Month: October 2019

31 Oct 2019
Employees Worldwide Welcome ‘AI Coworkers’ To The Office

Employees Worldwide Welcome ‘AI Coworkers’ To The Office

Last year, many Americans worried that artificial intelligence (AI) might replace them at work. This year, employees around the world are wondering why their employers don’t provide them with the kind of AI-enabled technology they’re starting to use at home. 

That’s one way to think about the results of a second annual survey about AI in the workplace, conducted by Oracle and research firm Future Workplace. This year, 50% of survey respondents say they’re currently using some form of AI at work—a major leap compared to only 32% in last year’s survey.

Last year, many Americans worried that artificial intelligence (AI) might replace them at work. This year, employees around the world are wondering why their employers don’t provide them with the kind of AI-enabled technology they’re starting to use at home. 

That’s one way to think about the results of a second annual survey about AI in the workplace, conducted by Oracle and research firm Future Workplace. This year, 50% of survey respondents say they’re currently using some form of AI at work—a major leap compared to only 32% in last year’s survey.

Source: https://www.forbes.com/sites/oracle/2019/10/31/employees-worldwide-welcome-ai-coworkers-to-the-office/#47aa68266681

30 Oct 2019
Using AI to Eliminate Bias from Hiring

Using AI to Eliminate Bias from Hiring

Like any new technology, artificial intelligence is capable of immensely good or bad outcomes. The public seems increasingly focused on the bad, especially when it comes to the potential for bias in AI. This concern is both well-founded and well-documented. But what is AI? It is the simulation of human processes by machines. This fear of biased AI ignores a critical fact: The deepest-rooted source of bias in AI is the human behavior it is simulating. It is the biased data set used to train the algorithm. If you don’t like what the AI is doing, you definitely won’t like what humans are doing because AI is purely learning from humans.

Let’s focus on hiring. The status quo of hiring is deeply flawed and quite frankly dystopian for three primary reasons.

Unconscious human bias makes hiring unfair. The typical way of reviewing applicants prior to an interview is through recruiters reviewing résumés. Numerous studies have shown this process leads to significant unconscious bias against women, minorities and older workers.

Large pools of applicants are being ignored. LinkedIn and other sourcing platforms have been so successful that, on average, 250 applicants apply for any open role. This translates into millions of applicants for a few thousand open roles. This process obviously cannot be handled manually. So, recruiters limit their review of the applicant pool to the 10% to 20% they think will show most promise: those coming from Ivy League campuses, passive candidates from competitors of the companies seeking to fill positions, or employee-referral programs. But guess what? Top colleges and employee-referral programs are much less diverse than the broader pool of applicants submitting résumés.

Traditional hiring tools are already biased. This is permitted by a loophole in U.S. law: Federal regulations state that a hiring tool can be biased if it is job-related. “Job-related” means that the people who are successful in a role show certain characteristics. But if all “successful employees” are white men, due to a history of biased human hiring practices, then it is almost certain that your job-related hiring assessment will bias towards white men and against women and minorities. An African American woman from a non-Ivy League college who is lucky enough to become part of the pipeline, whose résumé is reviewed, and who passes the human recruiter evaluating her résumé may then be asked to take a biased assessment.

Is it any wonder we struggle to hire a diverse workforce? What has led to today’s chronic lack of diversity, and what will continue to stunt diversity, are the human paradigms in place today, not AI.

AI holds the greatest promise for eliminating bias in hiring for two primary reasons:

1. AI can eliminate unconscious human bias. Many current AI tools for recruiting have flaws, but they can be addressed. A beauty of AI is that we can design it to meet certain beneficial specifications. A movement among AI practitioners like OpenAI and the Future of Life Institute is already putting forth a set of design principles for making AI ethical and fair (i.e., beneficial to everyone). One key principle is that AI should be designed so it can be audited and the bias found in it can be removed. An AI audit should function just like the safety testing of a new car before someone drives it. If standards are not met, the defective technology must be fixed before it is allowed into production.

