The economy grew by 6.9% in the April-to-June period from a year earlier, according to official figures.
IBM is doing its damnedest to keep the mainframe relevant in a modern context, and believe it or not, there are plenty of monster corporations throughout the world who still use those relics from the earliest days of computing. Today, the company unveiled the z14, its latest z-Series mainframe, which comes with the considerable draw of full encryption. Is that enough for even corporate giants… Read More
The debate over the role robots will play in the future of warfare is one that is taking place right now as the development of automated lethal technology is truly beginning to take shape. Predator drone style combat machines are just the tip of the iceberg for what is to come down the line of lethal weaponry and some are worried that when robots are calling the shots, things could get a little out of hand.
Recently there has been some debate at the U.N. about “killer robots,” with prominent scientists, researchers, and Human rights organizations all warning that this type of technology – lethal tech. that divorces the need for human control – could cause a slew of unintended consequence to the detriment of humanity.
A study conducted the University of British Columbia shows that this type of terminator-like weaponry isn’t sitting well with the general public, as an overwhelming majority of people, regardless of country or culture, want a complete ban placed upon any further development of these autonomous systems of war.
Despite the warnings of risk and concern, this is not stopping arms manufacturers from taking warfare into the twilight zone and bringing the futuristic battlefield scenario where A.I. robots and human are fighting with each other, side by side, closer to everyday reality.
Kalashnikov, the maker of the iconic AK-47, is one of those manufacturers bringing lethal automation and robotics into the present-day as it is currently building a range of products based on neural networks,’ including a fully automated combat module’ that can identify and shoot at its targets.
Defense One is reporting:
The maker of the famous AK-47 rifle is building “a range of products based on neural networks,” including a “fully automated combat module” that can identify and shoot at its targets. That’s what Kalashnikov spokeswoman Sofiya Ivanova told TASS, a Russian government information agency last week. It’s the latest illustration of how the U.S. and Russia differ as they develop artificial intelligence and robotics for warfare.
The Kalashnikov “combat module” will consist of a gun connected to a console that constantly crunches image data “to identify targets and make decisions,” Ivanova told TASS. A Kalashnikov photo that ran with the TASS piece showed a turret-mounted weapon that appeared to fire rounds of 25mm or so.
Defense One points out that in 2012 then-Deputy Defense Secretary Ash Carter signed a directive forbidding the U.S. to allow any robot or machine to take lethal action without the supervision of a human operator.
Then in 2015, then-Deputy Defense Secretary Bob Work said fully automated killing machines were un-American.
“I will make a hypothesis: that authoritarian regimes who believe people are weaknesses,” Work said, “that they cannot be trusted, they will naturally gravitate toward totally automated solutions. Why do I know that? Because that is exactly the way the Soviets conceived of their reconnaissance strike complex. It was going to be completely automated. We believe that the advantage we have as we start this competition is our people.”
According to Sergey Denisentsev, a visiting fellow at the Center For Strategic International Studies, Russian weapons makers see robotics and the artificial intelligence driving them as key to future sales to war makers.
“There is a need to look for new market niches such as electronic warfare systems, small submarines, and robots, but that will require strong promotional effort because a new technology sometimes finds it hard to find a buyer and to convince the buyer that he really needs it, ” Denisentsev said earlier this year.
With my previous reporting dealing with robotics and war, I always point out the incredible advances made by Softbank owned Boston Dynamics in the field of A.I., using it as an example of what future warfare could (or most likely will) look like it. And to be honest, it really is nightmarish.
The bottom line is war is a racket. Killing for political reasons is always disastrous. So the fact that governments are on the verge of possessing this terminator technology should send chills down everyone’s spine.
H/T Nicholas West of ActivistPost.com
New York City real estate, particularly the luxury market, is a popular refugee for world’s corrupt, self-dealing public servants and the crooked businessmen who bribe them. China cracked down on wealthy citizens seeking to stash their wealth in international real estate by adding several deterrents to its capital controls earlier this year (Among them, Chinese investors moving money out of the country must now sign a pledge saying it won’t be used to buy real estate, or investment securities). Shortly after, the New York real-estate – literally half a world away – was rattled by a crush of stalled deals.
