有没有可以分析历史事件关联性的AI

processes and mechanisms pursuant to Article 74 of that Directive.[heading3]Amendment[content]1.1.High-risk AI systems shall be designed and developed with capabilities enabling the automatic recording of events(‘logs’)while the high-risk AI systems is operating.Those logging capabilities shall conform to the state of the art and recognised standards or common specifications.[heading3]Amendment[content]1.2.In order to ensure a level of traceability of the AI system’s functioning throughout its entire lifetime that is appropriate to the intended purpose of the system,the logging capabilities shall facilitate the monitoring of operations as referred to in Article 29(4)as well as the post market monitoring referred to in Article 61.In particular,they shall enable the recording of events relevant for the identification of situations that may:(a)result in the AI system presenting a risk within the meaning of Article65(1);or(b)lead to a substantial modification of the AI system.[heading2]Proposal for a regulation Article 12–paragraph 2[content]Text proposed by the Commission[heading2]Amendment 298[heading2]Proposal for a regulation Article 12–paragraph 3[content]Text proposed by the Commission1.3.In particular,logging capabilities shall enable the monitoring of the operation of the high-risk AI system with respect to the occurrence of situations that may result in the AI system presenting a risk within the meaning of Article 65(1)or lead to a substantial modification,and facilitate the postmarket monitoring referred to in Article 61.deleted[heading2]Amendment 299[heading2]Proposal for a regulation Article 13–title[content]Text proposed by the CommissionTransparency and provision of information to users[heading2]Amendment 300

fundamental rights of the affected persons,notably their rights to free movement,nondiscrimination,protection of private life and personal data,international protection and good administration.It is therefore appropriate to classify as high-risk AI systems intended to be used by or on behalf of competent public authorities or by Union agencies,offices or bodies charged with tasks in the fields of migration,asylum and border control management as polygraphs and similar tools insofar as their use is permitted under relevant Union and national law,for assessing certain risks posed by natural persons entering the territory of a Member State or applying for visa or asylum;for verifying the authenticity of the relevant documents of natural persons;for assisting competent public authorities for the examination and assessment of the veracity of evidence in relation to applications for asylum,visa and residence permits and associated complaints with regard to the objective to establish the eligibility of the natural persons applying for a status;for monitoring,surveilling or processing personal data in the context of border management activities,for the purpose of detecting,recognising or identifying natural persons;for the forecasting or prediction of trends related to migration movements and border crossings.AI systems in the area of migration,asylum and border control management covered by this Regulation should comply with the relevant procedural requirements set by the Directive 2013/32/EU of the European Parliament and of the Council49,the Regulation(EC)No 810/2009 of the European Parliament and of the Council50 and other relevant legislation.The use of AI systems in migration,asylum and border control management should in no circumstances be used by Member States or Union institutions,agencies or bodies as a means to circumvent their international obligations under the Convention of 28 July 1951 relating to the Status of__________________49 Directive 2013/32/EU of the European Parliament and of the Council of 26 June 2013 on common procedures for granting and withdrawing international protection(OJ L 180,29.6.2013,p.60).

探索的开始，想以一个去年(23年)年中颇具戏剧性的两个事件为开端...故事的背景和起因是这样的，自AlphaGO为AI制造的涟漪还在、ChatGPT为AIGC掀起了更大的浪潮之后，以及基于LLM之上Agent模式初露头角后，人们将目光更多的关注在如何使得AI在达到AGI之后迈向ASI，而ASI的其中一条印证路径就是超越人类实现AI4S的突破...直到我们在去年底OpenAI内部的一次乌龙事件，似乎暴露出了一些隐藏在其背后的野心和端倪..Think：这里可以关联到Agent探索&体会中的一篇关于XOT的paper中MCTS DRL路径探寻的模式思考，其中AOT那篇paper中也有部分思想的重合与指导性。回望23年中的6月7日，曾经在最复杂的智力博弈领域风光无限的DeepMind，继AlphaGO神来之笔后，在LLM风靡世界的冷静期(2023.2H，Gemini和SORA发布前夕)，又将强化学习带向了巅峰，又双叒叕带着重磅成果登上Nature了..在计算机领域最基础的两个算法上实现了人类未发现的新突破：针对基础排序算法和哈希算法实现了汇编指令层的算法突破，分别提升70%及30%效率。而正是因为这一最新成果·AlphaDev，使得十年都没有更新的LLVM标准C++库都更新了，并且数十亿人将会受益。这个AI名叫AlphaDev，属于Alpha家族“新贵”，并且基于AlphaZero打造。DeepMind的研究员给它设计了一种单人“组装”游戏，如下图所示：只要能够搜索并选择出合适的指令（下图A流程），正确且快速地排好数据（下图B流程），就能获得奖励.