2. AI can assess the entire pipeline of candidates rather than forcing time-constrained humans to implement biased processes to shrink the pipeline from the start. Only by using a truly automated top-of-funnel process can we eliminate the bias due to shrinking the initial pipeline so the capacity of the manual recruiter can handle it. It is shocking that companies today unabashedly admit how only a small portion of the millions of applicants who apply are ever reviewed. Technologists and lawmakers should work together to create tools and policies that make it both possible and mandatory for the entire pipeline to be reviewed.

Additionally, this focus on AI fairness should have us evaluate existing pre-hire assessments with the same standards. The U.S. Equal Employment Opportunity Commission (EEOC) wrote the existing fair-hiring regulations in the 1970s — before the advent of the public internet and the explosion in the number of people applying for each job. The EEOC didn’t anticipate modern algorithms that are less biased than humans yet also able to evaluate a much larger, more diverse pipeline. We need to update and clarify these regulations to truly encourage equal opportunity in hiring and allow for the use of algorithmic recruiting systems that meet clear criteria. Some precedents for standards have already occurred. The California State Assembly passed a resolution to use unbiased technology to promote diversity in hiring, and the San Francisco DA is using “blind sentencing” AI in criminal justice proceedings.

The same standards should be applied to existing hiring tools. Amazon was nationally lambasted for months due to its male-biased hiring algorithm. Yet in the United States today, employers are legally allowed to use traditional, biased assessments that discriminate against women or minorities. How can this be? Probably because most people are unaware that biased assessments are prominently used (and legal). If we are going to call for unbiased AI — which we absolutely should — we should also call for the elimination of all biased traditional assessments.

It is impossible to correct human bias, but it is demonstrably possible to identify and correct bias in AI. If we take critical steps to address the concerns that are being raised, we can truly harness technology to diversify the workplace.

Source: https://hbr.org/2019/10/using-ai-to-eliminate-bias-from-hiring

29 Oct 2019
What's Blockchain Actually Good for, Anyway? For Now, Not Much

What’s Blockchain Actually Good for, Anyway? For Now, Not Much

Not long ago, blockchain technology was touted as a way to track tuna, bypass banks, and preserve property records. Reality has proved a much tougher challenge.

In early 2018, Amos Meiri got the kind of windfall many startup founders only dream of. Meiri’s company, Colu, develops digital currencies for cities—coupons, essentially, that encourage people to spend their money locally. The company was having some success with pilot projects in the UK and Israel, but Meiri had an idea for something bigger. He envisioned a global network of city currencies, linked together using blockchain technology. So he turned to a then-popular way to fund his idea: the initial coin offering, or ICO. Colu raised nearly $20 million selling a digital token it called CLN.

Now, Meiri is doing something unusual: Giving the money back. After a year of regulatory and technical headaches, he stopped trying to fit blockchain into his business plan. He thinks other blockchain projects will follow suit.

It’s not unusual for startup efforts to fail or pivot when the product doesn’t work or the funding runs out. But blockchain has offered a wilder ride than most new technologies. Two years ago, ICOs like Meiri’s lured billions of dollars into blockchain companies and spawned a cottage industry of pilot projects. For a while, a blockchain seemed a salve for just about any problem: Fraudulent tuna. Unreliable health records. Homelessness. Remember WhopperCoin? Burger King’s crypto-for-burgers scheme, along with thousands of other projects, has long lost its sizzle. Many were scams from the start. But even among the more legitimate enterprises, there are relatively few winners. Enter, as a recent report from Gartner put it, “blockchain fatigue.”

“What you’re seeing right now is lethargy,” says Emin Gun Sirer, a professor of computer science at Cornell and founder of Ava Labs. “The current technologies fall really short.”

