AI navigation goes with the flow

Read an article the other day (Engineers Teach AI to Navigate Ocean with Minimal Energy) about a simulated robot that was trained to navigate 2D turbulent water flow to travel between locations. They used a combination reinforcement learning with a DNN derived policy. The article was reporting on a Nature Communications open access paper (Learning efficient navigation in vortical flow fields).

The team was attempting to create an autonomous probe that could navigate the ocean and other large bodies of water to gather information. I believe ultimately the intent was to provide the navigational smarts for a submersible that could navigate terrestrial and non-terrestrial oceans.

One of the biggest challenges for probes like this is to be able to navigate turbulent flow without needing a lot of propulsive power and using a lot of computational power. They said that any probe that could propel itself faster than the current could easily travel wherever it wanted but the real problem was to go somewhere with lower powered submersibles.. As a result, they set their probe to swim at a constant speed at 80% of the overall simulated water flow.

Even that was relatively feasible if you had unlimited computational power to train and inference with but trying to do this on something that could fit in a small submersible was a significant challenge. NLP models today have millions of parameters and take hours to train with multiple GPU/CPU cores in operation and lots of memory Inferencing using these NLP models also takes a lot of processing power.

The researchers targeted the computational power to something significantly smaller and wished to train and perform real time inferencing on the same hardware. They chose a “Teensy 4.0 micro-controller” board for their computational engine which costs under $20, had ~2MB of flash memory and fit in a space smaller than 1.5″x1.0″ (38.1mm X 25.4mm).

The simulation setup

The team started their probe turbulent flow training with a cylinder in a constant flow that generated downstream vortices, flowing in opposite directions. These vortices would travel from left to right in the simulated flow field. In order for the navigation logic to traverse this vortical flow, they randomly selected start and end locations on different sides.

The AI model they trained and used for inferencing was a combination of reinforcement learning (with an interesting multi-factor reward signal) and a policy using a trained deep neural network. They called this approach Deep RL.

For reinforcement learning, they used a reward signal that was a function of three variables: the time it took, the difference in distance to target and a success bonus if the probe reached the target. The time variable was a penalty and was the duration of the swim activity. Distance to target was how much the euclidean distance between the current probe location and the target location had changed over time. The bonus was only applied when the probe was in close proximity to the target location, The researchers indicated the reward signal could be used to optimize for other values such as energy to complete the trip, surface area traversed, wear and tear on propellers, etc.

For the reinforcement learning state information, they supplied the probe and the target relative location [Difference(Probe x,y, Target x,y)], And whatever sensor data being tested (e.g., for the velocity sensor equipped probe, the local velocity of the water at the probe’s location).

They trained the DNN policy using the state information (probe start and end location, local velocity/vorticity sensor data) to predict the swim angle used to navigate to the target. The DNN policy used 2 internal layers with 64 nodes each.

They benchmarked the Deep RL solution with local velocity sensing against a number of different approaches. One naive approach that always swam in the direction of the target, one flow blind approach that had no sensors but used feedback from it’s location changes to train with, one vorticity sensor approach which sensed the vorticity of the local water flow, and one complete knowledge approach (not shown above) that had information on the actual flow at every location in the 2D simulation

It turned out that of the first four (naive, flow-blind, vorticity sensor and velocity sensor) the velocity sensor configured robot had the highest success rate (“near 100%”).

That simulated probe was then measured against the complete flow knowledge version. The complete knowledge version had faster trip speeds, but only 18-39% faster (on the examples shown in the paper). However, the knowledge required to implement this algorithm would not be feasible in a real ocean probe.

More to be done

They tried the probes Deep RL navigation algorithm on a different simulated flow configuration, a double gyre flow field (sort of like 2 circular flows side by side but going in the opposite directions).

The previously trained (on cylinder vortical flow) Deep RL navigation algorithm only had a ~4% success rate with the double gyre flow. However, after training the Deep RL navigation algorithm on the double gyre flow, it was able to achieve a 87% success rate.

So with sufficient re-training it appears that the simulated probe’s navigation Deep RL could handle different types of 2D water flow.

The next question is how well their Deep RL can handle real 3D water flows, such as idal flows, up-down swells, long term currents, surface wind-wave effects, etc. It’s probable that any navigation for real world flows would need to have a multitude of Deep RL trained algorithms to handle each and every flow encountered in real oceans.

However, the fact that training and inferencing could be done on the same small hardware indicates that the Deep RL could possibly be deployed in any flow, let it train on the local flow conditions until success is reached and then let it loose, until it starts failing again. Training each time would take a lot of propulsive power but may be suitable for some probes.

