The Benefits of Artificial Intelligence


Asking about the benefits of artificial intelligence and machine learning reminds me a little of the transition to suitcases with wheels.

Do you remember lugging around those old suitcases? If not, good for you -- this original advertisement from US Luggage will take you back! Thank Bernard Sadow for persistence with his idea to add wheels, because when he pitched his idea people thought he was crazy. Surely no one would want to pull their own suitcase? His patent application stated, “Whereas formerly, luggage would be handled by porters and be loaded or unloaded at points convenient to the street, the large terminals of today, particularly air terminals, have increased the difficulty of baggage-handling...Baggage-handling has become perhaps the biggest single difficulty encountered by an air passenger.”

We can wheel our own suitcases these days, but baggage handling is still a challenge for airlines. But one of the benefits of artificial intelligence and machine learning is the improvements companies like Amadeus are employing to baggage handling in airports to reduce the risk of lost bags. And in the Frankfurt Airport, Fraport uses predictive modeling from SAS to improve the overall customer experience moving through the airport.

I hear plenty of verbal and online chatter predicting that artificial intelligence and machine learning will eliminate jobs. But a review of history shows that many such past predictions have not come true. Remember the introduction of ATMs? The expectation was that bank tellers would become an anachronism, but in fact demand for tellers has increased greater than average. Automation reduced the number of tellers needed per bank, but this savings allowed banks to open new branches, thus stimulating demand for tellers.

The same pattern repeated with the introduction of grocery store scanners and cashiers and electronic document discovery and paralegals. Today your friendly bellhop still greets you at the hotel as you roll your suitcase to the entrance because in fact the US Bureau of Labor Statistics predicts average growth in demand for baggage porters and bellhops. I believe that the benefits of artificial intelligence and machine learning include increased productivity that will lead to job creation. Plenty of enthusiastic electronic ink has been spilled about the benefits of artificial intelligence and machine learning for business, so I’m going to focus on another reason why I’m excited about this field -– the public benefit in areas like our health, economic development, the environment, child welfare, and public services.

In a blog post on LinkedIn, Microsoft CEO Satya Nadella envisions a future where computers and humans work together to address some of society’s biggest challenges. Instead of believing computers will displace humans, he argues that at Microsoft “we want to build intelligence that augments human abilities and experiences.” He understands the trepidation some have about jobs and even the supposed Singularity (the idea that machines will run amok and take over), writing “...we also have to build trust directly into our technology,” to address privacy, transparency, and security. He cites an example of the social benefits of machine learning and artificial intelligence in the form of a young Microsoft engineer who lost his sight at an early age but who works with his colleagues to build what is essentially a mini-computer working like glasses to give him information in an audible form he can consume.

Nadella's example of his young colleague is one of many where machine learning and artificial intelligence are making fantastic advances in providing great help for people with disabilities in the form of various health care wearables and prosthetics. Health care is replete with examples, as deep learning and other techniques show rapid gains on humans for diagnosis. For example, the deep learning startup, Enlitic, makes software that in trials is 50% more accurate than humans in classifying malignant tumors, with no false-negatives (i.e. saying that scans show no cancer when in fact there is malignancy) when tested against three expert human radiologists (who produced false-negatives 7% of the time). In the field of population health management AICure makes a mobile phone app that increases medication adherence among high-risk populations using facial recognition and motion detection. Their technology makes sure that the right person is taking the right medication at the right time.

There are nonprofits that have been drawn to the benefits of artificial intelligence and machine learning, such as DataKind, which “harnesses the power of data science in the service of humanity.” In a project with the nonprofit GiveDirectly, DataKind volunteers worked on an algorithm to classify satellite images to identify the poorest households in rural villages in Kenya and Uganda. A team from SAS is working with DataKind and the Boston Public Schools to improve transportation for their students, using optimization. Thorn: Digital Defenders of Children, uses technology and innovation to fight child sexual exploitation. Much of the trafficking is done online, so analysis of chatter, images, and other data can aid in identifying children and the predators.

Trafficking in elephant ivory leads to an estimated 96 elephant deaths every day, but a machine learning app is helping wildlife patrols predict the best routes to track poachers. The app drew on 14 years of poaching data activity, produces routes that are randomized so poachers can be foiled, and learns from new data entered. So far its routes have outperformed those by previous ranger patrols. Protection Assistant for Wildlife Security (PAWS) was developed by Milind Tambe, a professor from the University of Southern California, based on security game theory. Tambe has also built these kinds of algorithms for federal agencies like Homeland Security, the Transportation Security Administration, and the Coast Guard to optimize the placement of staff and surveillance to combat smuggling and terrorism.

