Kunihiko Fukushima (Japanese: 福島 邦彦, born 16 March 1936) is a Japanese computer scientist, most noted for his work on artificial neural networks and deep learning. He is currently working part-time as a senior research scientist at the Fuzzy Logic Systems Institute in Fukuoka, Japan. == Notable scientific achievements == In 1980, Fukushima published the neocognitron, the original deep convolutional neural network (CNN) architecture. Fukushima proposed several supervised and unsupervised learning algorithms to train the parameters of a deep neocognitron such that it could learn internal representations of incoming data. Today, however, the CNN architecture is usually trained through backpropagation. This approach is now heavily used in computer vision. In 1969 Fukushima introduced the ReLU (Rectifier Linear Unit) activation function in the context of visual feature extraction in hierarchical neural networks, which he called "analog threshold element". (Though the ReLU was first used by Alston Householder in 1941 as a mathematical abstraction of biological neural networks.) As of 2017 it is the most popular activation function for deep neural networks. == Education and career == In 1958, Fukushima received his Bachelor of Engineering in electronics from Kyoto University. He became a senior research scientist at the NHK Science & Technology Research Laboratories. In 1989, he joined the faculty of Osaka University. In 1999, he joined the faculty of the University of Electro-Communications. In 2001, he joined the faculty of Tokyo University of Technology. From 2006 to 2010, he was a visiting professor at Kansai University. Fukushima acted as founding president of the Japanese Neural Network Society (JNNS). He also was a founding member on the board of governors of the International Neural Network Society (INNS), and president of the Asia-Pacific Neural Network Assembly (APNNA). He was one of the board of governors of the International Neural Network Society (INNS) in 1989-1990 and 1993-2005. == Awards == In 2020, Fukushima received the Bower Award and Prize for Achievement in Science. In 2022, Fukushima became a laureate of the Asian Scientist 100 by the Asian Scientist. He also received the IEICE Achievement Award and Excellent Paper Awards, the IEEE Neural Networks Pioneer Award, the APNNA Outstanding Achievement Award, the JNNS Excellent Paper Award and the INNS Helmholtz Award.
TipTop Technologies
TipTop Technologies is a real-time web and social search engine with a platform for semantic analysis of natural language. Tip-Top Search provides results capturing individual and group sentiment, opinions, and experiences there from the content of various sorts such as real-time messages from Twitter or consumer product reviews on Amazon.com. TipTop Technologies and ITC Infotech collaborated to create a search interface suitable for both enterprise and consumer applications. Tip-Top's products are part of the "emerging Web 3.0 applications which use semantic technologies to augment the underlying Web system's functionalities." Their main product is 360, an AI tool that incorporates multiple AI applications under one wing. Jonathan AlBright professor at Elon University, found videos generated by TipTop Technologies software on YouTube in his research into artificial intelligence, described it as AI-generated "fake news". Through semantic analysis of large data sets, TipTop gleaned behavioral insights from Tweets around events like Halloween, Thanksgiving, Holiday Gifting, the Super Bowl, and the Oscar Nominees for the Academy Awards coverage. Sentiment analysis, concept trend tracking, and real-time market research are other applications included in the TipTop Search product. TipTop's insight engine solves the problem of real-time data noise, and its ability to "sort the 'good tweets' from the 'bad tweets' when it comes to a product, service, or a region..." In addition, products like TipTop Shopping with customizable search widgets bring together consumer reviews, social search, and sentiment analysis enabling product comparisons across attributes like the overall value and aiding purchasing decisions through user-driven product tips and pits. TipTop Finance adds another complexity to real-time search results by incorporating corporate sentiment, company stock tickers, and social media into TipTop's existing social search platform. Additional success applying semantic technologies has been with polling, "if you compare these Gallup results with TipTop, a sentiment engine based on Twitter, the results are not way off. It does surprise you but it tells me that sentiment analysis in case of public opinion about a burning social issue or a famous personality is relatively easier." With the increasing amount of unstructured, opinion-oriented, and user-generated content available on the Web, TipTop's technology aims to make sense of all this data, and deliver it in a useful way for consumer and enterprise users alike. TipTop Technologies is a privately held company with its headquarters in the San Francisco Bay Area, and team members are located globally.