The researchers have 3D printed a submersible with a Teensy microcontroller and an Arduino controller board with propellers surrounding it to be able to swim in any 3D direction. They have also constructed a water tank for use for in real life testing of their Deep RL navigation algorithms.

Picture credit(s):

The problem with smarter robots

Read an article the other week about how Deepmind (at Google) is approaching the training of robotics using simulation, reinforcement learning, elastic weights, knowledge distillation and progressive learning.

It seems relatively easy to train a robot to handle some task like grabbing or walking. But doing so can take an awfully long time. If you want to try to train a robot to grab something and put it someplace. You can have it start out making some random movements of its arm, wrist and fingers (if they have such things) and then use reinforcement learning to help it improve its movements over time.

But if each grab attempt takes 10 seconds, using reinforcement learning may take 10,000 attempts before it starts to make any significant progress and perhaps another 20,000-50,000 more to get expert at it. Let’s see 60K *10 seconds is 10,000 minutes or ~170 hours. And that’s just one object pick and place. But then maybe you would like to grab different parts and maybe place them in different locations. All these combinations start adding up.

And of course doing 1000s of movements will wear out gears, motors, mechanisms etc. If only this could all be done in electronic simulations. Then assuming the simulations are accurate enough the whole thing could be done in a matter of hours without wearing anything out. Enter robot simulators such as NVIDIA Isaac Sim, OpenAI RoboSchool/PyBullet

But the problems with simulation are …

Simulations are getting more accurate but at some point their accuracy defeats its purpose because the real world is always noisy, windy and not as deterministic as any simulation. One researcher said you could conceivable have a two armed robot be trained to throw all of a cell phones components up into the air and they will all land in their proper places, proper orientations. But in the real world this could never actually happen, or if it did, it could only happen once.

Hurricane Ike - 2008/09/12 - 21:26 UTC by CoreBurn (cc) (from Flickr)
Hurricane Ike – 2008/09/12 – 21:26 UTC by CoreBurn (cc) (from Flickr)

Weather researchers have been dealing with this problem in spades for a long time. There appears to be a fundamental limit to how far in advance we can predict weather and it’s due to the accuracy with which sensors operate and the complexity of feedback loops between the atmosphere, oceans, landforms, etc. So at a fundamental level, simulations can never be completely accurate. But they can be better.

Today’s weather simulations we see on TV/radio use models that average a number of distinct simulations, where sensor information has been slightly and randomly modified. Something similar could be done for robotic simulation environments, to make them more realistic.

But there are other problems with training robots to do lots of tasks.

Forget me not…

AI deep learning and reinforcement learning algorithms are great when charged with learning a single task, but having it learn multiple tasks is much harder to do. Because each task requires its own deep neural network (DNN) and if you train a DNN on one task and then try to train in on a another task, it forgets all the learnings from the original task. Researchers call this catastrophic forgetting.

One way researchers have dealt with this problem is to effectively freeze certain DNN nodes from having their weights changed during subsequent training rounds and leave others flexible or changeable. One can see this when one trains an image recognition DNN to classify different objects by importing a well trained object classifier and freezing all of it’s layers except the top one or two and then training these layers to classify new objects.

This works well but you have effectively changed the DNN to forget the original object classification training and replaced it with a new one. One solution to this approach is to have multiple passes of training, after each one, certain nodes and connections (of importance to that particular task) are selectively frozen. This works well for a limited number of different tasks but over time all nodes become frozen which means that no more learning can take place. Researchers call this approach to the catastrophic forgetting problem elastic weights.

One way to get around the all nodes frozen issue in elastic weights is to have multiple NNs. One which is trained on a specific task and whose weights are frozen and then a DNN that exists alongside this one with it’s own initialized set of weights. But which uses the original DNN as part of the new DNN inputs. This effectively includes and incorporates all the previously learned knowledge into the new, combined DNN. This is called Progressive Neural Networks.

In this fashion one progressive DNN can be sequentially trained on any number of tasks each of which ends up providing input to all subsequent task training activity. Such a progressive network never forgets and can use previously learned knowledge on new tasks.

The problem with progressive DNNs is a proliferation of DNN column. one for each trained task. However there are a couple of approaches to shrinking an ensemble of DNN like progressive training creates into one that is simpler and just as effective. One way is to perturb weights in DNN nodes and see how model prediction accuracy is impacted on all its tasks. If accuracy isn’t impacted that much, then that node and all its connections could be deleted from the model with minimal impact on model accuracy.

Another approach is to use one DNN to train another. Sort of like a teacher-student. This is called Knowledge Distilation. Where one DNN is a large network (the teacher) and a smaller (student) network that is trained to mimic the teacher DNN to achieve similar accuracy. This is done by training the smaller student network to match the predictions/classifications of the larger one.

