Artificial Intelligence: Drawing Inspiration from Human Capabilities

This video is both an introduction to the recent paper by Francesca Rossi and her team at IBM, and to Luis Lamb's most recent paper . Thinking Fast and Slow in AI Neurosymbolic AI: the 3rd Wave of AI Both these papers are drawing inspiration from human capabilities to build a future generation of artificial intelligence that would be more general and trustworthy. Then, there are 10 important questions for the AI community to consider in their research. Enjoy the video! Chapters: Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise. 0:00 Neuro-Symbolic AI: The Third Wave 5:33 Thinking Fast and Slow 11:01 Thinking Fast and Slow in AI - 10 Questions for the AI Research Community 16:06 References: Thinking Fast and Slow by Daniel Kahneman (Book): https://amzn.to/2XHehG1 Neurosymbolic AI: The 3rd Wave: https://arxiv.org/pdf/2012.05876.pdf Thinking Fast And Slow in AI: https://arxiv.org/abs/2010.06002 AI Debate 2 by Montreal AI: https://youtu.be/VOI3Bb3p4GM Follow me for more AI content: Instagram: https://www.instagram.com/whats_ai/ LinkedIn: https://www.linkedin.com/in/whats-ai/ Twitter: https://twitter.com/Whats_AI Facebook: https://www.facebook.com/whats.artifi... Medium: https://medium.com/@whats_ai Youtube: https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join Video Transcript ( : this transcript is auto-generated by YouTube and may not be 100% accurate) Note 00:00 okay let me say let me tell you about my 00:03 experience you know in dealing with 00:05 trust and 00:06 ai ethics in general and also what i 00:09 think are some 00:11 main points so some of them are 00:12 technical and some of them are not 00:14 in really achieving this ecosystem of 00:17 trust around the ai so this is the 00:19 overall picture that i think that 00:21 many of the previous panelists put 00:23 forward you know we want the future 00:25 i would say of and with ai because of 00:28 course i don't think as ai 00:30 uh at ai as just autonomous systems but 00:33 also systems that work with us 00:34 so it's not just the future of the eye 00:36 for me but also with ai 00:38 uh and it has all these uh desirable 00:41 properties of course trustworthiness is 00:44 one but of course general collaborative 00:46 and 00:46 as already mentioned for gdp three and 00:48 language very huge launch and matter 00:50 also sustainably 00:51 uh computationally but how to focus on 00:55 this 00:55 third panel how do we build an ecosystem 00:58 of trust 00:58 and i and i talk about an ecosystem of 01:01 trust because 01:02 it has many dimensions just like 01:04 trusting other people as many dimensions 01:07 so of course we want ai system to be 01:09 accurate 01:10 and that's but beyond accuracy we really 01:13 want 01:14 a lot of desirable properties one of 01:16 them i called it and 01:18 some other people call it value 01:19 alignment which is around fairness you 01:22 know what how do we want these 01:25 machines to behave to behave according 01:27 to some values that we care about 01:28 one of them of course is fairness so we 01:31 want the bias to be 01:32 identified removed and so on but 01:35 it may have maybe other values that are 01:37 beyond fairness 01:38 robustness also you know 01:40 generalizability beyond 01:42 you know some data distribution and 01:44 explainability explainability is very 01:46 important especially in the context of 01:48 machines that work together with human 01:50 beings 01:52 now but differently from what we would 01:55 expect in building trust with another 01:56 human being 01:58 here we are not in in the presence of 02:01 another human being we are the presence 02:03 of ai systems that are created by human 02:05 beings 02:06 so just like margaret and others have 02:08 said we want 02:09 something also from those that create 02:12 that ai 02:12 system from the developers from the 02:14 deployers for those that use the ai 02:16 and one of the things that i think 02:18 margaret pointed out very clearly and 02:21 we have a very similar approach is we 02:23 want transparency we want transparency 02:25 about 02:26 the decisions that have been made during 02:28 the ai pipeline 02:30 whether their decision about the 02:31 training data or other decision and 02:34 in that very nice visual way margaret 02:37 showed that bias can be injected in many 02:39 different uh 02:40 