Rendezvous hashing
Rendezvous or highest random weight (HRW) hashing is an algorithm that allows clients to achieve distributed agreement on a set of k {\displaystyle k} options out of a possible set of n {\displaystyle n} options. A typical application is when clients need to agree on which sites (or proxies) objects are assigned to. Consistent hashing addresses the special case k = 1 {\displaystyle k=1} using a different method. Rendezvous hashing is both much simpler and more general than consistent hashing (see below). == History == Rendezvous hashing was invented by David Thaler and Chinya Ravishankar at the University of Michigan in 1996. Consistent hashing appeared a year later in the literature. Given its simplicity and generality, rendezvous hashing is now being preferred to consistent hashing in real-world applications. Rendezvous hashing was used very early on in many applications including mobile caching, router design, secure key establishment, and sharding and distributed databases. Other examples of real-world systems that use Rendezvous Hashing include the GitHub load balancer, the Apache Ignite distributed database, the Tahoe-LAFS file store, the CoBlitz large-file distribution service, Apache Druid, IBM's Cloud Object Store, the Arvados Data Management System, Apache Kafka, and the Twitter EventBus pub/sub platform. One of the first applications of rendezvous hashing was to enable multicast clients on the Internet (in contexts such as the MBONE) to identify multicast rendezvous points in a distributed fashion. It was used in 1998 by Microsoft's Cache Array Routing Protocol (CARP) for distributed cache coordination and routing. Some Protocol Independent Multicast routing protocols use rendezvous hashing to pick a rendezvous point. == Problem definition and approach == === Algorithm === Rendezvous hashing solves a general version of the distributed hash table problem: We are given a set of n {\displaystyle n} sites (servers or proxies, say). How can any set of clients, given an object O {\displaystyle O} , agree on a k-subset of sites to assign to O {\displaystyle O} ? The standard version of the problem uses k = 1. Each client is to make its selection independently, but all clients must end up picking the same subset of sites. This is non-trivial if we add a minimal disruption constraint, and require that when a site fails or is removed, only objects mapping to that site need be reassigned to other sites. The basic idea is to give each site S j {\displaystyle S_{j}} a score (a weight) for each object O i {\displaystyle O_{i}} , and assign the object to the highest scoring site. All clients first agree on a hash function h ( ⋅ ) {\displaystyle h(\cdot )} . For object O i {\displaystyle O_{i}} , the site S j {\displaystyle S_{j}} is defined to have weight w i , j = h ( O i , S j ) {\displaystyle w_{i,j}=h(O_{i},S_{j})} . Each client independently computes these weights w i , 1 , w i , 2 … w i , n {\displaystyle w_{i,1},w_{i,2}\dots w_{i,n}} and picks the k sites that yield the k largest hash values. The clients have thereby achieved distributed k {\displaystyle k} -agreement. If a site S {\displaystyle S} is added or removed, only the objects mapping to S {\displaystyle S} are remapped to different sites, satisfying the minimal disruption constraint above. The HRW assignment can be computed independently by any client, since it depends only on the identifiers for the set of sites S 1 , S 2 … S n {\displaystyle S_{1},S_{2}\dots S_{n}} and the object being assigned. HRW easily accommodates different capacities among sites. If site S k {\displaystyle S_{k}} has twice the capacity of the other sites, we simply represent S k {\displaystyle S_{k}} twice in the list, say, as S k , 1 , S k , 2 {\displaystyle S_{k,1},S_{k,2}} . Clearly, twice as many objects will now map to S k {\displaystyle S_{k}} as to the other sites. === Properties === Consider the simple version of the problem, with k = 1, where all clients are to agree on a single site for an object O. Approaching the problem naively, it might appear sufficient to treat the n sites as buckets in a hash table and hash the object name O into this table. Unfortunately, if any of the sites fails or is unreachable, the hash table size changes, forcing all objects to be remapped. This massive disruption makes such direct hashing unworkable. Under rendezvous hashing, however, clients handle site failures by picking the site that yields the next largest weight. Remapping is required only for objects currently mapped to the failed site, and disruption is minimal. Rendezvous hashing has the following properties: Low overhead: The hash function used is efficient, so overhead at