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Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do.

But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own.

When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter a must be added to end of a word to make the masculine form feminine in Serbo-Croatian.

This model can also automatically learn higher-level language patterns that can apply to many languages, enabling it to achieve better results.

The researchers trained and tested the model using problems from linguistics textbooks that featured 58 different languages. Each problem had a set of words and corresponding word-form changes. The model was able to come up with a correct set of rules to describe those word-form changes for 60 percent of the problems.

This system could be used to study language hypotheses and investigate subtle similarities in the way diverse languages transform words. It is especially unique because the system discovers models that can be readily understood by humans, and it acquires these models from small amounts of data, such as a few dozen words. And instead of using one massive dataset for a single task, the system utilizes many small datasets, which is closer to how scientists propose hypotheses they look at multiple related datasets and come up with models to explain phenomena across those datasets.

One of the motivations of this work was our desire to study systems that learn models of datasets that is represented in a way that humans can understand. Instead of learning weights, can the model learn expressions or rules? And we wanted to see if we could build this system so it would learn on a whole battery of interrelated datasets, to make the system learn a little bit about how to better model each one, says Kevin Ellis 14, PhD 20, an assistant professor of computer science at Cornell University and lead author of the paper.

Joining Ellis on the paper are MIT faculty members Adam Albright, a professor of linguistics; Armando Solar-Lezama, a professor and associate director of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of CSAIL; as well as senior author

Timothy J. ODonnell, assistant professor in the Department of Linguistics at McGill University, and Canada CIFAR AI Chair at the Mila -Quebec Artificial IntelligenceInstitute.

The research is published today in Nature Communications.

Looking at language

In their quest to develop an AI system that could automatically learn a model from multiple related datasets, the researchers chose to explore the interaction of phonology (the study of sound patterns) and morphology (the study of word structure).

Data from linguistics textbooks offered an ideal testbed because many languages share core features, and textbook problems showcase specific linguistic phenomena. Textbook problems can also be solved by college students in a fairly straightforward way, but those students typically have prior knowledge about phonology from past lessons they use to reason about new problems.

Ellis, who earned his PhD at MIT and was jointly advised by Tenenbaum and Solar-Lezama, first learned about morphology and phonology in an MIT class co-taught by ODonnell, who was a postdoc at the time, and Albright.

Linguists have thought that in order to really understand the rules of a human language, to empathize with what it is that makes the system tick, you have to be human. We wanted to see if we can emulate the kinds of knowledge and reasoning that humans (linguists) bring to the task, says Albright.

To build a model that could learn a set of rules for assembling words, which is called a grammar, the researchers used a machine-learning technique known as Bayesian Program Learning. With this technique, the model solves a problem by writing a computer program.

In this case, the program is the grammar the model thinks is the most likely explanation of the words and meanings in a linguistics problem. They built the model using Sketch, a popular program synthesizer which was developed at MIT by Solar-Lezama.

But Sketch can take a lot of time to reason about the most likely program. To get around this, the researchers had the model work one piece at a time, writing a small program to explain some data, then writing a larger program that modifies that small program to cover more data, and so on.

They also designed the model so it learns what good programs tend to look like. For instance, it might learn some general rules on simple Russian problems that it would apply to a more complex problem in Polish because the languages are similar. This makes it easier for the model to solve the Polish problem.

Tackling textbook problems

When they tested the model using 70 textbook problems, it was able to find a grammar that matched the entire set of words in the problem in 60 percent of cases, and correctly matched most of the word-form changes in 79 percent of problems.

The researchers also tried pre-programming the model with some knowledge it should have learned if it was taking a linguistics course, and showed that it could solve all problems better.

One challenge of this work was figuring out whether what the model was doing was reasonable. This isnt a situation where there is one number that is the single right answer. There is a range of possible solutions which you might accept as right, close to right, etc., Albright says.