Bitcoin appears to be here to stay, even if the price has recently slumped. An entire industry has been built around holding and trading digital assets like it. But attempts to build more complex applications using blockchain are hobbled by the underlying technology. Blockchains offer an immutable ledger of data without relying on a central authority—that’s core to the hype behind the technology. But the cryptographic machinery behind blockchains is notoriously slow. Early platforms, like Ethereum, which gave rise to the ICO frenzy, are far too sluggish to handle most commercial applications. For that reason, “decentralized” projects represent a tiny portion of corporate blockchain efforts, perhaps 3 percent, says Apolline Blandin, a researcher at the Cambridge Centre for Alternative Finance.

The rest take shortcuts. So-called permissioned blockchains borrow ideas and terms from Bitcoin, but cut corners in the name of speed and simplicity. They retain central entities that control the data, doing away with the central innovation of blockchains. Blandin has a name for those projects: “blockchain memes.” Hype and lavish funding fueled many such projects. But often, the same applications could be built with less-splashy technology. As the buzzwords wear off, some have begun asking, what’s the point?

When Donna Kinville, the city clerk in South Burlington, Vermont, was approached by a startup that wanted to put the city’s land records on a blockchain, she was willing to listen. “We had the reputation of being ahead of things,” she says. The company, called Propy, had raised $15 million through an ICO in 2017 and forged Vermont connections, including lobbying for blockchain-friendly state legislation.

Propy pitched blockchain as a more secure way to handle land records. “It didn’t take long for them to say that they were overzealous,” Kinville says. She worked with Propy for about a year as it designed its platform and recorded the city’s historical data on the Ethereum blockchain. Propy also recorded one sale for the city, for a parcel of empty land whose owners weren’t in much of a rush.

Last month, Propy pitched Kinville a nearly finished product. She was uninspired. The system lacked practical features she uses all the time, like a simple way to link documents. She liked the software she uses now. It was built by an established company that was just a call away, in case anything fritzed.

“I’m having a hard time understanding how blockchain is going to really positively affect my citizens,” Kinville says. “Is it the speed of the blockchain? The security? Between faxes and emails, things get done just as quickly.” The city’s data is backed up on three servers; Kinville keeps a print copy, just in case. “We Vermonters are cautious. We like paper; you can always go back to it.” She sent Propy notes on how to improve its product, but doesn’t expect to buy it.

Natalia Karayaneva, Propy’s founder, says the land records platform is being tested in another Vermont town that didn’t have a computer system. But she acknowledges that privacy issues, as well as local rules and legacy computer systems, mean blockchain isn’t always a good fit for government. Propy is now focusing on an automated platform for realtors. It also uses blockchain, but the company doesn’t always trumpet it.

“In 2017, it was enough to have blockchain technology and everyone reaches out to you,” says Karayaneva. “But now working with traditional investors, we actually avoid the word blockchain in many of our materials.”

For a while, blockchain was seen as a panacea, says Andrew Stevens, a Gartner analyst who coauthored the “blockchain fatigue” study. Stevens’ team focused on projects that touted blockchain as a way to identify fraudulent and tainted goods in supply chains. They predicted 90 percent of those projects would eventually stall. Blockchain evangelizers were finding that supply chains more complex than expected, and that blockchain offered no ready-made solutions. When it comes to mission-critical blockchain projects, “there are no deployments across any supply chains,” he says.

Read more: https://www.wired.com/story/whats-blockchain-good-for-not-much/

28 Oct 2019
SCIENTISTS SAY THEY FINALLY FIGURED OUT HOW TO SPOT WORMHOLES

SCIENTISTS SAY THEY FINALLY FIGURED OUT HOW TO SPOT WORMHOLES

Thinking With Portals

Scientists think they’ve come up with a way to detect traversable wormholes, assuming they exist.

There’s never been any sort of evidence that traversable wormholes — portals between two distant parts of the universe, or two universes within a theoretical multiverse — are real. But if they are, a team of scientists think they know what that evidence might look like, breathing new life into a far-out theory that could finally achieve faster-than-light travel.

Telltale Wobbles

If a wormhole were to exist, then the gravitational pull of objects on one side, like black holes or stars, would influence the objects on the other side.

If a star wobbled or had otherwise inexplicable perturbations in its orbit around a black hole, researchers could hypothetically argue that they’re being influenced by the gravity of something on the other end of a wormhole, according to research published this month in the journal Physical Review D.