Other public sector organizations also realize the benefits of artificial intelligence and machine learning. The New York Police Department has developed the Domain Awareness System, which uses sensors, databases, devices, and more, along with operations research and machine learning, to put updated information in the hands of cops on the beat and at the precincts. Delivering this information even faster than the dispatchers means cops are better prepared when they arrive on the scene. Teams from the University of Michigan’s Flint and Ann Arbor campuses are working together with the City of Flint to use machine learning and predictive algorithms to predict where lead levels are highest and build an app to help both residents and city officials with resources to better identify issues and prioritize responses. It took a lot of work to gather all the disparate information together, but interestingly their initial findings indicate that the troubles are not in the lines themselves but in individual homes, although the distribution of the problems doesn’t cluster like you’d expect.

These are just a few of the many examples of the social benefits of artificial intelligence and machine learning, but they illustrate why I’m excited about their potential to improve our society. Automation fueled by artificial intelligence is likely to result in what economists call "structural unemployment," when there is a mismatch between the skills some workers have and those the economy demands, typically a result of technological change. This disruption is undoubtedly devastating for those who lose their jobs, and I believe as a society we have an obligation to provide workforce development programs and training to help those impacted shift to new skills. But I am hopeful that machine learning will be able to offer help to those disrupted by these changes.

And it may even offer job opportunities. SAS is working with our local Wake Technical Community College, which has launched the nation's first Associate's Degree in Business Analytics, fueled in part by a grant from the US Trade Adjustment Assistance Community College and Career Training initiative. They will also offer a certificate program aimed at displaced or underemployed workers will be targeted and required to earn 12 credit hours to gain a certificate of training. While these graduates will not likely start off doing machine learning, they may move in that direction, and at a minimum contribute to teams that do use these methods.

And LinkedIn uses machine learning extensively, for recommendations, image analysis, and more, but through their Economic Graph and LinkedIn for Good initiatives the company aims to connect talent to opportunities by filling in gaps in skills. In partnership with the Markle Foundation their new LinkedIn Cities program offers training for middle skill workers, those with a high school diploma and some college but no degree, and is piloting in Phoenix and Denver. The combination of online and offline tools with connections to educators and employers will help these individuals improve their opportunities.

SAS will highlight the data for good movement at our upcoming Analytics Experience conference in Las Vegas September 12-14. Jake Porway, the Founder and Executive Director of DataKind, will be one of the keynote speakers. My colleague Jinxin Yi will be giving a super demo on the SAS/DataKind project I mentioned that aims to improve transportation for the Boston Public Schools. His session is one of several that have been tagged in the program as Data for Good sessions. We’ll have a booth where you can learn more and get engaged with #data4good. Stop by and say hi to me if you're there!

Suitcase image credit: Photo courtesy of U.S. Luggage, Briggs & Riley

Bank teller image credit: photo by AMISOM Public Information // attribution by creative commons

Xray image credit: photo by Yale Rosen // attribution by creative commons

Elephants image credit: photo by Justin Norton // attribution by creative commons

NYPD image credit: photo by michele ursino // attribution by creative commons

Bus image credit: photo by ThoseGuys119 // attribution by creative commons

This content was reposted from SAS Subconscious Musings. Go there to read the original post.

Polly Mitchell-Guthrie, SAS R&D Project and Program Management

Polly Mitchell-Guthrie leads the Advanced Analytics Customer Liaison Group in R&D, connecting with customers to improve SAS products. At SAS for 14 years, Polly has held a variety of roles in finance and alliances, and the Global Academic Program. She has a BA and MBA from the University of North Carolina at Chapel Hill.

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The Benefits of Artificial Intelligence

Learn how artificial intelligence is contributing to the good of society and making our lives better. Presentations at Analytics Experience.


Re: Sweet and sour of AI
  • 9/29/2016 8:19:25 AM
NO RATINGS

Lyndon, not to dismiss your concerns because I believe what is happening today is on an unprecedented scale of sleaze, but let's be honest that our culture has always included an element of deception around data and facts. Look at Enron. Look at the Vietnam War. Look at the Spanish American war or the origins of the pyramid scheme. What is most surprising today is that the skepticism that Americans developed post watergate has seemingly disappeared for half the population.

