when a open AI developed a gp4 and they wanted to test what this new AI can do they gave it the task of solving capture puzzles it's these puzzles you encounter online when you try to access a website and the website needs to decide whether you're a human or a robot now uh gp4 could not solve the capture but it accessed a website task rabbit where you can hire people online to do things for you and it wanted to hire a human worker to solve the capture puzzle

the number one issue is to get world leaders  immediately to sit down together and, recognize that we need to urgently get back  into the safe space of planetary boundaries.

Create an account using the sidebar on the right of the screen. Pin the Hypothesis extension in Chrome (1 and 2), then activate the sidebar by clicking the button in the location bar (3).

A task is used further down in the workflow. When I think about planning features, they occur before development takes off. The task is used by the developer as they are breaking down the issue into smaller components.

You can see how the constant jumping between these two tools in the first scenario is super annoying, and also very risky as none of the changes you make in Figma are also automatically being updated in the same GitLab designs.

As a positive example of where this works well: Our VS Code GitLab Workflow extension allows users to not only see comments that were written inside the GitLab UI, but also allows these users to respond to these comments right from the IDE, the tool where they actually have to make these changes.

Practice (Dash): Undone Action Item — Individual items (action items and ideas) are marked with a dash preceding them. All items, no matter what they are, are therefore treated as items to be processed. (Plus): Done Action Item — If the item is an action item (todo), when the item is complete, a vertical line is drawn through the “dash” thus making it resemble a “plus”. This makes the dashed items stand out quite well despite the fact that the same color pen or pencil may be used. (Right Arrow): Waiting – (i.e. for another action) — Drawing an arrow pointing to the the item denotes that it is something that is waiting on another action to happen or deliverable. (Left Arrow): Delegated — Drawing an arrow pointing to the left of the item denotes that it has been delegated (with a note to whom and the date) . (Triangle): Data Point — Turning the dash into a triangle denotes a data point (a fact or figure you wish to remember for instance). (Circle) — A circle around any of the above means that it has been carried forward, moved to another list or otherwise changed status — i.e. a “Waiting” item has now become an Action Item elsewhere (with a note about where that item has gone). The beauty of this system is that it is all built upon, and extensions of, the original dash. Therefore, it is easy to change items from one state to another (an undone action item to a done one, an undone action item to waiting or delegated) and in the case of an non-dashed item changing completely the item is circled to denote that.

Use David Allen’s GTD method and put your MIT (Most Important Task) at the top, and don’t attempt anything below it until that one task is done.

Throughout the day, mark each task as completed, in-progress, or delegated. Feel free to create your own symbols.

如果你已经在文件列表中创建了文件夹的话，你可以右键点击文件夹，选择 新建笔记 选项来创建笔记。此时，新建笔记会存放在该文件夹下。当然，你也可以使用文件列表顶部的按钮来创建笔记或者文件夹。当你停止输入两秒，或关闭面板后，Obsidian 都会自动为你进行保存。你也可以通过快捷键 Ctrl/Cmd+S 来手动保存。相关条目 重命名笔记 Links to this page索引由此开始

So she was considering in her own mind (as well as she could, for the hot day made her feel very sleepy and stupid), whether the pleasure of making a daisy-chain would be worth the trouble of getting up and picking the daisies, when suddenly a White Rabbit with pink eyes ran close by her.

Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, “and what is the use of a book,” thought Alice “without pictures or conversations?”

e argue that mutual learningwould benefit sentiment classification since it enriches theinformation required for the training of the sentiment clas-sifier (e.g., when the word “incredible” is used to describe“acting” or “movie”, the polarity should be positive)

. However, such a framework is not applicablehere since the learned latent topic representations in topicmodels can not be shared directly with word or sentencerepresentations learned in classifiers, due to their differentinherent meanings

Obsidian Kanban for Someday Maybe

It was Ranke, too, who set the historian’s task: to find out wie eseigentlich gewesen ist, what really happened, or, literally, how itreally was. His goal is one that will remain forever just beyond ourgrasp for reasons I explained in a “Note on Sources” in The Guns ofAugust (a paragraph that no one ever reads but I think is the bestthing in the book). Summarized, the reasons are that we who writeabout the past were not there. We can never be certain that we haverecaptured it as it really was. But the least we can do is to stay withinthe evidence.