So, it’s unsurprising that the mystery behind the largest residential foreclosure auction in NYC history would have this kind of sordid backstory. Last month, we met Kola Aluko, a Nigerian oil magnate and the purported owner of One57’s Apartment 79, a $50 million apartment that will be sold next week in what appears to be the largest foreclosure auction in New York City history.
And now the US government has added a new twist: In a lawsuit filed Friday in Houston by the Justice Department’s Kleptocracy Asset Recovery Initiative, the Feds are seeking to recover $144 million in assets, including proceeds from a luxury condominium on Manhattan’s Billionaires’ Row in New York, which prosecutors claim were spoils from bribes paid for Nigerian oil contracts, according to Bloomberg.
The targets of the suit were none other than Aluko and another Nigerian businessman, Olajide Omokore. However, judging by the government’s price tag, Aluko’s assets – including the One57 condo and Aluko’s $80 million yacht, 213-feet (65 meters) luxury yacht the Galactica Star – appear to be the focus of the suit. In the past, Aluko would frequently rent out his yacht to his friends. In 2015, Jay-Z and Beyonce rented it for the bargain-basement price of $900,000 per week to sail around the Mediterrainean.
The Justice Department alleges that two Nigerian businessmen made corrupt payments to a Nigerian official who oversaw the country’s state-owned oil company in exchange for contracts, according to Bloomberg. The official used her influence to direct contracts to two of their shell companies — Atlantic Energy Drilling Concepts Nigeria Ltd. and Atlantic Energy Brass Development Ltd. — through a subsidiary of the Nigerian National Petroleum Corp, according to the complaint. The companies failed to abide by the terms the contracts, yet were permitted to sell more than $1.5 billion worth of Nigerian crude oil, the U.S. alleges. They then laundered the money through the US.
"Corrupt foreign officials and business executives should make no mistake: if illicit funds are within the reach of the United States, we will seek to forfeit them and to return them to the victims from whom they were stolen," Acting Assistant Attorney General Kenneth Blanco said in a statement.
The Justice Department’s recovery lawsuit comes just days before Aluko’s penthouse at One57, one of Manhattan’s most expensive buildings, is scheduled to be sold at a foreclosure auction forced by his mortgage lender, the Luxembourg-based Banque Havilland SA, which said in court filings earlier this year that he failed to pay back the full loan amount in September.
As we’ve previously noted, Aluko took out an 'unusually large' ($35.3 million) mortgage with an even more unusual term: one-year.
Aluko’s condo is a full-floor, 6,240-square-foot (580-square-meter) penthouse that was the eighth-priciest sold in the building located at 157 W. 57th Street, just across the street from Carnegie Hall, according to real estate data firm PropertyShark.
Now of course one lawsuit doesn’t necessarily prove that a market is infested with criminality, but the opaque nature of real-estate transactions, and the ease with which buyers and sellers can conceal their true identities behind LLCs, make buying real estate in a market like NYC an attractive option for any would-be money launderer.
And while one foreclosure certainly doesn’t signal that the market is collapsing, there are other more worrying trends in NYC luxury real estate. As we’ve previously noted, buildings like One57 are struggling with unsustainable vacancy rates. To wit: Nearly 40% of apartments in one comparable building remained on the market years after it had opened.
As Bloomberg points out, One57, along with a cluster of ultra-high end luxury buildings around Central Park collectively known as “Billionaire’s Row,” has become a symbol of New York’s luxury-development boom — and eventual slowdown. The tower, which broke ground in 2009, drew investors willing to pay large sums for lavish residences they rarely lived in, inspiring other developers to build similar offerings, creating an effective “Billionaires' Row” along West 57th Street. One57 still holds the record for the most-expensive residential sale in New York in December 2014 at $100.5 million.
