Relief From Decision Fatigue

Decisions I would normally agonize over, like travel logistics or whether to scuttle dinner plans because my mother-in-law wants to visit, A.I. took care of in seconds.

And it made good decisions, such as advising me to be nice to my mother-in-law and accept her offer to cook for us.

A.I. Takes the Wheel

I’d been wanting to repaint my home office for more than a year, but couldn’t choose a color, so I provided a photo of the room to the chatbots, as well as to an A.I. remodeling app. “Taupe” was their top suggestion, followed by sage and terra cotta.

In the Lowe’s paint section, confronted with every conceivable hue of sage, I took a photo, asked ChatGPT to pick for me and then bought five different samples.

I painted a stripe of each on my wall and took a selfie with them — this would be my Zoom background after all — for ChatGPT to analyze. It picked Secluded Woods, a charming name it had hallucinated for a paint that was actually called Brisk Olive.

The Consequences of Relieving Decision Fatigue

Just as we’ve outsourced our sense of direction to mapping apps, and our ability to recall facts to search engines, this explosion of A.I. assistants might tempt us to hand over more of our decisions to machines.

Judith Donath, a faculty fellow at Harvard’s Berkman Klein Center, who studies our relationship with technology, said constant decision making could be a “drag.” But she didn’t think that using A.I. was much better than flipping a coin or throwing dice, even if these chatbots do have the world’s wisdom baked inside.

“You have no idea what the source is,” she said. “At some point there was a human source for the ideas there. But it’s been turned into chum.”

The Dark Side of A.I.-Generated Decisions

The information in all the A.I. tools I used had human creators whose work had been harvested without their consent. (As a result, the makers of the tools are the subject of lawsuits, including one filed by The New York Times against OpenAI and Microsoft, for copyright infringement.)

There are also outsiders seeking to manipulate the systems’ answers; the search optimization specialists who developed sneaky techniques to appear at the top of Google’s rankings now want to influence what chatbots say. And research shows it’s possible.

The Risks of Over-Reliance on A.I.

Ms. Donath worries we could get too dependent on these systems, particularly if they interact with us like human beings, with voices, making it easy to forget there are profit-seeking entities behind them.

“It starts to replace the need to have friends,” she said. “If you have a little companion that’s always there, always answers, never says the wrong thing, is always on your side.”

Conclusion

While A.I. can provide relief from decision fatigue, it is essential to be aware of the potential consequences of relying too heavily on these systems. As A.I. continues to evolve, it is crucial that we consider the ethical implications of its use and strive to maintain a balance between technology and human judgment.

FAQs

Q: What are the potential risks of relying too heavily on A.I.-generated decisions?

A: Relying too heavily on A.I.-generated decisions can lead to a lack of understanding of the source of the information and the potential for manipulation by external entities.

Q: What are the ethical implications of using A.I. in decision-making?

A: The ethical implications of using A.I. in decision-making include the potential for bias, the lack of transparency in the decision-making process, and the potential for manipulation by external entities.

Q: How can we maintain a balance between technology and human judgment?

A: Maintaining a balance between technology and human judgment requires being aware of the potential risks and biases associated with A.I.-generated decisions and ensuring that human judgment is still involved in the decision-making process.

Trump’s Pledge to Crypto and Tech

The most raucous cheers of the night were prompted by Trump’s promise to fire Gary Gensler, chair of the Securities and Exchange Commission, a regulatory agency that has brought a volley of lawsuits against crypto businesses under the Biden administration.

Separately, Trump has promised to commute the sentence of Ross Ulbricht, the creator of darknet marketplace Silk Road, who is currently serving life in prison. Silk Road, through which people bought and sold drugs and other contraband, was among the first online services to accept bitcoin as payment. The severity of Ulbricht’s sentence is widely considered to be disproportionate by bitcoiners, who have long called for his release.

Antitrust

FTC Chair Lina Khan

An early indicator of the relationship the Trump administration intends to have with Big Tech will be the fate of the Federal Trade Commission chair Lina Khan.

Khan, the youngest ever FTC chair at 35, became a flashpoint in the election campaign. Among Democrat donors, her approach to antitrust enforcement and corporate power was deeply controversial. Google, Meta, Amazon, and Microsoft all faced legal challenges under her tenure, although some were more successful than others.

“Lina Khan is … a person who is not helping America,” LinkedIn cofounder and Democrat donor Hoffman told CNN in July. Trump donor Elon Musk also expressed his dislike. “She will be fired soon,” he said of Khan last week.

Dan Ives, an analyst at financial services firm Wedbush, described Khan as a “nightmare for the tech sector,” adding there was a belief among analysts that her departure would act as a catalyst for more Big Tech deals. “The Musk influence for Trump could also catalyze and accelerate a potential Khan exit,” he said.

Trump’s Vague Promises

Trump has suggested, vaguely, that “something” should be done about Google, to make the company “more fair.” Vance has been more explicit, praising Khan for “doing a pretty good job.”

