Symbolic vs Subsymbolic AI Paradigms for AI Explainability by Orhan G. Yalçın

It had 400 light sensors that together acted as a retina, feeding information to about 1,000 “neurons” that did the processing and produced a single output. In 1958, a New York Times article quoted Rosenblatt as saying that “the machine would be the first device to think as the human brain.” The thing symbolic processing can do is provide formal guarantees that a hypothesis is correct. This could prove important when the revenue of the business is on the line and companies need a way of proving the model will behave in a way that can be predicted by humans. In contrast, a neural network may be right most of the time, but when it’s wrong, it’s not always apparent what factors caused it to generate a bad answer. The performance of AlphaProof and AlphaGeometry 2 at the International Mathematical Olympiad is a notable leap forward in AI’s capability to tackle complex mathematical reasoning.

While effective for simple problems, this symbolic AI encounters difficulties in flexibility, particularly when faced with unconventional or new geometric scenarios. The inability to predict hidden puzzles or auxiliary points crucial for proving complex geometry problems highlights the limitations of relying solely on predefined rules. Moreover, creating exhaustive rules for every conceivable situation becomes impractical as problems increase in complexity, resulting in limited coverage and scalability issues.

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In the black box world of ML and DL, changes to input data can cause models to drift, but without a deep analysis of the system, it is impossible to determine the root cause of these changes. This is why many forward-leaning companies are scaling back on single-model AI deployments in favor of a hybrid approach, particularly for the most complex problem that AI tries to address – natural language understanding (NLU). “It is from there that the basic need for hybrid architectures that combine symbol manipulation with other techniques such as deep learning most fundamentally emerges,” Marcus says.

(13), is still valid when the travel time in the shortest path(s) is used. Hence, the travel time of the shortest path(s) prevails over the secondary paths. It is a simple network with loops and ChatGPT only one branch corresponding to the pipe feeding the system from the single reservoir. The aim of this case study is to start understanding the role of the loops, although in a small network.

Game developers used GPUs to do sophisticated kinds of shading and geometric transformations. Computer scientists in need of serious compute power realized that they could essentially trick a GPU into doing other tasks—such as training neural networks. Nvidia noticed the trend and created CUDA, a platform that enabled researchers to use GPUs for general-purpose ChatGPT App processing. Among these researchers was a Ph.D. student in Hinton’s lab named Alex Krizhevsky, who used CUDA to write the code for a neural network that blew everyone away in 2012. The main advantage of connectionism is that it is parallel, not serial. If one neuron or computation if removed, the system still performs decently due to all of the other neurons.

Apulian WDN

Symbolica approaches building AI models through structured models that define tasks through manipulating symbols, as opposed to Transformers, which use the contextual and statistical relationships between inputs and learn from past content given to them. Symbols in symbolic AI represent a set of rules, allowing them to be pretrained for particular tasks — such as coding or word processing capabilities. Most machine learning techniques employ various forms of statistical processing.

There are more low-code and no-code solutions now available that are built for specific business applications. Using purpose-built AI can significantly accelerate digital transformation and ROI. Kahneman states that it “allocates attention to the effortful mental activities that demand it, including complex computations” and reasoned decisions. System 2 is activated when we need to focus on a challenging task or recognize that a decision requires careful consideration and analysis. One solution is to take pictures of your cat from different angles and create new rules for your application to compare each input against all those images. Even if you take a million pictures of your cat, you still won’t account for every possible case.

The world of neural networks

The topic of neuro-symbolic AI has garnered much interest over the last several years, including at Bosch where researchers across the globe are focusing on these methods. At the Bosch Research and Technology Center in Pittsburgh, Pennsylvania, we first began exploring and contributing to this topic in 2017. Their optimal use is actually as a component of a larger AI architecture.

Our minds create abstract symbolic representations of objects such as spheres and cubes, for example, and do all kinds of visual and nonvisual reasoning using those symbols. We do this using our biological neural networks, apparently with no dedicated symbolic component in sight. “I would challenge anyone to look for a symbolic module in the brain,” says Serre. He thinks other ongoing efforts to add features to deep neural networks that mimic human abilities such as attention offer a better way to boost AI’s capacities. This study showed that similar EPR formulas are found for WDNs with distinctive hydraulic, geometrical, and topological attributes.

You can train linguistic models using symbolic AI for one data set and ML for another. Then, combining them both in a pipeline achieves even greater accuracy. However, in the 1980s and 1990s, symbolic AI fell out of favor with technologists whose investigations required procedural knowledge of sensory or motor processes. Today, symbolic AI is experiencing a resurgence due to its ability to solve problems that require logical thinking and knowledge representation, such as natural language.

GPT-3 had 175 billion parameters in total; GPT-4 reportedly has 1 trillion. By comparison, a human brain has something like 100 billion neurons in total, connected via as many as 1,000 trillion synaptic connections. Vast though current LLMs symbolic artificial intelligence are, they are still some way from the scale of the human brain. Ai-Da wants to support designers and artists whose work is being undermined by artificial intelligence and is happy for people to use the symbol freely without any royalties.

Don’t get distracted

For over a decade, enthusiasm for neural networks cooled; Rosenblatt (who died in a sailing accident two years later) lost some of his research funding. In standard deep learning, back-propagation calculates gradients to measure the impact of the weights on the overall loss so that the optimizers can update the weights accordingly. In the agent symbolic learning framework, language gradients play a similar role. AI agents are showing impressive capabilities in tackling real-world tasks by combining large language models (LLM) with tools and multi-step pipelines.