Re: Sweet and sour of AI
  • 9/23/2016 5:18:15 PM
NO RATINGS

..

Jim writes


What is data? How factual does it have to be?

You cite the traditional view that data is facts validated in evidence. However, we increasingly draw conclusions from "data" that cannot be validated. With AI we typically have a mix of solid facts and what I will call "possibilities".


 

Jim is absolutely right. But what I'm raising concern about (and why the issue of lying by a major public figure and contender for the presidency is relevant) is the CULTURE of deception, the CULTURE of acceptbility for lying (and, say, fabricating of data) that I think this is fostering – and what problems it may nurture for scientific research and data analysis.

To use Jim's example: I can imagine researchers in AI fudging sensor data (e.g., rigging the system to either detect false data or skewing the interpretation algorithms) to claim some kind of breakthrough in machine vision technology and qualify for a grant for continuing research. 

You can come up with youtr own scenarios of how company personnel might tamper with customer sales data to try to achieve some result, perhaps for either personal or department benefit.

Obviously, such manipulation is outrageously unacceptable and the offenders might (or might not) get caught. But the point is that this new climate of "informally acceptable" lying, it seems to me, is shifting a significant ethical standard – virtually movig it to another planet.

..

Re: Sweet and sour of AI
  • 9/23/2016 9:33:48 AM
NO RATINGS

@Lyndon. You've opened up an interesting question, and one that we should air in another forum for all of the A2 community. What is data? How factual does it have to be?

You cite the traditional view that data is facts validated in evidence. However, we increasingly draw conclusions from "data" that cannot be validated. With AI we typically have a mix of solid facts and what I will call "possibilities". With machine vision, for example, video sensors that identify a large or angular non-movable object feed "data" about that object to a system that identifies it as probably being a rock or other solid object that should be avoided. But the system doesn't really know if it is non-movable or that it is solid. It just appears that way. So, not validated facts, but useful data.

We are in an era when not all data can be validated to the point of being proven fact. However, it is close enough to fact to draw conclusions with varying degrees of certainty. The level of certainty needed of course varies with the nature of the application.

We look at sales data and conclude that Customer X likes a certain type of product or is a DIY homeowner. So we promote related products to them. However, we don't know if it's a couple doing the shopping and using Customer X's credit or loyalty card, or maybe a purchase on behalf of a friend. We also don't know if they bought that product by mistake or if they didn't like it and returned it later (unless we have more provable data). For the retailer's purpose, they can speculate that Customer X likes the product and they can risk a promotional email or direct mail piece.

How much trust you put in "data"? More and more, it depends on what you want to use it for and the risk vs reward. 

Re: Sweet and sour of AI
  • 9/22/2016 10:30:52 PM
NO RATINGS

..

Broadway writes "But we are supposed to be talking about analytics right?"

Yeah, certainly ... and to relate this to analytics, I'll briefly address the issue of data, validity, and trust. Because certainly, if we cannot trust data, and what's real or truthful or not is a matter of contentious public debate, then the very basis of Big Data and analytics is jeopardized.

This is something that does worry me – the widespread erosion not just of trust in data – facts validated by evidence – but even worse, of a commonly held concept of what's true or not, i.e., what's valid data and what isn't.

This erosion has been happening for quite a while, but with this political campaign, the issue has intensified into a yawning dichotomy of dispute over what's regarded as factual and what is not. To the point that a presidential candidate who basically lies whenever his lips move is regarded as something of a national role model.

The model that major leaders present matters. What much of the country –  and its youngest members – are seeing as such a model is someone who apparently espouses a kind of nihilism, and sends the message that fudging the facts, making up facts, employing deception, baldly lying can all be done with impunity, with no meaningful consequences. Imagine the impact that this is probably having on wide swaths of the U.S. public, and particularly the younger generations.

If you've been bothered about fudging on resumes, I'd suggest that may be the equivalent of a lofty standard of honesty compared to what may be coming in the future with a public ethos influenced by this presidential campaign.

..

 

Re: Sweet and sour of AI
  • 9/22/2016 8:57:21 PM
NO RATINGS

Lyndon, I will have to read that --- though I feel guilt anytime I read one of these trump stories. The media is certainly on a click bait frenzy with him and has been since the primaries. They will all be guilty of conflating his Candidacy if he gets elected --- and knowing him, he will make them pay. But we are supposed to be talking about analytics right?

