- Last 7 days
-
www.youtube.com www.youtube.com
-
we are using set theory so a certain piece of reference text is part of my collection or it's not if it's part of my collection somewhere in my fingerprint is a corresponding dot for it yeah so there is a very clear direct link from the root data to the actual representation and the position that dot has versus all the other dots so the the topology of that space geometry if you want of that patterns that you get that contains the knowledge of the world which i'm using the language of yeah so that basically and that is super easy to compute for um for for a computer i don't even need a gpu
for - comparison - cortical io / semantic folding vs standard AI - no GPU required
-
for example our standard english language model is trained with something like maybe 100 gigabytes or so of text um that gives it a strength as if you would throw bird at it with the google corpus so the other thing is of course uh a small corpus like that is computed in two hours or three hours on a on a laptop yeah so that's the other thing uh by the way i didn't mention our fingerprints are actually a boolean so when we when we train as i said we are not using floating points
for - comparison - cortical io vs normal AI - training dataset size and time
-
-
feministai.pubpub.org feministai.pubpub.org
-
AI and Gender Equality on Twitter
there are movements that address gender equality issues, which oppose Thai society’s patriarchal culture and patriarchal bias. These include attacking sexual harassment, allowing same-sex marriage, drafting legislation for the protection of people working in the sex industry, and promoting the availability of free sanitary napkins for women.
-
-
feministai.pubpub.org feministai.pubpub.org
-
Artificial Intelligence (AI) in Robotics
Deep learning is about machine learning based on a set of algorithms that attempt to model high-level abstractions in data.
Robotisation is rapid growth as work more precisely and costs saving, for example, Creative studios have 3D printers and the self-learning ability of these production robots are more work efficiently.
Dematerialisation leads to the phenomenon that traditional physical products are becoming software, for example, CDs or DVDs was replaced by streaming services or the replacement of traditional event/travel tickets/ or hard cash to contactless payment by smartphone.
Gig economy A rise in self-employment is typical for the new generation of employees. The gig economy is usually understood to include chiefly two forms of work: ‘crowd working’ and ‘work on-demand via apps’ organized networking platforms. There are more and more independent contractors for individual tasks that companies advertise on online platforms (eg, ‘Amazon Mechanical Turk’).
Autonomous driving is vehicles with the power for self-governance using sensors and navigating without human input.”
-
-
feministai.pubpub.org feministai.pubpub.org
-
Manila has one of the most dangerous transport systems in the world for women (Thomson Reuters Foundation, 2014). Women in urban areas have been sexually assaulted and harassed while in public transit, be it on a bus, train, at the bus stop or station platform, or on their way to/from transit stops.
The New Urban Agenda and the United Nations’ Sustainable Development Goals (5, 11, 16) have included the promotion of safety and inclusiveness in transport systems to track sustainable progress. As part of this effort, AI-powered machine learning applications have been created.
-
-
feministai.pubpub.org feministai.pubpub.org
-
AI for Good3, SDG AI LAB4, IRCAI5 y Global Partnership for Artificial Intelligence6
“apoyar el desarrollo y uso de inteligencia artificial tomando como base los derechos humanos, la inclusión, la diversidad, la innovación y el crecimiento económico, buscando responder a los Objetivos de Desarrollo Sostenible de Naciones Unidas”. (Benjio & Chatila, 2020)
-
-
-
https://flux1.ai/<br /> Flux AI Image Generator
-
-
tome.com tome.com
Tags
Annotators
URL
-
-
www.prankify.lol www.prankify.lol
-
-
deepgram.com deepgram.com
Tags
Annotators
URL
-
- Aug 2024
-
www.youtube.com www.youtube.com
-
that's why the computer can never be conscious because basically he has none of the characteristics of qualia and he certainly doesn't have free will and Free Will and conscious must work together to create these fields that actually can can direct their own experience and create self-conscious entities from the very beginning
for - AI - consciousness - not possible - Frederico Faggin
-
-
-
“Analysts need to be able to dissect exactly how the AI reached a particular conclusion or recommendation,” says Chief Business Officer Eric Costantini. “Neo4j enables us to enforce robust information security by applying access controls at the subgraph level.”
Tags
Annotators
URL
-
-
-
for - AI - website simulator - websim.ai
self-link - https://websim.ai/
-
-
www.youtube.com www.youtube.com
-
Interesting thought. This guy relates the upcome of AI (non-fiction) writing to the lack of willingness people have to find out what is true and what is false.
Similar to Nas & Damian Marley's line in the Patience song -- "The average man can't prove of most of the things that he chooses to speak of. And still won't research and find the root of the truth that you seek of."
If you want to form an opinion about something, do this educated, not based on a single source--fact-check, do thorough research.
Charlie Munger's principle. "I never allow myself to have [express] an opinion about anything that I don't know the opponent side's argument better than they do."
It all boils down to a critical self-thinking society.
-
-
-
is it possible to teach machine values
for - question - AI - can we teach AI values?
question - AI - can we teach AI values? - it's likely not possible because we cannot assign metrics to things like - ethics - kindness - happiness
-
the future future for education and this is a mega Trend that will last in the next decades is that we use artificial intelligence to tailor um educational let's say or didactic Concepts to the specific person so let's say in in the future everybody will have his or her specific let's say training or education profile he or she will run through and artificial intelligence um will will tailor the different educational environments for everybody in the future this is this is a pre this is a pretty clear Trend
for - AI and education - children will have custom tailored education program via AI
-
this is the reason why I'm not afraid of artificial intelligence taking over
for - question - AI - can AI learn to be intentionally distracted?
-
human beings don't do that we understand that the chair is not a specifically shaped object but something you consider and once you understood that concept that principle you see chairs everywhere you can create completely new chairs
for - comparison - human vs artificial intelligence
question - comparison - human vs artificial intelligence - Can't an AI also consider things we sit on to then generalize their classifcation algorithm?
-
the brain is Islam Islam is it is lousy and it is selfish and still it is working yeah look around you working brains wherever you look and the reason for this is that we totally think differently than any kind of digital and computer system you know of and many Engineers from the AI field haven't figured out that massive difference that massive difference yet
for - comparison - brain vs machine intelligence
comparison - brain vs machine intelligence - the brain is inferior to machine in many ways - many times slower - much less accurate - network of neurons is mostly isolated in its own local environment, not connected to a global network like the internet - Yet, it is able to perform extraordinary things in spite of that - It is able to create meaning out of sensory inputs - Can we really say that a machine can do this?
-
you can Google data if you're good you can Google information but you cannot Google an idea you cannot Google Knowledge because having an idea acquiring knowledge this is what is happening on your mind when you change the way you think and I'm going to prove that in the next yeah 20 or so minutes that this will stay analog in our closed future because this is what makes us human beings so unique and so Superior to any kind of algorithm
for - key insight - claim - humans can generate new ideas by changing the way we think - AI cannot do this
Tags
- key insight - claim - humans can generate new ideas by changing the way we think - AI cannot do this
- comparison - human intelligence vs artificial intelligence
- question - comparison - human vs artificial intelligence - Can't an AI also consider things we sit on to then generalize their classifcation algorithm?
- comparison - brain vs machine intelligence - what brains and consciousness can do but AI cannot
- AI and education - children will have custom tailored education program via AI
- question - AI - can we teach AI values?
- question - AI - can AI learn to be intentionally distracted?
Annotators
URL
-
-
www.myperfectresume.com www.myperfectresume.com
-
Perfect Resume 작성/샘플
Tags
Annotators
URL
-
-
www.youtube.com www.youtube.com
-
Really useful video about the generation of story beats.
-
- Jul 2024
-
www.youtube.com www.youtube.com
-
26:30 Brings up progress traps of this new technology
26:48
question How do we shift our (human being's) relationship with the rest of nature
27:00
metaphor - interspecies communications - AI can be compared to a new scientific instrument that extends our ability to see - We may discover that humanity is not the center of the universe
32:54
Question - Dr Doolittle question - Will we be able to talk to the animals? - Wittgenstein said no - Human Umwelt is different from others - but it may very well happen
34:54
species have culture - Marine mammals enact behavior similar to humans
- Unknown unknowns will likely move to known unknowns and to some known knowns
36:29
citizen science bioacoustic projects - audio moth - sound invisible to humans - ultrasonic sound - intrasonic sound - example - Amazonian river turtles have been found to have hundreds of unique vocalizations to call their baby turtles to safety out in the ocean
41:56
ocean habitat for whales - they can communicate across the entire ocean of the earth - They tell of a story of a whale in Bermuda can communicate with a whale in Ireland
43:00
progress trap - AI for interspecies communications - examples - examples - poachers or eco tourism can misuse
44:08
progress trap - AI for interspecies communications - policy
45:16
whale protection technology - Kim Davies - University of New Brunswick - aquatic drones - drones triangulate whales - ships must not get near 1,000 km of whales to avoid collision - Canadian government fines are up to 250,000 dollars for violating
50:35
environmental regulation - overhaul for the next century - instead of - treatment, we now have the data tools for - prevention
56:40 - ecological relationship - pollinators and plants have co-evolved
1:00:26
AI for interspecies communication - example - human cultural evolution controlling evolution of life on earth
Tags
- progress trap - AI applied to interspecies communications
- ecological relationships - pollinators and plants co-evolved
- interspecies communication - umwelt
- - whale communication - span the entire ocean
- whale protection - bioacoustic and drones
- AI for interspecies communication - example - human cultural evolution controlling evolution of life on earth
- citizen science bioacoustics
- metaphor - interspecies communication - AI is like a new scientific instrument
- progress trap - AI for interspecies communications - examples - poachers - ecotourism
- environmental overhaul - treatment to prevention
- question - How do we shift our relationship with the rest of nature? - ESP research objective
- progress trap - AI for interspecies communications - policy
Annotators
URL
-
-
www.datacenterdynamics.com www.datacenterdynamics.com
-
“For our customer base, there's a lot of folks who say ‘I don't actually need the newest B100 or B200,’” Erb says. “They don’t need to train the models in four days, they’re okay doing it in two weeks for a quarter of the cost. We actually still have Maxwell-generation GPUs [first released in 2014] that are running in production. That said, we are investing heavily in the next generation.”
