This is your office on AI

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Article by Jeffrey Brown at a Special Issue of the Wilson Quarterly on AI: “The future has arrived and it’s your first day at your new job. You step across the threshold sporting a nervous smile and harboring visions of virtual handshakes and brain-computer interfaces. After all, this is one of those newfangled, modern offices that science-fiction writers have been dreaming up for ages. Then you bump up against something with a thud. No, it’s not one of the ubiquitous glass walls, but the harsh reality of an office that, at first glance, doesn’t appear much different from what you’re accustomed to. Your new colleagues shuffle between meetings clutching phones and laptops. A kitchenette stocked with stale donuts lurks in the background. And, by the way, you were fifteen minutes late because the commute is still hell.

So where is the fabled “office of the future”? After all, many of us have only ever fantasized about the ways in which technology – and especially artificial intelligence – might transform our working lives for the better. In fact, the AI-enabled office will usher in far more than next-generation desk supplies. It’s only over subsequent weeks that you come to appreciate how the office of the future feels, operates, and yes, senses. It also slowly dawns on you that work itself has changed and that what it means to be a worker has undergone a similar retrofit.

With AI already deployed in everything from the fight against ISIS to the hunt for exoplanets and your cat’s Alexa-enabled Friskies order, its application to the office should come as no surprise. As workers pretty much everywhere can attest, today’s office has issues: It can’t intuitively crack a window when your officemate decides to microwave leftover catfish. It seems to willfully disregard your noise, temperature, light, and workflow preferences. And it certainly doesn’t tell its designers – or your manager – what you are really thinking as you plop down in your annoyingly stiff chair to sip your morning cup of mud.

Now, you may be thinking to yourself, “These seem like trivial issues that can be worked out simply by chatting with another human being, so why do we even need AI in my office?” If so, read on. In your lifetime, companies and workers will channel AI to unlock new value – and immense competitive advantage….(More)”.

Tech Platforms and the Knowledge Problem

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Frank Pasquale at American Affairs: “Friedrich von Hayek, the preeminent theorist of laissez-faire, called the “knowledge problem” an insuperable barrier to central planning. Knowledge about the price of supplies and labor, and consumers’ ability and willingness to pay, is so scattered and protean that even the wisest authorities cannot access all of it. No person knows everything about how goods and services in an economy should be priced. No central decision-maker can grasp the idiosyncratic preferences, values, and purchasing power of millions of individuals. That kind of knowledge, Hayek said, is distributed.

In an era of artificial intelligence and mass surveillance, however, the possibility of central planning has reemerged—this time in the form of massive firms. Having logged and analyzed billions of transactions, Amazon knows intimate details about all its customers and suppliers. It can carefully calibrate screen displays to herd buyers toward certain products or shopping practices, or to copy sellers with its own, cheaper, in-house offerings. Mark Zuckerberg aspires to omniscience of consumer desires, by profiling nearly everyone on Facebook, Instagram, and WhatsApp, and then leveraging that data trove to track users across the web and into the real world (via mobile usage and device fingerprinting). You don’t even have to use any of those apps to end up in Facebook/Instagram/WhatsApp files—profiles can be assigned to you. Google’s “database of intentions” is legendary, and antitrust authorities around the world have looked with increasing alarm at its ability to squeeze out rivals from search results once it gains an interest in their lines of business. Google knows not merely what consumers are searching for, but also what other businesses are searching, buying, emailing, planning—a truly unparalleled matching of data-processing capacity to raw communication flows.

