Good afternoon everybody. I think we're about ready to get rolling here. Everyone could take their seats. I'd like to welcome you to the US high-level side event on nuclear policy. My name is Adam Scheinman, the State Department. I am the head of the US delegation here to the MPT review conference and again welcome to those who are here in the audience with us and joining us virtually as well. We're holding this side event in consideration of our long-standing support for the NPT. It is the most successful nuclear treaty and for our support to all of its aims among the arms control and pursuit of eventual nuclear disarmament and in recognition of the fact that transparency is the best antidote to distrust and misunderstanding which of course we all hope to avoid. So with transparency in mind we have assembled a very senior US government panel for you today to talk about all aspects of US policy bearing on nuclear weapons. Our speakers are Undersecretary Colin Coll from the Department of Defense who's joining us on screen. We have Undersecretary Jill Ruby from the Department of Energy's National Nuclear Security Administration and my boss, Secretary of State Bonnie Jenkins and I'll provide more detailed bio before each speaks. We'll speak in the order that just presented and after the three presentations we'll open it up to questions which we only can do here in the room. We can't take questions unfortunately from our virtual audience but when I call on you if you do have a question I would ask that you please turn on your microphone introduce yourself your affiliation and proceed with your question if it's to a particular member of the panel let us know otherwise I'll try to address it to the appropriate speaker. So with that I will introduce our first speaker Dr. Colin Coll who has had a distinguished career in government and academia he was sworn in as the Secretary of Defense for Policy in April 2021 in this role he is the principal advisor to the Secretary of Defense for Defense Policy and lead formulation and coordination of national security policy within the Department of Defense. He was most recently the co-director of the Center for International Security and Cooperation and a professor by courtesy in the Department of Political Science at Stanford University. Previously he served as deputy assistant to the president and national security advisor to the vice president from October 2014 to January 2017 where he advised President Obama and vice president Biden on all matters related to U.S. foreign policy and national security affairs and represented the office of the vice president as a standing member of the National Security Council's deputies committee. Dr. Coll has also held a number of positions in the Department of Defense prior to that and in June 2011 he was awarded the Secretary of Defense Medal for Outstanding Public Service by Secretary of Defense Robert Gates. Dr. Coll was previously an assistant and associate professor in the security studies program at Georgetown University, a senior fellow at the Center for a New American Security Assistant Professor of Political Science at the University of Minnesota and a Council on Formulations International Affairs fellow in the office of the Secretary of Defense. Dr. Coll received his bachelor's degree in political science from the University of Michigan, Go Blue, and his PhD in political science from Columbia University in 2000. So Dr. Coll over to you. Thanks Ambassador Scheinman and I apologize that you had to read that long bio. You know I went to school for 29 straight years and so I require everybody to read my full bio otherwise I get very insecure about how much time I spent in school but no more seriously thank you very much and thank you for leading the US delegation at this year's NPT RevCon. Although I'm not able to join you all in person today I I'm certainly delighted to share the panel with my colleagues under Secretary Jenkins and under Secretary Ruby so that we can provide a broad perspective of US government policies that inform this important event. Participants to this RevCon both government and non-governmental experts have an important role to play in achieving the continued success of the treaty on the non-proliferation of nuclear weapons and thus making progress towards the ultimate goal that we all share of a world free of nuclear weapons. As you are all aware today's security environment is more challenging than at any time since the end of the Cold War and arguably a more complex one as we see many challenges that impact progress towards achievement of the NPT's aspirations. Whether it's Russia's unprovoked invasion of Ukraine and reckless nuclear rhetoric, China's rapid expansion, modernization and diversification of its nuclear weapons capabilities to say nothing of their military activities across the Taiwan Strait this week, Iran's refusal to resolve outstanding IAEA concerns and return to the Joint Comprehensive Plan of Action or JCPOA, or North Korea's possible preparations to conduct another nuclear test and its steady stream of missile tests all present a significant challenge to stability and security and none can be solved by the United States alone. The United States for our part continues to face those challenges head on. We recognize both the continued important role of nuclear deterrence and the equal importance of transparency, communication and dialogue in reducing the risks of nuclear war. This balanced approach recognizes that nuclear deterrence is not mutually exclusive to bolstering arms control, promoting strategic stability and working toward a world without nuclear weapons. Each of us today will focus on a key component of US national of the US national security enterprise. The integrated relationship between those components is essential to our responsibility as a nuclear weapons state under the NPT. Even as we continue the important work of ensuring a safe, secure and effective nuclear deterrent, the United States remains committed to the goals of the NPT and each of our departments and agencies in the US government work every day to realize those goals. Together we sustain a credible nuclear deterrent, uphold a commitment to reducing nuclear risk and fulfill a pledge to make sure that our nuclear stockpile remains safe, secure and effective. We have and will continue to support dialogues and transparency that reduce risk and enhance stability. With that being said, let me spend a few minutes talking about US policy in particular from the perspective of the Defense Department. The US Department of Defense released the National Defense Strategy and the Nuclear Posture Review in classified form to the US Congress in March. The unclassified version of the NPR is a distillation of the classified document and I expect unclassified versions of all of our strategic reviews to be released publicly in the relatively near future following the release of the National Security Strategy. My remarks today will detail some of the Nuclear Posture Review or NPR's key decisions which have already been released publicly. We develop these strategic reviews in a truly integrated manner, aligning for the first time key elements such as our assessment of the security environment as well as our defense priorities, implementation approaches and our approach to risk management. As part of this security environment, we face a China whose approach to nuclear deterrence has fundamentally changed. The in state of China's nuclear expansion remains uncertain, but the current trajectory points to a large diverse nuclear arsenal with a high degree of survivability, reliability and effectiveness. The changes we see in China paired with its overall conventional force posture is deeply concerning. Meanwhile, Russia's invasion of Ukraine has been exacerbated by Russia's overt nuclear threats directed towards other nations, especially states that have foresworn nuclear weapons. Russia's continued modernization of its nuclear forces, including non strategic nuclear weapons and novel strategic systems, as well as their willingness to use aggressive nuclear rhetoric that amounts to nuclear coercion is a stark reminder of the nuclear risk in the in contemporary conflict. Despite calls to work cooperatively toward further reducing nuclear weapons stockpiles, China and Russia are continuing to expand significantly their nuclear forces and introducing wholly new forms of nuclear delivery systems without regard to their impact on strategic stability. We have all we also have growing concern about the parallel development of non nuclear capabilities that could have strategic level consequences and create new escalation risks, including ones that could potentially cross into the nuclear domain. Moreover, the growing potential for crises and conflicts to escalate into and emanate from both the space and cyber domains and potentially lead to miscalculations that exacerbate the risks of further strategic escalation is troublesome. The complex array of challenges reinforces that the US nuclear deterrent remains an essential to our security and to that of our allies and partners for the foreseeable future. With all that said, I'm confident that the nuclear posture review positions us to address current challenges, not only because these real world events were discussed throughout the NPR deliberations, but also as a result of the deliberate process that the NPR took. Turning to NPR outcomes, I'll provide a kind of short summary or bumper sticker upfront. This NPR takes a comprehensive and balanced approach to our nuclear posture. Our North Star amid all of this was to maintain a safe, secure and effective nuclear deterrent and strong and credible extended deterrence commitments with the equally important goal of taking steps to reduce the role of nuclear weapons and recommit ourselves to arms control, risk reduction and strategic stability. When the unclassified version is released, I believe you will find that the Biden administration's NPR presents a balanced approach to reducing the risks of nuclear war. This NPR and system with previous NPRs across multiple US administrations reflects a great degree of continuity and stability in our nuclear policy, both as it regards deterrence and our commitment to arms control and nonproliferation. Some highlights of this year's NPR include a continued commitment by the United States to nuclear modernization. The US nuclear modernization program is replacing existing systems that are nearing the end of their service lives. This program is not, I repeat not, an extension of US nuclear capabilities and is fully compliant with our obligations under the New Start Treaty. The NPR also represents a new integrated approach to deterrence that seeks to work more seamlessly across warfighting domains, theaters and the spectrum of conflict, other instruments of national power and our unrivaled network of alliances and partnerships. One facet of this approach is the potential to identify other options to complement the role of nuclear weapons and to seek opportunities to accomplish deterrent effects with other non nuclear capabilities. Third, the NPR expresses a commitment to taking steps to reduce the role of nuclear weapons in our strategy, including the retirement of the B 83-1 nuclear gravity bomb and a decision to discontinue efforts to develop and field a sea launched nuclear cruise missile to slick them in. The NPR also represents a strong commitment to reducing risk through mutual verifiable arms control agreements, including compliance with all obligations under existing agreements and pursuing new agreements when conditions permit. And finally, the NPR recognizes a continued commitment to discussions among the major nuclear powers on other means to sustain and enhance strategic stability and reduce the risks of nuclear war. Let me highlight a key NPR conclusion underpinning