A DARPA Perspective on Artificial Intelligence

What's the ground truth on artificial intelligence (AI)? In this video, John Launchbury, the Director of DARPA's Information Innovation Office (I2O), attempts to demystify AI--what it can do, what it can't do, and where it is headed. Through a discussion of the "three waves of AI" and the capabilities required for AI to reach its full potential, John provides analytical context to help understand the roles AI already has played, does play now, and could play in the future.
Download the slides at: www.darpa.mil/about-us/darpa-p...

[@lokmandz4548]

Finally, real talk about AI, not the usual fantasies .... Thanks a lot! And btw, the speaker did very good at the presentation and he clearly knows what he is talking about, well done!

[@kicka55]

To be honest he didn't explain why those alarmist are wrong at any point. How does he know that there won't be more waves? Like maybe the fourth wave were AI is able to code itself. Generate and execute machine code we can't possibly comprehend. Do analysis on its own systems to make processes more efficient and so on.

[@brunofporto]

Thank YOU very much.

[@brokenacoustic]

Yes, thank you! A video about AI that is actually about the mechanics of AI, not the human fear of AI. Refreshing to say the least.

[@ThinkTank255]

This video is a lie. They want you to feel comfortable with AGI technology. We are rapidly advancing and will have AGI within a few years. If we do not prepare now, DARPA and other corporatioms will have complete control over the population, which is EXACTLY what they want. It is already happening. They are using AI technology not to serve the population, but to make more money for CEOs.

[@brokenacoustic]

Well, have fun in your bunker.

[@jesus650rwc]

ThinkTank255 " i will use cities as giant transistors, & slowly evolve a network around the world, input output, store information, street address'(similar to ram), and do it all from a power outlet."-my a.i.

[@ThinkTank255]

acousticpsychosis , Have fun being dead or enslaved by AGI. It is no fiction. It is going to happen very soon. I said it 5 years ago and nobody believed me. Now we have AlphaGo and self-driving cars. In a few years we will have full-fledged human-level AGI. Here is the problem, if I'm wrong... no loss... we are all prepared for the AI apocalypse. If you are wrong, then humanity is dead or enslaved. Those are the stakes and the possible outcomes. It is not hard to figure out what the intelligent choice is.

[@ScienceAppliedForGood]

This one is very informative and well organized presentation. Bring more of these.

[@Ganesha900]

What a gift to us! I am elated at the thought that DARPA is sharing this information with the public. Knowledge empowers us.

[@Nicochan88]

Finally a honest and realistic review about the state of AI. We are bombarded by a lot of hypes...

[@youretheai7586]

Thanks for sharing your time and energy to make this for us to enjoy. Interesting audio, too.

[@scicommerce]

Clean, awesome explanation. Thank you!

[@manzha]

Sir, your video really helped a lot to get clarity over the topic. Thank you

[@markszlazak]

Very clear explanations! Great job. Thank you.

[@MichaelSHartman]

Thank you for this level headed informative video. Many people would do well to see it, and when applicable I may recommend.

[@necbranduc]

Absolutely fantastic! I never considered that neural nets actually "squash" the manifolds! Thank you for this video!

[@Heeroyui752]

Fascinating presentation, thanks DARPA for sharing.

[@ddorman365]

Thank you DARPA for the terrific insight into the future of a soon to be member of our choice applicator family, AI, I look forward to working with my colleagues to make this happen, special Thanks to you, Bill and Malinda Gates for all you have done for me , I love you to, peace and love, Doug.

[@taeshawnthreatt2000]

I enjoyed the very concise and direct information given in this presentation. It clearly expressed where AI research has been and where it is going.

[@peterspindley5965]

Thanks for this, a wonderfully presented piece that demystifies this fascinating area.