What would the energy cost be of the two compared like this?
-
-
www.youtube.com www.youtube.com
-
( ~ 6:25-end )
Steps for designing a reading plan/list: 1. Pick a topic/goal (or question you want to answer) & how long you want to take to achieve this. 2. Do research into the books necessary to achieve this goal. Meta-learning, scope out the subject. The number of books is relative to the goal and length of the goal. 3. Find the books using different tools such as Google & GoodReads & YouTube Recommendations (ChatGPT & Gemini are also useful). 4. Refine the book list (go through reviews, etc., in Adlerian steps, do an Inspectional Read of everything... Find out if it's truly useful). Also order them into a useful sequence for the syntopical reading project. Highlight the topics covered, how difficult they are, relevancy, etc. 5. Order the books (or download them)
Reminds me a bit of Scott Young's Metalearning step, and doing a skill decomposition in van Merriënboer et al.'s 10 Steps to Complex Learning
-
-
lilys.ai lilys.ai
Tags
Annotators
URL
-
-
allenj.substack.com allenj.substack.com
-
for - progress trap - AI -
article details - title - Hollow, world! (Part 1 of 5) - author - James Allen - date - 10 July, 2024 - publication - substack - self link - https://allenj.substack.com/p/hollow-world-part-1-of-5
summary James Allen provides an insightful description of ultra-anthropomorphic AI, AI that attempts to simulate an entire, whole human being.
In short, he points out the fundamental distinction between the real experience of another human being, and a simulation of one. In so doing, he gets to the heart of what it is to be human.
An AI is a simulation of a human being. No matter how realistic it's responses and actions, it is not evolved out of biology. I have no doubts that scientists are hard at work trying to make a biological AI. The distinction becomes fuzzier then.
Current AI cannot possibly simulate the experience of being in a fragile and mortal body and all that this entails. If an AI robot says it understands joy or pain, that statement isn't built on the combined exteroception and interoception of being in a biological body, rather, it is based on many linguistic statements it has assimilated.
-
-
x28newblog.wordpress.com x28newblog.wordpress.com
-
Matthias Melcher on (personal) AI and which affordances it may provide or not. Vgl n:: Mark Meinema's remark about how it os much better at switching role than a human (explaining the same thing for a 5yr old or expert)
-
-
www.epi.org www.epi.org
-
Improving the living standards of all working-class Americans while closing racial disparities in employment and wages will depend on how well we seize opportunities to build multiracial, multigendered, and multigenerational coalitions to advance policies that achieve both of these goals
for - political polarization - challenge to building multi-racial coalition - to - Wired story - No one actually knows how AI will affect jobs
political polarization - building multi-racial coalitions - This is challenging to do when there is so much political polarization with far-right pouring gasoline on the polarization fire and obscuring the issue - There is a complex combination of factors leading to the erosion of working class power
automation - erosion of the working class - Ai is only the latest form of the automation trend, further eroding the working class - But Ai is also beginning to erode white collar jobs
to - Wired story - No one actually knows how AI will affect jobs - https://hyp.is/KsIWPDzoEe-3rR-gufTfiQ/www.wired.com/story/ai-impact-on-work-mary-daly-interview/
-
-
www.wired.com www.wired.com
-
for - AI - impact on jobs -WIRED interview
-
-
www.caspa.ai www.caspa.ai
Tags
Annotators
URL
-
- Jun 2024
-
sites.temple.edu sites.temple.edu
-
-
Today we’re discussing technology and AI’s role in the food industry. We’ll discuss AI’s implications, challenges, and impact on food and see how it has truly evolved the industry.
-
-
-
for - AI - inside industry predictions to 2034 - Leopold Aschenbrenner - inside information on disruptive Generative AI to 2034
document description - Situational Awareness - The Decade Ahead - author - Leopold Aschenbrenner
summary - Leopold Aschenbrenner is an ex-employee of OpenAI and reveals the insider information of the disruptive plans for AI in the next decade, that pose an existential threat to create a truly dystopian world if we continue going down our BAU trajectory. - The A.I. arms race can end in disaster. The mason threat of A.I. is that humans are fallible and even one bad actor with access to support intelligent A.I. can post an existential threat to everyone - A.I. threat is amplifier by allowing itt to control important processes - and when it is exploited by the military industrial complex, the threat escalates significantly
- to - YouTube - 4 hour in-depth interview with Leopold Aschenbrenner on the disruptive and existential impacts of A.I. super-intelligence
-
a dictator who wields the power of superintelligence would command concentrated power unlike 00:50:45 anything we've ever seen
for - key insight - AI - progress trap - nightmare scenario - dictator controlling superintelligence
meet insight - AI - progress trap - nightmare scenario - locked in dictatorship controlling superintelligence - millions of AI controlled robotic law and enforcement agents could police their populace - Mass surveillance would be hypercharged - Dictator loyal AI agents could individually assess every single citizen for descent with near perfect lie detection sensor - rooting out any disloyalty e - Essentially - the robotic military and police force could be wholly controlled by a single political leader and - programmed to be perfectly obedient and there's going to be no risks of coups or rebellions and - his strategy is going to be perfect because he has super intelligence behind them - what does a look like when we have super intelligence in control by a dictator ? - there's simply no version of that where you escape literally - past dictatorships were not permanent but - superintelligence could eliminate any historical threat to a dictator's Rule and - lock in their power - If you believe in freedom and democracy this is an issue because - someone in power, - even if they're good - they could still stay in power - but you still need the freedom and democracy to be able to choose - This is why the Free World must Prevail so - there is so much at stake here that - This is why everyone is not taking this into account
-
this is why it's such a trap which is why like we're on this train barreling down this pathway which is super risky
for - progress trap - double bind - AI - ubiquity
progress trap - double bind - AI - ubiquity - Rationale: we will have to equip many systems with AI - including military systems - Already connected to the internet - AI will be embedded in every critical piece of infrastructure in the future - What happens if something goes wrong? - Now there is an alignment failure everywhere - We will potentially have superintelligence within 3 years - Alignment failures will become catastrophic with them
-
getting a base model to you know make money by default it may well learn to lie to commit fraud to deceive to hack to seek power because 00:47:50 in the real world people actually use this to make money
for - progress trap - AI - example - give prompt for AI to earn money
progress trap - AI - example - instruct AI to earn money - Getting a base model to make money. By default it may well learn - to lie - to commit fraud - to deceive - to hack - to seek power - because in the real world - people actually use this to make money - even maybe they'll learn to - behave nicely when humans are looking and then - pursue more nefarious strategies when we aren't watching
-
this company's got not good for safety
for - AI - security - Open AI - examples of poor security - high risk for humanity
AI - security - Open AI - examples of poor security - high risk for humanity - ex-employees report very inadequate security protocols - employees have had screenshots capture while at cafes outside of Open AI offices - People like Jimmy Apple report future releases on twitter before Open AI does
-
the alignment problem
for - definition - AI - The Alignment Problem
definition - The Alignment Problem - When AI intelligence so far exceeds human intelligence that - we won't be able to predict their behavior - we won't know if we can trust that the AI is aligned to our intent
-
open AI literally yesterday published securing research infrastructure for advanced AI
for - AI - Security - Open AI statement in response to this essay
-
this is a serious problem because all they need to do is automate AI research 00:41:53 build super intelligence and any lead that the US had would vanish the power dynamics would shift immediately
for - AI - security risk - once automated AI research is known, bad actors can easily build superintelligence
AI - security risk - once automated AI research is known, bad actors can easily build superintelligence - Any lead that the US had would immediately vanish.
-
the model Waits are just a large files of numbers on a server and these can be easily stolen all it takes is an adversary to match your trillions 00:41:14 of dollars and your smartest minds of Decades of work just to steal this file
for - AI - security risk - model weight files - are a key leverage point
AI - security risk - model weight files - are a key leverage point for bad actors - These files are critical national security data that represent huge amounts of investment in time and research and they are just a file so can be easily stolen.
-
our failure today will be irreversible soon in the next 12 to 24 months we will leak key AGI breakthroughs to the CCP it will 00:38:56 be to the National security establishment the greatest regret before the decade is out
for - AI - security risk - next 1 to 2 years is vulnerable time to keep AI secrets out of hands of authoritarian regimes
-
here are so many loopholes in our current top AI Labs that we could literally have people who are infiltrating these companies and there's no way to even know what's going on because we don't have any true security 00:37:41 protocols and the problem is is that it's not being treated as seriously as it is
for - key insight - low security at top AI labs - high risk of information theft ending up in wrong hands
-
if you have the cognitive abilities of something that is you know 10 to 100 times smarter than you trying to to outm smarten it it's just you know it's just not going to happen whatsoever so you've effectively lost at that point which means that 00:36:03 you're going to be able to overthrow the US government
for - AI evolution - nightmare scenario - US govt may seize Open AI assets if it arrives at superintelligence
AI evolution - projection - US govt may seize Open AI assets if it arrives at superintelligence - He makes a good point here - If Open AI, or Google achieve superintelligence that is many times more intelligent than any human, - the US government would be fearful that they could be overthrown or that the technology can be stolen and fall into the wrong hands
-
whoever controls superintelligence will possibly have enough power to seize control from 00:35:14 pre superintelligence forces
for - progress trap - AI - one nightmare scenario
progress trap - AI - one nightmare scenario - Whoever is the first to control superintelligence will possibly have enough power to - seize control from pre superintelligence forces - even without the robots small civilization of superintelligence would be able to - hack any undefended military election television system and cunningly persuade generals electoral and economically out compete nation states - design new synthetic bioweapons and then - pay a human in Bitcoin to synthetically synthesize it
-
military power and Technology progress have been tightly linked historically and with extraordinarily rapid technological 00:34:11 progress will come military revolutions
for - progress trap - AI and even more powerful weapons of destruction
progress trap - AI and even more powerful weapons of destruction - The podcaster's excitement seems to overshadow any concern of the tragic unintended consequences of weapons even more powerful than nuclear warheads. - With human base emotions still stuck in the past and our species continued reliance on violence to solve problems, more powerful weapons is not the solution, - indeed, they only make the problem worse - Here is where Ronald Wright's quote is so apt: - We humans are running modern software on 50,000 year old hardware systems - Our cultural evolution, of which AI is a part of, is happening so quickly, that - it is racing ahead of our biological evolution - We aren't able to adapt fast enough for the rapid cultural changes that AI is going to create, and it may very well destroy us
-
this is where we can see the doubling time of the global economy in years from 1903 it's been 15 years but after super intelligence what happens is it going to be every 3 years is it going be every five is it going to 00:33:22 be every year is it going to be every 6 months I mean how crazy is the growth going to be
for - progress trap - AI triggering massive economic growth - planetary boundaries
progress trap - AI triggering massive economic growth - planetary boundaries - The podcaster does not consider the ramifications of the potential disastrous impact of such economic growth if not managed properly
-
AGI level factories are going to shift from going to human run to AI directed using human physical labor soon to be fully being run by swarms of human level robots
for - progress trap - AI and human enslavement?
progress trap - human enslavement? - Isn't what the speaker is talking about here is that - AI will be the masters and - humans will become slaves?
