define what task is

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Meaning of task – Learner’s Dictionary

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• I had the sad task of sorting through her papers after she died .
• Max has undertaken the task of restoring an old houseboat .
• Robson's first task will be to inspire his team with some confidence .
• We ranked the tasks in order of importance .
• UN troops were assigned the task of rebuilding the hospital .

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A stuffed animal or bird is filled with special material so that it keeps the shape it had when it was alive.

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Example sentences perform a task

Definition of 'task' task.

Definition of 'perform' perform

Cobuild collocations perform a task, browse alphabetically perform a task.

• perform a somersault
• perform a song
• perform a stunt
• perform a task
• perform a trick
• perform a wedding
• perform acrobatics
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What does task mean?

Definitions for task tæsk, tɑsk task, this dictionary definitions page includes all the possible meanings, example usage and translations of the word task ., princeton's wordnet rate this definition: 5.0 / 2 votes.

undertaking, project, task, labor noun

any piece of work that is undertaken or attempted

"he prepared for great undertakings"

job, task, chore verb

a specific piece of work required to be done as a duty or for a specific fee

"estimates of the city's loss on that job ranged as high as a million dollars"; "the job of repairing the engine took several hours"; "the endless task of classifying the samples"; "the farmer's morning chores"

assign a task to

"I tasked him with looking after the children"

tax, task verb

use to the limit

"you are taxing my patience"

Wiktionary Rate this definition: 3.5 / 2 votes

A piece of work done as part of one's duties.

A difficult or tedious undertaking.

An objective.

A process or execution of a program.

To assign a task to, or impose a task on.

Etymology: From tasque, variant of tasche, from tasca, alteration of taxa, from taxare.

Samuel Johnson's Dictionary Rate this definition: 0.0 / 0 votes

Etymology: tasche, French; tassa, Italian.

Relieves me from my task of servile toil Daily in the common prison else enjoin’d me. John Milton.

His mental powers were equal to greater tasks. Francis Atterbury.

No happier task these faded eyes pursue, To read and weep is all they now can do. Alexander Pope.

A holy man took a soldier to task upon the subject of his profession. Roger L'Estrange.

He discovered some remains of his nature when he met with a foot-ball, for which Sir Roger took him to task. Addis.

To Task verb

To burthen with something to be done.

Etymology: tascu, Welsh, or from the noun.

He depos’d the king, Soon after that depriv’d him of his life, And, in the neck of that, task’d the whole state. William Shakespeare.

Forth he goes, Like to a harvestman, that’s task’d to mow, Or all, or lose his hire. William Shakespeare , Coriolanus.

Some things of weight, That task our thoughts, concerning us and France. William Shakespeare.

I have drunk but one cup to-night, and that was craftily qualified too; and behold what innovation it makes here. I am unfortunate in the infirmity, and dare not task my weakness with any more. William Shakespeare , Othello.

Divert thy thoughts at home, There task thy maids, and exercise the loom. Dryden.

Wikipedia Rate this definition: 0.0 / 0 votes

The two-pore-domain or tandem pore domain potassium channels are a family of 15 members that form what is known as leak channels which possess Goldman-Hodgkin-Katz (open) rectification. These channels are regulated by several mechanisms including signaling lipids, oxygen tension, pH, mechanical stretch, and G-proteins . Their name is derived from the fact that the α subunits consist of four transmembrane segments, and each pair of transmembrane segments contains a pore loop between the two transmembrane segments. Thus, each subunit has two pore loops. As such, they structurally correspond to two inward-rectifier α subunits and thus form dimers in the membrane (whereas inward-rectifier α subunits form tetramers). Each single channel does not have two pores; the name of the channel comes from the fact that each subunit has two P (pore) domains in its primary sequence. To quote Rang and Dale (2015), "The nomenclature is misleading, especially when they are incorrectly referred to as two-pore channels".Below is a list of the 15 known two-pore-domain human potassium channels:

ChatGPT Rate this definition: 0.0 / 0 votes

A task is a specific piece of work or activity that needs to be completed, typically within a certain timeframe, in order to achieve a goal or objective. It often involves the allocation of resources, the execution of specific actions or steps, and may require the involvement of individuals or teams. Tasks can range from simple or routine actions to complex and multi-step processes, depending on the nature and complexity of the goal to be accomplished.

Webster Dictionary Rate this definition: 5.0 / 1 vote

labor or study imposed by another, often in a definite quantity or amount

business; employment; undertaking; labor

to impose a task upon; to assign a definite amount of business, labor, or duty to

to oppress with severe or excessive burdens; to tax

to charge; to tax; as with a fault

Freebase Rate this definition: 2.3 / 6 votes

In project management, a task is an activity that needs to be accomplished within a defined period of time or by a deadline. A task can be broken down into assignments which should also have a defined start and end date or a deadline for completion. One or more assignments on a task puts the task under execution. Completion of all assignments on a specific task normally renders the task completed. Tasks can be linked together to create dependencies. In most projects, tasks may suffer one of two major drawbacks: ⁕Task dependency: Which is normal as most tasks rely on others to get done. However, this can lead to the stagnation of a project when many tasks cannot get started unless others are finished. ⁕Unclear understanding of the term complete: For example, if a task is 90% complete, does this mean that it will take only 1/9 of the time already spent on this task to finish it? Although this is mathematically sound, it is rarely the case when it comes to practice.