The model often came up with unexpected solutions. In one instance, it discovered the expected answer to a Polish language problem, but also another correct answer that exploited a mistake in the textbook. This shows that the model could debug linguistics analyses, Ellis says.

The researchers also conducted tests that showed the model was able to learn some general templates of phonological rules that could be applied across all problems.

One of the things that was most surprising is that we could learn across languages, but it didnt seem to make a huge difference, says Ellis. That suggests two things. Maybe we need better methods for learning across problems. And maybe, if we cant come up with those methods, this work can help us probe different ideas we have about what knowledge to share across problems.

In the future, the researchers want to use their model to find unexpected solutions to problems in other domains. They could also apply the technique to more situations where higher-level knowledge can be applied across interrelated datasets. For instance, perhaps they could develop a system to infer differential equations from datasets on the motion of different objects, says Ellis.

This work shows that we have some methods which can, to some extent, learn inductive biases. But I dont think weve quite figured out, even for these textbook problems, the inductive bias that lets a linguist accept the plausible grammars and reject the ridiculous ones, he adds.

This work opens up many exciting venues for future research. I am particularly intrigued by the possibility that the approach explored by Ellis and colleagues (Bayesian Program Learning, BPL) might speak to how infants acquire language, says T. Florian Jaeger, a professor of brain and cognitive sciences and computer science at the University of Rochester, who was not an author of this paper. Future work might ask, for example, under what additional induction biases (assumptions about universal grammar) the BPL approach can successfully achieve human-like learning behavior on the type of data infants observe during language acquisition. I think it would be fascinating to see whether inductive biases that are even more abstract than those considered by Ellis and his team such as biases originating in the limits of human information processing (e.g., memory constraints on dependency length or capacity limits in the amount of information that can be processed per time) would be sufficient to induce some of the patterns observed in human languages.

This work was funded, in part, by the Air Force Office of Scientific Research, the Center for Brains, Minds, and Machines, the MIT-IBM Watson AI Lab, the Natural Science and Engineering Research Council of Canada, the Fonds de Recherche du Qubec Socit et Culture, the Canada CIFAR AI Chairs Program, the National Science Foundation (NSF), and an NSF graduate fellowship.

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AI that can learn the patterns of human language - MIT News

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Despite conservationists efforts, animal poaching continues to devastate vulnerable species. So, when New Yorker Anika Puri came across ivory jewelry at a market in India four years ago, she felt inspired to do her part in stopping elephant hunting. The solution: she invented a low-cost machine learning software that can detect poachers in real time with 91% accuracy.

Discovering the numerous ivory objects in Mumbai was the catalyst for her project. I was quite taken aback because I always thought, Well, poaching is illegal, how come it really is still such a big issue? she says about the incident. So, the 17-year-old delved into the poaching numbers and discovered that Africa's forest elephant population declined by about 61% between 2002 and 2011, with numbers that continue to drop.

Poachers are usually detected by drones; however, Puri noticed the success rate could be significantly higher. I realized that we could use this disparity between these two movement patterns in order to actually increase the detection accuracy of potential poachers, she explains. As a result, Puri spent two years developing her solution: a machine learning software named ElSa (an abbreviation for Elephant Savior). It analyzes the movement patterns of humans and elephants in thermal infrared videos and is four times more accurate than the existing detection methods. Even better, the software can be used with low-cost cameras, eliminating the need for high-resolution thermal cameras.