The bribes were paid between 2011 and 2015 to Diezani Alison-Madueke, then Nigeria’s minister for petroleum resources, according to the complaint. The defendants are accused of spending millions to fund a lavish lifestyle for Alison-Madueke. They acquired real estate in London that was used by the minister and her family, and bought her more than $1 million of furniture and artwork from several stores in Houston, Texas, the complaint said.
We wonder: In what tony neigborhood is her apartment?
According to an insider account, the Clinton team, put together the Russia Gate narrative within 24 hours of her defeat. The Clinton account explained that Russian hacking and election meddling caused her unexpected loss. Her opponent, Donald Trump, was a puppet of Putin. Trump, they said, “encourages espionage against our people.” The scurrilous Trump dossier, prepared by a London opposition research firm, Orbis, and paid for by unidentified Democrat donors, formed a key part of the Clinton narrative: Trump’s sexual and business escapades in Russia had made him a hostage of the Kremlin, ready to do its bidding. That was Hillary's way to say that Trump is really not President of the United States—a siren call adopted by the Democratic party and media.
Hillary and the Orbis Dossier
The most under-covered story of Russia Gate is the interconnection between the Clinton campaign, an unregistered foreign agent of Russia headquartered in DC (Fusion GPS), and the Christopher Steele Orbis dossier. This connection has raised the question of whether Kremlin prepared the dossier as part of a disinformation campaign to sow chaos in the US political system. If ordered and paid for by Hillary Clinton associates, Russia Gate is turned on its head as collusion between Clinton operatives (not Trump’s) and Russian intelligence. Russia Gate becomes Hillary Gate.
Neither the New York Times, Washington Post, nor CNN has covered this explosive story. Two op-eds have appeared in the Wall Street Journal (Holman Jenkins and David Satter). The possible Russian-intelligence origins of the Steele dossier have been raised only in conservative publications, such as in The Federalist and National Review.
The Fusion story has been known since Senator Chuck Grassley (R-Iowa) sent a heavily-footnoted letter to the Justice Department on March 31, 2017 demanding for his Judiciary Committee all relevant documents on Fusion GPS, the company that managed the Steele dossier against then-candidate Donald Trump. Grassley writes to justify his demand for documents that: “The issue is of particular concern to the Committee given that when Fusion GPS reportedly was acting as an unregistered agent of Russian interests, it appears to have been simultaneously overseeing the creation of the unsubstantiated dossier of allegations of a conspiracy between the Trump campaign and the Russians.” (Emphasis added.)
Former FBI director, James Comey, refused to answer questions about Fusion and the Steele dossier in his May 3 testimony before the Senate Intelligence Committee. Comey responded to Lindsey Graham’s questions about Fusion GPS’s involvement “in preparing a dossier against Donald Trump that would be interfering in our election by the Russians?” with “I don’t want to say.” Perhaps he will be called on to answer in a forum where he cannot refuse to answer.
The role of Fusion GPS and one of its key associates, a former Soviet intelligence officer, must raise the question as to whether the Steele dossier, which was orchestrated by a suspected unregistered agent of Russia, was a plant by Russian intelligence to harm Donald Trump?
David Satter, one of our top experts on Russia and himself expelled by the Kremlin, writes:
Perhaps most important, Russian intelligence also acted to sabotage Mr. Trump. The ‘Trump dossier, full of unverified sexual and political allegations, was published in January by BuzzFeed, despite having all the hallmarks of Russian spy agency ‘creativity.’ The dossier was prepared by Christopher Steele, a former British intelligence officer. It employed standard Russian techniques of disinformation and manipulation.
Much of the credibility of the Orbis dossier hinges on Steele’s reputation as a former M15 intelligence agent. Satter writes, however, that “after the publication of the Trump dossier, Mr. Steele went into hiding, supposedly in fear for his life. On March 15, however, Michael Morell, the former acting CIA director, told NBC that Mr. Steele had paid the Russian intelligence sources who provided the information and never met with them directly. In other words, his sources were not only working for pay. Furthermore, Mr. Steele had no way to judge the veracity of their claims.”