Chambers 20th Century Dictionary Rate this definition: 0.0 / 0 votes

task, n. a set amount of work, esp. of study, given by another: work: drudgery.— v.t. to impose a task on: to burden with severe work.— ns. Task′er , one who imposes a task, or who performs it; Task′ing , task-work; Task′master , a master who imposes a task: an overseer:— fem. Task′mistress ; Task′work , work done as a task, or by the job.— Take to task , to reprove. [O. Fr. tasque (Fr. tâche )—Low L. tasca , taxa —L. taxāre , to rate.]

Editors Contribution Rate this definition: 4.0 / 1 vote

A simple defined piece of work.

The project task list was so easy and simple for all to follow.

Submitted by MaryC on January 23, 2020  

Suggested Resources Rate this definition: 0.0 / 0 votes

What does TASK stand for? -- Explore the various meanings for the TASK acronym on the Abbreviations.com website.

Matched Categories

British national corpus.

Spoken Corpus Frequency

Rank popularity for the word 'task' in Spoken Corpus Frequency: #1070

Written Corpus Frequency

Rank popularity for the word 'task' in Written Corpus Frequency: #2347

Nouns Frequency

Rank popularity for the word 'task' in Nouns Frequency: #333

Anagrams for task »

How to pronounce task.

Alex US English David US English Mark US English Daniel British Libby British Mia British Karen Australian Hayley Australian Natasha Australian Veena Indian Priya Indian Neerja Indian Zira US English Oliver British Wendy British Fred US English Tessa South African

How to say task in sign language?

Chaldean Numerology

The numerical value of task in Chaldean Numerology is: 1

Pythagorean Numerology

The numerical value of task in Pythagorean Numerology is: 6

Examples of task in a Sentence

German Chancellor Angela Merkel :

The German government says the Dublin approach is not working anymore, because so many refugees are arriving at our external borders, that we can't leave Italy or Greece alone to deal with this task , at the same time, if we say that Italy and Greece can't be left alone with this task , then neither can it be that three countries, like Sweden, Austria and Germany, are left alone with the lion's share of the task .

Andriy Filonenko :

It's a war. They're not handing out sweets, think of it this way: There's a task , for the task you need a vehicle to get there and back – but they don't give you any vehicle or petrol to fulfill the task ... You have to pick up wounded ... so what do you do? ... Of course, you stop a car and take it.

Salim Virani :

The risk enhancers may increase a patient's short-term, 10-year risk, or in some cases, their lifetime risk of having a cardiovascular event, then those guidelines recommend that clinicians should err on the side of early treatment, something not considered in The Task Force The Task Force, but I do want to emphasize, even if clinicians were to take the more conservative The Task Force recommendations and follow them very, very aggressively, we definitely will see a population-level impact because of the statin therapy. The Task Force's really been studied for a very, very long time and now we know The Task Force works.

Theodore Roosevelt :

We cannot afford merely to sit down and deplore the evils of city life as inevitable, when cities are constantly growing, both absolutely and relatively. We must set ourselves vigorously about the task of improving them; and this task is now well begun.

People who are too concerned with how well they are doing will be less successful and feel less competent than those who focus on the task itself... Some psychologists call it a conflict between ego-orientation, or between extrinsic and intrinsic motivation... but in all cases, what counts is whether attention is turned away from the task at hand and focused on the self and its future rewards, or whether it is instead trained on the task itself. The latter attitude seems the more fruitful.

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Nearby & related entries:

• tasis schools
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How do you define a task?

Collaboration has progressed from a buzzword to an article of faith in today’s organisations. Books talk about how to do it better and tools claim to make it ever easier to share ideas and plan projects. But what task are you collaborating on?

Something so simple, and yet something so often neglected—how to properly define a task. If you want to markedly improve productivity without anything other than the content of your own mind, it will pay to pay attention to the proper definition of a task.

[ Listen to audio version , read by David Hodes ]

But first, let’s get our language standardised. We have no issue when applying our minds to the hard sciences about the need for precision in language. A volt, an ohm and an ampere have very precise meanings and can be measured in a consistent way at any point in time and in any given place. Whether you have travelled to the moon or are in Outer Mongolia, at the bottom of the Pacific Ocean or the top of Mount Everest, the meaning and measurement of these physical properties of electricity are always the same. So, what about the terms we use when we go to work? What is work? How is it different from a task? How do you measure it? Is it similar in any way to the definition used by physicists: the product of the force applied and the distance over which it has been applied?

