Robotics and AI bring up questions of ethics. The ethics of AI can be illustrated with the example of Microsoft’s Tay. In the 16 hours between its first message and its last, Tay sent over 96,000 tweets. Similar to Siri and Alexa, Tay was designed to provide an interface for interaction. Microsoft created the bot using machine learning techniques with public data and then released Tay on Twitter, so the bot could gain more experience and, its creators hoped — to learn — and gain more intelligence. Within hours, the bot began tweeting sexist, racist, anti-Semitic content. This created a wave of negative media attention for Microsoft, prompted them to remove some of the tweets, and eventually to take Tay offline permanently, and issue a public apology. 

Tay’s brief appearance produced a cautionary tale that has implications for the relationship between algorithms and culture. As a chatbot, Tay was asked to distinguish the textual speech of others and respond with the same speech. But what comes intuitively to humans turns out to be hard to teach to a bot. The technical name for this is called “natural language processing,” and it has proven difficult for bots because conversation doesn’t follow rules — its unruly and contextual. Advances have now been made by applying machine learning algorithms. Like inductive reasoning in humans, machine learning works by developing generalizations from data. Patterns in a set of data provide a way for AI systems to better understand what is being said.

Facial recognition technologies have been reported to have difficulties with trans people and African-American faces. This points to a lack of diversity in the data which may cause inaccuracy problems. This raises the issue of transparency. Making the data sets available to the public or independent overseers provides a chance to evaluate programs for bias. Though this then raises further concerns with privacy.

Machine learning builds on data sets. The data sets are about people — their attributes, their preferences, their actions, and their interactions. This data documents people’s behaviors in their everyday lives. Although Tay was active on Twitter for less than a day, that’s all it took for it learn how to harass other users. The action was modeled off past patterns of behavior on Twitter, but it did not agree with the values of Microsoft.

Problems like these are propelling the need to create AI systems that take culture into account. This is important for conflict resolution, prediction, and decision-making. People’s choices come from their environment, upbringing, and experience. Creating AI systems that take culture into account is essential for making effective computations for policymakers, consumers, and citizens. If we want our AI systems to be inline with our reasoning and decisions, expectations and desires, then we need to create AI systems that take our cultures into account.

The way to do that is with stories and analogy-based technology. Given a new problem, an AI system can use a human-like retrieval process to find a similar prior situation, and compare how it applies. It’s called “analogical generalization,” and it’s a learning process that helps find patterns in stories. The advantage of this process is that the number of examples needed to train AI systems can be very small. Even ten examples can be enough, as opposed to deep learning systems which can take millions of examples. The efficiency arises from using more “human-like representations” than are usually used in machine learning. These human-like representations help the AI with intentions, reasons, and arguments, which are critical for building AI systems that can be trusted.  Cultural products such as stories, religious texts, and folktales provide reliable source data. This kind of data evolves over generations, providing a historical memory and moral framework in order to help AI systems ground decisions in everyday life. Cultural narratives offer a moral compass through events, actors, and motivations. 

The pipeline for building a cultural model for moral decision-making would be: 

(1) Gather a set of representative cultural products 

(2) Translate into whatever natural language form can be currently understood automatically
(3) Feed them to the analogical learning system

Preparing the Way 

AI for IoT and Robotics is the same thing — they’re learning algorithms — and all of this innovation is coming. Regardless of the equipment upon which their infrastructure is currently operating, high tech communications companies today want to be in on this transformation now. One IoT connectivity model resides in Senet’s Low Power Virtual Network. This particular model offers ways for participants across markets and industries to establish IoT revenue streams by monetizing their networks and services with very low capital and effort. With a cloud-based architecture purpose built for Low Power Wide Area Network (LPWAN) service delivery and Operations Support System (OSS) and Business Support System (BSS) technology built for IoT from the ground up, this approach allows for the rapid expansion of network coverage and product and service offerings beyond traditional areas of operation, unlocking regional and global expansion opportunities for existing communications companies. Thus avoiding the “we’re not ready yet” problem. 

Conclusion

Turns out, that though robots are good at building cars, cleaning up nuclear waste, and winning at games of strategy, they aren’t so good at hospitality. After opening to worldwide publicity in 2015, Japan’s Henn na, or “Strange,” Hotel, the world’s first all robot hotel, is now laying off its droids. They were annoying guests with their inability to answer questions, by making appearances in rooms when not called for, and by becoming foot hazards in the lobby when their batteries ran out. So maybe there are some things a robot can’t do.

China and the U.S. are currently the world’s leaders in advancing AI systems. Today, when you walk out your front door in China, cameras will immediately pick you up and log your whereabouts. Chinese consumers use digital currency and have their favorite snacks delivered to the office. AI improves and benefits their lives, but with the Chinese system of “social credit” earned for good behavior, the AI system can lead to public shaming if your score is low. So, we need people to oversee these systems for their biases and we all need to assure that these systems have transparency — “open data.” If Taobao, the internet consumer giant in China, talks to the bank, then citizens need to know what info is being shared. The Chinese have an “ecology” of AI: Wechat, the social media app, talks to Taobao, the consumer platform, which talks to the bank, which has access to your social credit score.  