places in the ai pipeline uh we don't 02:42 have the concept of a model card but a 02:44 very similar one called the ai faction 02:46 and in fact we work together with 02:48 margaret and others also within the 02:50 partnership and ai 02:51 to compare and learn from each other 02:53 these different ways to achieve 02:55 uh transparency the second point that i 02:58 want to make is that 02:59 of course we want these developers also 03:02 to work according to some ai 03:04 ethics and guidelines and principles but 03:07 principles are just the first step 03:10 in in in a corporate place where 03:13 ai is being produced and deployed so it 03:16 really needs a lot of multi-stakeholder 03:18 consultation 03:19 education training and as margaret 03:22 already mentioned diverse teams 03:24 you know to bring all many different 03:26 backgrounds it needs a lot of technical 03:28 tools 03:29 for example to detect mitigate bias to 03:31 generate explanation and so on 03:33 it needs a lot of work in helping 03:36 developers understand 03:37 how to change the way they're doing 03:39 things how to make it as easy as 03:41 possible to adopt a 03:43 new methodology and how to build an 03:45 overall governance 03:47 in a company within you know that is a 03:50 kind of an 03:50 umbrella over what developers are doing 03:53 what the business units are doing and so 03:55 on so it's really a process 03:57 and that's why i put this picture of 03:58 trust with all the cranes because it's a 04:00 process to build trust in ai 04:04 so the last point that i want to make is 04:06 that uh 04:07 for all these properties that we want in 04:09 the ai systems in order to be able to 04:12 trust them 04:12 unfortunately current ayai is not there 04:15 yet 04:16 these are the reasons why francisca 04:18 rossi and her team at ibm 04:20 published this paper proposing a 04:22 research direction to advance ai 04:24 drawing inspiration from cognitive 04:26 theories of human decision making 04:29 where the premise is if we gain insights 04:32 into human capabilities that are still 04:34 lacking in 04:35 ai such as adaptability robustness 04:38 abstraction generalizability common 04:41 sense and causal reasoning 04:43 we may obtain similar capabilities as we 04:46 have 04:46 in an ai system nobody knows yet what 04:49 will be the future of ai 04:51 will it be neural networks or do we need 04:53 to integrate machine learning with 04:55 symbolic and 04:56 logic based ai techniques the latest 04:59 is similar to the neurosymbolic learning 05:01 systems 05:02 which integrate two fundamental 05:04 phenomena of intelligent behavior 05:06 reasoning and the ability to learn from 05:09 experience 05:10 they argue that a better comprehension 05:12 of how humans have 05:14 and have evolved to obtain these 05:16 advanced capabilities 05:18 can inspire innovative ways to imbue ai 05:21 systems with these competencies 05:23 but nobody will be better placed than 05:25 lewis lamb 05:26 himself to explain this learning system 05:28 shown in his recent paper 05:30 neurosymbolic ai the third wave 05:34 what we want to do here is exactly this 05:36 convergence because one of the key 05:38 questions is to identify 05:40 the building blocks of ai and how to 05:44 make 05:44 a.i more trustworthy ai 05:48 explainable but not only explainable 05:50 interpretable as well 05:52 so in order to make ai interpretable 05:55 sound and to use the right models 05:59 right computational models so that one 06:01 can explain what's going on in ai 06:03 we need better representations we need 06:06 models that are sound 06:07 and soundness and the results that come 06:10 from logic 06:12 the correctness results and all of that 06:14 can benefit 06:15 of course the great results you are 06:17 having on deep learning so 06:19 our work corroborates this point that uh 06:22 gary marcus made and also that danny 06:25 kanema made 06:26 at tripoli i that system one i mean the 06:29 fast system one that's associated with 06:32 concepts like deep learning 06:34 certainly knows language as daniel 06:36 kahneman said and system 06:37 2 which is more reflective certainly 06:40 does involve 06:41 certain manipulation of symbols so this 06:44 analogy of system 1 and 2 06:46 leads us to build the ideas that are 06:50 the inspiration the inspiration that 06:52 gary brought in his book the algebraic 06:54 the algebraic mind and also