Hang-Ups in Emergency Land

How do you get help when you are in dire straits? Plant rocks in the ground spelling “help” and hope someone flying above sees them? Smoke signals and fire alerts? Not too good if the emergency is a fire or an earthquake… Flooding kind of negates most of that signaling too. What, you say, I am missing the obvious? That even mobile phones with unpaid bills have an emergency function? CALL someone? Duh!

But what if you can’t? Even assuming you have an adequately charged cell phone – noting that utilities are now shutting down power service during certain emergencies, from heavy storms knocking high voltage power lines to the ground or places where older powerlines could easily spark in heavy winds and ignite raging forest fires in inaccessible locales filled with decades of accumulating debris and tinder… like federal forest land controlled by cabinet-level heads appointed by the President. 

Landlines, slowly disappearing at least a residential level, are fixed. If the structure in which they are housed is destroyed or if the residents are forced to abandon the building, they are useless, especially if the hard lines that connect them are broken. Oddly enough, however, landlines in some cases still worked when cell access was down. Nevertheless, we are back to cell phones as the primary “go to” answer. But as recent experiences in the California wildfires, easily replicated in regions impacted by earthquakes, tsunamis, severe coastal surges and massive flooding, tell us that cell phones, even when fully charged, don’t work when the cell towers built to receive their signals are damaged or destroyed… or where the towers themselves loose the power that keeps them operating. 

The California lesson is a lesson for us all: “California saw significant interruptions of cellphone service due to the planned power shut-offs at precisely the time customers needed to be alerted about evacuation warnings — raising questions about how prepared California is for future electric shut-offs and other public safety emergencies, such as a major earthquake.

“At one point, Marin County saw 57% of its 280 cellphone tower sites out of service. Other counties also saw major disruptions: Sonoma, Lake, Santa Cruz, Humboldt and Calaveras all encountered days when more than 20% of cellphone towers were out; Napa County saw a day when 19% of cell towers were not working, according to data released by the Federal Communications Commission.

“In the central Bay Area, San Mateo and Contra Costa counties saw more than 11% of cell towers fail to work. The problems weren’t limited to cellphones. Some customers who get their landline phone service through their broadband internet service provider saw their phone lines go out, despite having their phones charged and equipped with battery backups.

“Local government officials and consumer safety advocates were incensed at the widespread phone service failures, which came despite days of warnings that the power would be shut off to help prevent ignition of wildfires by power lines and other electrical equipment damaged from severe Diablo winds… Sonoma County Supervisor Lynda Hopkins, whose district includes areas under evacuation during the Kincade fire, said that the cellphone network outages posed significant safety concerns.

“Fire stations were forced to communicate by radio, creating excess traffic at a time when officials had to rapidly deliver information about an active fire… And while evacuation warnings were delivered before the planned power shut-offs, Hopkins said residents in rural communities without cellphone or internet service would have had no way to receive additional warnings.

“‘Had there been a second fire, had the fire suddenly been reinvigorated and moved quickly toward some of these communities, we would not have been able to effectively communicate with the residents,’ she said… Federal regulators said they do not release data on how many cell signal sites went down by company… Phone carriers said they did the best they could.

“Heidi Flato, a spokeswoman for Verizon, said in an email that the company has generators and backup batteries at most of its cell towers and at all of its switch locations… ‘While we are doing everything we can to minimize the impact of the [power shut-off], there are discrete areas of our network that will experience service disruption or degradation, due to topographical and other technological constraints,’ she said.

“AT&T spokesman Ryan Oliver said that before the Kincade fire, the company deployed hundreds of additional generators and technicians from across the country, and had crews working 24 hours a day to refuel and deploy generators as needed.

“A statement provided by T-Mobile spokesman Joel Rushing said the company deployed and refueled generators to more than 260 sites. Permanent generators are in place at key cell sites, while others are prepared with a battery backup; the company also has a fleet of temporary generators that can be deployed as needed.

“Comcast’s Xfinity services require commercial power to operate, spokeswoman Joan Hammel said in a statement… ‘Generators may be deployed in a limited manner to address Comcast outages in vital public safety facilities,’ the statement said. There may be situations when a home’s power is on, but the part of the network that provides a connection to the home has no power, and as a result, communication services are unavailable.

