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Interesting article by Yana Chekan from dida Machine Learning on #XAI I think the concept of eXplainable Artificial Intelligence (XAI) mirrors challenges faced in fields like hydrodynamic simulation, where, despite an understanding of underlying principles, the complexity often precludes a full explanation of specific outcomes. This issue, highlighted by the use of tools like Shapley values, emphasizes the practical limits of transparency in complex systems. The increased visibility of AI has brought this longstanding issue to the forefront, especially given AI’s widespread application. To address this, it's proposed that machine learning models should not be over-engineered and should maintain accessibility for under-the-hood examination by specialists. This approach will enable data scientists and ML engineers to derive and communicate the necessary explanations tailored to varying audience requirements, thus balancing technical transparency with practical applicability in business and technology contexts. #MachineLearning #XAI #DataScience

Day 69 of 100 Days of AI: Rules of Inference in Artificial Intelligence In the field of artificial intelligence (AI), rules of inference serve as the backbone for reasoning and decision-making processes. These rules are fundamental in enabling AI systems to draw logical conclusions from given premises, thereby facilitating intelligent behavior. Understanding Rules of Inference: Rules of inference are logical principles or techniques used to derive new propositions or make deductions based on existing knowledge or premises. In AI, these rules are essential for tasks such as problem-solving, expert systems, knowledge representation, and decision-making. Terminology Related to Inference Rules: Implication (→): Represents a logical connection between two propositions. If P implies Q, it can be written as P → Q. Converse: The converse of an implication swaps the left-hand side and right-hand side propositions: Q → P. Contrapositive: The negation of the converse: ¬Q → ¬P. Inverse: The negation of the original implication: ¬P → ¬Q. Types of Inference Rules: 1. Modus Ponens:   Modus Ponens, also known as affirming the antecedent, is a rule of inference that asserts if we have a conditional statement where the antecedent (if-part) is true, then we can infer the consequent (then-part) is true. Mathematically, it can be represented as:   If P implies Q (P → Q) is true,   And if P is true (P),   Then Q is true (Q).   Example:   If it is raining (P → Q),   And it is indeed raining (P),   Then the ground is wet (Q). 2. Modus Tollens:   Modus Tollens, also known as denying the consequent, is another important rule of inference. It states that if a conditional statement is true, and the consequent is false, then the antecedent must be false as well. Mathematically, it can be represented as:   If P implies Q (P → Q) is true,   And if Q is false (¬Q),   Then P is false (¬P).   Example:   If it is raining (P → Q),   And the ground is not wet (¬Q),   Then it is not raining (¬P). 3. Hypothetical Syllogism:   Hypothetical Syllogism is a rule of inference that allows for the transitive property of implications. It asserts that if one proposition implies a second, and the second implies a third, then the first proposition implies the third. Mathematically, it can be represented as:   If P implies Q (P → Q) is true,   And if Q implies R (Q → R) is true,   Then P implies R (P → R) is true.   Example:   If it is raining (P → Q),   And if the ground is wet (Q → R),   Then it is raining implies the ground is wet (P → R). In conclusion, rules of inference are indispensable tools in artificial intelligence, enabling systems to perform logical deductions and make informed decisions. By leveraging these rules, AI systems can emulate human-like reasoning processes, leading to more intelligent and effective problem-solving capabilities. #ArtificialIntelligence #computerscience #softwareengineering

Using machine learning (ML) 🧠 and artificial intelligence (AI) 🤖, Cognistx's Data Quality Engine improves data quality through cleansing 🧼, normalization, anomaly detection 🔍, and enrichment. Applying these methods to route optimization offers businesses significant benefits. Discover the specific benefits in our most recent blog post. https://lnkd.in/eRAmSekP #cognistx #routeoptimization #dataqualityengine #DQE

Let's talk about AI hallucinations 😮 All of the current AI models (GPT-4, DALL·E 2, etc.) 'hallucinate'. However, this term may be misleading. Wikipedia defines a hallucination as 'a perception in the absence of an external stimulus that has the qualities of a real perception.' In the case of these AIs, it is not about perception of any sort. So, what would be the correct term here? 'Extrapolation' would be more appropriate (Wikipedia: "extrapolation is a type of estimation, beyond the original observation range") This is similar to when a person, often a child, discusses topics they are unfamiliar with. Children lack experience in a particular field but still want to contribute to their peer group. Therefore they attempt to deduce or infer the most likely information based on their existing knowledge. Are they hallucinating by doing that? No. The more an AI model knows (the bigger the training set was), the more accurate its output is. The same rule applies to human beings! 😱 This phenomenon is not limited to learning and recall. It also affects how we process memories and it is not a conscious decision. For instance, 'Even questioning by a lawyer can alter the witness’s testimony because fragments of the memory may unknowingly be combined with information provided by the questioner, leading to inaccurate recall.' - from 'Why Science Tells Us Not to Rely on Eyewitness Accounts' (by Scientific American). As you can see, the phenomenon observed in AI models like GPT-4 is not isolated to the computer world only. We all do this on a larger or smaller scale.

You cannot have ML/AI without bias, because these systems depend on bias as a core element that allows them to function in the first place. - But how does #bias get into #MachineLearning and #AI Systems? - By looking more closely at how various types of bias make their way into these systems, we can better control and harness their power for good. Or… at least to do no harm. - Learn more in our recently updated blog: https://hubs.ly/Q02s67nc0 - #TechnologyTrends #TechnologyConsulting #IT #Data

Solving the Mystery: Artificial Intelligence (AI) and Machine Learning (ML) use patterns and computations for predictions and data analysis. Unlike humans, they don't have curiosity or critical thinking. What sets us apart from machines is our capacity to reason, to question the status quo, and to explore the unknown. Harnessing our innate ability to question and reason is the key to advancing with AI and ML. Our natural curiosity drives progress and innovation. Explore how we employ our reasoning and questioning abilities in our collaboration with AI: https://lnkd.in/dkmTTMGt

"Demystifying Machine Learning: The Power of Counterfactual Explanations" Ever wondered how machine learning models make decisions that affect our lives? 🤔 Counterfactual explanations are the key to unlocking this mystery! 😀 Read my latest blog post to learn how this powerful tool can increase transparency, fairness, and trust in AI decision-making. 🤓 https://lnkd.in/gAUJPTZP #MachineLearning #AI #Explainability #Transparency #Fairness #Trust #CounterfactualExplanations #dice_ml"

This AI Paper Introduces Investigate-Consolidate-Exploit (ICE): A Novel AI Strategy to Facilitate the Agent’s Inter-Task Self-Evolution Quick read: https://lnkd.in/graJPj5a Paper: https://lnkd.in/gJWs38jq #artificialintelligence #llms #largelanguagemodels

How Do We Know if AI Is Smoke and Mirrors? #AI #AIio #BigData #ML #NLU #Futureofwork http://ow.ly/Ykz930sCcl0

As a result of recent developments in the field of #AI, the impact of automation on the #labormarket has begun to be reassessed. Non-routine cognitive or manual jobs, long considered unaffected by previous #technologies, are increasingly under threat from self-learning #algorithms, leading many economists to argue that this time the impact on the labor market is fundamentally different from other technologies. In today's blog post, Benjamin Schaefer summarizes an article, which outlines different ways of AI`s #influence on #occupations. https://lnkd.in/dcWv-Esz