Agent Spotlight: Emmanuel Sam, An Avid Learner With a Heart Of Gold
January 27, 2023
July 15, 2022
2
min to Read
Automate
New data from Crunchbase indicates that startup funding fell even further in Q2 after a sharp decline in the first quarter of 2022.
These findings mirror concerns we heard from business leaders in a survey we conducted last month: overall, over 45% of business leaders reported feeling worried about raising a next round of funding.
This concern snowballs into two others for decision-makers.
1. They’re worried about cash burn because they can’t raise additional funds.
2. They’re worried about burning out their employees because they can’t increase headcount.
What can business leaders do to reduce both burn and burnout at their organizations?
Here are 5 solutions:
1. Invisible
At Invisible, we do two things remarkably well.
We augment your existing team with a powerful blend of humans and technology called worksharing to help you accomplish business goals. Delegating rote, time-consuming tasks to us unburdens your team and enables you to instead focus on the remaining work needed to grow.
Invisible likens this to an Ironman suit for your team. Tony Stark was brilliant; IronMan was a superhero.
We also unlock scale for our clients. Our worksharing model not only removes burdensome processes off your plate, it minimizes your need to increase headcount and burnout your employees with evolving work.
Once the business process is in our hands the Invisible team continuously improves the process for efficiency, manages quality, and keeps you updated the entire way.
Here’s an example of worksharing at its best: A food delivery giant had demand double near the start of the pandemic and needed to onboard restaurants fast.
After a month, Invisible had doubled the output of existing vendors - but that wasn’t enough.
We built tech to help automate the process and cut costs for the client by 50% in just 3 months. The result - hundreds of thousands of restaurants were onboarded onto the platform, and business boomed.
Read more about that success story here.
If we’re not a great fit for your needs at the moment, here are some other solutions in the marketplace:
2. Pareto
Pareto focuses on carrying out repeatable tasks and saving businesses time. Some examples of processes that Pareto runs for clients: lead-generation, hiring (specifically for engineers), competitor research, and other data operations.
3. Clay.run
Clay.run specializes in automating workflows starting with spreadsheet templates. Clay’s core offerings include no-code sourcing and data-enrichment for sales pipelines and recruiting, while syncing data across platforms.
4. TaskUs
TaskUs digitally outsources work for partners. The company’s core competency is in CX, scaling customer support teams and content moderation for growing companies, but it offers AI services and consulting as well.
5. Tonkean
Tonkean simplifies complex workflows. The no-code solution automates tasks including legal operations processes like triaging requests, as well knowledge transfer across functions and even employee onboarding.
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New data from Crunchbase indicates that startup funding fell even further in Q2 after a sharp decline in the first quarter of 2022.
These findings mirror concerns we heard from business leaders in a survey we conducted last month: overall, over 45% of business leaders reported feeling worried about raising a next round of funding.
This concern snowballs into two others for decision-makers.
1. They’re worried about cash burn because they can’t raise additional funds.
2. They’re worried about burning out their employees because they can’t increase headcount.
What can business leaders do to reduce both burn and burnout at their organizations?
Here are 5 solutions:
1. Invisible
At Invisible, we do two things remarkably well.
We augment your existing team with a powerful blend of humans and technology called worksharing to help you accomplish business goals. Delegating rote, time-consuming tasks to us unburdens your team and enables you to instead focus on the remaining work needed to grow.
Invisible likens this to an Ironman suit for your team. Tony Stark was brilliant; IronMan was a superhero.
We also unlock scale for our clients. Our worksharing model not only removes burdensome processes off your plate, it minimizes your need to increase headcount and burnout your employees with evolving work.
Once the business process is in our hands the Invisible team continuously improves the process for efficiency, manages quality, and keeps you updated the entire way.
Here’s an example of worksharing at its best: A food delivery giant had demand double near the start of the pandemic and needed to onboard restaurants fast.
After a month, Invisible had doubled the output of existing vendors - but that wasn’t enough.
We built tech to help automate the process and cut costs for the client by 50% in just 3 months. The result - hundreds of thousands of restaurants were onboarded onto the platform, and business boomed.
Read more about that success story here.
If we’re not a great fit for your needs at the moment, here are some other solutions in the marketplace:
2. Pareto
Pareto focuses on carrying out repeatable tasks and saving businesses time. Some examples of processes that Pareto runs for clients: lead-generation, hiring (specifically for engineers), competitor research, and other data operations.