“Consumer advocates, however, say the carriers failed to keep phone signals alive at precisely the moment they were needed most — when customers needed to communicate with loved ones and receive evacuation warnings… There are no federal or state regulations that mandate cell carriers have any backup power for cell service, said Ana Maria Johnson, program manager with the Public Advocates Office, an independent organization of the California Public Utilities Commission that advocates on behalf of consumers… ‘What this tells us is that the communications network is vulnerable. It’s not resilient. The companies were not prepared. And requirements must be put in place to require backup power,’ Johnson said.” Leila Miller and Rong-Gong Lin II  in the Los Angeles Times, November 5^th. What an understatement!

What are the answers? Fleets of aircraft at the ready with cell receptors on board? Expensive and not particularly useable in massive fires, where fire-fighters need open and unobstructed air space in which to operate. Increasing the ability of cell towers to operate on a standalone basis even if their network goes down? Long-term batteries, perhaps with solar chargers to keep them powered in emergencies?

“In Japan, it is required that cellphones have a 48-hour backup power supply, said seismologist Lucy Jones, author of ‘The Big Ones: How Natural Disasters Have Shaped Us.’

“There was some discussion as to whether such a standard should be required in Los Angeles to prepare for earthquakes, but it became clear several years ago that neighborhood groups would oppose having diesel-powered generators at wireless tower sites across the city, which would need to be tested monthly, said Jones, a former science advisor to Mayor Eric Garcetti.

“A prolonged power outage — and lack of cell service — can cause major problems for recovery. The hardest-hit areas in Japan after the 2011 earthquake and tsunami ran through the 48 hours of backup supply, but electricity wasn’t going to come back soon. ‘When they lost cellphones is when people gave up and left the area,’ Jones said.” LA Times. Whatever the answer, we know one thing for sure: what we’ve got just ain’t workin’!

              I’m Peter Dekom, and we surely must move away from being a “reactive” and unprepared nation to totally “active” and prepared; ultimately it will save us billions of dollars and untold numbers of lives.

Ai, Y-Ai, Y-Ai -They’re Here!

Privacy?! Personal financial information? Exceptionally personal information? Lots of legislation to protect consumers from hacking and revealing their most intimate secrets? Powerful companies supporting a level playing field as to access to information and implement commercial transactions and trades? LOLOLOLOLOLOLOL. What are you smoking? We live in a world of accelerating artificial intelligence (AI) – software that enables high speed computers to assimilate vast pools of big data, learn all possible combinations and trends asked of them, and able to learn and reprogram themselves – all while using supercomputers. Snail computers compared to what will happen when quantum computing come online… just around the corner. There is too much tempting personal information out there, stuff that can generate billions and billions of dollars a year in additional profits, no matter the statutory and regulatory risks.

We cannot forget that Wall Street is always looking for shortcuts, advantages, fast-tracks, real-time analysis of massive data perhaps with automated decision-making (programmed trading on steroids)… even to the point of shortening the physical path between computer and data inputs, across long distances and even within data centers themselves. Let’s look at the efforts emanating from Wall Street when it comes to AI. Nanoseconds matter. 

Wall Street is on a hiring binge for academics and AI specialists to generate the next generation of financial tools based on AI. And getting, configuring and collating data for AI is hideously expensive. The fastest computers, the least expensive part, are also outrageously expensive. The advantage is staggering to those with the resources to build AI business units and deploy the best minds to create strategic capacities not even possible just a year ago. The playing field would be so tilted as to slide all but the richest financial institutions right off the table. But with that computing power comes unbridled financial, economic and, inevitably, political power.

While scientific and medical research, infrastructure maximization and energy allocation are and will be beneficiaries of this technology leap, the potential damages (from glitches to job displacement) have yet to be fully appreciated. If hacks and glitches have been nasty to date, with these high-speed autonomous systems, risks are amplified and multiplied thousands of times over… when mistakes can penetrate a system and spread much more rapidly than we have ever seen before. “Financial technology start-ups have begun using machine-learning algorithms to model credit ratings and detect fraud. And hedge funds and high-frequency traders are using AI to make investment decisions.