each of these outcomes, namely that nuclear threats and risk cannot be managed or reduced by deterrence alone. The NPR places value on arms control, nonproliferation and disarmament to strengthen strategic stability, head off costly arms races and reduce the salience of nuclear weapons globally. Mutual verifiable arms control provides the most effective and durable path to reduce the role of nuclear weapons in U.S. strategy. Despite the challenges in the current security environment, the United States will continue to pursue engagement with other nuclear arms states where possible to reduce nuclear risks. We will do so with realistic expectations, understanding that progress requires partners prepared to engage responsibly on the basis of reciprocity, and with whom we can establish a degree of trust. Finally, speaking briefly on our nuclear declaratory policy, the president has articulated the following. President Biden has said, and I quote, as long as nuclear weapons exist, the fundamental role of U.S. nuclear weapons is to deter nuclear attack on the United States, our allies and partners. The United States would only consider the use of nuclear weapons in extreme circumstances to defend the vital interests of the United States or our allies and partners, unquote. This declaratory policy sets a high bar for nuclear employment while also confronting adversaries with a certain degree of uncertainty and assuring allies and partners of U.S. security commitments. Allies and partners views feature prominently in our declaratory policy discussions during the NPR process. And of course, underlying our entire approach was the imperative that we consider the current and future security environments. A sole purpose declaratory policy has long been supported by President Biden, but the NPR concluded that now is not the time for making such a change. We retain the goal of moving towards a sole purpose declaration in the future, and the NPR makes that clear, and we will work with our allies and partners to identify concrete steps that will allow us to do so. We also continue to adhere to a negative security assurance not to use or threaten to use nuclear weapons against non-nuclear weapon states that are party to the MPT and are in compliance with their nuclear non-proliferation obligations. Of course, there will be more to discuss once the unclassified NPR has been published, and I'm hopeful that that will be released in the near term. In conclusion, the 2022 NPR takes a balanced approach to nuclear posture that is deeply informed by today's security environment. The NPR is focused on sustaining and modernizing U.S. nuclear forces, maintaining a safe, secure, and effective nuclear deterrent, and strong, incredible extended deterrent commitments, while simultaneously seeking to reassert U.S. leadership in arms control, risk reduction, and strategic stability. On this latter point, let me be clear, the United States wholeheartedly recognizes and reaffirms that a nuclear war cannot be won and must never be fought, and urges all P5 members to ensure their actions are consistent with the joint statement made earlier this year to that effect. We encourage all nuclear weapon states to engage with the United States on risk reduction measures and provide transparency about nuclear posture and doctrine. Thank you, and I look forward to your questions. And I think next up is Jill Ruby. Thank you very much. And indeed, we'll move to our next speaker, Administrator Jill Ruby. Who was sworn in as the Undersecretary for Nuclear Security of the U.S. Department of Energy and Administrator of the National Nuclear Security Administration, or NNSA, in July 26, 2021, to wear two hats. Administrator Ruby leads NNSA in achieving our nation's nuclear security missions by ensuring the nuclear security enterprise is bringing cutting-edge science and creativity to sustain and fully understand our stockpile without testing and developing advanced capabilities to enhance nuclear security, arms control, and AV reactors. She has an extensive background in science and engineering, spanning more than 30 years, and served most recently as Director of the Sandia National Laboratories from 2015 through 2017. Following that, Administrator Ruby served as the inaugural Sam Nunn Distinguished Fellow at the Nuclear Threat Initiative from 2018 to 2019, and has been a member of the Defense Science Board, the National Nuclear Security Administration, Defense Programs Advisory Committee, and the National Academy of Science Committee for International Security and Arms Control. Administrator Ruby earned her bachelor's degree from Purdue University and her master's degree from the University of California at Berkeley, both in mechanical engineering, of note holds three patents, received an R&D 100 award, and in 2017 Business Insider named her the second most powerful female engineer. Dr. Ruby has received the Department of Energy Secretary's Exceptional Service Award, the National Nuclear Security Administrators Distinguished Service Gold Award, and the Office of the Secretary of Defense Medal for Exceptional Public Service. Over to you. Thank you, Adam. Good afternoon, everyone. It's a pleasure to be here this afternoon with my colleagues under Secretary Jenkins and under Secretary Kahl at the 10th Nuclear Nonproliferation Treaty Review Conference. Although this conference has been delayed, our work to meet our obligations under the MP has not stopped. As President Biden has affirmed, the U.S. is committed to strengthening the treaty as the cornerstone of the international nuclear amplification and disarmament regime. At the National Nuclear Security Administration, we design, produce, and maintain a safe, secure U.S. nuclear weapon stockpile to serve as an effective deterrent for ourselves and our allies. We also support broad objective of nuclear deterrence by advancing nuclear security, nonproliferation, and arms control using our technical expertise and knowledge and by working with our partners in the U.S. government, foreign counterparts, and international organizations. A safe, secure, transparent nuclear deterrent combined with arms control creates stability and helps set the conditions needed to consider disarmament. While our efforts maintain global nuclear stability as an enduring mission, our approach for responsible stockpile stewardship and nuclear nonproliferation are informed by current conditions. This is a critical moment for the nonproliferation regime. Russia's further invasion of Ukraine has raised radiological health and safety concerns through its reckless military actions at Chernobyl and Zaparition nuclear sites and has unraveled important nonproliferation norms. North Korea's continued expansion of its nuclear weapons and delivery capabilities and the uncertainty of negotiations with Iran over return to the JCPOA, pose proliferation risks, and undermine both regional and global security. At the same time, we see opportunity for increased use of nuclear power to combat climate change. An increased use of nuclear energy is accompanied by more nuclear material, nuclear-related dual use technology, and specialized equipment around the world, making it critical to encourage all countries to put in place strong safeguards consistent with the nonproliferation priorities in the tree. We are committed to work with the IAEA and international partners to prevent nuclear material misuse by developing effective and affordable monitoring, detection, and verification technologies and architectures. Addressing these challenges requires us to recognize that nuclear deterrent and nonproliferation are tightly linked, not just through capabilities that advance post-op pile stewardship and nonproliferation capabilities, but also by implementing policies that are mutually reinforcing. In the face of these uncertain of this certain global environment, NNSA must be responsive to the moment and the mission. As a responsible nuclear power, the United States is committed to finding a balance between our deterrence needs and our nonproliferation obligations and by promoting transparency. Last October, I announced the number of warheads in our nuclear weapons complex, which shows that the overall size of the US nuclear stockpile has decreased significantly. At the height of the Cold War, the US possessed over 31,000 nuclear weapons. As of 2020, that number has shrunk to 3,750, representing an 88% reduction from the peak and an 83% reduction from the end of the Cold War. And the overall weapons, including an even higher percent reduction in non-strategic weapons. Just to be clear, this is the total number of nuclear weapons possessed by the United States. The number of deployed strategic weapons meets the New START Treaty obligation. While we are working to further reduce the number of weapons in our stockpile, maintaining an effective deterrent requires us to execute a modernization program. Since the end of the Cold War, our programs have been centered on sustaining a capable but smaller and less diverse stockpile. Because of the age of the weapons, the US has embarked on a refurbishment program for over a decade. We've worked to extend the life of our weapons systems that are being retained. As the weapons continue to age, the refurbishment program needs to touch all remaining weapons and is increasingly becoming a program that fully rebuilds update systems. The US program of record also now includes a new weapon, the W-93. In the absence of eliminating nuclear weapons, the future US stockpile is the minimum needed to maintain a safe, secure, and effective deterrent. The US remains committed to refurbishing and modernizing our nuclear stockpile without resuming underground nuclear testing. And to be clear, we are not expanding the size of our nuclear arsenal with this modernization program, even though the other weapons states are increasing both types and numbers of strategic and or tactical nuclear weapons systems. As we reconcile ourselves to the current global environment and our nuclear modernization plans, we also recognize the need to upgrade our production and science facilities. Infrastructure recapitalization is a critical component of the US nuclear deterrent effort. Infrastructure recapitalization will replace outdated Cold War era US facilities with resilient modernized infrastructure. The capitalization effort will reduce the overall footprint of our nuclear weapons enterprise and provide flexibility to respond to global dynamics. Our hope is that with improved facilities and our ever improving science-based understanding, sometime in the future we may be able to reduce the number of weapons we retain in our hedge. All that being said, make no mistake. NNSA is fully committed to our mission of preventing the spread of nuclear weapons programs while doing everything we can to promote strong non-proliferation standards as an enabler to the peaceful uses of nuclear energy. Rebus safeguards and early proactive engagement promote non-proliferation while supporting international climate goals. Since it is clearly better to prevent a crisis than manage it, we will work with nations and stakeholders interested in pursuing nuclear energy to incorporate non-proliferation policies into all aspects of the nuclear discussion. NNSA is investing in the people and the science while at the same time partnering with industry and internationally to incorporate non-proliferation standards into civil nuclear commerce. For example, NNSA is implementing the proliferation-resistant optimization program or Pro-X, which develops proliferation-resistant fuel for industrial applications. In addition, together with the IAEA and international partners, we are developing a program to enhance proliferation resistance in newly built research reactors and their related facilities. In this program, NNSA seeks to eliminate or minimize the