the clients is very low. Load balancing: Since the hash function is randomizing, each of the n sites is equally likely to receive the object O. Loads are uniform across the sites. Site capacity: Sites with different capacities can be represented in the site list with multiplicity in proportion to capacity. A site with twice the capacity of the other sites will be represented twice in the list, while every other site is represented once. High hit rate: Since all clients agree on placing an object O into the same site SO, each fetch or placement of O into SO yields the maximum utility in terms of hit rate. The object O will always be found unless it is evicted by some replacement algorithm at SO. Minimal disruption: When a site fails, only the objects mapped to that site need to be remapped. Disruption is at the minimal possible level. Distributed k-agreement: Clients can reach distributed agreement on k sites simply by selecting the top k sites in the ordering. == O(log n) running time via skeleton-based hierarchical rendezvous hashing == The standard version of Rendezvous Hashing described above works quite well for moderate n, but when n {\displaystyle n} is extremely large, the hierarchical use of Rendezvous Hashing achieves O ( log n ) {\displaystyle O(\log n)} running time. This approach creates a virtual hierarchical structure (called a "skeleton"), and achieves O ( log n ) {\displaystyle O(\log n)} running time by applying HRW at each level while descending the hierarchy. The idea is to first choose some constant m {\displaystyle m} and organize the n {\displaystyle n} sites into c = ⌈ n / m ⌉ {\displaystyle c=\lceil n/m\rceil } clusters C 1 = { S 1 , S 2 … S m } , C 2 = { S m + 1 , S m + 2 … S 2 m } … {\displaystyle C_{1}=\left\{S_{1},S_{2}\dots S_{m}\right\},C_{2}=\left\{S_{m+1},S_{m+2}\dots S_{2m}\right\}\dots } Next, build a virtual hierarchy by choosing a constant f {\displaystyle f} and imagining these c {\displaystyle c} clusters placed at the leaves of a tree T {\displaystyle T} of virtual nodes, each with fanout f {\displaystyle f} . In the accompanying diagram, the cluster size is m = 4 {\displaystyle m=4} , and the skeleton fanout is f = 3 {\displaystyle f=3} . Assuming 108 sites (real nodes) for convenience, we get a three-tier virtual hierarchy. Since f = 3 {\displaystyle f=3} , each virtual node has a natural numbering in octal. Thus, the 27 virtual nodes at the lowest tier would be numbered 000 , 001 , 002 , . . . , 221 , 222 {\displaystyle 000,001,002,...,221,222} in octal (we can, of course, vary the fanout at each level - in that case, each node will be identified with the corresponding mixed-radix number). The easiest way to understand the virtual hierarchy is by starting at the top, and descending the virtual hierarchy. We successively apply Rendezvous Hashing to the set of virtual nodes at each level of the hierarchy, and descend the branch defined by the winning virtual node. We can in fact start at any level in the virtual hierarchy. Starting lower in the hierarchy requires more hashes, but may improve load distribution in the case of failures. For example, instead of applying HRW to all 108 real nodes in the diagram, we can first apply HRW to the 27 lowest-tier virtual nodes, selecting one. We then apply HRW to the four real nodes in its cluster, and choose the winning site. We only need 27 + 4 = 31 {\displaystyle 27+4=31} hashes, rather than 108. If we apply this method starting one level higher in the hierarchy, we would need 9 + 3 + 4 = 16 {\displaystyle 9+3+4=16} hashes to get to the winning site. The figure shows how, if we proceed starting from the root of the skeleton, we may successively choose the virtual nodes ( 2 ) 3 {\displaystyle (2)_{3}} , ( 20 ) 3 {\displaystyle (20)_{3}} , and ( 200 ) 3 {\displaystyle (200)_{3}} , and finally end up with site 74. The virtual hierarchy need not be stored, but can be created on demand, since the virtual nodes names are simply prefixes of base- f {\displaystyle f} (or mixed-radix) representations. We can easily create appropriately sorted strings from the digits, as required. In the example, we would be working with the strings 0 , 1 , 2 {\displaystyle 0,1,2} (at tier 1), 20 , 21 , 22 {\displaystyle 20,21,22} (at tier 2), and 200 , 201 , 202
Materials informatics
