Closed of her three accounts with Silicon Valley bank failurecookbook author Anna Fosino spent the weekend in a state of high anxiety, uncertain about the future of her sauces and seasonings company.

“I spent most of my Friday afternoon writing all our creditors and saying, ‘Hey, I know we owe you money now, but I hope it all works out over the weekend,’” she said. “If it doesn’t, please have mercy on us.”

First thing on Monday morning, Vocino was able to successfully log into the Silicon Valley Bank’s website and begin the process of closing her accounts. She is moving her money into the National City Bank.

“I would feel more comfortable somewhere else,” said one Solvang resident.

Many other small business owners felt the same after Federal Deposit Insurance Corp. He seized the Bank of Santa Clara, California on Friday, It was followed by state regulators’ takeover of New York’s Signature Bank on Sunday.

Monday has become a day of massive money moves and account closures after what one winemaker called a “crisis cleanse” over the weekend, with account holders panicking that they won’t be able to access their cash easily or quickly. Customers logged on to the Silicon Valley bank’s website en masse while others rushed to the branch sites of other weak financial institutions.

The financial stampede came though President Biden’s reassurances, Who told Americans that the moves of the US Treasury, the Federal Reserve and the FDIC would ensure “that the banking system is secure. Your deposits will be there when you need them.”

“I thought my business was done, and I was pissed,” said Anthony Combs, CEO of Santa Monica lingerie company Splendies. He described the past 48 hours as “an absolute mess” and said he had sent 80% of his company’s money from Silicon Valley Bank.

“This wasn’t a stupid investment. This wasn’t bad planning – this was the company’s money in a bank where it’s supposed to be safe.

Before Combs knew if the transfer had been made on Monday, he pooled his savings, prepared to use them to meet the payroll of his 13 employees, and reached out to the vendors, who told him payments due within the next two weeks might have been spilled over into the next two months.

Many startup founders have spent the weekend racing to figure out ways to make ends meet for their business.

Lauren Wang, who runs sustainable period products company Flex, was denied her company’s funds at a Silicon Valley bank on Friday. The next day, she drove to Chase Bank in Calabasas to open a business account and tied half of her family’s liquid savings to it to make payrolls for Flex’s 30 employees by Monday.

Wang said the order was to “take action first to protect our employees and find out later”. “We had no idea what would happen to the bank.”

For people who used Silicon Valley Bank as their main source of banking services, the crash was a lesson in diversification. King Alandy Dy, founder of San Francisco-based AI logistics company Expedock, spent Friday waiting in line at Chase and Wells Fargo locations in Piedmont to create new accounts — along with several other startup owners doing the same.

On Monday, he logged into a Silicon Valley bank and sent his money. “I’m just trying to get a good spread,” he said of his new banking strategy.

Tegan Passalacqua of Sandlands Vineyards in Napa found out about the bank failure last week from his boss, who “called me and said, ‘I wish you didn’t have any money in a Silicon Valley bank,’” and I was like, “I got all my money in a Silicon Valley bank.”

Passalacqua has been in banking with the financial institution for 11 years and has more than half a million dollars across two accounts that he uses to pay for business expenses such as farming contractors, glass and cork makers, and shipping services.

“I didn’t have much wiggle room on my balance sheet,” he said. “A lot of people were like, ‘It’s going to be okay at the end of the day,’ but you don’t know until you get to it.”

Things were generally smooth on Monday after that mad bank last week, But there were still hiccups.

Shortly before noon, Issa Watson, founder of social media startup Squad, said the company still could not access its Silicon Valley Bank account and kept getting error messages.

“It’s definitely another day of scrambling,” she said.

Her company is moving over to Chase and she hopes to have those new accounts in place by the end of the day. But until the Silicon Valley bank’s money is reached, Watson is on the hook for the Squad’s expenses. She began receiving payment failure notices on the company’s Silicon Valley Bank credit cards on Saturday and was paying the bills with her personal credit cards.