Puri presented her project at the Regeneron Internation Science and Engineering Fair, winning the $10,000 Peggy Scripps Award for Science Communication and first place in the earth and environmental sciences category. It's quite remarkable that a high school student has been able to do something like this, Jasper Eikelboom, an ecologist at Wageningen University in the Netherlands, comments. Not only the research and the analysis but alsobeing able to implement it in the prototypes. Puri will be attending MIT in fall 2022 with hopes to expand her project to protect other endangered animal species.

h/t: [Smithsonian]

Meet the All-Female Anti-Poaching Team Changing the Face of Conservation in Africa [Interview]

Gorillas Pose for a Selfie with Virunga National Parks Anti-Poaching Rangers

Watch Olive the White Rhino Give Birth to a Healthy Baby Calf

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17-Year-Old Invents Software That Detects Elephant Poaching - My Modern Met

To make decisions more quickly and accurately, enterprises are increasingly turning to Machine Learning, arguably todays most practical application of Artificial Intelligence (AI). Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. Industry pundits share insights why Machine Learning has been made a central part of business operations.

As organisations emerge from the lockdown restrictions that were imposed on businesses because of the COVID-19 pandemic, Machine Learning has taken centre stage because it gives enterprises a view of trends in customer behaviour and business operational patterns, as well as supports the development of new products. Many of todays leading multinational companies, such as Facebook, Google and Uber, have made Machine Learning a central part of their operations. Machine Learning has become a significant competitive differentiator for many companies across the Middle East and Africa (MEA).

According to research firm Gartner, the adoption of Machine Learning in the enterprise is being catalysed by Digital Transformation, the need for democratisation and the urgency of industrialisation. The firm says 48% of respondents to the 2022 Gartner CIO and Technology Executive Survey have already deployed or plan to deploy AI/Machine Learning in the next 12 months. And Gartner said that the on-going Digital Transformation requires better and faster but also ethical decision making, enabled by advances in decision intelligence and AI governance.

Gartner said one of the most prominent reasons why the IT industry is seeing an increasing enterprise adoption of Machine Learning is the desire to bring the power of Machine Learning to a widening audience the democratisation of data science and Machine Learning (DSML), lowering the barrier to entry which is enabled by technical advances in automation and augmentation.

Farhan Choudhary, Principal Analyst, Gartner, said to assess where Machine Learning can be applied in the enterprise, the CIO and IT team first need to determine the what of the problem statement, for example, what business KPIs does the organisation want to be impacted through the work in Machine Learning, and second, the how of the problem statement, i.e., how will the organisation accomplish this task.

Choudhary said Machine Learning can be applied across many parts of the business, some applications or opportunities could be low hanging fruits, some could be money-pits or some cutting edge. He said a thorough and systematic assessment of opportunities should be conducted before determining where Machine Learning can be applied by enterprise IT, and where a democratised approach can be followed.

This should be a top-down approach. Lets assume were in retail business and we want to leverage Machine Learning while working in collaboration with enterprise IT to generate tangible business value. The first order of business is to conduct an assessment on business value we expect the project to generate or KPIs that it would impact, and the feasibility of using Machine Learning in the enterprise. Say our priorities are revenue growth, and we want to use Machine Learning to impact the volume of sales; then, this could be done through use of Machine Learning in products and services, sales and marketing or in customer service (these are our separate lines of businesses that can leverage Machine Learning), he said.

Choudhary pointed out that there are opportunities in sales and marketing, R&D, corporate legal, human capital management, customer service, IT operations, software development and testing, and many other areas where Machine Learning can be applied.

Mike Brooks, Global Director, Asset Performance Management, Aspen, said: Machine Learningalgorithms are basically free from many open sources. It seems everybody is using it but Machine Learning itself is hardly the secret sauce, but it is how you use it and what for. The biggest issue with Machine Learningis the data science skills required to implement and the absolute necessity to engage the subject matter experts with deep familiarity of the problem space, including perhaps, process, mechanical, reliability, planning/scheduling personnel, etc.

Brooks said Aspen has embed Machine Learningand engineering smarts in anomaly and failure/degradation agents that exercise every few minutes to do the Machine Learning and guidance to ensure they hunt for causation rather than simple correlation is differentiating methodology.