that we 06:56 formalized in several 06:58 neural symbolic systems since the early 07:01 2000s 07:02 and some of them several of them 07:04 temporal reasoning model reasoning 07:06 reason about knowledge are formalized in 07:08 this book and 07:09 of course we have been evolving this 07:11 concept so that we one can deal with 07:13 combinatory explosion 07:14 and several other symbolic problems 07:17 within a neurosymbolic framework 07:20 and so the approach that we have been 07:22 defending over the years 07:24 is that we need a foundational approach 07:26 for neurosymbolic computing 07:28 neurosymbolic ai 07:29 that's based both on logical 07:31 formalization and we have francesca here 07:34 judy pearl that have been that have been 07:36 outstanding results on symbolic ai 07:39 and machine learning and we use logic 07:42 and knowledge representation to 07:43 represent the reasoning process 07:45 that is integrated with machine learning 07:47 systems 07:48 so that we can also effective 07:50 effectively perform 07:52 neural learning using deep learning 07:54 machinery so our approach has been 07:56 tested in training 07:58 assessment simulators by tno which is a 08:01 dutch subsidiary of the government it 08:03 has been applied in robotics and ai 08:06 and several other applications but what 08:08 we offer here 08:09 is a sound way including some formal 08:12 results 08:13 that our neurosymbolic systems in order 08:16 so that we can have more effective and 08:18 more trustful 08:19 ai we need to have models 08:23 interpretive models that are based on 08:25 sound logical models 08:27 and in this way we can explain what the 08:29 neural learning process is doing 08:32 at the same way that we can prove that 08:35 the results that we obtain via machine 08:38 learning 08:39 can even be related to the formal 08:41 results that one typically expects from 08:43 symbolic logic for instance here 08:46 in a system that we call connections 08:47 connectionist modal logic 08:49 which was by the way published in the 08:51 same issue of neuro computation that 08:54 jeff hinton published one of his 08:56 inflation paper 08:57 on deep belief nets we proved that model 09:01 and temporal logic programs 09:02 can be computed soundly in neural 09:06 network models so in this way what we 09:08 provide 09:09 in a way is a way of providing neural 09:12 networks 09:12 as a learning system which can also 09:15 learn to compute 09:16 in a deep way the evolution of knowledge 09:19 in time 09:20 and this is what we explained in several 09:23 of our papers 09:24 and also in recent work that we 09:26 published gary in 09:27 um in tripoli 2018 09:31 and now each guy 2020 where we present a 09:34 survey paper 09:36 so the the the final message here 09:39 is that there have been some 09:41 developments including 09:43 uh the ai debate the great ai debate 09:46 between banjo and 09:47 gary marcus last year which we saw also 09:50 at triple i 2020 that we need more 09:52 convergency 09:53 towards building more effective ai 09:56 systems 09:57 and ai systems that most people can 09:59 trust since ai 10:00 is becoming a lingua franca for science 10:03 these days 10:04 neurosymbolic is basically another type 10:06 of ai system that is trying to use a 10:09 well-funded knowledge representation 10:11 and reasoning it integrates neural 10:13 network-based learning 10:15 with symbolic knowledge representation 10:17 and logical reasoning 10:18 with the goal of creating an ai system 10:20 that is both interpretable 10:22 and trustworthy this is where francesca 10:25 rossi's work comes into play 10:27 with their purpose called thinking fast 10:29 and slow in ai 10:30 as the name suggests they focus on 10:32 daniel kahneman's theory regarding the 10:35 two systems 10:36 explained in his book thinking fast and 10:38 slow 10:39 an attempt to connect them into a 10:41 unified theory 10:43 with the aim to identify some of the 10:45 roots of the desired human capabilities 10:48 here is a quick extract taken from the 10:50 ai debate 2 10:51 organized by montreal ai where daniel 10:54 cantman himself clearly explained these 10:56 two systems 10:58 and their link with artificial 10:59 intelligence 11:01 i seem to be identified with the idea of 11:04 two systems system one and system two 11:06 although they're not my idea but i did 11:08 write a book 11:09 that described them and 11:12 