“I run a software company, and we’re in the consumer social space, and we have a tech app and an audio app,” she said. “I can’t take out my back-end database because it didn’t pay.”

Watson said the sudden collapse of Silicon Valley Bank, which served more than half of all venture-backed tech startups in the country, left the founders “rethinking how we do banking.”

Going forward, startups will have to take “more of a front row seat and strategy for how to bank,” she said, “and that’s not something we’ve been thinking about with the same intent before.”

The Silicon Valley bank’s fallout has spilled over into other financial institutions, with First Republic Bank Shares fell 62% on Monday despite assurances from the San Francisco-based bank that funding from the Federal Reserve and JPMorgan Chase has boosted its finances.

The Studio City branch of First Republic was filled with customers on Monday. Someone said he arrived at 9:30 am to withdraw $340,000 and send it to Bank of America.

“They told me it would take half an hour,” he said. “It’s one o’clock now, and we still don’t have the money. They tell me it’s three o’clock now. I’m a little worried.”

A First Republic employee tried to reassure him, saying, “It’s a busy day so it’s just taking a little longer.”

Another client said he decided to withdraw a $200,000 certificate of deposit to reach the FDIC insurance limit of $250,000. He said he was worried about the bank failing and decided to pay a $4,000 fine for early withdrawal of the CD.

As customers scrambled Monday to move their cash, vendors watching from the sidelines said they hoped the turmoil did not seep into their businesses.

Besides being a major bank for tech startups, Silicon Valley Bank has also been deep in the wine industry. For days, said Jennifer Thompson, owner of Thomson Vineyards, a contract grower in Napa, grape growers were trying to figure out which of their clients used the Silicon Valley bank, fearing they wouldn’t get paid on time.

“The first thing the tech guys who own the winery don’t pay is the grower,” she said.

Times staff writers Terry Castleman, Daniel Miller, Ross Mitchell and Melody Petersen contributed to this report.

Imagine a time when a virus-blocking face covering served as an umbrella. Most days, it stays in your locker or stowed somewhere in your car. But when the COVID-19 outbreak is in the forecast, you can use it.

Moreover, the intense viral forecast may prompt you to choose an outdoor table when meeting a friend for coffee. If contracting the coronavirus has the potential to make you seriously ill, you can choose to work from home or attend church services online until the threat has passed.

Such a future assumes that Americans will heed public health warnings about a pandemic virus — and that matters. It also assumes a system that can reliably predict impending outbreaks with few false alarms, and with enough timing and geographic accuracy that the public can trust its predictions.

A group of would-be forecasters says it has the makings of such a system. that it an offer To build a viral weather report published this week in the journal Science Advances.

Like the meteorological models driving weather forecasts, the COVID-19 outbreak prediction system emerges from a river of data fed by hundreds of local and global information streams. They include time-stamped online searches for symptoms such as chest tightness, loss of smell, or fatigue; geotagged tweets that include terms like “corona,” “pandemic,” or “panic buying”; location data aggregated from smartphones that reveal how many people are traveling; and a drop in online requests for directions, indicating fewer people are getting out.

The resulting volume of information is far greater than humans can manage, let alone interpret. But with the help of powerful computers and software trained to winnow, interpret and learn from the data, the map is beginning to emerge.

If you check this map against historical data—in this case, two years of epidemiological experience in 93 counties—and update accordingly, you might have the makings of an outbreak forecasting system.

This is exactly what the team from Northeastern University is leading Computer scientist it’s over. In their attempt to create an early warning system for the COVID-19 outbreak, the study authors built a “machine learning” system capable of chewing through millions of digital traces, integrating new local developments, improving its focus on subtle signs of disease, and issuing timely notifications of impending local surges of COVID. -19.

Of his many Internet searches, one proved to be an especially good warning sign of an impending outbreak: “How long will COVID last?”

Tested against real data, the researchers’ machine-learning method predicted an increase in local virus prevalence up to six weeks early. Alarm bells were going off almost at the point where every infected person was likely to spread the virus to at least one other person.