If Steele disappeared for fear of his life, we must suspect that he feared murder by Russian agents. The only secret he might have had to warrant such a drastic Russian action would be knowledge that Russian intelligence prepared the dossier.
According to a Vanity Fair article, Fusion GPS was first funded by an anti-Trump Republican donor, but, after Trump’s nomination, Fusion and Steele were paid by Democratic donors whose identity remains secret. Writes Satter: “Perhaps the time has come to expand the investigation into Russia’s meddling to include Mrs. Clinton’s campaign as well.”
As someone who has read every word of the Steele Trump dossier and has studied the Soviet Union/Russia for almost a half century, I can say that the Steele dossier consists of raw intelligence from informants identified by capital letters, who claim (improbably) to have access to the highest levels of the Kremlin. The dossier was not, as the press reports, written by Steele. No matter how experienced (or gullible) Steele might be, there is no way for him to know whether his sources are clandestine Russian intelligence agents.
In Stalin's day, some of the most valued KGB (NKVD) agents were called "novelists," for their ability to conjure up fictional plots and improbable tales to use against their enemies. Some of Steele's sources claim detailed knowledge of the deepest Kremlin secrets, such as Putin's personal control of Clinton emails or negotiations with Putin's head of the national oil company. If they truly had such knowledge, why would they "sell" it to Steele? The most likely explanation is that the Steele dossier is the work of Russian intelligence "novelists" charged by the Kremlin with defaming Trump and adding chaos to the American political system.
Mueller’s Difficult Task
While leaks from within the investigation focus on possible obstruction of justice, Special Counsel Robert Mueller’s writ – to investigate Russian interference in the 2016 election – requires him to consider “matters” that Dems would prefer be left alone.
Special Counsel Mueller has been given a broad charge and no deadline — a formula for trouble. He is supposed to “investigate Russia’s intervention in the 2016 election.” Given the many accounts of Russian contacts of Trump campaign officials and hangers-on, Mueller must follow these leads, which apparently have lead nowhere over a nine month investigation as reported even by Trump unfriendly sources like CNN. Mueller, therefore, should not require much time to rule out coordination between the Trump campaign and Russia state actors. Mueller must be careful to avoid detours into loosely related issue by scalp-hunting investigators. Mueller also must shut down leaks from within his office, if he wishes his reports to be credible to the American people.
Mueller must also conduct an investigation which is perceived as fair to both sides. On the Clinton/Democratic side, there are a number of unanswered questions related to Russian electoral intervention. Among them is the question of whether the “wiped clean” Clinton e-mails are in Russian hands (as asserted by the Steele dossier), whether the tarmac meeting of Bill Clinton and the Attorney General quashed the investigation of Hillary’s e-mails, and whether the Clintons and Russian uranium interests engaged in quid pro quo and “pay to play” operations.
The most important unanswered question is whether the Clinton campaign funded the Orbis Trump smear campaign and did they understand the campaign could be conducted by Russian intelligence?
Mueller must question Steele himself on his sources and some of the sources themselves, investigate whether they could be Russian intelligence agents, and determine the role of Clinton donors and campaign officials in the funding of the anti-Trump dossier.
The Fusion-Steele matter is explosive because it suggests that Russia’s most damaging intervention in the 2016 campaign may have been its creation of the Steele Dossier, remarkably paid for by the Clinton campaign! If so, the Clinton campaign (not Trump) was the prime sponsor of Russia’s intervention in the 2016 election.
Following more dismal data from the US, hope for global growth remains in China and they did not disappoint. Despite slumping macro data, a major slowdown in real estate, and the nation's deleveraging efforts in the last three months, GDP beat, Retail Sales beat, Industrial Production surged, and even fixed asset investment was above expectations. The Yuan hasn't moved.
