Paper by Tan Yigitcanlar, Duzgun Agdas & Kenan Degirmenci: “Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI is on the rise in urban policy circles, there is limited understanding and lack of empirical studies on the city manager perceptions concerning urban AI systems. Bridging this gap is the rationale of this study. The methodological approach adopted in this study is twofold. First, the study collects data through semi-structured interviews with city managers from Australia and the US. Then, the study analyses the data using the summative content analysis technique with two data analysis software. The analysis identifies the following themes and generates insights into local government services: AI adoption areas, cautionary areas, challenges, effects, impacts, knowledge basis, plans, preparedness, roadblocks, technologies, deployment timeframes, and usefulness. The study findings inform city managers in their efforts to deploy AI in their local government operations, and offer directions for prospective research…(More)”.
AI and the next great tech shift
Book review by John Thornhill: “When the South Korean political activist Kim Dae-jung was jailed for two years in the early 1980s, he powered his way through some 600 books in his prison cell, such was his thirst for knowledge. One book that left a lasting impression was The Third Wave by the renowned futurist Alvin Toffler, who argued that an imminent information revolution was about to transform the world as profoundly as the preceding agricultural and industrial revolutions.
“Yes, this is it!” Kim reportedly exclaimed. When later elected president, Kim referred to the book many times in his drive to turn South Korea into a technological powerhouse.
Forty-three years after the publication of Toffler’s book, another work of sweeping futurism has appeared with a similar theme and a similar name. Although the stock in trade of futurologists is to highlight the transformational and the unprecedented, it is remarkable how much of their output appears the same.
The chief difference is that The Coming Wave by Mustafa Suleyman focuses more narrowly on the twin revolutions of artificial intelligence and synthetic biology. But the author would surely be delighted if his book were to prove as influential as Toffler’s in prompting politicians to action.
As one of the three co-founders of DeepMind, the London-based AI research company founded in 2010, and now chief executive of the AI start-up Inflection, Suleyman has been at the forefront of the industry for more than a decade. The Coming Wave bristles with breathtaking excitement about the extraordinary possibilities that the revolutions in AI and synthetic biology could bring about.
AI, we are told, could unlock the secrets of the universe, cure diseases and stretch the bounds of imagination. Biotechnology can enable us to engineer life and transform agriculture. “Together they will usher in a new dawn for humanity, creating wealth and surplus unlike anything ever seen,” he writes.
But what is striking about Suleyman’s heavily promoted book is how the optimism of his will is overwhelmed by the pessimism of his intellect, to borrow a phrase from the Marxist philosopher Antonio Gramsci. For most of history, the challenge of technology has been to unleash its power, Suleyman writes. Now the challenge has flipped.
In the 21st century, the dilemma will be how to contain technology’s power given the capabilities of these new technologies have exploded and the costs of developing them have collapsed. “Containment is not, on the face of it, possible. And yet for all our sakes, containment must be possible,” he writes…(More)”.
Unlocking AI’s Potential for Everyone
Article by Diane Coyle: “…But while some policymakers do have deep knowledge about AI, their expertise tends to be narrow, and most other decision-makers simply do not understand the issue well enough to craft sensible policies. Owing to this relatively low knowledge base and the inevitable asymmetry of information between regulators and regulated, policy responses to specific issues are likely to remain inadequate, heavily influenced by lobbying, or highly contested.
So, what is to be done? Perhaps the best option is to pursue more of a principles-based policy. This approach has already gained momentum in the context of issues like misinformation and trolling, where many experts and advocates believe that Big Tech companies should have a general duty of care (meaning a default orientation toward caution and harm reduction).
In some countries, similar principles already apply to news broadcasters, who are obligated to pursue accuracy and maintain impartiality. Although enforcement in these domains can be challenging, the upshot is that we do already have a legal basis for eliciting less socially damaging behavior from technology providers.
When it comes to competition and market dominance, telecoms regulation offers a serviceable model with its principle of interoperability. People with competing service providers can still call each other because telecom companies are all required to adhere to common technical standards and reciprocity agreements. The same is true of ATMs: you may incur a fee, but you can still withdraw cash from a machine at any bank.
In the case of digital platforms, a lack of interoperability has generally been established by design, as a means of locking in users and creating “moats.” This is why policy discussions about improving data access and ensuring access to predictable APIs have failed to make any progress. But there is no technical reason why some interoperability could not be engineered back in. After all, Big Tech companies do not seem to have much trouble integrating the new services that they acquire when they take over competitors.
In the case of LLMs, interoperability probably could not apply at the level of the models themselves, since not even their creators understand their inner workings. However, it can and should apply to interactions between LLMs and other services, such as cloud platforms…(More)”.
City CIOs urged to lay the foundations for generative AI
Article by Sarah Wray: “The London Office of Technology and Innovation (LOTI) has produced a collection of guides to support local authorities in using generative artificial intelligence (genAI) tools such as ChatGPT, Bard, Midjourney and Dall-E.
