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Over the past several weeks, the flurry of new generative AI products and capabilities from ChatGPT to Bard and numerous variations from others built around large language models (LLMs) has created an excessive hype cycle. However, many argue that these generalized models are unsuitable for enterprise use. Most AI engines show signs of struggling when assigned niche or domain-specific tasks. Could hybrid AI be the answer?

Hybrid AI is the expansion or enhancement of AI models using machine learning, deep learning, and neural networks alongside human subject matter expertise to develop use-case-specific AI models with the greatest accuracy or potential for prediction.

The rise of hybrid AI tackles many significant and legitimate concerns. More than AI models built on large datasets are required in numerous scenarios or domains for maximum benefit or actual value creation. For example, consider ChatGPT being asked to write a long and detailed economic report.

Adopting or enhancing the model with domain-specific knowledge can be the most effective way to reach a high forecasting probability. Hybrid AI combines the best aspects of neural networks (patterns and connection formers) and symbolic AI (fact and data derivers) to achieve this.

Todays LLMs have several flaws, including inadequate performance on mathematical tasks, a propensity to invent data, and a failure to articulate how the model yields results. All of these issues are typical of connectionist neural networks, which depend on notions of how the human brain operates.

These issues are typical of connectionist neural networks, which depend on notions of the human brains operation.

Classical AI is also referred to as symbolic AI. It attempts to plainly express human knowledge in a declarative form, such as rules and facts interpreted from symbol inputs. It is a branch of AI that attempts to connect facts and events using logical rules.

From the mid-1950s to the end of the 1980s, the study of symbolic AI saw considerable activity.

In the 1960s and 1970s, technological advances inspired researchers to investigate the relationship between machines and nature. They believed that symbolic techniques would eventually result in an intelligent machine, which was viewed as their disciplines long-term objective.

In this context, John Haugeland coined good old-fashioned artificial intelligence or GOFAI in his 1985 book Artificial Intelligence: The Very Idea.

The GOFAI method is best suited for inert issues and is far from a natural match for real-time dynamic problems. It favors a restricted definition of intellect as abstract reasoning, whereas artificial neural networks prioritize pattern recognition. Consequently, the latter connectionist or non-symbolic method has gained prominence recently.

The genesis of non-symbolic artificial intelligence is the attempt to simulate the human brain and its elaborate web of neural connections.

To discover solutions to issues, non-symbolic AI systems refrain from manipulating a symbolic representation. Instead, they conduct calculations based on principles that have been empirically proven to solve problems without first understanding precisely how to arrive at a solution.

Neural networks and deep learning are two examples of non-symbolic AI. Non-symbolic AI is also known as connectionist AI, several present-day artificial intelligence apps are based on this methodology, including Googles automated transition engine (which searches for patterns) and Facebooks face recognition program.

In the context of hybrid artificial intelligence, symbolic AI serves as a supplier to non-symbolic AI, which handles the actual task. Symbolic AI offers pertinent training data from this vantage point to the non-symbolic AI. In turn, the information conveyed by the symbolic AI is powered by human beings i.e., industry veterans, subject matter experts, skilled workers, and those with unencoded tribal knowledge.

Web searches are a popular use of hybrid AI. If a user inputs 1 GBP to USD, the search engine detects a currency conversion challenge (symbolic AI). It uses a widget to perform the conversion before employing machine learning to retrieve, position, and exhibit web results (non-symbolic AI). This is a fundamental example, but it does illustrate how hybrid AI would work if applied to more complex problems.

According toDavid Cox,director of the MIT-IBM Watson AI Lab, deep learning and neural networks thrive amid the messiness of the world, while symbolic AI does not. As previously mentioned, however, both neural networks and deep learning have limitations. In addition, they are susceptible to hostile instances, dubbed as adversarial data, which may influence the behavior of an AI model in unpredictable and possibly damaging ways.

However, when combined, symbolic AI and neural networks can establish a solid foundation for enterprise AI development.

Business problems with insufficient data for training an extensive neural network or where standard machine learning cant deal with all the extreme cases are the perfect candidates for implementing hybrid AI. When a neural network solution could cause discrimination, lack of full disclosure, or overfitting-related concerns, hybrid AI may be helpful (i.e., training on so much data that the AI struggles in real-world scenarios).

A prime instance is an AI initiative by Fast Data Science, an AI consulting firm. The objective is to assess the potential hazards of a clinical trial.

The user sends a PDF document detailing the plan for conducting a clinical trial to the platform. A machine learning model can identify vital trial characteristics like location, duration, subject number, and statistical variables. The machine learning models output will be incorporated into a manually crafted risk model. This symbolic model converts these parameters into a risk value, which then appears as a traffic light signaling high, medium, or low risk to the user.

Human intelligence is essential to specify a reasonable and logical rule for converting protocol data into a risk value.

A second illustration is Googles search engine. It is a sophisticated, all-encompassing AI system composed of revolutionary deep learning tools like transformers and symbol manipulation mechanisms like the knowledge graph.

No technique or combination of techniques resolves every problem equally well; therefore, it is necessary to understand their capabilities and limitations. Hybrid AI is not a magic bullet, and both symbolic and non-symbolic AI will continue to be powerful technologies in their own right. The fact that expert understanding and context from everyday life are seldom machine-readable is another impediment. Coding human expertise into AI training datasets presents another issue.

Most organizations fail to fully recognize the cognitive, computational, carbon output, and financial barriers that arise from placing the complex jumble of our lived worlds into a context that AI can comprehend. Therefore, the timeline for AI implementation in any meaningful way may take much longer than expected.

AI initiatives are notoriously problematic; only 1 in 10 pilots and prototypes lead to significant results in production.

Progressive businesses are already aware of the limits of single-mode AI models. They are acutely aware of the need for technology to be versatile, capable of delving deeper into stored data, less expensive, and far easier to use.

Hybrid AI provides solutions to some of these problems, though not all. Since it integrates symbolic AI and ML, it can efficiently use the advantages of each approach while staying explainable, which is vital for industries like finance and healthcare.

ML may focus on specific elements of a problem where explainability doesnt matter, whereas symbolic AI will arrive at decisions using a transparent and readily understandable pathway. The hybrid approach to AI will only become increasingly prevalent as the years go by.

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The Future of AI in Hybrid: Challenges & Opportunities - TechFunnel