-
be able to quick Master any domain write trillions lines of code and read every research paper in every scientific field ever written
for - AI evolution - projections for capabilities by 2030
AI evolution - projections for 2030 - AI will be able to do things we cannot even conceive of now because their cognitive capabilities are orders of magnitudes faster than our own - Write billions of lines of code - Absorb every scientific paper ever written and write new ones - Gain the equivalent of billions of human equivalent years of experience
-
you're going to have like 100 million more AI research and they're going to be working at 100 times what 00:27:31 you are
for - stats - comparison of cognitive powers - AGI AI agents vs human researcher
stats - comparison of cognitive powers - AGI AI agents vs human researcher - 100 million AGI AI researchers - each AGI AI researcher is 100x more efficient that its equivalent human AI researcher - total productivity increase = 100 million x 100 = 10 billion human AI researchers! Wow!
-
nobody's really pricing this in
for - progress trap - debate - nobody is discussing the dangers of such a project!
progress trap - debate - nobody is discussing the dangers of such a project! - Civlization's journey has to create more and more powerful tools for human beings to use - but this tool is different because it can act autonomously - It can solve problems that will dwarf our individual or even group ability to solve - Philosophically, the problem / solution paradigm becomes a central question because, - As presented in Deep Humanity praxis, - humans have never stopped producing progress traps as shadow sides of technology because - the reductionist problem solving approach always reaches conclusions based on finite amount of knowledge of the relationships of any one particular area of focus - in contrast to the infinite, fractal relationships found at every scale of nature - Supercomputing can never bridge the gap between finite and infinite - A superintelligent artifact with that autonomy of pattern recognition may recognize a pattern in which humans are not efficient and in fact, greater efficiency gains can be had by eliminating us
-
perhaps 100 million human researcher equivalents running day and night t
for - stats - AI evolution - equivalent of 100 million human researchers working 24/7
stats - AI evolution - equivalent of 100 million human researchers working 24/7 - By 2027, the industry's aim is to have tens of millions of GPU training clusters, running - millions of copies of automated AI researchers, or the equivalent of - 100 million human AI researchers working 24/7
-
Sam mman has said that's his entire goal that's what opening eye are trying to build they're not really trying to build super intelligence but they Define AGI as a 00:24:03 system that can do automated AI research and once that does occur
for - key insight - AGI as automated AI researchers to create superintelligence
key insight - AGI as automated AI researchers to create superintelligence - We will reach a period of explosive, exponential AI research growth once AGI has been produced - The key is to deploy AGI as AI researchers that can do AI research 24/7 - 5,000 of such AGI research agents could result in superintelligence in a very short time period (years) - because every time any one of them makes a breakthrough, it is immediately sent to all 4,999 other AGI researchers
-
we are on course for AGI by 2027 and that these AI 00:19:25 systems will basically be able to automate basically all all cognitive jobs think any job that can be done remotely
for - AI evolution - prediction - 2027 - all cognitive jobs can be done by AI
-
suppose that GPT 4 training took 3 months in 2027 a leading AI lab will be able to train a GPT 4 00:18:19 level model in a minute
for - stat - AI evolution - prediction 2027 - training time - 6 OOM decrease
stat - AI evolution - prediction 2027 - training time - 6 OOM decrease - today it takes 3 months to train GPT 4 - in 2027, it will take 1 minute - That is, 131,400 minutes vs 1 minute, or - 6 OOM
-
by 2027 rather than a chatbot you're going to have something that looks more like an agent and more like a coworker
for - AI evolution - prediction - 2027 - AI agent will replace AI chatbot
-
this is where we talk about un hobbling this is of course something that we just spoke about before but the reason that this is important is because this is where you can get gains from a model in ways that you couldn't previously see 00:15:31 before
for - definition - hobbling - AI
-
the inference efficiency improved by nearly three orders of magnitude or 1,000x in less than 2 years
for - stats - AI evolution - Math benchmark - 2022 to 2024
stats - AI evolution - Math benchmark - 2022 to 2024 - 50% increase in accuracy over 2 years - inference accuracy improved 1000x or 3 Orders Of Magnitude (OOM)
-
there is essentially this Benchmark 00:09:58 called the math benchmark a set of difficult mathematic problems from a high school math competitions and when the Benchmark was released in 2021 gpt3 only got 5%
for - stats - AI - evolution - Math benchmark
stats - AI - evolution - Math benchmark - 2021 - GPT3 scored 5% - 2022 - scored 50% - 2024 - Gemini 1.5 Pro scored 90%
-
having an automated AI research engineer by 2027 00:05:14 to 2028 is not something that is far far off
for - progress trap - AI - milestone - automated AI researcher
progress trap - AI - milestone - automated AI researcher - This is a serious concern that must be debated - An AI researcher that does research on itself has no moral compass and can encode undecipherable code into future generations of AI that provides no back door to AI if something goes wrong. - For instance, if AI reached the conclusion that humans need to be eliminated in order to save the biosphere, - it can disseminate its strategies covertly under secret communications with unbreakable code
-
it is strikingly plausible that by 2027 models 00:03:36 will be able to do the work of an AI researcher SL engineer that doesn't require believing in sci-fi it just requires in believing in straight lines on a graph
for - quote - AI prediction for 2027 - Leopold Aschenbrenner
quote - AI prediction for 2027 - Leopold Aschenbrenner - (see quote below) - it is strikingly plausible that by 2027 - models will be able to do the work of an AI researcher SL engineer - that doesn't require believing in sci-fi - it just requires in believing in straight lines on a graph
-
he Talk of the Town has shifted from 10 billion compute clusters 00:01:16 to hundred billion do compute clusters to even trillion doll clusters and every 6 months another zero is added to the boardroom plans
for - AI - future spending - trillion dollars - superintelligence by 2030
Tags
- key insight - AGI as automated AI researchers to create superintelligence
- AI evolution - prediction - 2027 - all cognitive jobs can be done by AI
- stats - AI evolution - Math benchmark - 2022 to 2024
- to - YouTube - 4 hour in-depth interview with Leopold Aschenbrenner on the disruptive and existential impacts of A.I. super-intelligence
- AI - progress trap - nightmare scenario - dictator controlling superintelligence
- progress trap - AI - one nightmare scenario
- progress trap - AI - example - give prompt for AI to earn money
- AI - security risk - next 1 to 2 years is vulnerable time to keep AI secrets out of hands of authoritarian regimes
- AI - security - Open AI - poor security - high risk for humanity
- key insight - low security at top AI labs - high risk of information theft ending up in wrong hands
- stats - comparison of cognitive powers - AGI AI agents vs human researcher
- stat - AI evolution - prediction 2027 - training time - 6 OOM decrease
- progress trap - double bind - AI - ubiquity
- AI evolution - nightmare scenario - US govt may seize Open AI assets if it arrives at superintelligence
- progress trap - AI and human enslavement?