“If you have two stars, one on each side of the wormhole, the star on our side should feel the gravitational influence of the star that’s on the other side,” said University at Buffalo physicist Dejan Stojkovic. “The gravitational flux will go through the wormhole.”

Occam’s Razor

While the researchers hope to look for wobbles in the orbits of stars orbiting near Sagittarius A*, the supermassive black hole at the center of the Milky Way, Stojkovic concedes that spotting some wouldn’t guarantee that a wormhole exists there.

He added “we cannot say that, ‘Yes, this is definitely a wormhole.’ There could be some other explanation, something else on our side perturbing the motion of this star.”
 
27 Oct 2019
Big Tech Is Making A Massive Bet On AI … Here’s How Investors Can, Too

Big Tech Is Making A Massive Bet On AI … Here’s How Investors Can, Too

Artificial intelligence is becoming the future of everything. Yet, only a few large companies have the talent and the technology to perfect it.

That’s the gist of New York Times story published late last week. Rising costs for AI research are locking out university researchers and garage entrepreneurs, two of the traditional — and historically best — founts of innovation.

But it’s not all bad news for investors.

In the past, software engineers used code to build platforms and new business models. A prime example is Netflix.

Managers there transformed the mail-order DVD business into a digital media behemoth. They revolutionized how we view and interact with media. They also shook up traditional Hollywood studios by giving new and independent voices a huge platform.

In the process, the companies with the best algorithms will start to solve the medical, economic and social problems that have vexed researchers and scientists for decades.

Investors need to understand that winners and losers are being determined right now as the cost of AI research becomes prohibitive.

Think of the research process as a set of increasingly complex math problems. Researchers throw enormous amounts of data at custom algorithms that learn through trial and error. As the number of simulations mount, so do costs.

Big problems like self-driving cars or finding the cause of disease at the cellular level require immense amounts computing power.

An August research report from the Allen Institute for Artificial Intelligence determined that the number of calculations required to perform cutting-edge AI research soared 300,000x over the course of the past six years.

Only a handful of companies have the resources to compete at that level.

Long ago, executives at Amazon.comMicrosoftAlphabet and Facebook had the foresight to begin building massive cloud computing scale. Their data centers, many the size of football fields, are strewn all over the globe. Millions of servers, connected with undersea cables and fiber optic lines, have replaced the mainframes of old.

If you want to do great things in AI research, you’ll probably need to deal with at least one of these four big firms.

It’s a pinch being felt even by large technology companies …

Adobe and SAP joined an open data alliance with Microsoft in September 2018. A day later, salesforce.com hooked up with Amazon Web Services, Amazon’s cloud computer arm.

There has been some effort to break up the concentration of power. But critics are still mostly focused on the wrong things. In their view, data is the new oil, and it begs for regulation.

In the early 1900s, oil was the lifeblood of industry. It was central to the development of new game-changing chemicals. It powered the nascent automobile and steel complex.

The oil barons were the gatekeepers to innovation. In the process, they amassed fantastic wealth, as did many other industrialists. Income inequality soared.

Eventually, this led to calls for regulation, and trust-busters were brought in to break up (and control) the oil giants.

The parallels to today are convenient, and lazy.

Writers at The Economist in 2017, painted a dystopian picture of our future — one where the tech giants remain unregulated. The influential finance magazine concluded antitrust regulators must step in to control the flow of data, just as they did with oil companies in the early 1900s.

However, data is not oil. It’s not dear. It’s abundant.

Thanks to inexpensive sensors and lightweight software, there is a gusher of digital information everywhere. It comes from our wrists, cars and television sets. Soon it will shoot out of traffic lights, buses and trains; mining pits, farm fields and factories.

The limited resource is computing power. Enterprises, governments and researchers will need to pay up if they want to turn their data into something of value.

McKinsey, a global research and consulting firm, argues unlocking data should be a strategic priority at every enterprise. Analysts predict data will change business models in every industry, every business going forward.

