Hybrid AI

Artificial intelligence (AI) encompasses technologies for automating intelligent behavior, from machine learning to generative AI, which is characterized by models such as neural networks. Hybrid AI combines analytical and generative approaches to ensure flexibility, energy efficiency and precision by switching between specialized and generative methods as needed, as in MORESOPHY-DAPHY technology, which optimally combines creativity and control.

1. First of all, what is artificial intelligence?

We humans describe ourselves as intelligent because we have the ability to solve problems. And we don’t do this somehow, by force or in endless discussions, but as efficiently as possible – i.e. in as little time and with as little energy expenditure as possible.

Humanity has long claimed intelligence as a differentiating characteristic. We now know that not only other living beings exhibit intelligence, but computers can also demonstrate intelligent behavior by helping us to solve problems.

2. How do I measure the value of AI?

Intelligence is not a value in itself. People can also use intelligence criminally or to the detriment of others. There are simple principles for the use of artificial intelligence that should be used to measure the value of an AI:

1. Reliability

Reliability is measured by whether the AI does what we expect it to do. The use of AI is usually associated with solving a specific problem. We want to achieve a goal more quickly. Therefore, the crucial question is how confident we can be that the AI will do exactly what we want it to do. Reliability has a lot to do with trust. And trust is based on the fact that I can understand and comprehend how a human or, similarly, an artificial intelligence behaves.

2. Business KPI

The use of AI in companies should always be seen in conjunction with specific business key performance indicators (KPIs). This is because the use of AI costs money, even a lot of money in the case of widely used applications. It is therefore essential to check how the use of AI affects one or other KPI and whether the return on investment in the use of AI is positive. This can result in higher customer satisfaction, cost savings, higher productivity or even improved quality. KPIs make it possible to measure targets. And they can also be used to measure the reliability of the AI.

3. Energy Efficiency

In all of this, the aim is to achieve the goal with as little effort and cost as possible. In the case of AI, this primarily relates to the energy consumption required to operate the AI model. Generative AI is criticized here because a comparatively extremely large amount of computing time and electricity is required for each query. This in turn results in high water consumption for cooling.

3. What forms of artificial intelligence are there?

Artificial intelligence encompasses a wide range of technologies that deal with the automation of intelligent behavior. The focus is on machine learning (ML) methods. Here, complex mathematical functions are used that are able to predict what will result from a given constellation of data. Whether for weather forecasts, assistance systems in cars or a chatbot that answers a question. The principle is similar for all applications. The type, quantity and structure of the data and the expected result differ greatly.

In all cases, the AI must be trained beforehand. Only when it has seen and “experienced” a sufficient number of cases can it recognize systematic differences and patterns in the data that affect the possible outcome. For good, reliable predictions, this usually requires a large number of well-prepared cases so that the AI can learn accordingly.

There are many different criteria by which an AI can be distinguished. For example, there are many different algorithms, i.e. computing methods. The most powerful are so-called neural networks, which can also deal with very complex data structures and are now standard. ML methods also differ in the way they are trained. There are techniques in which the AI learns from direct input from a human. Like humans, however, AI can also learn automatically by repeatedly seeing, reading or hearing data. In technical jargon, this is called unsupervised learning.

When companies use AI, the most important criterion is to differentiate between what a company uses AI for. Here it is helpful to distinguish between analytical and generative AI.

Analytical AI

AI can be used very well to identify relevant patterns, signals or trends in an unmanageable amount of data and information for humans. Analytical AI can help people to evaluate complex situations and make the right decisions based on this. Analytical AI can evaluate large amounts of text as well as numerical data.

Generative AI

ChatGPT has established a new form of AI, generative AI. From a technological point of view, generative AI is based on large language models (LLM) that are trained on massive data (practically a complete dump of the WWW). They can be used very universally – for any kind of domain and for very different tasks – and especially for generative tasks. Generative AI can therefore do much more than analytical AI. While generative AI stores all knowledge in a gigantic model, highly specialized, specific models are used for analytical purposes.

4. What is hybrid AI?

Hybrid AI is more than just a toolbox that provides tools for both analytical and generative purposes. Rather, a system equipped with hybrid AI uses both analytical and generative AI. And it does so intelligently so that an optimum result is achieved with as little energy expenditure as possible.

Hybrid systems always follow the same principle: they combine different approaches flexibly in order to achieve the best result with the right approach depending on the situation and given requirements. Humans, for example, also exhibit hybrid intelligence. There are problems that we solve linguistically, others logically and mathematically. Sometimes we follow simple rules, in other cases we rely on our (not explicitly justifiable) gut feeling. A hybrid engine combines the advantages of a combustion engine (long range, mature refueling infrastructure) with those of an electric drive (zero emissions, low noise, low maintenance).

It is the same with hybrid AI: for specific tasks, specialized analytical models are much more efficient than consulting a large model every time. Generative AI is used where it has capabilities that analytical models cannot provide – and vice versa.

