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• Published: 18 May 2020

AI for social good: unlocking the opportunity for positive impact

• Nenad Tomašev 1   na1 ,
• Julien Cornebise 2   na1 ,
• Frank Hutter 3 , 4   na1 ,
• Shakir Mohamed 1   na1 ,
• Angela Picciariello 5 ,
• Bec Connelly 6 ,
• Danielle C. M. Belgrave 7 ,
• Daphne Ezer   ORCID: orcid.org/0000-0002-1685-6909 8 , 9 ,
• Fanny Cachat van der Haert 10 ,
• Frank Mugisha 11 ,
• Gerald Abila 12 ,
• Hiromi Arai 13 ,
• Hisham Almiraat 14 ,
• Julia Proskurnia 15 ,
• Kyle Snyder 6 ,
• Mihoko Otake-Matsuura   ORCID: orcid.org/0000-0003-3644-276X 13 ,
• Mustafa Othman 16 ,
• Tobias Glasmachers 17 ,
• Wilfried de Wever 18 , 19 ,
• Yee Whye Teh   ORCID: orcid.org/0000-0001-5365-6933 1 , 20 ,
• Mohammad Emtiyaz Khan 13   na1 ,
• Ruben De Winne 21   na1 ,
• Tom Schaul   ORCID: orcid.org/0000-0002-2961-8782 1   na1 &
• Claudia Clopath   ORCID: orcid.org/0000-0003-4507-8648 22   na1  

Nature Communications volume  11 , Article number:  2468 ( 2020 ) Cite this article

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Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.

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Introduction.

The challenges facing our world today have grown in complexity and increasingly require large, coordinated efforts: between countries; and across a broad spectrum of governmental and non-governmental organisations (NGOs) and the communities they serve. These coordinated efforts work towards supporting the Sustainable Development Goals (SDGs) 1 , and there continues to be an important role for technology to support the developmental organisations and efforts active in this field to deliver the highest impact.

Artificial intelligence (AI) and machine learning (ML) have attracted widespread interest in recent years due to a series of high-profile successes. AI has shown success in games and simulations 2 , 3 , and is being increasingly applied to a wide range of practical problems, including speech recognition 4 and self-driving cars 5 . These commercial applications often have indirect positive social impact by increasing the availability of information through better search and language-translation tools, providing improved communication services, enabling more efficient transportation, or supporting more personalised healthcare 6 . With this interest come a lot of questions regarding social impact, malicious uses, risks, and governance of these innovations, which are of foremost importance 7 , 8 .

Targeted applications of AI to the domain of social good have recently come into focus. This field has attracted many actors, including charities like DataKind (established in 2012) 9 , academic programmes such as the Data Science for Social Good (DSSG) programme at the University of Chicago (established in 2013) 10 , the UN Global Pulse Labs 11 , AI for Social Good workshops in conferences such as the 2018 and 2019 NeurIPS conference 12 , 13 , the 2019 ICML conference 14 and the 2019 ICLR conference 15 , along with corporate funding programmes such as Google AI for Good Grants 16 , Microsoft AI for Humanity 17 , Mastercard Center for Inclusive Growth and the Rockefeller Foundation’s Data Science for Social Impact 18 , amongst several others.

Results from several recent studies hint at the potential benefits of using AI for social good. Amnesty International and ElementAI demonstrated how AI can be used to help trained human moderators with identifying and quantifying online abuse against women on Twitter 19 . The Makerere University AI research group supported by the UN Pulse Lab Kampala developed automated monitoring of viral cassava disease 20 , and this same group collaborated with Microsoft Research and other academic institutions to set up an electronic agricultural marketplace in Uganda 21 . Satellite imagery was used to help predict poverty 22 and identify burned-down villages in conflict zones in Darfur 23 , and collaborative efforts between climate and machine learning scientists initiated the field of climate informatics 24 , 25 that continues to advance predictive and interpretive tools for climate action. Future improvements in both data infrastructure and AI technology can be expected to lead to an even more diverse set of potential AI4SG applications.

This wealth of projects, sometimes isolated, has led to several meta-initiatives. For example, the Oxford Initiative on AIxSDGs 26 , launched in September 2019, is a curated database of AI projects addressing SDGs that indexes close to 100 projects. Once publicly accessible, it should support a formal study of such projects’ characteristics, success factors, geographical repartition, gaps, and collaborations. Attempts at similar repositories include the ITU AI Repository 27 . Another growing initiative, focused on networking AI4SG and making their blueprints easily accessible and reproducible by anyone, is the AI Commons knowledge hub 28 backed by 21 supporting organisations and 71 members. These meta-initiatives can help aggregate the experience and transfer knowledge between AI4SG projects, as well as establish connections between teams and organisations with complementary aims.

Despite the optimism, technical and organisational challenges remain that make successful applications of AI/ML hard to deliver within the field and that make it difficult to achieve lasting impact. Some of the issues are deeply ingrained in the tech culture that involves moving fast and breaking things while iterating towards solutions, and a lack of familiarity with the non-technical aspects of the problems 29 . There is also a long history of tech for good, including 30 years of Information and Communication Technology for Development (ICT4D). Not all applications of technology aimed at delivering positive social impact manage to achieve their goals 30 , leaving us with important experiences from which we must learn. Importantly, technology should not be imagined as a solution on its own 31 , outside of the context of its application: it merely aligns with human intent and magnifies human capacity 32 . It is therefore critical to put it in service of application-domain experts early, through deep partnerships with technical experts.

To achieve positive impact, AI solutions need to adhere to ethical principles and both the European Commission 33 as well as OECD 34 have put together guidelines for developing innovative and trustworthy AI. Related principles are encoded in the Montreal Declaration for Responsible AI 35 and the Toronto Declaration 36 . The European Commission states that AI needs to be lawful, ethical and robust, to avoid causing unintended harm. OECD Principles on AI state that AI should be driving inclusive growth and sustainable development; designed so as to respect the rule of law, human rights, democratic values and diversity; transparent, so that people can understand AI outcomes; robust, safe and secure; deployed with accountability, so that organisations can be held responsible for AI systems they develop and use. Proper ethical design and governance of AI systems is a broad research topic of fundamental importance, and has been the focus of institutions and initiatives like the AI Now Institute 37 and the ACM Conference on Fairness, Accountability and Transparency 38 .

Also, it is important to recognise the interconnectedness of the Sustainable Development Goals (SDGs) and of efforts to achieve them. The UN stresses that each goal needs to be achieved so that no one is left behind. Yet, an intervention with a positive impact on one SDG could be detrimental to another SDG and its targets. Awareness of this interconnectedness should also be a driving principle for fair and inclusive AI for social good: AI applications should aim to maximise a net positive effect on as many SDGs as possible, without causing avoidable harm to other SDGs. Therefore, while being careful to avoid the pitfalls of analysis paralysis 39 , both application-domain experts and AI researchers should aspire to measure the effects, both positive and negative, of their AI for social good applications across the five areas of people, planet, prosperity, peace and partnerships, which are the targets of the sustainable development agenda.

A recent UN report 40 details how over 30 of its agencies and bodies are working towards integrating AI within their initiatives. According to the report, AI4SG projects need to be approached as a collaborative effort, bringing communities together to carefully assess the complexities of designing AI systems for SDGs. These initiatives should aim to involve NGOs, local authorities, businesses, the academic community, as well as the communities which these efforts support. The report highlights the vast potential of the technology across a wide spectrum of applications, while recognising the need for improving data literacy and a responsible approach to AI research and deployment. Our own efforts to put these considerations into practice have led us to put forward in the next section a set of guidelines with which to approach AI4SG, which we then exemplify with a set of case studies before concluding with a call to action for technical communities and their important role in supporting the success of our social and global goals.

Guidelines for AI4SG collaborations

To address the challenges involved with setting up successful collaborations between AI researchers and application-domain experts working on SDGs, we facilitated a series of structured multidisciplinary discussions at a dedicated seminar 41 bringing together experts from both communities to identify key aspects of successful partnerships, and potential obstacles. This process involved setting up focused working groups around key topics and repeatedly coming together to disseminate the results, obtain feedback and discuss within the wider group. We present the conclusions in the form of guidelines to inform future AI4SG initiatives and ground our recommendations in practical examples of successful AI4SG collaborations.

These guidelines summarise what we see as key principles for successful AI4SG collaborations and should therefore be applicable across different types of organisations aiming to utilise AI for sustainable development. These guidelines pertain to the overall use of AI technology ( G1 , G2 , G3 ), applications ( G4 , G5 , G6 , G7 , G8 ) and data handling ( G9 , G10 ). The list is by no means exhaustive and we expect there to be notable differences in how each of the guidelines is implemented in practice, depending on the data readiness of each organisation and the theory of change underpinning the projects. The recent report from the Google AI Impact Challenge identifies NGOs in particular as having a low rate of utilising AI in their existing projects, which is why we feel they might be the ones to benefit the most from the guidelines provided here 42 .

The fast pace of AI research may sometimes make it difficult for organisations outside the field to correctly assess the applicability of the current state of the art. It is therefore important to set expectations early ( G1 ), to distinguish between short-term and long-term opportunities and help select projects accordingly.

Despite the apparent appeal of using the latest ML methods, these may require large quantities of high-quality training data. AI4SG projects may sometimes benefit from simpler solutions 43 , aiming to solve the problem at hand with minimum overall complexity ( G2 ). Such solutions tend to be faster to implement, easier to maintain, interpret and justify—and are sometimes sufficient to solve valuable practical problems, as demonstrated by the winning solution in a recent food safety predictive challenge 44 . Data analysis and visualisation can be a useful tool in informing practical decision-making and can potentially deliver value to organisations.

AI systems need to be fair, inclusive and accessible ( G3 ). Fairness in particular should be explicitly accounted for, to avoid reinforcing existing societal biases reflected in the data used for model development 45 , 46 , 47 . Unfairness may result in violations of the right to equality, manifesting as inequity in model performance and associated outcomes across race, ethnicity, age, gender, etc. Fairness of AI applications should be deeply anchored in existing international human rights standards, guiding all practical decisions. Ethics compliance should be appropriately formalised and involve setting up internal and external processes to review sensitive decisions and design choices.

To be actionable, practical problems need to be translated into concrete, well-defined goals ( G4 ) that can be addressed by technical solutions. For example, water shortages in case of drought could be addressed by a use case of predicting water demand based on flow data 48 . Alternatively, one could approach the problem by providing better weather predictions or tracking water supply and reservoir levels or helping individual consumers reduce their daily water usage. For each goal, it is crucial to provide the correct metric for measuring the desired effect, as well as define the minimum viable performance for a solution to be adding value to its stakeholders.

