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[Bilingual as delivered; scroll down for all-English]

Mr. President,Excellencies,

I thank the United Kingdom for convening the first debate on Artificial Intelligence ever held in this Council.

I have been following the development of AI for some time. Indeed, I told the General Assembly six years ago that AI would have a dramatic impact on sustainable development, the world of work, and the social fabric.

But like everyone here, I have been shocked and impressed by the newest form of AI, generative AI, which is a radical advance in its capabilities.

The speed and reach of this new technology in all its forms are utterly unprecedented.

It has been compared to the introduction of the printing press. But while it took more than fifty years for printed books to become widely available across Europe, ChatGPT reached 100 million users in just two months.

The finance industry estimates AI could contribute between $10 and $15 trillion US dollars to the global economy by 2030.

Almost every government, large company and organization in the world is working on an AI strategy.

But even its own designers have no idea where their stunning technological breakthrough may lead.

It is clear that AI will have an impact on every area of our lives including the three pillars of the United Nations.

It has the potential to turbocharge global development, from monitoring the climate crisis to breakthroughs in medical research.

It offers new potential to realize human rights, particularly to health and education.

But the High Commissioner for Human Rights has expressed alarm over evidence that AI can amplify bias, reinforce discrimination, and enable new levels of authoritarian surveillance.

Todays debate is an opportunity to consider the impact of Artificial Intelligence on peace and security where it is already raising political, legal, ethical, and humanitarian concerns.

I urge the Council to approach this technology with a sense of urgency, a global lens, and a learners mindset.

Because what we have seen is just the beginning.

Never again will technological innovation move as slow as it is moving today.

Mr. President,

AI is being put to work in connection with peace and security, including by the United Nations.

It is increasingly being used to identify patterns of violence, monitor ceasefires and more, helping to strengthen our peacekeeping, mediation and humanitarian efforts.

But AI tools can also be used by those with malicious intent.

AI models can help people to harm themselves and each other, at massive scale.

Lets be clear:

The malicious use of AI systems for terrorist, criminal or state purposes could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale.

AI-enabled cyberattacks are already targeting critical infrastructure and our own peacekeeping and humanitarian operations, causing great human suffering.

The technical and financial barriers to access are low including for criminals and terrorists.

Both military and non-military applications of AI could have very serious consequences for global peace and security.

The advent of generative AI could be a defining moment for disinformation and hate speech undermining truth, facts, and safety; adding a new dimension to the manipulation of human behaviour; and contributing to polarization and instability on a vast scale.

Deepfakes are just one new AI-enabled tool that, if unchecked, could have serious implications for peace and stability.

And the unforeseen consequences of some AI-enabled systems could create security risks by accident.

Look no further than social media. Tools and platforms that were designed to enhance human connection are now used to undermine elections, spread conspiracy theories, and incite hatred and violence.

Malfunctioning AI systems are another huge area of concern.

And the interaction between AI and nuclear weapons, biotechnology, neurotechnology, and robotics is deeply alarming.

Generative AI has enormous potential for good and evil at scale. Its creators themselves have warned that much bigger, potentially catastrophic and existential risks lie ahead.

Without action to address these risks, we are derelict in our responsibilities to present and future generations.

Monsieur le Prsident,

La communaut internationale a une longue histoire de rponses aux nouvelles technologies susceptibles de dstabiliser nos socits et nos conomies.

Nous avons joint nos efforts au sein de lONU pour tablir de nouvelles rgles internationales, signer de nouveaux traits et crer de nouveaux organismes mondiaux.

Si de nombreux pays ont prconis diffrentes mesures et initiatives relatives la gouvernance de lintelligence artificielle, une approche universelle est ncessaire.

Et les questions de gouvernance seront complexes divers gards :

Premirement, certains modles puissants dintelligence artificielle sont dores et dj largement accessibles au grand public.

Deuximement, et contrairement aux matires nuclaires et aux agents chimiques et biologiques, les outils dIA peuvent tre expdis partout dans le monde en laissant trs peu de traces.

