What makes AI algorithms different from traditional computer algorithms?

Rule-based algorithms are based in the context of If-Then-Else, where different actions are taken, depending on whether a condition is met or not. It is a useful process if you have less complex problems to solve. It was the default mode of most of the programs in the past. But, the rule-based model does not work with complex cases. Let's take the example of chess: how do you calculate a tree with 10ˆ123 nodes? AI algorithms do not follow any rule. They work on identifying patterns and cluster options into these patterns, making it far more effective for large datasets and complex problems, where statistics becomes the central piece of the process.

Traditional computer algorithms follow predefined rules to process data. For example, sorting algorithms arrange data based on a set of instructions. In contrast, AI algorithms, being data-driven, learn patterns and rules from the data itself. An example is a recommendation system that suggests movies based on a user's watching history, adapting its suggestions as the user interacts more. This data-driven approach enables AI to discover insights and patterns that may not be explicitly programmed.

The distinction between AI algorithms and traditional ones lies in their approach to problem-solving. Traditional algorithms follow explicit instructions to reach a predetermined outcome. They're linear, deterministic, and operate within defined parameters. Conversely, AI algorithms, particularly those in machine learning, learn from data. They discern patterns, adapt, and make decisions, often uncovering solutions beyond the explicit programming of their creators. This ability to learn and evolve makes them inherently dynamic and non-linear. This fundamental difference marks a shift from rigid programming to a fluid, learning-centric approach in computing, opening vast new possibilities in technology and application.

🚀 AI algorithms are game-changers! I'd say they differ from traditional algorithms mainly because they're like sponges, soaking up data to make intelligent decisions. They adapt and evolve, unlike standard algorithms that follow set rules. AI thinks in probabilities, offering best-guess answers, while traditional methods are more like math problems with one correct answer. AI can get creative, dreaming up new ideas, which traditional algorithms don't do. Plus, AI brings ethical questions, making us think hard about their impact. In short, AI is dynamic, creative, and ever-evolving! 🤖✨

I can share an experience of mine when it comes to this distinction between traditional computer algorithms and AI algorithms from audit perspective. By audit I am referring to financial audit. Not all AI solutions will go through financial audit, only a few will have to experience it. One expectation in audit is that given the input is the same and methodology (in AI application it's the AI a algorithm) is the same, output will also be the same. Now that will be true for a rule based system. However for a random forest, or XGBoost algorithms this will not be the case (unless we fix it which is not very common). This is new for the auditor & he/she has to find ways to audit this type of a solution which is time consuming.

AI powered applications can adapt to the changing business environment whereas the rule based applications will remain static. This let's AI systems to remain relevant for a longer period while the business environment changes as compared to the rule based application. When the business environment changes, the rule based application need to be modified by adding new rules and altering existing rules to capture the latest business environment.

Dynamic vs Static. This is single greatest area of difference in a real system. AI algorithms can get better and also decay over time hence maintenance requires both technical and domain expertise. Example: A static image de-noising algorithm based on a simple filter could work fine for all images at 70% accuracy. An ML based density estimation algorithm could be working at 95% accuracy for images similar to ones it has seen in training, but could massively overfit as well, making the whole system open to adversarial attacks unless regularly re-trained. Hence, sometimes simple rules are not bad, unless we believe in the investment.

AI algorithms stand out for their dynamic nature, allowing them to update parameters, structure, or behavior in response to new data or feedback. This adaptability makes them highly flexible for various applications. In contrast, traditional computer algorithms are static, maintaining a fixed structure once implemented. This rigidity limits their adaptability but provides consistency and predictability in specific scenarios. Understanding this distinction is crucial for leveraging the appropriate algorithm type based on the needs of a task or project, balancing flexibility and stability.

AI algorithms differ from traditional computer algorithms in terms of their dynamism. Traditional algorithms are often static, following predetermined rules and logic set by developers. In contrast, AI algorithms, especially in machine learning, exhibit dynamic behavior. They can adapt and evolve based on the data they encounter, allowing for flexibility and the ability to improve over time. This dynamic nature enables AI algorithms to handle complex tasks and learn from diverse datasets, contributing to their effectiveness in various applications.