Re: Sweet and sour of AI
  • 9/21/2016 2:21:30 PM
NO RATINGS

..

Broadway writes


I stand corrected that telephone operators were around a lot more recently in larger numbers. What I dont concede is that someone who lost their job 25 years ago is still overly bitter about it. Some yes. But most have hopefully moved on. Bad memories yes. But grounds for joining the alt right no. 


 

Well, we'll have to agree to disagree about that. Who knows? There might even be some embittered unemployed buggy whip craftsmen attending those Trump rallies ...

But it is important to try to have some understanding of what's motivating approximately half the country to adulate a crooked, conniving, cheating, lying, bigoted, racist, xenophobic, race-baiting, violence-mongering, sociopathic con artist in a bid to head the government of perhaps the world's most powerful nation. In this regard, I would strongly recommend a recent New York Times article:

• We Need 'Somebody Spectacular': Views From Trump Country

Enlightening. Check it out.

 

 

 

Re: Sweet and sour of AI
  • 9/20/2016 11:55:37 PM
NO RATINGS

I stand corrected that telephone operators were around a lot more recently in larger numbers. What I dont concede is that someone who lost their job 25 years ago is still overly bitter about it. Some yes. But most have hopefully moved on. Bad memories yes. But grounds for joining the alt right no.

Re: Sweet and sour of AI
  • 9/20/2016 4:10:25 PM
NO RATINGS

..

Jim writes


Most of the country didn't get direct-dial long distance until the 1960s. Until then you needed an operator just to call two towns away. So, the die-off of operators probably started with that direct dial across local exchanges. However, there were still plenty of operators 15-20 years ago handling things like collect and credit card calls (remember those?) and directory assistance. Plus, even with PBX systems that allowed direct dial within a company, there were still lots of receptionists and operators within most businesses outside of THE phone company.


 

Even as late as the 1990s, AT&T had thousands of telephone operators. A decade before that, the company had tens of thousands.

I think that time period is enough for lots of former operators to still bitterly remember the difficulties when those jobs were phased out.

And the impact of the loss of these (and similar kinds of) jobs was not just on those for whom the job was their primary livelihood. These were entry-level and transitional, stepping-stone jobs for lots of people (overwhelmingly women in this case) as they attended college or otherwise tried to advance to higher professional levels in the earlier periods of their lives.

Jim adds:

 


However, my point in citing operators wasn't to know what became of them but to point out that automation doesn't mean the end of the world even for the employees.


 

It doesn't mean the end of the world for every single one of them, but for many it probably was a stunning blow, and in this YOYO (You're On Your Own) society, there are virtually no real, meaningful re-training, job-upgrading programs available (at least not on any significant scale) – no  college tuition, income-supplementation, childcare, etc. that would be needed to assist people to acquire new skills and make that transition to new careers and livelihoods.

Now multiply this by a factor of X millions (coalminers, former steelworkers, former autoworkers, former office secretaries, former brick-and-mortar store retail employees, and soon former truckdrivers and taxidrivers, etc.). Then throw in vulnerability to fear, racism, bigotry, xenophobia, Islamophobia, antisemitism, nativism, etc., and I think you have a very potent mixture of bitter, misguided potential voters ripe for the next proto-fascistic demagogue ...  

..

Re: Sweet and sour of AI
  • 9/19/2016 11:34:06 AM
NO RATINGS

@Broadway. Most of the country didn't get direct-dial long distance until the 1960s. Until then you needed an operator just to call two towns away. So, the die-off of operators probably started with that direct dial across local exchanges. However, there were still plenty of operators 15-20 years ago handling things like collect and credit card calls (remember those?) and directory assistance. Plus, even with PBX systems that allowed direct dial within a company, there were still lots of receptionists and operators within most businesses outside of THE phone company.

However, my point in citing operators wasn't to know what became of them but to point out that automation doesn't mean the end of the world even for the employees. The evolution of most companies created other roles for those who served as receptionists/operators, at least within the non-Bell system sectors.

 

Re: Sweet and sour of AI
  • 9/18/2016 9:33:56 PM
NO RATINGS

Gotta figure that those telephone operators have moved on by now. When was their heydey? In the 1950? The 1920s? I cant imagine anyone being bitter about that. Perhaps many of them moved on to become grandmothers and great-grandmothers, fantastic role models for their daughters, granddaughters, and other kin, who have gone on to college, become entrepreneurs, doctors, executives as telecom companies.

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