Materials informatics is a field of study that applies the principles of informatics and data science to materials science and engineering to improve the understanding, use, selection, development, and discovery of materials. The term "materials informatics" is frequently used interchangeably with "data science", "machine learning", and "artificial intelligence" by the community. This is an emerging field, with a goal to achieve high-speed and robust acquisition, management, analysis, and dissemination of diverse materials data with the goal of greatly reducing the time and risk required to develop, produce, and deploy new materials, which generally takes longer than 20 years. This field of endeavor is not limited to some traditional understandings of the relationship between materials and information. Some more narrow interpretations include combinatorial chemistry, process modeling, materials databases, materials data management, and product life cycle management. Materials informatics is at the convergence of these concepts, but also transcends them and has the potential to achieve greater insights and deeper understanding by applying lessons learned from data gathered on one type of material to others. By gathering appropriate meta data, the value of each individual data point can be greatly expanded. == Databases == Databases are essential for any informatics research and applications. In material informatics many databases exist containing both empirical data obtained experimentally, and theoretical data obtained computationally. Big data that can be used for machine learning is particularly difficult to obtain for experimental data due to the lack of a standard for reporting data and the variability in the experimental environment. This lack of big data has led to growing effort in developing machine learning techniques that utilize data extremely data sets. On the other hand, large uniform database of theoretical density functional theory (DFT) calculations exists. These databases have proven their utility in high-throughput material screening and discovery. Some common DFT databases and high throughput tools are listed below: Databases: MaterialsProject.org, MaterialsWeb.org (University of Florida) HT software: Pymatgen, MPInterfaces, Matminer == Beyond computational methods? == The concept of materials informatics is addressed by the Materials Research Society. For example, materials informatics was the theme of the December 2006 issue of the MRS Bulletin. The issue was guest-edited by John Rodgers of Innovative Materials, Inc., and David Cebon of Cambridge University, who described the "high payoff for developing methodologies that will accelerate the insertion of materials, thereby saving millions of investment dollars." The editors focused on the limited definition of materials informatics as primarily focused on computational methods to process and interpret data. They stated that "specialized informatics tools for data capture, management, analysis, and dissemination" and "advances in computing power, coupled with computational modeling and simulation and materials properties databases" will enable such accelerated insertion of materials. A broader definition of materials informatics goes beyond the use of computational methods to carry out the same experimentation, viewing materials informatics as a framework in which a measurement or computation is one step in an information-based learning process that uses the power of a collective to achieve greater efficiency in exploration. When properly organized, this framework crosses materials boundaries to uncover fundamental knowledge of the basis of physical, mechanical, and engineering properties. == Challenges == While there are many who believe in the future of informatics in the materials development and scaling process, many challenges remain. Hill, et al., write that "Today, the materials community faces serious challenges to bringing about this data-accelerated research paradigm, including diversity of research areas within materials, lack of data standards, and missing incentives for sharing, among others. Nonetheless, the landscape is rapidly changing in ways that should benefit the entire materials research enterprise." This remaining tension between traditional materials development methodologies and the use of more computationally, machine learning, and analytics approaches will likely exist for some time as the materials industry overcomes some of the cultural barriers necessary to fully embrace such new ways of thinking. == Analogy from Biology == The overarching goals of bioinformatics and systems biology may provide a useful analogy. Andrew Murray of Harvard University expresses the hope that such an approach "will save us from the era of "one graduate student, one gene, one PhD". Similarly, the goal of materials informatics is to save us from one graduate student, one alloy, one PhD. Such goals will require more sophisticated strategies and research paradigms than applying data-science methods to the same tasks set currently undertaken by students.
