Paper by Jens Ludwig & Sendhil Mullainathan: “While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about who to jail. We begin with a striking fact: The defendant’s face alone matters greatly for the judge’s jailing decision. In fact, an algorithm given only the pixels in the defendant’s mugshot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: They are not explained by demographics (e.g. race) or existing psychology research; nor are they already known (even if tacitly) to people or even experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional dataset (e.g. cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our paper is that hypothesis generation is in and of itself a valuable activity, and hope this encourages future work in this largely “pre-scientific” stage of science…(More)”.
Urban AI Guide
Guide by Popelka, S., Narvaez Zertuche, L., Beroche, H.: “The idea for this guide arose from conversations with city leaders, who were confronted with new technologies, like artificial intelligence, as a means of solving complex urban problems, but who felt they lacked the background knowledge to properly engage with and evaluate the solutions. In some instances, this knowledge gap produced a barrier to project implementation or led to unintended project outcomes.
The guide begins with a literature review, presenting the state of the art in research on urban artificial intelligence. It then diagrams and describes an “urban AI anatomy,” outlining and explaining the components that make up an urban AI system. Insights from experts in the Urban AI community enrich this section, illuminating considerations involved in each component. Finally, the guide concludes with an in-depth examination of three case studies: water meter lifecycle in Winnipeg, Canada, curb digitization and planning in Los Angeles, USA, and air quality monitoring in Vilnius, Lithuania. Collectively, the case studies highlight the diversity of ways in which artificial intelligence can be operationalized in urban contexts, as well as the steps and requirements necessary to implement an urban AI project.
Since the field of urban AI is constantly evolving, we anticipate updating the guide annually. Please consider filling out the contribution form, if you have an urban AI use case that has been operationalized. We may contact you to include the use case as a case study in a future edition of the guide.
As a continuation of the guide, we offer customized workshops on urban AI, oriented toward municipalities and other urban stakeholders, who are interested in learning more about how artificial intelligence interacts in urban environments. Please contact us if you would like more information on this program…(More)”.
The False Promise of ChatGPT
Article by Noam Chomsky: “…OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney are marvels of machine learning. Roughly speaking, they take huge amounts of data, search for patterns in it and become increasingly proficient at generating statistically probable outputs — such as seemingly humanlike language and thought. These programs have been hailed as the first glimmers on the horizon of artificial general intelligence — that long-prophesied moment when mechanical minds surpass human brains not only quantitatively in terms of processing speed and memory size but also qualitatively in terms of intellectual insight, artistic creativity and every other distinctively human faculty.
That day may come, but its dawn is not yet breaking, contrary to what can be read in hyperbolic headlines and reckoned by injudicious investments. The Borgesian revelation of understanding has not and will not — and, we submit, cannot — occur if machine learning programs like ChatGPT continue to dominate the field of A.I. However useful these programs may be in some narrow domains (they can be helpful in computer programming, for example, or in suggesting rhymes for light verse), we know from the science of linguistics and the philosophy of knowledge that they differ profoundly from how humans reason and use language. These differences place significant limitations on what these programs can do, encoding them with ineradicable defects.
It is at once comic and tragic, as Borges might have noted, that so much money and attention should be concentrated on so little a thing — something so trivial when contrasted with the human mind, which by dint of language, in the words of Wilhelm von Humboldt, can make “infinite use of finite means,” creating ideas and theories with universal reach…(More)”.
I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique
Book by Thomas Chamorro-Premuzic: “For readers of “Sapiens” and “Homo Deus” and viewers of “The Social Dilemma,” psychologist Tomas Chamorro-Premuzic tackles one of the biggest questions facing our species: Will we use artificial intelligence to improve the way we work and live, or will we allow it to alienate us? It’s no secret that AI is changing the way we live, work, love, and entertain ourselves. Dating apps are using AI to pick our potential partners. Retailers are using AI to predict our behavior and desires. Rogue actors are using AI to persuade us with bots and misinformation. Companies are using AI to hire us–or not. In “I, Human” psychologist Tomas Chamorro-Premuzic takes readers on an enthralling and eye-opening journey across the AI landscape. Though AI has the potential to change our lives for the better, he argues, AI is also worsening our bad tendencies, making us more distracted, selfish, biased, narcissistic, entitled, predictable, and impatient. It doesn’t have to be this way. Filled with fascinating insights about human behavior and our complicated relationship with technology, I, Human will help us stand out and thrive when many of our decisions are being made for us. To do so, we’ll need to double down on our curiosity, adaptability, and emotional intelligence while relying on the lost virtues of empathy, humility, and self-control. This is just the beginning. As AI becomes smarter and more humanlike, our societies, our economies, and our humanity will undergo the most dramatic changes we’ve seen since the Industrial Revolution. Some of these changes will enhance our species. Others may dehumanize us and make us more machinelike in our interactions with people. It’s up to us to adapt and determine how we want to live and work. The choice is ours. What will we decide?…(More)”.