We further expand on this and try to identify communities amongst a plethora of tweets using clustering techniques.

Abstract

In her book Meaning in Life and Why it Matters (2010), the US philosopher Susan Wolf has argued, against Taylor, that even if Sisyphus is feeling fulfilled, owing to the nature of the activity in which he is engaged, his feeling fulfilled is not reasonable. She argues that the object of an agent’s feeling of fulfilment is represented in the mind of the agent as being something that is objectively good. In the case of Sisyphus, even if he is fulfilled, he misrepresents the source of his fulfilment as good. So Wolf argues that the meaningfulness of an activity or goal is a function not only of one’s being fulfilled in engaging in it/pursuing it, but the feeling of fulfilment also needs to be a fitting response to the activity or goal. And it is only a fitting response if the activity or goal is objectively pursuit-worthy. What would render an activity or goal objectively pursuit-worthy? Objective pursuit-worthiness is in part owing to the value of the end or activity coming from outside of oneself. Wolf admits that determining objective pursuit-worthiness is not easy, and involves a process of discovery. A possibility not explicitly endorsed by Wolf (but not rejected by her, either) is that an activity or goal is objectively pursuit-worthy if engaging in that activity is conducive to the cultivation of virtues. My suggestion is that, so long as an activity is generally conducive to developing character traits and intellectual traits that will enhance and not impede the flourishing of both an agent and those with whom they interact, it is objectively pursuit-worthy.

The Value of Philosophy

I’ve been promising for many years and it turned out I couldn’t have fully designed it without the tools we do have now.

Source maps are on my radar, but i'm playing catchup. See: #124 (comment) for a rundown of where we are right now. Source maps are a pretty big project, it's not li

The purpose of this toolkit is to familiarize the user with intellectual and developmental disabilities (I/DD) and communication techniques to assist in successfully facilitating a healthy living program for individuals with I/DD. These are suggested techniques particularly helpful for those who don’t have any experience working with individuals who have I/DD.

One of the primary tasks of engineers is to minimize complexity. JSX changes such a fundamental part (syntax and semantics of the language) that the complexity bubbles up to everything it touches. Pretty much every pipeline tool I've had to work with has become far more complex than necessary because of JSX. It affects AST parsers, it affects linters, it affects code coverage, it affects build systems. That tons and tons of additional code that I now need to wade through and mentally parse and ignore whenever I need to debug or want to contribute to a library that adds JSX support.

Henrich defends this sweeping thesis with several studies, including a test known as the Triad Task. Subjects are shown three images—say, a rabbit, a carrot, and a cat. The goal is to match a “target object”—the rabbit—with a second object. A person who matches the rabbit with the cat classifies: The rabbit and the cat are animals. A person who matches the rabbit with the carrot looks for relationships between the objects: The rabbit eats the carrot.

If something is important enough to write down and keep track of, it’s important enough to schedule.

One of the main goals is to avoid some giant, never-ending task backlog, either within the current week’s “To do later” section, constantly being carried over week after week and growing in size, or in a separate backlog.md, or similar, file, also just growing in size and causing most tasks to be completely burried & lost. Either a task should be scheduled as “I plan to work on this” or it should be completely discarded (it can always come back later if it turns out to be important).

Things that arose throughout the week that captured my interest or demanded my attention and which I felt the need or capacity to do now.

Tasks which I am giving myself permission to avoid doing at all this week. The checkboxes here are for indicating that these tasks have been scheduled elsewhere.

Tasks that I may have been forwarding along or which I may have initially added to the “To do later” list which I no longer plan to do – if they happen, great, but I’m not actively planning to complete them.

scripts are plenty powerful and often easier to live with.