E-Science librarianship
E-Science librarianship refers to a role for librarians in e-Science. == Early scholars == Early references to e-Science and librarianship involve information studies scholars researching cyberinfrastructure and emerging networked information and knowledge communities. Notably Christine Borgman, Professor and Presidential Chair in Information Studies at the University of California, Los Angeles (UCLA) was a key player in bringing e-Science, and the idea of networked knowledge communities, to the attention of the library profession. In 2004, as a visiting fellow at the Oxford Internet Institute, she conducted research and lectured publicly on e-Science, Digital Libraries, and Knowledge Communities. In 2007 Anna K. Gold, formerly of MIT and Cal Poly, San Luis Obispo, authored a series of articles in D-Lib Magazine that opened the door for academic libraries to begin exploring roles, skills, and strategies for engaging in e-Science: Cyberinfrastructure, Data, and Libraries, Part 1: A Cyberinfrastructure Primer for Librarians and Cyberinfrastructure, Data, and Libraries, Part 2: Libraries and the Data Challenge: Roles and Actions for Libraries. == Academic research and health sciences libraries == In 2007, the Association of Research Libraries (ARL) e-Science task force issued its report on e-Science and librarianship. The ARL's report encouraged its member libraries to position themselves to engage with researchers involved in e-Science (eScience) by cultivating new research support strategies and developing their digital scholarship infrastructure. E-Science has multiple attributes; Tony and Jessie Hey framed e-Science for the library community by characterizing it as a research methodology: "e-Science is not a new scientific discipline in its own right: e-Science is shorthand for the set of tools and technologies required to support collaborative, networked science". In addition to academic libraries' interests in providing support for their researchers engaging in e-Science, the health sciences library community also emerged as a major proponent for creating librarian positions for supporting the information needs of large-scale, networked, research collaborations on their campuses. Neil Rambo, current director of NYU's Health Sciences Library and former director of University of Washington Health Sciences Library, was the first to use the term in the Journal of the Medical Library Association, in his 2009 editorial e-Science and the Biomedical Library. Rambo's definition of e-Science highlighted the potential e-Science held for creating data as a research product: "E-science is a new research methodology, fueled by networked capabilities and the practical possibility of gathering and storing vast amounts of data." In response to this article the University of Massachusetts Medical School Lamar Soutter Library and National Network of Libraries of Medicine, New England Region encouraged health sciences libraries to cooperate to identify skills and develop a program for training e-Science Librarians. Then, in 2013, Shannon Bohle, an archivist who was employed in the library at Cold Spring Harbor Laboratory, an NCI-designated basic cancer research facility, used experience gained there and previous papers and presentations about preserving scientific archival materials to expand the traditional definition of e-Science by including the terms, principles, and practices used in archival science. These included in the definition the "long-term storage and accessibility of all materials generated through the scientific process," as well as examples of material types traditionally preserved in archives, like "electronic/digitized laboratory notebooks, raw and fitted data sets, manuscript production and draft versions, pre-prints," as well as library materials ("print and/or electronic publications"). == Roles == Many areas of science are about to be transformed by the availability of vast amounts of new scientific data that can potentially provide insights at a level of detail never before envisaged. However, this new data dominant era brings new challenges for the scientists and they will need the skills and technologies both of computer scientists and of the library community to manage, search and curate these new data resources. Libraries will not be immune from change in this new world of research. Karen Williams identifies roles in the following areas for librarians in the developing world of e-Science. Campus Engagement Content/Collection Development and Management Teaching and Learning Scholarly Communication E-Scholarship and Digital Tools Reference/Help Services Outreach Fund Raising Exhibit and Event Planning Leadership == Challenges for research libraries == E-science tends toward inter- and multidisciplinary approaches that depend on computation and computer science. Research libraries have traditionally been discipline focused and, although increasingly technologically sophisticated, do not have systems of the scale or complexity of the e-science