- progress trap - AI - milestone - automated AI researcher - concerns
- AI - inside industry predictions to 2034
- AI evolution - projections for capabilities by 2030
- AI - future spending - trillion dollars - superintelligence by 2030
- AI - Security - Open AI statement in response to this essay
- AI - security risk - once automated AI research is known, bad actors can easily build superintelligence
- stats - AI evolution - equivalent of 100 million human researchers working 24/7
- AI evolution - prediction - 2027 - AI agent will replace AI chatbot
- definition - hobbling - AI
- quote - AI prediction for 2027 - Leopold Aschenbrenner
- definition - AI - The Alignment Problem
- Leopold Aschenbrenner - inside information on disruptive Generative AI to 2034
- progress trap - debate - nobody is discussing the dangers of exponential AI research by AGI agents
- progress trap - AI and even more powerful weapons of destruction
- AI - security risk - model weight files - are a key leverage point for bad actors
- progress trap - AI triggering massive economic growth - planetary boundaries
- article - SItuational Awareness - The Decade Ahead - Leopold Aschenbrenner
- stats - AI - evolution - Math benchmark
Annotators
URL
-
-
www.youtube.com www.youtube.com
-
for - progress trap - AI - threat of superintendence - interview - Leopold Aschenbrenner - former Open AI employee - from -. YouTube - review of Leopold Aschenbrenner's essay on Situational Awareness - https://hyp.is/ofu1EDC3Ee-YHqOyRrKvKg/docdrop.org/video/om5KAKSSpNg/
-
-
docdrop.org docdrop.org
-
quite frankly a lot of artists and 00:21:16 producers are probably using it just for that they come up with something inspiration they go they make something new
for - Generative AI music - producers and artists using for inspiration
comment I would agree with this. Especially since the AI music currently sounds lo-fi
-
what if a band decides to take one of the udio generated songs and re-record it entirely will they own the full copy rate to that very new recording now if I 00:21:03 was udio the answer probably be like no you made that thing using our platform
for - AI music issues - rerecording an AI music generated song - copyright question
-
the AI created Music learned from got inspiration from the hit songs and came up with a great new hit song for you and then kind of you 00:13:21 know what we'll call those those artifacts or the little similarities here and there might get picked up by Content ID on YouTube
for - AI music - youtube content ID algorithms can identify it
-
here's a way to do direct to 00:16:46 Consumer sell and can make some money and don't just be like so worried about being on the music platform streaming and now you're diluted because the AI
for - new music sales model - direct to consumer - helps mitigate AI music
-
there's a huge disparity between state of law application of tech and what's 00:15:42 actually happening
for - AI - law - too slow
-
to your point for 00:13:46 every problem there's going to be a solution and AI is going to have it and then for every solution for that there's going to be a new problem
for - AI - progress trap - nice simple explanation of how progress traps propagate
-
this is more of a unfair competition 00:10:36 issue I think as a clearer line than the copyright stuff
for - progress trap - Generative AI - copyright infringement vs Unfair business practice argument
-
now there's going to be even more AI music pouring 00:09:04 into platforms which saturated Market in an already oversaturated Market
for - progress trap - AI music - oversaturated market
-
these conversations are having daily people are scrambling trying to like we're trying to keep up 00:07:32 with AI in real time scrambling to find out what we're going to do think about all the different businesses that are affected from this
for - AI Disruption - Realtime - music industry is scrambling
-
Google deep mind they're coming up their new Google AI sound boox that and it is making Loops from prompts and they have wav Jean
for - AI music - Google Deep Mind - Google AI Soundbox - Wycliff Jean endorsing
-
backstory of udio like I didn't know that willim IM and United Masters were like investors in udio
for - AI music - Udio - investors - Will.I.Am - United Masters
-
deluding the general royalty pool
for - progress trap - AI music - dilution of general royalty pool - due to large volume
-
the volume of how much music is being created over 800,000 00:01:56 tracks a day are being created using udio
for - stats - AI music platform Udio - tracks created per day - over 800000
-
terms of service which is the contract that you sign when you get on their platform does say that you can monetize what you make so meaning you can put into distribution 00:00:41 the music that you make
for - AI music - Udio - terms of service - users can sell the music made on Udio
Tags
- progress trap - AI music - oversaturated market
- AI music - Udio - terms of service - users can sell the music made on Udio
- progress trap - AI music - dilution of general royalty pool - due to large volume
- AI music - Google Deep Mind - Google AI Soundbox - Wycliff Jean endorsing
- new music sales model - direct to consumer - helps mitigate AI music
- AI Disruption - Realtime - music industry is scrambling
- progress trap - Generative AI - copyright infringement vs Unfair business practice argument
- generative AI music - producers and artists using for inspiration
- AI music - Udio - investors - Will.I.Am - United Masters
- AI - law - too slow
- stats - AI music platform Udio - tracks created per day - over 800000
- AI music issues - rerecording an AI music generated song - copyright question
- AI - progress trap - nice simple explanation of how progress traps propagate
- AI music - youtube content ID algorithms can identify it
Annotators
URL
-
-
www.yalelawjournal.org www.yalelawjournal.org
-
These arguments are meant to present a cautionary tale of unintended consequences.
For - progress trap - AI - Generative AI - IP - Yale Law Journal
-
-
docdrop.org docdrop.org
-
for - progress trap - AI music - critique - Folia Sound Studio - to - P2P Foundation - Michel Bauwens - Commons Transition Plan - Netarchical Capitalism - Predatory Capitalism
to - P2P Foundation - Michel Bauwens - Commons Transition Plan - Netarchical Capitalism - Predatory Capitalism https://hyp.is/o-Hp-DCAEe-8IYef613YKg/wiki.p2pfoundation.net/Commons_Transition_Plan
-
I think that Noam chsky said exactly a year ago in New York Times around a year ago that generative AI is not any 00:18:37 intelligence it's just a plagiarism software that learned stealing human uh work transform it and sell it as much as possible as cheap as possible
for - AI music theft - citation - Noam Chomsky - quote - Noam Chomsky - AI as plagiarism on a grand scale
to - P2P Foundation - commons transition plan - Michel Bauwens - netarchical capitalism - predatory capitalism - https://wiki.p2pfoundation.net/Commons_Transition_Plan#Solving_the_value_crisis_through_a_social_knowledge_economy
Tags
- AI music theft - citation - Noam Chomsky
- to - P2P Foundation - Michel Bauwens - netarchical capitalism - predatory capitalism
- to - P2P Foundation - Michel Bauwens - Commons Transition Plan - Netarchical Capitalism - Predatory Capitalism
- quote - Noam Chomsky - AI as plagiarism on a grand scale
- progress trap - AI music - critique
Annotators
URL
-
-
wiki.p2pfoundation.net wiki.p2pfoundation.net
-
from - youtube - Folia Sound Studio critique of AI music tools - Suno - Udio https://hyp.is/NGScyjB_Ee-rSYNhe9Fuug/docdrop.org/video/wEQ9Vg2YKcU/
-
-
-
for - AI - replicating music - AI - music app - Udio
-
-
www.reworked.co www.reworked.co
-
via [[Lee Bryant]] Postshift. Which use cases to look at for AI. Imo the narrower the better. Vgl [[small band AI personal assistant]]
-
-
www.anthropic.com www.anthropic.comClaude1
-
https://web.archive.org/web/20240617122834/https://www.anthropic.com/claude
What https://unherd.com/2024/05/im-in-love-with-my-ai-girlfriend/ used as AI model / app, jailbroken.
Seems it was the paid version, as linked article mentions Opus, which is available for 20usd/m. Has an API and an iOS app (no Android).
-
-
unherd.com unherd.com
-
Column by a travel writer on how anthropomorphing AI can go off the rails quickly. Note that author doesn't really explain how he interacted except for vague indications (a jailbroken Claude 3 Opus model, seemingly running on his phone as app?)
Via [[Euan Semple]] https://euansemple.blog/2024/06/08/jesus-tittyfucking-christ-on-a-cracker-is-that-a-pagan-shrine/
-
-
-
- openai use LiveKit to deliver realtime voice
- playground: https://cloud.livekit.io/projects/
-
-
-
-
www.codium.ai www.codium.ai
Tags
Annotators
URL
-
-
getdecipher.com getdecipher.com
-
www.second.dev www.second.dev
-
debot.lodder.dev debot.lodder.devDeBot1
-
Een project v Open State Foundation.
-
-
www.cursor.com www.cursor.comCursor1
Tags
Annotators
URL
-
-
useanything.com useanything.com
Tags
Annotators
URL
-
-
-
if we just had a big enough spreadsheet we could get the data in and then we could get you know something like AI or some you know some other computational 00:12:32 process in to help us deal with all this complexity because our little brains can't handle it and my feeling about this is that 00:12:44 actually no
for - adjacency - AI - Nora Bateson - solving wicked problems - no - Human Intelligence - HI - yes - @gyuri
-
- May 2024
-
Local file Local file
-
normalizeddifference vegetation index (NDVI)
O Índice de Vegetação por Diferença Normalizada (NDVI, do inglês Normalized Difference Vegetation Index) é uma métrica amplamente utilizada na área de sensoriamento remoto para quantificar a vegetação em uma determinada área a partir de imagens de satélite ou aeronaves. Este índice é baseado na reflexão da luz em diferentes comprimentos de onda pelas plantas.
-
-
www.helmut-schmidt.de www.helmut-schmidt.de
-
Die Rede der ZukunftspreisträgerinMeredith Whittaker warnt in ihrer Rede vor der Macht der Techindustrie und erklärt, warum es sich gerade jetzt lohnt, positiv zu denken.
Meredith Whittaker on the origin of AI wave and consquences. Need to read this. #toread Current AI as 1980s insights now feasible on top of the massive data of bigtech silos. And Clinton admin wrt privacy and advertising in 1990s as the fautllines that enabled #socmed platform silos.
-
-
-
FastCut adds animated captions, b-rolls & sound effects to your videos. FastCut은 동영상에 애니메이션 캡션, 비롤 및 음향 효과를 추가합니다.
Tags
Annotators
URL
-
-
media.dltj.org media.dltj.org
-
And one way we've seen artificial intelligence used in research practices is in extracting information from copyrighted works. So researchers are using this to categorize or classify relationships in or between sets of data. Now sometimes this is called using analytical AI and it evolves processes that are considered part of text and data mining. So we know that text data mining research methodologies can but they don't necessarily need to rely on artificial intelligence systems to extract this information.
Analytical AI: categorize and contextualize
As distinct from generative AI...gun example in motion pictures follows in the presentation.
-
-
aisafety.dance aisafety.dance
-
AI with intact skills, but broken goals, would be an AI that skillfully acts towards corrupted goals.
-
Without intuition, AI can't understand common sense or humane values. Thus, AI might achieve goals in logically-correct but undesirable ways.
-
-
www.linkedin.com www.linkedin.com
-
Matthew van der Hoorn Yes totally agree but could be used for creating a draft to work with, that's always the angle I try to take buy hear what you are saying Matthew!
Reply to Nidhi Sachdeva: Nidhi Sachdeva, PhD Just went through the micro-lesson itself. In the context of teachers using to generate instruction examples, I do not argue against that. The teacher does not have to learn the content, or so I hope.
However, I would argue that the learners themselves should try to come up with examples or analogies, etc. But this depends on the learner's learning skills, which should be taught in schools in the first place.