We recognise the critical role of focused short-term initiatives like workshops and hackathons in gathering momentum and bringing application-domain experts together with AI researchers to deepen their mutual understanding of opportunities for achieving SDGs. Yet, we believe that for achieving sustained impact, it is necessary to establish long-term collaborations ( G5 ) between application-domain experts and AI researchers and form deep integrated partnerships that allow for enough time to reach good practical solutions 49 .

In interdisciplinary collaborations with a large set of stakeholders, it is important to closely align organisational incentives towards the common goals ( G6 ). Taking the involvement of academic researchers as an example, measures of academic success should explicitly take into account the wider societal impact of the work 50 , as citations are known to be a poor proxy for measuring real-world impact 51 .

In some cases, AI4SG collaborations may need to overcome existing organisational barriers to adoption of technology and investment in high-tech solutions ( G7 ). Scepticism towards AI is partially rooted in depictions of AI in mainstream media, as well as prior examples of technological solutions that failed to live up to the expectations 52 . Failed attempts to utilise technology for social good come with an associated opportunity cost, given the limited resources available. For effective applications of AI, these barriers will need to be overcome through establishing trust and long-term equal partnerships dedicated to delivering lasting impact.

AI4SG solutions should aim to be cost-effective ( G8 ) and this needs to be taken into account early on in the solution design process. There are several ways in which the development cost of AI solutions can potentially be reduced. Skills-based volunteering is a framework through which businesses can enable researchers to offer pro-bono services to NGOs and volunteer for causes that they are passionate about. Hackathons and platforms for crowd-sourcing technical solutions are equally promising, as well as the use of AutoML tools 53 for automating low-level tasks. Some of AI4SG development costs can be covered by grants, for example those offered by the Google AI Impact Challenge 42 or the 2030 Vision 54 aiming to support projects aligned with SDGs.

It is important to be conscious of the different levels of data readiness 55 across organisations ( G9 ) and how they map onto potential ML solutions. Deep learning approaches tend to require large quantities of high-quality data, whereas smaller and noisier datasets may be amenable to exploratory data analysis. Transfer learning and zero-shot learning approaches should be considered in cases where existing trained models can be re-purposed for the relevant use case 56 , 57 . In the absence of bespoke high-quality data, model development process might benefit from utilising external open datasets like satellite imagery or the existing language corpora and concept ontologies.

Secure data storage, data anonymisation and restricted data access are required to ensure that sensitive data are handled with utmost care ( G10 ) 58 . Research data should only include minimal information required to deliver the solution. Responsible information governance should be deeply rooted in respect for human rights, and combined with a high level of physical data security. Data should be encrypted both at rest and in transit. Collaborations should aim to implement existing data governance frameworks for humanitarian action 59 and consider established standards like the European Union’s General Data Protection Regulation (GDPR) and the United States’ Health Insurance Portability and Accountability Act of 1996 (HIPAA).

Case studies

Here we highlight three case studies to reflect on how AI4SG collaboration guidelines can be incorporated in mature projects (Troll Patrol), new projects that are just being initiated (Shaqodoon), as well as community-wide initiatives within the AI community aiming to use AI for sustainable development (Deep Learning Indaba).

Troll Patrol

Having women working in the heart of our democracy is an important step towards achieving gender equality (SDG 5) and strong institutions (SDG 16). This involves creating and protecting inclusive spaces for discussing important political issues. Social media have become an integral part of these conversations and represent an important way of sharing ideas and disseminating information. For women to be equally represented on these digital platforms, they need to be able to share their opinions without fear of abuse.

In Troll Patrol 60 , 61 , Amnesty International partnered with Element AI’s former AI for Good team to utilise computational statistics and natural language processing methods for quantifying abuse against women on Twitter, based on crowd-sourcing that involved participation of over 6500 volunteers who sorted through 288,000 tweets sent to 778 women politicians and journalists in the UK and USA in 2017. The results of the study have revealed worrying patterns of online abuse, estimating 1.1 million toxic tweets being sent to women in the study across the year, black women being 84% more likely than white women to experience abuse on the platform. The core of the analysis was based on using machine learning approaches to pre-filter the data, followed by applying computational statistics methods. The team has additionally evaluated the feasibility of using a fine-tuned deep learning model for automatic detection of abusive tweets 61 . The evaluation suggests that AI could potentially be used to enrich the work of trained human moderators and make abusive tweet detection easier, despite not being ready to be used without human supervision.

The project involved a deep partnership ( G5 ) between an NGO and an AI team, using established methods ( G1, G2 ) for a well-defined goal of identifying abusive tweets ( G4 ) in order to make digital platforms more inclusive ( G3 ). To perform the study, obtaining labelled data was key ( G9 ). Given the sensitivity of the data and possibility of increasing exposure to abuse, all study participants were asked if they wanted to remain anonymous in the reports ( G10 ). The AI technology developed in the project is not bespoke to tracking abuse against women, making it reusable and of long-lasting value for the team involved in the development ( G6 ). Amnesty International having previously engaged with the team on other AI projects helped build trust to make the collaboration possible ( G7 ), and brought their deep domain expertise that the quantitative study had complemented. In terms of technical project execution, using a pretrained model from a larger dataset helped reduce the minimum sample size needed for a performant AI abuse detection system, reducing overall costs ( G8 ).

Shaqodoon: AI for improving citizen feedback

Citizens play a pivotal role both in helping deliver on SDGs as well as holding development actors accountable by keeping track of their progress. It is especially important to actively involve communities that may not have the means of making their voice heard. Shaqodoon 62 is an NGO aiming to improve citizen feedback in Somalia by hosting an interactive voice response platform allowing the citizens to leave feedback on infrastructural projects that affect them. Given that an estimated 65% of the Somali population does not read or write 63 , voice recordings provide an inclusive way of aiming to involve everyone in the conversation ( G3 ).

Manually extracting relevant feedback from voice recordings is a laborious process, and Shaqodoon has been looking at ways of using AI for automating the labelling of incoming responses in order to efficiently identify complaints.

Early on in this process, Shaqodoon estimated that there might be up to 80,000 voice recordings available for model development, until a subsequent analysis revealed that only 72 voice recordings had high-quality labels available in an accessible format. This was insufficient for developing an AI solution, making it necessary to reset expectations ( G1 ) and improve data readiness ( G9 ) through better data collection practices, increasing the number of high-quality labels in a machine-readable format. Shaqodoon worked jointly with ML experts towards identifying the minimum viable AI solution for automated triaging of voice recordings ( G2 ) under a formal specification of categories of interest ( G4 ), while keeping in mind the privacy of the callers ( G10 ). Through this collaboration, Shaqodoon managed to accelerate the project towards the stage where working on AI automation was feasible. The project was selected to be among the finalists of the MIT Solve Challenge 64 , an opportunity for Shaqodoon to obtain resources for model development ( G6, G8 ).

Deep Learning Indaba

Successful implementation of each of our ten guidelines (see Box ) relies on bridge-builders who bring disparate AI4SG stakeholders together. These bridge-builders are people who are embedded within local communities, understand development or humanitarian work, and/or have strong technical skills in data science and AI. Our third case study looks at the Deep Learning Indaba 65 , a grassroots organisation that aims to build strong and locally led capacity in artificial intelligence and its applications across Africa. Such organisations can support the realisation of the Sustainable Development Goals by building strong partnerships (SDG 17) and by fostering innovation (SDG 9). Of particular relevance to supporting successful AI4SG outcomes is the ability of such organisations to support greater diversity and inclusion within the field of AI, and in technical communities more generally. Such inclusivity is the best guarantee that AI applications will effectively work towards social good.

The Deep Learning Indaba was established with the mission to strengthen machine learning and AI in Africa, and towards greater self-ownership and self-confidence in AI by pan-African developers and communities. Over the last 3 years, with leadership driven by Africans within their countries and abroad, they contributed to a positive shift in the visibility and ability of Africans in AI. The Indaba, through the critical mass of African AI researchers and engineers it brings together, supports a growing number of AI4SG efforts, including helping to create new datasets like those of African masks 66 , developing new research for language translation 67 , creating new continent-wide distributed research groups to develop on natural language tools for African languages that are often considered ‘low-resourced’ 68 , improving outcomes for malaria 69 and in addressing conservation challenges 70 . The Snapshot Serengeti Challenge 70 was done in partnership with DeepMind and had involved using thousands of geo-located, time-stamped and labelled images from camera traps in the Serengeti, to develop AI solutions for tracking migration and activity patterns of potentially endangered animal species to help the conservation efforts (SDG 15). The IBM-Zindi Malaria Challenge 69 was done in partnership with IBM Research Africa and was looking at using reinforcement learning for combining interventions for reducing the likelihood of transmission and reducing prevalence of malaria infections (SDG 3). Groups like the Indaba also work in strong partnership with many other groups, such as Data Science Africa, Black-in-AI and Women in Machine Learning, emphasising the importance of sector-wide collaboration to improve representation. This also makes these organisations better positioned to do justice to the interconnectedness of SDGs. This grassroots approach to building stronger socio-technical communities has also been replicated in other regions, in eastern Europe, south-east Asia and South America, showing the growing ability of global communities in strengthening their own capacities and of their eventual contributions towards AI for Social Good.

Dialogue with technical grassroots organisations like the Deep Learning Indaba informs several of our guidelines: these organisations can ensure inclusive and accessible applications ( G3 ); they can create the environment and provide the embedded capacity that supports long-term partnerships ( G5 ); they can act as translators between different stakeholders ( G1 ); they can help facilitate teams whose work is informed by resource constraints ( G6, G8 ) and in need of simple, low-cost solutions ( G2 ); they can share the knowledge and experience needed to help establish trust and buy-in necessary for AI4SG collaborations ( G7 ). For example, the hackathons that Deep Learning Indaba hosted on AI solutions for conservation and malaria efforts 69 , 70 brought together experts from leading centres of research excellence and the local developers to work on finding solutions to important problems of interest to the local community. Industry partners provided high-quality data for these challenges ( G9 ) for local developers to find simple prototype solutions ( G2 ) in a hackathon format, aimed at reducing development costs ( G8 ).

Call for action

We encourage AI experts to actively seek out opportunities for delivering positive social impact. Ethics and inclusivity should be central to AI systems and application-domain experts should inform their design. Numerous recent advances suggest that there is a huge opportunity for adding value to the non-profit sector and partnerships with NGO experts can help ensure that theoretical advances in AI research translate into good for us all.

We equally encourage all organisations working on sustainable development to consider opportunities for utilising AI solutions as powerful tools that might enable them to deliver greater positive impact, while working around resource constraints by tapping into cost-efficient opportunities, such as skills-based volunteering or crowd-sourcing. To do this with the least amount of friction, we highlighted the need to engage with technical experts early, and to gain insights into prerequisites and feasibility given the level of data readiness. We see the need to create more spaces and opportunities to facilitate partnerships and make it easier to get access to AI expertise.