The resources include a guide for local authority leaders and another aimed at all staff, as well as a guide designed specifically for council Chief Information Officers (CIOs), which was developed with AI software company Faculty.
Sam Nutt, Researcher and Data Ethicist at LOTI, a membership organisation for over 20 boroughs and the Greater London Authority, told Cities Today: “Generative AI won’t solve every problem for local governments, but it could be a catalyst to transform so many processes for how we work.
“On the one hand, personal assistants integrated into programmes like Word, Excel or Powerpoint could massively improve officer productivity. On another level there is a chance to reimagine services and government entirely, thinking about how gen AI models can do so many tasks with data that we couldn’t do before, and allow officers to completely change how they spend their time.
“There are both opportunities and challenges, but the key message on both is that local governments should be ambitious in using this ‘AI moment’ to reimagine and redesign our ways of working to be better at delivering services now and in the future for our residents.”
As an initial step, local governments are advised to provide training and guidelines for staff. Some have begun to implement these steps, including US cities such as Boston, Seattle and San Jose.
Nutt stressed that generative AI policies are useful but not a silver bullet for governance and that they will need to be revisited and updated regularly as technology and regulations evolve…(More)”.
EU leadership in trustworthy AI: Guardrails, Innovation & Governance
Article by Thierry Breton: “As mentioned in President von der Leyen’s State of the Union letter of intent, Europe should lead global efforts on artificial intelligence, guiding innovation, setting guardrails and developing global governance.
First, on innovation: we will launch the EU AI Start-Up Initiative, leveraging one of Europe’s biggest assets: its public high-performance computing infrastructure. We will identify the most promising European start-ups in AI and give them access to our supercomputing capacity.
I have said it before: AI is a combination of data, computing and algorithms. To train and finetune the most advanced foundation models, developers need large amounts of computing power.
Europe is a world leader in supercomputing through its European High-Performance Computing Joint Undertaking (EuroHPC). Soon, Europe will have its first exascale supercomputers, JUPITER in Germany and JULES VERNE in France (able to perform a quintillion -that means a billion billion- calculations per second), in addition to various existing supercomputers (such as LEONARDO in Italy and LUMI in Finland).
Access to Europe’s supercomputing infrastructure will help start-ups bring down the training time for their newest AI models from months or years to days or weeks. And it will help them lead the development and scale-up of AI responsibly and in line with European values.
This goes together with our broader efforts to support AI innovation across the value chain – from AI start-ups to all those businesses using AI technologies in their industrial ecosystems. This includes our Testing and Experimentation Facilities for AI (launched in January 2023), our Digital Innovation Hubs, the development of regulatory sandboxes under the AI Act, our support for the European Partnership on AI, Data and Robotics and the cutting-edge research supported by HorizonEurope.
Second, guardrails for AI: Europe has pioneered clear rules for AI systems through the EU AI Act, the world’s first comprehensive regulatory framework for AI. My teams are working closely with the Parliament and Council to support the swift adoption of the EU AI Act. This will give citizens and businesses confidence in AI developed in Europe, knowing that it is safe and respects fundamental rights and European values. And it serves as an inspiration for global rules and principles for trustworthy AI.
As reiterated by President von der Leyen, we are developing an AI Pact that will convene AI companies, help them prepare for the implementation of the EU AI Act and encourage them to commit voluntarily to applying the principles of the Act before its date of applicability.
Third, governance: with the AI Act and the Coordinated Plan on AI, we are working towards a governance framework for AI, which can be a centre of expertise, in particular on large foundation models, and promote cooperation, not only between Member States, but also internationally…(More)”
AI often mangles African languages. Local scientists and volunteers are taking it back to school
Article by Sandeep Ravindran: “Imagine joyfully announcing to your Facebook friends that your wife gave birth, and having Facebook automatically translate your words to “my prostitute gave birth.” Shamsuddeen Hassan Muhammad, a computer science Ph.D. student at the University of Porto, says that’s what happened to a friend when Facebook’s English translation mangled the nativity news he shared in his native language, Hausa.
Such errors in artificial intelligence (AI) translation are common with African languages. AI may be increasingly ubiquitous, but if you’re from the Global South, it probably doesn’t speak your language.
That means Google Translate isn’t much help, and speech recognition tools such as Siri or Alexa can’t understand you. All of these services rely on a field of AI known as natural language processing (NLP), which allows AI to “understand” a language. The overwhelming majority of the world’s 7000 or so languages lack data, tools, or techniques for NLP, making them “low-resourced,” in contrast with a handful of “high-resourced” languages such as English, French, German, Spanish, and Chinese.