The late Elliott Jaques spent a lifetime using his professional skills as a psychoanalyst to give us an entire framework for understanding work in a way which allows us to derive tremendous value. It forms a part of his major contribution to the field of organisational design, Stratified Systems Theory , which he codified in his book Requisite Organisation . In that book, he gave us the following definitions:

Task: An assignment to produce specified output (including quantity and quality) within a targeted completion time, with allocated resources and within specified limits (policies, procedures etc)

Role: The position occupied in the organisation

Work: What the person has to do in order to achieve the task: use judgement and make decisions in overcoming obstacles

The focus of this article will be on how we go about defining a task, consistent with Jaques’s framework. Having the discipline to correctly and fully define a task is a prerequisite of achieving the ideal of what I call Just Work —that everyone has the right to be well managed. Amongst other things, being well managed means that people have a right to understand the context of any given task—how it fits into the bigger picture and what difference it could make to the longer horizon.

It is critical for them to know why you are calling for the task to be done. What is the purpose of the work in every circumstance a task is assigned, whether to shovel a piece of dirt or develop a new mine? What outcomes are you looking for when you issue the instruction, in terms of both the quality to which it is to be produced and the quantity required? Is it a single prototype to be used in a proof of concept or is it a final design for mass production of the first million units? What resources will be required to complete the task, be they human, material, financial or information? And by when would you like the work to be completed?

The answer to these questions represents the minimum necessary set of instructions required for anyone to reliably and successfully complete tasks assigned to them. They can be summarised in the acronym CPORT or:

C – Context P – Purpose O – Outcome in terms of quality and quantity R – Resources T – Timing

In my experience, people are quite poor at providing clarity on each of these dimensions. In addition, whilst each is a necessary condition for successfully assigning a task, individually they are insufficient to ensure consistent outcomes. Only once you have considered and communicated each dimension can you claim to have fulfilled your obligation to the idea of everyone having the right to be well managed.

Levels of work

Jaques discovered that complexity is correlated with the time horizon of the task being considered. These horizons can be arranged into time spans that reflect the cognitive ability required to effectively complete the given task. With each horizon, there is a distinct mode of thinking that is as different one from the other as ice is from water and water is from steam. A feature of the CPORT is that it can be used to define a task at any of Jaques’s levels of work.

The table below shows the horizon of work, the level (what Jaques calls a stratum), the mental process involved and the related position in the hierarchy of a commercial or military organisation:

You will notice in the table that the mental processes repeat themselves from stratum V to stratum VIII. It is analogous to a note played on the piano, followed by one played an octave higher. They both share fundamental characteristics but are nevertheless different in their nature. This is because the object of the mental processing has moved from the task of the individual as a unit within the stratum to the single business unit within the larger enterprise.

From stratum I-IV, the mental processing is largely symbolic and verbal and addresses the specific case in hand, such as the business or military unit. From strata V to VIII it is conceptual and abstract, meaning that the thinking generally involves the class of problem being solved and how the specific instance can be understood as but an example of the general case.

What might a CPORT look like?

Here’s one for someone at stratum one:

THE TASK Inspect and repair pump #1234

Context: As we operate a plant which requires fluids containing corrosive mineral elements to be pumped around the purification circuit, we need to regularly maintain those pumps to avoid unplanned breakdowns. Our reliability engineers have determined the optimum interval for inspections of all pumps and developed a task list to repair or replace them if they are at the end of their planned life or if any faults are found.

Purpose: The purpose of the task is to ensure the safe and reliable operation of the pump.

Outcomes: The following outcomes are required for this task.

• Review all safety and work-related documentation attached to the notification for the service of the pump
• Confirm replacement parts have been picked by stores in accordance with the bill of materials
• Confirm with operations when the pump will be offline and available to carry out the work
• Isolate the pump in accordance with the standard operating procedures
• Inspect the pump in accordance with the maintenance task list for pump type ABC
• Replace mandatory and worn parts
• Reassemble, reconnect and test pump in accordance with the blueprint
• Remove locks on pump and notify operations that pump is available
• Return unrequired parts to stores
• Complete confirmation of work in the maintenance system

Resources: The following resource will be required to complete this work.

• A mechanical fitter and trade assistant for 4 hours
• A list of parts in accordance with the bill of materials for the routine work
• All tools and equipment documented in the bill of materials
• All manuals and documentation associated with the work order

Timing: The inspection and repair of the pump must be completed no later than 25th July, 2019.

By way of contrast and as an example, what might a stratum four CPORT look like?

THE TASK Implement Theory of Constraints scheduling methods of Critical Chain Project Management (CCPM) and Drum Buffer Rope (DBR) across all engineering projects at EngCo.

Context: Our engineering division, has a record amount of capital spend over the next five years for sustaining, improving and expanding the business. As an organisation, we are not very good at running our capital projects to deliver the full scope, on time and on or below budget. As the biggest asset in the business our division will lead the adoption of TOC, given its proven ability to deliver superior outcomes to any comparable method. For many years we have struggled to effectively use the pool of engineering resources we have in a way that does justice to the demand generated by both technical queries and capital projects.