Google researchers have shown that knowledge distillation works best when the gap in the sizes of the two networks (teacher and student) aren’t that large. They have solved this problem by introducing an intermediate step (called teachers assistent). They train this TA first then use the TA to train the student.

In the above graphic, when using a teacher of size 110 and a student of size 8 the resulting accuracy suffers but if one uses an intermediate DNN, with a size 20 the resultant accuracy of the student is much closer to the teacher..

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So with realistic simulation we can train a robot to do any specific task, all using only compute resources. And using progressive DNN training, a robot could conceivably be trained to do any number of tasks. And with appropriate knowledge distillation one can reduce the DNN from progressive training into something much smaller (<10%) than the original DNN.

Want a personal robot that can clean up around your place, do the wash, cook your food and do anything else needed. You know what to do.

Photonic [AI] computing seeing the light of day – part 2

Read an interesting article in Analytics India Magazine (MIT Researchers Make New Chips That Work On Light) about a startup out of MIT focused on using photonics for AI/ML/DL activities. Not exactly neuromorphic chips, but using analog photonics interactions to perform computational intensive operations required by todays deep neural net training.

We’ve written about photonics computing before ( see Photonic computing seeing the light of day [-part 1]). That post was about spin outs from Princeton and MIT back in 2019. We showed a bit more on how photonics can perform multiplication and other computations with less power.

The article (noted above) talked about LightIntelligence, an MIT spinout/ startup that’s been around since ~2017, but there’s another company in the same space, also out of MIT called LightMatter that just announced early access to their hardware system.

The CEOs of both companies collaborated on a paper (#1&2 authors of the 10 author paper) written back in 2017 on Deep Learning with Coherent Nanophotonic Circuits. This seemed to be the event that launched both companies.

LightMatter just received $80M in Series B funding ( bringing total funding to $113M) last month and LightIntelligence seems to have $40M in total funding So both have decent funding but, LightMatter seems further ahead in funding and product technology.

LightMatter

LightMatter Envise Photonics-RISC AI processing chip

LightMatter Envise AI chip uses standard RISC electronic cores together with Photo Arithmetic Units for accelerated AI computations. Each Envise chip has 500MB of SRAM for large models, offers 400Gbps chip to chip interconnect fabric, 256 RISC cores, a Graph processor, 294 photonic arithmetic units and PCIe 4.0 connectivity.

LightMatter has just announced early access for their Envise AI photonics server. It’s an 4U, AI server with 16 Envise chips, 2 AMD EPYC CPUs, (16×400=)6.4Tpbs optical fabric for inter-chip communications, 1TB of DDR4 DRAM, 3TB of NVMe SSD and supports 2-200GbE SmartNICs for outside communications.

Envise also offers Idiom Software that interfaces with standard AI frameworks to transform models for photonics computing to use Envise hardware . Developers select Envise hardware to run their AI models on and Idiom automatically re-compiles (IdCompile) their model into more parallelized, photonics operations. Idiom also has a model profiler (IdProfiler) to help debug and visualize photonic models in operation (training or inferencing?) on Envise hardware. Idiom also offers an AI model library (IdML) which provides a PyTorch frontend to help compress and quantize a standard set of AI models.

LightMatter also announced their Passage optical interconnect chip that supplies 100Tbps optical switch for photonics, CPU or GPU processing. It’s huge, 8″x8″ and built on 5nm/7nm node process. Passage can connect up to 48 photonics, CPU or GPU chips that are built onto of it (one can see the space for each of these 48 [sub-]chips on the chip). LightMatter states that 40 Passage (photonic/optical) lanes are the width of one optical fibre. Passage chips are sampling now.

LightMatter Passage photonics-transistor chip (carrier) that provides a photonics programmable interconnect for inter-[photonics-electronic-]chip communications.

LightIntelligence

They don’t appear to be announcing any specific hardware just yet but they are at work in creating the world largest integrated photonics processing system. But LightIntelligence have published a number of research papers focused on photonic approaches to CNNs, RNNs/LSTMs/GRUs, Recurrent ISING machines, statistical computing, and invisibility cloaking.

Turns out the processing power needed to provide invisibility cloaking is very intensive and as its all pixels, photonics offers serious speedups (for invisibility, see Nature article, behind paywall).

Photonics Recurrent ISLING Sampler (PRIS)

LightIntelligence did produce a prototype photonics processor in 2019. And they believe the will have de-risked 80-90% of their photonics technology by year end 2021.