For the last three months, Chinese data has been disappointing, along with US, as the collapsing credit impulse leaks into reality…
But exports and consumer spending have been pillars for the economy over the second quarter, offsetting the curb on leverage, and tonight's data shows that none of that matters.. because the deleveraging economy beat across the board
- China GDP BEAT 6.9% (exp +6.8%, prior +6.9%)
- China Retail Sales BEAT 11.0% (exp +10.6%, prior +10.7%)
- China Fixed Asset Investment BEAT 8.6% (exp +8.5%, prior +8.6%)
- China Industrial Production BEAT 7.6% (exp +6.5%, prior +6.5%)
As the charts below show, more of the same well-managed data to show that all is well enough that hope remains…Strong growth again reflects an economy awash in credit, foretold in the latest new yuan loans (1.54 trillion yuan) and aggregate social financing (1.78 trillion yuan).
Enda Curran, Bloomberg's Chief Asia Economics Correspondent, notes that at first glance there's not a lot for the bears in these numbers given they appear strong across the board. The backdrop though continues to be one of cheap credit and mounting risks. That's an issue policy makers say they are aware of but for now, it seems like growth above all else is key.
Iris Pang, greater China economist at ING Bank in Hong Kong:
"Higher than expected GDP growth comes from strong industrial production. That said, the gap between FAI growth and industrial production growth tells the story that it is consumption and export driven growth."
Julian Evans-Pritchard, China economist at Capital Economics, said the strength seen in the data seems unlikely to last:
"The recent crackdown on financial risks has driven a slowdown in credit growth, which will weigh on the economy during the second half of this year.
"What’s more, the National Financial Work Conference that concluded over the weekend has signaled that further regulatory tightening remains on the horizon."
We wonder how long before the lagged response to the credit impulse collapse hits GDP... (NOTE the weaker and weaker reactions in GDP to credit impulse surges)
The reaction in Yuan is underwhelming for now… (after its biggest weekly gain since March)
China's stock market ripped back higher (after an early plunge) ahead of China's data dump, and held those gains as the data hit (we wonder if someone got wind of the data a little early?).
As a reminder, Japan is closed for a holiday so we are not getting the usual juice from BoJ shenanigans on any move.
Robots that serve dinner, self-driving cars and drone-taxis could be fun and hugely profitable. But don’t hold your breath. They are likely much further off than the hype suggests.
A panel of experts at the recent 2017 Wharton Global Forum in Hong Kong outlined their views on the future for artificial intelligence (AI), robots, drones, other tech advances and how it all might affect employment in the future. The upshot was to deflate some of the hype, while noting the threats ahead posed to certain jobs.
Their comments came in a panel session titled, “Engineering the Future of Business,” with Wharton Dean Geoffrey Garrett moderating and speakers Pascale Fung, a professor of electronic and computer engineering at Hong Kong University of Science and Technology; Vijay Kumar, dean of engineering at the University of Pennsylvania, and Nicolas Aguzin, Asian-Pacific chairman and CEO for J.P.Morgan.
Kicking things off, Garrett asked: How big and disruptive is the self-driving car movement?
It turns out that so much of what appears in mainstream media about self-driving cars being just around the corner is very much overstated, said Kumar. Fully autonomous cars are many years away, in his view.
One of Kumar’s key points: Often there are two sides to high-tech advancements. One side gets a lot of media attention — advances in computing power, software and the like. Here, progress is quick — new apps, new companies and new products sprout up daily. However, the other, often-overlooked side deeply affects many projects — those where the virtual world must connect with the physical or mechanical world in new ways, noted Kumar, who is also a professor of mechanical engineering at Penn. Progress in that realm comes more slowly.
At some point, all of that software in autonomous cars meets a hard pavement. In that world, as with other robot applications, progress comes by moving from “data to information to knowledge.” A fundamental problem is that most observers do not realize just how vast an amount of data is needed to operate in the physical world — ever-increasing amounts, or, as Kumar calls it — “exponential” amounts. While it’s understood today that “big data” is important, the amounts required for many physical operations are far larger than “big data” implies. The limitations on acquiring such vast amounts of data severely throttle back the speed of advancement for many kinds of projects, he suggested.