Purpose: The purpose of this task is to secure the long-term benefits of using TOC as a competitive operating philosophy for our business.

Outcomes: The project will be run in accordance with the newly released corporate standard for projects and project management. The first phase, pre-feasibility, will require the following deliverables:

• A review of the marketplace for expertise in the application of TOC-based solutions in engineering environments
• Selection of a consulting partner for EngCo to assist with the process design, organisational change management and technology implementation of the system of engineering management
• Development of a business case which can be taken to the EngCo board to approve an overall budget, broken down into mandated stage gates, in accordance with our new project management standards
• A clear articulation of the business benefits to be gained from the successful adoption of TOC
• An analysis of the risks associated with a project of this nature and mitigations required to set up the project for success

Resources: The following resources will be required to complete phase one of the work.

• A project manager at superintendent level of the engineering function
• Access to all subject matter experts to fully understand the problem we are solving for
• Up to $100,000 investment in the use of consultants to assist with the deliverables outlined above

Timing: Whilst it is expected that EngCo will be able to achieve the quick wins achieved by most companies who adopt TOC, our intention is that this endeavour is for the long haul. We therefore expect the new TOC system of engineering management will be fully mature within the next three years. That is, the standard operating procedures will have been documented, and people will have been educated and trained to the level required by their roles. Online and class-based training will be available as well as a suite of quick reference guides (QRGs). We will have integrated our variety of vendors and business partners into our new ways of working and will be a learning centre of excellence for the broader Eng Holdings business.

In summary, at all levels of work, we have:

Context: How does this work fit in with the big picture?

Purpose: Why is this task or project necessary? What do you want to achieve with it?

Outcomes: You’re probably familiar with SMART objectives: Specific, Measurable, Ambitious, Realistic and Time-bound. (The A and R often stand for different things: achievable/agreed, say, or relevant/reasonable. The SMT are universal.)

Resources: What human, material, financial and information resources do you need to achieve the outcome you desire? How much estimated actual touch time, by resource?

Timing: If your outcome is SMART, timing will be included. But, how much calendar time will the work take. When will be required to finish it?

Like Jaques’s time horizon strata, the CPORT is also fractal; it works for everything from small tasks to large projects. So, the next time you’re briefing someone—even for a smallish task due, say, next week—ask yourself if you’ve been as clear about the context, purpose, outcomes and resources as you’ve been about the timing.

Making the CPORT standard procedure also encourages written requests. For small tasks, sent by email, you may decide you don’t need explicit CPORT headings. But do mentally check you’ve covered them. You’ll often discover you’ve been assuming too much on the part of your recipient. Much better to make your expectations clear than just chat it through and hope for the best. If you want a high-quality result, set yourself up for success at the outset.

____________________________

What’s next?

The change from standard thinking to Theory of Constraints (TOC) is both profound and exhilarating. To make it both fun and memorable, we use a business simulation we call The Right Stuff Workshop .

We’d love to run it with you. To learn more:

• download the brochure (no email required)

“Work as though you would live forever, 
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Measurably reliable and agile

Few performance standards deliver the competitive advantage you gain by keeping your promise to deliver on time, doing so faster than your competitors, and suffering no defects while you’re about it. (more…)

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Eli Goldratt famously said, ‘Tell me how you measure me, and I will tell you how I will behave. If you measure me in an illogical way… do not complain about illogical behaviour.’ If you measure and reward activity, then activity’s what you’ll get. (more…)

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Definition of task noun from the Oxford Advanced Learner's Dictionary

• to accomplish/perform/undertake/complete a task
• a difficult/a daunting/an impossible task
• Getting hold of this information was no easy task (= was difficult) .
• a thankless task (= an unpleasant one that nobody wants to do and nobody thanks you for doing)
• The first task for the new leader is to focus on the economy.
• The new role involves a variety of specific tasks.
• task of doing something Detectives are now faced with the task of identifying the body.
• task of something The government now has to take on the task of reconstruction of the country.
• We should stop chatting and get back to the task at hand .
• You need to concentrate on the task in hand .
• It was a challenge to adapt this novel for the screen, but the writer proved himself equal to the task .
• Our first task will be to set up a communications system.
• Your duties will include setting up a new computer system.
• They undertook a fact-finding mission in the region.
• I’ve got various jobs around the house to do.
• household chores
• the task/​mission/​job/​chore of (doing) something
• (a) daily/​day-to-day task/​duties/​job/​chore
• (a) routine task/​duties/​mission/​job/​chore
• (a/​an) easy/​difficult task/​mission/​job
• (a) household/​domestic task/​duties/​job/​chore
• to do a task/​a job/​the chores
• to finish a task/​a mission/​a job/​the chores
• to give somebody a task/​their duties/​a mission/​a job/​a chore
• It was my task to wake everyone up in the morning.
• Our first task is to set up a communications system.
• She felt daunted by the enormity of the task ahead.
• How do you tackle a task like that?
• I left her to get on with the task of correcting the errors.
• I was engaged in the delicate task of clipping the dog's claws.
• She failed to complete the task that she had been set.
• The primary task of the chair is to ensure the meeting runs smoothly.
• The team have no illusions about the size of the task confronting them.
• The unenviable task of telling my parents fell to my teacher.
• We need to think realistically about the task ahead.
• the simple task of making a sandwich
• How exactly do you intend to approach this task?
• You'll be required to do several routine tasks in the office.
• challenging
• take upon yourself
• involve something
• require something
• fall to somebody
• be no easy task
• the task ahead
• the task at hand