as quite a few of you sure know 11:16 we talk about the contrast between one 11:18 system that works fast 11:20 another that works slow uh 11:23 but the main difference between system 11:25 one and system two as i described them 11:27 was that system one is something that 11:30 happens to you 11:32 you are not active the thought that 11:35 the words come to you the ideas come to 11:38 you the emotions come to you they happen 11:40 to you 11:40 you do not do them and the essential 11:43 distinction that i was drawing 11:45 between the two systems was really that 11:47 one 11:48 something that happens to you and 11:50 something that you do 11:52 high level skills in my description of 11:55 of things 11:56 were absolutely in system one anything 11:58 that they can do automatically 12:00 anything that happens associatively is 12:03 in system one 12:06 another distinction between system one 12:08 and system two 12:09 as psychologists see them in that 12:12 operation the system one tend to be 12:14 parallel 12:14 operations or system two tend to be 12:16 serial 12:18 so it's true that anything 12:21 any activity that we would describe as 12:23 non-symbolic 12:25 i think does belong to system one 12:28 that system one i think cannot be 12:30 described 12:31 as a non-symbolic system for one thing 12:35 uh it's it's much too complicated and 12:39 rich for that 12:40 it knows language for one thing 12:42 intuitive thoughts 12:44 are in language uh the most interesting 12:47 component 12:48 of system one the basic component as i 12:51 conceive of that 12:52 notion is that it holds a representation 12:54 of the world 12:56 and and the representation that actually 12:59 allows something that resembles the 13:01 simulation of the world 13:03 as i describe it we we live 13:06 with that representation of the world 13:08 and most of the time 13:10 we are in what i call the valley of the 13:12 normal 13:13 there are events that we positively 13:15 expect 13:16 there are events that surprise us but 13:19 most of what happens to us 13:21 neither surprises us nor is it expected 13:24 what i'm going to do 13:25 to say next will not surprise you but 13:28 you didn't actually expect it 13:30 so there is that model that compares 13:33 that 13:34 accepts many many 13:38 events as normal continuations of what 13:41 happens 13:41 but it rejects some and it distinguishes 13:45 what is surprising from what is normal 13:51 that's very difficult to describe in 13:54 terms of symbolic or non-symbolic 13:56 certainly what happens is a lot of 13:58 counter factual thinking 14:00 is in fact system one thinking because 14:04 surprise is something that happens 14:07 automatically 14:08 you're surprised when something that 14:10 happens 14:11 is not normal is not expected 14:14 and that forces common sense 14:17 and causality to be in system one and 14:20 not in system two 14:22 in short can man explains that humans 14:25 decisions 14:26 are guided by two main kinds of 14:28 capabilities 14:29 or systems which he referred to as 14:32 system 1 and system 2. 14:34 the first provides us tools for 14:36 intuitive fast and unconscious 14:38 decisions which could be viewed as 14:41 thinking 14:41 fast while the second system handles 14:44 complex situations 14:46 where we need rational thinking and 14:48 logic to make a decision 14:50 here viewed as thinking slow 14:53 if we come back to the thinking fast and 14:55 slow in ai paper 14:56 scratch frachanska russi and her team 14:58 argue that we can make a very loose 15:00 comparison 15:01 between these two systems one and two 15:04 and the two main lines of work in ai 15:06 which are machine learning and symbolic 15:09 logic reasoning 15:10 or rather data-driven versus knowledge 15:13 driven ai 15:14 systems where the comparison between 15:17 kanman's 15:18 system one and machine learning is that 15:20 both seem to be able to build 15:22 models from sensory data such as 15:25 seeing and reading where both system 1 15:28 and machine learning produce 15:29 possibly imprecise and biased results 15:32 indeed what we call deep learning is 15:35 actually not 15:36 deep enough to be explainable similarly 15:38 to system one 15:39 however the main difference is that 15:41 current machine learning algorithms 15:43 lack basic notions of causality and 15:46 common sense 15:47 reasoning compared to our system one 15:50 we can also see a