How ChatGPT Hijacks Democracy
Article by Nathan E. Sanders and Bruce Schneier:”…But for all the consternation over the potential for humans to be replaced by machines in formats like poetry and sitcom scripts, a far greater threat looms: artificial intelligence replacing humans in the democratic processes — not through voting, but through lobbying.
ChatGPT could automatically compose comments submitted in regulatory processes. It could write letters to the editor for publication in local newspapers. It could comment on news articles, blog entries and social media posts millions of times every day. It could mimic the work that the Russian Internet Research Agency did in its attempt to influence our 2016 elections, but without the agency’s reported multimillion-dollar budget and hundreds of employees.Automatically generated comments aren’t a new problem. For some time, we have struggled with bots, machines that automatically post content. Five years ago, at least a million automatically drafted comments were believed to have been submitted to the Federal Communications Commission regarding proposed regulations on net neutrality. In 2019, a Harvard undergraduate, as a test, used a text-generation program to submit 1,001 comments in response to a government request for public input on a Medicaid issue. Back then, submitting comments was just a game of overwhelming numbers…(More)”
ChatGPT reminds us why good questions matter
Article by Stefaan Verhulst and Anil Ananthaswamy: “Over 100 million people used ChatGPT in January alone, according to one estimate, making it the fastest-growing consumer application in history. By producing resumes, essays, jokes and even poetry in response to prompts, the software brings into focus not just language models’ arresting power, but the importance of framing our questions correctly.
To that end, a few years ago I initiated the 100 Questions Initiative, which seeks to catalyse a cultural shift in the way we leverage data and develop scientific insights. The project aims not only to generate new questions, but also reimagine the process of asking them…
As a species and a society, we tend to look for answers. Answers appear to provide a sense of clarity and certainty, and can help guide our actions and policy decisions. Yet any answer represents a provisional end-stage of a process that begins with questions – and often can generate more questions. Einstein drew attention to the critical importance of how questions are framed, which can often determine (or at least play a significant role in determining) the answers we ultimately reach. Frame a question differently and one might reach a different answer. Yet as a society we undervalue the act of questioning – who formulates questions, how they do so, the impact they have on what we investigate, and on the decisions we make. Nor do we pay sufficient attention to whether the answers are in fact addressing the questions initially posed…(More)”.
‘There is no standard’: investigation finds AI algorithms objectify women’s bodies
Article by Hilke Schellmann: “Images posted on social media are analyzed by artificial intelligence (AI) algorithms that decide what to amplify and what to suppress. Many of these algorithms, a Guardian investigation has found, have a gender bias, and may have been censoring and suppressing the reach of countless photos featuring women’s bodies.
These AI tools, developed by large technology companies, including Google and Microsoft, are meant to protect users by identifying violent or pornographic visuals so that social media companies can block it before anyone sees it. The companies claim that their AI tools can also detect “raciness” or how sexually suggestive an image is. With this classification, platforms – including Instagram and LinkedIn – may suppress contentious imagery.
Two Guardian journalists used the AI tools to analyze hundreds of photos of men and women in underwear, working out, using medical tests with partial nudity and found evidence that the AI tags photos of women in everyday situations as sexually suggestive. They also rate pictures of women as more “racy” or sexually suggestive than comparable pictures of men. As a result, the social media companies that leverage these or similar algorithms have suppressed the reach of countless images featuring women’s bodies, and hurt female-led businesses – further amplifying societal disparities.
Even medical pictures are affected by the issue. The AI algorithms were tested on images released by the US National Cancer Institute demonstrating how to do a clinical breast examination. Google’s AI gave this photo the highest score for raciness, Microsoft’s AI was 82% confident that the image was “explicitly sexual in nature”, and Amazon classified it as representing “explicit nudity”…(More)”.