The most important takeaway is that all future key AI breakthroughs are likely to come out of the big four. They have the technological and financial resources to attract talent. They have the scale to push the envelope.

It’s not a surprise that Amazon is leading in advanced robotics and language processing, or that Alphabet started developing self-driving cars in 2009.

Microsoft is building the biggest connected car platform in the world: Its engineers in Redmond, Wash., imagine a world of vehicle synchronization and the end of traffic.

Across town, Facebook researchers are working on augmented reality and brain computer interfaces.

These are big ideas with huge potential payoffs.

Amazon, Microsoft, Alphabet and Facebook are as important today as Standard Oil, Royal Dutch Shell and British Petroleum were a century ago.

Their resource is not oil, or data for that matter. It’s computing power. They’re leveraging that position to dominate AI research, the most important technology of the future.

For their investors, this is a good thing.

Growth investors should consider buying the stocks into any significant weakness. The story of AI is only getting started.

Source: https://www.forbes.com/sites/jonmarkman/2019/10/26/big-tech-is-making-a-massive-bet-on-ai–heres-how-investors-can-too/#a3cfea856d73

26 Oct 2019
Expert: VR Headsets Should Have Brain Interfaces

Expert: VR Headsets Should Have Brain Interfaces

Brain-computer interfaces could make VR gaming way more immersive.

Mind Control
Virtual reality headsets are already pretty good at fooling our eyes and ears into thinking we’re in another world. And soon, we might be able to navigate that world with our thoughts alone.

Speaking at this year’s Game Developer’s Conference in San Francisco, Mike Abinder, in-house psychologist and researcher for game developer and distributor Valve, gave a talk on the exciting possibilities of adding brain-computer interfaces to VR headsets.

Personalized Gaming
The idea is to add non-invasive electroencephalogram (EEG) sensors to the insides of existing VR headsets. EEG readers detect the electrical signals firing in the brain and turn them into data points. And by analyzing that data, according to Abinder, game designers could make games that respond differently depending on whether you’re excited, happy, sad or bored.


“So think about adaptive enemies. What kinds of animals do you like playing against in gaming?” Ambinder said, as quoted by VentureBeat. “If we knew the answers to these questions, you could have the game give you more of a challenging time and less of the boring time.”

Game design could become almost perfectly tailored to the person wearing the VR headset — or even recreate a perfect representation of you inside a virtual world. Your avatar could perfectly mimic your current state of mind or mood.

“All of a sudden, we start becoming able to assess how you’re responding to various elements in game,” Ambinder continued. “We can make small changes to make big changes.”

Brain Extensions
There are a handful of companies already trying to harness brain signals for enhancing gaming experiences. A startup called Neurable is already testing out BCIs built into off-the-shelf VR headsets “to create a natural extension of our brains, creating new possibilities for human empowerment,” according to its website.

Of course, Abinder’s vision of the future of gaming is mostly a fun thought experiment at this stage. Even hospital-grade EEGs have to deal with a huge amount of noise — and that’s especially the case for consumer-grade, non-invasive scanners that are not planted to the scalp or surgically implanted.

Source: https://futurism.com/brain-computer-interface-vr-headsets

24 Oct 2019
These Scientists Want to Bring Back Zeppelins in a Big Way

These Scientists Want to Bring Back Zeppelins in a Big Way

Here’s an explosive situation: An Austrian team of scientists are proposing an airship, otherwise known as a zeppelin, that’s ten times the size of the Hindenburg — the 800-foot German passenger airship that infamously caught fire in 1937, ending in a disaster, while landing in New Jersey.

Despite their inherent dangers, zeppelins could revolutionize cargo transportation in the 21st century, the scientists argue. For one, according to a terrific NBC News feature on the project, hydrogen-filled zeppelins could greatly reduce carbon dioxide emissions from the maritime shipping industry.

Thanks to the tenfold increase in dimensions, its capacity to lift cargo could increase 1000 fold, the team argues in a paper submitted to the September issue of the journal Energy Conversion and Management.