In other words, many optimistic articles about autonomous vehicles overlook the fact that it will take many years to get enough data to make fully self-driving cars work at a large scale — not just a couple of years.
Getting enough data to be 90% accurate “is difficult enough,” noted Kumar. Some object-recognition software today “is 90% accurate, you go to Facebook, there are just so many faces — [but there is] 90% accuracy” in identification. Still, even at 90% “your computer-vision colleagues would tell you ‘that’s dumb’…. But to get from 90% accuracy to 99% accuracy requires a lot more data” — exponentially more data. “And then to get from 99% accuracy to 99.9% accuracy, guess what? That needs even more data.” He compares the exponentially rising data needs to a graph that resembles a hockey stick, with a sudden, sharply rising slope. The problem when it comes to autonomous vehicles, as other analysts have noted, is that 90% or even 99% accuracy is simply not good enough when human lives are at stake.
Exponentially More Data
“To have exponentially more data to get all of the … cases right, is extremely hard,” Kumar said. “And that’s why I think self-driving cars, which involve taking actions based on data, are extremely hard [to perfect]…. “Yes, it’s a great concept, and yes, we’re making major strides, but … to solve it to the point that we feel absolutely comfortable — it will take a long time.”
So why is one left with the impression from reading mainstream media that self-driving cars are just around the corner?
To explain his view of what is happening in the media, Kumar cited remarks by former Fed chairman Alan Greenspan, who famously said there was “irrational exuberance” in the stock market not long before the crash of the huge tech stock bubble in the early 2000s. Kumar suggested a similar kind of exaggeration is true for today for self-driving cars. “That’s where the irrational exuberance comes in. It’s a technology that is almost there, but it’s going to take a long time to finally assimilate.”
“To have electric power and motors and batteries to power drones that can lift people in the air — I think this is a pipe dream.”–Vijay Kumar
Garrett pointed out that Tesla head Elon Musk claims all of the technology to allow new cars to drive themselves already exists (though not necessarily without a human aboard to take over in an emergency) and that the main problem is “human acceptance of the technology.”
Kumar said he could not disagree more. “Elon Musk will also tell you that batteries are improving and getting better and better. Actually, it’s the same battery that existed five or 10 years ago.” What is different is that batteries have become smaller and less expensive, “because more of us are buying batteries. But fundamentally it’s the same thing.”
Progress has been slow elsewhere, too. In the “physical domain,” Kumar explained, not much has changed when it comes to energy and power, either. “You look at electric motors, it’s World War II technology. So, on the physical side we are not making the same progress we are on the information side. And guess what? In the U.S., 2% of all of electricity consumption is through data centers. If you really want that much more data, if you want to confront the hockey stick, you are going to burn a lot of power just getting the data centers to work. I think at some point it gets harder and harder and harder….”
Similar constraints apply to drone technology he said. “Here’s a simple fact. To fly a drone requires about 200 watts per kilo. So, if you want to lift a 75-kilo individual into the air, that’s a lot of power. Where are you going to get the batteries to do that?” The only power source with enough “power density” to lift such heavy payloads is fossil fuels. “You could get small jet turbines to power drones. But to have electric power and motors and batteries to power drones that can lift people in the air — I think this is a pipe dream.”
That is not to say one “can’t do interesting things with drones, but whatever you do — you have to think of payloads that are commensurate what you want to do.”
In other areas, like electric cars, progress is moving along smartly and Kumar says there is lots of potential. “The Chinese have shown that, they are leading the world. The number of electric cars in China on an annual basis that are being produced is three times that of the U.S…. I do think electric cars are here to stay, but I’m not so sure about drones using electric power.”