If I had to guess, it would appear as if LightIntelligence is trying to re-imagine deep learning taking a predominately all photonics approach.

Why photonics for AI DL

It turns out that one can use the interaction/interference between two light beams to perform matrix multiplication and other computations a lot faster, with a lot less power than using standard RISC (or CISC) electronic processor architectures. Typical GPUs run 400W each and multi-GPU training activities are commonplace today.

The research documented in the (Deep learning using nanophotonics) paper was based on using an optical FPGA which we have talked about before (See Photonics or Optical FPGAs on the horizon) to prototype the technology back in 2017.

Can photonics change the technology underpinning AI or computing?

If by using photonics, one could speed up AI inferencing by 3-5X and do it with 5-6X less power, you might have a market. These are LightMatter Envise performance numbers on ResNet50 with ImageNet and BERT-Base with SQUAD v1.1 against NVIDIA DGX-A100 (state of the art) AI processing system.

The challenge to changing the technology behind multi-million/billion/trillion dollar industry is that it’s not sufficient to offer a product better than the competition. One has to offer a technology that’s better enough to fund the building of a new (multi-million/billion/trillion dollar) ecosystem surrounding that technology. In order to do that it’s got to be orders of magnitude faster/lower power/better so that commercial customers adopt it en masse.

I like where LightMatter is going with their Passage chip. But their Envise server doesn’t seem fast enough to give them enough traction to build a photonics ecosystem or to fund Envise 2, 3, 4, etc. to change the industry.

The 2017 (Deep learning using nanophotonics) paper predicted that an all optical/photonics implementation of CNN would use 3 orders of magnitude less power for small models and that advantage would only go up for larger models (not counting power for data movement, photo detectors, etc.). Now if that’s truly feasible and maybe it takes a more photonics intensive processor to get there, then photonics technology could truly transform the AI or for that matter the computing industry.

But the other thing that LightIntelligence and LightMatter may be counting on is the slowdown in Moore’s law which may inhibit further advances in electronics processing power. Whether the silicon industry is ready to throw in the towel yet on Moore’s law is TBD.

Comments?

Photo Credit(s):

New era of graphical AI is near #AIFD2 @Intel

I attended AIFD2 ( videos of their sessions available here) a couple of weeks back and for the last session, Intel presented information on what they had been working on for new graphical optimized cores and a partner they have, called Katana Graph, which supports a highly optimized graphical analytics processing tool set using latest generation Xeon compute and Optane PMEM.

What’s so special about graphs

The challenges with graphical processing is that it’s nothing like standard 2D tables/images or 3D oriented data sets. It’s essentially a non-Euclidean data space that has nodes with edges that connect them.

But graphs are everywhere we look today, for instance, “friend” connection graphs, “terrorist” networks, page rank algorithms, drug impacts on biochemical pathways, cut points (single points of failure in networks or electrical grids), and of course optimized routing.

The challenge is that large graphs aren’t easily processed with standard scale up or scale out architectures. Part of this is that graphs are very sparse, one node could point to one other node or to millions. Due to this sparsity, standard data caching fetch logic (such as fetching everything adjacent to a memory request) and standardized vector processing (same instructions applied to data in sequence) don’t work very well at all. Also standard compute branch prediction logic doesn’t work. (Not sure why but apparently branching for graph processing depends more on data at the node or in the edge connecting nodes).

Intel talked about a new compute core they’ve been working on, which was was in response to a DARPA funded activity to speed up graphical processing and activities 1000X over current CPU/GPU hardware capabilities.

Intel presented on their PIUMA core technology was also described in a 2020 research paper (Programmable Integrated and Unified Memory Architecture) and YouTube video (Programmable Unified Memory Architecture).

Intel’s PIUMA Technology

DARPA’s goals became public in 2017 and described their Hierarchical Identity Verify Exploit (HIVE) architecture. HIVE is DOD’s description of a graphical analytics processor and is a multi-institutional initiative to speed up graphical processing. .

Intel PIUMA cores come with a multitude of 64-bit RISC processor pipelines with a global (shared) address space, memory and network interfaces that are optimized for 8 byte data transfers, a (globally addressed) scratchpad memory and an offload engine for common operations like scatter/gather memory access.

Each multi-thread PIUMA core has a set of instruction caches, small data caches and register files to support each thread (pipeline) in execution. And a PIUMA core has a number of multi-thread cores that are connected together.

PIUMA cores are optimized for TTEPS (Tera-Traversed Edges Per Second) and attempt to balance IO, memory and compute for graphical activities. PIUMA multi-thread cores are tied together into (completely connected) clique into a tile, multiple tiles are connected within a single node and multiple nodes are tied together with a 8 byte transfer optimized network into a PIUMA system.