The airship could also be much quicker than cargo ships. The idea is to harness powerful jet stream winds at altitudes between ten and 20 kilometers where winds reach an average of 165 km/h. In fact, the proposed zeppelins could circle the entire planet in as little as two weeks according to the paper — while hauling more than 20,000 tons of cargo.

“I didn’t invent this,” Julian Hunt, a postdoctoral fellow at the International Institute for Applied Systems Analysis in Laxenburg, Austria, told NBC, referring to the idea of harnessing the power of strong air currents. “The Hindenburg used to do it. They had this path which would go from New York to Tokyo and then come back,” he said. “The jet stream hasn’t changed much in 100 years.”

Today, technologies like carbon fiber could greatly increase the structural rigidity of airships’ hulls. We’ve also got far more detailed weather data and forecasting systems.

Still, plenty of risks remain despite these advancements. Hydrogen is hydrogen — a flammable gas that could still end in disaster even 82 years after the Hindenburg’s demise that killed 36 people. Today’s much smaller blimps rely on inert helium gas, but it’s a resource that’s far more expensive to extract.

Hunt’s solution: autonomous operation. “The idea would be that the whole process would be automated so that in case you have an accident, no one will be injured — only the equipment and the cargo,” Hunt told NBC News.

Source: https://futurism.com/scientists-bring-back-zeppelin

23 Oct 2019
How Should We Measure the Digital Economy?

How Should We Measure the Digital Economy?

Suppose we make you an offer. You give up access to Google search for one month, and we pay you $10. No? How about $100? $1,000? What would we need to pay you to forgo access to Wikipedia? Your answer can help us understand the value of the digital economy.

In 2018, Americans spent an average of 6.3 hours a day on digital media—not just Google and Wikipedia but social networks, online courses, maps, messaging, videoconferencing, music, smartphone apps, and more. Digital media consumes a large and growing share of our waking lives, but these goods and services go largely uncounted in official measures of economic activity such as GDP and productivity (which is simply GDP per hour worked). We listen to more and better music, navigate with ease, communicate with coworkers and friends in a rich variety of ways, and enjoy myriad other benefits we couldn’t have imagined 40 years ago. But if you were to look only at GDP numbers, you’d think that the digital revolution never happened. The contribution of the information sector as a share of total GDP has barely budged since the 1980s, hovering between 4% and 5% annually and reaching a high of only 5.5% in 2018. To paraphrase the economist Robert Solow, we see the digital age everywhere except in the GDP statistics.

The reason the value of digital offerings is underrepresented is that GDP is based on what people pay for goods and services. With few exceptions, if something has a price of zero, then it contributes zero to GDP. But most of us get more value from free digital goods such as Wikipedia and online maps than we did from their more expensive paper predecessors.

Policy makers use GDP data to make decisions about how to invest in everything from infrastructure and R&D to education and cyberdefense. Regulators use it to set policy that affects technology firms and other organizations. Because the benefits of digitization are dramatically underestimated, those decisions and policies are being made with a poor understanding of reality.

Effective management of the digital economy depends on our ability to accurately assess the value of free digital goods and services. That’s why we developed a new technique to measure not only how much consumers pay for digital products but how much they benefit from them. And that uncounted benefit is substantial. For example, our research with Felix Eggers, of the University of Groningen, found that Facebook alone has created more than $225 billion worth of uncounted value for consumers since 2004.

Capturing the unmeasured benefit of free goods is not a new problem: Think of earlier waves of innovation that produced free and nearly free offerings like antibiotics, radio, and television, which clearly delivered significant value to the consumer. Given the exceptionally rapid growth of digital goods and services in our economy, it’s past time to solve this problem.

What GDP Doesn’t Measure

GDP is often used as a proxy for how the economy is doing. It’s a relatively precise number that signals every quarter whether the economy is growing or shrinking. However, GDP captures only the monetary value of all final goods produced in the economy. Because it measures only how much we pay for things, not how much we benefit, consumer’s economic well-being may not be correlated with GDP. In fact, it sometimes falls when GDP goes up, and vice versa.