Recently I was thrilled to learn that the web platform offers such an affordance in the form of the <details> tag.

Montaigne called “On Vanity” one of those essays which, being quite long and not at all confined by the titles he gave them, “require a decision to read them and time set aside.”

What would it take, I wonder, to post-process the kinds of PDF files that governments typically produce, in order to add Purple Numbers?

The goal here is explicitly not to improve the state of the art in the narrow domain of restaurantbooking, but to take a narrow domain where traditional handcrafted dialog systems are known toperform well, and use that to gauge the strengths and weaknesses of current end-to-end systemswith no domain knowledge

Unsurprisingly, perfectly coded rule-based systems can solve the simulated tasks T1-T5 perfectly,whereas our machine learning methods cannot. However, it is not easy to build an effective rule-based

SUPERVISEDEMBEDDINGMODELS

We implemented a rule-based system for this task in the followingway. We initialized a dialog state using the 3 relevant slots for this task: cuisine type, location andprice range. Then we analyzed the training data and wrote a series of rules that fire for triggers likeword matches, positions in the dialog, entity detections or dialog state, to output particular responses,API calls and/or update a dialog state. Responses are created by combining patterns extracted fromthe training set with entities detected in the previous turns or stored in the dialog state. Overall webuilt 28 rules and extracted 21 patterns. We optimized the choice of rules and their application priority(when needed) using the validation set, reaching a validation per-response accuracy of 40.7%. Wedid not build a rule-based system forConciergedata as it is even less constrained.

Finally, the policy network output is generated bya three-way matrix transformation,

a distributed representationgenerated by an intent network and a probabilitydistribution over slot-value pairs called the beliefstate

In learning such neural network based dialogmodel, we propose hybrid offline training and on-line interactive learning methods. We first let theagent to learn from human-human conversationswith offline supervised training. We then improvethe agent further by letting it to interact with usersand learn from user demonstrations and feedbackwith imitation and reinforcement learning.

We design neural net-work based dialog system that is able to ro-bustly track dialog state, interface with knowl-edge bases, and incorporate structured queryresults into system responses to successfullycomplete task-oriented dialog.

A potential draw-back with such pre-training approach is that themodel may suffer from the mismatch of dialoguestate distributions between supervised training andinteractive learning stages. While interacting withusers, the agent’s response at each turn has a di-rect influence on the distribution of dialogue statethat the agent will operate on in the upcoming di-alogue turns.

These system components areusually trained independently, and their optimiza-tion targets may not fully align with the overallsystem evaluation criteria (e.g. task success rateand user satisfaction). Moreover, errors made inthe upper stream modules of the pipeline propa-gate to downstream components and get amplified,making it hard to track the source of errors

The attentionmechanism later introduced in [12] enables the encoder-decodermodel to learn a soft alignment and to decode at the same time.

We present a general solution towards building task-orienteddialogue systems for online shopping, aiming to assist on-line customers in completing various purchase-related tasks,such as searching products and answering questions, in a nat-ural language conversation manner. As a pioneering work, weshow what & how existing natural language processing tech-niques, data resources, and crowdsourcing can be leveragedto build such task-oriented dialogue systems for E-commerceusage. To demonstrate its effectiveness, we integrate our sys-tem into a mobile online shopping application. To the bestof our knowledge, this is the first time that an dialogue sys-tem in Chinese is practically used in online shopping scenariowith millions of real consumers. Interesting and insightful ob-servations are shown in the experimental part, based on theanalysis of human-bot conversation log. Several current chal-lenges are also pointed out as our future directions

System Formalization

Retrieval-based MethodsRetrieval-based methods choose a response from candidateresponses. The key to retrieval-based methods is message-response matching. Matching algorithms have to overcomesemantic gaps between messages and responses [28].