“Politicians are starting to take notice. In mid-October, the newly formed Task Force on Artificial Intelligence of the House Financial Service Committee held hearings on how AI could raise data privacy concerns in the financial industry. In June, Sen. Elizabeth Warren called on federal regulators to crack down on ‘algorithmic discrimination’ by financial institutions, noting that financial technology companies often charge minorities higher interest rates.

“Artificial intelligence also could fundamentally change the way that our financial system works. And until we understand how those changes could play out, we will be ill-equipped to deal with them.

“In the last decade, the broader field of artificial intelligence has made remarkable strides. We have seen AI beat the world’s best players of ‘Jeopardy’ and the ancient board game Go, identify unknown genes related to Lou Gehrig’s disease and power driverless cars around the streets of Phoenix. These achievements have been enabled by better algorithms, more powerful computers and ever- bigger data sets.

“For many reasons, the rise of artificial financial intelligence on Wall Street should be applauded. It is a good thing if we can find ways to deploy capital more efficiently, identify risk more accurately or simply make money faster. It can smooth the gears of commerce and, at least theoretically, raise all boats.

“But every new tool has its quirks and its risks, and AI is no exception… The problems with AI in finance stem from the way AI algorithms work. Today, when people talk about AI they are really talking about a specific field of computer science known as machine learning. Machine-learning algorithms are fed large amounts of information and predict future events by identifying patterns in the information. At the base of this complex system is data, which drives AI.

“But the very features of AI that have allowed it to be so successful in other arenas also make it dangerous when applied to the financial world. These threats mirror the problems that created the last financial crisis — when complex derivatives and poorly understood subprime mortgages sent the world into a deep depression — and must be taken seriously.

“For one, AI could lead to financial bubbles growing bigger or lasting longer by feeding the flames of irrational exuberance. Machine-learning algorithms rely on large data sets to make predictions about the world… If the data used to make these predictions is outdated, financial chaos could ensue… AI optimists would say that, sure, AI has limitations, but responsible decision-makers are aware of them and will respond appropriately. AI is simply another tool in the toolshed.

“But because AI algorithms are so complex and data-dependent, it is extremely hard to understand how they work. The spread of complex, inscrutable financial instruments was at the root of the 2007 financial crisis and may well be at the root of the next.

“We learned from the last crash that when something is hard to understand — such as the collateralized debt obligations that packaged together collections of risky subprime mortgages in a way that purported to make them safe — it is also hard to second-guess. If financial decision-makers have an AI recommendation, which contains a clear ‘answer’ and purports to be based on millions of pieces of information, they will be hard-pressed to ignore it. It could become not so much a tool as a crutch.

“Perhaps most importantly, we are not sure how AI algorithms will interact with each other in the jungles of Wall Street. In capital markets, stock prices depend heavily on the decisions of other participants in the market. If most of the participants are AI-driven, and they adopt broadly similar machine-learning strategies, they might create echo effects where they all pile into (or out of) a stock at a moment’s notice. Flash crashes might become more frequent as a result.” William Magnuson  writing for the Los Angeles Times, November 1^st. The winners will be bigger, but the income inequality gap could widen to socially unacceptable limits. And oh those mistakes… they could take down our entire financial system in a day.

              I’m Peter Dekom, and increasingly, we should be exceptionally skeptical of big private institutions, beholden legal only to make profits for their shareholders, with this much expensive power at their carnivorous fingertips… the same players who make those massive campaign contributions.

How at Risk is Your Job?

When most Americans think of who gets impacted by accelerating automation empowered by artificial intelligence, images of manufacturing robots leap to mind. It’s pretty visual and easy to imagine. They might miss automated detailed financial analysis, accelerating as self-learning machines gather masses of freely available data combined with propriety data generated by mega-wealthy financial institutions… and analyze, predict and forecast. These same machines then get to test their forecasts against results, refining their ability as time passes.

The vision of robotic medical diagnostics, exponentially increasing their accuracy as they scan results and accumulate patterns of experience, is not particularly visual either. Robotic surgery, as depicted above, is too gory for most laypeople to contemplate. Legal drafting and research are increasingly fully automated. These functions have been the bailiwick of some of this nation’s most brilliant minds, her best educated students from our finest universities, and some of our most successful and most highly paid professionals. 