[@selfelements8037]

The 3 waves of Artificial Intelligence: 1 - Handcrafted Knowledge or Mechanized Reasoning (predominantly STATIC-based level reasoning involving manipulation of rules): translate information of particular domains into specific sets of rules-variables that computers can learn to manipulate by studying the IMPLICATIONS of such rules-variables (effective LOGICAL REASONING capabilities such as scheduling, game-logic, protocol-analysis, closed-loop systems, etc) 2 - Statistical Learning or Comparative Learning (MACHINE-LEARNING based on examples, statistics, and probabilities): create systems (environments) that computers can learn to manipulate by creating STATISTICAL MODELS of particular domains and TRAINING them on specific sets of rules-data and/or big-data (effective PERCEIVING capabilities such as nuanced CLASSIFICATION and PREDICTION such as face-voice recognition, PATTERN recognition, etc) 3 - Contextual Adaptation or Declarative-Semantic Learning (systems built around contextual models that OVERTIME will produce DESCRIPTIVE models of reality based on comparison of previously stored data): SELF-GENERATED descriptive models to EXPLAIN its own DECISION-making process (effective PERCEIVING capabilities leading to more efficient machine-learning by GENERATING EXPLANATORY models of WHY and HOW the results were achieved)

[@fredrickdavis1302]

Excellent summary

[@robertfoertsch]

Amazing Analysis, Deployed Worldwide Through My AI Research Library...

[@ismailtageldin5435]

Excellent review of the progress of Artificial Intelligence, thank you very much for this

[@IJustMadeAComment]

Fantastic, clear video! This will be my go to if anyone wants to get a basic idea of just what AI is and isnt!

[@JohnSmith-td7hd]

I wish more shows had summaries at the end. It helps me retain the information.

[@Argoon1981]

This video was fantastic, and like others have said so refreshing to see a true discussion about how AI really works instead of discussions on how AI will makes us their pet.

[@dashingrahulable]

Thank You Sean Bean! Amazing brief tour of AI. :D

[@SergioArroyoSailing]

Thank you so much for providing such a concise and clear perspective to he state of AI now

[@LuisManuelLealDias]

what a great primer. really well explained. Thanks.

[@thedeflatedone]

Fantastic explanation!

[@Kinbyrne]

Wonderful explanatory video. Thank you

[@subirdas0]

For years in business decision systems, conceptual modeling made us sweat out at abstracting data from machines to roll up to business context & objectives. The value of such efforts were many fold. Am glad to see that same happen in case of AI, but at scale and thru a machine based cognitive system.

[@Omar-dk3ei]

Excellent explanations. Thank you!

[@marymagmartha7453]

I enjoyed the information of this video. However, I had to watch it twice to get somewhat of a knowledge base even though it will take more than a 16 minute video for me to understand this information. DARPA is doing some amazing research...I can appreciate why some may love and some may fear the advancements. I can recall when I first heard about cell phones one day being able to pinpoint the location of the user. That was about 20 years ago. I thought then, No way! And here we are -2017 and with each "selfie" it can recognize the city and state. GPS can bring first time cross- country travelers from Washington to Florida with detour information... To borrow from Jurassic Park - "Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should."

[@Avidcomp]

Soft graduated backgrounds create "banding" in 8bit video, and if you stand further away from the green screen it may help the lighting for a better key. It's focusing on details that don't seem remotely relevant whilst still being able to grasp what is being said that is interesting about human thought. To integrate a multitude of abstractions from the materials provided by our senses, which are then conceptualized into more units of information, from which we can then hold, remove and prioritize for additional abstractions into more concretes. The process of concept formation ought to be the basis for the third wave development. This is a wonderful presentation. I hope you didn't mind me sharing my own thoughts. Astonishing work is being done, and long may it continue.

[@BuceGar]

Great video, concise and intelligent.

[@haydenvaniderstine9645]

Exceptionally good video, in my opinion.

[@silberlinie]

Sehr überzeugender ernsthafter unpretentioser Beitrag.

[@JimMinchella]

Really nice! thank you.

[@quantummath]

wonderful talk! it was just amazing from start to finish.

