Intra-institution collaboration—such as partnershipsbetween academic departments, career centers, andstudent services—creates an integrated ecosystem whereskill development is reinforced across both curricular andco-curricular experiences. Meanwhile, inter-institutioncollaboration—sharing frameworks, tools, and best practicesacross universities, colleges, and professional programs—canaccelerate innovation and ensure more consistent preparationfor students entering a rapidly evolving workforce

Embed professional skills teaching into generaleducation requirements and across disciplines toensure they’re identified and evaluated.

The answer most technocrats are leaning towards is vector search technology and Retrieval-Augmented Generation (RAG) models that improve AI experiences. These intelligent search systems are fundamentally changing how users discover information, interact with applications, and receive personalized experiences across industries.

discussed above? In other words, how do we revise logic to grant paradox whereit is required? What new kinds of paradoxical concepts might better expressthe complexities of our ecological, economic, and other post-postmoderncontexts and systems? Is it possible to work them into the syntax of our exist-

flexibility

“the practice of purposefully involving minoritized communities throughout a design process with the goal of allowing their voices to directly affect how the solution will address the inequity at hand.”

approaches to align data analytics microcredentials with undergraduate experiences;

SECURE, DIGITALSTUDENT RECORDS

We now have the capacity to ensurethat all possible pathways – andthe essential information about allthe providers, credentials, skills,assessments, quality indicators,outcome measures, transfer values,and links to job skills critical tounderstanding and building thosepathways – can be made fully open,transparent and interoperable sothat a new generation of tools tocustom pathways to meet everyone’sindividual need

It seems like the neuron basically adds the embedding of “ an” to the residual stream, which increases the output probability for “ an” since the unembedding step consists of taking the dot product of the final residual with each token2.

‘networked accumulation’ platform firms (hereafter NAPFs) rely on existing or easily replaceable assets with minimal infrastructure, as distinct to platform firm models that extend or complement transport and accommodation infrastructures through the acquisition of their own fleets of vehicles or suites of properties (Stehlin et al., 2020). NAPFs typically launch local services under a cloud of ‘regulatory indeterminacy’ (Stehlin et al., 2020, 1256), relying on being “simultaneously embedded and disembedded from the space-times they mediate” (Graham, 2020, 454)

same groups that are going to makethe scale happen can also perpetuate theinequities. We have to be asking the rightquestions with the right stakeholders toensure that we are not recreating anotherinequitable system that marginalizes thepeople we are trying to support

Employer associations drive consistent skills languageacross job postings in their sectors

L)earners can make themselves visible toemployers around the country and around theglobe by “opting in” to digital sector recruitingnetworks for internships, gigs, and full-time jobs

To ensure an equitable and inclusive participation, Workcred invited executive directors and directors of certification that represented certification bodies based on selected criteria—whether their certification(s) could be aligned at the cognitive content level of a bachelor’s degree, whether the organization participates in Workcred’s Credentialing Body Advisory Council, and if they are accredited by a third-party.4 For purposes of this project, accreditation served as a proxy for the industry value of the certification. Select employers in industries related to the focus of a convening were also invited to participate.

t-SNE visualizations of word embeddings.

The second-to-last layer is what Han settled on as a reasonable sweet-spot.

ECMAScript 6th Edition (ECMA-262) introduces template literals which are intended to be used for embedding DSL in ECMAScript.

<Playground> ```html filename=index.html

for query, query_embedding in zip(queries, query_embeddings): distances = scipy.spatial.distance.cdist([query_embedding], corpus_embeddings, "cosine")[0]

simple approach is to average the second to last hiden layer of each token producing a single 768 length vector

It is worth noting that word-level similarity comparisons are not appropriate with BERT embeddings because these embeddings are contextually dependent, meaning that the word vector changes depending on the sentence it appears in. This allows wonderful things like polysemy so that e.g. your representation encodes river “bank” and not a financial institution “bank”, but makes direct word-to-word similarity comparisons less valuable. However, for sentence embeddings similarity comparison is still valid such that one can query, for example, a single sentence against a dataset of other sentences in order to find the most similar. Depending on the similarity metric used, the resulting similarity values will be less informative than the relative ranking of similarity outputs since many similarity metrics make assumptions about the vector space (equally-weighted dimensions, for example) that do not hold for our 768-dimensional vector space.

For out of vocabulary words that are composed of multiple sentence and character-level embeddings, there is a further issue of how best to recover this embedding. Averaging the embeddings is the most straightforward solution (one that is relied upon in similar embedding models with subword vocabularies like fasttext), but summation of subword embeddings and simply taking the last token embedding (remember that the vectors are context sensitive) are acceptable alternative strategies.

It should be noted that although the [CLS] acts as an “aggregate representation” for classification tasks, this is not the best choice for a high quality sentence embedding vector. According to BERT author Jacob Devlin: “I’m not sure what these vectors are, since BERT does not generate meaningful sentence vectors. It seems that this is is doing average pooling over the word tokens to get a sentence vector, but we never suggested that this will generate meaningful sentence representations.”

In order to get the individual vectors we will need to combine some of the layer vectors…but which layer or combination of layers provides the best representation?

This object has four dimensions, in the following order: The layer number (12 layers) The batch number (1 sentence) The word / token number (22 tokens in our sentence) The hidden unit / feature number (768 features) That’s 202,752 unique values just to represent our one sentence!

BERT offers an advantage over models like Word2Vec, because while each word has a fixed representation under Word2Vec regardless of the context within which the word appears, BERT produces word representations that are dynamically informed by the words around them.

BERT for feature extraction The fine-tuning approach isn’t the only way to use BERT. Just like ELMo, you can use the pre-trained BERT to create contextualized word embeddings. Then you can feed these embeddings to your existing model – a process the paper shows yield results not far behind fine-tuning BERT on a task such as named-entity recognition.

([CLS]).The final hidden state corresponding to this token is used as the ag- gregate sequence representation for classification tasks.

I extracted embeddings from a pytorch model (pytorch_model.bin file). The code to extract is pasted here. It assumes the embeddings are stored with the name bert.embeddings.word_embeddings.weight.

about 30,000 vectors or embeddings (we can train the model with our own vocabulary if needed- though this has many factors to be considered before doing so, such as the need to pre-train model from scratch with the new vocabulary). These vectors are referred to as raw vectors/embeddings in this post to distinguish them from their transformed counterparts once they pass through the BERT model.These learned raw vectors are similar to the vector output of a word2vec model — a single vector represents a word regardless of its different meanings or senses. For instance, all the different senses/meanings (cell phone, biological cell, prison cell) of a word like “cell” is combined into a single vector.

MDX is a superset of Markdown. It allows you to write JSX inside markdown. This includes importing and rendering React components!

Text embedding models convert any input text into an output vector of numbers, and in the process map semantically similar words near each other in the embedding space: Figure 2: Text embeddings convert any text into a vector of numbers (left). Semantically similar pieces of text are mapped nearby each other in the embedding space (right). Given a trained text embedding model, we can directly measure the associations the model has between words or phrases. Many of these associations are expected and are helpful for natural language tasks. However, some associations may be problematic or hurtful. For example, the ground-breaking paper by Bolukbasi et al. [4] found that the vector-relationship between "man" and "woman" was similar to the relationship between "physician" and "registered nurse" or "shopkeeper" and "housewife"

Grid devices can be nested or layered along with other devices and your plug-ins,

Publishers and other sites can include a simple line of javascript to enable annotation by default across their content.

Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Section 3.1 and 3.2 in Mikolov et al.).