production of nuclear material by adjusting to the design of the facility before construction while maintaining or improving facilities capabilities and performance. When it comes to enhancing the global safeguards regime, NNSA partners with industry and IAEA to demonstrate the benefits of deploying next-generation nuclear technology in a safe and secure manner. NNSA engages U.S. industry through voluntary safeguards by design security by design efforts to address non-proliferation and security risks posed by advanced and small modular reactor designs. Through its advanced reactor international safeguards engagement or ARISE, the ARISE NNSA informs U.S. industry partners and international safeguards legal requirements and conducts technical analysis to recommend ways to integrate safeguards considerations into the design process of new and modified nuclear facilities as early as possible. Our efforts do not stop there. NNSA works with partners around the globe to implement safeguards and reduce radiological sources and replace radioactive technologies with more proliferation-resistant options. We also partner with the medical community to provide peaceful use assistance where it is needed most to meet development goals. We are fully committed to using our scientific and policy expertise to bring non-proliferation and the peaceful uses of nuclear energy to the forefront of global discussion. Finally, let me touch briefly on arms control. NNSA remains committed to supporting strong and verifiable future arms control agreements. Moving forward, the next generation of arm control agreements will emerge in a more complicated, unpredictable, and technologically advanced environment. The emergence of new technologies that significantly lower the barrier to proliferation and explosion of open-source research and the proliferation of new types of warheads and delivery vehicles all require reciprocal advancements of verification technologies. We call on the international community to work together to address this daunting challenge. We must be proactive rather than reactive in addressing this issue. At NNSA, we continue to support the development and maturation of technologies in anticipation of future arms control agreements under these conditions. As you know, this review conference is occurring at a pivotal moment. We must look for a balanced approach to maintaining a sufficient nuclear deterrent, advancing non-proliferation, and laying groundwork for next generation arms control. This means modernizing our enterprise so that it is capable of meeting both our defense and non-proliferation needs and advancing peaceful uses. Thank you again for the opportunity to speak with you today, and I look forward to your questions. Great, thank you so much. And we'll turn now to our third speaker, Ambassador Bonnie Jenkins, who is the Under Secretary for Arms Control and International Security at the U.S. Department of State. She previously served in the Obama Administration as Special Envoy Coordinating U.S. Efforts on Global Threat Reduction and U.S. Government Programs in Chemical, Biological, Nuclear, and Radiological Security. She was the State Department lead for four of the Head of State Nuclear Security Summits held from 2010 to 2016, and the Global Health Security Agenda from 2014 to 2017 developed threat reduction programs in Africa and served as the U.S. Representative to the G7 Global Partnership. Prior to that, Ambassador Jenkins was a legal advisor to the U.S. Arms Control and Disarmament Agency and to multiple treaty implementation bodies, including the Chemical Weapons Convention, Biological Weapons Convention, and the Comprehensive Nuclear Test Ban Treaty, and provided counsel to the 9-11 Commission and the U.S. Commission to assess the Organization of the Federal Government at the proliferation of weapons of mass destruction. Ambassador Jenkins has extensive experience working in civil society, including at the Ford Foundation, Brookings, Perry Worldhouse at the University of Pennsylvania, the Belfer Center at the Harvard University Kennedy School, and women of color advancing peace, security, and conflict transformation, and organizations in solidarity. Ambassador Jenkins, over to you. Thank you, Ambassador Scheiben, and thanks to my fellow panelists on the Secretary of Defense Carl and on the Secretary Ruby. As you can see, the United States government is working in lockstep to advance a nuclear policy that maintains deterrence, enhances stability, and enables further progress on arms control, disarmament, and non-proliferation. As you have heard, the U.S. Nuclear Post Review, or NPR, affirms that deterrence and arms control and non-proliferation are clearly enforcing and all essential to maintaining stability. As Under Secretary Kahl outlined, the 2022 Nuclear Post Review represents a comprehensive, balanced approach to U.S. nuclear strategy, policy, posture, and forces. Maintaining a safe, secure, and effective nuclear deterrent and strong, incredible, extended deterrent commitments remains a top U.S. priority. The 2022 NPR underscores our commitment to take steps to reduce the role of nuclear weapons and reestablish our leadership in arms control. We approach deterrence, arms control, and non-proliferation as mutually reinforcing complementary elements of an integrated strategy for preserving stability, deterring aggression and escalation, and avoiding arms racing and nuclear war. We will continue to emphasize strategic stability, seek to avoid costly arms races, and facilitate risk reduction in arms control arrangements where possible. As President Biden recently wrote, today, perhaps any other time since the Cold War, we must work to reduce the risk of an arms race or nuclear escalation. That sentiment helps guide us actions on nuclear policy, not in spite of the security environment, but because of it. Upon taking office in January 2021, President Biden took immediate steps to extend the New START Treaty with the Russian Federation for the full five years provided for under the treaty. The United States then pushed for the resumption of a U.S.-Russia's Strategic Stability Dialogue, or SSD, aimed at reducing risk and laying the groundwork for future arms control. Unfortunately, President Putin has chosen war, chaos, and violence over stability and diplomacy for Russia. Russia's invasion of Ukraine directly contradicts commitments Russia made in the Budapest memorandum. President Putin has also chosen to level implied nuclear threats against any country that will come to Ukraine's aid. This behavior places significant strain on the global non-proliferation regime. Responsible behavior by nuclear weapons state is critical to the health of the NPT. As we contemplate the future of arms control with Russia, it is important to note two things. The U.S. goal to pursue next steps in arm control with Russia has not changed, nor has the expiration date for a new START, February 5th, 2026. We know that there is a ticking clock counting down towards a world with no binding limits on the two largest nuclear arsenals and their greater potential for instability. Such a world would not be a safer one. Seeing this challenge, President Biden made it clear that we must continue beyond new START extension. And early this week, President Biden reiterated his readiness to negotiate expeditiously a framework to replace new START if Russia is prepared to operate in good faith. With that change, we will continue to prepare to advance our objectives for bilateral arms control with Russia, which include negotiating continued verifiable limits on incontinental range nuclear weapons, including new kinds of weapons and addressing non-deployed nuclear weapons in theater range or non-strategic nuclear weapons and missiles. The world is also facing facing new nuclear challenges as you have heard. The people of the Republic of China is rapidly building a larger more diverse nuclear arsenal. As recently as 2020, our Department of Defense assessed that the PRC would at least double the size of its nuclear stockpile, which at that time was estimated in the low 200s within the decade. Since then, the PRC has accelerated this growth may possess up to 700 deliverable nuclear warheads by 2027 and likely intends to have at least 1,000 warheads by 2030. We will continue to explore measures for reducing and managing strategic risks with the PRC in the near term, including through improved crisis communication and information sharing mechanisms, even in a period of intensified military competition. But progress in these areas will require a willing partner in Beijing. Guardrails built today can also help us explore what forms of mutual restraint and arms limitations may be in both sides' interests in the long term. It is also important to note that Washington and Beijing have the same obligations on the Article 6 of the NPT. And the world is right to expect us to advance discussions that would help fulfill these obligations. As President Biden stated earlier this week, there is no benefit to any of our nations or for the world to resist substantive engagement on arms control and nuclear non-proliferation. The United States supports multilateral arms control and non-proliferation including the NPT and other multilateral treaties. This conference includes many opportunities to discuss U.S. support for the NPT and other measures advancing a common goal of nuclear non-proliferation. So here I will only briefly highlight a few examples. The United States supports the Comprehensive Nuclear Test Plan Treaty or CTBT and is committed to work to achieve its entry into force, recognizing the significant challenges that lie ahead in reaching this goal. For nearly 30 years, the United States has observed a zero-year moratorium on nuclear explosive testing. We call on all states possessing nuclear weapons to declare or maintain such a moratorium. We also continue to support the commencement of negotiations on a fissile material call of treaty. And FMCT remains a key component in building towards a world without nuclear weapons. We strongly encourage all states to follow our lead and to declare and maintain moratoria on production of fissile material for use in nuclear weapons or other nuclear explosive devices. The United States will continue to champion work for advanced disarmament, like the Creating and Environment for Nuclear Disarmament Initiative or CEND and the International Partnership for Nuclear Disarmament Foundation or IPNDV. We will continue to partner with all who seek to make substantive progress on these issues, such as the Stockholm Initiative and the non-proliferation and disarmament initiative or MPDI. As we work to advance our goals, we will continue to be transparent about our nuclear policy, strategy and forces. Such transparency is a long-standing priority of practice of the United States. And in 2010 and 2018, we issued public reports on the U.S. nuclear posture, explaining our nuclear policy and strategy, describing our nuclear force structure the rationale for specific nuclear weapons systems and how we posture them and U.S. nuclear modernization plans. Following both reviews, the United States has also issued publicly available explanations of U.S. nuclear employment strategy in reports to the U.S. Congress. As you just heard, the Biden administration completed its nuclear posture review or NPR in March of this year. The Department of Defense publicly released a fact sheet upon completion of the review and a more declassified classified report of that review is forthcoming as you've heard. The United States has been extraordinarily transparent about its nuclear posture and policies, the size of its nuclear arsenal and changes to U.S. nuclear forces. We know that transparency is a key confidence-building measure that contributes to our broader aim of reducing strategic risk. We believe such