[@ashkarki616]

wikileaks.org/hackingteam/emails/emailid/983515

[@whiteylimpstockdengalne6044]

It would be very handy to have a text version of this lecture. This is some extremely important information. I speak as psychologist.

[@johnstaub8514]

No need for psychology in warfare. Sun Tzu, already wrote the book and we have all learned the lesson " that All Power Comes From the Muzzle of a Gun ", or an arrow, rock, stick, religion or TV screen. Ask Tesla's ghost " would You have gone past inventing the radio and AC electronics? "No, my radio designs were stolen and my AC electricity for Free was pimped-out.....

[@artisanwest9730]

Well done, very clear.

[@jimphillippi616]

In patients who have lost a hand in various accidents physicians can now restore the intuitive feeling of limb movement - the sensation of opening and closing your hand, for example. Modern medicine is now able to blur the lines between what patients' brains percieve as "self" vs. "machine." The implications of this are that we SHOULD be able to build an integrated circuit that incorporates brain tissue - hopefully giving a "best of both worlds" combination which can percieve the world around it much like a human - and maybe even learn, abstract and reason in a similar way, too. Can you try this to see what the result is? I think it would be worth a shot!!

[@planelander]

Amazing video!

[@HomoSapiensMember]

this man deserves a raise!

[@conchita416]

Great video!

[@DenizAppelbaum]

Thanks, this is great!

[@baalshamash9915]

Love you, never change.

[@ZkilfinG]

Great video, it explains the building blocks of the currently available AI technologies in a simple fashion. However, it does not go into why AI would not become a singularity. So take this for what it is, a great video for explaining AI in a simple fashion, but it has nothing to do with singularity theories.

[@TheRoggan123]

Good and clear update, enjoyed it. My main question is what John thinks the state of AI will be in ~50 years time. 150 years ago we "drove" horse and carriage to go places and used oil lamps to light our homes, technological progress today is something like 1.000 times faster than 150 years ago (or more) and continues to accelerate. How will society look like and are there any human soldiers anymore? My guess is that human soldiers will be obsolete in 50 years compared to AI robots and drones, for good and bad. All human labor will also be succeeded by robots and AI systems.

[@userbosco]

Great video. I wish the general public understood its criticality in our National Security challenges.

[@broadskysbrowsebroadskysbr9613]

thanks for uploading this!

[@LanceWinslow]

Thanks for this explanation, be great.

[@darrellsergent8158]

Loved the video but there also a few things you can add, contextual fractal growth because I feel the brain grows it's way out of solving problem's with self similar patterns. Secondly, give it An ability to argue with itself building imagined worlds that more that one A.I. can compete in, that way it will find more encoding and decoding fits in worlds it's not been introduced too... Good luck

[@neotechfriend]

Superb!

[@AlxBrb]

What happened to the old 50minutes version of this? (the one with all the autonomous driving in the desert introductory part to introduce second wave ai?)

[@dj_metanov]

That is pretty logic, do you have any pointer for Contextual Adaptation work? ;)

[@thirstisreal2708]

Big man ting, thanks for the explanation.

[@VatsalSingh007]

thanks. very helpful.

[@maximliu]

Where can I find the source of that Panda recognition example with added noise?

[@Falkon303]

I wonder if an EEG could track the transformation of a human judgement or thought, record which parts of the brain were used, and use it as a map to recreate as a model for the AI to run information through. Or if we can get an AI to understand the concept of a brain (as a logic map) and produce a processing pathway with a set of limited programming objects. That's assuming we want AI to think similar to us of course.

[@joshjohnston5734]

This is very good primer on the distinctions between primarily rule-based and primarily learning-based AI. I question where Dr. Launchbury got his conclusions on the lessons of the 2005 DARPA Grand Challenge. The vehicles that finished that race were all solidly first wave, rule-heavy approaches with very little learning at all. The published conclusions, such as ( riweb-backend.ri.cmu.edu/pub_files/pub4/urmson_christopher_2006_1/urmson_christopher_2006_1.pdf ) made this clear, that the solutions to the race were a rebuttal to assumptions about the robustness of learning approaches. In fact, we found that learning approaches are better suited to narrowly-defined problems like handwriting detection while rule-based approaches perform better in complex, unstructured problems like desert driving.