Nor is this logic limited to the online context. Concentration is paying dividends for the largest banks (widely assumed to be too big to fail), and major health insurers (now squeezing and expanding the medical supply chain like an accordion). Like the digital giants, these finance and insurance firms not only act as middlemen, taking a cut of transactions, but also aspire to capitalize on the knowledge they have gained from monitoring customers and providers in order to supplant them and directly provide services and investment. If it succeeds, the CVS-Aetna merger betokens intense corporate consolidations that will see more vertical integration of insurers, providers, and a baroque series of middlemen (from pharmaceutical benefit managers to group purchasing organizations) into gargantuan health providers. A CVS doctor may eventually refer a patient to a CVS hospital for a CVS surgery, to be followed up by home health care workers employed by CVS who bring CVS pharmaceuticals—allcovered by a CVS/Aetna insurance plan, which might penalize the patient for using any providers outside the CVS network. While such a panoptic firm may sound dystopian, it is a logical outgrowth of health services researchers’ enthusiasm for “integrated delivery systems,” which are supposed to provide “care coordination” and “wraparound services” more efficiently than America’s current, fragmented health care system.

The rise of powerful intermediaries like search engines and insurers may seem like the next logical step in the development of capitalism. But a growing chorus of critics questions the size and scope of leading firms in these fields. The Institute for Local Self-Reliance highlights Amazon’s manipulation of both law and contracts to accumulate unfair advantages. International antitrust authorities have taken Google down a peg, questioning the company’s aggressive use of its search engine and Android operating system to promote its own services (and demote rivals). They also question why Google and Facebook have for years been acquiring companies at a pace of more than two per month. Consumer advocates complain about manipulative advertising. Finance scholars lambaste megabanks for taking advantage of the implicit subsidies that too-big-to-fail status confers….(More)”.

How the Math Men Overthrew the Mad Men

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 in the New Yorker: “Once, Mad Men ruled advertising. They’ve now been eclipsed by Math Men—the engineers and data scientists whose province is machines, algorithms, pureed data, and artificial intelligence. Yet Math Men are beleaguered, as Mark Zuckerberg demonstrated when he humbled himself before Congress, in April. Math Men’s adoration of data—coupled with their truculence and an arrogant conviction that their “science” is nearly flawless—has aroused government anger, much as Microsoft did two decades ago.

The power of Math Men is awesome. Google and Facebook each has a market value exceeding the combined value of the six largest advertising and marketing holding companies. Together, they claim six out of every ten dollars spent on digital advertising, and nine out of ten new digital ad dollars. They have become more dominant in what is estimated to be an up to two-trillion-dollar annual global advertising and marketing business. Facebook alone generates more ad dollars than all of America’s newspapers, and Google has twice the ad revenues of Facebook.

In the advertising world, Big Data is the Holy Grail, because it enables marketers to target messages to individuals rather than general groups, creating what’s called addressable advertising. And only the digital giants possess state-of-the-art Big Data. “The game is no longer about sending you a mail order catalogue or even about targeting online advertising,” Shoshana Zuboff, a professor of business administration at the Harvard Business School, wrote on faz.net, in 2016. “The game is selling access to the real-time flow of your daily life—your reality—in order to directly influence and modify your behavior for profit.” Success at this “game” flows to those with the “ability to predict the future—specifically the future of behavior,” Zuboff writes. She dubs this “surveillance capitalism.”

However, to thrash just Facebook and Google is to miss the larger truth: everyone in advertising strives to eliminate risk by perfecting targeting data. Protecting privacy is not foremost among the concerns of marketers; protecting and expanding their business is. The business model adopted by ad agencies and their clients parallels Facebook and Google’s. Each aims to massage data to better identify potential customers. Each aims to influence consumer behavior. To appreciate how alike their aims are, sit in an agency or client marketing meeting and you will hear wails about Facebook and Google’s “walled garden,” their unwillingness to share data on their users. When Facebook or Google counter that they must protect “the privacy” of their users, advertisers cry foul: You’re using the data to target ads we paid for—why won’t you share it, so that we can use it in other ad campaigns?…(More)”

AI trust and AI fears: A media debate that could divide society

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Article by Vyacheslav Polonski: “Unless you live under a rock, you probably have been inundated with recent news on machine learning and artificial intelligence (AI). With all the recent breakthroughs, it almost seems like AI can already predict the future. Police forces are using it to map when and where crime is likely to occur. Doctors can use it to predict when a patient is most likely to have a heart attack or stroke. Researchers are even trying to give AI imagination so it can plan for unexpected consequences.