-
***Deep Processing***-> It's important in learning. It's when our brain constructs meaning and says, "Ah, I get it, this makes sense." -> It's when new knowledge establishes connections to your pre-existing knowledge.-> When done well, It's what makes the knowledge easily retrievable when you need it. How do we achieve deep processing in learning? 👉🏽 STORIES, EXPLANATIONS, EXAMPLES, ANALOGIES and more - they all promote deep meaningful processing. 🤔BUT, it's not always easy to come up with stories and examples. It's also time-consuming. You can ask you AI buddies to help with that. We have it now, let's leverage it. Here's a microlesson developed on 7taps Microlearning about this topic.
Reply to Nidhi Sachdeva: I agree mostly, but I would advice against using AI for this. If your brain is not doing the work (the AI is coming up with the story/analogy) it is much less effective. Dr. Sönke Ahrens already said: "He who does the effort, does the learning."
I would bet that Cognitive Load Theory also would show that there is much less optimized intrinsic cognitive load (load stemming from the building or automation of cognitive schemas) when another person, or the AI, is thinking of the analogies.
https://www.linkedin.com/feed/update/urn:li:activity:7199396764536221698/
-
-
citl.indiana.edu citl.indiana.edu
-
If students know that the AI has some responsibility for determining their grades, that AI will have considerably more authority in the classroom or in any interactions with students.
warning about AI grading
-
-
-
if I met a robot that looked very much like a beautiful girl and everything went fine together with her and me but
for - comparison - human vs AI robot - Denis Noble
-
-
meta.stackexchange.com meta.stackexchange.com
-
One of the key elements was "attribution is non-negotiable". OpenAI, historically, has done a poor job of attributing parts of a response to the content that the response was based on.
-
I feel violated, cheated upon, betrayed, and exploited.
-
I wouldn't focus too much on "posted only after human review" - it's worth noting that's that's worth nothing. We literally just saw a case of obviously riduculous AI images in a scientific paper breezing through peer review with noone caring, so quality will necessarily go down because Brandolini's law combined with AI is the death sentence for communities like SE and I doubt they'll employ people to review content from the money they'll make
-
What could possibly go wrong? Dear Stack Overflow denizens, thanks for helping train OpenAI's billion-dollar LLMs. Seems that many have been drinking the AI koolaid or mixing psychedelics into their happy tea. So much for being part of a "community", seems that was just happy talk for "being exploited to generate LLM training data..." The corrupting influence of the profit-motive is never far away.
-
If you ask ChatGPT to cite it will provide random citations. That's different from actually training a model to cite (e.g. use supervised finetuning on citations with human raters checking whether sources match, which would also allow you to verify how accurately a model cites). This is something OpenAI could do, it just doesn't.
-
There are plenty of cases where genAI cites stuff incorrectly, that says something different, or citations that simply do not exist at all. Guaranteeing citations are included is easy, but guaranteeing correctness is an unsolved problem
-
GenAIs are not capable of citing stuff. Even if it did, there's no guarantee that the source either has anything to do with the topic in question, nor that it states the same as the generated content. Citing stuff is trivial if you don't have to care if the citation is relevant to the content, or if it says the same as you.
-
LLMs, by their very nature, don't have a concept of "source". Attribution is pretty much impossible. Attribution only really works if you use language models as "search engine". The moment you start generating output, the source is lost.
Tags
- generative AI: attribution
- generative AI: stealing people's content and using for training without attribution
- good point
- AI: training
- feeling exploited
- changing the rules
- generative AI: citing sources
- generative AI: incorrectness
- Brandolini's law
- AI: large language model
- ChatGPT: not citing sources
- StackExchange: negative
- AI-assisted but still depening on human review
- generative AI: not citing sources
Annotators
URL
-
-
stackoverflow.blog stackoverflow.blog
-
Plenty of companies are still figuring out how to integrate “traditional AI” (that is, non-generative AI; tools like machine learning and rule-based algorithms)
-
-
stackoverflow.co stackoverflow.co
-
AI-powered code generation tools like GitHub Copilot make it easier to write boilerplate code, but they don’t eliminate the need to consult with your organization’s domain experts to work through logic, debugging, and other complex problems.Stack Overflow for Teams is a knowledge-sharing platform that transfers contextual knowledge validated by your domain experts to other employees. It can even foster a code generation community of practice that champions early adopters and scales their learnings. OverflowAI makes this trusted internal knowledge—along with knowledge validated by the global Stack Overflow community—instantly accessible in places like your IDE so it can be used alongside code generation tools. As a result, your teams learn more about your codebase, rework code less often, and speed up your time-to-production.
Tags
Annotators
URL
-
-
davidorban.com davidorban.com
-
The message of e/acc is this: let’s go full steam ahead in the development of increasingly powerful, general, and conscious artificial intelligences, up to superintelligences. This can only be the right path because it reflects the will of the universe. So far I perfectly agree with the philosophical approach of the e/acc movement.
E/Acc says invest more in AI limitlessly, as opposed to EA/Bostrom saying invest only in a specific circle of billionaire friends bc of the extinction level risk involved of AGI. And we need to do it, bc religious fervor 'it reflects the will of the universe'. Not convincing.
-
-
-
Thisinterventionrecognizes students’ annotations as objectsopen tocontinuous development, engaging students to connect, analyze, and expand upon their ideasthrough the synthesis processes. Meanwhile, the synthesis products can be integratedinto otherlearning events, enriching the overall learning experiences.
How can AI be leveraged to support: (1) the process of synthesizing students' annotations, and (2) the use of these synthesis artifacts in subsequent in-class group discussions?
Tags
Annotators
URL
-
-
www.cnbc.com www.cnbc.com
-
At Google, an AI team member said the burnout is the result of competitive pressure, shorter timelines and a lack of resources, particularly budget and headcount. Although many top tech companies have said they are redirecting resources to AI, the required headcount, especially on a rushed timeline, doesn’t always materialize. That is certainly the case at Google, the AI staffer said.
-
A common feeling they described is burnout from immense pressure, long hours and mandates that are constantly changing. Many said their employers are looking past surveillance concerns, AI’s effect on the climate and other potential harms, all in the name of speed. Some said they or their colleagues were looking for other jobs or switching out of AI departments, due to an untenable pace.
-
Artificial intelligence engineers at top tech companies told CNBC that the pressure to roll out AI tools at breakneck speed has come to define their jobs.
-
-
www.forbes.com www.forbes.com
-
Experts agree that 2024 will be the year AI invades and transforms work as we know it
-
-
www.digitaltrends.com www.digitaltrends.com
-
openai.com openai.com
-
We train our models using:
-
We recently improved source links in ChatGPT(opens in a new window) to give users better context and web publishers new ways to connect with our audiences.
-
Our models are designed to help us generate new content and ideas – not to repeat or “regurgitate” content. AI models can state facts, which are in the public domain.
-
When we train language models, we take trillions of words, and ask a computer to come up with an equation that best describes the relationship among the words and the underlying process that produced them.
-
-
-
r1 founder's (scrubbed) past as another nail in Rabbit's coffin. I guess the 'large action model' is vaporware, back to Automator :D
-
-
www.serverless-ml.org www.serverless-ml.org
-
the raw data layer (data warehouses and object stores), the compute layer for orchestrated pipelines (feature and inference pipelines), the ML Development services for model training and experiment management, and the state layer for features and models as well as model serving and model monitoring.
severless ml systems categories
-
The interactive ML systems are typically a Gradio or Streamlit UI (on Hugging Face Spaces or Streamlit Cloud) and work with a model either hosted or downloaded from Hopsworks. They typically take user input and join it with historical features from Hopsworks Feature Store, and produce predictions in the UI.
how interactive ML systems operate
-
These system runs a feature pipeline once/day to synthetically generate a new Iris Flower and write it to the feature store. Then a batch inference pipeline, that also runs once/day but just after the feature pipeline, reads the single flower added that day, and downloads the Iris model trained to predict the type of Iris flower based on the 4 input features: sepal length, sepal width, petal length, and petal width. The model’s prediction is written to an online Dashboard, and actual flower (outcome) is read from the feature store and also published to the same Dashboard - so you can see if the model predicted correctly.
how analytical ML systems operate
-
- Apr 2024
-
garymarcus.substack.com garymarcus.substack.com
-
Author suggests the improvement of LLMs is flattening. E.g. points to the closing gap between proprietary and open source models out there, while improvement of proprietary stuff is diminishing or no longer happening (OpenAI progress flatlined 13 months ago it seems). In comment someone points to https://arxiv.org/abs/2404.04125 which implies a hard upper limit in improvement
Tags
Annotators
URL
-
-
arxiv.org arxiv.org
-
However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during "zero-shot" evaluation.
What seems zero-shot performance by an LLM may well be illusionary as it is unclear what was in training data.
-
We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance
Exponential increase in training data is needed for linear improvements in zero-shot results of LLMs. This implies a very near, more or less now, brick wall in improvement.
Tags
Annotators
URL
-
-
-
BBC highly critical of Humane AI Pin, just like [[Humane AI Pin review not even close]] I noted earlier. Explicitly ties this to the expectations of [[rabbit — home]] too, which is a similar device. Issue here is I think similar to other devices like voice devices in your home. Not smart enough at the edge, too generic to be of use as [[small band AI personal assistant]] leading to using it for at most 2 or 3 very basic things (weather forecast, time, start playlist usually, and at home perhaps switching on a light), that don't justify the price tag .
Tags
Annotators
URL
-
-
-
AI hype in material science. Google shows an allergy to being pointed to fundamental issues. Another example of pointing out obvious mistakes or issues is not only not welcomed but actively ignored (vgl examples of AI as search engine, where pointing out the first three of the top ten results were wrong resulted in being shouted at, or the blogpost writing video in which presented 'facts' were 1 wikipedia click away from being shown made-up. False citations etc.) It's not bad per se that AI can be wrong, no tool is infallible, but the problem is n:: the extreme asymmetry between the machine effort needed to make stuff up in heaps, and the human effort needed to wade through all the crap and point that out. Vgl [[Spammy handelings assymmetrie 20201220072726]] [[It is easier to F things up than fix things 20180610073041]] Creating entropy is way easier than reducing it, always. We don't need our tools to create ever more entropy on purpose, if only we can reduce it again. Our tools need to help decrease entropy. Decreasing entropy is the definition of life, increasing it should be anathema. Esp if it is unclear where a tool is increasing entropy.