P[I]UMA (labeled PUMA in the video) multi-thread cores apparently eschew extensive data and instruction caching to focus on creating a large number of relatively simple cores, that can process a multitude of threads at the same time. Most of these threads will be waiting on memory, so the more threads executing, the less likely that whole pipeline will need to be idle, and hopefully the more processing speedup can result.

Performance of P[I]UMA architecture vs. a standard Xeon compute architecture on graphical analytics and other graph oriented tasks were simulated with some results presented below.

Simulated speedup for a single node with P[I]UMAtechnology vs. Xeon range anywhere from 3.1x to 279x and depends on the amount of computation required at each node (or edge). (Intel saw no speedups between a single Xeon node and multiple Xeon Nodes, so the speedup results for 16 P[I]UMA nodes was 16X a single P[I]UMA node).

Having a global address space across all PIUMA nodes in a system is pretty impressive. We guess this is intrinsic to their (large) graph processing performance and is dependent on their use of photonics HyperX networking between nodes for low latency, small (8 byte) data access.

Katana Graph software

Another part of Intel’s session at AIFD2 was on their partnership with Katana Graph, a scale out graph analytics software provider. Katana Graph can take advantage of ubiquitous Xeon compute and Optane PMEM to speed up and scale-out graph processing. Katana Graph uses Intel’s oneAPI.

Katana graph is architected to support some of the largest graphs around. They tested it with the WDC12 web data commons 2012 page crawl with 3.5B nodes (pages) and 128B connections (links) between nodes.

Katana runs on AWS, Azure, GCP hyperscaler environment as well as on prem and can scale up to 256 systems.

Katana Graph performance results for Graph Neural Networks (GNNs) is shown below. GNNs are similar to AI/ML/DL CNNs but use graphical data rather than images. One can take a graph and reduce (convolute) and summarize segments to classify them. Moreover, GNNs can be used to understand whether two nodes are connected and whether two (sub)graphs are equivalent/similar.

In addition to GNNs, Katana Graph supports Graph Transformer Networks (GTNs) which can analyze meta paths within a larger, heterogeneous graph. The challenge with large graphs (say friend/terrorist networks) is that there are a large number of distinct sub-graphs within the graph. GTNs can break heterogenous graphs into sub- or meta-graphs, which can then be used to understand these relationships at smaller scales.

At AIFD2, Intel also presented an update on their Analytics Zoo, which is Intel’s MLops framework. But that will need to wait for another time.

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It was sort of a revelation to me that graphical data was not amenable to normal compute core processing using today’s GPUs or CPUs. DARPA (and Intel) saw this defect as a need for a completely different, brand new compute architecture.

Even so, Intel’s partnership with Katana Graph says that even today compute environment could provide higher performance on graphical data with suitable optimizations.

It would be interesting to see what Katana Graph could do using PIUMA technology and appropriate optimizations.

In any case, we shouldn’t need to wait long, Intel indicated in the video that P[I]UMA Technology chips could be here within the next year or so.

Comments?

Photo Credit(s):

  • From Intel’s AIFD2 presentations
  • From Intel’s PUMA you tube video

Swarm learning for distributed & confidential machine learning

Read an article the other week about researchers in Germany working with a form of distributed machine learning they called swarm learning (see: AI with swarm intelligence: a novel technology for cooperative analysis …) which was reporting on a Nature magazine article (see: Swarm Learning for decentralized and confidential clinical machine learning).

The problem of shared machine learning is particularly accute with medical data. Many countries specifically call out patient medical information as data that can’t be shared between organizations (even within country) unless specifically authorized by a patient.

So these organizations and others are turning to use distributed machine learning as a way to 1) protect data across nodes and 2) provide accurate predictions that uses all the data even though portions of that data aren’t visible. There are two forms of distributed machine learning that I’m aware of federated and now swarm learning.

The main advantages of federated and swarm learning is that the data can be kept in the hospital, medical lab or facility without having to be revealed outside that privileged domain BUT the [machine] learning that’s derived from that data can be shared with other organizations and used in aggregate, to increase the prediction/classification model accuracy across all locations.

How distributed machine learning works

Distributed machine learning starts with a common model that all nodes will download and use to share learnings. At some agreed to time (across the learning network), all the nodes use their latest data to re-train the common model and share new training results (essentially weights used in the neural network layers) with all other members of the learning network.

Shared learnings would be encrypted with TLS plus some form of homomorphic encryption that allowed for calculations over the encrypted data.