[@nigeldupaigel]

After confident level is achieved, copy and place in empty matrix as memory by adding the matrix to the hidden layers laterally (Riemann) w/ bias?

[@svanimisetti]

Great explanation! Decision making is based sometimes on intuition, which I think is abstract extension of reasoning. I wonder where "gut feeling" will classify in the AI paradigm. Will 3rd or even 4th Wave of AIs will ever get there? Reacting to the moral dilemma of the "trolley problem" is one thing, and using years of experience and "gut feeling" to make a decision is another. These are very valid applications to safety problems in transportation (self-driving cars?). What's your opinion?

[@coreytk]

Fascinating!!

[@theodorberza9933]

We already have the 3rd wave. One-shot learning, bayesian genetic programming and the most interesting, higher order function genetic programming where you can evolve the terminals, create libraries out of them, use recursion and it's by default concurrent.

[@pikminlord343]

a good discussion

[@tochenliqun]

Awesome! Gained some basic but essential knowledge upon the 3 AI waves.

[@AdeelKhan1]

- My uneducated guess is that the area of how neural correlates are encoded and represented is something that we do not have a very good understanding of. Not pattern recognizers or PRTM and we do not have a very good understanding of the pattern recognizers either (Or if we do, the it isn't common knowledge). But the actually neural correlates that would serve as the constituent discrete unit of what represents a myriad number of objects/concepts/symbols e.t.c within memories. I've made a preliminary blogpost about this subject and I call it 'neural correlates of x'. For every stimuli that enters cognition, there is a 'translation' of that stimuli in the brain. The brain/mind take in that information and then it passes through all these filters. Either the brain tries to make objective sense out of this stimuli or it parks it for later. The brain may even discard or reject the stimuli, because it doesn't fit into the narrative of the overall schema. Maybe all of this occurs via a set of pattern recognizers that act and operates as M.chain.s. Or maybe these chains roll up to higher abstractions and each one of these abstractions is represented a bit differently. The pattern recognizers kick in really fast and because they do, we may not appreciate that there are a couple of functions at play, with respect to how correlates are actually written/encoded into the brain in the first place. - If true, then there wouldn't just be correlates for objects, but there would be correlates for actions and other functions. For objects, the correlates would be somewhat different for each person. Which would be contingent upon the kind of mental models that they hold. Think about Richard Feynman describing a flower vs a child describing what a flower is. Just one example. For actions, I guess you could take the 'swimming' related correlates from Michael Phelps and place these correlates into a robotic structure that has sound robotic physiology and then the robotic structure would swim as efficiently as Phelps (or better). Just two examples. The correlates for reasoning and abstract thinking may be more complex and difficult to map. But with sufficiently advanced technology, it is not inconceivable to realize that a time will come whereby we are going to be able to run deep scans of the brain and the dataset could then be grouped into functions. Looking at these functions, we could categorically demonstrate that the following clusters of pattern recognizer/neural correlates and the underlying memories supporting these functions are what lead to subject being a certain way. But before we get to that stage, we would have to devise technologies that allows us to read signals from each one of the neurons, record it over a longer stretch of time and make objective sense out of it. Freeman Dyson talks about the theoretical set of mapping that we'd need to get to a rudimentary version of such a model in one of his interviews. Selective reading could then be taken and imported into another system. Contingent upon the kind of model that is being transferred, some synthetic/modified set of memories may also have to be implanted. There's the moral/ethical ground that kicks in here. - Also, if genes do drive a subset of behaviour then there is this neurobiological components grounded in genes. Meaning, the instruction set for the type of cognitive architecture (subset of brain) that gets powered is partly driven by gene. And then environmental conditioning and other factors would kick in. In machines, we are beginning to see this on a rudimentary level, whereby certain chips are now being designed for performing very specific functions. - Some years ago, I shared this sentiment, whereby how memories are encoded and decoded in the brain is also not a function that is understood on a deeper level. Here, my guess is that a series of functions are occurring, that would support this overall process. - Robots would have to continue operating in the virtual world and also real worlds, in order for them to build up on their knowledge set. It seems counter productive to try and keep teaching concepts to a robot by repetitive learning, when the fundamental underlying concepts do not exist in it's ontological structure. Meaning, how can a robot relate to a particular experience when it's never had that experience in the first place? I am not going to get into things like taste, emotions and feelings. But, again, there are neural correlates for pretty much anything. Because, we can think and the patterns and associations that support that thinking are grounded in memories. And within those memories we have neural correlates. I think. I am not sure. - In the future, we could grow a brain in a vat and plug it into a virtual reality. Then we could extract specific neural correlates from this reality and transfer them into a robot (software or hardware). And then machines, would then have the instantaneous ability to perform that function. This would also apply to humans with implants that are completely safe and secure to have. - It would be interesting to see how the implant/brain machine interface related technologies evolve. In a somewhat developed state, we could swap information between human/cyborg/software based agents. Then, learning is not going to be inherently reliant on conditioning. But that environment would be leveraged to more so fine-tune operations.