The methodology copied from the iPhone ideas is that the smarts are on the inside doing the complex and hard work, so you do not have to. That approach assures it is easier and faster to do Machine Learningimplementations on specific equipment with an application that scales rapidly and easily, meaning faster time to cash for many assets. The alternative is a pure Machine Learning approach on a specific Machine Learning platform that takes the user nowhere near the problem space where every application is an open project every time complete with fragility and grand requirements for domain expertise.

With Machine Learning witnessing enterprise-wide adoption of the technology in various business environments across MEA, organisations are being urged to establish a business case before embarking on any project.

Ramprakash Ramamoorthy, Director, AI Research, ManageEngine, said since the onset of the pandemic, the first touchpoint for many businesses has been digital. Ramamoorthy said organisations must remain digitally competitive to stay afloat, and they achieve this by implementing newer technologies like Machine Learning. He said another factor is the ongoing AI summer, during which there have been a lot of investments in AI and other associated technologies, which in turn has increased the adoption of Machine Learning across the globe.

Ramamoorthy pointed out that because Machine Learning enables enterprise software to move from process automation to decision automation, using Machine Learning involves rewriting current, traditionally deterministic processes and workflows to make them probabilistic.

For instance, a traditional anomaly system uses the bell curve to identify anomalies, whereas an Machine Learning-powered anomaly system identifies anomalies along with the probability of an outage occurring. CIOs have to drive these changes and incentivise teams to use and integrate new technologies like ML into their everyday workflows by citing the impact they could have on business growth, he said.

Walid Issa, Senior Manager, Pre-sales and Solutions Engineering Middle East Region, NetApp, said Artificial Intelligence and Machine Learning have moved beyond the realm of concept into real-world application, representing the great opportunity to stay competitive, drive growth, and cut costs.

Issa said AI and ML are well suited in different verticals such as manufacturing, healthcare, telecom, public sector, retail, finance and automatise. If I select healthcare as an example, Artificial Intelligence is transforming healthcare in ways we never thought possible. And it really is all about data. Using data, AI and ML can help healthcare professionals make more informed, accurate, and proactive assessments and diagnoses. The ability to analyse data in real time enables healthcare professionals to improve the quality of life for patients and ultimately save lives. This will enable proactive diagnoses using smarter healthcare tools, help physicians find the right data faster and keep patients and healthcare organisations safe from cyber criminals and attacks, he said.

CIOs and IT leaders should involve business to ensure buy-in for a Machine Learning system deployment in their organisation as that ensures success in the organisation.

Chris Royles, EMEA Field CTO, Cloudera, said CIOs and IT leaders will be influential in building and maintaining a data culture in the organisation. Royles said helping develop a data literacy programme and working across lines of business to instill the importance of data in each domain is an important start. We then suggest a democratised approach to data management where ownership of the business domain and data problems are managed by those closest to the systems. It is then for each domain to identify the opportunities they can apply to their data processes to introduce Machine Learning, he said.

Kevin Thompson, Cloud Operations Manager, Sage Africa, Middle East and Asia Pacific, said one of the key elements to consider is change management since ML and AI could potentially take over many of the tasks human workers currently execute manually. Thompson said businesses should look at how these new technologies can augment, rather than replace their people, and show people how the technology will free them from routine, repetitive processes so they can focus on work that needs more creative, strategic, or emotional intelligence.

According to Thompson, within a few years, ML will be so deeply embedded into every computer system that the industry will take it for granted. To get ROI, organisations should start out with a clear idea of the business outcome they would like to achieve and how they will measure success. For example, they might want to use Machine Learning to generate efficiencies in customer service. In this case, they could measure call centre volumes versus customers served by a ML/AI-powered chatbot. An insurance company could use ML for fraud detection and measure the value of the fraudulent claims the system picks up, he said.