transparency is not just beneficial, it is essential. I want to close by discussing the January 2022 P5 leader statement on preventing nuclear war and avoiding arms races and how it aligns with U.S. nuclear policy. In the statement, all five nuclear weapons states in the NPT affirm that a nuclear war cannot be won and must never be fought. The U.S. demonstrates its commitment to this statement in both word and D. We do not use nuclear weapons to intimidate others or as part of an expansionist policy. U.S. nuclear weapons consistent with the P5 statement as Undersecretary Colin Coll has said serves defensive purposes, deter aggression and prevent war. This policy of restraint continues to shape the role of nuclear weapons in the U.S. strategy while we continue to ensure our strategic deterrence remains safe, secure and effective and our extended deterrence commitments remain strong and credible. The P5 statement also declares that nuclear use have far-reaching consequences. While we sometimes use the term non-strategic to describe certain nuclear weapons, the reality is that any deployment of nuclear weapons regardless of location or yield would fundamentally alter the nature of a conflict, create the potential for uncontrolled escalation and would have strategic effects. With this in mind, the U.S. believes all U.S. and Russian nuclear weapons should be included in future arms control regimes. It is the basis of our conviction that we all have a responsibility to continue the record of non-use of nuclear weapons since the end of World War II. While we know that a nuclear war cannot be won, these difficult times make clear the continuing need to maintain effective U.S. capabilities for deterring attacks on U.S. forces, allies and partners, as well as on the U.S. homeland. But let's iterate and underscore two other long-standing elements of U.S. policy. First, the United States would only consider nuclear weapons in extreme circumstances to defend the vital interests of the United States or allies and partners. And second, the United States will not use or threaten to use nuclear weapons against non-nuclear weapons states that are party to the NPT and in compliance with their nuclear non-proversion obligations. Finally, the P5 statement underlies our desire to work with all States to create a security environment more conducive to progress on disarmament with the ultimate goal of a world without nuclear weapons with undiminished security for all. We must and will continue to pursue armed control and non-properation steps that reduce nuclear risks as long as such weapons exist. Coupled with deterrence element of our strategy, these armed patrol and non-properation efforts will reduce nuclear dangers and provide critical contributions to the security of the United States and our allies and partners. They will also contribute to global security and stability that will benefit all countries and people around the world. Thank you. Thank you very much Under Secretary Jenkins and to each of our panelists. We've got about 25 minutes and I would like to have the opportunity to conduct some questions and answers. I know the temptation to end comments to your questions but in the interest of time perhaps it would be best just to focus on your question and a particular panelist if you would like to address one or the other. I'm going to take two questions at the at the front here and we'll see where we are on time at that point. And I neglected to mention earlier we also have with us Assistant Secretary Mallory Stewart from the Arms Control and Verification and Compliance Bureau at the State Department who may wish to amplify certain points if inviting her to if she wishes. So questions maybe make our way. I see I promised Daryl yesterday that I'd give him the first question. So I know it well. So I'm going to give Daryl the first question. One could interpret the statements as being very similar if not the same. So if you could just please elaborate in your view what the differences are between the stated declaratory policies of the United States and Russia. Okay let me grab one more question maybe coming over this way please in the middle. Hi there I'm Aaron Humphress and I'm here on behalf of the Christian Campaign for Nuclear Disarmament. Jill Ruby said the current U.S. stockpile is the minimum required for a credible deterrence. Earlier this week NGOs were able to consider a report by SIPRI called revisiting minimal nuclear deterrence laying the ground for multilateral disarmament. I'd be interested to know what the U.S. defines and quantifies minimum deterrence and how it seeks to apply the concept within its own nuclear policy. Thank you. Okay thank you. Perhaps under Secretary Call if you were able to hear the first question invite you to respond. Thanks Ambassador. You know I heard the end which was to distinguish between Russian declaratory policy and the U.S. declaratory policy but I did not hear my friend Daryl Kimball as the first minute or so of his question so if there was a part before that that you wanted to emphasize otherwise I'll just focus on the two sentences at the end that I heard. Is there anything else to throw out? All right let me answer let me answer the fundamental question I heard at the end and then Daryl can tell me a few once more. So I think there are a number of differences I think in large part President Biden has made clear through his declaratory policy that the fundamental role of nuclear weapons is to deter nuclear attack and so while there is a very narrow set of circumstances in which threats might be so extreme in other domains that possible nuclear use cannot be ruled out the fundamental purpose is to deter nuclear attack my sense is that Russia does not hold that view and that they see a much larger range of contingencies whereby they may use or threaten to use nuclear weapons I think one of the things that we're so troubling about some of the statements that we saw coming out especially at the beginning of the current Ukraine conflict where the Russians engaged in such a heinous and unprovoked invasion of Ukraine was to precisely threaten that any country that might seek to help Ukraine in defending its territorial integrity and sovereignty you know core norms at the heart of the rules based international order would face consequences that they had never seen in all of history which I think many interpreted as a veiled nuclear threat that is certainly not the type of statement you would hear from a responsible U.S. policymaker it would not be consistent with our declaratory policy it is not appropriate for a mature nuclear power to make reckless statements like that and it is certainly inconsistent with our negative security assurance consistent across numerous administrations not to threaten nuclear use against non-nuclear states that are compliant with their NPT obligations so I think that when you look behind some of the surface wording there are substantial differences between how the United States envisions the utility of nuclear weapons and the much lower bar that the Russian Federation has obtained for itself thanks thank you and Administrator Rue okay thank you thank you for the question on minimum deterrence so this is a difficult question to answer precisely but let me just generally answer this question the the U.S. thinks of its weapons and a couple of categories one is deployed strategic weapons consistent with the New START Treaty which you know we not interested in unilaterally decreasing this wall number of forward deployed weapons that we have and our hedge and in each of these categories we try to minimize the numbers but provide the deterrence we feel is essential for ourselves and our allies in the hedge we have a hedge for a couple of reasons both technical and political reasons and I discussed in my comments the some of the technical reasons the need to rebuild our infrastructure and the need to refurbish what are now very old weapons that we had hoped that we might not need in the future but we now know that we are going to maintain these weapons so they're in the the hedge category we feel that the need for the technical hedge and then the political hedge with an advancing Chinese capability diversify Russian capabilities and other nuclear weapon states as I discussed especially North Green Iran so it is our sense that these three categories and minimum numbers in each of these categories represent what we consider today in the absence of new arms control agreements the minimum deterrent thank you we'll take two more Tony thank you my microphone so this is a question to undersecretary Jenkins I want to ask for some comments or elaboration of the declaratory policy on negative security assurances and why nuclear weapon free zones are not included in the explicitly in the declaratory or whether there is a separate declaration regarding nuclear weapon free zones this is an issue that it's of utmost importance because the extinction between NPT state parties that are in compliance with the treaty might correspond to the nuclear weapon free zones so they could be explicitly included in the declaratory so that's more or less my question and request for comment thank you let me try the side of the hall Nobu thank you very much I have Nobu Akiyama advisor to the Japanese delegation and I have two questions and one goes to the Dr. Carl and the other is go to the other Peter Ruby the one about the question to Dr. Carl is about the possible arms control with China and what do you think would be the sort of a condition for you to engage in the China and arms control but particularly now China doesn't disclose its number of nuclear number of nuclear weapons and so in that case it is impossible to establish a base for the arms control dialogue so what would be the necessary condition for United States and China to engage in the arms control the question to Mr. that Mr. Ruby is about the small module reactors so if in the U.S. industry is to export SMRs then maybe disposition after the lifetime would be one of the questions and in that case is U.S. accept the take back of the SMR to the United States thank you okay thank you for the questions Under Secretary Jenkins Hi thanks for the question I think the best way that I would answer issue about the declaratory policy is you know as you said you know there's there's two things here so I would we have a declaratory policy about you know we said about nuclear NPT parties who are complying with the NPT and I think that's a state of policy I think that's very clear but we also have as you said and not necessarily contradictory but we also have our security assurances that are part of our nuclear weapon the commitments we make under the nuclear weapon free zones so I wouldn't necessarily think of them in terms of whether they're contradictory or not or whether you know what's the complication I think they're just very clear one is through the declaratory policy and the other ones do the security assurances so they both exist where the U.S. has signed and ratified the security assurances but I wouldn't approach them in terms of whether they're contradictory or not I think they're very clear obligations that we make under each one thank you oh I should also mention that we have a security assurance document that you should look at the P2P and I don't think there's any state in a nuclear weapon free zone who isn't covered by the formulation that we currently have there are no gaps there so the next question to under a call on aims for U.S. China nuclear discussions yes so as my opening remarks made clear and also under Secretary Jenkins made clear China is frankly in the midst of a kind of breathtaking expansion of its nuclear arsenal there in the midst of tripling or potentially quadrupling the size of their nuclear arsenal new kinds of nuclear weapons new capabilities and it raises the