Of course, many decisions in our lives require a good forecast, and AI agents are almost always better at forecasting than their human counterparts. Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions. Recent cases show that people don’t like relying on AI and prefer to trust human experts, even if these experts are wrong.

If we want AI to really benefit people, we need to find a way to get people to trust it. To do that, we need to understand why people are so reluctant to trust AI in the first place….

Many people are also simply not familiar with many instances of AI actually working, because it often happens in the background. Instead, they are acutely aware of instances where AI goes terribly wrong:

These unfortunate examples have received a disproportionate amount of media attention, emphasising the message that humans cannot always rely on technology. In the end, it all goes back to the simple truth that machine learning is not foolproof, in part because the humans who design it aren’t….

Fortunately we already have some ideas about how to improve trust in AI — there’s light at the end of the tunnel.

  1. Experience: One solution may be to provide more hands-on experiences with automation apps and other AI applications in everyday situations (like this robot that can get you a beer from the fridge). Thus, instead of presenting the Sony’s new robot dog Aibo as an exclusive product for the upper-class, we’d recommend making these kinds of innovations more accessible to the masses. Simply having previous experience with AI can significantly improve people’s attitudes towards the technology, as we found in our experimental study. And this is especially important for the general public that may not have a very sophisticated understanding of the technology. Similar evidence also suggests the more you use other technologies such as the Internet, the more you trust them.
  2. Insight: Another solution may be to open the “black-box” of machine learning algorithms and be slightly more transparent about how they work. Companies such as Google, Airbnb and Twitter already release transparency reports on a regular basis. These reports provide information about government requests and surveillance disclosures. A similar practice for AI systems could help people have a better understanding of how algorithmic decisions are made. Therefore, providing people with a top-level understanding of machine learning systems could go a long way towards alleviating algorithmic aversion.
  3. Control: Lastly, creating more of a collaborative decision-making process will help build trust and allow the AI to learn from human experience. In our work at Avantgarde Analytics, we have also found that involving people more in the AI decision-making process could improve trust and transparency. In a similar vein, a group of researchers at the University of Pennsylvania recently found that giving people control over algorithms can help create more trust in AI predictions. Volunteers in their study who were given the freedom to slightly modify an algorithm felt more satisfied with it, more likely to believe it was superior and more likely to use in in the future.

These guidelines (experience, insight and control) could help making AI systems more transparent and comprehensible to the individuals affected by their decisions….(More)”.

Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

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Paper by Martin Mueller and Marcel Salath: “In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams.

At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community.

Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases…(More)”.

How the Enlightenment Ends

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Henry Kissinger in the Atlantic: “…Heretofore, the technological advance that most altered the course of modern history was the invention of the printing press in the 15th century, which allowed the search for empirical knowledge to supplant liturgical doctrine, and the Age of Reason to gradually supersede the Age of Religion. Individual insight and scientific knowledge replaced faith as the principal criterion of human consciousness. Information was stored and systematized in expanding libraries. The Age of Reason originated the thoughts and actions that shaped the contemporary world order.

But that order is now in upheaval amid a new, even more sweeping technological revolution whose consequences we have failed to fully reckon with, and whose culmination may be a world relying on machines powered by data and algorithms and ungoverned by ethical or philosophical norms.

he internet age in which we already live prefigures some of the questions and issues that AI will only make more acute. The Enlightenment sought to submit traditional verities to a liberated, analytic human reason. The internet’s purpose is to ratify knowledge through the accumulation and manipulation of ever expanding data. Human cognition loses its personal character. Individuals turn into data, and data become regnant.