In both federated and swarm learning, the sharing mechanism was facilitated by a privileged block chain (apparently Etherium for swarm). All learning nodes would use this blockchain to share learnings and download any updates to the common model after sharing.

Federated vs. Swarm learning

The main difference between federated and swarm learning is that with federated learning there is a central authority that updates the model(s) and with swarm learning that processing is replaced by a smart contract executing within the blockchain. Updating model(s) is done by each node updating the blockchain with shared data and then once all updates are in, it triggers a smart contract to execute some Etherium VM code which aggregates all the learnings and constructs a new model (or at least new weights for the model). Thus no node is responsible for updating the model, it’s all embedded into a smart contract within the Etherium block chain. .

Buthow does the swarm (or smart contract) update the common model’s weights. The Nature article states that they used either a straight average or a weighted average (weighted by “weight” of a node [we assume this is a function of the node’s re-training dataset size]) to update all parameters of the common model(s).

Testing Swarm vs. Centralized vs. Individual (node) model learning

In the Nature paper, the researchers compared a central model, where all data is available to retrain the models, with one utilizing swarm learning. To perform the comparison, they had all nodes contribute 20% of their test data to a central repository, which ran the common swarm updated model against this data to compute an accuracy metric for the swarm. The resulting accuracy of the central vs swarm learning comparison look identical.

They also ran the comparison of each individual node (just using the common model and then retraining it over time without sharing this information to the swarm versus using the swarm learning approach. In this comparison the swarm learning approach alway seemed to have as good as if not better accuracy and much narrower dispersion.

In the Nature paper, the researchers used swarm learning to manage the machine learning model predictions for detecting COVID19, Leukemia, Tuberculosis, and other lung diseases. All of these used public data, which included PBMC (peripheral blood mono-nuclear cells) transcription data, whole blood transcription data, and X-ray images.

Swarm learning also provides the ability to onboard new nodes in the network. Which would supply the common model and it’s current weights to the new node and add it to the shared learning smart contract.

The code for the swarm learning can be downloaded from HPE (requires an HPE passport login [it’s free]). The code for the models and data processing used in the paper are available from github. All this seems relatively straight forward, one could use the HPE Swarm Learning Library to facilitate doing this or code it up oneself.

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The myth of AGI

Sorry seem to be on an AGI bent this month…

Read an article the other day about a new book (The myth of AI, by Erik. J. Larson) that explains how the present direction of AI-ML-DL will be very unlikely to achieve artificial general intelligence (AGI) given it’s current direction. Amazon and others offer a short preview of the book which is where most of this discussion comes from.

Types of (human) reasoning

Near as I can tell, (don’t have the book), the book discusses the three types of reasoning that exist in human intellect, i.e., deduction, induction and abduction.

  • Deduction uses formal logic (or its equivalents) to derive facts or theorems from basic principles.
  • Induction uses a multitude of samples and constructs general principles from the analysis of them
  • Abduction uses a set of probabilistic assertions and formal logic, to come up with a probabilistic principle.

Deduction is most famously observed in geometry and arithmetic proofs and was most evident in the early years of AI through its use of expert systems. The challenge with expert systems is that the real world is vastly more complex than any geometrical or arithmetical artifice that humankind can produce.

Expert systems became champions of checkers, chess and some other games but in the end was not easily generalizable beyond a few (gaming and medically) restricted domains.

Induction is presently all the rage and represents what machine learning and deep neural networks (DNN) are doing with all that training data and resultant classification inferencing.

Today we have DNNs that can classify the objects in an image, can learn to play any game on the planet better than humans, and can even safely drive a car down the road.

The current AI world view is that this form of reasoning, DNN induction, will if taken to its extreme will ultimately result in some level of AGI, or human-equivalent levels of intelligence in a system. The author of the book begs to differ.

Abduction is less well known or discussed in rational circles. It’s essentially what any human does when presented with real world examples/experiences to derive an understanding (or principe) of what happened.

For example, a plate full of cookies last night becomes an almost empty plate of crumbs and two cookies. So what happened, your son woke up early, consumed most if not all of them, and left for work. This is a probabilistic (most likely) inference, but has a high probability of being true.

Any AGI will need all forms of reasoning

The challenge is that AI has been through the deduction phase through the rise of expert systems which crashed and burned because of the cost and time required to produce an exhaustive and correct expert system. And AI is currently in the induction phase, via DNN training, which seems to be entirely more generalizable and successfully usable in many different domains, but no one is talking seriously about doing abduction in AI (anymore).

The author claims (again, have not read the book) that any AGI will require as much abduction as induction (as well as perhaps deduction), and therefore, AGI is not inevitable based on our current AI DNN (or induction) intensive path.