Given that much of the AI talent is currently involved in industrial or academic research, we would encourage key stakeholders in leading research labs to further empower researchers in donating a percentage of their time to AI4SG initiatives, where appropriate and possible. The complexity of real-world challenges can in fact help to boost the understanding of existing methods and demonstrate impact where it matters the most.

Finally, we invite everyone to join the discussion and help shape the strategy around how to tackle the world’s most pressing challenges with some of the most powerful technological solutions available. Only together can we build a better future.

The opinions presented in this paper represent the personal views of the authors and do not necessarily reflect the official policies or positions of their organisations.

Change history

07 august 2020.

The original version of this Article was updated shortly after publication following an error that resulted in the ORCID IDs of Daphne Ezer, Mihoko Otake-Matsuura, Yee Whye Teh, Tom Schaul and Claudia Clopath being omitted.

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Acknowledgements

We thank the Dagstuhl foundation for supporting the AI for Social Good Seminar (19082) 41 . This project was funded by the Alan Turing Institute Research Fellowship under EPSRC Research grant (TU/A/000017); EPSRC Innovation Fellowship (EP/S001360/1); UKRI Research Strategic Priority Fund (R-SPES-107), funded by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) ERC grant agreement no. 617071, as well as supported by JSPS KAKENHI Grant Number JP16H06395 and 17H05920. We would like to thank Haibo E., Pierre Mousset, and Toby Norman for their support in preparing the workshop.

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These authors contributed equally: Nenad Tomašev, Julien Cornebise, Frank Hutter, Shakir Mohamed, Mohammad Emtiyaz Khan, Ruben De Winne, Tom Schaul, Claudia Clopath.

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DeepMind, London, UK

Nenad Tomašev, Shakir Mohamed, Yee Whye Teh & Tom Schaul

Department of Computer Science, University College London, London, UK

Julien Cornebise

Department of Computer Science, University of Freiburg, Freiburg, Germany

Frank Hutter

Bosch Center for Artificial Intelligence, Renningen, Germany

Oxfam GB, Oxford, UK

Angela Picciariello

RNW Media, Hilversum, The Netherlands

Bec Connelly & Kyle Snyder

Microsoft Research, Cambridge, UK

Danielle C. M. Belgrave

University of Warwick, Warwick, UK

Daphne Ezer

Alan Turing Institute, London, UK

International Commission of Jurists, Brussels, Belgium

Fanny Cachat van der Haert

Chemonics International Inc., Kigali, Rwanda

Frank Mugisha

BarefootLaw, Kampala, Uganda

Gerald Abila

RIKEN Center for AI Project, Tokyo, Japan

Hiromi Arai, Mihoko Otake-Matsuura & Mohammad Emtiyaz Khan

Justice and Peace Netherlands, The Hague, The Netherlands

Hisham Almiraat

Google, Zurich, Switzerland

Julia Proskurnia

Shaqodoon Organization, Hargeisa, Somaliland

Mustafa Othman

Institute for Neural Computation, Ruhr-University Bochum, Bochum, Germany

Tobias Glasmachers

SEMA, Kampala, Uganda

Wilfried de Wever

Humanity Solutions, The Hague, The Netherlands

University of Oxford, Oxford, UK

Yee Whye Teh

Oxfam Novib, The Hague, The Netherlands

Ruben De Winne

Department of Bioengineering, Imperial College London, London, UK

Claudia Clopath

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N.T., F.H., S.M., J.C., and R.D.W. wrote the paper. N.T., F.C.v.d.H., J.C., A.P., B.C., C.C., D.C.M.B., D.E., F.C.v.d.H., F.M., G.A., H.Arai, H.Almiraat, J.P., K.S., M.Otake, M.E.K., M.Othman, R.D.W., S.M., T.G., T.S., W.d.W., Y.W.T. contributed to the guidelines presented in the paper.

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Tomašev, N., Cornebise, J., Hutter, F. et al. AI for social good: unlocking the opportunity for positive impact. Nat Commun 11 , 2468 (2020). https://doi.org/10.1038/s41467-020-15871-z

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Artificial intelligence enabled project management: a systematic literature review.

1. Introduction

2. related work, 2.1. hints on ai basics, 2.2. emerging pm, 3. methodology, 4.1. bibliometric analysis, 4.2. literature review, 4.2.1. stakeholder pd, 4.2.2. team pd, 4.2.3. development approach and life cycle pd, 4.2.4. planning pd.

• Fuzzy approaches: S.R. Sree and Ramesh [ 40 ] presented a model based on fuzzy logic. It was tested using the NASA93 dataset and concluded that the fuzzy model with triangular membership function outperforms the rest of the models. Furthermore, the authors in [ 41 ] provided a model by cascading fuzzy logic controllers, which improves the efficiency with clustering techniques. The NASA93 dataset was used as a case study, revealing that fuzzy models developed using subtractive clustering provide better results. Han et al. [ 42 ] presented an effective and accurate approach based on historical project data using the Gauss–Newton model to calibrate the parameters of the Constructive Cost Model and fuzzy logic to optimize it, thus Deming regression, expert judgment, and ML were also applied to enhance the model. González-Carrasco et al. [ 43 ] consider fuzzy input values in NN;
• Methods based on ML or/and NN: The work [ 44 ] suggested a k-nearest neighbour ML-algorithm, concluding that the combination of k-nearest neighbour and quadratic regression has the best response, accuracy improvement, and relative error reduction. Nassif et al. [ 45 ] presented a comparative study of different NN models (multilayer perceptron, general regression NN, radial basis function NN, and cascade correlation NN) and the International Software Benchmarking Standards Group dataset was used in the evaluation. The results showed that cascade correlation NN outperforms the other models. Different AI techniques (Artificial NN, GA, and fuzzy logic) were applied in [ 46 ] using data from past NASA projects, concluding that ANN methods give the best performance. An effective ML ensemble model composed of SVM, NN, and Generalized Linear Models is provided in [ 32 ]. In addition, Twala [ 47 ] investigated the effect of noisy domains on the learning accuracy of eight ML algorithms (SVM and ANN among them) and statistical pattern recognition algorithms. The study derived a solution from a probabilistic perspective that improves prediction for software effort corrupted by noise with better accuracy.

4.2.5. Project Work PD

4.2.6. delivery pd, 4.2.7. measurement pd, 4.2.8. uncertainty pd, 4.2.9. generic investigations.

• Darko et al. [ 148 ] presented a scientometric study about the state-of-the-art of research on AI in the Architecture, Engineering, and Construction (AEC) industry. This work corroborated that the most often-used AI techniques in PM include GA, NNs, ML, and fuzzy logic and sets, becoming a trend convolutional NNs with DL (especially for damage detection). It was commented that cost, productivity, safety, and risk management were the mainstream issues in AI-assisted Architecture, Engineering, and Construction (AEC) research;
• By a literature search, [ 149 ] identified existing implementations that apply DL for construction PM in topics such as construction cost prediction, workforce activity assessment, construction site safety, and structural health monitoring and prediction. Future challenges in the application of DL include cash flow prediction, project risk analysis, and mitigation; DL-based voice chatbots integrated with BIM; and on-site safety and health monitoring by means of video feeds or even robots;
• Fayek [ 150 ] gave examples of applications of fuzzy hybrid techniques for construction PM: fuzzy ML combined with GA to predict labor productivity, fuzzy ML with fuzzy multicriteria decision making to identify the competencies that most significantly contribute to enhancement in project key performance indicators, fuzzy ML with fuzzy system dynamics to perform risk analysis, and fuzzy agent-based modeling to predict crew performance based on crew motivation levels;
• Makaula et al. [ 151 ] developed a framework for AI in construction management. A theoretical framework based on the research findings was developed which illustrates the application of AI technologies across the project lifecycle and the results of each application;
• Wu et al. [ 152 ] provided a state-of-the-art review appraising studies and applications of NLP in construction PM. They highlight that NLP is used to extract and exchange information and to support downstream applications.

5. Discussion

• Lack of a DL-based PM: While the literature has emphasised ML-enabled PM, DL is key for processing complex BD but it has been applied to a limited extent. Therefore, the potential of DL has not been fully considered in the digital PM.
• Lack of AI-powered PM proposals in an agile environment: Despite the fact that a couple of studies discuss AI in agile PM, it is a topic that requires deeper investigation.
• Lack of evidence of AI adoption for project managers : Although AI-enabled PM seems encouraging, its design, standardization, and implementation in project-based firms are still a challenge. Thus, AI adoption in PM is yet to be noted.
• Lack of security issues of BD within the AI–PM ecosystem : The project BD used AI algorithms to assist PM is a major concern. Companies will be affected if data security, privacy, and authentication are not protected. However, we find that data security matters for AI-based BD analytics in the PM context are missing.
• Lack of sustainability-aware AI-assisted PM: Industry 5.0, in line with the United Nations 2030 Agenda for Sustainable Development, highlights the inclusion of sustainability in emerging technology-enabled industries. Nonetheless, we have only identified two works in the AI–PM theme that take into account the sustainability criteria in project evaluation; hence, there is a hole in sustainable AI-based PM.
• Regarding AI as an enabler for project BD analytics, the future question is to what extent BD analytics requirements meet the promising features of cutting-edge AI, such as DL;
• Searching for comprehensive solutions to AI-powered agile PM remains a subsequent task;
• An AI-based PM approach will create an environment that will involve both project managers and IT people to work collaboratively to make disruptive AI technologies perform effectively. This builds a complex framework that demands the project manager’s opinion on the adoption of AI in PM;
• Coming work needs to deal with security aspects in the AI–BD ecosystem within project-based firms;
• A study about the sustainable impact of AI-assisted PM will be desirable.