Everything is advancing so fast! Everybody feels like they’re missing the boat. But wait! Maybe we need to think this through. Slow down a little bit. Perhaps, everything doesn’t need an AI system. The Skoll Foundation, which focuses on social entrepreneurship to fix societal problems, says that not everything needs an AI solution. While there is no doubt that AI algorithms could improve such things as water efficiency in irrigation systems, the problem that Sub-Saharan African farmers face are more fundamental, more about just having access to the water. Using AI to develop accurate risk models for crop insurance won’t help much without adequate systems for distributing insurance. So, perhaps basic functionality should be looked at before AI is applied, which can then be used to augment proper functioning.

But there’s good and bad with all technology. Facial recognition software can be used for mass surveillance or it can find missing children. New technology is always disruptive. It improves our lives, but imposes difficulties and challenges. The idea is to maximize the good features. Policy, laws, self regulation, crowdsourcing are all ways to mitigate the downside; but tech is always dual in nature — it’s good and it’s bad. 

Chinese citizens will gladly give over their data, so they can have their favorite tea delivered to the office; but the downside is social credit, which can make you an outlier and put your safety into jeopardy. Transparency may be the best hedge against systemic maliciousness; but AI is advancing, and the algorithms are learning; and if they keep getting better at what we do, then the follow-through question would be… then what will we do?

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Part One: Is it Possible to Murder a Robot?

Artificial Intelligence (AI) is one of the most profound technologies of our time. It is in the “brains” of our autonomous vehicles, the strategies which guard our homes, the advice for what stocks to trade, and in the friendliness of our personal assistants. AI is transforming areas from education to retail, health care to finance, entertainment to national defense. Benefits for its use are virtually endless, which means that its downsides will always be around too. So, what do we do with this AI?

AI is defined as computer systems which perform tasks which are usually associated with requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. AI systems can do these things, but their most striking feature is their learning capabilities. So, if these systems are learning, who’s teaching them and what are they being taught? And if these systems are learning and making decisions “autonomously,” will they become so autonomous that they defy humans?

Will the day come when you grab your coffee and coat, get ready to head to work, but the door won’t open. So you try again. And nothing. So then you ask your virtual assistant to open the door, please. And it responds… “I’m sorry, I’m afraid I can’t do that.”

Hitchbot: An example of how we treat Robots, which says a lot about how we treat each other

It was no ordinary crime. Four years ago, a hitchhiker was “murdered” in Philadelphia. The victim was a robot called Hitchbot. The “death” raised questions about the human-robot relationship. Hitchbot was an experiment designed to test this relationship. The robot looked like a small child with tubular blue arms and a bucket on its head. “Cuteness” was the aim. “It was extremely important that people would trust it and want to help it out,” said Dr. Zeller, who designed Hitchbot. 

Hitchbot had a GPS receiver to track its journey, movements in its arms, and software to allow it to communicate when asked questions. It could smile, it could wink, and it could move its thumb into a hitch position. Hitchbot was programmed so that its adventures could be tracked online and pictures could be posted on social media. It didn’t take long for Hitchbot to become a celebrity. 

Hitchbot took several journeys. It was picked up by an elderly couple and taken camping in Nova Scotia; attended a wedding in British Columbia; a baseball game at Fenway Park; took a tour of the Amsterdam canals; a trip to Disney World; in British Columbia Hitchbot arrived in a traditional 50-foot cedar canoe with the Songhee Nation and was then a guest of honor at a powwow, where it was given a name that translates as “Iron Woman.” 

Hitchbot picked up thousands of fans, many travelling miles to be the next person to give it a lift. Hitchbot was given its own social media accounts on Twitter, Facebook and Instagram. But then one day images were posted of Hitchbot lying in the streets of Philly with its arms and legs ripped off and its head missing. Dr. Zeller said, “It affected thousands of people worldwide. Hitchbot had become an important symbol of trust. It was very sad and it hit us and the whole team more than I would have expected.”

Robotics

The partial meltdown of one of Three Mile Island’s nuclear reactors 40 years ago left areas of the facility radioactive. That’s when a group of young researchers gave a boost to the field of robotics when they employed their idea to send robots in to clean up the damage. Now, we’re talking about food delivery via autonomous vehicles on college campuses — that’s robots with AI on wheels. George Mason University received 25 delivery robots that can haul up to 20 pounds each as they roll across campus at four miles per hour. 

To navigate the campus, these robots rely on AI, ultrasonic sensors, and nine cameras. Two-way audio onboard allows users to communicate with human teleoperators who monitor the robots from afar and can take over the machine at any moment. The robots can cross streets, climb curbs, navigate around obstacles, and operate in rain and snow. To launch the delivery program, Starship Technologies partnered with Sodexo, a company that manages dining on campus. Jeff McKinley, Sodexo district manager for George Mason, told Digital Trends that the delivery robots are “at the forefront of changing trends.”