Users of the internet emphasize retrieving and manipulating information over contextualizing or conceptualizing its meaning. They rarely interrogate history or philosophy; as a rule, they demand information relevant to their immediate practical needs. In the process, search-engine algorithms acquire the capacity to predict the preferences of individual clients, enabling the algorithms to personalize results and make them available to other parties for political or commercial purposes. Truth becomes relative. Information threatens to overwhelm wisdom.

Inundated via social media with the opinions of multitudes, users are diverted from introspection; in truth many technophiles use the internet to avoid the solitude they dread. All of these pressures weaken the fortitude required to develop and sustain convictions that can be implemented only by traveling a lonely road, which is the essence of creativity.

The impact of internet technology on politics is particularly pronounced. The ability to target micro-groups has broken up the previous consensus on priorities by permitting a focus on specialized purposes or grievances. Political leaders, overwhelmed by niche pressures, are deprived of time to think or reflect on context, contracting the space available for them to develop vision.
The digital world’s emphasis on speed inhibits reflection; its incentive empowers the radical over the thoughtful; its values are shaped by subgroup consensus, not by introspection. For all its achievements, it runs the risk of turning on itself as its impositions overwhelm its conveniences….

There are three areas of special concern:

First, that AI may achieve unintended results….

Second, that in achieving intended goals, AI may change human thought processes and human values….

Third, that AI may reach intended goals, but be unable to explain the rationale for its conclusions…..(More)”

Superminds: The Surprising Power of People and Computers Thinking Together

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Book by Thomas W. Malone: “If you’re like most people, you probably believe that humans are the most intelligent animals on our planet. But there’s another kind of entity that can be far smarter: groups of people. In this groundbreaking book, Thomas Malone, the founding director of the MIT Center for Collective Intelligence, shows how groups of people working together in superminds — like hierarchies, markets, democracies, and communities — have been responsible for almost all human achievements in business, government, science, and beyond. And these collectively intelligent human groups are about to get much smarter.

Using dozens of striking examples and case studies, Malone shows how computers can help create more intelligent superminds not just with artificial intelligence, but perhaps even more importantly with hyperconnectivity:  connecting humans to one another at massive scales and in rich new ways. Together, these changes will have far-reaching implications for everything from the way we buy groceries and plan business strategies to how we respond to climate change, and even for democracy itself. By understanding how these collectively intelligent groups work, we can learn how to harness their genius to achieve our human goals….(More)”.

The Future of Fishing Is Big Data and Artificial Intelligence

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Meg Wilcox at Civil Eats: “New England’s groundfish season is in full swing, as hundreds of dayboat fishermen from Rhode Island to Maine take to the water in search of the region’s iconic cod and haddock. But this year, several dozen of them are hauling in their catch under the watchful eye of video cameras as part of a new effort to use technology to better sustain the area’s fisheries and the communities that depend on them.

Video observation on fishing boats—electronic monitoring—is picking up steam in the Northeast and nationally as a cost-effective means to ensure that fishing vessels aren’t catching more fish than allowed while informing local fisheries management. While several issues remain to be solved before the technology can be widely deployed—such as the costs of reviewing and storing data—electronic monitoring is beginning to deliver on its potential to lower fishermen’s costs, provide scientists with better data, restore trust where it’s broken, and ultimately help consumers gain a greater understanding of where their seafood is coming from….

Muto’s vessel was outfitted with cameras, at a cost of about $8,000, through a collaborative venture between NOAA’s regional office and science center, The Nature Conservancy (TNC), the Gulf of Maine Research Institute, and the Cape Cod Commercial Fishermen’s Alliance. Camera costs are currently subsidized by NOAA Fisheries and its partners.

The cameras run the entire time Muto and his crew are out on the water. They record how the fisherman handle their discards, the fish they’re not allowed to keep because of size or species type, but that count towards their quotas. The cost is lower than what he’d pay for an in-person monitor.The biggest cost of electronic monitoring, however, is the labor required to review the video. …

Another way to cut costs is to use computers to review the footage. McGuire says there’s been a lot of talk about automating the review, but the common refrain is that it’s still five years off.