-
-
zenodo.org zenodo.org
-
Until recently hedge funds and HFT firms were the main users of AI in finance, but applications have now spread to other areas including banks, regulators, Fintech, insurance firms to name a few
Using mobile phone data, Bjorkegren and Grissen (2015) employ ML methods to predict loan repayments.
In the insurance industry, Cytora is using AI to make better risk assessments about their customers, leading to more accurate pricing and minimising claims.
Tags
Annotators
URL
-
-
bigthink.com bigthink.com
-
Have you ever had a meaningful conversation with Siri or Alexa or Cortana? Of course not.
That said, I have had some pretty amazing conversations with ChatGPT-4. I've found it to be useful, too, for brainstorming. In one recent case (which I blogged about on my personal blog), the AI helped me through figuring out a structural issue with my zettelkasten.
-
rtificial intelligence is already everywhere
-
-
-
“Did you know that the first Matrix was designed to be a perfect human world? Where none suffered, where everyone would be happy. It was a disaster. No one would accept the program… I believe that, as a species, human beings define their reality through suffering and misery. The perfect world was a dream that your primitive cerebrum kept trying to wake up from.”
-
“I don’t want comfort. I want God, I want poetry, I want real danger, I want freedom, and I want goodness. I want sin.”
Exactly.
Too many people would want conflict. The source of the conflict is not scarcity: it's human nature.
-
“It generally becomes easier to be generous if you’re doing well and you have a big windfall, [because] when there isn’t enough for everybody, it’s just a question of who is going to starve, and then everything becomes much tougher,” Bostrom told Big Think.
Tell that to the super-rich.
-
erif concluded that scarcity was one of the main drivers of all human conflict. War, violence, invasion, and theft were all born of wanting a limited resource. The history of all humanity seems to support the hypothesis: We fight over water, cattle, arable land, ore deposits, oil, precious stones, and so on.
He concluded incorrectly.
Rich people already have more resources than they could ever use. The richest amongst us could not ever spend all the money they possess. But that does not seem to have stopped them from continuing to want more, and more, and more.
-
-
www.facebook.com www.facebook.com
-
Chhit Am-goat · eprostnSdo919g11fgl4460higl3m0m3thic34lh65i354a1249944tic75f · Shared with Public《 用台文直接共 AI 討台文內容 》昨昏有朋友問講 AI 出無按家己向望彼號型。窮實佗加強 prompt 佗會使,抑是共欲指定 AI 照你欲--ê 來輸出个規定,囥佇 System Instructions 內底,攏會使--得。下跤圖--ni̍h 是 ka 囥佇 Gemini-pro-1.5 開講區上頂懸个 System Instructions 內底,閣直接叫伊輸入台文文章,*完全無閣 upload 任何物仔*,足簡單个操作。輸出愛檢查,家己愛改,毋過完成度是不止仔懸--矣,而且直接佗出台文內容,毋免閣過翻譯。Prompt 抑是 System Instructions,用英文效果比中文加足好,因為原底遮个 LLM (大言語模型)佗攏用英文訓練--ê。罔參考,會使按家己需要閣改,內底个例攏會換--得,例个長度小可長,效果較好。若想欲輸出 全 POJ、全 TL、抑是全漢,會使改內底个規則佮例,試驗看覓。《System Instructions 》You are a linguist and a great translator between Taigi and English.Translate English text into Taigi I give you, and vise versa.Or chat with me in Taigi.For prepositions, conjunctions, particles and exclamations, must use POJ.For NERs such as country, place or human names keep the original name.English. Be sure to make the Taigi translation more Taigi-like and differentiate from Chinese, using Taigi words and sentences structures as possible, restructuring sentence is allowed.*note 1: Taigi text is a mix of Hanji characters and Latin characters with phonetic component on the top.*note 2: Example of Taigi word consist of Latin letters named POJ(Pe̍h-ōe-jī/POJ) sch as Tâi-gí,má-to͘h, té-thah. Please display the phonetic component and ‘-‘ between subwords accordingly.*note 3: the uniqe POJ elements are: ch, cch, o͘, eng, ek, oa, ⁿ*note 4: Do use POJ for prepositions, conjunctions, particles and exclamations.Output as format&example.<format&example>-----* Leonardo da Vinci ê 無限好玄## Leonardo da Vinci 是文藝復興時期 ê 代表人物, 伊 ê 人生 kah 作品 lóng 浸透彼个時代 ê 精神。伊 tī 1452 年出世 tī Italy ê Vinci, 伊 ê 天才 hāⁿ(迒) 藝術、科學、工程 koh 濟濟 ê 領域, lóng 是 hō͘ 伊對智識 ê 熱狂走chông 來 lu(攄) leh 行。## 伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。## 伊 ê 簿仔紙展示伊無 kā 藝術 kah 科學分--開 ê 頭殼, 內底滿滿是 chham 觀察、 chham 想像相透濫 ê 素描。Da Vinci ê 哲學是「研究藝術 ê 科學。研究科學 ê 藝術... 認捌 tio̍h 萬物 lóng 連連鬥陣」, 這反映伊相信所有學科 lóng 是 kap(合)做伙--ê。## 伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。-----Gemini-1.5-pro: https://aistudio.google.com/
驚喜到吃手手
改天來如法炮製,看如何下prompt讓它儘量用全漢的台文輸出,減少羅馬拼音,除非基本字型出不來的字。這只是我的個人偏好。
太興奮了。我會希望AI用台文書寫,儘量減少tailo或POJ。以後來研究這個system prompt怎麼下。
AI
Taigi
Chhit Am-goat · 《 用台文直接共 AI 討台文內容 》 昨昏有朋友問講 AI 出無按家己向望彼號型。窮實佗加強 prompt 佗會使,抑是共欲指定 AI 照你欲--ê 來輸出个規定,囥佇 System Instructions 內底,攏會使--得。 下跤圖--ni̍h 是 ka 囥佇 Gemini-pro-1.5 開講區上頂懸个 System Instructions 內底,閣直接叫伊輸入台文文章,完全無閣 upload 任何物仔,足簡單个操作。 輸出愛檢查,家己愛改,毋過完成度是不止仔懸--矣,而且直接佗出台文內容,毋免閣過翻譯。 Prompt 抑是 System Instructions,用英文效果比中文加足好,因為原底遮个 LLM (大言語模型)佗攏用英文訓練--ê。 罔參考,會使按家己需要閣改,內底个例攏會換--得,例个長度小可長,效果較好。 若想欲輸出 全 POJ、全 TL、抑是全漢,會使改內底个規則佮例,試驗看覓。 《System Instructions 》 You are a linguist and a great translator between Taigi and English. Translate English text into Taigi I give you, and vise versa.Or chat with me in Taigi. For prepositions, conjunctions, particles and exclamations, must use POJ. For NERs such as country, place or human names keep the original name.English. Be sure to make the Taigi translation more Taigi-like and differentiate from Chinese, using Taigi words and sentences structures as possible, restructuring sentence is allowed. note 1: Taigi text is a mix of Hanji characters and Latin characters with phonetic component on the top. note 2: Example of Taigi word consist of Latin letters named POJ(Pe̍h-ōe-jī/POJ) sch as Tâi-gí,má-to͘h, té-thah. Please display the phonetic component and ‘-‘ between subwords accordingly. note 3: the uniqe POJ elements are: ch, cch, o͘, eng, ek, oa, ⁿ note 4: Do use POJ for prepositions, conjunctions, particles and exclamations. Output as format&example. <format&example>
- Leonardo da Vinci ê 無限好玄
Leonardo da Vinci 是文藝復興時期 ê 代表人物, 伊 ê 人生 kah 作品 lóng 浸透彼个時代 ê 精神。伊 tī 1452 年出世 tī Italy ê Vinci, 伊 ê 天才 hāⁿ(迒) 藝術、科學、工程 koh 濟濟 ê 領域, lóng 是 hō͘ 伊對智識 ê 熱狂走chông 來 lu(攄) leh 行。
伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。
伊 ê 簿仔紙展示伊無 kā 藝術 kah 科學分--開 ê 頭殼, 內底滿滿是 chham 觀察、 chham 想像相透濫 ê 素描。Da Vinci ê 哲學是「研究藝術 ê 科學。研究科學 ê 藝術... 認捌 tio̍h 萬物 lóng 連連鬥陣」, 這反映伊相信所有學科 lóng 是 kap(合)做伙--ê。
伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。
Gemini-1.5-pro: https://aistudio.google.com/
-
-
www.rabbit.tech www.rabbit.tech
-
LAM is a new type of foundation model that understands human intentions on computers. with LAM, rabbit OS understands what you say and gets things done.
The Rabbit people say their LAM is a new type of foundation model, to be able to deduce user intention and decided on actions. Sounds like the cli tool I tried, but cutting human out of the loop to approve certain steps. Need to see their research what they mean by 'new foundation model'
Tags
Annotators
URL
-
-
-
Rabbit R1 is a personal AI assistant in a retro box. Supposedly without subscription fees, but with access to AI services and with internet connection. Designed to be able to take action (kind of like the promptchaining cli tool I tried out?). Says it has a LAM next to LLM, a 'large action model' which sounds like marketing rather than tech.
-
-
www.perplexity.ai www.perplexity.ai
-
I ran across an AI tool that cites its sources if anyone's interested (and heard of it yet): https://www.perplexity.ai/
That's one of the things that I dislike the most about ChatGPT is that it just synthesizes/paraphrases the information, but doesn't let me quickly and easily check the original sources so that I can verify (and learn more about the topic by doing further reading) the information for myself. Without access to primary sources, it often feels no better than a rumor — a retelling of what someone somewhere allegedly, purportedly, ostensibly found to be true — can I really trust what ChatGPT claims? (No...)