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• Getting Started
• Introduction
• The Teacher
• The Learner

Foreign Language Teaching Methods: Speaking

Lesson 3: designing communicative tasks.

• What is a Task?
• Design Principles
• Unsuccessful Tasks
• Analyzing Tasks
• Review and Reflect

What Is a Task?

Defining a "task."

Duration: 01:31

This lesson focuses on defining what a task is. And actually there's been a lot written on this in the literature on task-based language teaching. Some people say that a task is not really a language unit at all, that it's really a unit of activity. But how we're going to define a task in this lesson is: a human activity (that is, you are going to be doing something) that is goal-directed (that is, you're doing something to accomplish a particular goal). But the real crux here is it's going to require interaction -- interaction between two people -- partners -- or a small group.

So for example, say you want to hire a job candidate. You have an opening in your company and you need to hire somebody. That's a task. And, if you break it down, the first thing you are going to do you might review some resumes, you might post the job, you go over the resumes that you get from the applicants. You then have to match their qualifications to the actual job. And then as a group you're going to have to have that difficult decision-making process and come to some kind of agreement. So that's a pretty good example of what I mean here by task. There is an activity, you're hiring somebody, and the activity is goal-oriented and it requires interaction among a small group.

A task is (1) a classroom activity or exercise that has (a) an objective attainable only by the interaction among participants, (b) a mechanism for structuring and sequencing interaction, and (c) a focus on meaning exchange; (2) a language learning endeavor that requires learners to comprehend, manipulate, and/or produce the target language as they perform some set of workplans. (Lee 2000:32)

Lee, J. 2000. Tasks and Communicating in Language Classrooms . New York: McGraw-Hill.

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Synonyms of tasks

• as in duties
• as in purposes
• as in entrusts
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Thesaurus Definition of tasks

 (Entry 1 of 2)

Synonyms & Similar Words

• assignments
• responsibilities
• enterprises
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Thesaurus Definition of tasks  (Entry 2 of 2)

Thesaurus Entries Near tasks

taskmistresses

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“Tasks.” Merriam-Webster.com Thesaurus , Merriam-Webster, https://www.merriam-webster.com/thesaurus/tasks. Accessed 24 Nov. 2023.

More from Merriam-Webster on tasks

Nglish: Translation of tasks for Spanish Speakers

Britannica English: Translation of tasks for Arabic Speakers

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Google DeepMind wants to define what counts as artificial general intelligence

AGI is one of the most disputed concepts in tech. These researchers want to fix that.

• Will Douglas Heaven archive page

AGI, or artificial general intelligence , is one of the hottest topics in tech today. It’s also one of the most controversial. A big part of the problem is that few people agree on what the term even means. Now a team of Google DeepMind researchers has put out a paper that cuts through the cross talk with not just one new definition for AGI but a whole taxonomy of them .

In broad terms, AGI typically means artificial intelligence that matches (or outmatches) humans on a range of tasks. But specifics about what counts as human-like, what tasks, and how many all tend to get waved away: AGI is AI, but better.

To come up with the new definition, the Google DeepMind team started with prominent existing definitions of AGI and drew out what they believe to be their essential common features. 

The team also outlines five ascending levels of AGI: emerging (which in their view includes cutting-edge chatbots like ChatGPT and Bard ), competent, expert, virtuoso, and superhuman (performing a wide range of tasks better than all humans, including tasks humans cannot do at all, such as decoding other people’s thoughts, predicting future events, and talking to animals). They note that no level beyond emerging AGI has been achieved.

“This provides some much-needed clarity on the topic,” says Julian Togelius, an AI researcher at New York University, who was not involved in the work. “Too many people sling around the term AGI without having thought much about what they mean.”

The researchers posted their paper online last week with zero fanfare. In an exclusive conversation with two team members—Shane Legg, one of DeepMind’s co-founders, now billed as the company’s chief AGI scientist, and Meredith Ringel Morris, Google DeepMind’s principal scientist for human and AI interaction—I got the lowdown on why they came up with these definitions and what they wanted to achieve.

A sharper definition

“I see so many discussions where people seem to be using the term to mean different things, and that leads to all sorts of confusion,” says Legg, who came up with the term in the first place around 20 years ago. “Now that AGI is becoming such an important topic—you know, even the UK prime minister is talking about it—we need to sharpen up what we mean.”