Et troisimement, le rle de premier plan jou par le secteur priv dans le domaine de lIA a peu dquivalents dans dautres technologies stratgiques.

Mais nous avons dj des points de dpart.

Par exemple, les principes directeurs de 2018-2019 sur les systmes darmes ltaux autonomes, adopts dans le cadre de la Convention sur certaines armes classiques.

Je suis daccord avec les trs nombreux experts qui ont recommand linterdiction des armes autonomes ltales utilises sans contrle humain.

Nous disposons aussi des Recommandations sur lthique de lintelligence artificielle adoptes par lUNESCO en 2021.

Le Bureau de lutte contre le terrorisme, en collaboration avec lInstitut interrgional de recherche des Nations Unies sur la criminalit et la justice, a quant lui formul des recommandations sur la manire dont les tats Membres peuvent lutter contre lutilisation potentielle de lIA des fins terroristes.

Et les sommets "AI for Good" de l'Union internationale des tlcommunications ont rassembl des experts, le secteur priv, des institutions des Nations unies et des gouvernements autour defforts visant garantir que lIA serve le bien commun.

Mr. President,

The best approach would address existing challenges while also creating the capacity to monitor and respond to future risks. It should be flexible and adaptable, and consider technical, social and legal questions.

It should integrate the private sector, civil society, independent scientists and all those driving AI innovation.

The need for global standards and approaches makes the United Nations the ideal place for this to happen.

The Charters emphasis on protecting succeeding generations gives us a clear mandate to bring all stakeholders together around the collective mitigation of long-term global risks. AI poses just such a risk.

I therefore welcome calls from some Member States for the creation of a new United Nations entity to support collective efforts to govern this extraordinary technology, inspired by such models as the International Atomic Energy Agency, the International Civil Aviation Organization, or the Intergovernmental Panel on Climate Change.

The overarching goal of this body would be to support countries to maximize the benefits of AI for good, to mitigate existing and potential risks, and to establish and administer internationally-agreed mechanisms of monitoring and governance.

Lets be honest: There is a huge skills gap around AI in governments and other administrative and security structures that must be addressed at the national and global levels.

A new UN entity would gather expertise and put it at the disposal of the international community. And it could support collaboration on the research and development of AI tools to accelerate sustainable development.

Mr. President,

As a first step, I am convening a multistakeholder High-Level Advisory Board for Artificial Intelligence that will report back on the options for global AI governance, by the end of this year.

My upcoming Policy Brief on A New Agenda for Peace will also make recommendations on AI governance to Member States:

First, it will recommend that Member States develop national strategies on the responsible design, development and use of AI, consistent with their obligations under International Humanitarian Law and Human Rights Law.

Second, it will call on Member States to engage in a multilateral process to develop norms, rules and principles around military applications of AI, while ensuring the engagement of other relevant stakeholders.

Third, it will call on Member States to agree on a global framework to regulate and strengthen oversight mechanisms for the use of data-driven technology, including artificial intelligence, for counter-terrorism purposes.

The Policy Brief on a New Agenda for Peace will also call for negotiations to be concluded by 2026 on a legally-binding instrument to prohibit lethal autonomous weapons systems that function without human control or oversight, and which cannot be used in compliance with international humanitarian law.

I hope Member States will debate these options and decide on the best course of action to establish the AI governance mechanisms that are so urgently needed.

In addition to the recommendations of the New Agenda for Peace, I urge agreement on the general principle that human agency and control are essential for nuclear weapons and should never be withdrawn.

The Summit of the Future next year will be an ideal opportunity for decisions on many of these inter-related issues.

Mr. President,

I urge this Council to exercise leadership on Artificial Intelligence and show the way towards common measures for the transparency, accountability, and oversight of AI systems.

We must work together for AI that bridges social, digital, and economic divides, not one that pushes us further apart.

I urge you to join forces and build trust for peace and security.

We need a race to develop AI for good.

To develop AI that is reliable and safe and that can end poverty, banish hunger, cure cancer, and supercharge climate action;

AI that propels us towards the Sustainable Development Goals.