Previous and current attempts at abduction reasoning

Some may recall fuzzy logic as one of the avenues taken after expert systems seemed to fail at doing successful and realistic inferencing around the end of last century. Fuzzy logic was a way of bring probabilities into deduction, not unlike abduction as defined above. With fuzzy logic each assertion or base assumption was given a probabilistic value (of being true) and the final derivation was assigned some level of probability of being true.

The wikipedia article has definitions for fuzzy logic and, or and not which of course would allow any system to make these assertions. But fuzzy logic (like expert systems above) suffered from the inability to exhaustively cover all examples in a real world situation.

Furthermore, the (funny) thing about DNNs is that they are much more probabilistic than it appears. If one examines classification outputs of any DNN, it is extremely rare to see some sort of boolean (true or false) yes or no answers. Mostly one sees a series of probabilities that are assigned to each classification bucket.

DNN systems hide these probabilities by just selecting the maximum (or minimum) probability generated as its final classification. This is entirely an artifact of needing to have some discrete output (classification selection). But DNN (internal) results always result in probabilistic values.

So although, pure induction doesn’t include probabilities, DNN induction as practiced today in AI systems, uses probabilistic reasoning in every layer of a DNN and in its final results.

What else may be missing from AI to allow AGI to be developed

Personally, AGI seems to require not just the reasoning approaches above, but a more workable and general purpose planning solution. I’ve tried to identify to see whether some researchers are using DNNs to provide general purpose planning solutions but have been yet to find any (in publcly available research). These are probably the one place where expert (or control) fuzzy systems still shine. But again they are hard to generalize and prove almost impossible to be completely exhaustive.

Nonetheless, in the end, I think that all the above just proves, that there are a number of distinct reasoning and other (planning) techniques that may need to come together to provide AGI. As any of us can attest, all of these different approaches are available within any human intellect.

And if we assume that any AGI will need to follow the human design to intelligence (not a given), they will all need to be stitched together, combined and brought to bear to realize AGI.

But, at present, with all the focus on DNN/induction, we, as AI researchers, are not making any progress on using these other techniques or in combining them into a single system.

And for that I am happy. I would be very pleased to have any AGI be farther out than nearer term. Because for the life of me, AGI scares the s&#t out of me.

Mostly because I don’t see any real way to control AGI, once unleashed. That and given the diversity of motives around this world, I don’t see any realistic mechanism to instill a universal and firm (unalterable) belief in the sanctity of human and other life, the dependance this life has on our environment/biosphere and the rule of law needed to maintain peace across humankind (and I’m probably missing a half dozen more things that we would want any AGI to adhere to).

Maybe, if I saw more effort on how, we as a species can come up with universal views on these and other topics and can come up with some way of instilling, essentially a system of programs, with these unalterable beliefs and AGI controls based on these, I’d be less fearful of AGI emerging.

Lacking that, any way of delaying its emergence, is fine by me.

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AI inferencing using light alone

Researchers at UCLA have taken a trained DL neural network and implemented it into a series of passive optical only, 3D printed diffraction gratings to perform fashion MNIST object classification. And did the same with a MNIST handwritten digit and ImageNet DL neural network classifiers.

But first please take our new poll:

Experimental testing of 3D-printed D2NNs.(A and B) After the training phase, the final designs of five different layers (L1, L2, …, L5) of the handwritten digit classifier, fashion product classifier, and the imager D2NNs are shown. To the right of the network layers, an illustration of the corresponding 3D-printed D2NN is shown. (C and D) Schematic (C) and photo (D) of the experimental terahertz setup. An amplifier-multiplier chain was used to generate continuous-wave radiation at 0.4 THz, and a mixer-amplifier-multiplier chain was used for the detection at the output plane of the network. RF, radio frequency; f, frequency.

See the article on SlashGear, 3D printed all-optical diffractive deep learning neural network…. The research article is only available on Optical Society of America’s website/magazine (see Residual D2NN: training diffractive deep neural networks via learnable light shortcuts behind hard paywall). However, I did find a follow on article on ArchivX (see Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks) that discussed how to integrate D2NN approaches with an electronic NN to create a hybrid inference engine. And another earlier Science article (see All-optical machine learning using diffractive deep neural networks) that was available which described earlier versions of D2NN technology for MNIST digit classification, fashion MNIST classification and ImageNet object classification.

How does it work

Apparently the researchers trained a normal (electronic based) deep learning neural network on the MNIST, Fashion MNIST and ImageNet and then converted the resultant trained NNs into a set of multiple diffraction grids. They did some computer simulation of the D2NN and once satisfied it worked and achieved decent accuracy, 3D printed the diffraction plates.