Graphic Differnt kinds of articifical intelligence

5. What are the advantages of hybrid AI?

Hybrid AI initially has the same advantage that all hybrid systems have: the availability of a wide range of options means that the system can adapt much more flexibly to the respective circumstances. Hybrid office work, for example, is characterized by the fact that time-consuming arrival and departure times and resource consumption are saved. For work that benefits from physical collaboration, however, the use of resources makes sense and is possible in a hybrid office mode.

Hybrid systems have many other advantages, especially with regard to AI:

1. Costs and energy consumption

Hybrid AI systems only use resource-intensive, generative AI where it is absolutely sensible and necessary. Other tasks are performed by specific AI models that do not require GPUs and often cost up to a factor of 1000 or more less.

2. Speed

The lower energy consumption is also accompanied by a significantly higher processing speed of analytical models. Repeated specific tasks can also be completed orders of magnitude faster with analytical AI than with generative AI. Big data analyses, e.g. for media analysis or the evaluation of sensor data, cannot be performed in real time with generative AI.

3. Traceability

A major problem with generative AI is the lack of transparency. Generative AI is ultimately nothing more than statistics. But it is not clear why a generative AI answers a question in one way or another. Even the smallest changes to the question lead to unexpectedly large changes to the result, as the calculation involves billions of parameters whose dependency is not comprehensible. Analytical AI, on the other hand, can be set up in such a way that it shows people how and why it arrives at a result. This capability can also be used in conjunction with generative AI.

4. Controllability

True to the motto “problem identified, problem solved”, traceability also creates the conditions for targeted controllability. Analytical AI uncovers relevant patterns in the background. This enables systematic optimizations to be made or misconduct to be detected at an early stage and the right measures to be taken.

5. Precision

Hybrid AI beats generative AI, especially in cases where the focus is on qualitative aspects (in the form of how, what or why questions) in conjunction with quantitative key figures (sum, average, trend, …).

6. What does MORESOPHY's hybrid AI involve?

Hybrid AI from MORESOPHY is not just a toolbox that offers both types of AI. Instead, MORESOPHY’s AI platform offers concrete solutions that combine the advantages of both AI methods in an end-to-end process.

AI solutions from MORESOPHY enable end-to-end value creation on any unstructured data. Analytical methods are primarily used where existing data needs to be qualified, e.g. cleansed, standardized or classified. Generative AI, on the other hand, is used in particular at the user interface – i.e. wherever a user simply wants to ask a question or have a document processed. In the application, however, generative AI not only draws on its own knowledge, but can also make use of analytically obtained data.

Technically, the analytically obtained data is dynamically integrated into prompts to control the generative AI. We call this process DAPHY (DATA DRIVEN PROMPTING WITH HYBRID AI) – in other words, “data-driven prompting with hybrid AI”.

7. What are the advantages of MORESOPHY's DAPHY technology?

With the patent-pending DAPHY® technology, moresophy combines the advantages of generative AI with the traceability and controllability of analytical AI in a superior solution. As a result, you receive reliably optimizable results for an optimal compromise between creativity and control.

The main advantages of the technology are

  • Less effort in prompt engineering: In many cases, building complex prompts requires extensive domain knowledge. In addition, the prompting of an AI is very unpredictable. The smallest changes in the prompt can sometimes lead to surprisingly large changes in the output. With DAPHY, you can rely on standardized prompts that are dynamically adapted with structured data.
  • Dynamic control of prompts with valid, verifiable data: When it comes to control, you rely on data that can be validated and verified thanks to analytical AI processes. This is where the advantages of analytical AI in terms of transparency and traceability pay off immediately. The process reduces possible hallucinations of generative AI, forcing it to focus on concrete facts provided by data.
  • Optimization based on the context of the target group and goal of the process: The process can be used to automatically take into account data that defines the context of a task. This allows AI solutions to be pragmatically optimized.

8. How can I use hybrid AI for my company?

With the context optimizer, existing content can be optimized in a targeted manner for different needs, target groups and channels. DAPHY integrates data provided by MORESOPHY, which identifies motives and the specific language of target groups from large amounts of data. The solution offers multiple benefits. The hybrid process allows content to be created in a much more customized way. In addition, the content is optimized on the basis of validated, relevance-optimized data. This gives editorial teams a very transparent, reliable management tool.

MORESOPHY’s hybrid AI is also integrated into chatbots or AI assistants for answering questions. The hybrid approach offers significantly more precise results than the exclusive use of generative AI. MORESOPHY’s knowledge management solutions are particularly superior when the AI should not only answer qualitative questions, but also take quantitative measures into account. Hybrid AI also offers significantly improved control thanks to procedural embedding of the AI with various instruments for evaluating and controlling the AI’s behavior. In conjunction with CONTEXTSUITE ‘s platform architecture, the hybrid AI architecture creates the necessary conditions for using AI in regulated industries and in connection with sensitive data.

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