To spur faster action, TNC last year spearheaded an online competition, offering a $50,000 prize to computer scientists who could crack the code—that is, teach a computer how to count fish, size them, and identify their species.

“We created an arms race,” says McGuire. “That’s why you do a competition. You’ll never get the top minds to do this because they don’t care about your fish. They all want to work for Google, and one way to get recognized by Google is to win a few of these competitions.”The contest exceeded McGuire’s expectations. “Winners got close to 100 percent in count and 75 percent accurate on identifying species,” he says. “We proved that automated review is now. Not in five years. And now all of the video-review companies are investing in machine leaning.” It’s only a matter of time before a commercial product is available, McGuire believes….(More).

The promise and peril of military applications of artificial intelligence

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Michael C. Horowitz at the Bulletin of the Atomic Scientists: “Artificial intelligence (AI) is having a moment in the national security space. While the public may still equate the notion of artificial intelligence in the military context with the humanoid robots of the Terminatorfranchise, there has been a significant growth in discussions about the national security consequences of artificial intelligence. These discussions span academia, business, and governments, from Oxford philosopher Nick Bostrom’s concern about the existential risk to humanity posed by artificial intelligence to Tesla founder Elon Musk’s concern that artificial intelligence could trigger World War III to Vladimir Putin’s statement that leadership in AI will be essential to global power in the 21st century.

What does this really mean, especially when you move beyond the rhetoric of revolutionary change and think about the real world consequences of potential applications of artificial intelligence to militaries? Artificial intelligence is not a weapon. Instead, artificial intelligence, from a military perspective, is an enabler, much like electricity and the combustion engine. Thus, the effect of artificial intelligence on military power and international conflict will depend on particular applications of AI for militaries and policymakers. What follows are key issues for thinking about the military consequences of artificial intelligence, including principles for evaluating what artificial intelligence “is” and how it compares to technological changes in the past, what militaries might use artificial intelligence for, potential limitations to the use of artificial intelligence, and then the impact of AI military applications for international politics.

The potential promise of AI—including its ability to improve the speed and accuracy of everything from logistics to battlefield planning and to help improve human decision-making—is driving militaries around the world to accelerate their research into and development of AI applications. For the US military, AI offers a new avenue to sustain its military superiority while potentially reducing costs and risk to US soldiers. For others, especially Russia and China, AI offers something potentially even more valuable—the ability to disrupt US military superiority. National competition in AI leadership is as much or more an issue of economic competition and leadership than anything else, but the potential military impact is also clear. There is significant uncertainty about the pace and trajectory of artificial intelligence research, which means it is always possible that the promise of AI will turn into more hype than reality. Moreover, safety and reliability concerns could limit the ways that militaries choose to employ AI…(More)”,

Creating a Machine Learning Commons for Global Development

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Blog by Hamed Alemohammad: “Advances in sensor technology, cloud computing, and machine learning (ML) continue to converge to accelerate innovation in the field of remote sensing. However, fundamental tools and technologies still need to be developed to drive further breakthroughs and to ensure that the Global Development Community (GDC) reaps the same benefits that the commercial marketplace is experiencing. This process requires us to take a collaborative approach.

Data collaborative innovation — that is, a group of actors from different data domains working together toward common goals — might hold the key to finding solutions for some of the global challenges that the world faces. That is why Radiant.Earth is investing in new technologies such as Cloud Optimized GeoTiffs, Spatial Temporal Asset Catalogues (STAC), and ML. Our approach to advance ML for global development begins with creating open libraries of labeled images and algorithms. This initiative and others require — and, in fact, will thrive as a result of — using a data collaborative approach.

“Data is only as valuable as the decisions it enables.”

This quote by Ion Stoica, professor of computer science at the University of California, Berkeley, may best describe the challenge facing those of us who work with geospatial information:

How can we extract greater insights and value from the unending tsunami of data that is before us, allowing for more informed and timely decision making?…(More).