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Why Machine Learning is a central part of business operations - Intelligent CIO

Artificial Intelligence (AI) is transforming our way of life from new forms of social organization and scientific discovery to defense and intelligence. This explosive progress is especially apparent in the subfield of machine learning (ML), where AI systems learn autonomously by identifying patterns in large volumes of data.[1] Indeed, over the last five years, the fields of AI and ML have witnessed stunning advancements in computer vision (e.g., object recognition), speech recognition, and scientific discovery.[2], [3], [4], [5] However, these advances are not without risk as transformative technologies are generally accompanied by a significant risk profile, with notable examples including the discovery of nuclear energy, the Internet, and synthetic biology. Experts are increasingly voicing concerns over AI risk from misuse by state and non-state actors, principally in the areas of cybersecurity and disinformation propagation. However, issues of control for example, how advanced AI decision-making aligns with human goals are not as prominent in the discussion of risk and could ultimately be equally or more dangerous than threats from nefarious actors. Modern ML systems are not programmed (as programming is typically understood), but rather independently developed strategies to complete objectives, which can be mis-specified, learned incorrectly, or executed in unexpected ways. This issue becomes more pronounced as AI becomes more ubiquitous and we become more reliant on AI decision-making. Thus, as AI is increasingly entwined through tightly coupled critical systems, the focus must expand beyond accidents and misuse to the autonomous decision processes themselves.

The principal mid- to long-term risks from AI systems fall into three broad categories: risks of misuse or accidents, structural risks, and misaligned objectives. The misuse or accident category includes things such as AI-enabled cyber-attacks with increased speed and effectiveness or the generation and distribution of disinformation at scale.[6] In critical infrastructures, AI accidents could manifest as system failures with potential secondary and tertiary effects across connected networks. A contemporary example of an AI accident is the New York Stock Exchange (NYSE) Flash Crash of 2010, which drove the market down 600 points in 5 minutes.[7] Such rapid and unexpected operations from algorithmic trading platforms will only increase in destructive potential as systems increase in complexity, interconnectedness, and autonomy.

The structural risks category is concerned with how AI technologies shape the social and geopolitical environment in which they are deployed. Important contemporary examples include the impact of social media content selection algorithms on political polarization or uncertainty in nuclear deterrence and the offense-to-defense balance.[8],[9] For example, the integration of AI into critical systems, including peripheral processes (e.g., command and control, targeting, supply chain, and logistics), can degrade multilateral trust in deterrence.[10] Indeed, increasing autonomy in all links of the national defense chain, from decision support to offensive weapons deployment, compounds the uncertainty already under discussion with autonomous weapons.[11]

Misaligned objectives is another important failure mode. Since ML systems develop independent strategies, a concern is that the AI systems will misinterpret the correct objectives, develop destructive subgoals, or complete them in an unpredictable way. While typically grouped together, it is important to clarify the differences between a system crash and actions executed by a misaligned AI system so that appropriate risk mitigation measures can be evaluated. Understanding the range of potential failures may help in the allocation of resources for research on system robustness, interpretability, or AI alignment.

At its most basic level, AI alignment involves teaching AI systems to accurately capture what we want and complete it in a safe and ethical manner. Misalignment of AI systems poses the highest downside risk of catastrophic failures. While system failures by themselves could be immensely damaging, alignment failures could include unexpected and surprising actions outside the systems intent or window of probability. However, ensuring the safe and accurate interpretation of human objectives is deceptively complex in AI systems. On the surface, this seems straightforward, but the problem is far from obvious with unimaginably complex subtleties that could lead to dangerous consequences.

In contrast with nuclear weapons or cyber threats, where the risks are more obvious, risks from AI misalignment can be less clear. These complexities have led to misinterpretation and confusion with some attributing the concerns to disobedient or malicious AI systems.[12] However, the concerns are not that AI will defy its programming but rather that it will follow the programming exactly and develop novel, unanticipated solutions. In effect, the AI will pursue the objective accurately but may yield an unintended, even harmful, consequence. Googles Alpha Go program, which defeated the world champion Go[13] player in 2016, provides an illustrative example of the potential for unexpected solutions. Trained on millions of games, Alpha Gos neural network learned completely unexpected actions outside of the human frame of reference.[14] As Chris Anderson explains, what took the human brain thousands of years to optimize Googles Alpha Go completed in three years, executing better, almost alien solutions that we hadnt even considered.[15] This novelty illustrates how unpredictable AI systems can be when permitted to develop their own strategies to accomplish a defined objective.