3. Clay.run
Clay.run specializes in automating workflows starting with spreadsheet templates. Clay’s core offerings include no-code sourcing and data-enrichment for sales pipelines and recruiting, while syncing data across platforms.
4. TaskUs
TaskUs digitally outsources work for partners. The company’s core competency is in CX, scaling customer support teams and content moderation for growing companies, but it offers AI services and consulting as well.
5. Tonkean
Tonkean simplifies complex workflows. The no-code solution automates tasks including legal operations processes like triaging requests, as well knowledge transfer across functions and even employee onboarding.
LLM Task
Benchmark Dataset/Corpus
Common Metric
Dataset available at
Sentiment Analysis
SST-1/SST-2
Accuracy
https://huggingface
.co/datasets/sst2
Natural Language Inference / Recognizing Textual Entailment
Stanford Natural Language Inference Corpus (SNLI)
Accuracy
https://nlp.stanford.edu
projects/snli/
Named Entity Recognition
conll-2003
F1 Score
https://huggingface.co/
datasets/conll2003
Question Answering
SQuAD
F1 Score, Exact Match, ROUGE
https://rajpurkar.github.i
o/SQuAD-explorer/
Machine Translation
WMT
BLEU, METEOR
https://machinetranslate
.org/wmt
Text Summarization
CNN/Daily Mail Dataset
ROUGE
https://www.tensorflow
.org/datasets/catalog/
cnn_dailymail
Text Generation
WikiText
BLEU, ROUGE
Paraphrasing
MRPC
ROUGE, BLEU
https://www.microsoft.
com/en-us/download/details.a
spx?id=52398
Language Modelling
Penn Tree Bank
Perplexity
https://zenodo.org/recor
d/3910021#.ZB3qdHbP
23A
Bias Detection
StereoSet
Bias Score, Differential Performance
Table 1 - Example of some LLM tasks with common benchmark datasets and their respective metrics. Please note for many of these tasks, there are multiple benchmark datasets, some of which have not been mentioned here.
Metric
Usage
Pros
Cons
Accuracy
Measures the proportion of correct predictions made by the model compared to the total number of predictions.
Simple interpretability. Provides an overall measure of model performance.
Sensitive to dataset imbalances, which can make it not informative. Does not take into account false positives and false negatives.
Precision
Measures the proportion of true positives out of all positive predictions.
Useful when the cost of false positives is high. Measures the accuracy of positive predictions.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone).Sensitive to dataset imbalances.
Recall
Measures the proportion of true positives out of all actual positive instances.
Useful when the cost of false negatives is high.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone)and Sensitive to dataset imbalances.
F1 Score
Measures the harmonic mean of precision and recall.
Robust to imbalanced datasets.
Assumes equal importance of precision and recall.May not be suitable for multi-class classification problems with different class distributions.
Perplexity
Measures the model's uncertainty in predicting the next token (common in text generation tasks).
Interpretable as it provides a single value for model performance.
May not directly correlate with human judgment.
BLEU
Measures the similarity between machine-generated text and reference text.
Correlates well with human judgment.Easily interpretable for measuring translation quality.
Does not directly explain the performance on certain tasks (but correlates with human judgment).Lacks sensitivity to word order and semantic meaning.
ROUGE
Measures the similarity between machine-generated and human-generated text.
Has multiple variants to capture different aspects of similarity.
May not capture semantic similarity beyond n-grams or LCS.Limited to measuring surface-level overlap.
METEOR
Measures the similarity between machine-generated translations and reference translations.
Addresses some limitations of BLEU, such as recall and synonyms.
May have higher computational complexity compared to BLEU or ROUGE.Requires linguistic resources for matching, which may not be available for all languages.
Table 2 - Common LLM metrics, their usage as a measurement tool, and their pros and cons. Note that for some of these metrics there exist different versions. For example, some of the versions of ROUGE include ROUGE-N, ROUGE-L, and ROUGE-W. For context, ROUGE-N measures the overlap of sequences of n-length-words between the text reference and the model-generated text. ROUGE-L measures the overlap between the longest common subsequence of tokens in the reference text and generated text, regardless of order. ROUGE-W on the other hand, assigns weights (relative importances) to longer common sub-sequences of common tokens (similar to ROUGE-L but with added weights). A combination of the most relevant variants of a metric, like ROUGE is selected for comprehensive evaluation.