• But the widening array of triumphs in deep learning have relied on increasing the number of layers in neural nets and increasing the GPU time dedicated to training them.
• In fact, Bloomberg Intelligence estimates that “demand for generative AI products could add about $280 billion of new software revenue, driven by specialized assistants, new infrastructure products, and copilots that accelerate coding.”
• This is essentially a neuro-symbolic approach, where the neural network, Gemini, translates natural language instructions into the symbolic formal language Lean to prove or disprove the statement.
• Elsewhere, a report (unpublished) co-authored by Stanford and Epoch AI, an independent AI research Institute, finds that the cost of training cutting-edge AI models has increased substantially over the past year and change.
• NetHack probably seemed to many like a cakewalk for deep learning, which has mastered everything from Pong to Breakout to (with some aid from symbolic algorithms for tree search) Go and Chess.
• Deep-learning systems are outstanding at interpolating between specific examples they have seen before, but frequently stumble when confronted with novelty.

Researcher, intelligence is based on humans’ ability to understand the world around them by forming internal symbolic representations. They then create rules for dealing with these concepts, and these rules can be formalized in a way that captures everyday knowledge. That’s not my opinion; it’s the opinion of David Cox, director of the MIT-IBM Watson A.I. Lab in Cambridge, MA. In a previous life, Cox was a professor at Harvard University, where his team used insights from neuroscience to help build better brain-inspired machine learning computer systems. In his current role at IBM, he oversees a unique partnership between MIT and IBM that is advancing A.I.

The Missing Piece: Symbolic AI’s Role in Solving Generative AI Hurdles

For Marcus, if you don’t have symbolic manipulation at the start, you’ll never have it. They also discuss how humans gather bits of information, develop them into new symbols and concepts, and then learn to combine them together to form new concepts. These directions of research might help crack the code of common sense in neuro-symbolic AI. There are several attempts to use pure deep learning for object position and pose detection, but their accuracy is low. In a joint project, MIT and IBM created “3D Scene Perception via Probabilistic Programming” (3DP3), a system that resolves many of the errors that pure deep learning systems fall into. But unlike other branches of AI that use simulators to train agents and transfer their learnings to the real world, Tenenbaum’s idea is to integrate the simulator into the agent’s inference and reasoning process.

This is possible because the breadth of data that goes into training LLMs is eye-wateringly large, something that is both a strength and a weakness. They know enough to understand your language but too much to generate grounded answers. Deep learning and neural networks excel at exactly the tasks that symbolic AI struggles with. They have created a revolution in computer vision applications such as facial recognition and cancer detection. Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs.

The water quality analysis refers to the evaluation of the advective diffusion of any substance in drinking water infrastructures from source nodes. Such substances could be a contamination for the system or planned for the disinfection, e.g., chlorine. The water quality analysis is performed by integrating the differential equation in the pipes network domain using the kinetics of the substance decay and the Lagrangian scheme. The kinetics can be formulated using a specific reaction order depending on the substance characteristics. The basis for the integration is the pipes velocity field calculated by means of hydraulic analysis. We demonstrated, using one real network and two test networks, that the concentration at each node of the network can be predicted using the travel time along the shortest path(s) between the source and each node.

The basic deep learning performed modestly on descriptive challenges and poorly on the rest. Some of the advanced models performed decently on descriptive challenges. Pure neural network–based AI models lack understanding of causal and temporal relations between objects and their behavior. They also lack a model of the world that allows them to foresee what happens next and figure out how alternative counterfactual scenarios work. But interestingly, today’s most advanced artificial intelligence systems would struggle to answer them. Questions such as the ones asked above require the ability to reason about objects and their behaviors and relations over time.

This enables the AI to employ a deductive approach, mirroring human legal reasoning, to understand the context and subtleties of legal arguments. They’re essentially pattern-recognition engines, capable of predicting what text should come next based on massive amounts of training data. This leads to well-documented issues like hallucination—where LLMs confidently generate information that’s completely false. They may excel at mimicking human conversation but lack true reasoning skills. For all the excitement about their potential, LLMs can’t think critically or solve complex problems the way a human can.

Deep neural networks, the main component of deep learning algorithms, can find intricate patterns in large sets of data. This enables them to perform tasks that were previously off-limits or very difficult for computer software, such as detecting objects in images or recognizing speech. You can foun additiona information about ai customer service and artificial intelligence and NLP. The greatest promise here is analogous to experimental particle physics, where large particle accelerators are built to crash atoms together and monitor their behaviors. In natural language processing, researchers have built large models with massive amounts of data using deep neural networks that cost millions of dollars to train.

Further studies can investigate the accuracy of surrogating the water age by the travel time on the shortest path(s) based on the characteristic of the network domain of the hydraulic system such the average nodal degree or the density of loops. Such comparison between both parameters should be done also in WDNs with contrasting characteristics, such as multiple reservoirs with different chlorine doses, in which several secondary paths take place, or booster chlorination points. For first order data, the best performance was obtained using KmSPn, followed by KSPvar, KmSP and Knet. The MAE of KmSPn is slightly lower as the F parameter increases, which is due to a steeper decay that causes lower chlorine concentrations and absolute errors.