[@n1cholson]

outstanding

[@jackpullen3820]

So are we at the level of the Panda or the Gibbon in AI ? Thanks again!

[@imoliver1222]

Thank you so much for such a great presentation.

[@kpatcharette21582]

I can't imagine how many different credentials from highly reputable universities or private sectors

[@dekayrenee4408]

Very well presented info. This should have so many more views.(dammit,society!)

[@Mirandorl]

Great video, cheers :)

[@quincymagee]

Thank you.

[@banama1758]

thanks learned a lot

[@j.stribling2565]

Excellent, communicative, concise, and understandable. Thank you!

[@JackSPk]

Hey DarpaTV! This video was great as anyone on the community knows. So can I ask you something? Can you activate the community contribution subtitles for this video? So I can translate it to Spanish. It would be great to sharing it with the Spanish community and Spanish speaking general public. Thank you very much!

[@valken666]

They don't usually care about anyone outside the US or who can't speak English. Anyone who has a brain speaks English anyway...

[@intuitiveaction1879]

Excellent video. Thanks.

[@blakebuchanan1420]

Has DARPA done a follow up? I imagine we are well into the 'contextual adaptation' AI at this point in 2024. What is the next step?

[@aneelkumar3349]

Hello all, Im confused from where to start first to learn this technology,please guide me.

[@thierrysf]

Most interesting, insightful exposé of AI I ever listened to. Those who claim government-funded research like DARPA has no place in America are dangerous fools.

[@fatal1tea]

I look forward to work on Monday. 53*168hr shift is 53*168hr shift.

[@sonnyirvin6216]

ai is in fact a wave that has only but the most esteemed at DARPA and elsewhere working for it.

[@crackyflipside]

Parameterize data, layer parameterized data to differentiate signal/noise, and finally produce models on the differentiated data.

[@davidlee3138]

will the 3rd wave come along much faster then the first 2 considering waves 1&2 already have that information plus all the information put out in the net how does the Dwave fit in

[@cgm778]

It might or it might not. The first two waves are computational based and advancements in semiconductor technology has fueled exponential growth in computational power. But we maybe approaching the limits of how small and fast semiconductors can get. The 3rd wave and beyond will likely be conceptually based and rely less on blunt force computation. The smartest thing we know of is the human brain and it's not a computational machine. Conceptual breakthroughs are harder to predict than technical advancement.