To appreciate how AI systems pose these risks, by default, it is important to understand how and why AI systems pursue objectives. As described, ML is designed not to program distinct instructions but to allow the AI to determine the most efficient means. As learning progresses, the training parameters are adjusted to minimize the difference between the pursued objective and the actual value by incentivizing positive behavior (known as reinforcement learning, or RL).[16],[17] Just as humans pursue positive reinforcement, AI agents are goal-directed entities, designed to pursue objectives, whether the goal aligns with the original intent or not.

Computer science professor Steve Omohundro illustrates a series of innate AI drives that systems will pursue unless explicitly counteracted.[18] According to Omohundro, distinct from programming, AI agents will strive to self-improve, seek to acquire resources, and be self-protective.[19] These innate drives were recently demonstrated experimentally, where AI agents tend to seek power over the environment to achieve objectives most efficiently.[20] Thus, AI agents are naturally incentivized to seek out useful resources to accomplish an objective. This power-seeking behavior was reported by Open AI, where two teams of agents, instructed to play hide-and-seek in a simulated environment, proceeded to horde objects from the competition in what Open AI described as tool use distinct from the actual objective.[21] The AI teams learned that the objects were instrumental in completing the objective.[22] Thus, a significant concern for AI researchers is the undefined instrumental sub-goals that are pursued to complete the final objective. This tendency to instantiate sub-goals is coined the instrumental convergence thesis by Oxford philosopher Nick Bostrom. Bostrom postulated that intermediate sub-goals are likely to be pursued by an intelligent agent to complete the final objective more efficiently.[23] Consider an advanced AI system optimized to ensure adequate power between several cities. The agent could develop a sub-goal of capturing and redirecting bulk power from other locations to ensure power grid stability. Another example is an autonomous weapons system designed to identify targets that develop a unique set of intermediate indicators to determine the identity and location of the enemy. Instrumental sub-goals could be as simple as locking a computer-controlled access door or breaking traffic laws in an autonomous car, or as severe as destabilizing a regional power grid or nuclear power control system. These hypothetical and novel AI decision processes raise troubling questions in the context of conflict or safety of critical systems. The range of possible AI solutions are too large to consider and can only get more consequential as systems become more capable and complex. The effect of AI misalignment could be disastrous if the AI discovers an unanticipated optimal solution to a problem that results in a critical system becoming inoperable or yielding a catastrophic result.

While the control problem is troubling by itself, the integration of multiagent systems could be far more dangerous and could lead to other (as of now unanticipated) failure modes between systems. Just like complex societies, complex agent communities could manifest new capabilities and emergent failure modes unique to the complex system. Indeed, AI failures are unlikely to happen in isolation and the roadmap for multiagent AI environments is currently underway in both the public and private sectors.

Several U.S. government initiatives for next-generation intelligent networks include adaptive learning agents for autonomous processes. The Armys Joint All-Domain Command and Control (JADC2) concept for networked operations and the Resilient and Intelligent Next-Generation Systems (RINGS) program, put forth by the National Institute of Standards and Technology (NIST), are two notable ongoing initiatives.[24], [25] Literature on cognitive Internet of Things (IoT) points to the extent of autonomy planned for self-configuring, adaptive AI communities and societies to steer networks through managing user intent, supervision of autonomy, and control.[26] A recent report from the worlds largest technical professional organization, IEEE, outlines the benefits of deep reinforcement learning (RL) agents for cyber security, proposing that, since RL agents are highly capable of solving complex, dynamic, and especially high-dimensional problems, they are optimal for cyber defense.[27] Researchers propose that RL agents be designed and released autonomously to configure the network, prevent cyber exploits, detect and counter jamming attacks, and offensively target distributed denial-of-service attacks.[28] Other researchers submitted proposals for automated penetration-testing, the ability to self-replicate the RL agents, while others propose cyber-red teaming autonomous agents for cyber-defense.[29], [30], [31]

