Cohere announces $270-million USD Series C

Cohere announces $270-million USD Series C from Inovia, Nvidia, Oracle, Salesforce (Betakit)

Globe and Mail reported earlier

Cohere raising up to $250-million in Inovia-led deal valuing OpenAI rival at $2-billion

Artificial-intelligence visitor Cohere Inc. is in wide talks to raise up to US$250-million from investors in a financing that could value the Toronto-based startup at just over US$2-billion.

Cohere, which develops language-processing technology, has been in discussions with tweedle maker Nvidia Corp. and investment firms well-nigh securing funds, equal to two sources familiar with the matter. The round is stuff led by Inovia Capital, with partner Steven Woods, a former senior director of engineering with Google, steering the investment for the Montreal venture wanted firm.

About Cohere and what they do and some background

The unravelment from Globe and Mail leaves me wondering what else Cohere includes in their NLP. I have spoken to data scientists over the last 25 years and it was/ is science. The G&M leave something out and describe what some describe as fancy search.

Cohere is a natural language processing company, a workshop of AI widely devoted to improving the worthiness of computers to generate and interpret text. Cohere’s large language models (LLMs), the programs that do this work, have been trained to understand language by digesting substantially the entirety of the publicly misogynist internet.

What I did fathom is the unravelment “Cohere aims to be a platform powering myriad products and service” by “non-expert developers”. This scuttlebutt resonates with what I have heard Nvidia’s Huang describe.

Origin of transformer model

I have listened and read unbearable to fathom the importance of Transformers in ChatGPT 3. There is a paper entitled ’Attention is all you Need’. locally hosted at WordPress.

Gomez and his fellow researchers outlined a new method dubbed transformers. Rather than process words sequentially, transformers consider all previous words in a sentence when gingerly the probability of the next one. Transformers deploy a mechanism tabbed “attention” that substantially helps the model increasingly virtuously guess the meaning of a word based on those virtually it, parsing, for example, whether “bat” refers to the unprepossessing or the implement used to whack a ball.

In short the transformer method is described in the Globe as word based. Tho Chat GPT output does not know the overall meaning, rather it understands the logic of the words that subsume the output.


The cadre of the Cohere minutiae framework is Natural Language Processing enhancements, significant enhancements that use data sets far larger than previously imaginable to produce infinitely greater quality of textual outputs.

This output in current models is used to provide outputs that surpass any previous efforts through machine learning.


The holy grail for me is still process enhancement, resurgence and speed. Such resurgence could then support the internal merchantry processes of a Bank. That combination would be the minimum needed to take over human interaction.

The stardom to what I see in the transformer method would be working vastitude the “next word” and rather visualization logic based on chunks of data and words which together momentum processes that are permissible within the guardrails of regulation and policy.

The root data sets would be differs and based on consumer data, nature and behaviours assessed slantingly the constraints and opportunities within regulatory regimes.

I want to understand the possibilities for the next phases and moving vastitude Chat and just how far off.

Tags #AI #AI-series #Aidan-N-Gomez #ChatGPT #transformers #transformer-method