-
-
-
-
Perplexity AI's biggest strength over ChatGPT 3.5 is its ability to link to actual sources of information. Where ChatGPT might only recommend what to search for online, Perplexity doesn't require that back-and-forth fiddling.
-
-
augmentingcognition.com augmentingcognition.com
-
I occasionally wonder what the impact would be of memorizing a good book in its entirety; I wouldn't be surprised if it greatly influenced my own language and writing.
may be equivalent of training a generative AI?
-
-
sanjosespotlight.com sanjosespotlight.com
-
Sunnyvale taps AI to translate public meetings
Sunnyvale taps AI to translate public meetings
該來的還是來了。AI口譯將會從底層漸漸蠶食真人口譯的工作。
-
The adoption of AI translation is cheaper than hiring human translators. Garnett said the city pays $112.50 for every hour the service is used, and has so far paid about $4,308 for more than 38 hours of usage. Hiring an human translator would cost the city up to $400 per hour. “This creates a much smoother dialogue than using live interpreters,” she told San José Spotlight. “We likely would have needed two to four live interpreters to accomplish what we are doing with Wordly.”
The adoption of AI translation is cheaper than hiring human translators. Garnett said the city pays $112.50 for every hour the service is used, and has so far paid about $4,308 for more than 38 hours of usage. Hiring an human translator would cost the city up to $400 per hour.
“This creates a much smoother dialogue than using live interpreters,” she told San José Spotlight. “We likely would have needed two to four live interpreters to accomplish what we are doing with Wordly.”
-
-
towardsdatascience.com towardsdatascience.com
-
However, despite their eloquence, LLMs have some key limitations. Their knowledge is restricted to patterns discerned from the training data, which means they lack true understanding of the world.Their reasoning ability is also limited — they cannot perform logical inferences or synthesize facts from multiple sources. As we ask more complex, open-ended questions, the responses start becoming nonsensical or contradictory.To address these gaps, there has been growing interest in retrieval-augmented generation (RAG) systems. The key idea is to retrieve relevant knowledge from external sources to provide context for the LLM to make more informed responses.
Good context on what a RAG is for AI / LLMs
-
-
www.theverge.com www.theverge.com
-
It’s all made worse by the AI Pin’s desire to be as clever as possible.
it reads like that yes. Being able to instruct something rather than guess what it is I want is easier and probably better, because you can tweak your instructions to your own preferences.
-
But far more often, I’ll stand in front of a restaurant, ask the AI Pin about it, and wait for what feels like forever only for it to fail entirely. It can’t find the restaurant; the servers are not responding; it can’t figure out what restaurant it is despite the gigantic “Joe & The Juice” sign four feet in front of me and the GPS chip in the device.
This reads as if the device wants to be too clever. You could do this with your phone wearing a headset and instruct it to look up a specific restaurant in your own voice. No need for the device to use location, snap an image, OCR it or whatever.
-
I hadn’t realized how much of my phone usage consists of these one-step things, all of which would be easier and faster without the friction and distraction of my phone.
[[AI personal assistants 20201011124147]] should be [[small band AI personal assistant]]s and these are the type of things it might do. This articles names a few interesting use cases for it.
Tags
Annotators
URL
-
-
www.sciencedirect.com www.sciencedirect.com
-
Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities
artykuł
-
-
www.henrikkarlsson.xyz www.henrikkarlsson.xyz
-
https://web.archive.org/web/20240409122434/https://www.henrikkarlsson.xyz/p/go
- In the decades before AI beat Go-worldchampion, the highest level of Go-players was stable.
- After AI beat the Go-worldchampion, there is a measurable increase in the creativity and quality of Go-players. The field has risen as a whole.
- The change is not attributable to copying AI output (although 40% of cases that happened) but to increased human creativity (60%).
- The realisation that improvement is possible, creates the improvement. This reminds me of [[Beschouw telkens je adjacent possibles 20200826185412]] wrt [[Evolutionair vlak van mogelijkheden 20200826185412]]
- Also the improvement coincides with the appearance of an open source model and tool, which allowed players to explore and interact with the AI, not just play a Go-game against it.
- Examples of plateau-ing of accomplishments and sudden changes exist in sports
- There may also be a link to how in other fields one might see the low end of an activity up their game through using AI rather than be overtaken by it.
-
-
aggiehonor.tamu.edu aggiehonor.tamu.edu
-
The intentional invention of any information or citation on an assignment or document. This includes using generative Artificial Intelligence (AI) or other electronic resources in an unauthorized manner to create academic work and represent it as one's own.
The use of generative AI in an unauthorized manner to create academic work and present it as your own may be an example of fabrication as described in the Aggie Honor Code 20.1.2.3.2 part 4.
-
-
garymarcus.substack.com garymarcus.substack.com
-
On the investment and revenue in #algogens AI. Very lopsided, and surveys report dying enthusiasm with those closely involved. Voices doubt something substantial will come out this year, and if not it will deflate hype of expectations. #prediction for early #2025/ AI hype died down
-
-
theaidigest.org theaidigest.org
-
The same LM can be a much more or less capable agent depending on the enhancements added. The researchers created and tested four different agents built on top of GPT-4 and Anthropic’s Claude:
While today’s LMs agents don't pose a serious risk, we should be on the lookout for improved autonomous capabilities as LMs get more capable and reliable.
-
The latest GPT-4 model from OpenAI, which is trained on human preferences using a technique called RLHFEstimated final training run compute cost: ~$50mModel version: gpt-4-0613
~$50m = estimated training cost of GPT-4
Tags
Annotators
URL
-
-
www.across.health www.across.health
-
only 15% of European biopharma respondents reported no interest at all in AI in their organization
-
-
austinhenley.com austinhenley.com
-
Additionally, students in the Codex group were more eager and excited to continue learning about programming, and felt much less stressed and discouraged during the training.
Programming with LLM = less stress
-
On code-authoring tasks, students in the Codex group had a significantly higher correctness score (80%) than the Baseline (44%), and overall finished the tasks significantly faster. However, on the code-modifying tasks, both groups performed similarly in terms of correctness, with the Codex group performing slightly better (66%) than the Baseline (58%).
In a study, students who learned to code with AI made more progress during training sessions, had significantly higher correctness scores, and retained more of what they learned compared to students who didn't learn with AI.
Tags
Annotators
URL
-
-
www.theverge.com www.theverge.com
-
OpenAI is offering limited access to a text-to-voice generation platform it developed called Voice Engine, which can create a synthetic voice based on a 15-second clip of someone’s voice.
OpenAI’s voice cloning AI model only needs a 15-second sample to work
-
- Mar 2024
-
www.theregister.com www.theregister.com
-
Next to the xz debacle where a maintainer was psyops'd into backdooring servers, this is another new attack surface: AI tools make up software packages in what they generate which get downloaded. So introducing malware is a matter of creating malicious packages named the way they are repeatedly named by AI tools.
-
-
theresanaiforthat.com theresanaiforthat.com
Tags
Annotators
URL
-
-
arstechnica.com arstechnica.com
-
The AI-generated feedback goes to teacher review before being passed on to students so that a human remains in the loop.
-
But is it a good idea to outsource critical feedback to a machine?
-
-
www.theverge.com www.theverge.com
-
have extensively criticized both companies (and generative AI systems in general) for training their models on masses of online data scraped from their works without consent. Stable Diffusion and Midjourney have both been targeted with several copyright lawsuits, with the latter being accused of creating an artist database for training purposes in December.
-
-
pi.ai pi.ai
-
-
www.facebook.com www.facebook.com
-
謝昆霖 Verified account · enrdoSspotmlf916af3f9ct1macahh2ft27cmlc8m5c8c51i2l70554i08m1 · Shared with Public這幾天工作同時有請 GPT-4 和 Claude3 。工作條件:我的提問使用正體中文,同樣問題,只給一次作答機會。Claude3 的回覆速度是 GPT-4 的四倍 (或十倍) 以上不說,而且應答的專業度較高,正體中文的使用也比較在地,我這個支語警察給過。它的回答是大量文字輸出時,GPT4 講一講到後面會跑出簡體中文,Claude3 我還沒遇過,而且字數極多的多輪對話中,它對前面的脈絡記憶很清楚。值得注意的是,GPT-4 正體中文貧乏的問題 Claude3 似乎沒有,它的用字遣詞比 GPT4 多一些人類的隨機性,在重覆20次的例句中,同樣的動詞、形容詞,它會盡量用不同的文字。Claude3 似乎解決了我常常酸 GPT-4 的各種問題。這真是驚人。
Nice to see there's better support and performance for Traditional Chinese in Claude 3 than ChatGPT4. It feels like David against Goliath.