Vance’s Antitrust Views

Vance appears to see break-ups as a solution for what he claims is Big Tech’s censorship of conservatives. “When you have companies like Facebook and Google censoring American citizens, making it harder for Americans to speak in their own political process, that is a major problem,” the vice president-elect said in September, giving Google’s acquisition of YouTube in 2006 as an example. “I do think that there should be an antitrust solution to it.”

Antitrust Cases

A new Trump administration is unlikely to abandon antitrust cases against Big Tech, said Adam Kovacevich, CEO of Chamber of Progress, a left-leaning technology trade group, in a memo on Wednesday, noting several of these began under his first term. “But he will likely try to use these suits as leverage over the companies to get favorable treatment on speech and content concerns.”

Khan’s Future

Whether Khan would serve under Trump is unclear. Her team declined to comment on Wednesday. Bill Kovacic, a former FTC chair, said the chances of that happening beyond a few weeks were “close to zero.”

Conclusion

The Trump administration’s stance on crypto and tech is likely to be shaped by his promises to fire Gary Gensler and commute Ross Ulbricht’s sentence. The fate of FTC chair Lina Khan remains uncertain, but it is clear that the Trump administration intends to take a tough stance on Big Tech.

FAQs

Q: What did Trump promise regarding Gary Gensler?

Trump promised to fire Gary Gensler, chair of the Securities and Exchange Commission, a regulatory agency that has brought a volley of lawsuits against crypto businesses under the Biden administration.

Q: What did Trump promise regarding Ross Ulbricht?

Trump promised to commute the sentence of Ross Ulbricht, the creator of darknet marketplace Silk Road, who is currently serving life in prison.

Q: What is the status of FTC chair Lina Khan?

The fate of FTC chair Lina Khan remains unclear, but it is uncertain whether she will serve under the Trump administration.

Q: What are the implications of Trump’s antitrust views?

Trump’s antitrust views suggest that he intends to take a tough stance on Big Tech, potentially using antitrust cases as leverage to address concerns over speech and content.

Q: What is the likelihood of antitrust cases being abandoned?

It is unlikely that the Trump administration will abandon antitrust cases against Big Tech, but they may use these suits as leverage to address concerns over speech and content.

Unraveling the Complexity of Gene Expression

Researchers at MIT have developed a new method to identify the best way to aggregate genes into related groups, allowing them to efficiently learn the underlying cause-and-effect relationships between many genes. This approach uses only observational data, eliminating the need for costly and sometimes infeasible interventional experiments.

The Challenge of Gene Expression

Studying changes in gene expression can provide valuable insights into how cells function at a molecular level, potentially leading to a better understanding of the development of certain diseases. However, the human genome contains approximately 20,000 genes that can interact with each other in complex ways, making it a daunting task to identify the most relevant genes to target.

The Problem of Causal Disentanglement

Previous research has focused on using causal disentanglement to learn how to combine related groups of genes into a representation that allows for efficient exploration of cause-and-effect relationships. However, this approach typically requires interventional data, which can be expensive and difficult to obtain.

A New Approach

The MIT researchers developed a machine-learning algorithm that uses observational data to identify and aggregate groups of genes. This approach allows for the reconstruction of an accurate underlying representation of the cause-and-effect mechanism, without the need for interventional experiments.

The Layerwise Representation

The algorithm uses statistical techniques to compute the variance for the Jacobian of each variable’s score. Causal variables that don’t affect any subsequent variables should have a variance of zero. The researchers then reconstruct the representation in a layer-by-layer structure, starting by removing the variables in the bottom layer that have a variance of zero.

Efficient Algorithm

Identifying the variances that are zero quickly becomes a combinatorial objective that is hard to solve. Deriving an efficient algorithm to solve this problem was a major challenge. The researchers developed an algorithm that can efficiently disentangle meaningful causal representations using only observational data.

Future Applications

The researchers plan to apply this technique in real-world genetics applications, such as identifying genes that function together in the same program. This could help identify drugs that target those genes to treat certain diseases. They also plan to explore how their method could provide additional insights in situations where some interventional data are available.

Conclusion

The MIT researchers have developed a new method to identify the best way to aggregate genes into related groups, allowing for efficient learning of the underlying cause-and-effect relationships between many genes. This approach uses only observational data, eliminating the need for costly and sometimes infeasible interventional experiments. The potential applications of this technique are vast, and it could lead to a better understanding of the development of certain diseases.

FAQs

Q: What is the goal of the new method?

A: The goal is to identify the best way to aggregate genes into related groups, allowing for efficient learning of the underlying cause-and-effect relationships between many genes.

Q: What is the advantage of using observational data?

A: Using observational data eliminates the need for costly and sometimes infeasible interventional experiments.

Q: How does the algorithm work?

A: The algorithm uses statistical techniques to compute the variance for the Jacobian of each variable’s score. Causal variables that don’t affect any subsequent variables should have a variance of zero. The researchers then reconstruct the representation in a layer-by-layer structure, starting by removing the variables in the bottom layer that have a variance of zero.

Q: What are the potential applications of this technique?

A: The potential applications include identifying genes that function together in the same program, which could help identify drugs that target those genes to treat certain diseases.