Picking up on Kumar’s theme, Fung, who also helps run the Human Language Technology Center at her university, outlined some of the limits of artificial intelligence (AI) in the foreseeable future, where again the hype often outruns reality. While AI may perform many impressive and valuable tasks, once again physical limitations remain almost fixed.
“… A deep-learning algorithm that than can do just speech recognition, which is translating what you are saying, has to be trained on millions of hours of data “and uses huge data farms,” Fung noted. And while a deep-learning network might have hundreds of thousands of neurons, the human brain has trillions. Humans, for the time being, are much more energy-efficient. They can work “all day on a tiny slice of pizza,” she joked.
“The jobs safest from robot replacement will be those at the top and the bottom, not those in the middle.”–Vijay Kumar
The Human Brain Conundrum
This led to the panelists to note a second underappreciated divide: the scope of projects that AI can currently master. Kumar pointed out that tasks like translation are relatively narrow. We have “figured out how to go from data to information to some extent, though … with deep learning it’s very hard even to do that. To go from information to knowledge? We have no clue. We don’t know how the human brain works…. It’s going to be a long time before we build machines with the kind of intelligence we associate with humans.”
Not long ago, Kumar noted, IBM’s supercomputer Watson could not even play tic tac toe with a five-year-old. Now it beats humans at Jeopardy!. But that speedy progress can blind us to the fact that computers today can best handle only narrow tasks or “point solutions. When you look at generalizing across the many things that humans do — that’s very hard to do.”
Still, the stage is being set for bigger things down the road. To date, getting those narrow tasks that have been automated have required humans to “learn how to communicate with machines,” and not always successfully, as frustration with call centers and often Apple’s Siri suggests, noted Fung.
Today, the effort is to reverse the teacher and pupil relationship so that, instead, machines begin to learn to communicate with humans. The “research and development, and application of AI algorithms and machines that will work for us,” cater to us, is underway, Fung said. “They will understand our meaning, our emotion, our personality, our affect and all that.” The goal is for AI to account for the “different layers” of human-to-human communication.
“We look at each other, we engage each other’s emotion and intent,” said Fung, who is among the leaders worldwide in efforts to make machines communicate better with humans. “We use body language. It’s not just words. “That’s why we prefer face-to-face meetings, and we prefer even Skype to just talking on the phone.”
Fung referenced an article she wrote for Scientific American, about the need to teach robots to understand and mimic human emotion. “Basically, it is making machines that understand our feelings and intent, more than just what we say, and respond to us in a more human way.”
Such “affective computing” means machines will ultimately show “affect recognition” picked up from our voices, texts, facial expressions and body language. Future “human-robot communication must have that layer of communication.” But capturing intent as well as emotion is an extremely difficult challenge, Fung added. “Natural language is very hard to understand by machines — and by humans. We often misunderstand each other.”
So where might all this lead when it comes to the future of jobs?
Machines Are Still ‘Dumb’
“In the near future, no one needs to worry because machines are pretty dumb….” Kumar said. As an example, Fung explained that she could make a robot today capable of doing some simple household chores, but, “it’s still cheaper for me to do it, or to teach my kids or my husband to do it. So, for the near future there are tons of jobs where it would be too expensive to replace them with machines. Fifty to 100 years from now, that’s likely to change, just as today’s world is different from 50 years ago.”
But even as new tech arrives it is not always clear what the effect will be ultimately. For example, after the banking industry first introduced automatic teller machines [ATMs], instead of having fewer tellers “we had more tellers,” noted Aguzin. ATMs made it “cheaper to have a branch, and then we had more branches, and therefore we had more tellers in the end.”
“With blockchain technology, eventually the cost of doing a transaction will be ‘like sending an email, like zero.’ Imagine applying that to trade finance.”–Nicolas Aguzin
On the other hand, introducing blockchain technology as a ledger system into banking will likely eliminate the need for a third-party to double-check the accounting. Anything requiring reconciliation can be done instantly, with no need for confirmation, Aguzin added. Eventually the cost of doing a transaction will be “like sending an email, it will be like zero … without any possibility of confusion, there’s no cost. Imagine if you apply that to trade finance, etc.”