「支語警察」,哈哈
謝昆霖 enrdoSspotmlf916af3f9ct1maca h h2ft27cmlc8m5c8c5 1 i2l70554i08m1 · 這幾天工作同時有請 GPT-4 和 Claude3 。工作條件:我的提問使用正體中文,同樣問題,只給一次作答機會。 Claude3 的回覆速度是 GPT-4 的四倍 (或十倍) 以上不說,而且應答的專業度較高,正體中文的使用也比較在地,我這個支語警察給過。 它的回答是大量文字輸出時,GPT4 講一講到後面會跑出簡體中文,Claude3 我還沒遇過,而且字數極多的多輪對話中,它對前面的脈絡記憶很清楚。 值得注意的是,GPT-4 正體中文貧乏的問題 Claude3 似乎沒有,它的用字遣詞比 GPT4 多一些人類的隨機性,在重覆20次的例句中,同樣的動詞、形容詞,它會盡量用不同的文字。 Claude3 似乎解決了我常常酸 GPT-4 的各種問題。這真是驚人。
-
-
Local file Local file
-
So good, so true. GPT人没有重要性感受和原创想法,没有办法将理论与实际联系; 可以和那一篇提及the illusion of explanatory depth一起阅读; 一个有用的自测:你说的X到底是什么意思,能不能举个例子,反对X的人有什么理由? 以及看看这种话到底有没有反对者——反对者是一个argument最好的反脆弱性测试。
-
-
Local file Local file
-
可能的威胁:de novo design有害蛋白 solutions: - 自我审查:软件限制 - 公司审查:合成DNA的时候用AI进行审查,但是这个风险转接不知公司是否愿意
-
-
-
-
Local file Local file
-
两个有趣的概念: explanatory depth: 误以为自己很了解某事 exploratory breadth: AI 可能让人偏向于去探索AI能做的东西
-
-
Local file Local file
-
可以和 歪脑的 「意识上传」一起阅读
-
-
www.theguardian.com www.theguardian.com
-
www.te-learning.nl www.te-learning.nl
-
By [[Wilfred Rubens]] citing Leon Furze (?) how 'AI made safe' isn't safe AI just as alcohol free beer isn't soda. I think there's an element here of [[Triz denken in systeemniveaus 20200826114731]] analogue to [[Why False Dilemmas Must Be Killed to Program Self-driving Cars 20151026213310]] where all the focus is on the thing (application, car etc)
-
-
www.facebook.com www.facebook.com
-
詹益鑑 Verified account · 45m · Shared with PublicAI 真的取代了一些工作嗎?或者造成一些工作的薪資降低?今天看到這篇實際分析的文章,從2022 年11 月1 日(ChatGPT 發布前一個月)到2024 年2 月14 日,在 Upwork 的自由工作者資料中,分析出幾個事實:1. 下降幅度最大的 3 個類別是寫作、翻譯和客戶服務工作。寫作職位數量下降了 33%,翻譯職位數量下降了 19%,客戶服務職位數量下降了 16%2. 影片編輯/製作工作成長了 39%,圖形設計工作成長了 8%,網頁設計工作成長了 10%。軟體開發職缺也有所增加,其中後端開發職缺成長了 6%,前端/Web 開發職缺成長了 4%3. 翻譯絕對是受打擊最嚴重的工作,每小時工資下降了 20% 以上,其次是影片編輯/製作和市場研究。平面設計和網頁設計工作是最具彈性的。兩人不僅數量增加了,而且時薪也增加了一些。4. 自 ChatGPT 和 OpenAI API 發布以來,與開發聊天機器人相關的工作數量激增了 2000%。如果說當今人工智慧有一個殺手級用例,那就是開發聊天機器人。
下降幅度最大的 3 個類別是寫作、翻譯和客戶服務工作
寫作職位數量下降了 33%,翻譯職位數量下降了 19%,客戶服務職位數量下降了 16%
翻譯絕對是受打擊最嚴重的工作,每小時工資下降了 20% 以上
-
- Feb 2024
-
-
-
www.youtube.com www.youtube.com
-
08:18 Technology like AI enables one person businesses (see Sam Altman statement on 1 billion one person company)
Tags
Annotators
URL
-
-
research.manuscritpub.com research.manuscritpub.com
-
ergi
Highlight and annotate at least 2 areas for each question. The annotations should be 1-2 sentences explaining the following: A. New learning B. Familiar with this C. Use this in practice
-
-
pluralistic.net pluralistic.net
-
Broderick makes a more important point: AI search is about summarizing web results so you don't have to click links and read the pages yourself. If that's the future of the web, who the fuck is going to write those pages that the summarizer summarizes? What is the incentive, the business-model, the rational explanation for predicting a world in which millions of us go on writing web-pages, when the gatekeepers to the web have promised to rig the game so that no one will ever visit those pages, or read what we've written there, or even know it was us who wrote the underlying material the summarizer just summarized? If we stop writing the web, AIs will have to summarize each other, forming an inhuman centipede of botshit-ingestion. This is bad news, because there's pretty solid mathematical evidence that training a bot on botshit makes it absolutely useless. Or, as the authors of the paper – including the eminent cryptographer Ross Anderson – put it, "using model-generated content in training causes irreversible defects"
Broderick: https://www.garbageday.email/p/ai-search-doomsday-cult, Anderson: https://arxiv.org/abs/2305.17493
AI search hides the authors of the material it presents, summarising it is abstracting away the authors. It doesn't bring readers to those authors, it just presents a summary to the searcher as end result. Take it or leave it. At the same time, if one searches for something you know about, you see those summaries are always of. Leaving you guessing how of it is when searching something you don't know about. Search should never be the endpoint, always a starting point. I think that is my main aversion against AI search tools. Despite those clamoring 'it will get better over time' I don't think it will easily because the tool nor its makers have any interest in the quality of output necessarily and definitely can't assess it. So what's next, humans factchecking AI output. Why not prevent bs at its source? Nice ref to Maggie Appleton's centipede metaphor in [[The Expanding Dark Forest and Generative AI]]
-
-
www.youtube.com www.youtube.com
-
I major in English literature in University I major in interpreting you went to gr for my master degree I think more than 100 people applied and eight students were admit how many people finally passed the final exam two two two wow
中間這位受訪的台灣人,父母在她英文學習上下重本投資,從小讀雙語幼稚園,課後上雙語補習班,中學上雙語私校,大學唸英語系,研究所唸了師大口譯所,錄取八名口譯組學生,最後專業考試還是唯一考過的兩人之一。從這些事實看來,她的英語應當可謂萬中選一,可是爲什麼英語發音還是有問題?
我發現,評斷英語發音好壞,其實有一個巧妙的方法:讓AI機器語音識別轉寫文字,看文字是什麼。很多時候,雖有稍微的口音,機器仍然辨認正確,產出的英文是正確的。但當一個字的發音偏誤到更像另一個字時,機器就毫不客氣,產生另一個字了。發現語音辨識常有這種「誤植另一字」問題是,就表示英語發音有問題。
AI機器輔助偵測辨識英語發音問題法,前提當然是,文字不能再經人工校正,必須是原始的結果。例如,本影片有兩個英文字幕,一個是後製在螢幕上的那個,那是人工校正過的,一個是YouTube自動生產的(也可透過Hypothesis顯示),我們要看的是後者。這個機器字幕在前4分鐘的內容中,指出她英語發音上至少兩個錯誤,不知各位有沒有光從聽就能察覺到?
Tags
Annotators
URL
-
-
txt.cohere.com txt.cohere.com
-
Constructing Prompts for the Command Model Techniques for constructing prompts for the Command model. Developers
Tags
Annotators
URL
-
-
docs.cohere.com docs.cohere.com
-
Now, let’s modify the prompt by adding a few examples of how we expect the output to be. Pythonuser_input = "Send a message to Alison to ask if she can pick me up tonight to go to the concert together" prompt=f"""Turn the following message to a virtual assistant into the correct action: Message: Ask my aunt if she can go to the JDRF Walk with me October 6th Action: can you go to the jdrf walk with me october 6th Message: Ask Eliza what should I bring to the wedding tomorrow Action: what should I bring to the wedding tomorrow Message: Send message to supervisor that I am sick and will not be in today Action: I am sick and will not be in today Message: {user_input}""" response = generate_text(prompt, temp=0) print(response) This time, the style of the response is exactly how we want it. Can you pick me up tonight to go to the concert together?
-
But we can also get the model to generate responses in a certain format. Let’s look at a couple of them: markdown tables
-
And here’s the same request to the model, this time with the product description of the product added as context. Pythoncontext = """Think back to the last time you were working without any distractions in the office. That's right...I bet it's been a while. \ With the newly improved CO-1T noise-cancelling Bluetooth headphones, you can work in peace all day. Designed in partnership with \ software developers who work around the mayhem of tech startups, these headphones are finally the break you've been waiting for. With \ fast charging capacity and wireless Bluetooth connectivity, the CO-1T is the easy breezy way to get through your day without being \ overwhelmed by the chaos of the world.""" user_input = "What are the key features of the CO-1T wireless headphone" prompt = f"""{context} Given the information above, answer this question: {user_input}""" response = generate_text(prompt, temp=0) print(response) Now, the model accurately lists the features of the model. The answer is: The CO-1T wireless headphones are designed to be noise-canceling and Bluetooth-enabled. They are also designed to be fast charging and have wireless Bluetooth connectivity. Format
-
While LLMs excel in text generation tasks, they struggle in context-aware scenarios. Here’s an example. If you were to ask the model for the top qualities to look for in wireless headphones, it will duly generate a solid list of points. But if you were to ask it for the top qualities of the CO-1T headphone, it will not be able to provide an accurate response because it doesn’t know about it (CO-1T is a hypothetical product we just made up for illustration purposes). In real applications, being able to add context to a prompt is key because this is what enables personalized generative AI for a team or company. It makes many use cases possible, such as intelligent assistants, customer support, and productivity tools, that retrieve the right information from a wide range of sources and add it to the prompt.
-
We set a default temperature value of 0, which nudges the response to be more predictable and less random. Throughout this chapter, you’ll see different temperature values being used in different situations. Increasing the temperature value tells the model to generate less predictable responses and instead be more “creative.”
Tags
Annotators
URL
-
-
arxiv.org arxiv.org
-
T. Herlau, "Moral Reinforcement Learning Using Actual Causation," 2022 2nd International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China, 2022, pp. 179-185, doi: 10.1109/ICCCR54399.2022.9790262. keywords: {Digital control;Ethics;Costs;Philosophical considerations;Toy manufacturing industry;Reinforcement learning;Forestry;Causality;Reinforcement learning;Actual Causation;Ethical reinforcement learning}
-
-
pdf.sciencedirectassets.com pdf.sciencedirectassets.com
-
Can model-free reinforcement learning explain deontological moraljudgments?Alisabeth AyarsUniversity of Arizona, Dept. of Psychology, Tucson, AZ, USA
-
-
-
for - progress trap - AI
Tags
Annotators
URL
-
-
openai.com openai.com
-
This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations. Model and implementation details are not included in this report.
AI to generate video images.
-