After testing the prediction of 367 actual outbreaks at the county level, the program provided accurate early warnings for 337 of them, or 92%. Of the 30 remaining outbreaks, 23 have been identified just as they would have become apparent to human health officials.

Once the Omicron variant began to spread widely in the United States, the early warning system was able to detect early evidence of 87% of the outbreaks at the county level.

A predictive system with these capabilities could be useful to local, state, and national public health officials who need to plan for COVID-19 outbreaks and warn vulnerable citizens that the coronavirus threatens an imminent local resurgence.

But “we’re looking beyond” COVID, he said Mauricio Santayanawho runs Northeastern’s Machine intelligence group to improve health and the environment.

“Our work aims to document technologies and approaches that may be useful not only for this, but for the next pandemic,” he said. “We’re gaining the trust of public health officials, so they won’t need any more convincing” when yet another disease begins to spread across the country.

This may not be an easy sell for state public health agencies and the US Centers for Disease Control and Prevention, which have all struggled to keep up with pandemic data and incorporate new ways to track the spread of the virus. The CDC’s inability to adapt and communicate effectively during the pandemic has led to some “dramatic and very public missteps,” said Dr. Rochelle Walensky, the agency’s administrator, I confess. And she warned that only a “changing culture” would prepare the agency for the next pandemic.

CDC’s lackluster efforts to develop prediction tools haven’t paved the way for easy acceptance either. 2022 appreciation Of the forecasting efforts used by the CDC it concluded that most “failed to reliably predict rapid changes” in COVID-19 cases and hospitalizations. The authors of this assessment cautioned that the systems developed to date “should not be relied upon to make decisions about the possibility or timing of rapid changes in trends.”

Anas Barryan expert in machine learning at New York University, called the new early warning system “very promising.”

“The machine learning methods presented in the paper are good, mature and well-researched,” said Barry, who was not involved in the research. But he warned that in a once-in-a-lifetime emergency such as a pandemic, it would be dangerous to rely too heavily on a new model to predict events.

For starters, Barry noted, the coronavirus’ first encounter with humanity didn’t yield the long historical record needed to fully test the model’s accuracy. And the three-year period of the pandemic gave researchers little time to recognize the “noise” that comes when too much data is thrown into a jar.

The Centers for Disease Control and state health departments have begun to use epidemiological techniques such as phylodynamic gene sequencing And Wastewater monitoring To monitor the spread of the Corona virus. Using machine learning to predict the location of upcoming viral spikes, Santillana said, could take another leap of imagination by these agencies.

In fact, accepting early warning tools like the one developed by Santillana’s group may require some leaps of faith, too. As computer programs digest large amounts of data and begin to discern patterns they can reveal, they often generate surprising “features” — variables or search terms that help predict an important event, such as a viral mutation.

Even if these visible signs prove to accurately predict such an event, their relevance to a public health emergency may not be immediately apparent. A sudden signal may be the first sign of a new trend – for example, a previously unseen symptom caused by a new variant. But they may also seem so random to public health officials that they cast doubt on the software’s ability to predict an imminent outbreak.

My review, said Santayana, who also teaches at the Harvard School of Public Health early work of his group She responded with some skepticism to some of the signals that appeared as warning signs of an upcoming outbreak. He said one of them — the tweets implying “panic buying” — seemed like a false signal from machines that latched on to a random event and made sense of it.

He defended the inclusion of the “panic buying” signal as a signal of an imminent outbreak domestically. (After all, the early days of the pandemic were marked by Shortage of basic materials Including rice and toilet paper.) But he acknowledged that the “black box” early warning system could face resistance from public health officials who need to trust its forecasts.

“I think the concerns of decision-makers are a legitimate concern,” Santayana said. “When we find a signal, it has to be reliable.”

Rodney Brooks knows the difference between true technological advances and unfounded hype.