very good question of how do we think about potential strategic stability conversations and arms control conversations with Beijing and how are they thinking about it you know in our view we are open to strategic stability dialogue conversations with the PRC we are open to initiating conversations on arms control whether that relates whether those strategic stability arms control conversations relate specifically to nuclear weapons or other strategic capabilities that have potential implications in the nuclear domain we think this is the type of activity that mature responsible nuclear powers should engage in and we would welcome the opportunity to speak with our Chinese colleagues the necessary condition for that conversation to happen is a reciprocal willingness from Beijing to entertain those conversations and to date they have not expressed a willingness to engage in either sustained strategic stability dialogue or arms control now in a number of exchanges between President Biden and President Xi Jinping they have talked about the importance of initiating such conversations and engaging in risk reduction but to date we have not seen the PRC express an openness to follow through on that in the meantime I think it's imperative that both the United States and China establish some guardrails and keep open lines of communication between our two countries and the reason for that is obvious we don't want to have a situation in which heightened tensions or heightened competition spiral into conflict certainly something we do not seek we certainly don't want circumstances in which there could be miscommunication or misperception and so establishing these guardrails opening you know keeping open lines of communication is very important I think it's especially important question given the events in recent days you know the good news is as we've seen tensions increase in the Taiwan Strait the PRC has not made nuclear threats that's that's the good news the bad news however is that you know mature responsible nuclear powers don't cut off military to military contacts and communication in the midst of heightened tensions they don't do that because it increases the prospect for misperception and miscalculation so and yet that's precisely what Beijing has done in this current context they've kind of manufactured a crisis out of US legislature legislators visiting the island of Taiwan and they are seeking to I guess in their view to punish the United States by cutting off military to military contacts with us I don't really think see that as a punishment I see it as an irresponsible move because we have a responsibility to our bilateral relationship but also frankly the rest of the world to keep those lines of communication open thanks I just want to add briefly you know I cannot emphasize enough what the Undersecretary of College has said about the importance of establishing some kind of dialogue with with the PRC on these issues really to deal with the issues of miscalculation because you know there's a lot of countries that have concerns and questions and you know in many of the bilateral discussions I have this question comes up about when you know is there going to be bilateral discussion some sort between the US and the PRC so just to highlight this is not just an issue of concern with the US and you know having discussions with the PRC this is a question a lot of a lot of countries have and I hear it all the time so you know as as the the secretary call has said you know this is something that many countries should focus on and also encourage a dialogue with the PRC and also Adam has reminded me one of the things that we take very seriously is the issue of risk reduction we as we look at the issues of arms control and everyone's always asking about the future of arms control and the focus is not only on with Russia what's going on because we are not talking with Russia right now and the SSD and the dialogue I think we also have to think about there are many ways in which we can promote dialogue and many ways in which we can engage countries and one of the things that we do want to do and echoing what's on the secretary call has said is by having risk reduction discussions having some kind of dialogue we can try to develop the trust or the that we can that can be the foundation of a dialogue and to try to address the question why is this so challenging to do so I think arms control is important but as the secretary call said there are many ways we can engage and risk reduction is one of the things that we also want to do with the policy and Administrator Ruby yeah to the question about small modular reactors their lifetimes and whether or not the U.S. would have the U.S. small modular reactors return to the to return back this is I don't have an answer to this question yet but it is you know one of the considerations and the reasons why the government and NSA in particular wants to work closely with industry on safeguards and security by design of course the U.S. industry as industry on the world are very focused on safety but we must focus also on these other things when it comes to small modular reactors so we don't yet have an answer we are very keen on continuing partnerships so that we can get answers like that and have you know what we're sort of colloquially calling fit for purpose one two three agreements as the technology advances thank you okay I think we have time for two or three more questions I'm gonna look over to my right and make my way back sir thank you very much Adam Bugajski security policy director of Poland Polish MFA and first of all I would like to thank the U.S. for consulting allies while drafting your NPR we appreciate it we hope that in any new arms control negotiations you will address the question of so-called non-strategic nuclear weapons because well at least in our region probably the Russian arsenal constitute the biggest threat for original stability I wonder and this question I think I will refer to both Dr. Cal and under the criteria Jenkins whether it will require a different approach for previous negotiations on new start do you think that additional efforts will be will be required here and what what what kind of processes you can initiate with regard to non-strategic arsenal thank you thanks very much sir thank you very much Bricksy Williams director of basic in London I've noticed that the language of responsibility has come up a great deal in the review conference in both your statements but also not least in the P3 working paper on responsible practices and I think that's it's a great start to start to try and draw some contours around what you see as responsible and irresponsible behavior not so much defined who is a responsible state and who's not a responsible state which you know it's a bit like saying someone's in the axis of evil it puts them off a bit but but but focusing on behavior I think is a good thing so the question I'd have is what kind of activities is the United States undertaking internally to explore and reflect on its responsibilities in relation to nuclear weapons and is there more you can do and then would you be interested in engaging with nuclear and non-nuclear armed states beyond the P3 on these questions as a means of developing stronger norms and I'm thinking here of the late great Michael Crepon who in his recent book was talking about a similar idea about trying to sort of strengthen these norms of behavior in spite of the difficulties of of agreeing legal arms control measures thank you okay thanks very much that might kill our time between those two questions but we'll see how we do undersecretary call on the the first question regarding new start treaty which undersecretary Jenkins may also want to address yeah thanks so a couple of things I mean obviously the new start treaty is set to expire in 2026 one of the first foreign policy priorities for president Biden of course was to extend new start when he came into office for for five years but obviously as we get closer to 2026 it will be important for us in the Russians to engage in in thorough dialogue about extending it further and and either you know expanding the treaty or looking at other arms control arrangements to address activities that are that are not adequately covered in the current text I think is everybody in the room fully appreciates obviously a new start is a fundamental cornerstone of global of the global arms control regime it is really what puts a lid on strategic nuclear competition between the United States and Russia in terms of potential costly and dangerous arms race but it doesn't cover certain novel nuclear systems that the Russian Federation has developed nor does it cover the thousands of non-strategic nuclear weapons in the in the in the Russian arsenal so I think you know consistent with the with the questioner it will be imperative for us to explore what modalities there may be to address those non-strategic nuclear weapons because I mean as under Secretary Jenkins noted in our opening statement there is no scenario in which the use of something that is called a non-strategic nuclear weapon doesn't nevertheless have strategic and in fact global consequences so there is a responsibility for us to engage in conversations over these weapons I think we also find certain aspects of the Russian development and the doctrine around the potential use of these systems to be particularly troubling because it seems to lower the threshold and increase the perceived utility for the use of nuclear weapons outside of the context of certainly what we would be comfortable with in thinking about the appropriate use of nuclear weapons as it relates to the specific ways in which that might be done I'm going to defer to my colleagues from the State Department both under Secretary Jenkins and also Mallory Stewart may have some ideas on this and I would also defer to them on the on the other question about kind of norms of responsible behavior and which norms in particular we think we can build on and further instantiate I mean obviously we're committed to certain norms like transparency and the state stewardship of our stockpile and remain committed to our our safeguards commitments and over time reducing the role of nuclear weapons and moving towards a world without nuclear weapons but as it relates to specific responsible state behavior again I'm going to defer to our colleagues at State and then we're running out of time so I'll be brief just to repeat what on the Secretary of State Colleges just said yes non-strategic nuclear weapons is going to be included and has been included to date in terms of the early parts of the dialogue that we did have with Russia last year their invasion of Ukraine and we have heard very clearly from our European colleagues and others about the interest and the concern about non-strategic nuclear weapons so we know that that's an issue that is of concern for a number of countries including our own as far as the question about the different approach I would say I think you know as we approach the SSD this time we did approach it in a way that it's a dialogue and we want to build to the negotiations but we want to bring a number of issues into this discussion so it's not just you know looking at the same issue the same type of weapons that are covered in new start but we wanted to recognize that there's a lot of new types of weapons there's a lot of issues that we have to deal with now whether it's issues related to emerging technology whether it's issues related to space I mean there's a lot of things that we think should be brought into a larger discussion not that all of them will lead and end up in a legally binding document of an arms control tree but there are certainly issues that we need to talk about and discuss and have a dialogue with Russia about because we're looking at the strategic environment