TASK-ORIENTED DIALOGUESYSTEMSTask-oriented dialogue systems have been an important branchof spoken dialogue systems. In this section, we will reviewpipeline and end-to-end methods for task-oriented dialoguesystems.

2.2 End-to-End Methods

TASK-ORIENTED DIALOGUESYSTEMS

2.1.3 Policy learning

A statistical dialog system

Dialogue State Tracking

Slot filling

Language Understanding

The first step is lexical analysis, i.e. word segmentation and part-of-speech (POS)tagging. The words and POS labels are used as features in the subsequent models. Forthe shared task we used HanLP [1] as our Chinese lexical analyzer.

Each rule is of the form “if thequery q is listed in a particular lexicon L, and the preceding queries and their predicteddomain labels satisfy certain conditions, then q is assigned a certain intent label and,with the exception of short commands, the entire q is regarded as a slot of the typecorresponding to L.” The rules are arranged in sequential order in accordance with theirpriorities

Figure 1 shows the framework of our SLU system, which consists of the context-dependent rules for entity-only queries and the context-independent model for querieswith IISPs. The entire system feeds the query to the rules first. If the rule-based compo‐nent returns null result, that means the query is judged to contain IISPs and the model-based component will continue to process it. Otherwise, it means the query is regardedas entity-only and the result of the rules is returned as the final output

s in real use cases of dialog systems, the queries in the shared task can be roughlydivided into two kinds, viz. queries with intent-indicating salient phrases and querieswithout. By intent-indicating salient phrase (IISP) it is meant a phrase in the query thatshows the intent of the query. E.g. the phrase “” in the query “” andthe phrases “” in the query “” are IISPs.

Spoken language understanding (SLU) comprises two tasks, intent identification andslot filling. That is, given the current query along with the previous queries in the samesession, an SLU system predicts the intent of the current query and also all slots (entitiesor labels) associated with the predicted intent. The significance of SLU lies in that eachtype of intent corresponds to a particular service API and the slots correspond to theparameters required by this API. SLU helps the dialog system to decide how to satisfythe user’s need by calling the right service with the right information

对话管理也可以看成是一个分类任务，即每个对话状态和一个合适的对话动作相对应．和其它有监督的学习任务一样，分类器可以从标注的语料库中训练得到．但是，在某状态下系统应该选择的动作不能仅仅是模仿在训练数据中同一状态对应的动作，而应该是选择合适的动作能够导致一个成功的对话．因此，把对话过程看成是一个决策过程更为合适，从而根据对话的整体成功来优化动作的选择过程［３２］．因而这是一个规划问题，并且可以用强化学习［３３］方法学习获得最优的结果

对话系统从本体构成和业务逻辑角度，可分为领域任务型和开放型的信息交互．领域任务型系统针对具体应用领域，具有比较清晰的业务语义单元的定义、本体结构以及用户目标范畴，例如航班查询、视频搜索、设备控制等等，这类交互往往是以完成特定的操作任务作为交互目标；而开放型信息交互则不针对特定领域，或说面向非常广泛的领域，交互目标并非业务任务，而是满足用户其它方面的需求，例如开放的百科问答、聊天等．它虽然能一定程度上显示人工智能的力量，但因其并不专注于帮助人解决现实任务问题，其实际使用范围较为狭窄．近年来，随着移动终端的高速发展，面向任务的自然人机对话系统和相关的认知控制理论得到了越来越多的学术和产业界重视，这也是本文讨论的重点

We com-plement recent work by showing the effec-tiveness of simple sequence-to-sequenceneural architectures with a copy mecha-nism. Our model outperforms more com-plex memory-augmented models by 7% inper-response generation and is on par withthe current state-of-the-art on DSTC2, areal-world task-oriented dialogue dataset