Even if you were not a fan of Mad Men, we all have images of advertising executives tossing around ideas, mock ad campaigns as well as catchy slogans and tunes. Today, it’s all about data scraping, quantative analysis and interactive adjustment based on consumer behavior. Campaigns are predicated on that analysis, adjusted by AI in the field, and advertising of all forms is tracked with linkage to resulting sales. Educated, white collar work. 

But if a November 20^th Brookings Institution report – What Jobs are affected by AI? Better-paid, better-educated workers face the most exposure by Mark Muro, Jacob Whiton and Robert Maxim – is accurate, it isn’t the remaining lower-paid blue-collar workers who face the greatest job insecurity… The headline: “White-collar, well-paid America—radiologists, legal professionals, optometrists, and many more—will likely get no free pass… and better-educated, better-paid workers will be the most affected” by artificial intelligence (AI).

What makes the Brooking’s report different is how this study separates itself from the world of academic speculation vs some anchor in some empiricism, measurable and quantified. “In part because the technologies have not yet been widely adopted, previous analyses have had to rely either on case studies or subjective assessments by experts to determine which occupations might be susceptible to a takeover by AI algorithms. What’s more, most research has concentrated on an undifferentiated array of ‘automation’ technologies including robotics, software, and AI all at once. The result has been a lot of discussion—but not a lot of clarity—about AI, with prognostications that range from the utopian to the apocalyptic.

“Given that, the analysis presented here demonstrates a new way to identify the kinds of tasks and occupations likely to be affected by AI’s machine learning capabilities, rather than automation’s robotics and software impacts on the economy. By employing a novel technique developed by Stanford University Ph.D. candidate Michael Webb, the new report establishes job exposure levels by analyzing the overlap between AI-related patents and job descriptions. In this way, the following paper homes in on the impacts of AI specifically and does it by studying empirical statistical associations as opposed to expert forecasting.” The report.

The November 20^th FastCompany.com explains further: “This modified view is based on a novel research technique developed by… Webb, who built his own algorithm to compare language from 16,400 AI patents with the specific words used to describe 769 different jobs in the government’s official occupational database, known as O*NET.

“For example, Webb unearthed verb-object combinations in patents related to marketing that included ‘measure, effectiveness’; ‘analyze, data’; ‘identify, markets’; and ‘monitor statistics.’ To a considerable extent, these terms mirror those found on O*NET to explain what a marketing specialist does. Among them: ‘measure the effectiveness of marketing, advertising, and communications programs and strategies,’ ‘collect and analyze data on customer demographics, preferences, needs, and buying habits to identify potential markets,’ and ‘monitor industry statistics and follow trends in trade literature.’

“Such a high degree of overlap between the two sets of texts indicates that AI is poised to have a significant impact on a particular occupation… In all, according to Brookings, some 25 million workers in the U.S. stand to be touched the most by AI. That’s about 15% of the nation’s labor force.” We are learning fast, but there is a “devil may care” attitude among those with professional educational skills and experience that for the foreseeable future, their jobs will be safe. But that myth is eroding as AI is becoming the analytical decider. You see, as an AI driven system consumes data, predicts, measures the results of its predictions, it learns. It gets better, more accurate, and more sophisticated.  Able to replace more sophisticated work.

“All that said, [report co-author Mark] Muro and his colleagues stress that AI is a ‘moving target’ since computers are constantly gaining new forms of ‘intelligence’—planning, reasoning, problem-solving, perceiving, forecasting, and ‘learning’ by gleaning statistical patterns within huge pools of data. ‘Much more inquiry—qualitative and empirical—is needed to tease out AI’s special genius,’ they write.

“Even with the insights provided by Webb, Brookings also takes care not to speculate on how AI will reshape the world of work. AI might eat a ton of jobs. But many, or even most, AI applications could wind up needing a person to work in tandem with the technology. And AI might give rise to new occupations that require the intervention of human hands—and brains… ‘Nobody knows how this will play out,’ says Tom Mitchell, a professor at Carnegie Mellon University and a pioneer in machine learning. ‘It’s a wild card.’”

What we do know is that college tuition is skyrocketing, well beyond the normal inflation rate. We know massive student loans are often required. And we know that a stable job market is essential not just for the repayment of those student loans… but for the reasonable health of our entire national economic health.

              I’m Peter Dekom, and for those white-collar workers so imperiled, I have two words: “red alert!”