and so that's how we have decided to approach that and that's the way that both of the U.S. and Russia had agreed to approach it prior to the invasion of Ukraine we also recognize that we need to look at new types of verification if we're going to be including all types of nuclear weapons so that's the way we have wanted to approach it much more holistically because that's much more realistic of the situation we have right now and on the on the second question about you know norms I mean and and you know on the Secretary we talked about you know how how transparent we have been I think the real question is how do we make the other country other P5 countries as transparent as we are and I think we can always do more and one of the messages that we are sending in this time in this in this review conference is we do want to continue to talk with all countries you mentioned non-nuclear weapons states we already do that in the creating the creating an environment for nuclear disarmament we have these discussions we have a number of forums like that where we are engaging non-nuclear weapons states and other in in NGOs as well so we do want to have that dialogue we do want to find ways which we can promote norms but you know I think one of the things that we need to also do is make sure that other countries are also transparent I'm conscious of the time but I just want to add a little bit on this question of non-strategic nuclear weapons and arms control which requires new verification technologies or at least verification technologies that we haven't deployed yet that are not that are that will be more complicated and so again we we are looking at those technologies and we invite international participation into those activities wherever possible thanks okay well thank you very much and I think that will bring to a close our event I want to thank everyone who's here for joining us I'd like you to join me in thanking speakers it's not often you get through U.S. Undersecretaries to to talk to you at one time I don't have that pleasure very often so let's thank the speakers


to Erin. Today I'll be talking about machine learning for everyone. So who am I? Well I am Erin Maugh. I'm 12 years old and I have a tremendous passion in computer science ever since I was five. I'm a hardcore software and hardware developer and I'm the world's youngest potential contributor. I love robotics, machine learning algorithms, TensorFlow, Python, C++ and much more. I'm also the world's youngest graduated from Udacity so I'm trying to start an engineering program and much more. And you can follow me on Twitter at ErinH Maugh. So for those of you who've seen me talk before, hello there. For those of you who are new to see me talk, hi there. So today we'll be going on a journey to the moon. Will we come back? Let's find out. Oh wait, we're at Flastal. So hold on tight and get ready for Flastal. So now let's start talking about machine learning core concepts. So first of all, machine learning is not a joke. So you're laughing? No. Very unacceptable. Let's begin. So machine learning is the study of its complex algorithm and statistical model. It has the ability to learn and improve from its own experience without being explicitly programmed by any human intervention or assistance. The goal in machine learning is to find the algorithm along the weights and biases that comes with it when tweaking the input hyperparameters that we as humans give it. So machine learning is not magic or a black box. It's writing tools like technologies to answer questions based on data. So here's the machine learning pipeline. On the left hand side, we gather our data and apply preprocessing techniques to the data to make sure it's in a stable format for machine learning. Then on the right hand side, we apply a learning algorithm to the data set and then train our model. Our model has a pretty good accuracy. We won't want to deploy it to the real world. If not, we might want to update our model again and reach me in it in hope of getting a better accuracy. Don't worry if this doesn't make sense. We'll cover all of this in my talk. So let's take that machine learning in a human friendly way. So let's meet John. John loves new music. He loves music with a fast tempo and the genre of rock. But he dislikes music with a slow tempo and the genre of pop. So let's we can plot his like music and dislike music on an x y axis graph as you can see here. The x axis is a music genre from pop to rock. The y axis is the music tempo from relaxed to fast. So let's say John was listening to a new song. Master a puppet by Metallica. Where do you think the point will go at? If you guess it was around here, you're correct. How about bad guy by Billy Elish? Where do you think the point will be at? So now some of you might be saying John probably would like this song and some of you might be saying don't probably dislike the song. Well, here we can use a machine learning algorithm known as K nearest neighbors. So what we do here is we need to set a K parameter. So if K was four, then we draw a circle with four data points inside. Now we can go with the majority and say that John would probably like the song. So let's take a look at a few more scenarios of machine learning in the real world. For example, Twitter, I'm pretty sure most of us here uses Twitter. So have you ever wondered how Twitter gives you recommended tweets? So whenever you follow someone, there's always an algorithm watching you to bring the content for you. But how? Well, whenever you follow someone, the algorithm automatically gets tweets from that user and then calculates probability if you will like this tweet or not. And then I'll show the tweets from the best to the worst in your Explorer card. How about Uber? Well, Uber uses machine learning and a wide majority of their products. For example, in the rideshare app, they can predict the number of sales they're going to have in a day and also supply and demand. Also for their support, they have automated checkbox to help users quickly get the support they need. Also, there's account personalization, dynamic pricing, and also detecting front activity. All of this is based on machine learning. So let's learn machine learning today. So there are three fields of study in machine learning. The first field of study is artificial intelligence or AI. This is the broad discipline of creating intelligent machines to think for their own selves. Machine learning or ML is a subset of AI. It's a system that can learn from its own experience about any human intervention. Deep learning or DL is a subset of AI and machine learning. It's a system that can learn from experience on humongous data sets. So when we're talking about machine learning, we're actually referring to three different things. The first thing is called surprise learning. It's test driven to take the data as the input in the labels as the target. Without training, tomorrow we'll find some correlation between the input and the target. Surprise learning is best at solving regression and classification problems. Regression is where we need to predict a continuous value. For example, what is the price of the stock apple going to be on a particular day? Classification on the other hand comes in the form of yes, no question. For example, is this picture a cat? Yes or no? There's also unsurprised learning. This is data driven, so which means there's no labels and the data is not structured. We can only solve question problem where we need to group similar things together. Finally, there's reinforcement learning and it's algorithm driven, so it learns from its own experience. So if it does something good, it gets a reward. If it doesn't move back, the reward is taken away. So surprise learning is what we're going to focus on in this talk. Unsurprisingly, beyond the scope right now, and you want to learn about reinforcement learning, don't forget to go to my website at AaronHsma.com and click on view my previous talks and you can see my talk on reinforcement learning where you get to build your own sell-getting car in the browser. How cool is that? So let's take a look at a simple super exciting call. For example, classification. So have you ever wondered how Gmail spam folder works? Well, here's how it works.  Then we feed it into a computer and the computer will learn the relationship between emails marked as spam or not spam. So when email comes in, you can automatically categorize if it's spam or not spam. But why shouldn't you use machine learning where? Well, let's take a look at that in a traditional approach. In the traditional way, we will study the problem first and write a bunch of rules. For example, the text can be awesome for you or free many, many times, and it's probably going to be spam. And if not, it's probably not going to be spam. So we're going to evaluate this approach and it's pretty good. Then we'll probably just launch it. If it's bad, we want to write some more rules. But for example, what is a spam or change of the O and awesome to a zero? Sorry, consumer, we forgot to block that spam email. So this is why we should use machine learning. Once we study the problem, then we can trade our machine learning algorithm based on the data. And it's pretty good, then we can launch it. And if not, we might want to update our model a little bit. But the reason we use the machine learning approach is that this can be automated. So the user reports us email a spam. As long as we get new data, get online to train our model again to get even better results. How about clustering in unsupervised learning? Take a look at these images on this slide. What if there was a million of these images? And then I ask the story into three different groups, it can take more than 20 years just to do this. This is where unsupervised learning shines. Using the unsupervised learning algorithm, I like to identify which items are similar to each other. In this case, the triangles will go together, the cubes together, and the circles will go together. In total, there will be three groups. How about reinforcement learning? Take a look at this maze. Pretty easy, right? But how can we teach this to a computer? Well, to do this, we better need to better understand what reinforcement learning is. The mouse in this place is the machine learning agent, which will play in our environment, which is the maze. So in the first learning epoch, the agent might just keep moving forward and it gets electrocuted because it dies. But after many, many learning epochs eventually will get to the desired output, which is the cheese, and it will get there quickly and efficiently. How about neural networks? Neural networks are the foundational building blocks for machine learning. It's a computer software that was inspired by the brain and made up of units like neuron and our brain to solve a problem together. So a neural network is basically a stack of layers. Each layer is made up of units. For example, in our input layer, we have three units. You may also notice that each of these layers are fully connected. We can call this a dense layer. You may also notice in the hidden layer in the output layer, we have Ws and Bs. These are called the Ws and Bs, which are the internal levels of our model, which are going to be updated to our training. And by model, we're basically representing a neural network. So here, literally, is the simplest neural network. All we do is just taking an x and y, and if you remember from your algebra one classes, that function, which is multiplied x by y to get the output. So if x was three and y was seven, the output will be 21. So here it is in Python. Basically, this is the exact same thing, except for we're just we're just multiplying 8.5 by 0.1. And we have 0.85. But that's just silly. In today's monorail, there's many lots, lots of hidden layers. That's why let's take a look at