research breakthroughs, reducing the error rate for ImageNet — a massive database used for visual recognition research — from 11.5% to 1% could cost 100 billion billion dollars. (Yes that's two "billions.")
• All that compute requires lots of energy, which in turn means that AI research has a growing environmental footprint.

The bottom line: It doesn't make sense scientifically or ethically for high-level AI research to be done only by those companies that can afford it, but changing the paradigm won't be easy.

The new report also shows that biology is experiencing its "AI moment."

Why it matters: Thanks in part to the needs of the COVID-19 pandemic, researchers are finding success using the tools of AI on the challenges of human biology — and the startups behind them are cleaning up.

By the numbers: Researchers have published nearly 14,000 papers involving both AI and biology — compared to fewer than 1,000 papers in 2010 — and there are still months to go.

• The first AI-created drug entered clinical trials this year, while deep learning models are being employed on everything from COVID-19 infections to macular degeneration.
• The most promising area is likely in drug discovery, as AI allows researchers to quickly sift through potential molecules far faster than they could do alone.
• These developments also show that while the highest-level AI research may only be affordable to deep-pocketed tech giants, there's still room for innovation in more targeted areas.

The bottom line: Drug discovery has gotten more and more expensive, but there's legitimate hope that AI could finally bend that cost curve.

The startup IonQ this week announced what it's calling "the world's most powerful quantum computer."

Why it matters: Quantum is the next frontier in computing, theoretically capable of solving problems beyond the ability of classical computers. IonQ's next-generation computer looks set to push the boundaries of quantum, but it will still take years before the technology becomes truly reliable.

How it works: IonQ reports its new quantum computer system has 32 "perfect" qubits — the basic unit of information in a quantum computer — that the company says gives it an expected quantum volume of more than 4,000,000.

• Quantum volume is a metric that attempts to calculate the computing effectiveness of a quantum computer. These types of metrics are necessary because quantum computers are built in different ways and to different specifications.
• "The way we achieved it is by having good fidelity in our qubits," says Peter Chapman, IonQ's president and CEO. "You can have a million qubits, but if your fidelity isn't good enough, it doesn't really matter."

Background: In the mid-1990s, IonQ co-founder Chris Monroe began working on entangling atoms to make more precise atomic clocks, the most accurate timekeeping devices known.

• IonQ's approach to quantum computing builds out of that research, using trapped ions in a way that Chapman says reduces the errors that qubits are prone to.

The catch: IonQ hasn't yet released detailed specifications of its new system, and its research needs to be verified.

Context: IonQ's announcement comes in the same week that its competitor Honeywell, which also use a version of trapped ions, reported achieving a quantum volume of 128, and the Canadian startup D-Wave announced a 5,000-qubit system built yet another way that would be available for customers, including via the cloud.

Be smart: Comparing different kinds of quantum computing systems is difficult because they function in fundamentally different ways.

• But given that quantum computers tap the confounding principles of quantum physics, where qubits can be superposed in two different states at the same time, perhaps that makes a kind of sense.

Electric cars get lots of attention, but new analysis provides sobering numbers that show why EVs are not, as the authors say, a "silver bullet" for wringing emissions out of passenger travel, my Axios colleague Ben Geman writes.

Why it matters: The paper in Nature Climate Change arrives as officials in California, the country's largest auto market, are pledging aggressive regulations to ramp up EV sales.

• Joe Biden's energy plan has EV provisions too, including expanded consumer tax credits and a goal of 500,000 public charging stations deployed in a decade.

How it works: It models a long-term CO2 "budget" for U.S. light-duty vehicles that represents that sector's contribution to an emissions pathway consistent with holding temperature rise to 2°C above preindustrial levels.

Threat level: Current policies are nowhere close to putting passenger vehicle emissions on track to meet that budget, it concludes.

• Using EVs alone to right the ship would be daunting, to say the least. Sales are growing, but right now they're about a percent of the total vehicles on U.S. roads.
• Closing the sector's CO2 "mitigation gap" with EVs alone would require 90% of the U.S. fleet to be electric by 2050, they find.
• The authors note that this exceeds even optimistic projections and would mean more than 350 million EVs on the road in 2050.

The bottom line: The paper concludes that a "wide range" of policies are needed to slash vehicle emissions, including electrification but also very stringent fuel efficiency standards, better mass transit and more.

Audrey Tang on her “conservative-anarchist” vision for Taiwan’s future (E. Tammy Kim — Rest of World)

• A profile of the digital minister of Taiwan, which has emerged low-key as a model of "digital democracy."

This overlooked variable is the key to the pandemic (Zeynep Tufekci — The Atlantic)

• A long lesson in the importance of k, the measure of a virus' dispersion, and what it means for the pace of the pandemic.

Europe is building a "digital twin" of Earth to revolutionize climate forecasts (Paul Voosen — Science)

• European climate scientists are building a massive simulation of the Earth, to understand how our real planet will respond to climate change.

A Message From the Future II: The Years of Repair (The Leap and the Intercept)

• A short film spearheaded by the writer and environmentalist Naomi Klein imagines a future where the hard lessons of COVID-19 lead to a better world.

A new paper makes the case that the social returns of investing in innovation are enormous.

Why it matters: Things seem not great, to say the least, but humans have faced far worse material challenges for most of our existence. That changed largely thanks to the innovations brought about by science, which is why we need to ensure they keep coming.

By the numbers: In a working paper for the National Bureau of Economic Research, former Treasury Secretary Lawrence Summers and Northwestern economist Benjamin Jones attempt to calculate the social returns of innovation investment.

• They write that "even under very conservative assumptions, it is difficult to find an average return below $4 per $1 spent. Accounting for health benefits, inflation bias, or international spillovers can bring the social returns to over $20 per $1 spent."
• That's one reason why per-capita income in the U.S. has risen by 25-fold since 1820.

The catch: Government spending on R&D is currently at a 60-year low as a percentage of the federal budget.

• Corporate R&D spending is making up for some of that, but it is far less likely to be allocated to basic science — the foundational discoveries that must be made before we get the cool stuff.

The bottom line: We may obsess over politics, but scientific innovation is the engine that moves us forward. We fail to fuel it at our peril.