Considering the host of problems discussed from AI alignment, unexpected side effects, and the issue of control, jumping headfirst into efforts that give AI meaningful control over critical systems (such as the examples described above) without careful consideration of the potential unexpected (or potentially catastrophic) outcomes does not appear to be the appropriate course of action. Proposing the use of one autonomous system in warfare is concerning but releasing millions into critical networks is another matter entirely. Researcher David Manheim explains that multiagent systems are vulnerable to entirely novel risks, such as over-optimization failures, where optimization pressure allows individual agents to circumvent designed limits.[32] As Manheim describes, In many-agent systems, even relatively simple systems can become complex adaptive systems due to agent behavior.[33] At the same time, research demonstrates that multiagent environments lead to greater agent generalization, thus reducing the capability gap that separates human intelligence from machine intelligence.[34] In contrast, some authors present multiagent systems as a viable solution to the control problem, with stable, bounded capabilities, and others note the broad uncertainty and potential for self-adaptation and mutation.[35] Yet, the author admits that there are risks and the multiplicative growth of RL agents could potentially lead to unexpected failures, with the potential for the manifestation of malignant agential behaviors.[36],[37] AI researcher Trent McConaughy highlights the risk from adaptive AI systems, specifically decentralized autonomous organizations (DAO) in blockchain networks. McConaughy suggests that rather than a powerful AI system taking control of resources, as is typically discussed, the situation may be far more subtle where we could simply hand over global resources to self-replicating communities of adaptive AI systems (e.g., Bitcoins increasing energy expenditures that show no sign of slowing).[38]

Advanced AI capabilities in next-generation networks that dynamically reconfigure and reorganize network operations hold undeniable risks to security and stability.[39],[40] A complex landscape of AI agents, designed to autonomously protect critical networks or conduct offensive operations, would invariably need to develop subgoals to manage the diversity of objectives. Thus, whether individual systems or autonomous collectives, the web of potential failures and subtle side-effects could unleash unpredictable dangers leading to catastrophic second- and third-order effects. As AI systems are currently designed, understanding the impact of the subgoals (or even their existence) could be extremely difficult or impossible. The AI examples above illustrate critical infrastructure and national security cases that are currently in discussion, but the reality could be far more complex, unexpected, and dangerous. While most AI researchers expect that safety will develop concurrently with system autonomy and complexity, there is no certainty in this proposition. Indeed, if there is even a minute chance of misalignment in a deployed AI system (or systems) in critical infrastructure or national defense it is important that researchers dedicate a portion of resources to evaluating the risks. Decision makers in government and industry must consider these risks and potential means to mitigate them before generalized AI systems are integrated into critical and national security infrastructure, because to do otherwise could lead to catastrophic failure modes that we may not be able to fully anticipate, endure, or overcome.

Disclaimer: The authors are responsible for the content of this article. The views expressed do not reflect the official policy or position of the National Intelligence University, the National Geospatial Intelligence Agency, the Department of Defense, the Office of the Director of National Intelligence, the U.S. Intelligence Community, or the U.S. Government.

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Etzioni, Oren. No, the Experts Dont Think Superintelligent AI is a Threat to Humanity. MIT Technology Review. September 20, 2016. https://www.technologyreview.com/2016/09/20/70131/no-the-experts-dont-think-superintelligent-ai-is-a-threat-to-humanity/ (accessed August 19, 2022).

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Jumper, John, et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 (August 2021): 583589.