a deep dive and deep neural networks. But hey, congratulations on trying the most criminal part on the rocket. So let's say we're going to build a neural network to classify a square, a triangle and a star. So we'll feed these images into our input layer. Then we'll pass in those images from our input layer to our hidden layers. So perform the required computation for our model. Finally, the output layer will hold our outputs. There will be three yards in our output layer. The number of yards in a classification form like this one depends on the number of types that you're feeding in to your model. In this case, three, a square, a triangle and a star. Let's say we're going to feed it an image of a triangle to a neural network. So this image is 20 by 28 pixels, or in total, 784 pixels. So there will be 784 neurons in our input layer, one for each pixel, then we'll pass those into our hidden layers, the lines called channels. These channels has its own number known as the weight at each neuron in the hidden layer as its own number, the bias. Thus, we can formulate the equation y equals wx plus b, where x is the data in the input neuron, w is the weight and b is the bias. Once we have that number, we need to go through an activation function. In this case, we want to go through a sigmoid function, where it gives us value from zero to one. So we can use it for predicting probabilities as the output. And also it's called sigmoid because if we point it out on chart, we get the shape of an s, thus sigmoid. This step is calculated every step of our hidden layer. Now, if we go to our end, our output layer, we can see we have a wrong prediction. But this is only half of the story. This is called forward propagation. Right after forward propagation, we see how much we need to improve. But here, we can see our machine's prediction, and also our errors, and also the targets. And the one here beside the triangle tells us that is the desired output. Now for the other half of the story, back propagation. Now we know how to improve our model, we go through back propagation where we adjust the weight and biases in hopes of reducing the error. So at the many epochs of forward propagation, back propagation, forward propagation, and so much more, we eventually have a correct prediction. And you can see that after many iterations of training, we have reduced error and increased accuracy. So now you might be thinking, how long does this training take? What can take anyone from minutes to hours to even a continuous amount of days? So let's take a look at the machine learning process. So here it is, blah, blah, blah, blah. Let's take a look at that in an easier way. The first step is to gather our data. So machine learning depends largely on our training data. So we need a large line of data and high quality data. So where can you find a large line of data and high quality data? Well, you can find it from one of these sources. For example, Google's data sets, Kaggle, USAN Machine Learning repo, the hacker Earth, Amazon, Microsoft, etc. The next step is to pre process our data. This basically allows selecting your data, filtering it, transforming it, and also visualizing the data to get better sense of it. Basically, just cleaning our data to make it similar for machine learning. Next, we choose an algorithm. This algorithm will be used to train our model. So make sure you choose wisely. So if you choose a model of an algorithm that is not suitable for your data, then you'll have very bad accuracy to choose an algorithm that's suited for your data to have a high accuracy. So you can get some algorithm from popular lines like scikit-learn and keras. So most commonly used machine learning algorithms include linear regression, logistic regression, decision tree, k-means, etc. The next step is to build and compile our model. Here, we'll build our model layer by layer. And each layer will have its own ways that correspond to the layer. And also don't forget to add activation function. So here, we will build a model by first defining a sequential layer, which basically defines a linear stack of network layers. And here we can add layers to our model using the model.add API. And also here, we can see that we're adding a dense layer, which is basically a fully connected neural network. That's the Rayleigh activation function. So then we need to compile our model using the optimizer, like how we're going to improve our model, the loss, calculating the error of our model, and the metrics, and also the weights. The next step is to train our model. So we make a prediction based on the current state of the model, and then calculate how bad the prediction is. And then we go to the backup application. So we update the weights and biases to minimize this error and make this model better. So we train our model using the model of fit API, with the training data, and also the training label. Then we also add the number of epochs, and also the back size, and the callback. Make sure that when choosing your epochs, you're choosing a good amount. For example, we're choosing such a very high number of epochs, you'll go through something called overfitting, which basically means your model has already memorized the training data, and it does horribly on the testing data set, and underfitting is where your model didn't learn anything during training, and also it performed badly during testing. And also the back size says the number of samples that will iterate through our data set in an epoch. And also a callback is, for example, if we reach a specific accuracy or loss, we might want to stop training in order to not overfit. For the next step, it's a tester model. So you see how well our model data set throughout training, and we predict our prediction using model.predict along with the testing x data. And then we repeat this process. So machinery is this easy, who uses it? Well, there's researchers, data scientists, machinery engineers, and users, sorry, developers, and me. So congratulations on making it this far. Now we're at the second stage separation. Okay, let's talk about traditional learning versus machine learning. To make sure you pay attention to this part, because this is a very commonly asked interview question. In fact, if you go to a machine learning interview, this might be the first question that we ask you. What is the difference between traditional learning and machine learning? Well, let's take a look at that. So let's say a game. We're not really a game because media controllers aren't allowed. So you don't have one. Good job. So traditional software development, we already know the input and the algorithm, and we just write a function that gets the alpha in machine learning on the other hand, we're taking pairs and pairs and pairs, and put it out the data, and we create a model that will figure out the algorithm in machine learning. We're more focusing on how data has been represented, while in traditional software development, we're more focusing on our code. So let's say we're solving the Celsius different problem using traditional software development. Well, here we're taking our input, which is Celsius, and can use the algorithm, which is Celsius times 1.8 plus 32 to get the alpha. But in machine learning, we're taking a pair of infinite alpha data, and we create a model and through iterations, we'll figure out the algorithm. So let's take a look at that in a simple scenario. For example, different languages say hello, hello, there are so many languages to say hello. So in traditional software development, based on the text, let's say what language it is. So the text is hello, then the language is English. And the text is French, and hello in French is Bonjour. So the text is not hello, the language is French. Spanish is Spanish, low is Hola. So the text is not low or Bonjour. Then it's going to be Spanish. But what language is this in? What language? It's Chinese. Oh, no, our model has no idea what that means. So the problem was, was that in traditional software development, we have to explicitly set every single possible thing that might happen. By the time you're done with that, you'd be like this. But then you ask, oh, no, there's so many other phrases that you have to write in traditional software. For example, bye, what are you going to do in so many other stuff like that? No, well, let's use the magic wand of machine learning. Start, we'll gather our data set, train our model where we'll learn the relationship between the data and the answers to figure out the rules. So let's try it again. What language is this in? Hold on to what language? And as expected, the language is Chinese. Much better. So if you're lazy like me, you can use something called BERT or bidirectional encoder representation from Transformers. This is a state of the art natural language processing model that was rolled up by Google AI. It's a mechanism that allows you to learn relationship between words in a text. And now we're at the third stage ignition. So we're almost at the moon. Let's talk about the history of machine learning. So now as you probably know, we have these fancy, water-cooled neural networks, and also scary water-cooled TPUs. But let's just hold on and then rewind to the year of 1941. In 1940, the idea of machine learning was introduced by the ally power during World War II. The ally power thought, if we can have this machine learning thing, we'll definitely win World War II and beat the axis power. But sadly, there wasn't enough computational power for this to happen. Then surprisingly, 11 years later, the first neural learner, by Marvel MC from the MIT, created a box that was made using a neural network. Then just one year later, the first reinforcement learning agent by Archive Daniel was made. Then people were like, this machine learning is so good, it can definitely match the ability of humans. So people started funding to machine learning. But when that happened, in 1974, the first machine learning so cold winter started. But then out of the blue, 12 years later, in 1996, I mean, the defeat of the world's chess champion, Gary Petra. Just four years later, that propagation for neural networks was introduced, draws neural networks to train faster and more accurately. Then in 2014, Dean Ryan was formed. Just two years later, Dean went off ago, he's the world's best co-player. And in 2030, AI ROF will be taking over our job in daily tasks. Oh, did you hear that? You're not going to have a job in 2030. So how do you still have a job in 2030? My learning tensor flow, tensor flow, tensor flow, the only one I've got to learn to still have a job in 2030. Okay, so what is this tensor flow thing? Well, tensor was originally titled disbelief, that the global brain team started in 2011. Then they needed some more help. They decided to open source it and reading the product to tensor flow in 2015. Then in 2017, 10th rule one was announced, and it also became the world's most honest machine learning and it also included Keras, which allowed people to develop their models quickly, and easier to use. Then in 10th rule two was released last year in September. So what's new in 10th rule two? Well, your execution is available by default. And instead of importing Keras, you can use TF dot Keras. Also, TF dot data is a simplified API that allows you to read your training data based on input pipeline. Also, the TF function decorator automatically translates Python products into central grass for you. So you no longer have to use TensorFlow session and can still run 10th rule one code of the 2.0 release. But if you're professional, don't do it. So the architecture of 10th rule two actually is pretty easy. We start by reading and preprocessing your data. Then once we have that, we use Keras or a premade estimator to build and compile our model. Then we can train our models using the distribution chain. I'll take full