One of the world’s most accomplished experts in robotics and artificial intelligence, Brooks is one of the founders of IRobot, maker of the Roomba robotic vacuum cleaner. the co-founder and chief technology officer of RobustAI, which makes robots for factories and warehouses; He is the former director of the Computer and Artificial Intelligence Laboratories at the Massachusetts Institute of Technology.

So when, in 2018, Australian-born Brooks encountered a wave of unwarranted optimism about self-driving cars — “People were saying outrageous things, like, Oh, my teenage son will never have to learn to drive” — he took it as a personal challenge. In response, he compiled List of predictions On self-driving vehicles, artificial intelligence, robotics and space travel, he promised to review them every year until January 1, 2050, when he would have turned 95 if he was still alive.

His goal was to “inject some reality into what I saw as an irrational exuberance.”

Each prediction carries a time frame – maybe something happened on a certain date, not before a certain date, or “not in my life”.

Brooks published his book Fifth annual scorecard On New Year’s Day. The majority of his predictions were spot on, though this time he admitted he thought he, too, had let the hype make him overly optimistic about some developments.

“My current belief,” he wrote this year, “is that things will go, on the whole, more slowly than I thought five years ago.”

As a veteran technologist, Brooks has insights into what makes ordinary people, or even experts, overly optimistic about new technologies.

People have been “trained by Moore’s Law,” Brooks told me, to expect that technologies will continue to improve at ever faster rates.

His reference is to an observation made in 1965 by semiconductor engineer Gordon Moore that the number of transistors that could be fitted on a microchip doubled approximately every two years. Moore’s observation became a proxy for the idea that computing power will improve exponentially over time.

This tempts people, even experts, to underestimate the difficulty of reaching a chosen target, whether they be self-aware robots or people living on Mars.

He told me, “They don’t understand how hard it is to get there, so they assume it’s just going to keep getting better.”

One such example is self-driving cars, a technology with limitations that ordinary people rarely recognize.

Books about brooks experience with Cruza service that uses self-driving taxis (with no one ever in the front seat) in parts of San Francisco, Phoenix, and Austin, Texas.

In San Francisco, Cruise only operates between 10pm and 5:30am—that is, when traffic is lighter—and only in limited parts of the city and in good weather.

On his three cruises, Brooks found that vehicles avoided left turns, preferring to make three right turns around a block instead, driving very slowly and once trying to carry him in front of a construction site that would have exposed him to oncoming traffic.

“The result is that it was two times slower than any human-operated transportation service,” Brooks wrote. “It may work in specific geographic areas, but it won’t compete with human-run systems for a long time.” He also said that it is “decades away from profitability”. In his annual scorecard this year, he predicted that “there will be human drivers on our roads for decades to come.”

The annual scorecard is one of several outlets Brooks relies on to mitigate the “irrational exuberance” around technology in general and artificial intelligence in particular. He has been a frequent contributor to IEEE Spectrum, the home member of the leading professional society for electronics engineers.

In an article entitled An inconvenient truth about artificial intelligence In September 2021, for example, he noted how each wave of new developments in AI was accompanied by “breathless predictions about the end of human dominance in intelligence” amid “a tsunami of promise, hype, and lucrative applications.”

In fact, Brooks writes, nearly every successful deployment of AI in the real world has either had a human “somewhere in the loop” or a very low cost of failure. The Roomba works autonomously, he wrote, but its more serious failure could involve “missing a plot and failing to catch a dust ball.”

When IRobots were deployed to Afghanistan and Iraq to disable improvised explosive devices, “a failure there could kill someone, so there was always a human in the loop giving supervisory orders.”

Robots are common today in industry and even around the home, but their capabilities are very limited. Robotic hands with human-like dexterity haven’t advanced much in 40 years, Brooks says. This also applies to independent movement around any home with clutter, furniture and moving objects. “What is easy for humans is still very, very difficult for robots,” he writes.

For ChatGPT, the creator of AI prose that has garnered a lot of attention from high-tech enthusiasts, along with warnings that it could usher in a new era of machine-driven plagiarism and academic forgery, Brooks argues for caution.