It wasn’t always this way. Talk of AGI was once derided in serious conversation as vague at best and magical thinking at worst. But buoyed by the hype around generative models, buzz about AGI is now everywhere.

When Legg suggested the term to his former colleague and fellow researcher Ben Goertzel for the title of Goertzel’s 2007 book about future developments in AI , the hand-waviness was kind of the point. “I didn’t have an especially clear definition. I didn’t really feel it was necessary,” says Legg. “I was actually thinking of it more as a field of study, rather than an artifact.”

His aim at the time was to distinguish existing AI that could do one task very well, like IBM’s chess-playing program Deep Blue, from hypothetical AI that he and many others imagined would one day do many tasks very well. Human intelligence is not like Deep Blue, says Legg: “It is a very broad thing.”

But over the years, people started to think of AGI as a potential property that actual computer programs might have. Today it’s normal for top AI companies like Google DeepMind and OpenAI to make bold public statements about their mission to build such programs.

“If you start having those conversations, you need to be a lot more specific about what you mean,” says Legg.

For example, the DeepMind researchers state that an AGI must be both general-purpose and high-achieving, not just one or the other. “Separating breadth and depth in this way is very useful,” says Togelius. “It shows why the very accomplished AI systems we’ve seen in the past don’t qualify as AGI.”

They also state that an AGI must not only be able to do a range of tasks, it must also be able to learn how to do those tasks, assess its performance, and ask for assistance when needed. And they state that what an AGI can do matters more than how it does it.  

It’s not that the way an AGI works doesn’t matter, says Morris. The problem is that we don’t know enough yet about the way cutting-edge models, such as large language models, work under the hood to make this a focus of the definition.

“As we gain more insights into these underlying processes, it may be important to revisit our definition of AGI,” says Morris. “We need to focus on what we can measure today in a scientifically agreed-upon way.”

Measuring up

Measuring the performance of today’s models is already controversial , with researchers debating what it really means for a large language model to pass dozens of high school tests and more. Is it a sign of intelligence? Or a kind of rote learning?

Assessing the performance of future models that are even more capable will be more difficult still. The researchers suggest that if AGI is ever developed, its capabilities should be evaluated on an ongoing basis, rather than through a handful of one-off tests.

The team also points out that AGI does not imply autonomy. “There’s often an implicit assumption that people would want a system to operate completely autonomously,” says Morris. But that’s not always the case. In theory, it’s possible to build super-smart machines that are fully controlled by humans.

One question the researchers don’t address in their discussion of what AGI is, is why we should build it. Some computer scientists, such as Timnit Gebru , founder of the Distributed AI Research Institute, have argued that the whole endeavor is weird. In a talk in April on what she sees as the false (even dangerous) promise of utopia through AGI , Gebru noted that the hypothetical technology “sounds like an unscoped system with the apparent goal of trying to do everything for everyone under any environment.” 

Most engineering projects have well-scoped goals. The mission to build AGI does not. Even Google DeepMind’s definitions allow for AGI that is indefinitely broad and indefinitely smart. “Don’t attempt to build a god,” Gebru said.

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Computer Science > Machine Learning

Title: mechanistically analyzing the effects of fine-tuning on procedurally defined tasks.

Abstract: Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy. Despite its clear importance, there has been minimal work that explains how fine-tuning alters the underlying capabilities learned by a model during pretraining: does fine-tuning yield entirely novel capabilities or does it just modulate existing ones? We address this question empirically in synthetic, controlled settings where we can use mechanistic interpretability tools (e.g., network pruning and probing) to understand how the model's underlying capabilities are changing. We perform an extensive analysis of the effects of fine-tuning in these settings, and show that: (i) fine-tuning rarely alters the underlying model capabilities; (ii) a minimal transformation, which we call a 'wrapper', is typically learned on top of the underlying model capabilities, creating the illusion that they have been modified; and (iii) further fine-tuning on a task where such hidden capabilities are relevant leads to sample-efficient 'revival' of the capability, i.e., the model begins reusing these capability after only a few gradient steps. This indicates that practitioners can unintentionally remove a model's safety wrapper merely by fine-tuning it on a, e.g., superficially unrelated, downstream task. We additionally perform analysis on language models trained on the TinyStories dataset to support our claims in a more realistic setup.

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What Are Large Language Models Used For?

AI applications are summarizing articles, writing stories and engaging in long conversations — and large language models are doing the heavy lifting.

A large language model, or LLM , is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other forms of content based on knowledge gained from massive datasets.

Large language models are among the most successful applications of transformer models . They aren’t just for teaching AIs human languages, but for understanding proteins, writing software code, and much, much more.

In addition to accelerating natural language processing applications — like translation, chatbots and AI assistants — large language models are used in healthcare , software development and use cases in many other fields .

Language is used for more than human communication.