advantage of our CPU GPU and CPU. Once it's done training, we can use safe model, which will last the player model to cloud on a phone that on a browser and also other TensorFlow language binding. So consider this your first whole application in TensorFlow. So here we can import TensorFlow as TF. Now we can print out the TensorFlow version. And here you can see I'm on the latest TensorFlow version 2.2. Now here there's so many machine learning Python languages out there. So why should I use TensorFlow? Well, more quickly, which machine library should you use? Well, I got to cover, friend. The top three most popular machine learning libraries as of today are TensorFlow, PyTorch, and scikit-learn. TensorFlow, by far, has the most popular get-of-stars, and it's backed by Google. And under the hood is based on Keras. PyTorch, on the other hand, has 40k stars, and it's backed by Facebook, and it's built on top of K2. Finally, scikit-learn has 41k get-of-stars, and it's backed by the open source community. Let's take a look at statistics. Statistics never lie. But clearly, in the past 12 months, worldwide, TensorFlow is the world's most popular search term in the machine learning category. So the winner of the machine learning Python library is, Drone World, please. TensorFlow! Yes! Everyone loves TensorFlow. So now we're at a third stage burnout. No one else has got some other tools for machine learning. The first one is Jupyter Notebook, which allows you to quickly write Python, markdown, and other language bindings from the comfort of your own browser. There's also Google Collab, which is basically Google's copy of Jupyter. So here you can see a simple Google Collab here, but ha ha ha, hidden the rest of the Collab. But what I think is good about Collab is they're free stuff. Oh, yes, it's completely free. They are free GPUs and TPUs for youth. How about NumPy? NumPy, or numerical Python, is an open source library that you can use for scientific and numerical computing. It was built using Python and C, and it allows efficient computation on arrays. There's also pandas. So here you can see pandas, pandas is a high performance data manipulation and analysis tool.  So here you can see in under three lines, you can import a data set from the internet. How cool is that? Let's take a look at that in a human friendly way. So here we're importing pandas as PD. Okay, we imported pandas. So it's reading our pandas.csv file. Okay, we have our CSV file. Now let's clean our data using df.drop. Okay, we're left with shreds of bamboo. And now we have a great meal. So beautiful. So now let's take a look at Matplotlib. Matplotlib allows you to create beautiful data visualizations with high quality graphs, charts, and figures, and much more. So here you can see some Matplotlib graphs that you can create here. Now for your first product, fashion eminence. So now you might be a little sad because it's product time. But coding? Yes. Let's take a look at that. Okay. So here I'm in the colab here. So today we'll be taking a look at a fashion eminence demo where we will build a neural network to classify images of closing. So here we're going to import packages and load our data set. If you're basically import everything in the print out potential version. So next, we'll load our data set from the Keras fashion eminence here. And here we're actually going to split it into the training, training set and the testing set. So the training data is what I'm going to use to teach my model and the testing set is what I'm going to use to see how all my models have learned the data set. So next I'm going to explore let's explore our data set. So we actually have 10 different types of data inside our data. So we have t-shirt trousers and all the way to ankle boots here. So in our training set, we have 60,000 images each 20 by 28 pixels.  So here we can see a simple image and it's an ankle boot. But there's one thing we need to convert it to grayscale because machine model can't take in a colored image right now. So here in Python, we can actually do it pretty easy. We only need to do it's just divided by 255 to convert it to grayscale.  So now we'll build our model. So here I'll build our model. So here, first of all, in our input layer, we'll convert our data to a one dimensional array. Then in our hidden layer, we'll have 128 neurons, and we'll be using the Rayleigh activation function. So introduce non-linearity to our model. And our output layer has 10 neurons because there's going to be 10 different types of input that's going to be fed to our model here. And then we're going to compile our model using the odd atom automizer, which will find the individual learning rates for each parameter. Finally, the sparse category across entropy will measure the similarity between the predicted class probability and observed class labels. Here we'll also calculate our metrics, and here we'll be using the accuracy metric. So now let's train our model. We can train our models by passing the x data, which is images, and also training labels. And here we have a capacity number of epochs. We can also pass verbose so we don't see any output, and also callbacks, but here I'm just going to set an epoch number and our data. So now let's test them all. And can see we have a 87% testing accuracy. Hmm, I think we overfitted a little bit here. Because here you can see in our final epoch, we have 98% accuracy on the training data. But here in our testing accuracy, we only have 87% accuracy. So you can see that we have overfitted a little bit. So here's a challenge for you. Try and play around with my model and also play around with the number of epochs to see if you can have an accuracy that's greater than 95%. If you need any help, feel free to reach out to me on Discord. Now we'll take a look at our predictions. And you can see here, our predictions is not. So remember from our class names, right? So the nine is our index insider array, and see it's an echo boot. So let's take a look at the correct label. And it's also nice. So this is a correct prediction. Let's plot out that prediction and can see that we got it correct. So the blue means that we got it correct. And the red means we got it wrong. So our model got sandal wrong here. So let's plot everything. So our model is actually doing pretty good here. All except for the sneaker. So congratulations on solving your first project in machine learning. So now we're at the moon. Congratulations. So now for my grand finale drum roll please. What is what that's why where there's no code required to create machine learning models and no machine learning experience required from robles introducing teachable machine. Teachable machine is a web based tool that allows you to create machine learning models fast, easy and accessible for everyone. Teachable machine is great for kids. But more importantly, it's something for everyone. The teachable machine currently can handle images, sounds and body poses. So here's how it works. First step, you need to gather all of your data set that you train your model. And then you can test your model to see how it performs on training and also export it for your own product. Let's take a look at a live demo here. So here I'm going to create a new image monitor. So today I'm going to let it classify images of me holding three and five. So I'm going to be able to create five. So here I'm going to upload images of me holding three. Okay, I have my data, three now five. Now let's train them all. So you can see we have we train for 50 epochs and for each epoch 16 the batches in the learning will be 0.001. Okay, now it's trained. You can see I can test my model. So here I can see three and it's pretty good here how about five pretty good. But what I think is great about teachable machine is that once you have this all you need to do is put expert model and can put it on Peter website loaded from TensorFlow and also put it on your own mobile app. So what just happened there? Well, what just happened was transfer learning transfer learning allows you to train on your own input data and then and then change the input data and retrain again is about the same accuracy. So an example of this is for example, neural style transfer here we have a content image and a style image, we get a generate image. The generating image will be the style image with the content image on the top. So here you can see the person points along style of Van Gogh into the final generating image. A more advanced example of this is style G&M. We're basically here we're generating faces of fake people based on faces of real people. So congratulations on making it this far. So now for some takeaways of my talk. Machine learning is everywhere and machinery was for everyone, which means you, yeah, you senior chair, you can become a machine learning engineer with a little help from Google TensorFlow. We're back on earth you survive. Congratulations. Yeah, everyone might listen to this quote from the founder of Coursera and deep green dot AI Andrew and G AI is akin to building a rocket ship. We need a huge engine and a lot of fuel. Rocket engine is the learning algorithms, but the fuel is the humanism of the data that gives me to those algorithms, Andrew and G. So what he's saying is that to build a really good machine learning model, you need a good algorithm and a ton of data. The more data we have, the better. So here's some next steps in your machine learning journey. You can install TensorFlow from the official TensorFlow site. And also I highly recommend you check out Google's free machine learning crash course where they cover the most of the machine learning algorithms. And also awesome TensorFlow, which contains a great resource for learning TensorFlow and also central playground. Yes, central playground. It allows you to build a neural network in your browser. And don't forget to check out machine learning to hear on YouTube, where it's where my good friend learns morning on the Google TensorFlow team. Now give yourself a round of applause for learning the basics of such a hard concept. So thank you for listening. I'm Erin Ma. Don't forget to check out my my website at erinagema.com. And also don't forget to send an email at high at erinagema.com. I'm available at 14 hours, seven days. And don't forget to follow me on Twitter at erinagema, where I'll be posting out the slide deck, also the tips and tricks into machine learning. Thank you for listening and have a great day. Goodbye. Hey, that's great. That's a very, very good talk. It's like one of the best talk I've ever seen in Europe, Python, to be honest. And actually, there's one question for you.  So do you find actually the things in school a bit boring for you? I would say it's actually pretty boring, to be honest. It's not that fun, because basically follow the basics of Comcore. They don't teach a lot. They really teach this, like this, he goes blah, blah, blah. Because I don't think I think it's both pretty boring. Yeah, so you want to just stick with machine learning and all this AI and all this stuff, right? Yeah, pretty cool. That's really a limited thing to do. Yeah, but I think like you can maybe do both, because like the school stuff is so easy for you, you can do everything actually. Yeah, that's great. Or maybe you can use machine learning to finish for the school work. Yeah, okay. So yeah, so I think now is almost the time for the closing sessions. So that would happen in Brian. So thank you so much. And we would love to see you again, or maybe show something like exciting next time. Yeah, so if you have any more questions, you can maybe continue the chat on Discord server. There's actually a dedicated room for this talk. So you can actually continue the discussion there. And so that's it for the Pirates room now. And the closing session again is in the Microsoft room. So I'll see you all there. Okay, bye.