environment. E-science is data intensive, but research libraries have not typically been responsible for scientific data. E-science is frequently conducted in a team context, often distributed across multiple institutions and on a global scale. The primary constituency of libraries generally comprises those affiliated with the local institution. Licenses for electronic content are typically restricted to a particular institutional community, and the infrastructure to move institutional licenses into a multi-institutional environment is not well developed. E-science challenges all these traditional paradigms of research library organization and services. == Skills == Garritano & Carlson were among the first to outline a skill set for librarians seeking to support the data needs of e-Science; they identified five skill categories librarians new to this area should expect to adapt or develop when participating on such projects: Library and information science expertise Subject expertise Partnerships and outreach (both internal and external) Participating in sponsored research Balancing workload An example of librarians reconfiguring traditional librarian skills to meet the needs of researchers engaging in e-Science is Witt & Carlson's adaptation of the traditional reference interview into a "data interview" in order to provide effective data management and e-Science services. This interview consists of ten practical queries necessary for understanding the provenance and expectations for the preservation of datasets typical of e-Science that also help illustrate some of the educational tools and skills needed by a librarian new to e-Science. "What is the story of the data? What form and format are the data in? What is the expected lifespan of the dataset? How could the data be used, reused, and repurposed? How large is the dataset, and what is its rate of growth? Who are the potential audiences for the data? Who owns the data? Does the dataset include any sensitive information? What publications or discoveries have resulted from the data? How should the data be made accessible?" == Resources == In 2009 the Lamar Soutter Library at the University of Massachusetts Medical School (UMMS) and the National Network of Libraries of Medicine, New England Region (NN/LM NER) funded an e-Science program for building the skills highlighted above for librarians. Elaine Russo Martin, Director of Library Services at the Lamar Soutter Library and Director of the NN/LM NER developed this comprehensive e-Science program to build librarians' subject expertise in the sciences, developing their data management skills, and their familiarity with cyberinfrastructure and e-Science. Three major products of this program are the e-Science web portal for librarians, the E-Science Symposium, and the New England Collaborative Data Management Curriculum (NECDMC). This portal includes educational resources for specific tools and subject/discipline tutorials and modules to assist librarians new to e-Science. UMMS and NN/LM NER also publish an open access journal called the Journal of eScience Librarianship.
CloudSim
CloudSim is a framework for modeling and simulation of cloud computing infrastructures and services. Originally built primarily at the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, the University of Melbourne, Australia, CloudSim has become one of the most popular open source cloud simulators in the research and academia. CloudSim is completely written in Java. The latest version of CloudSim is CloudSim v6.0.0-beta on GitHub. Cloudsim is suitable for implementing simulations scenarios based on Infrastructure as a service as well as with latest version Platform as a service, so get started here == CloudSim extensions == Initially developed as a stand-alone cloud simulator, CloudSim has further been extended by independent researchers. GPUCloudSim is an enhanced CloudSim tool for modeling GPU-based cloud infrastructures and data centers. It offers simulations for multi-GPU setups, customizable GPU policies, GPU remoting, etc. It also examines performance impacts and interactions within virtualized GPU environments. CloudSim Plus is a totally re-engineered CloudSim fork providing general-purpose cloud computing simulation and exclusive features such as: multi-cloud simulations, vertical and horizontal VM scaling, host fault injection and recovery, joint power- and network-aware simulations and more. Though CloudSim itself does not have a graphical user interface, extensions such as CloudReports offer a GUI for CloudSim simulations. CloudSimEx extends CloudSim by adding MapReduce simulation capabilities and parallel simulations. Cloud2Sim extends CloudSim to execute on multiple distributed servers, by leveraging Hazelcast distributed execution framework. RECAP DES extends the CloudSim Plus framework to model synchronous hierarchical architectures (such as ElasticSearch). ThermoSim extends CloudSim toolkit by incorporating thermal characteristics, and uses Deep learning-based temperature predictor for cloud nodes.