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[1] (Zewe 2022)

[2] (Littman, et al. 2021)

[3] (Jumper, et al. 2021)

[4] (Brown, et al. 2020)

[5] (Gary, Davis and Aaronson 2022)

[6] (Buchanan, et al. 2020)

[7] (Avatrade Staff 2021)

[8] (Russell 2019, 9-10)

[9] (Zwetsloot and Dafoe 2019)

[12] (Etzioni 2016)

[13] GO is an ancient Chinese strategy board game

[14] (Byford 2016)

[15] (Anderson 2019, 150)

[16] (Kegel 2021)

[17] (Krakovna 2020)

[18] (Omohundro 2008, 483-492)

[19] Ibid., 484.

[20] (Turner, et al. 2021, 8-9)

[21] (Baker, et al. 2020)

[22] Ibid.

[23] (Bostrom 2012, 71-85)

[24] (GCN Staff 2021)

[25] (Pomerleu 2022)

[26] (Berggren, et al. 2021)

[27] (Nguyen and Reddi 2021)

[28] Ibid.

[29] (Edison 2019)

[30] (Panfili, et al. 2018)

[31] (Winder n.d.)

[32] (Manheim 2018)

[33] Ibid.

[34] (Zeng, et al. 2022)

[35] (Drexler 2019, 18)

[36] Ibid.

[37] (Shah 2019)

[38] (Duettmann 2022)

[39] (Trevino 2019)

[40] (Pico-Valencia and Holgado-Terriza 2018)

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Artificial Intelligence, Critical Systems, and the Control Problem - HS Today - HSToday

The French government has collected nearly 10 million in additional taxes after using machine learning to spot undeclared swimming pools in aerial photos. In France, housing taxes are calculated based on a propertys rental value, so homeowners who dont declare swimming pools are potentially avoiding hundreds of euros in additional payments.

The project to spot the undeclared pools began last October, with IT firm Capgemini working with Google to analyze publicly available aerial photos taken by Frances National Institute of Geographic and Forest Information. Software was developed to identify pools, with this information then cross-referenced with national tax and property registries.

The project is somewhat limited in scope, and has so far analyzed photos covering only nine of Frances 96 metropolitan departments. But even in these areas, officials discovered 20,356 undeclared pools, according to an announcement this week from Frances tax office, the General Directorate of Public Finance (DGFiP), first reported by Le Parisien.

As of 2020, it was estimated that France had around 3.2 million private swimming pools, but constructions have reportedly surged as more people worked from home during COVID-19 lockdowns, and summer temperatures have soared across Europe.

Ownership of private pools has become somewhat contentious in France this year, as the country has suffered from a historic drought that has emptied rivers of water. An MP for the French Green party (Europe cologie les Verts) made headlines after refusing to rule out a ban on the construction of new private pools. The MP, Julien Bayou, said such a ban could be used as a last resort response. He later clarified his remarks on Twitter, saying: [T]here are ALREADY restrictions on water use, for washing cars and sometimes for filling swimming pools. The challenge is not to ban swimming pools, it is to guarantee our vital water needs.

Frances tax offices, the DGFiP (known more commonly as Le Fisc), says it now plans to expand the use of its AI-pool-spotter to the entirety of metropolitan France (excluding the countrys overseas departments), which could net an additional 40 million in taxes.

Early reports on the project suggested that the machine learning software had an unusually high error rate of 30 percent, and regularly mistook other architectural features like solar panel installations for swimming pools. Now, though, Le Fisc says its ironed out these problems, and is looking to expand the use of its software spotting pools to identifying other undeclared and taxable housing improvements, like extensions and annexes.

We are particularly targeting house extensions like verandas, but we have to be sure that the software can find buildings with a large footprint and not the dog kennel or the childrens playhouse, Antoine Magnant, the deputy director general of public finances, told Le Parisien, reports The Guardian.

Read more from the original source:
French government uses AI to spot undeclared swimming pools and tax them - The Verge