All-optical D2NN-based classifiers. These D2NN designs were based on spatially and temporally coherent illumination and linear optical materials/layers. (a) D2NN setup for the task of classification of handwritten digits (MNIST), where the input information is encoded in the amplitude channel of the input plane. (b) Final design of a 5-layer, phase-only classifier for handwritten digits. (c) Amplitude distribution at the input plane for a test sample (digit ‘0’). (d-e) Intensity patterns at the output plane for the input in (c); (d) is for MSE-based, and (e) is softmax- cross-entropy (SCE)-based designs. (f) D2NN setup for the task of classification of fashion products (Fashion-MNIST), where the input information is encoded in the phase channel of the input plane. (g) Same as (b), except for fashion product dataset. (h) Phase distribution at the input plane for a test sample. (i-j) Same as (d) and (e) for the input in (h),  refers to the illumination source wavelength. Input plane represents the plane of the input object or its data, which can also be generated by another optical imaging system or a lens, projecting an image of the object data onto this plane.

In their D2NN, they start with coherent (laser) light in the THz spectrum, used this to illuminate the input plane (I assume an image of the object/digit/fashion accessory) and passed this through multiple plates of diffraction grids onto THz detector which was used to detect the illuminated spot that indicated the classification.

The article in science has a supplementary materials download that show how the researchers converted NN weights into a diffraction grating. Essentially each pixel on the diffraction grating either transmits, refracts, or reflects a light path. And this represents the connections between layers. It’s unclear whether the 5 or 6 plates used in the D2NN correspond to the NN layers but it’s certainly possible.

And to the life of me I can’t understand what they mean by “Residual D2NN”, other than if it means using a trained (residual) NN and converting this to D2NN.

Some advantages of D2NN

3D printing diffraction gratings means anyone/lab could do this. The 3D printers they used had a spatial accuracy of 600 dpi, with 0.1mm accuracy, almost consumer grade 3D printers. In any case, being able to print these in a matter of hours, while not as easy as changing an all digital NN, seems like an easy way to try out the approach.

For example, for the MNIST digit classifier they used a pixel size of 400um and each diffraction layer they created was equivalent to 200X200 neural weights. Which means that 5 layer D2NN could handle about 0.2M neural weights which were completely connected to one another. This meant they could have (200×200)**2*5=8B connections in the MNIST D2NN. In the image classifier, each diffraction layer had 300×300 neural weights. So D2NN’s seem to scale very well.

Being an all passive optical device, the system is operates entirely in parallel, That is, the researchers indicated that the D2NN devices operate at the speed of light and would perform the inferencing activity in the time it takes a camera to capture the image.

Also the device uses very little energy (I assume just the energy for the THz generator, the input plane detector and the THz detector at the end.

And the researchers also claimed the device was cheap to manufacture, it could be created for less than $50. (Unclear if this included all the electronics or just the D2NN diffraction gratings and holder). And once you have locked into a D2NN that you wanted to use, could be manufactured in volume, very cheaply (sort of like stamping out CD platters). Finally, the number of neural network nodes and layers can be scaled up to a large number of layers and nodes per layer while still fitting on the diffraction gratings. In contrast, all electronic NN require more compute power as you scale up network layers and nodes per layer.

The other article (ArchivX) talked about potentially using a hybrid optical-electronic DNN approach with some layers being D2NN and others being purely digital (electronics). Such a system could potentially be used where some portion of the NN was more stable/more compute intensive than others and where the final output classification layer(s) was more changeable and much smaller/less compute intensive. Such a hybrid system could make use of the best of of the all optical D2NN to efficiently and quickly compress the input space and then have the electronic final classification layer provide the final classification step.

The Oracle

Combining a handful of D2NNs into a device that accepts speech input and provides speech output with the addition of say an offline copy of Wikipedia, Google Books etc. with a search engine that could be used to retrieve responses to questions asked would create an oracle device. Where you would ask a question and the device would respond with the best answer it could find (in it’s databases).

If this could be made out of an all passive optical components and use natural sunlight/electronic illumination to perform it’s functionality, such an all optical, question to answer oracle would be very useful to the populations of the world. And could be manufactured in volume very cheaply and would cost almost nothing to operate.

A couple of other tweaks, if we could collapse the multiple grating D2NNs into a single multi-layer plate/platter and make these replaceable in the device that would allow the oracle’s information base to be updated periodically.

Then if we could embed such a device into a Long Now Clock that would reflect sunlight onto the disk every Solstice, or Equinox, then we could have a quarterly oracle device that could last for 1000 of years. That would provide answers to queries one day every quarter. And that would be quite the oracle…

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