## 6 Future Directions  
We recommend that researchers carefully consider the differences between transfer learning and analogical inference before classifying research as one or the other, or perhaps \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Task Number** & **Task** & **Computer Vision** & **Natural Language Processing** \\ \hline \multirow{4}{*}{1} & Using any portion of a pre-trained model & Utilizing a pre-trained ANN architecture with frozen layers to predict a subset of the classes it was originally trained on & Utilizing a pre-trained vector space model to determine the similarity between two documents \\ \hline \multirow{4}{*}{2} & Parameter fine-tuning & Utilizing a pre-trained ANN architecture with unfrozen layers to predict image classes in the target dataset A more specific case of Task 1 & Adjusting the parameter values on a support vector machine to classify good and negative customer reviews \\ \hline \multirow{4}{*}{3} & Feature extraction on similar/ analogous data & Transfer learning: Identifying important features on a target dataset given the features of a source dataset & Transfer learning: Identifying the most relevant words within a text document \\ \cline{2-4}  & \multirow{2}{*}{2} & Analogical inference: Given the important visual elements of the source portion of an analogy, identifying the important elements of its target counterpart & Analogical inference: Given the important textual elements of the source portion of an analogy, identifying the important elements of its target counterpart \\ \hline \multirow{4}{*}{4} & Drawing inference on new data based on a similar knowledge space & Transfer learning: Identifying the location of four wheels on a truck given their locations on a car & Transferring the best D-word to complete the given incomplete textual analogy, _A:B:C:_? \\ \cline{2-4}  & \multirow{2}{*}{4} & Analogical inference: Inferring that since trucks have four wheels and cars are like trucks, cars must also have four wheels & Given two analogous stories, identify which elements (characters, plot, setting, etc.) in the source story maps to the target story (for example, Figure 6e, determining whether to map the military general to the surgeon or the patient) \\ \hline \multirow{4}{*}{5} & Mapping elements between two domains & Deciding which geometric elements of a source image corresponds best to the geometric elements of a target image (for example, utilizing \(A\) and \(C\) of Figure 6a, the large triangle of \(A\) can map to either the large or small square of _C_) & Identifying the underlying common relationship between two different words that are usually not synonymous (for example, the relationship between the two sentences, ”The runner trained for the marathon” and ”The student studied for the test,” could be re-representations of the word “preparing”) \\ \cline{2-4}  & \multirow{2}{*}{6} & \multirow{2}{*}{Re-representation} & Identifying where a knee would be on a tree given its location on a human & Identifying the underlying common relationship between two different sentences (for example, the relationship between the two sentences, ”The runner trained for the marathon” and ”The student studied for the test,” could be re-representations of the word “preparing”) \\ \hline \multirow{4}{*}{8} & Schema abstraction (generalization) & Creation of a general representational structure (or schema) that derives from the identified relationships within the source and target data \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Specific examples of transfer learning and analogical inference task(s) within computer vision and natural language processing. both. Although there are many elements that both techniques have in common, the differences we have reviewed show how the two approaches are distinctly different from one another. Future algorithms, methods, and applications should leverage concepts from both fields to advance the current state of AI.