“People are making the same mistake they’ve been making over and over,” he wrote on his scorecard, completely mistaking some new AI demo as a sign that everything in the world has changed. did not happen “.

He writes that ChatGPT repeats patterns in a human prompt, rather than showing any new level of intelligence.

None of this means that Brooks doubts the eventual creation of “truly artificial intelligence, with cognition and consciousness distinctly similar to our own.” Written in 2008.

He predicts that “the robots that will roam our homes and workplaces…will emerge gradually and symmetrically with our society” even as “a wide range of advanced sensory devices and prosthetics” emerge to enhance and strengthen our bodies: “As our devices become more like us, we will become more like them.” And I am optimistic. I think we’ll all get along.”

This brings us back to Brooks’ scorecard for 2023. This year, 14 of his original predictions were deemed accurate, whether because they occurred in the time frame he predicted or failed to happen before his deadline.

Among them are driverless package delivery services in a major US city, which he predicted won’t happen before 2023; It hasn’t happened yet. In terms of space travel and space tourism, expect a suborbital launch for humans by a private company to happen by 2018; Virgin Atlantic beat the deadline with such a flight on December 13, 2018.

He predicted that spaceflight with a handful of paying customers wouldn’t happen before 2020; regular flights no more than once a week no earlier than 2022 (possibly by 2026); and fly two paying customers around the moon no later than 2020.

All those deadlines have passed, which makes predictions accurate. Only three flights took place with paying customers in 2022, which indicates that there is “a long way to go to get to the sub-weekly flights,” notes Brooks.

Brooks constantly questions the predictions of the most-cited tech entrepreneur, Elon Musk, who Brooks notes “has a pattern of overly optimistic time-frame projections”.

Lunar orbit for customers pushing in Musk’s SpaceX Falcon Heavy capsule doesn’t seem possible before 2024, Brooks notes. Landing the payload on Mars for use by humans at a later date, which Musk predicted would happen by 2022, seems as though it won’t happen. Before 2026, and even this date is “overly optimistic.”

Musk has yet to deliver on his promise for 2019 That Tesla will put a million robotaxis on the road by 2020 — that is, a fleet of self-driving cars called through a Tesla Uber-like app. “I think the actual number is still firmly zero,” Brooks wrote.

As for Musk’s dream of regular service between two cities on his Hyperloop underground transit system, Brooks puts that in the “not in my life” hole.

Many of Brooks’ predictions remain open, including some relating to the electric vehicle market. In his original forecast, he predicted that electric vehicles would not reach 30% of US auto sales before 2027 or 100% before 2038.

Growth in electric vehicle sales becomes turbocharged in 2022 – increasing 68% in the third quarter over the same quarter a year earlier. If this growth rate continues, electric vehicles will account for 28% of new car sales in 2025.

This assumes that the driving forces for EV adoption continue. Head wind, however, should not be underestimated. Electric vehicle sales may have spiked due to the massive hike in gasoline prices in 2021 and last year, but that inflationary trend has now disappeared. Battery plants may take longer to come online than expected, which could lead to shortages of these critical components and drive up electric vehicle prices.

“There is clearly something going on,” Brooks wrote, though “the jury is still out” on whether the US will see 30% market share for electric vehicles by 2027.

Brooks does not wish to stifle human aspirations to build robots, artificial intelligence systems, or space exploration.

He told me “I’m a technician”. “I build robots — that’s what I’ve done with my life — and I’ve been a space fan forever. But I don’t think it helps people to be so overly optimistic off the charts” that they ignore difficult problems that stand in the way of progress.

“I don’t think we’re limited in our ability to build humanoid robots, eventually,” he says. “But whether we have any idea how to do it at the moment or whether all of the methods we think will work are just right is entirely up to you.”

The dream is compared to the dream of medieval alchemists researching how to turn lead into gold. “You can do that now with a particle accelerator to change atomic structures, but at the time they didn’t even know atomic structure existed. We might as well be at the level of human intelligence, but we have no idea how it works at all.”