GDP can be a misleading proxy for economic well-being.

The good news is that economics does provide a way, at least in theory, to measure consumer well-being. That measure is called consumer surplus, which is the difference between the maximum a consumer would be willing to pay for a good or service and its price. If you would have spent as much as $100 for a shirt but paid only $40, then you have a $60 consumer surplus.

To understand why GDP can be a misleading proxy for economic well-being, consider Encyclopedia Britannica and Wikipedia. Britannica used to cost several thousand dollars, meaning its customers considered it to be worth at least that amount. Wikipedia, a free service, has far more articles, at comparable quality, than Britannica ever did. Measured by consumer spending, the industry is shrinking (the print encyclopedia went out of business in 2012 as consumers abandoned it). But measured by benefits, consumers have never been better off. Our research found that the median value that U.S. consumers place on Wikipedia is about $150 a year—but the cost is $0. That translates into roughly $42 billion in consumer surplus that isn’t reflected in the U.S. GDP.

Consumer spending—the basis for GDP—can be counted at the cash register and shows up on companies’ revenue statements. In contrast, consumer surplus cannot be directly observed, which is one reason it hasn’t been used much for measuring the economy. Fortunately, the digital revolution has created not only tough measurement challenges but also powerful new measurement tools. In our research, we use digital survey techniques to run massive online choice experiments examining the preferences of hundreds of thousands of consumers. The results allow us to estimate the consumer surplus for a great variety of goods, including free ones that are missing from GDP statistics. We start by asking participants to make choices. In some cases, we ask them to choose between various goods (for example, “Would you rather lose access to Wikipedia or to Facebook for one month?”). In others, they choose between keeping access to a digital good or giving it up in exchange for monetary compensation (“Would you give up Wikipedia for a month for $10?”). To make sure that people have revealed their true preferences, we follow up with experiments in which participants actually must give up a service before they can receive compensation.

Source: https://hbr.org/2019/11/how-should-we-measure-the-digital-economy

22 Oct 2019
SCIENTISTS WORRIED THAT HUMAN BRAINS GROWN IN LAB MAY BE SENTIENT

SCIENTISTS WORRIED THAT HUMAN BRAINS GROWN IN LAB MAY BE SENTIENT

It’s Alive!

Some neuroscientists working with lab-grown human “mini brains” worry they could be experiencing an endless horror, with a conscious existence with no body.

At least, that’s the warning that a group of Green Neuroscience Lab researchers plan to deliver during a national meeting for the Society for Neuroscience on Monday, according to The Guardian. While it’s never been demonstrated that a mini brain has become conscious or sentient, the researchers believe that the risk is too great to continue using them.

Toeing The Line

Mini brains give scientists the opportunity to study neurological development and conditions in something more human-like than an animal model. While they don’t approach the complexity of a human brain, scientists have been able to make increasingly-complex mini brains for their work.

“We’re already seeing activity in organoids that is reminiscent of biological activity in developing animals,” Elan Ohayon, the director of the Green Neuroscience Laboratory, told The Guardian.

Hold Off

It’s that progress that has the neuroscientists concerned.

“If there’s even a possibility of the organoid being sentient, we could be crossing that line,” said Ohayon. “We don’t want people doing research where there is potential for something to suffer.”

Source: https://futurism.com/the-byte/scientists-worried-lab-grown-brains-sentient


 
 
21 Oct 2019
AI Can Help You—And Your Boss—Maximize Your Potential. Will You Trust It?

AI Can Help You—And Your Boss—Maximize Your Potential. Will You Trust It?

Would you trust an Artificial Intelligence (AI) to tell you how to become more effective and successful at your job? How would you feel if you knew your HR department uses AI to determine whether you are leadership material? Or that an AI just suggested to your boss that she should treat you better or else you might soon quit and join a competitor—well before the thought of jumping ship entered your mind?

Meet Yva, introduced by her creator David Yang in this fascinating podcast discussion.