Already, Aguzin’s bank is about to automate 1.7 million processes this year currently being done manually. “And those are not the lowest-level, manual types of jobs — it’s somewhere in the middle.” In an early foray in affective computing, his bank is working on software that will be able to sense what a client is feeling and their purpose when they call in for service. “It’s not perfect yet, but you can get a pretty good sense of how they are feeling, whether they want to complain or are they just going to check a balance? Are they going to do x, y — so you save a lot of time.”
Still, said he remains confident that new jobs will be created in the wake of new technologies, as was the case following ATMs. His view about the future of jobs and automation is not as “catastrophic” as some analysts’. “I am a bit concerned about the speed of change, which may cause us to be careful, but … there will be new things coming out. I tend to have a bit more positive view of the future.”
Fung reminded the audience that that even in fintech, progress will be throttled by the available data. “In certain areas, you have a lot of data, in others you don’t.” Financial executives have told Fung that they have huge databases, but in her experience, it often is not nearly large enough to accomplish many of their goals.
Kumar concedes that today we are creating more jobs for robots than humans, a cause for concern for the future of jobs for humans. But he also calls himself a “pathological optimist” on the jobs issue. AI and robotics will work best in “applications where they work with humans.” Echoing Fung, he added that “it’s going to take a long time before we build machines with the kind of intelligence associated with humans. When it comes to going from “information to knowledge, we have no clue. We don’t know how the human brain works.”
Security at the Top — and Bottom
Picking up on Fung’s point that many lower-skill level jobs likely will be preserved, Kumar added that the jobs most likely to be eliminated could surprise people. “What is the one thing that computers are really good at? They are good at taking exams. So, this expectation of, oh, I got a 4.0 from this very well-known university, I will have a job in the future — this is not true.” At the same time, for robots “cleaning up a room after your three-year old is just very, very hard. Serving dinner is very, very hard. Cleaning up after dinner is even harder. I think those jobs are secure.”
The panel’s consensus: The jobs safest from robot replacement will be those at the top and the bottom, not those in the middle.
What about many years down the road, when robots become advanced enough and cheap enough to take over more and more human activities. What’s to become of human work?
“You will still want to read a novel written by a human even though it’s no different from a novel written by a machine someday. You still appreciate that human touch.”–Pascale Fung
For one thing, Fung said, there will be a lot more AI engineers “and people who have to regulate machines, maintain machines, and somehow design them until the machines can reproduce themselves.”
But also, many jobs will begin to adapt to the new world. Suppose, for example, at some point in the distant future many restaurants have robot servers and waiters. People will “pay a lot more money to go to a restaurant where the chef is a human and the waiter is a human,” Fung said “So human labor would then become very valuable.”
She added that many people might “become artists and chefs, and performing artists, because you still want to listen to a concert performed by humans, don’t you, rather than just robots playing a concerto for you. And you will still want to read a novel written by a human even though it’s no different from a novel written by a machine someday. You still appreciate that human touch.”
What’s more, creativity already is becoming increasingly important, Fung notes. So, it’s not whether AI engineers or business people will be calling the shots in the future. “It’s really creative people versus non-creative people. There is more and more demand for creative people.” Already, it appears more difficult for engineering students “to compete with the best compared to the old days.”
In the past, for engineers, a good academic record guaranteed a good job. Today, tech companies interview applicants in “so many different areas,” Fung added. They look beyond technical skills. They look for creativity. “I think the engineers have to learn more non-engineering skills, and then the non-engineers will be learning more of the engineering skills, including scientific thinking, including some coding….”
Kumar agrees. Today, all Penn engineering students take business courses. “The idea of a well-rounded graduate, the idea of liberal education today, I think includes engineering and includes business, right? The thing I worry about is what happens to the anthropologist, the English majors, the history majors … I think those disciplines will come under a lot of pressure.”