6. Conclusions

• Stakeholder management would use ML, NLP, and NN to understand, classify, and analyze stakeholders;
• AI-assisted communication in projects using ML demonstrates the potential to improve team performance;
• ML, NNs, GA, expert system, ACO, SVM-GA, and DL show promising usefulness for planning, duration prediction, effort estimation, scheduling, assignment of human resources to project tasks, resource leveling, and project cost estimation;
• In project work PD, the fuzzy expert system, SVM, NLP, DL, and NN can help with effective procurement management, appropriate communication with stakeholders, continuous learning, and the management of physical resources;
• The automation of requirements meetings and project quality management using DL, NN, and fuzzy bring the prospect of efficient project delivery;
• Using AI techniques (e.g., ML, SVM, GA, fuzzy, and NN) to measure project performance indexes, assess delays and implement appropriate responses, and monitor activities, gives rise to precise project measurement;
• AI-enabled uncertainty features address risk identification, probability distribution modelling, risk assessment, stability prediction, dispute risk forecasting, and project riskiness classification. AI techniques that improve for uncertainty functions include ML, fuzzy, ANN, ACO, and NLP.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Share and Cite

Taboada, I.; Daneshpajouh, A.; Toledo, N.; de Vass, T. Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Appl. Sci. 2023 , 13 , 5014. https://doi.org/10.3390/app13085014

Taboada I, Daneshpajouh A, Toledo N, de Vass T. Artificial Intelligence Enabled Project Management: A Systematic Literature Review. Applied Sciences . 2023; 13(8):5014. https://doi.org/10.3390/app13085014

Taboada, Ianire, Abouzar Daneshpajouh, Nerea Toledo, and Tharaka de Vass. 2023. "Artificial Intelligence Enabled Project Management: A Systematic Literature Review" Applied Sciences 13, no. 8: 5014. https://doi.org/10.3390/app13085014

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• Updated Aug 27, 2024

shukur-alom / Spam_mail_detector_using_ML

This Model can detectany kind of spam mail. Here i use ML Algorithm. If use use my code pleace give me my cradit

• Updated Oct 6, 2023

w00000dy / ai-hand-detection

A web AI hand detection

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Interactive notes (Jupyter Notebooks) for building AI-powered applications

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A Django/React website for sharing datasets - It seems that we got beaten to it by Hugging Face. Work no longer in progress

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emirhanai / Tesla-Car-Range-Base-Price-and-Exact-Price-Prediction---Machine-Learning

Tesla Car Range, Base Price and Precise Price Prediction - Machine Learning

• Updated Aug 15, 2021

harshvardhan-anand / Python-Projects

Various self-made python projects

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XxcuriousxX / Pacman-Artificial-Intelligence

Artificial Intelligence Assignments DIT UoA

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neavepaul / Minesweeper

My attempt to create an AI that can play a game that I could never finish :')

• Updated Jun 21, 2024

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• Top IEEE Projects On Artificial Intelligence
• September 28 2023

What is IEEE projects?

IEEE projects are those that follow the standards and guidelines set by the Institute of Electrical and Electronics Engineers in fields such as electrical and electronics engineering, computer science, and related areas. These projects have a broad range of topics and technologies and aim to solve real-world issues and advance new technologies. Anyone, including undergraduate and graduate students, researchers, and industry professionals, can develop IEEE projects individually or as a team. These projects offer an excellent opportunity to apply theoretical concepts to practical problems, develop innovative technologies, and gain hands-on experience in the field of study. Ideas for IEEE projects can be found in various sources such as academic journals, conference proceedings, online communities, and research papers.

Advantages of IEEE projects on AI 

IEEE projects on AI offer exposure to cutting-edge technology, provide opportunities to work on real-world problems with interdisciplinary collaboration, can lead to career opportunities, and contribute to the advancement of the field through the development of new algorithms and techniques.

1. Cutting-edge technology:  AI is an emerging field that is growing rapidly and has the potential to revolutionize several industries. By working on IEEE projects on AI, students, researchers, and professionals can gain exposure to the latest developments and technologies in the field.

2. Real-world applications:  AI has several real-world applications such as image and speech recognition, natural language processing, autonomous vehicles, and medical diagnosis. IEEE projects on AI provide an opportunity to work on projects that solve real-world problems and have a significant impact on society.

3. Contribution to the field:  IEEE projects on AI can lead to the development of new algorithms, techniques, and technologies that can contribute to the advancement of the field. By working on IEEE projects on AI, researchers and professionals can make a significant contribution to the field and have a lasting impact.

Top 10 IEEE Projects On Artificial Intelligence In 2023

1. assistive object recognition and tracking system for the visually impaired using cnn”.

This project proposes an object recognition and tracking system using Convolutional Neural Networks (CNN) to assist visually impaired individuals. The system utilizes a camera to capture real-time images of the surroundings, which are then processed using the CNN algorithm to recognize and track objects of interest.

The system provides audio feedback to the user, which describes the object’s location and properties, such as size, shape, and color. The proposed system aims to enhance the independence and mobility of visually impaired individuals by providing them with a tool to navigate their surroundings safely and efficiently. Experimental results show that the system achieves high accuracy in object recognition and tracking, making it a promising solution for assisting visually impaired people in their daily activities.

2. Comparative Evaluation of R-CNN and YOLO Algorithms for Object Recognition in Urban Environments

Abstract: This research project focuses on evaluating the performance of two pre-trained deep learning algorithms, R-CNN and YOLO, for recognizing street objects in urban environments. The study utilizes the publicly available GRAZ-02 dataset consisting of 1476 raw images of cars, bicycles, and pedestrians.

The deep learning algorithms are fine-tuned and trained on large databases, ImageNet and COCO, and then tested on the dataset. Both algorithms achieved high accuracy, greater than 90%, in recognizing all three objects of interest. The results suggest that deep learning algorithms, particularly R-CNN and YOLO, have promising potential in the automated driving domain for object recognition in urban environments.

3. TensorFlow-based Automatic Personality Recognition Used in Asynchronous Video Interviews

With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human-computer interaction, and psychological assessment.

Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolutional neural network (CNN) models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera.

In this study, an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions and self-reported questionnaires of 120 real job applicants

4. Deep learning-based respiratory sound analysis to aid in the detection of chronic obstructive pulmonary disease.

In today’s world, the field of medicine is constantly being aided by technologies such as machine learning and deep learning, which have proven to be effective in tackling medical challenges. These technologies have improved the accuracy of early disease detection by analyzing medical imaging and audio.

Medical practitioners, faced with a shortage of trained personnel, have welcomed such technological advancements as a helping hand in managing an increasing number of patients. The prevalence of respiratory diseases is also on the rise and is becoming a serious threat to society, making it necessary to develop and implement technologies

5. Research on Intrusion Detection Based on Particle Swarm Optimization in IoT.

With the advent of the “Internet plus” era, the  Internet of Things (IoT)  is gradually penetrating into various _fields, and the scale of its equipment is also showing an explosive growth trend. The age of the “Internet of Everything” is coming.

The integration and diversification of IoT terminals and applications make IoT more vulnerable to various intrusion attacks. Therefore, it is particularly important to design an intrusion detection model that guarantees the security, integrity and reliability of the IoT.

Traditional intrusion detection technology has the disadvantages of low detection rate and poor scalability, which cannot adapt to the complex and changeable IoT environment. In this paper, we propose a particle swarm optimization-based gradient

6. Indian Cuisine Recipe Recommendation based on Ingredients using Machine Learning Techniques

There are plenty of different types of Indian delicacies available with the same ingredients. In India, traditional recipes are varied due to the locally available spices, vegetables, fruits & herbs. In this paper, we purposed a way that recommends Indian recipes based on readily available ingredients and popular dishes.

In this task, we perform a web search to create a collection of recipe types and apply a content-based approach to machine learning to recommend recipes. This system provides Indian food recommendations based on ingredients.

7. Medicine assistance application for visually impaired people

Visual written information nowadays is the basis for most of the tasks but for visually impaired people reading printed text is a challenging task. Nowadays smartphones are very common and accessible to each and everyone. The objective of this project is to assist visually challenged elderly people in taking correct and timely doses of medicines without being dependent on others using their smartphones.

Users need to take pictures of the backside of medicine strips with the help of their mobile camera in the app. The application will scan the text written on it with the help of optical character recognition (OCR) and with the help of text localization techniques it will extract medicine details from the wrapper of medicine.

App also allows users to set reminders to take dosage of their medicine on time. This project is proposed to help visually challenged people with the help of Artificial intelligence, machine learning, image-to-text recognition and voice assistance.

8. Online Smart Voting System Using Biometrics Based Facial and Fingerprint Detection on Image Processing and CNN.

India being a democratic country, still conducts its elections by using voting machines, which involves high costs and manual labor. The web-based system enables voters to cast their votes from anywhere in the world. The online website has a prevented IP address generated by the government of India for election purposes. People should register their name and address in the website

9. Recognition of Objects in the Urban Environment using R-CNN and YOLO Deep Learning Algorithms.

Over the course of the last decade, the subfield of artificial intelligence, called deep learning, becomes the main technology that provides breakthroughs in the computer vision area. Likewise, deep learning algorithms made a major impact in the automated driving domain.

This research aims to apply and evaluate the performance of two pre-trained deep learning algorithms in order to recognize different street objects. Both RCNN, as well as YOLO algorithms, are used to recognize bikes, cars and pedestrians using the public GRAZ-02 dataset composed of 1476 raw images of street objects. Accuracy greater than 90% is achieved in recognizing all considered objects. The

fine-tuning and training of both algorithms is established using databases named ImageNet and COCO, and afterwards, trained models are tried on the test data.

10. Soil Properties Prediction for Agriculture using Machine Learning Techniques.

Information about soil properties help the farmers to do effective and efficient farming, and yield more crops with less usage of resources. An attempt has been made in this paper to predict the soil properties using machine learning approaches. The main properties of soil prediction are Calcium, Phosphorus, pH, Soil Organic Carbon, and Sand.

These properties greatly affect the production of crops. Four well-known machine learning models, namely, multiple linear regression, random forest regression, support vector machine, and gradient boosting, are used for prediction of these soil properties. The performance of these models is evaluated on Africa Soil Property Prediction dataset.

Experimental results reveal that the gradient boosting outperforms the other models in terms of coefficient of determination. Gradient boosting is able to predict all the soil properties accurately except phosphorus. It will be helpful for the farmers to know the properties of the soil in their particular

Conclusion :

The field of artificial intelligence is constantly evolving, and the IEEE community is at the forefront of these advancements. The top 10 IEEE projects on artificial intelligence showcase the innovative and ground-breaking research being conducted in this field.

By exploring IEEE papers on   artificial intelligence projects , students and researchers can gain valuable insights and inspiration for their own projects. As we move into 2023, the demand for artificial intelligence IEEE projects is only expected to increase, and the IEEE community will undoubtedly continue to push the boundaries of what is possible in this exciting and rapidly growing field..

So, let us explore and create our own artificial intelligence IEEE projects to contribute to this exciting and promising field of technology.

1) How to get source code of AI Based IEEE Projects?

Visit our website  citl projects  and register your name and request for the source code depend on the project we can assist in solving that project and guide you through. we have vast collection solved papers 

2) How does the IEEE Project on AI helps students?

AI has several real-world applications such as image and speech recognition, natural language processing, autonomous vehicles, and medical diagnosis. IEEE projects on AI provide an opportunity to work on projects that solve real-world problems and have a significant impact on society.This will helps the students to showcase their skills to gain employment opportunities in this field.