Code is the language of computers. Protein and molecular sequences are the language of biology. Large language models can be applied to such languages or scenarios in which communication of different types is needed.

These models broaden AI’s reach across industries and enterprises, and are expected to enable a new wave of research, creativity and productivity, as they can help to generate complex solutions for the world’s toughest problems.

For example, an AI system using large language models can learn from a database of molecular and protein structures, then use that knowledge to provide viable chemical compounds that help scientists develop groundbreaking vaccines or treatments.

Large language models are also helping to create reimagined search engines, tutoring chatbots, composition tools for songs, poems, stories and marketing materials, and more.

How Do Large Language Models Work?

Large language models learn from huge volumes of data. As its name suggests, central to an LLM is the size of the dataset it’s trained on. But the definition of “large” is growing, along with AI.

Now, large language models are typically trained on datasets large enough to include nearly everything that has been written on the internet over a large span of time.

Such massive amounts of text are fed into the AI algorithm using unsupervised learning — when a model is given a dataset without explicit instructions on what to do with it. Through this method, a large language model learns words, as well as the relationships between and concepts behind them. It could, for example, learn to differentiate the two meanings of the word “bark” based on its context.

And just as a person who masters a language can guess what might come next in a sentence or paragraph — or even come up with new words or concepts themselves — a large language model can apply its knowledge to predict and generate content.

Large language models can also be customized for specific use cases, including through techniques like fine-tuning or prompt-tuning, which is the process of feeding the model small bits of data to focus on, to train it for a specific application.

Thanks to its computational efficiency in processing sequences in parallel, the transformer model architecture is the building block behind the largest and most powerful LLMs.

Top Applications for Large Language Models

Large language models are unlocking new possibilities in areas such as search engines, natural language processing, healthcare, robotics and code generation.

The popular ChatGPT AI chatbot is one application of a large language model. It can be used for a myriad of natural language processing tasks.

The nearly infinite applications for LLMs also include:

• Retailers and other service providers can use large language models to provide improved customer experiences through dynamic chatbots, AI assistants and more.
• Search engines can use large language models to provide more direct, human-like answers.
• Life science researchers can train large language models to understand proteins, molecules, DNA and RNA.
• Developers can write software and teach robots physical tasks with large language models.
• Marketers can train a large language model to organize customer feedback and requests into clusters, or segment products into categories based on product descriptions.
• Financial advisors can summarize earnings calls and create transcripts of important meetings using large language models. And credit-card companies can use LLMs for anomaly detection and fraud analysis to protect consumers.
• Legal teams can use large language models to help with legal paraphrasing and scribing.

Running these massive models in production efficiently is resource-intensive and requires expertise, among other challenges, so enterprises turn to NVIDIA Triton Inference Server , software that helps standardize model deployment and deliver fast and scalable AI in production.

When to Use Custom Large Language Models

Many organizations are looking to use custom LLMs tailored to their use case and brand voice. These custom models built on domain-specific data unlock opportunities for enterprises to improve internal operations and offer new customer experiences. Custom models are smaller, more efficient and faster than general-purpose LLMs.

Custom models offer the best solution for applications that involve a lot of proprietary data. One example of a custom LLM is BloombergGPT , homegrown by Bloomberg. It has 50 billion parameters and is targeted at financial applications. 

Where to Find Large Language Models

In June 2020, OpenAI released GPT-3 as a service, powered by a 175-billion-parameter model that can generate text and code with short written prompts.

In 2021, NVIDIA and Microsoft developed Megatron-Turing Natural Language Generation 530B , one of the world’s largest models for reading comprehension and natural language inference, which eases tasks like summarization and content generation.

And HuggingFace last year introduced BLOOM , an open large language model that’s able to generate text in 46 natural languages and over a dozen programming languages.

Another LLM, Codex , turns text to code for software engineers and other developers.

NVIDIA offers tools to ease the building and deployment of large language models:

• NVIDIA NeMo LLM Service provides a fast path to customizing large language models and deploying them at scale using NVIDIA’s managed cloud API, or through private and public clouds.
• NVIDIA NeMo framework , part of the NVIDIA AI platform, enables easy, efficient, cost-effective training and deployment of large language models. Designed for enterprise application development, NeMo provides an end-to-end workflow for automated distributed data processing; training large-scale, customized model types including GPT-3 and T5; and deploying these models for inference at scale.
• NVIDIA BioNeMo is a domain-specific managed service and framework for large language models in proteomics, small molecules, DNA and RNA. It’s built on NVIDIA NeMo for training and deploying large biomolecular transformer AI models at supercomputing scale.

Challenges of Large Language Models

Scaling and maintaining large language models can be difficult and expensive.

Building a foundational large language model often requires months of training time and millions of dollars.

And because LLMs require a significant amount of training data, developers and enterprises can find it a challenge to access large-enough datasets.

Due to the scale of large language models, deploying them requires technical expertise, including a strong understanding of deep learning, transformer models and distributed software and hardware.