I believe dynamic AI references "online learning", where the model can update itself on the fly. However, all AI models aren't necessarily able to do that. Many studies are done regarding dynamic systems, but it does not include all AI models. For example, a simple image segmentation model will always give the same outputs once in production. Similarly to "traditional" computer algorithms, only an update to the model by the developer will change its behaviour.

Unlike deterministic algorithms, which follow a fixed set of rules and produce predictable outputs, AI algorithms, particularly machine learning algorithms, leverage probabilistic methods. These algorithms learn from data, adapt, and make predictions or decisions based on statistical probabilities, allowing them to handle complex and uncertain scenarios where traditional algorithms may fall short. This probabilistic nature empowers AI algorithms to uncover patterns, make predictions, and provide insights in domains such as natural language processing, image recognition, and recommendation systems.

Distinction between ML-based algorithms and traditional algorithms lies in their approach to problem-solving and the nature of the outcomes they provide: approximate and empirical for ML-based algorithms, and exact and deterministic for traditional algorithms.

That is just the wrong categorization. But we should excuse the AI that generated this text... First, it is unclear what is considered to be traditional and whether there are non-traditional non-AI algorithms, or the categorisation is binary: AI and everything else. The text implies the later case. Just FYI, some traditional non-deterministic algorithms: RRT and PRM motion planners, and some variants actual reason and deal with uncertainty. Take the ConCERTT motion planner algorithm, it assumes an uncertain initial robot state, noisy robot motion, noisy proprioception, and uses random exploration, so, two executions of the algorithm with different seeds but for the same problem will result in different solutions.

Traditional computer algorithms are typically deterministic, meaning that they will always produce the same output given the same input. AI algorithms, on the other hand, are often probabilistic, meaning that they can produce different outputs given the same input. This is because AI algorithms learn from data, and data can be noisy or incomplete.

AI algorithms are inherently probabilistic, equipped to handle data uncertainty, ambiguity, or noise. They provide outputs based on probabilities or confidence levels, adept at managing varying degrees of information reliability. In contrast, traditional computer algorithms are deterministic, requiring clear, precise data to produce outputs with certainty and exactness. This fundamental difference highlights AI's suitability for complex, real-world scenarios where data may not be perfect, while traditional algorithms excel in environments with well-defined and consistent inputs. Understanding this contrast is key to choosing the right algorithmic approach for specific tasks or challenges.

In specific LLMs (for example GPT 3.5, GPT 4 driving ChatGPT) we can give the user preferences. The user can specify to the LLM to give the answer considering that I am the teacher with 10 years of experience or a company executive with 20 years of experience etc. and LLM will tailor the answer accordingly. This is difficult with rule based systems. If we still want to allow user preferences to come in the rule based systems driven, we can add rules in the configuration files and these rules can be switched on or kept off based on user's preferences. User has to make the selection. Rule based system can be configured so that a pop up comes where user selects his/her profession, years of experience etc.

Creative AI algorithms are focused on the generative paradigm. Current state-of-the-art generative models such as LLMs based on transformer-decoders can generate text by predicting the token with the highest probability at each step. By seeing tons of text data in the pre-training stage, we can enhance model creativity. Mechanical traditional algorithms are based on fixed steps, which can be fired depending in some predefined conditions. Thus, these algorithms are analogous to finite state machines.

Classical “rule-based” algorithms can also generate not so predictable and “creative” outputs. Think of cellular automata (game of life is the most famous example) or rule based reasoning systems (that can infer new knowledge from existing one). The difference between hand made algorithms and data-driven (learned) models is that either a human design the rules or these are learned from data, meaning that the model behind the scene can potentially learn very complex (and non-linear) rules. The consequences are 1) the scale of potential answers the model can output and 2) the possibility of adversarial inputs (where a small deviation in the input lead to a drastic change in the outputs) and 3) the possibility of not generalizing well.

This categorization is just another nonsense. Is this another AI hallucinating or a marketing pitch? Just one counter example: Traditional computer algorithm with influence from the system e.g. system clock or from the user e.g. an input integer used as seed value for randomization, like for the RRT motion planner, will result in different and unexpected outcomes. So, by the definition above RRT planners are creative.