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Top 30 Artificial Intelligence Projects in 2024 [Source Code]

Home Blog Data Science Top 30 Artificial Intelligence Projects in 2024 [Source Code]

AI ha wide range of applications today like marketing, automation, transport, supply chain, and communication, and many more. From cutting-edge research to real-world applications, here we will learn the top artificial intelligence projects. This article will help you in discovering plenty of fascinating ideas and insights to inspire you, whether you are a tech fanatic or want to know about the future of AI. 

Currently, most students and working professionals prefer a Data Science Course to make a smooth transition in the data science field. In this article, we will talk about the top AI project topics. Let us get started!

What are Artificial Intelligence Projects?

Artificial intelligence (AI) projects are software-based initiatives that utilize machine learning, deep learning, natural language processing, computer vision, and other AI technologies to develop intelligent programs capable of performing various tasks with minimal human intervention.

If you're interested in diving into the world of AI, consider exploring an Artificial intelligence course to gain valuable insights and practical knowledge in this exciting field.

List of Top AI Projects with Source Code in 2024

Artificial Intelligence projects with source code are available on various platforms and can be used by beginners to understand the project flow and build their projects. Let us check the top AI project ideas with their technicalities along with their source code.

• Stock Prediction
• Lane line detection while driving
• AI Health Engine
• AI-powered Search engine
• House Security
• Loan Eligibility Prediction
• Resume Parser
• Animal Species Prediction
• Hidden Interfaces for Ambient Computing
• Improved Detection of Elusive Polyps
• Document Extraction using FormNet
• Handwritten Notes recognition
• Consumer Sentiment Analysis
• Real-time Translation Tool
• Spam Email Detector
• Building Chatbot for Customer Service
• Face Detection System
• Object Detection with TensorFlow
• Traffic Sign Recognition
• Image Classification System
• Predictive Maintenance System
• Fake News Detector Project
• Building Teachable Machine
• Building Price Comparison Application
• Ethnicity Detection Model
• GPT-3 Applications
• Reinforcement Learning
• Computer vision system
• NLP application
• Recommendation system

AI Project Ideas for Beginner & Intermediate

Here are some examples of AI project topics for beginners, ranging from simple to complex. When choosing a project, it's important to consider your interests, skills, available resources, and tools. These can be considered ideal AI projects for students in their final year and budding AI engineers.

1. Stock Prediction

• Language: Python
• Data set: CSV file
• Source code : Build Your First stock prediction model

The use of artificial intelligence, such as machine learning and deep learning, to forecast future price movements of stocks and other financial instruments is known as stock prediction. Stock prediction aims to use AI to build models that can analyze historical stock data, spot patterns and trends, and forecast future prices.

Several variables can impact stock prices, including news events, market mood, and economic data. As a result, it's crucial to consider these things while developing an AI based stock prediction model. This can be one of the artificial intelligence topics for the project.

2. Lane line detection while driving

• Data set: mp4 file
• Source code: Lane-lines-detection-using-Python-and-OpenCV

Lane line detection is the simple and AI beginners project. The method of detecting and tracking the lanes on a road while driving using a computer vision system is known as lane line detection while employing machine learning. This is an important use of machine learning in autonomous driving systems since it helps the car stay in its lane and prevent accidents.

Lane line identification faces several difficulties, including shifting lighting, shifting road markers, and collisions with other cars. Therefore, it's critical to create reliable machine-learning models to address these issues and deliver precise lane detection in practical settings.

Overall, machine learning-based lane line identification is a crucial computer vision application in autonomous driving systems that can potentially increase the safety and dependability of self-driving cars.

3. AI Health Engine

• Source code : Patient-Selection-for-Diabetes-Drug-Testing

Artificial intelligence (AI) in healthcare is called the "AI Health Engine." It involves analyzing vast amounts of health-related data, including health records, medical images, and genetic information, using machine learning algorithms, natural language processing, computer vision, and other AI technologies to enhance the health of patients, lower costs, and boost the effectiveness of the delivery of healthcare.

By offering better patient outcomes, personalized treatment options, and more accurate diagnoses, AI Health Engines have the potential to transform the healthcare industry completely. The privacy and security of patient data and ensuring that AI algorithms are accurate, dependable, and impartial must be overcome. Therefore, creating ethical and reliable AI Health Engines that can be applied to healthcare safely and efficiently is crucial.

4. AI-powered Search engine

• Data set: text file
• Source code : ai-powered-search

Source: Towards Data Science

An AI-powered search engine is a search engine that incorporates artificial intelligence (AI) technology, such as machine learning and NLP, to deliver more precise and customized search results. These search engines can process data and employ cutting-edge algorithms to decipher the purpose of a user's query and provide relevant results.

AI-driven search engines may deliver more precise and pertinent search results while providing every user with a more individualized search experience. By removing the need for users to modify their searches or sort through unnecessary outcomes manually, they can also help to increase search efficiency.

5. House Security

• Data set: image file
• Source code: Machine-Learning-Face-Recognition-using-openCV

Using artificial intelligence to monitor and secure a home is known as "house security with AI." AI-powered security systems can detect and analyze various events and activities, including motion, sound, and facial recognition, using a variety of sensors and cameras.

By offering more precise and reliable detection of intrusions and other security breaches, AI-powered security systems have the potential to improve home security. By interacting with other intelligent home systems and gadgets, they can also offer a user experience that is more practical and smoother.

6. Loan Eligibility Prediction

• Source code : Loan_Status_Prediction

Source: GeeksforGeeks

The goal of loan eligibility prediction using AI is to forecast the likelihood of loan approval for new applicants by analyzing historical data on borrowers and their loan applications. This can assist banks and other lenders in setting appropriate terms and conditions for accepted loans, as well as helping them make better decisions about whether to approve or reject loan applications.

The security and privacy of borrower data and preventing unintended outcomes like unintentionally barring specific borrower categories are obstacles to be addressed. Creating moral and open loan eligibility prediction systems that work for both lenders and borrowers is therefore crucial. This is one of the best AI projects.

Artificial Intelligence Project Ideas For Advanced Level

These are a few of the many cutting-edge AI initiatives you might consider. It's crucial to consider your hobbies and areas of skill while selecting advanced AI projects and the initiative's potential influence and worth to the larger community.

1. Resume Parser

• Source cod e: keras-english-resume-parser-and-analyzer

Source: DaXtra Technologies

An AI-powered tool called a resume parser pulls pertinent data from resumes or CVs and turns it into structured data. The structured data can be utilized for various tasks, including applicant tracking, hiring, and talent management. Developing a resume parser might be a challenging but rewarding endeavor that can assist businesses and organizations in automating their hiring and talent management procedures.

2. Animal Species Prediction

• Data set: PNG file
• Source code:  animal_detection

In machine learning and computer vision, predicting animal species includes creating an AI system to recognize an animal's species from an image. To reliably categorize animal species using visual characteristics, including shape, color, and texture, animal species prediction attempts to build a model that can do so.

Because it involves dealing with a vast and diverse range of animals with varying physical characteristics, predicting animal species is difficult. However, recent deep learning and computer vision developments have made significant advancements possible in this field.

3. Hidden Interfaces for Ambient Computing

• Source code:  Hidden Interfaces for Ambient Computing

User interfaces that are smoothly incorporated into the environment allow users to engage with ambient computing devices without requiring explicit actions or inputs. These interfaces are referred to as hidden interfaces for ambient computing. The goal of ambient computing devices is to give consumers a smooth and natural experience without forcing them to engage with the device directly. These devices are embedded into the surroundings.

Voice assistants, smart speakers, and intelligent displays are a few examples of hidden interfaces for ambient computing.

4. Improved Detection of Elusive Polyps

• Source code: Polyp-Segmentation-using-UNET-in-TensorFlow-2.0

Source: Science Direct

Artificial intelligence (AI) and computer vision are two methods for enhancing the detection of evasive polyps. Large datasets of colonoscopy images can be used to train AI systems to identify patterns and traits common to various polyp kinds. Computer vision techniques can also improve photographs' quality and highlight important details that human viewers might overlook.

The development of new imaging methods, such as high-definition colonoscopes, and the use of specialized dyes or markers that can aid in identifying polyps are two more strategies for enhancing the detection of elusive polyps.

5. Document Extraction using FormNet

• Data set: PDF file
• Source code: Representation-Learning-for-Information-Extraction

The information must be extracted from unstructured data, such as text documents, PDFs, or photos, to create structured data that may be used for analysis or processing. A deep learning model called FormNet was explicitly designed for extracting documents from scanned forms.

FormNet extracts fields from structured forms using a convolutional neural network (CNN) architecture. The model can learn the common patterns and features associated with various shapes and areas because it is trained on vast datasets of labeled forms.

Applications for document extraction using FormNet include data entry, processing invoices, and form recognition in sectors like healthcare, banking, and law. FormNet may significantly reduce the time and effort needed for human data entry, improve accuracy, and increase the effectiveness of corporate processes by automating the document extraction process.

6. Handwritten Notes recognition

• Source code:  SimpleHTR

Source: AmyGB.ai

Turning handwritten text or notes into computer-readable digital text is called handwritten note recognition. Optical character recognition (OCR) technology, which recognizes and converts handwritten text into a digital format using computer vision techniques, is often used for this operation.

Various machine learning and deep learning algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and recurrent neural networks (RNNs), can be used to achieve OCR technology for handwritten note recognition. These algorithms can learn the patterns and features of various handwriting styles since they have been trained on enormous datasets of labelled handwritten notes.

7. Consumer Sentiment Analysis

• Source code: Consumer Sentiment Analysis

Consumer sentiment analysis examines consumers' attitudes, feelings, and views toward a specific good, service, or brand. Natural language processing (NLP) and machine learning techniques are usually used in this analysis, giving businesses insightful knowledge on how their customers see them.

The analysis entails extracting and categorizing pertinent data, such as keywords, sentiment, emotions, and themes, to detect patterns and trends in consumer feedback. Businesses can utilize consumer sentiment analysis to raise customer happiness, enhance the quality of their goods and services, and gain a competitive advantage.

8. Real-time Translation Tool

• Source code:  Real-time-voice-recognition-based-language-translation-bot

A software program known as a real-time translation tool enables users to translate speech, writing, or other forms of communication from one language to another in real time. Real-time translation tools rely on machine learning and natural language processing (NLP) approaches to translate languages rapidly and reliably.

Various contexts, including international business meetings, travel, and communication with non-native speakers, can benefit from real-time translation tools. They allow users to connect efficiently with persons who speak different languages since they can translate text or speech in real time. These tools simplify connecting and collaborating worldwide by enhancing communication and lowering language barriers.

List of More Artificial Intelligence Project Ideas

Apart from the above artificial intelligence project, here is the list of some more AI project ideas that you can work on: 

Open Source Artificial Intelligence Project Ideas: Additional Topics

Here are a few open source AI project suggestions that are popular right now on Google.ai and other sites of such nature:

Why Should You Work on AI Based Projects?