Hausa is the second most spoken African language, with an estimated 60 million to 80 million speakers, and it’s just one of more than 2000 African languages that are mostly absent from AI research and products. The few products available don’t work as well as those for English, notes Graham Neubig, an NLP researcher at Carnegie Mellon University. “It’s not the people who speak the languages making the technology.” More often the technology simply doesn’t exist. “For example, now you cannot talk to Siri in Hausa, because there is no data set to train Siri,” Muhammad says.
He is trying to fill that gap with a project he co-founded called HausaNLP, one of several launched within the past few years to develop AI tools for African languages…(More)”.
The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments
Paper by Tzuhao Chen, Mila Gascó-Hernandez, and Marc Esteve: “Although the use of artificial intelligence (AI) chatbots in public organizations has increased in recent years, three crucial gaps remain unresolved. First, little empirical evidence has been produced to examine the deployment of chatbots in government contexts. Second, existing research does not distinguish clearly between the drivers of adoption and the determinants of success and, therefore, between the stages of adoption and implementation. Third, most current research does not use a multidimensional perspective to understand the adoption and implementation of AI in government organizations. Our study addresses these gaps by exploring the following question: what determinants facilitate or impede the adoption and implementation of chatbots in the public sector? We answer this question by analyzing 22 state agencies across the U.S.A. that use chatbots. Our analysis identifies ease of use and relative advantage of chatbots, leadership and innovative culture, external shock, and individual past experiences as the main drivers of the decisions to adopt chatbots. Further, it shows that different types of determinants (such as knowledge-base creation and maintenance, technology skills and system crashes, human and financial resources, cross-agency interaction and communication, confidentiality and safety rules and regulations, and citizens’ expectations, and the COVID-19 crisis) impact differently the adoption and implementation processes and, therefore, determine the success of chatbots in a different manner. Future research could focus on the interaction among different types of determinants for both adoption and implementation, as well as on the role of specific stakeholders, such as IT vendors…(More)”.
Who Wrote This? How AI and the Lure of Efficiency Threaten Human Writing
Book by Naomi S. Baron: “Would you read this book if a computer wrote it? Would you even know? And why would it matter?
Today’s eerily impressive artificial intelligence writing tools present us with a crucial challenge: As writers, do we unthinkingly adopt AI’s time-saving advantages or do we stop to weigh what we gain and lose when heeding its siren call? To understand how AI is redefining what it means to write and think, linguist and educator Naomi S. Baron leads us on a journey connecting the dots between human literacy and today’s technology. From nineteenth-century lessons in composition, to mathematician Alan Turing’s work creating a machine for deciphering war-time messages, to contemporary engines like ChatGPT, Baron gives readers a spirited overview of the emergence of both literacy and AI, and a glimpse of their possible future. As the technology becomes increasingly sophisticated and fluent, it’s tempting to take the easy way out and let AI do the work for us. Baron cautions that such efficiency isn’t always in our interest. As AI plies us with suggestions or full-blown text, we risk losing not just our technical skills but the power of writing as a springboard for personal reflection and unique expression.
Funny, informed, and conversational, Who Wrote This? urges us as individuals and as communities to make conscious choices about the extent to which we collaborate with AI. The technology is here to stay. Baron shows us how to work with AI and how to spot where it risks diminishing the valuable cognitive and social benefits of being literate…(More)”.
Computing the Climate: How We Know What We Know About Climate Change
Book by Steve M. Easterbrook: “How do we know that climate change is an emergency? How did the scientific community reach this conclusion all but unanimously, and what tools did they use to do it? This book tells the story of climate models, tracing their history from nineteenth-century calculations on the effects of greenhouse gases, to modern Earth system models that integrate the atmosphere, the oceans, and the land using the full resources of today’s most powerful supercomputers. Drawing on the author’s extensive visits to the world’s top climate research labs, this accessible, non-technical book shows how computer models help to build a more complete picture of Earth’s climate system. ‘Computing the Climate’ is ideal for anyone who has wondered where the projections of future climate change come from – and why we should believe them…(More)”.
Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models
Paper by Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren: “The growing carbon footprint of artificial intelligence (AI) models, especially large ones such as GPT-3 and GPT-4, has been undergoing public scrutiny. Unfortunately, however, the equally important and enormous water footprint of AI models has remained under the radar. For example, training GPT-3 in Microsoft’s state-of-the-art U.S. data centers can directly consume 700,000 liters of clean freshwater (enough for producing 370 BMW cars or 320 Tesla electric vehicles) and the water consumption would have been tripled if training were done in Microsoft’s Asian data centers, but such information has been kept as a secret. This is extremely concerning, as freshwater scarcity has become one of the most pressing challenges shared by all of us in the wake of the rapidly growing population, depleting water resources, and aging water infrastructures. To respond to the global water challenges, AI models can, and also should, take social responsibility and lead by example by addressing their own water footprint. In this paper, we provide a principled methodology to estimate fine-grained water footprint of AI models, and also discuss the unique spatial-temporal diversities of AI models’ runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI…(More)”.