AI algorithms are distinguished by their creativity, capable of generating novel and original outputs beyond the constraints of existing data, rules, or formulas. They can adapt to the goals, preferences, or emotions of users or systems, offering unique, sometimes unexpected solutions. In contrast, traditional computer algorithms are mechanical, producing outputs that are predictable and strictly determined by predefined rules and input, without adapting to user or system influences. This distinction underlines AI's ability to offer innovative solutions in complex scenarios, while traditional algorithms provide reliable and consistent results in well-defined situations.

Ethically, the main difference lies in the decision-making process. Traditional algorithms execute predetermined rules, often reflecting human design choices. AI algorithms, however, can learn from data and may exhibit biases present in the training data, raising ethical concerns about fairness, transparency, and accountability in decision-making. Managing and mitigating biases in AI systems is a critical ethical consideration.

The ethical dimension of AI algorithms specific in medicine is a product of its training data and application context. There is a need to commit to transparency in AI design, to guarantee that algorithms are not only effective but also either universally equitable for diverse patient groups or explicitly specified for narrower patient demographics. This approach marks a significant shift from the traditionally neutral application of algorithms in broader contexts.

I don't necessarily agree with the distinction. It's clear to me that any automated solution (AI powered or not) carries significant ethical implications and consequences for humanity. There are countless examples of malicious software affecting the many systems we depend on and causing significant harm. Addressing these safety issues/concerns requires different approaches, but It's important to not leave them behind.

This is false. Both “traditional” and AI algorithms can and are informed by ethics and have ethics implications. It’s usually going to require more intention and rigor for AI algorithms (assuming ML), however.

This is section is also inaccurate. To say “traditional computer algorithms… have no inherent moral or ethical implications, as they are simply tools or methods for solving problems or performing tasks” is patently false. Here is a simple “traditional” algorithm written in natural language: “If someone is of gender X, deny them a loan.” Non-AI algorithms can cause harm and have profound moral implications as well. It’s *usually* easier to detect and prevent such harm, however.

1. Traditional algorithms: - Uses a fixed, predefined function f(x) as outcome. - Deterministically map inputs to outputs. 2. ML algorithms: - Learns the function fθ(x) such that θ is learnt from data. - probabilistic, producing predictions based on learned probabilities P(y∣x,θ)

AI algorithms differ from traditional computer algorithms in their learning capability, data-driven decision making, complexity handling, automation, and adaptability. Unlike traditional algorithms, AI algorithms can learn from data without explicit programming, make predictions based on large amounts of data, handle complex and non-linear relationships, automate tasks, and adapt to changing conditions. They often leverage techniques like machine learning, neural networks, and feature extraction to extract insights and improve performance. These characteristics enable AI algorithms to tackle complex problems and provide sophisticated solutions.

Major difference between Traditional algorithms and AI algorithms is that traditional algorithms use a fixed set of rules to process inputs and generate outputs, while AI algorithms use inputs and outputs to generate logic that can be used to process new inputs. AI algorithms are more complex and require training data to learn, while traditional algorithms are designed to perform a specific task and do not have the ability to learn from data

Autonomous vs Dependent: One significant aspect distinguishing AI algorithms from traditional ones is their autonomy. Traditional algorithms often rely on predefined rules and structures, following a rigid set of instructions. In contrast, AI algorithms, especially machine learning models, exhibit autonomy by learning and adapting from data. They operate more independently, making decisions based on patterns and insights gained during training. This autonomy introduces a level of self-adjustment and responsiveness that is distinct from the dependency seen in traditional algorithms. This difference has profound implications for adapting to dynamic environments and evolving datasets.

This article forgets to mention that traditional computer algorithms can transition towards AI algorithms through the integration of machine learning techniques. By incorporating learning capabilities and the ability to adapt to data, a traditional algorithm can evolve into an AI algorithm, particularly in the context of supervised or unsupervised machine learning. This transformation enables the algorithm to make decisions based on patterns and experiences rather than relying solely on predefined rules.