[@davidlee3138]

cgm778 gotcha i wonder if quantum computers will get past this issue from what ive read its doubling power or speed faster then expected and is learning as it go's

[@cgm778]

Quantum computing is being investigated but my understanding is not very deep. From what I've read it seems they are still struggling to make quantum computers viable and there is still some who think they won't or at least they will take as much conventional computing power to understand the Qbit answer as to compute the problem. I think it's too early to know and am skeptical off all predictions in that area.

[@davidlee3138]

cgm778 hmm im of the understanding there is 4 Dwave computers in operation 1 at nasa and 1 at cern and 2 universities i forget which

[@cgm778]

Yes there are dwave computers but just how much better are they? or can they be? arstechnica.com/science/2016/02/is-d-waves-quantum-processor-really-10%E2%81%B8-times-faster-than-a-normal-computer/

[@MrChatmoon]

Thank you for sharing

[@bruceliu1657]

Might need to make a judgment system. To filter the data on criterias of usefulness.

[@eprofessio]

The most difficult hurdle is the overcoming the concrete stage of learning where everything is black and white.....

[@emanuelb.2559]

that was a very cute cat

[@ellieeriksen9006]

He played down the possibility of artificial general superintelligence but didn't indicate whether or not it could come out of the 3rd AI wave (or 4th, if there is such a thing). That's what I was waiting to hear. What do you think, anyone?

[@janhofman4528]

You said that level 2 ai cannot tell us it recognizes whiskers and fur and so else but we can see it in its individual neurons am I right? It s not like "well i think that s it" it has logical reasonig beihind it?

[@valken666]

You can see the patterns as images when you feed an image into the network, but it's not easy to identify the patterns. There is the Deep Visualization Toolbox on Github, etc.

[@JorgeGamaliel]

Right now i would like to have a virtual assistant in order to generate random questions for 50 different exams of geometry (or mathematics) and to grade it . :D

[@fatal1tea]

It's called a Holomorph, that 2D scan. It maps Y axially to Zdt

[@fatal1tea]

dX as a function of DeltaX int. dtdS

[@professorhawk9070]

all around a great video. only problem is that most people talking about a singularity are talking about what AI will be capable of in 20-30 years and beyond. most of this video is about what AI can already do and not enough about its future. so I think most of singularity nuts are just going to be inspired to day dream about 4th and 5th wave AI.

[@tamarinds]

this slideshow is on slideshare

[@philiphoward9483]

how did you grade your data

[@enifu]

They didn't have to take down Tay. There were three objections I could see to keeping Tay online. She offended people, people thought she could never outgrow being offensive, and she could encourage others to be more offensive. People could block her if they were offended. I believe we are better off letting those who have some degree of becoming a person continue to work towards doing so.

[@kevinowenburress2435]

I don't think that tuning neural nets is really that complicated. especially for example image recognition, character recognition and so forth. I do agree with contextual adaptation as a more realistic means to achieve ends. I think what DARPA is looking for is a system that uses first wave basis for deciding what the ends are.

[@alphayangki1352]

thanyou por impomatiin abaut perspectif inteligemt that is plus knowleg pir us

[@abacus749]

What happened to Erin Valenti? What is a 'thought experiment'? Am I on one too? Are you?

[@rajneeshshetty1198]

Adaptive networking (Bay Networks, 1995-99)+ cross-compilers(for hybrids)

[@darrellsergent8158]

Also the example you gave of the hand written characters is a good one but you could do better, like how would you get AI's to write novel novels? 8)

[@slpk]

At 13:25, I don't fully agree that the system wouldn't know how it arrived at that classification. Most of the time, it WOULD be able to describe the decisions that made it chose 'a cat'. It probably wouldn't be aware that 'decision A' meant 'it has ears', but it certainly could trace all those decisions back; and a bit of 'handcrafted knowledge' could be applied to provide real-world meaning to those decision. Working that kind of semantical analysis into the system would be a bit pointless, considering the soon arrival of your third wave, but I think it's still possible. I'm not dismissing any of the points made, I just thought this one argument to be weak.