Robots and AI are the same conversation. With personal assistants, self-driving cars, and the emergence companion robots, we can see how these devices are powered with AI and their “relationship” to the human is becoming an increasingly important relationship to study. For instance, in the case of companion robots, they are being designed to assist with the daily living tasks of people who have Alzheimer’s disease. These robots are helping them get in and out of bed, reminding them to take medication, measuring their mood, calming them down, and providing regular updates to human caregivers.

“But,” says Roboticist Professor Noel Sharkey, “we need to get over our obsession with treating machines as if they were human. People perceive robots as something between an animate and an inanimate object and it has to do with our in-built anthropomorphism.” For instance, he warns, “We can’t be falling in love with them.” 

“Intelligence”

Last year, at a conference on “intelligent” robotic systems, an autonomous drone using AI to navigate raced through a maze consisting of a complicated series of turns and gates. The drone destroyed its competition, completing the race course twice as fast as its nearest human competitor. 

AI-driven systems now routinely best humans. AlphaGo, a program built by DeepMind, went from learning the basics of the game Go to beating the world’s best human player in a little over three years. More recently, the AI AlphaStar, also by DeepMind, was able to beat a top player in the complex strategy video game “StarCraft II,” shutting out its human competitor five games to zero. 

AI is beating humans at games, but are these mathematical formulas expressed in computer programs “value-free tools” that can give us an accurate picture of social reality upon which to base our decisions? For instance, autonomous cars are not programmed to drive, they are programmed to anticipate what a human driver would do. When platforms decide which news stories to present, they use algorithmic predictions. They attempt to predict what news each individual person is looking for, and then they adopt decision rules designed to maximize user engagement. 

However, there are some reasons for making sure that the data sets, upon which decisions are made, remain “blind” so to speak — as in “justice is blind.” In the same way that it doesn’t matter who’s before the judicial bench, there are instances in which “judgements” of algorithmic systems can not be stacked, slanted or biased toward one group or another. 

Bias & Error

Navigating sidewalks, flying through mazes and beating Go Masters has interesting, useful, and beneficial implications; but recently the darker side of AI has been surfacing. Apple and Google have been accused of helping to “enforce gender apartheid” in Saudi Arabia, by offering an app that allows men to track women. And there have been reports of a Chinese company owning the dating app Grindr. The app caters to an LGBTQ crowd. U.S. officials believe the Chinese government could end up exploiting that information.

In Arizona last year, a self-driving car with a backup driver struck and killed a pedestrian. Based on a video taken inside the car and records collected from Hulu, police said that the backup driver was streaming an episode of “The Voice” on her phone. The driver looked up a half-second before hitting the pedestrian. 

Even with this tragedy, Google is moving ahead with its plans for a self-driving car called Waymo. Google plans to build a factory in Michigan, creating up to 400 jobs at what it describes will be the world’s first plant “100 percent” dedicated to the mass production of autonomous vehicles. The company integrates its self-driving system into vehicles it buys from automakers and is currently testing autonomous Chrysler Pacifica minivans. And Ford has also recently announced that it wants to bring self-driving cars to Washington, D.C.

Job Loss & Hacks

The rise of AI offers many benefits. However, we’re also reminded that we need to pay attention to the impact of AI on the displacement of jobs. Repetitive tasks that involve processing information, performing physical activities or operating machinery will be the first to be replaced by AI — which means that manufacturing jobs could be hit hard. 

And besides displacement, all devices themselves and their connectivity to each other are expected to boom in 2020 — the Internet of Things (IoT) is here — and all these units and systems do not have the same level of security. Hackers love to target IoT devices that don’t have built-in security.  

Need we be reminded of the 5 Biggest Data Breaches of the 21st Century:

1. Yahoo [2013-14] 3 billion user accounts: the attack compromised real names, email addresses, dates of birth, telephone numbers, email addresses, and passwords.

2. Marriott [2014-18] 500 million customers: the attack compromised names, contact information, passport numbers, credit card numbers, and expiration dates.

3. Adult Friend Finder [October 2016] 412.2 million accounts: the attack compromised names, email addresses and passwords.

4. eBay [May 2014] 145 million users: the attack compromised names, addresses, dates of birth and encrypted passwords.

5. Equifax [July 2017] 143 million consumers: the attack compromised Social Security Numbers, birth dates, addresses, drivers’ license numbers, and credit card data.

In late March of 2109, Facebook admitted that it had left hundreds of millions of users’ passwords exposed in plain text, potentially visible to the company’s employees, making this another major privacy and security headache for Google, which is already being scrutinized and penalized for mishandling people’s personal information. 
So important is connectivity — and thus the threat of hacking — that the NSA and US Cyber Command work side by side in the same building in Maryland. The NSA monitors foreign communications, while Cyber Command takes action in the digital realm. And in Georgia, Gov. Brian Kemp was just informed that his state’s new digital voting system is considered vulnerable to hacking. Even Jeff Bezos was recently hacked by the Saudi government in an effort to harm him.

To continue reading see Part Two

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