comparison between the 15:52 system 2 and ai techniques 15:54 based on logic search optimization and 15:57 planning 15:58 techniques that are not using deep 16:00 learning rather employing explicit 16:02 knowledge 16:03 symbols and high level concepts to make 16:05 decisions 16:06 this is the similarity highlighted 16:08 between the humans decision making 16:10 system 16:11 and current artificial intelligence 16:13 systems i want to remind you that as 16:15 they state 16:16 the goal of this paper is mainly to 16:18 stimulate the ai research community to 16:20 define 16:21 try and evaluate new methodologies 16:24 frameworks and evaluation metrics in the 16:27 spirit of achieving a better 16:29 understanding 16:30 of both human and machine intelligence 16:33 they intend to do that by asking the ai 16:36 community to study 16:37 10 important questions and try to find 16:40 appropriate answers or at least 16:42 think about these questions here i will 16:44 only quickly list 16:45 these 10 important questions to be 16:47 considered in future research 16:50 but feel free to read their paper for 16:52 much more information regarding these 16:54 questions 16:54 and discuss it in the comments below so 16:57 here it goes 16:58 should we clearly identify the ai system 17:01 1 and system 2 capabilities 17:04 is the sequentiality of system 2 a bug 17:06 or a feature 17:07 should we carry it over to machines or 17:09 should we exploit parallel threads 17:12 performing system 2 reasoning will this 17:14 together with the greater computing 17:16 power of machines compared to humans 17:19 compensate the lack of other 17:20 capabilities in ai 17:23 what are the metrics to evaluate the 17:25 quality of a hybrid system 1 system 2 17:28 ai system should these matrix be 17:31 different 17:32 for different tasks and combination 17:34 approaches 17:35 how do we define ai's introspection in 17:38 terms of eye consciousness and 17:40 m consciousness how do we model the 17:43 governance of system 1 and 17:45 system 2 in an ai when do we switch or 17:48 combine them 17:49 which factors trigger the switch how can 17:53 we leverage a model based on system 1 17:55 and system 2 in ai to understand and 17:58 reason 17:58 in complex environments when we have 18:01 competing priorities 18:03 which capabilities are needed to perform 18:05 various forms of moral judging and 18:08 decision making 18:09 how do we model and deploy possibly 18:12 conflicting normative ethical theories 18:14 in ai 18:15 are the various ethics theories tied to 18:18 either system 1 or system 2 18:21 how do we know what to forget from the 18:23 input data during the abstraction step 18:26 should we keep knowledge at various 18:28 levels of abstraction 18:30 or just raw data and fully explicit 18:32 high-level knowledge 18:34 in a multi-agent view of several ai 18:37 systems communicating and learning from 18:39 each other 18:40 how to exploit adapt current results on 18:43 epistemic reasoning 18:44 and planning to build learn models of 18:47 the world and of others 18:49 and finally what architectural choices 18:52 best support the above vision of the 18:55 future of ai 18:58 feel free to discuss any of these 18:59 questions in the comments below 19:01 i would love to have your take on these 19:03 questions and debate over them 19:05 i definitely invite you to read the 19:07 thinking fast and slow in ai paper 19:10 as well as daniel canman's book thinking 19:13 fast and slow if you'd like to have more 19:15 information about this 19:17 theory if this subject interests you i 19:20 would also strongly recommend you to 19:22 follow the research of yeshua benjo 19:24 addressing consciousness priors and a 19:27 huge thanks to montreal ai for 19:29 organizing 19:29 this ai debate 2 providing a lot of 19:32 valuable information 19:33 for the ai community all the documents 19:36 discussed 19:37 in this video are linked in the 19:38 description below please 19:40 leave a like if you went this far in the 19:42 video and since there are over 90 19:44 of you guys watching that are not 19:46 subscribed yet consider subscribing to 19:48 the channel to not miss any further news 19:50 clearly explained 19:52 thank you for watching Learn AI: https://www.omologapps.com/whats-ai Join Our Discord channel, Learn AI Together: ► https://discord.gg/learnaitogether Song credit: https://soundcloud.com/mattis-rodrigu...