Work and meaning in the age of AI
Report by Daniel Susskind: “It is often said that work is not only a source of income but also of meaning. In this paper, I explore the theoretical and empirical literature that addresses this relationship between work and meaning. I show that the relationship is far less clear than is commonly supposed: There is a great heterogeneity in its nature, both among today’s workers and workers over time. I explain why this relationship matters for policymakers and economists concerned about the impact of technology on work. In the short term, it is important for predicting labour market outcomes of interest. It also matters for understanding how artificial intelligence (AI) affects not only the quantity of work but its quality as well: These new technologies may erode the meaning that people get from their work. In the medium term, if jobs are lost, this relationship also matters for designing bold policy interventions like the ‘Universal Basic Income’ and ‘Job Guarantee Schemes’: Their design, and any choice between them, is heavily dependent on policymakers’—often tacit—assumptions about the nature of this underlying relationship between work and meaning. For instance, policymakers must decide whether to simply focus on replacing lost income alone (as with a Universal Basic Income) or, if they believe that work is an important and non-substitutable source of meaning, on protecting jobs for that additional role as well (as with a Job Guarantee Scheme). In closing, I explore the challenge that the age of AI presents for an important feature of liberal political theory: the idea of ‘neutrality.’..(More)”
How Smart Are the Robots Getting?
Cade Metz at The New York Times: “…These are not systems that anyone can properly evaluate with the Turing test — or any other simple method. Their end goal is not conversation.
Researchers at Google and DeepMind, which is owned by Google’s parent company, are developing tests meant to evaluate chatbots and systems like DALL-E, to judge what they do well, where they lack reason and common sense, and more. One test shows videos to artificial intelligence systems and asks them to explain what has happened. After watching someone tinker with an electric shaver, for instance, the A.I. must explain why the shaver did not turn on.
These tests feel like academic exercises — much like the Turing test. We need something that is more practical, that can really tell us what these systems do well and what they cannot, how they will replace human labor in the near term and how they will not.
We could also use a change in attitude. “We need a paradigm shift — where we no longer judge intelligence by comparing machines to human behavior,” said Oren Etzioni, professor emeritus at the University of Washington and founding chief executive of the Allen Institute for AI, a prominent lab in Seattle….
At the same time, there are many ways these bots are superior to you and me. They do not get tired. They do not let emotion cloud what they are trying to do. They can instantly draw on far larger amounts of information. And they can generate text, images and other media at speeds and volumes we humans never could.
Their skills will also improve considerably in the coming years.
Researchers can rapidly hone these systems by feeding them more and more data. The most advanced systems, like ChatGPT, require months of training, but over those months, they can develop skills they did not exhibit in the past.
“We have found a set of techniques that scale effortlessly,” said Raia Hadsell, senior director of research and robotics at DeepMind. “We have a simple, powerful approach that continues to get better and better.”
The exponential improvement we have seen in these chatbots over the past few years will not last forever. The gains may soon level out. But even then, multimodal systems will continue to improve — and master increasingly complex skills involving images, sounds and computer code. And computer scientists will combine these bots with systems that can do things they cannot. ChatGPT failed Turing’s chess test. But we knew in 1997 that a computer could beat the best humans at chess. Plug ChatGPT into a chess program, and the hole is filled.
In the months and years to come, these bots will help you find information on the internet. They will explain concepts in ways you can understand. If you like, they will even write your tweets, blog posts and term papers.
They will tabulate your monthly expenses in your spreadsheets. They will visit real estate websites and find houses in your price range. They will produce online avatars that look and sound like humans. They will make mini-movies, complete with music and dialogue…
Certainly, these bots will change the world. But the onus is on you to be wary of what these systems say and do, to edit what they give you, to approach everything you see online with skepticism. Researchers know how to give these systems a wide range of skills, but they do not yet know how to give them reason or common sense or a sense of truth.
That still lies with you…(More)”.
Nine cities set standards for the transparent use of Artificial Intelligence
Press Release: “Nine cities, cooperating through the Eurocities network, have developed a free to use open-source ‘data schema’ for algorithm registers in cities. The data schema, which sets common guidelines on the information to be collected on algorithms and their use by a city, supports the responsible use of AI and puts people at the heart of future developments in digital transformation.
While most cities primarily use only simple algorithms and not advanced AI such as facial recognition, the joint effort by seven European municipalities aims to pre-empt any future data misuse and create an interoperable model that can be shared and copied by other cities. The data schema was developed by Barcelona, Bologna, Brussels Capital Region, Eindhoven, Mannheim, Rotterdam and Sofia, based on the example set by Amsterdam and Helsinki…
- Further information, including the full transparency standard can be viewed and downloaded here: https://www.algorithmregister.org/
- The cities of Barcelona, Bologna, Brussels Capital Region, Eindhoven, Mannheim, Rotterdam and Sofia cooperated through Eurocities Digital Forum Lab, basing their work on the previous initiative of Amsterdam and Helsinki. The Eurocities Digital Forum Lab aims to develop digital interoperable solutions for cities.
- The examples from Amsterdam and Helsinki can be found here:
a. https://algoritmeregister.amsterdam.nl/en/ai-register/
b. https://ai.hel.fi/en/ai-register/…(More)”.