That is the race we need, and that is a race that is possible and achievable.

Thank you.

*************************************************************************

[All-English]

Mr. President,Excellencies,

I thank the United Kingdom for convening the first debate on Artificial Intelligence ever held in this Council.

I have been following the development of AI for some time. Indeed, I told the General Assembly six years ago that AI would have a dramatic impact on sustainable development, the world of work, and the social fabric.

But like everyone here, I have been shocked and impressed by the newest form of AI, generative AI, which is a radical advance in its capabilities.

The speed and reach of this new technology in all its forms are utterly unprecedented.

It has been compared to the introduction of the printing press. But while it took more than fifty years for printed books to become widely available across Europe, ChatGPT reached 100 million users in just two months.

The finance industry estimates AI could contribute between $10 and $15 trillion US dollars to the global economy by 2030.

Almost every government, large company and organization in the world is working on an AI strategy.

But even its own designers have no idea where their stunning technological breakthrough may lead.

It is clear that AI will have an impact on every area of our lives including the three pillars of the United Nations.

It has the potential to turbocharge global development, from monitoring the climate crisis to breakthroughs in medical research.

It offers new potential to realize human rights, particularly to health and education.

But the High Commissioner for Human Rights has expressed alarm over evidence that AI can amplify bias, reinforce discrimination, and enable new levels of authoritarian surveillance.

Todays debate is an opportunity to consider the impact of Artificial Intelligence on peace and security where it is already raising political, legal, ethical, and humanitarian concerns.

I urge the Council to approach this technology with a sense of urgency, a global lens, and a learners mindset.

Because what we have seen is just the beginning.

Never again will technological innovation move as slow as it is moving today.

Mr. President,

AI is being put to work in connection with peace and security, including by the United Nations.

It is increasingly being used to identify patterns of violence, monitor ceasefires and more, helping to strengthen our peacekeeping, mediation and humanitarian efforts.

See the original post:
Secretary-General's remarks to the Security Council on Artificial ... - United Nations

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Artificial intelligence (AI) tools, like ChatGPT, have already started to transform the learning experience in classrooms at the University of Colorado Boulder. Faculty members from various departments are utilizing AI to enhance critical thinking skills and facilitate collaboration among students.

Professor Kirk Ambrose, the founding director of the Center for Teaching and Learning, believes that AI can be a valuable tool within and outside the classroom. He sees AI as a means of advancing student learning in a robust way by enabling conversations and collaborative activities.

Professor William Kuskin, Chair of the English department, acknowledges the potential benefits of AI but also emphasizes the need for caution in its usage. He points out that there is a risk of losing the ability to distinguish truth from falsehood if not properly instructed. However, Kuskin also sees the potential for a deeper understanding of writing and imagination through AI.

Associate Professor Kai Larsen, who has been teaching machine learning and business analytics, plans to incorporate AI into his upcoming class. He believes that AI, such as ChatGPT, can provide invaluable assistance to students in technical problem-solving. Its availability 24/7 and adaptability to students schedules make it a convenient support system.

The English Department is also exploring the role of AI in learning and coursework. Associate Teaching Professor Teresa Nugent has proposed courses on AI in writing, focusing on foundational writing principles and the impact of AI on those aspects. Nugent stresses the need for collaboration between faculty and students to navigate the challenges and opportunities presented by AI.

Despite the numerous benefits, there are ethical implications and concerns regarding AI. For instance, there is a risk of plagiarism when AI tools generate content on behalf of students. Instructors also need to consider how the use of AI might unintentionally create barriers for students with disabilities.

The issue of labor and the allocation of time for learning also arises. AI raises questions about where students should invest their effort for example, when to use AI tools for drafting papers versus engaging in a more traditional writing process.

When it comes to creating policies around AI, Katherine Eggert, Senior Vice Provost and Associate Vice Chancellor for Academic Planning and Assessment, believes that it should be left to the discretion of individual faculty members. She compares it to having a policy on using Google, which would be impractical.