Both NLU and NLG are implementedwith template-based models

Symptom ExtractionWe follow the BIO(begin-in-out) schema for symptom identification(Figure 1). Each Chinese character is assigned alabel of ”B”, ”I” or ”O”. Also, each extractedsymptom expression is tagged withTrueorFalseindicating whether the patient suffers from thissymptom or not. In order to improve the anno-tation agreement between annotators, we createtwo guidelines for the self-report and the conver-sational data respectively. Each record is anno-tated by at least two annotators. Any inconsis-tency would be further judged by the third one.The Cohen’s kappa coefficient between two anno-tators are71%and67%for self-reports and con-versations respectively

In this paper, we make a move to builda dialogue system for automatic diagno-sis. We first build a dataset collected froman online medical forum by extractingsymptoms from both patients’ self-reportsand conversational data between patientsand doctors. Then we propose a task-oriented dialogue system framework tomake the diagnosis for patients automat-ically, which can converse with patients tocollect additional symptoms beyond theirself-reports. Experimental results on ourdataset show that additional symptoms ex-tracted from conversation can greatly im-prove the accuracy for disease identifica-tion and our dialogue system is able tocollect these symptoms automatically andmake a better diagnosis

Open-Domain Neural Dialogue Systems

the dataset used in our experiment hasonly the tags of filled information slots extracted by patternmatching between dialogue log and final order information

the agent model swaps the in-put and output sequences, and it also takes the tag of filledinformation slots as an input which is extracted from dia-logue in previous turns by pattern matching with the orderinformation in ground truth

By directly learningfrom the raw dialogue logs, the network takes the agent ut-Figure 2: The network structure: encoder-decoder structurewith the attention mechanismteranceXa:xa1;xa2;:::;xanas the input sequence and takescorresponding user utteranceYu:yu1;yu2;:::;yumas the tar-get sequence.

In the task-oriented dialogues, a user usually firstly showsthe intention to the agent and then answers the agent’s ques-tions one by one to specify the demand.

we propose a uSer andAgent Model IntegrAtion (SAMIA) framework inspired byan observation that the roles of the user and agent models areasymmetric. Firstly, this SAMIA framework model the usermodel as a Seq2Seq learning problem instead of ranking ordesigning rules. Then the built user model is used as a lever-age to train the agent model by deep reinforcement learning.In the test phase, the output of the agent model is filtered bythe user model to enhance the stability and robustness. Ex-periments on a real-world coffee ordering dataset verify theeffectiveness of the proposed SAMIA framework.

Simply put, your frog is the most important task. It is the thing that moves the needle the most towards your ideal future. It is probably very important, but not very urgent. So our advice (and Brian Tracy’s as well) is to make sure you tackle this MIT at the beginning of your day when you have the most energy and willpower available. Tackling your MIT first thing is extremely important!

If I were to summarize the book in a single statement, it would be that you should do your most important task (MIT) first thing in the morning.

Instead of continuing the millennia old search for the universal ontology, different types of ontologies have been proposed in computer science. [.] [C]lassification of ontologies [is] based on their granularity and [their] thematic scope [differs] into top-level, domain, task, and application ontologies ... (Hitzler, Janowicz, Berg-Cross, Obrst, Sheth, Finin, Cruz 2012)

Or, as a soldier of a desert war wrote in last autumn’s New York Times, is our central task the task of learning how to die—not (as he put it) to die ‘as individuals, but as a civilization’ (Scranton, 2013), in the Anthropocene?

was extremely saddened to read of his children’s recent exposure to measles due the Disneyland outbreak. To read about my journey leaving the anti-vaccination movement, click here.

Learningto complete a whole task involves four levels ofinstruction: (a) the problem, (b) the tasks re-quired to solve the problem, (c) the operationsthat comprise the tasks, and (d) the actions thatcomprise the operations. Effective instructionshould engage students in all four levels of per-formance: the problem level, the task-level, theoperation-level, and the action-level.

Executive Summary

Activities such as time spent on task and discussion board interactions are at the forefront of research.

The team used a whiteboard with task cards, stand-up meetings, weekly iterations, and prioritization. They even estimated their work, measured flow, and held retrospectives.