Many leaders in tech are working to advance development and build resources that can expand access to large language models, allowing consumers and enterprises of all sizes to reap their benefits.

Learn more about large language models .

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The president once again pardons turkeys who did nothing wrong, but why?

Domenico Montanaro

Two turkeys, named Liberty and Bell, were "pardoned" Monday at the White House ahead of Thanksgiving. Shown here, they're at the Willard InterContinental Hotel in Washington on Monday. Jacquelyn Martin/AP hide caption

Two turkeys, named Liberty and Bell, were "pardoned" Monday at the White House ahead of Thanksgiving. Shown here, they're at the Willard InterContinental Hotel in Washington on Monday.

Joe Biden is one year older Monday. At 81, he's the oldest president in U.S. history.

But some things apparently never get old, notably the strange tradition of presidential turkey pardons, which is happening again Monday as well.

"This is the 76th anniversary of this event, and I want you to know, I wasn't there for the first one," Biden said with a chuckle right before he spared "Liberty" and "Bell," this year's turkeys from a flock in Minnesota.

The tradition of giving presidents turkeys to eat did, in fact, start in 1947, but of pardoning them, that's a more complicated tale .

Biden pardoned the Thanksgiving turkeys. Read the strange truth behind the tradition

This has come to be a tradition that ironically features an American president sanctioning an event sponsored by a lobbying group, which advocates the opposite of what actually takes place at said event.

The president makes a few jokes and lets a turkey go free in what only became a formalized occurrence at the White House in the 1980s. But the turkey lobby's actual goal, as most likely know, is to get people to eat more turkey.

A long, strange history

This dance between the turkey lobby and presidents started in the 1940s, but back then, it was — a more honest — gifting of a bird for the president and his family to eat at Thanksgiving.

But death is a hard sell.

Most Americans probably don't know or think about how their food gets to their tables. They really care, as surveys have found, about how it tastes and how cheap it is .

Politicians know this.

Realizing the awkwardness of the whole situation — of publicly accepting a live turkey that was destined for his dinner table — John F. Kennedy broke the tradition in 1963.

"I think we'll just let this one grow," Kennedy said of the gobbling fowl with a sign around its neck that read, "Good eating, Mr. President."

He and succeeding presidents would realize, it's better to be seen as a turkey liberator rather than the one to publicly send ol' Giblet to the executioner.

There was a close call, though, during the George W. Bush administration when Barney, the president's plucky Scottish Terrier, almost silenced the gobble of that year's bird .

It took Bush hustling out of a national security meeting to call him off. And remember, this is the same dog that bit a reporter .

Good thing Biden's German Shepherds were kept far away from the event or there might have been a real ... fowl up.

Charlie, Caroline Kennedy's pet Welsh terrier, inspects a turkey presented to President Kennedy after a traditional Thanksgiving week ceremony at the White House in Washington, Nov. 19, 1963. President Kennedy "pardoned" the bird, sending it back to the farm. Charlie had the run of the grounds during the ceremony. AP hide caption

Charlie, Caroline Kennedy's pet Welsh terrier, inspects a turkey presented to President Kennedy after a traditional Thanksgiving week ceremony at the White House in Washington, Nov. 19, 1963. President Kennedy "pardoned" the bird, sending it back to the farm. Charlie had the run of the grounds during the ceremony.

An expanding tradition ... for some reason

The birds get their royal treatment, their own hotel room and, of course, punny jokes. And despite the irony, pointed out by your author year after year , this event shows no signs of slowing down.

In fact, it only seems to be expanding.

Democratic Gov. Gretchen Whitmer brought the tradition to Michigan in 2022, pardoning " Mitch E. Gander " (get it!?). And it's slated to happen again this year.

President Biden speaks as he pardons the national Thanksgiving turkeys during a ceremony at the White House on Monday. Andrew Harnik/AP hide caption

President Biden speaks as he pardons the national Thanksgiving turkeys during a ceremony at the White House on Monday.

Alabama has apparently been doing this for decades . Oddly, the birds also, according to the Alabama Daily News, used to use the same names for the turkey every year for some reason — "Clyde" and "Henrietta."

That changed this year after an online poll of Alabamians picked Giblet and Puddin' .

"Today, by the powers vested in me as governor of the state of Alabama," Gov. Kay Ivey said at this year's event, "I hereby am granting a full pardon to Giblet and Puddin', so that they can spend their turkey day enjoying a meal of their own."

Let's just hope that meal isn't what everyone else eats for Thanksgiving.

It's become so embedded in the culture that a town in East Texas this year decided to join in on the strange tradition and let live a turkey by the name of ... Dolly Pardon.

"The turkey is named Dolly because she's a strong female role model," Lisa Mays-Gonzalez, Van Community Library director. "And it's a tribute to our Southern roots. And she is a very strong literacy advocate."

No word on whether the turkey can read.

Dolly is set to live out her days at the "Believe in Vegan" ranch.