While concerns about cheating exist, the universitys honor code already addresses plagiarism, encompassing the use of AI-powered writing technology. Cheating has always been a concern, and the introduction of AI does not fundamentally change that. Faculty members encourage students to experiment with AI while maintaining academic integrity.

The swift integration of AI into the classroom environment at the University of Colorado Boulder presents both opportunities and challenges. Faculty and students need to work together to navigate these new possibilities and ensure that AI is implemented in ways that truly benefit student learning outcomes.

Read the original here:
The Impact of Artificial Intelligence on Learning at the University of ... - Fagen wasanni

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The Generative AI News (GAIN) rundown for July 20, 2023, delves into the recent advancements in the field of generative artificial intelligence. The article sheds light on Metas Llama 2, an open source and free tool in the LLM market, and discusses its potential impact. Additionally, it examines the alleged performance degradation of GPT-4, scrutinizing the research paper results and contemplating a potential rebuttal. The article also features insights into the winners and losers of the week in the generative AI sector.

Metas decision to make Llama 2, a tool in the LLM market, open source and free for commercial use, is anticipated to have profound implications for the industry. This move may democratize access to advanced language models and foster innovation.

Claims surrounding GPT-4s performance degradation have been circulating, prompting the article to analyze the research paper results and question the methodology employed. The possibility of a rebuttal is also explored, raising doubts about the alleged decline in performance.

Furthermore, the article highlights the winners and losers of the week in the generative AI space, offering valuable insights into the latest developments and industry trends. It provides readers with a comprehensive overview of the progress made in the field.

In addition to the aforementioned topics, the article also covers funding news in the industry. Cognaize secured $18 million to develop an enhanced LLM for the finance sector, while Preply closed a Series C funding round, raising $120 million and focusing on strengthening its AI capabilities. Moreover, SAPs investments in Aleph Alpha, Anthropic, and Cohere are mentioned.

To conclude, this article offers a comprehensive overview of the latest advancements in generative artificial intelligence. It examines the potential ramifications of Metas decision to open source Llama 2, scrutinizes the claims concerning GPT-4s performance degradation, and provides insights into the winners and losers of the week in the generative AI sector. The funding news and investments in the industry are also covered. For readers intrigued by these topics, the provided links offer avenues for further exploration.

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Latest Developments in Generative Artificial Intelligence - Fagen wasanni

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We proposed the PCAIDS, an AutoML-based model, for the prediction of csPCa based on quick and economic routinely performed clinical examinations. The PCAIDS incorporated multimodal and multidimensional data, including laboratory tests, imaging tests, and demographic data, revealing encouraging discriminative power with AUCs of 0.820 in the validation cohort and 0.807 and 0.850 in the two prospective test cohorts.

Compared with previous prediction models, such as the ERSPC-RC [14], PCPT-RC [15] and CPCC-RC [16], the PCAIDS, for the first time, evaluated over 100 multimodal features with AI-based algorithms. These features, including demographics, laboratory tests, and imaging examinations, were assessed by a series of AI algorithms. Among these AI algorithms, AutoML outperformed logistic regression, random forest, and XGBoost. AutoML has become a popular and efficient modeling tool for data science that uses k-fold cross-validation through varying optimization algorithms, such as grid search, random search, and genetic algorithm (GA), to scan different feature combinations, feature transformations, supervised algorithms, and their corresponding hyperparameter combinations implemented in AutoWEKA [17], Autogluon [18], AutoSklearn 2.0 [19], and TPOT, [20] thereby identifying the optimal machine learning pipeline.

Additionally, AI-based methods have the potential to analyze high-volume data and to discover nonlinear and interactive prediction information. For cancer diagnosis, there were huge possibilities that currently applied predictive models only included a proportion of effective predictors. Although the application of AI-based methods may not always outperform linear models, the advantage of involving more features could help the models to be more stable and more applicable for different populations.