David Yang is an impressive serial entrepreneur: he has launched twelve companies, beginning when he was in fourth grade. David started training as a physicist, to follow in his parents’ footsteps. He won math and physics Olympiads; then his first entrepreneurial detour “distracted” him from his studies for a while and sparked his passion for computer science and AI—it’s really worth hearing the story from David’s own voice, especially his concern of possibly disappointing his parents even as he was launching a hugely successful entrepreneurial and scientific career.

Yva, David’s latest creation, is an AI-powered people analytics platform—a remarkable example of the powerful role that AI is starting to play in the workplace, with the ethical implications that quickly come to the fore.

Yva’s neural network can mine and analyze workers’ activities across a range of work applications: email, Slack, G-Suite, GitHub. With these data, the AI can pick up a treasure trove of nuanced insights about employee behaviors: how quickly an employee responds to certain types of emails; or the tree structure of her communications: how many to subordinates, how many to peers or superiors, how many outside the company; and much more.

These insights can provide value to an organization in two main ways:

First, in identifying which employees have high potential to be great performers or strong leaders. The company tells Yva which individuals it currently considers as best performers; Yva’s neural network identifies which behaviors are characteristic of these top performers, and then finds other employees who exhibit some if not all of the same traits. It can tell you who has the potential to become a top salesperson, or an extremely effective leader; and it can tell you which characteristics they already possess and which ones they need to develop.

Second, Yva helps minimize “regrettable attrition” by identifying employees who are a high resignation risk. A decision to resign never comes out of the blue. First the employee will feel increasingly frustrated or burnt out; then she will become more open to consider other opportunities; then she will actively seek another job. Each stage carries subtle changes in our behavior: maybe how early we send out our first email in the morning, or how quickly we respond, or something in the tone of our messages. We can’t detect these changes, but Yva can.

For large companies, reducing regrettable attrition is Yva’s top contribution: losing and having to replace valuable employees represents a substantial cost. This, notes David Yang, makes the Return On Investment from deploying Yva very easy to identify. For smaller companies, especially in their growth stage, attrition is less of a concern and the greater value comes from the way Yva helps them build talent and leadership from within their ranks.

Given the ubiquitous concerns that technology will eliminate jobs, it’s refreshing and reassuring to hear that Yva instead proves its value by boosting employee retention.

Yva can also help the individual worker; it can create your personal dashboard with insights and suggestions on how you can change your behavior to become more effective and successful.

There is a trade-off. By default, Yva will respect your privacy, working on anonymized data. But the more individual data you are willing to share, the more Yva can help. The choice is yours.

David Yang notes some interesting geographic differences in the share of employees who opt in; he also notes that across the board, close to one employee in five remains adamantly opposed to disclosing her individual data.

Privacy concerns are fully understandable when faced with an AI that can drive important HR decisions. But is it smart to trust humans more than AI? David Yang notes that AI can help eliminate the human biases that often influence hiring and promotion decisions. Provided—he stresses—that the AI gets trained in the right way, only on final outcomes, on objective performance criteria, without feeding into it intermediate variables such as race, gender or age, which could create a built-in bias in the AI itself.

David Yang, unsurprisingly, is very bullish on the role that AI can play in people analytics and in our lives. Bullish, but very realistic and thoughtful, and willing to put himself on the line—at the end of the podcast discussion he talks of the role that Morpheus, another AI, plays in his personal life.

David thinks that in the future smaller companies (500 employees or less) will rely completely on AI-powered people analytics platform; he believes that AI will play a major role in leveraging the creativity and efficiency of individuals, while HR (human) professionals will focus on business-specific HR-partner roles. He has a horse in the race—Yva. But there seems to be little doubt that whatever role AI takes in HR and people analytics, it will be one of its most powerful influences in our professional—and personal—lives.

Source: https://www.forbes.com/sites/marcoannunziata/2019/10/20/ai-can-help-youand-your-bossmaximize-your-potential-will-you-trust-it/#1b696bef6b7b