Working on Artificial intelligence based projects can be gratifying for several reasons, including:

• High demand: AI is a fast-expanding subject, and skilled individuals are in tall order. Gaining knowledge of AI can lead to various employment choices and job prospects.
• Innovation: AI initiatives frequently involve going beyond what is currently achievable, which results in fresh discoveries and advances in the area.
• Impact: AI can positively impact society, from healthcare and education to finance and transportation. You can make a meaningful contribution by working on AI-based projects.
• Personal growth: Working on AI-based projects can help you acquire new techniques and concepts in programming, data science, and machine learning, improving your personal and professional development.

Best Platforms to Work on AI Projects

To create machine learning models, these platforms offer a vast array of tools and resources, including pre-built algorithms, data visualization tools, and support for distributed computing. They also feature vibrant developer and research communities that exchange knowledge and support ongoing development. Future AI projects are all dependent on this platform.

Here are some of the top platforms to work on AI project Links:

• Scikit-learn
• Microsoft Cognitive Toolkit
• Apache MXNet

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Learn AI the Smart Way!

Learning AI can be a challenging but worthwhile endeavor. Here are some pointers for clever AI learning:

• Begin with the fundamentals: Start by being familiar with the foundational ideas of AI, such as machine learning, deep learning, and neural networks.
• Take online classes: Work with real-world datasets to put your knowledge into practice. Using real-world datasets is an excellent method to put your knowledge into practice. KnowledgeHut Data Science Course provides online courses with thorough AI instruction.
• Create your projects: Creating your own Artificial Intelligence projects is an excellent opportunity to practice what you've learned and put it to the test.
• Emphasise problem-solving: You can develop the skills to manage challenging AI projects by emphasizing problem-solving and critical thinking.

Studying AI generally involves commitment, perseverance, and a readiness to pick things up quickly and adapt. Using these pointers, you can learn AI intelligently and successfully and accomplish your objectives in this fascinating and promptly expanding topic. 

Frequently Asked Questions (FAQs)

• Stock Prediction 

Because they are relatively straightforward but still challenging enough to offer a worthwhile learning experience, these AI projects are great for beginners. They provide a solid foundation for anyone interested in learning AI because they cover many AI ideas and applications. The above can also be used as artificial intelligence research paper topics.

AI project failures can stem from various issues like poor planning, limited funding, subpar data quality, lack of domain knowledge, ineffective communication, unrealistic objectives, unvalidated assumptions, algorithm bias, ethical/legal issues, and changing business needs. Inadequate planning leads to unclear goals and insufficient resources, while poor data affects AI model accuracy. Insufficient expertise can lead to flawed algorithm selection, and poor communication causes misunderstandings and delays.

AI can be categorized into four types:

• Reactive machines: AI systems that respond to specific situations without using past experiences.
• Limited memory: AI that uses past information for decision-making but lacks critical thinking or long-term planning.
• Theory of mind: AI that understands others' emotions, thoughts, and intentions for informed decision-making.
• Self-aware: AI that is conscious of its own feelings and mental states, utilizing this for improved decisions and behavior adjustments.

You can take the following actions to launch your artificial intelligence career:

• Learn the fundamentals of computer science, statistics, and mathematics.
• Acquire knowledge of programming languages like Python, R.
• Learn how to use AI tools.
• Attend machine learning and AI boot camps or online courses from the  KnowledgeHut data science course .
• Take part in Kaggle tournaments to gain experience creating AI models.
• AI projects with source code can be used for learning

Ashish Gulati

Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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• Corpus ID: 252691806

ARTIFICIAL INTELLIGENCE IN PROJECT MANAGEMENT RESEARCH: A BIBLIOMETRIC ANALYSIS

• Published 2022
• Computer Science, Business

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8 Citations

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• Raghvendra Kumar Pandey 13 ,
• Saharsh Singh 14 ,
• Gaurav Tiwari 14 ,
• Ambuj Kumar 14 &
• Prateek Mishra 14  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1005))

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The fundamental aim of this research paper is to make Machine Learning-Based Smart Traffic Control System. The traffic light timer timing changes on detecting the traffic density count at each crossroad. Traffic congestion is most common problem in the major highly populated cities across the world, and it has made traveling very tough from one place to other. Traditional traffic lights run on the fixed timer concept assigned to each side of the road which can’t be changed as per changing vehicles density. In some situation lane with higher density demands longer green time as compared to the basic fixed time. The object or vehicles in the traffic signal are detected using cameras then processed into a simulator then its threshold is assumed on the basis of vehicle count in respect to each lane and compute the total number of vehicles present in the given area. After computing the total number of vehicles the system will acknowledge that which side the density of vehicles is high and based on the density the signals will be allotted for a particular side. Traffic mishaps or accidents are very common at overcast, rainy day, night when no street lights are available, foggy day and many others when there is minimum visibility. Traffic light control is one of the severe technical hazards of the Major cities in almost every country across the world. This is due to exponential rate of growth in the number of vehicles. In respect to minimize the time, a system has to be come up with the technology of artificial intelligence which makes a machine to think themselves. This modern developed technology will help the traffic light to switch the traffic signals from green to red based upon traffic density. This paper is related with the enhancement of traffic control system using machine learning which will be base on the different density on each lane.

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Acknowledgements

For future scope, there is potential to expand the system’s capabilities by incorporating additional data sources, such as weather and road conditions, to further optimize traffic flow. Additionally, the system can be integrated with other transportation modes, such as public transportation, to provide a more comprehensive and integrated transportation system.

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Department of Computer Science and Engineering, United Institute of Technology, Prayagraj, Uttar Pradesh, 211008, India

Amit Kumar Tiwari & Raghvendra Kumar Pandey

Department of Information Technology, United Institute of Technology, Prayagraj, Uttar Pradesh, 211008, India

Saharsh Singh, Gaurav Tiwari, Ambuj Kumar & Prateek Mishra

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Tiwari, A.K., Pandey, R.K., Singh, S., Tiwari, G., Kumar, A., Mishra, P. (2024). Artificial Intelligence-Based Smart Traffic Control System. In: Bhattacharya, A., Dutta, S., Dutta, P., Samanta, D. (eds) Innovations in Data Analytics. ICIDA 2023. Lecture Notes in Networks and Systems, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-97-4928-7_20

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• In AI Mysteries

Top Machine Learning Research Papers

• by Dr. Nivash Jeevanandam

Advances in machine learning and deep learning research are reshaping our technology. Machine learning and deep learning have accomplished various astounding feats, and key research articles have resulted in technical advances used by billions of people. The research in this sector is advancing at a breakneck pace and assisting you to keep up. Here is a collection of the most important scientific study papers in machine learning.

Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training

The authors of this work examined why ACGAN training becomes unstable as the number of classes in the dataset grows. The researchers revealed that the unstable training occurs due to a gradient explosion problem caused by the unboundedness of the input feature vectors and the classifier’s poor classification capabilities during the early training stage. The researchers presented the Data-to-Data Cross-Entropy loss (D2D-CE) and the Rebooted Auxiliary Classifier Generative Adversarial Network to alleviate the instability and reinforce ACGAN (ReACGAN). Additionally, extensive tests of ReACGAN demonstrate that it is resistant to hyperparameter selection and is compatible with a variety of architectures and differentiable augmentations.

This article is ranked #1 on CIFAR-10 for Conditional Image Generation.

For the research paper, read here .

For code, see here .

Dense Unsupervised Learning for Video Segmentation

The authors presented a straightforward and computationally fast unsupervised strategy for learning dense spacetime representations from unlabeled films in this study. The approach demonstrates rapid convergence of training and a high degree of data efficiency. Furthermore, the researchers obtain VOS accuracy superior to previous results despite employing a fraction of the previously necessary training data. The researchers acknowledge that the research findings may be utilised maliciously, such as for unlawful surveillance, and that they are excited to investigate how this skill might be used to better learn a broader spectrum of invariances by exploiting larger temporal windows in movies with complex (ego-)motion, which is more prone to disocclusions.

This study is ranked #1 on DAVIS 2017 for Unsupervised Video Object Segmentation (val).

Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases

The authors offer an atlas-based technique for producing unsupervised temporally consistent surface reconstructions by requiring a point on the canonical shape representation to translate to metrically consistent 3D locations on the reconstructed surfaces. Finally, the researchers envisage a plethora of potential applications for the method. For example, by substituting an image-based loss for the Chamfer distance, one may apply the method to RGB video sequences, which the researchers feel will spur development in video-based 3D reconstruction.

This article is ranked #1 on ANIM in the category of Surface Reconstruction. 

EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

The researchers propose a revolutionary interactive architecture called EdgeFlow that uses user interaction data without resorting to post-processing or iterative optimisation. The suggested technique achieves state-of-the-art performance on common benchmarks due to its coarse-to-fine network design. Additionally, the researchers create an effective interactive segmentation tool that enables the user to improve the segmentation result through flexible options incrementally.

This paper is ranked #1 on Interactive Segmentation on PASCAL VOC

Learning Transferable Visual Models From Natural Language Supervision

The authors of this work examined whether it is possible to transfer the success of task-agnostic web-scale pre-training in natural language processing to another domain. The findings indicate that adopting this formula resulted in the emergence of similar behaviours in the field of computer vision, and the authors examine the social ramifications of this line of research. CLIP models learn to accomplish a range of tasks during pre-training to optimise their training objective. Using natural language prompting, CLIP can then use this task learning to enable zero-shot transfer to many existing datasets. When applied at a large scale, this technique can compete with task-specific supervised models, while there is still much space for improvement.

This research is ranked #1 on Zero-Shot Transfer Image Classification on SUN

CoAtNet: Marrying Convolution and Attention for All Data Sizes

The researchers in this article conduct a thorough examination of the features of convolutions and transformers, resulting in a principled approach for combining them into a new family of models dubbed CoAtNet. Extensive experiments demonstrate that CoAtNet combines the advantages of ConvNets and Transformers, achieving state-of-the-art performance across a range of data sizes and compute budgets. Take note that this article is currently concentrating on ImageNet classification for model construction. However, the researchers believe their approach is relevant to a broader range of applications, such as object detection and semantic segmentation.

This paper is ranked #1 on Image Classification on ImageNet (using extra training data).

SwinIR: Image Restoration Using Swin Transformer

The authors of this article suggest the SwinIR image restoration model, which is based on the Swin Transformer . The model comprises three modules: shallow feature extraction, deep feature extraction, and human-recognition reconstruction. For deep feature extraction, the researchers employ a stack of residual Swin Transformer blocks (RSTB), each formed of Swin Transformer layers, a convolution layer, and a residual connection.

This research article is ranked #1 on Image Super-Resolution on Manga109 – 4x upscaling.

Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits

Ways to incorporate historical data are still unclear: initialising reward estimates with historical samples can suffer from bogus and imbalanced data coverage, leading to computational and storage issues—particularly in continuous action spaces. The paper addresses the obstacles by proposing ‘Artificial Replay’, an algorithm to incorporate historical data into any arbitrary base bandit algorithm. 

Read the full paper here . 

Bootstrapped Meta-Learning

Author(s) – Sean R. Sinclair et al.

The paper proposes an algorithm in which the meta-learner teaches itself to overcome the meta-optimisation challenge. The algorithm focuses on meta-learning with gradients, which guarantees performance improvements. Furthermore, the paper also looks at how bootstrapping opens up possibilities. 

Read the full paper here .

LaMDA: Language Models for Dialog Applications

Author(s) – Sebastian Flennerhag et al.

The research describes the LaMDA system which caused chaos in AI this summer when a former Google engineer claimed that it had shown signs of sentience. LaMDA is a family of large language models for dialogue applications based on Transformer architecture. The interesting feature of the model is its fine-tuning with human-annotated data and the possibility of consulting external sources. This is a very interesting model family, which we might encounter in many applications we use daily. 

Competition-Level Code Generation with AlphaCode

Author(s) – Yujia Li et al.

Systems can help programmers become more productive. The following research addresses the problems with incorporating innovations in AI into these systems. AlphaCode is a system that creates solutions for problems that require deeper reasoning. 

Privacy for Free: How does Dataset Condensation Help Privacy?

Author(s) – Tian Dong et al.

The paper focuses on Privacy Preserving Machine Learning, specifically deducting the leakage of sensitive data in machine learning. It puts forth one of the first propositions of using dataset condensation techniques to preserve the data efficiency during model training and furnish membership privacy.

Why do tree-based models still outperform deep learning on tabular data?

Author(s) – Léo Grinsztajn, Edouard Oyallon and Gaël Varoquaux

The research answers why deep learning models still find it hard to compete on tabular data compared to tree-based models. It is shown that MLP-like architectures are more sensitive to uninformative features in data compared to their tree-based counterparts. 

Multi-Objective Bayesian Optimisation over High-Dimensional Search Spaces 

Author(s) – Samuel Daulton et al.

The paper proposes ‘MORBO’, a scalable method for multiple-objective BO as it performs better than that of high-dimensional search spaces. MORBO significantly improves the sample efficiency and, where existing BO algorithms fail, MORBO provides improved sample efficiencies over the current approach. 

A Path Towards Autonomous Machine Intelligence Version 0.9.2

Author(s) – Yann LeCun

The research offers a vision about how to progress towards general AI. The study combines several concepts: a configurable predictive world model, behaviour driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised

learning. 

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

Author(s) –  Shreshth Tuli, Giuliano Casale and Nicholas R. Jennings

This is a specialised paper applying transformer architecture to the problem of unsupervised anomaly detection in multivariate time series. Many architectures which were successful in other fields are, at some point, also being applied to time series. The research shows improved performance on some known data sets. 

Differentially Private Bias-Term only Fine-tuning of Foundation Models

Author(s) – Zhiqi Bu et al. 

In the paper, researchers study the problem of differentially private (DP) fine-tuning of large pre-trained models—a recent privacy-preserving approach suitable for solving downstream tasks with sensitive data. Existing work has demonstrated that high accuracy is possible under strong privacy constraints yet requires significant computational overhead or modifications to the network architecture.

ALBERT: A Lite BERT

Usually, increasing model size when pretraining natural language representations often result in improved performance on downstream tasks, but the training times become longer. To address these problems, the authors in their work presented two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. The authors also used a self-supervised loss that focuses on modelling inter-sentence coherence and consistently helped downstream tasks with multi-sentence inputs. According to results, this model established new state-of-the-art results on the GLUE, RACE, and squad benchmarks while having fewer parameters compared to BERT-large. 

Check the paper here .

Beyond Accuracy: Behavioral Testing of NLP Models with CheckList

Microsoft Research, along with the University of Washington and the University of California, in this paper, introduced a model-agnostic and task agnostic methodology for testing NLP models known as CheckList. This is also the winner of the best paper award at the ACL conference this year. It included a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. 

Linformer is a Transformer architecture for tackling the self-attention bottleneck in Transformers. It reduces self-attention to an O(n) operation in both space- and time complexity. It is a new self-attention mechanism which allows the researchers to compute the contextual mapping in linear time and memory complexity with respect to the sequence length. 

Read more about the paper here .

Plug and Play Language Models

Plug and Play Language Models ( PPLM ) are a combination of pre-trained language models with one or more simple attribute classifiers. This, in turn, assists in text generation without any further training. According to the authors, model samples demonstrated control over sentiment styles, and extensive automated and human-annotated evaluations showed attribute alignment and fluency. 

Reformer 

The researchers at Google, in this paper , introduced Reformer. This work showcased that the architecture of a Transformer can be executed efficiently on long sequences and with small memory. The authors believe that the ability to handle long sequences opens the way for the use of the Reformer on many generative tasks. In addition to generating very long coherent text, the Reformer can bring the power of Transformer models to other domains like time-series forecasting, music, image and video generation. 

An Image is Worth 16X16 Words

The irony here is that one of the popular language models, Transformers have been made to do computer vision tasks. In this paper , the authors claimed that the vision transformer could go toe-to-toe with the state-of-the-art models on image recognition benchmarks, reaching accuracies as high as 88.36% on ImageNet and 94.55% on CIFAR-100. For this, the vision transformer receives input as a one-dimensional sequence of token embeddings. The image is then reshaped into a sequence of flattened 2D patches. The transformers in this work use constant widths through all of its layers.

Unsupervised Learning of Probably Symmetric Deformable 3D Objects

Winner of the CVPR best paper award, in this work, the authors proposed a method to learn 3D deformable object categories from raw single-view images, without external supervision. This method uses an autoencoder that factored each input image into depth, albedo, viewpoint and illumination. The authors showcased that reasoning about illumination can be used to exploit the underlying object symmetry even if the appearance is not symmetric due to shading.

Generative Pretraining from Pixels

In this paper, OpenAI researchers examined whether similar models can learn useful representations for images. For this, the researchers trained a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, the researchers found that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, it achieved 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full fine-tuning and matching the top supervised pre-trained models. An even larger model, trained on a mixture of ImageNet and web images, is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of their features.

Deep Reinforcement Learning and its Neuroscientific Implications

In this paper, the authors provided a high-level introduction to deep RL , discussed some of its initial applications to neuroscience, and surveyed its wider implications for research on brain and behaviour and concluded with a list of opportunities for next-stage research. Although DeepRL seems to be promising, the authors wrote that it is still a work in progress and its implications in neuroscience should be looked at as a great opportunity. For instance, deep RL provides an agent-based framework for studying the way that reward shapes representation, and how representation, in turn, shapes learning and decision making — two issues which together span a large swath of what is most central to neuroscience. 

Dopamine-based Reinforcement Learning

Why humans doing certain things are often linked to dopamine , a hormone that acts as the reward system (think: the likes on your Instagram page). So, keeping this fact in hindsight, DeepMind with the help of Harvard labs, analysed dopamine cells in mice and recorded how the mice received rewards while they learned a task. They then checked these recordings for consistency in the activity of the dopamine neurons with standard temporal difference algorithms. This paper proposed an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning. The authors hypothesised that the brain represents possible future rewards not as a single mean but as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. 

Lottery Tickets In Reinforcement Learning & NLP

In this paper, the authors bridged natural language processing (NLP) and reinforcement learning (RL). They examined both recurrent LSTM models and large-scale Transformer models for NLP and discrete-action space tasks for RL. The results suggested that the lottery ticket hypothesis is not restricted to supervised learning of natural images, but rather represents a broader phenomenon in deep neural networks.

What Can Learned Intrinsic Rewards Capture

In this paper, the authors explored if the reward function itself can be a good locus of learned knowledge. They proposed a scalable framework for learning useful intrinsic reward functions across multiple lifetimes of experience and showed that it is feasible to learn and capture knowledge about long-term exploration and exploitation into a reward function. 

AutoML- Zero

The progress of AutoML has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks, or similarly restrictive search spaces. In this paper , the authors showed that AutoML could go further with AutoML Zero, that automatically discovers complete machine learning algorithms just using basic mathematical operations as building blocks. The researchers demonstrated this by introducing a novel framework that significantly reduced human bias through a generic search space.

Rethinking Batch Normalization for Meta-Learning

Batch normalization is an essential component of meta-learning pipelines. However, there are several challenges. So, in this paper, the authors evaluated a range of approaches to batch normalization for meta-learning scenarios and developed a novel approach — TaskNorm. Experiments demonstrated that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient-based and gradient-free meta-learning approaches. The TaskNorm has been found to be consistently improving the performance.

Meta-Learning without Memorisation

Meta-learning algorithms need meta-training tasks to be mutually exclusive, such that no single model can solve all of the tasks at once. In this paper, the authors designed a meta-regularisation objective using information theory that successfully uses data from non-mutually-exclusive tasks to efficiently adapt to novel tasks.

Understanding the Effectiveness of MAML

Model Agnostic Meta-Learning (MAML) consists of optimisation loops, from which the inner loop can efficiently learn new tasks. In this paper, the authors demonstrated that feature reuse is the dominant factor and led to ANIL (Almost No Inner Loop) algorithm — a simplification of MAML where the inner loop is removed for all but the (task-specific) head of the underlying neural network. 

Your Classifier is Secretly an Energy-Based Model

This paper proposed attempts to reinterpret a standard discriminative classifier as an energy-based model. In this setting, wrote the authors, the standard class probabilities can be easily computed. They demonstrated that energy-based training of the joint distribution improves calibration, robustness, handout-of-distribution detection while also enabling the proposed model to generate samples rivalling the quality of recent GAN approaches. This work improves upon the recently proposed techniques for scaling up the training of energy-based models. It has also been the first to achieve performance rivalling the state-of-the-art in both generative and discriminative learning within one hybrid model.

Reverse-Engineering Deep ReLU Networks

This paper investigated the commonly assumed notion that neural networks cannot be recovered from its outputs, as they depend on its parameters in a highly nonlinear way. The authors claimed that by observing only its output, one could identify the architecture, weights, and biases of an unknown deep ReLU network. By dissecting the set of region boundaries into components associated with particular neurons, the researchers showed that it is possible to recover the weights of neurons and their arrangement within the network.

Cricket Analytics and Predictor

Authors: Suyash Mahajan,  Salma Shaikh, Jash Vora, Gunjan Kandhari,  Rutuja Pawar,

Abstract:   The paper embark on predicting the outcomes of Indian Premier League (IPL) cricket match using a supervised learning approach from a team composition perspective. The study suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player‘s batting and bowling performances, forming the basis of our approach.