In this aspect, Jungyo Suh et al. proposed the possibility of applying AI-based algorithms in the prediction of prostate biopsy. They developed an AI-based prediction tool with PSA, total prostate volume, age, hypoechoic lesion on ultrasonography, transitional zone volume, testosterone, and fPSA [21]. This study showed the promising future of using AI-based algorithms in predicting PCa; however, the investigated features were of limited number. To some extent, AI-based algorithms were not ideal for the analysis of limited features, which could have been done by traditional methods. In predicting colon cancer, researchers applied AI-based methods to data from health maintenance organizations by evaluating analytes from standard laboratory records, including hematology, liver function, and metabolism [22]. In breast cancer, the notion of applying AI-based methods to diagnose breast cancer was validated, and age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used in the prediction model [23]. Further studies validated that routine blood analysis features had a boosted performance for breast cancer diagnosis and supported the notion that this approach is of great potential to be used in a widespread manner to detect cancers [24]. These studies suggested the possibility of using routine health examinations to predict cancer based on AI algorithms.

The clinical scenario for the application of PCAIDS is between PSA-based screening and novel tests predicting PCa, including mpMRI, urinary PCA3 test, 4kScore, and Prostate Health Index. MpMRI, a potent modality in predicting biopsy results, is of great importance in patients who are at high risk of PCa. However, the application of mpMRI is limited by the accessibility of MRI machines and the professionalism of the radiologists who interpret the images. Meanwhile, these biomarkers were only available for patients in some countries and regions. In addition, mpMRI and these novel biomarkers are associated with high costs in most countries. The application of PCAIDS, on the other hand, does not require special examination equipment. The features included in the model were common, routinely performed, quick, and economic tests, which were also needed for a general health check-up. The application of B-ultrasound in evaluating the size of the prostate is also accessible for almost every hospital. In general, this AI-based modality is not here to perfect the diagnostic modality with mpMRI and novel biomarkers, rather than replacing them.

AutoML has the flaw of interpretability, which is consistently met with skepticism, similar to other complex models, especially in the medical field. To this end, we applied the SHAP [13] tool to explore the contribution of individual features to the model. To explore the rationality of this contribution, we also examined the interpretability of the LR compared to SHAP (Additional file4: Figure S1). First, the contribution of the key variables (the cross-sectional area of the prostate (B_AREA), AGE, and fPSA) is basically the same in the two prediction modalities. This is similar to the previous conclusions obtained by the RF model (Additional file1: Table S1). Second, the SHAP value from AutoML is roughly the same as the importance of LR calculated by model coefficients. Third, B_AREA is the most important variable. Significantly, the risk of PCa did not increase with B_AREA, which may be due to the increased concertation of PSA produced by a larger prostate, misstating that the risk of PCa and fPSA/tPSA are similar. In addition, age played the second most important role in the prediction model. Thereafter, the risk of PCa increases with age, which is intuitive, and the same holds true for other clinical indicators, although no direct cause can be inferred.

One of the limitations of this study is the lack of a head-to-head comparison with mpMRI or other novel biomarkers. However, the clinical scenario of this prediction mode is not to replace novel diagnostic methods but to assist in decision-making for novel diagnostic methods. In addition, we introduced the dimensions of the prostate from the B ultrasound in the model, and there might be inter- and intrarater differences among different centers in terms of ultrasound results. Furthermore, ultrasound images were not included in this study due to the lack of image storage in all centers. We believe that future studies may incorporate the images captured during ultrasound examinations. The findings of this study are applicable primarily to Asian populations due to the vast discrepancy between Asian and Caucasian patients. In the future, we intend to collect data from various populations to adapt our model to different ethnic groups. Finally, the performance of the PCAIDS is not better than that of the other algorithms, including LR. However, it is important to note that in the study, given the serious implications of missing a prostate cancer diagnosis, prioritizing sensitivity rather than specificity was chosen. This decision was made understanding that it might increase the false positive rate, but it's a reasonable trade-off given the potential severity of a missed diagnosis, where high sensitivity can often lead to lower specificity. We consider that further validation studies may help us to show its wide applicability.

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Artificial intelligence for the diagnosis of clinically significant prostate ... - BMC Medicine

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