Artificial intelligence in government
Artificial intelligence (AI) has a range of uses in government. It can be used to further public policy objectives (in areas such as emergency services, health and welfare), as well as assist the public to interact with the government (through the use of virtual assistants, for example). According to the Harvard Business Review, "Applications of artificial intelligence to the public sector are broad and growing, with early experiments taking place around the world." Hila Mehr from the Ash Center for Democratic Governance and Innovation at Harvard University notes that AI in government is not new, with postal services using machine methods in the late 1990s to recognise handwriting on envelopes to automatically route letters. The use of AI in government comes with significant benefits, including efficiencies resulting in cost savings (for instance by reducing the number of front office staff) and reducing the opportunities for corruption. However, it also carries risks (described below). == Uses of AI in government == The potential uses of AI in government are wide and varied, with Deloitte considering that "Cognitive technologies could eventually revolutionize every facet of government operations". Mehr suggests that six types of government problems are appropriate for AI applications: Resource allocation—such as where administrative support is required to complete tasks more quickly. Large datasets—where these are too large for employees to work efficiently and multiple datasets could be combined to provide greater insights. Expert shortage—including where basic questions could be answered and niche issues can be learned. Predictable scenario—historical data makes the situation predictable. Procedural tasks refer to repetitive tasks in which the answers to inputs or outputs are binary. Diverse data—where data takes various forms (such as visual and linguistic) and needs to be summarized regularly. Mehr states that "While applications of AI in government work have not kept pace with the rapid expansion of AI in the private sector, the potential use cases in the public sector mirror common applications in the private sector." Potential and actual uses of AI in government can be divided into three broad categories: those that contribute to public policy objectives, those that assist public interactions with the government, and other uses. === Contributing to public policy objectives === There are a range of examples of where AI can contribute to public policy objectives. These include: Receiving benefits at job loss, retirement, bereavement and child birth almost immediately, in an automated way (thus without requiring any actions from citizens at all) Social insurance service provision Classifying emergency calls based on their urgency (like the system used by the Cincinnati Fire Department in the United States) Detecting and preventing the spread of diseases Assisting public servants in making welfare payments and immigration decisions Adjudicating bail hearings Triaging health care cases Monitoring social media for public feedback on policies Monitoring social media to identify emergency situations Identifying fraudulent benefits claims Predicting a crime and recommending optimal police presence Predicting traffic congestion and car accidents Anticipating road maintenance requirements Identifying breaches of health regulations Providing personalised education to students Marking exam papers Assisting with defence and national security (see Artificial intelligence § Military and Applications of artificial intelligence § Other fields in which AI methods are implemented respectively) Artificial Intelligence in China has been used to drive both political and economic markets. In 2019, Shanghai’s government rolled out 100 billion yuan to assist in funding enterprises that used AI to introduce 22 new policy agendas. Shanghai invested in these enterprises to attract top international talent in order to set up the Shanghai Municipal Big Data Center. City Brain AI is an urban management platform made by Alibaba. China uses City Brain AI to maintain a significant share of capital investment through public and state owned enterprises. The synergy between public and private sectors are more than capital-driven with City Brain AI. The blend of both public and private shareholding is only made out to be through the role of provincial and sub-provincial governments. Both hold control over the direction that City Brain AI makes both socially and economically. === Assisting public interactions with government === AI can be used to assist members of the public to interact with government and access government services, for example by: Answering questions using virtual assistants or chatbots (see below) Directing requests to the appropriate area within government Filling out forms Assisting with searching documents (e.g. IP Australia's trade mark search) Scheduling appointments Various governments, including those of Australia and Estonia, have implemented virtual assistants to aid citizens in navigating services, with applications ranging from tax inquiries to life-event registrations. === Gerrymandering === Gerrymandering is a method of influencing political process by drawing map boundaries in favor of incumbent parties. Academic researchers Wendy Tam Cho and Bruce Cain have proposed partially automating the map-drawing process with an AI system to reduce partisan gerrymandering. Even with this AI system, the process may still be manipulated to favor partisan interests, so the researchers emphasized the importance of transparency and human involvement. === Other uses === Other uses of AI in government include: Translation Language interpretation pioneered by the European Commission's Directorate General for Interpretation and Florika Fink-Hooijer. Drafting documents == Potential benefits == AI offers potential efficiencies and cost savings for the government. For example, Deloitte has estimated that automation could save US Government employees between 96.7 million to 1.2 billion hours a year, resulting in potential savings of between $3.3 billion to $41.1 billion a year. The Harvard Business Review has stated that while this may lead a government to reduce employee numbers, "Governments could instead choose to invest in the quality of its services. They can re-employ workers' time towards more rewarding work that requires lateral thinking, empathy, and creativity—all things at which humans continue to outperform even the most sophisticated AI program." == Risks == Risks associated with the use of AI in government include AI becoming susceptible to bias, a lack of transparency in how an AI application may make decisions, and the accountability for any such decisions. For example, a 2026 lawsuit alleged that the U.S. Department of Government Efficiency used ChatGPT to flag and cancel federal humanities grants, including projects on Jewish history and Israeli culture, over some objections from NEH officials, illustrating how automated decision-making could affect funding outcomes.