Research Methodology: In this paper, two methodologies have been used. MySQL database is used for storing data whereas Java for the GUI. The algorithm used is Clustering Algorithm for prediction. The steps followed are as

• Begin with a decision on the value of k being the number of clusters.
• Put any initial partition that classifies the data into k clusters.
• Take every sample in the sequence; compute its distance from centroid of each of the clusters. If sample is not in the cluster with the closest centroid currently, switch this sample to that cluster and update the centroid of the cluster accepting the new sample and the cluster losing the sample.

For the research paper, read here

2.Real Time Sleep / Drowsiness Detection – Project Report

Author : Roshan Tavhare

Institute : University of Mumbai

Abstract : The main idea behind this project is to develop a nonintrusive system which can detect fatigue of any human and can issue a timely warning. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough.

Research Methodology : A training set of labeled facial landmarks on an image. These images are manually labeled, specifying specific (x, y) -coordinates of regions surrounding each facial structure.

• Priors, more specifically, the probability on distance between pairs of input pixels. The pre-trained facial landmark detector inside the dlib library is used to estimate the location of 68 (x, y)-coordinates that map to facial structures on the face.

A Study of Various Text Augmentation Techniques for Relation Classification in Free Text

Authors: Chinmaya Mishra Praveen Kumar and Reddy Kumar Moda,  Syed Saqib Bukhari and Andreas Dengel

Institute: German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

Abstract: In this paper, the researchers explore various text data augmentation techniques in text space and word embedding space. They studied the effect of various augmented datasets on the efficiency of different deep learning models for relation classification in text.

Research Methodology: The researchers implemented five text data augmentation techniques (Similar word, synonyms, interpolation, extrapolation and random noise method)  and explored the ways in which we could preserve the grammatical and the contextual structures of the sentences while generating new sentences automatically using data augmentation techniques.

Smart Health Monitoring and Management Using Internet of Things, Artificial Intelligence with Cloud Based Processing

Author : Prateek Kaushik

Institute : G D Goenka University, Gurugram

Abstract : This research paper described a personalised smart health monitoring device using wireless sensors and the latest technology.

Research Methodology: Machine learning and Deep Learning techniques are discussed which works as a catalyst to improve the  performance of any health monitor system such supervised machine learning algorithms, unsupervised machine learning algorithms, auto-encoder, convolutional neural network and restricted boltzmann machine .

Internet of Things with BIG DATA Analytics -A Survey

Author : A.Pavithra,  C.Anandhakumar and V.Nithin Meenashisundharam

Institute : Sree Saraswathi Thyagaraja College,

Abstract : This article we discuss about Big data on IoT and how it is interrelated to each other along with the necessity of implementing Big data with IoT and its benefits, job market

Research Methodology : Machine learning, Deep Learning, and Artificial Intelligence are key technologies that are used to provide value-added applications along with IoT and big data in addition to being used in a stand-alone mod.

Single Headed Attention RNN: Stop Thinking With Your Head 

Author: Stephen Merity

In this work of art, the Harvard grad author, Stephen “Smerity” Merity, investigated the current state of NLP, the models being used and other alternate approaches. In this process, he tears down the conventional methods from top to bottom, including etymology.

The author also voices the need for a Moore’s Law for machine learning that encourages a minicomputer future while also announcing his plans on rebuilding the codebase from the ground up both as an educational tool for others and as a strong platform for future work in academia and industry.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Authors: Mingxing Tan and Quoc V. Le 

In this work, the authors propose a compound scaling method that tells when to increase or decrease depth, height and resolution of a certain network.

Convolutional Neural Networks(CNNs) are at the heart of many machine vision applications. 

EfficientNets are believed to superpass state-of-the-art accuracy with up to 10x better efficiency (smaller and faster).

Deep Double Descent By OpenAI

Authors: Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal

In this paper , an attempt has been made to reconcile classical understanding and modern practice within a unified performance curve. 

The “double descent” curve overtakes the classic U-shaped bias-variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. 

The Lottery Ticket Hypothesis

Authors: Jonathan Frankle, Michael Carbin

Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. 

The authors find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, they introduce the “lottery ticket hypothesis:”

On The Measure Of Intelligence 

Authors: Francois Chollet

This work summarizes and critically assesses the definitions of intelligence and evaluation approaches, while making apparent the historical conceptions of intelligence that have implicitly guided them.

The author, also the creator of keras, introduces a formal definition of intelligence based on Algorithmic Information Theory and using this definition, he also proposes a set of guidelines for what a general AI benchmark should look like. 

Zero-Shot Word Sense Disambiguation Using Sense Definition Embeddings via IISc Bangalore & CMU

Authors: Sawan Kumar, Sharmistha Jat, Karan Saxena and Partha Talukdar

Word Sense Disambiguation (WSD) is a longstanding  but open problem in Natural Language Processing (NLP).  Current supervised WSD methods treat senses as discrete labels  and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen  during training.

The researchers from IISc Bangalore in collaboration with Carnegie Mellon University propose  Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD  by predicting over a continuous sense embedding space as opposed to a discrete label space.

Deep Equilibrium Models 

Authors: Shaojie Bai, J. Zico Kolter and Vladlen Koltun 

Motivated by the observation that the hidden layers of many existing deep sequence models converge towards some fixed point, the researchers at Carnegie Mellon University present a new approach to modeling sequential data through deep equilibrium model (DEQ) models. 

Using this approach, training and prediction in these networks require only constant memory, regardless of the effective “depth” of the network.

IMAGENET-Trained CNNs are Biased Towards Texture

Authors: Robert G, Patricia R, Claudio M, Matthias Bethge, Felix A. W and Wieland B

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. The authors in this paper , evaluate CNNs and human observers on images with a texture-shape cue conflict. They show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence.

A Geometric Perspective on Optimal Representations for Reinforcement Learning 

Authors: Marc G. B , Will D , Robert D , Adrien A T , Pablo S C , Nicolas Le R , Dale S, Tor L, Clare L

The authors propose a new perspective on representation learning in reinforcement learning

based on geometric properties of the space of value functions. This work shows that adversarial value functions exhibit interesting structure, and are good auxiliary tasks when learning a representation of an environment. The authors believe this work to open up the possibility of automatically generating auxiliary tasks in deep reinforcement learning.

Weight Agnostic Neural Networks 

Authors: Adam Gaier & David Ha

In this work , the authors explore whether neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. In this paper, they propose a search method for neural network architectures that can already perform a task without any explicit weight training. 

Stand-Alone Self-Attention in Vision Models 

Authors: Prajit Ramachandran, Niki P, Ashish Vaswani,Irwan Bello Anselm Levskaya, Jonathon S

In this work, the Google researchers verified that content-based interactions can serve the vision models . The proposed stand-alone local self-attention layer achieves competitive predictive performance on ImageNet classification and COCO object detection tasks while requiring fewer parameters and floating-point operations than the corresponding convolution baselines. Results show that attention is especially effective in the later parts of the network. 

High-Fidelity Image Generation With Fewer Labels 

Authors: Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Z, Olivier B and Sylvain Gelly 

Modern-day models can produce high quality, close to reality when fed with a vast quantity of labelled data. To solve this large data dependency, researchers from Google released this work , to demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting.

The proposed approach is able to match the sample quality of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels.

ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations

Authors: Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin G, Piyush Sharma and Radu S

The authors present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT and to address the challenges posed by increasing model size and GPU/TPU memory limitations, longer training times, and unexpected model degradation

As a result, this proposed model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.

GauGANs-Semantic Image Synthesis with Spatially-Adaptive Normalization 

Author: Taesung Park, Ming-Yu Liu, Ting-Chun Wang and Jun-Yan Zhu

Nvidia in collaboration with UC Berkeley and MIT proposed a model which has a spatially-adaptive normalization layer for synthesizing photorealistic images given an input semantic layout.

This model retained visual fidelity and alignment with challenging input layouts while allowing the user to control both semantic and style.

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Researching the White Paper:

The process of researching and composing a white paper shares some similarities with the kind of research and writing one does for a high school or college research paper. What’s important for writers of white papers to grasp, however, is how much this genre differs from a research paper.  First, the author of a white paper already recognizes that there is a problem to be solved, a decision to be made, and the job of the author is to provide readers with substantive information to help them make some kind of decision--which may include a decision to do more research because major gaps remain. 

Thus, a white paper author would not “brainstorm” a topic. Instead, the white paper author would get busy figuring out how the problem is defined by those who are experiencing it as a problem. Typically that research begins in popular culture--social media, surveys, interviews, newspapers. Once the author has a handle on how the problem is being defined and experienced, its history and its impact, what people in the trenches believe might be the best or worst ways of addressing it, the author then will turn to academic scholarship as well as “grey” literature (more about that later).  Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position.  Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting, and overlapping perspectives. When people research out of a genuine desire to understand and solve a problem, they listen to every source that may offer helpful information. They will thus have to do much more analysis, synthesis, and sorting of that information, which will often not fall neatly into a “pro” or “con” camp:  Solution A may, for example, solve one part of the problem but exacerbate another part of the problem. Solution C may sound like what everyone wants, but what if it’s built on a set of data that have been criticized by another reliable source?  And so it goes. 

For example, if you are trying to write a white paper on the opioid crisis, you may focus on the value of  providing free, sterilized needles--which do indeed reduce disease, and also provide an opportunity for the health care provider distributing them to offer addiction treatment to the user. However, the free needles are sometimes discarded on the ground, posing a danger to others; or they may be shared; or they may encourage more drug usage. All of those things can be true at once; a reader will want to know about all of these considerations in order to make an informed decision. That is the challenging job of the white paper author.     
 The research you do for your white paper will require that you identify a specific problem, seek popular culture sources to help define the problem, its history, its significance and impact for people affected by it.  You will then delve into academic and grey literature to learn about the way scholars and others with professional expertise answer these same questions. In this way, you will create creating a layered, complex portrait that provides readers with a substantive exploration useful for deliberating and decision-making. You will also likely need to find or create images, including tables, figures, illustrations or photographs, and you will document all of your sources. 

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Quantitative Biology > Quantitative Methods

Title: harnessing big data and artificial intelligence to study plant stress.

Abstract: Life finds a way. For sessile organisms like plants, the need to adapt to changes in the environment is even more poignant. For humanity, the need to develop crops that can grow in diverse environments and feed our growing population is an existential one. The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of stress resilience. Today, the proliferation of artificial intelligence (AI) allows scientists to rapidly screen through high-throughput datasets to uncover elusive patterns and correlations, enabling us to create more performant models for prediction and hypothesis generation in plant biology. This review aims to provide an overview of the availability of large-scale data in plant stress research and discuss the application of AI tools on these large-scale datasets in a bid to develop more stress-resilient plants.

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