cross pond high tech

How Fireflies.ai works? ​Users can connect their Google or Outlook calendars with Fireflies and have our AI system capture meetings in real-time across more than a dozen different web-conferencing platforms like Zoom, Google Meet, Skype, GoToMeeting, Webex, and many ​more ​systems. These meetings are then indexed, transcribed, and made searchable inside the Fireflies dashboard. You can comment, annotate key moments, and automatically extract relevant information around numerous topics like the next steps, questions, and red flags.

Instead of spending time frantically taking notes in meetings, Fireflies users take comfort knowing that shortly after a meeting they are provided with a transcript of the conversation and an easy way to collaborate on the project going forward.

Fireflies can also sync all this vital information back into the places where you already work thanks to robust integrations with Slack, Salesforce, Hubspot, and other platforms.

Fireflies.ai is the bridge that helps data flow seamlessly from your communication systems to your system of records.

This approach is possible today because of major technological changes over the last 5 years in the field of machine learning. Fireflies leverage recent enhancements in Automatic Speech Recognition (ASR), natural language processing (NLP), and neural nets to create a seamless way for users to record, annotate, search, and share important moments from their meetings.

Who is Fireflies for? ​The beauty of Fireflies is that it’s been adopted by people in different roles across organizations big and small:

• Sales managers​ use Fireflies to review their reps’ calls at lightning speed and provide on the spot coaching
• Marketers ​create key customer soundbites from calls to use in their campaigns.
• Recruiters ​no longer worry about taking hasty notes and instead spend more time paying attention to candidates during interviews.
• Engineers ​refer back to specific parts of calls using our smart search capabilities to make everyone aware of the decisions that were finalized.
• Product managers and executives​ rely on Fireflies to document knowledge and important initiatives that are discussed during all-hands and product planning meetings on how to get access ​Fireflies have a free tier for individuals and teams to easily get started. For more advanced capabilities like augmented call search, more storage, and admin controls, we offer different tiers for growing teams and enterprises. You can learn more about our pricing and tiers by going to fireflies.ai/pricing.

This year, people are expected to spend more than half their day reading e-mail, articles, or posts on social media, and it’s only going to get worse. To help solve this problem, researchers at Salesforce have developed an algorithm that uses machine learning to produce “surprisingly coherent and accurate” summaries according to MIT Technology Review.

Automatic summarization would be a particularly useful technology for Salesforce, which produces a variety of customer-service focused products. The company notes that the resulting summaries could be used by sales or customer service representatives to quickly digest e-mails and information, which would allow them to spend more time focused on their customers.

To that end, Salesforce is turning to machine learning to find ways to summarize longer blocks of texts, which it could eventually incorporate into its products. The company announced that it made two breakthroughs in natural language processing, introducing a new, “contextual word generation model,” and a “new way of training summarization models.” Together, the two advances allow researchers to automatically create summaries of longer texts that are accurate and readable. The company acquired a deep learning outfit MetaMind last year, which was behind the research.

The researchers explain that automatic text summarization works in two ways: extraction or abstraction. With extraction, computer can draw from pre-existing wording in a text, but it’s not very flexible. Abstraction allows the computer to introduce new words, but the system has to understand the original article enough to be able to introduce the right words.

This is where deep learning neural networks come into play. They processing numerous examples of sentences and words to spit out new representations of each phrase, which allow the system to interpret texts and introduce its own words. The researchers let their model to look back at the text it’s working off of for additional context. It also looks back at earlier generated examples, to ensure that it’s not repeating itself.

The other breakthrough concerns how the researchers train the system to learn and improve itself. They used two approaches: teacher forcing and reinforcement learning. Reinforcement learning is a method that draws inspiration from how animals learn, and been used to teach Google’s DeepMind how to play video games. In this instance, the model is allowed to generate a sequence of words, and the result is then scored with an automated evaluation metric known as ROUGE (Recall-Oriented Understudy for Gisting Evaluation). The algorithm updates itself with higher scores, leading to better outcomes with future summaries. Teacher forcing is when the results are scored word by word off of an established reference, which provide “very decent results,” but which doesn’t allow for much flexibility.

Researchers found that “ROUGE-optimized RL helps improve recall...and word level learning supervision ensures good language flow, making the summary more coherent and readable.” Scored against this system, they found that their joint model scored higher than other approaches, and Richard Socher, Salesforce’s chief scientist, noted that he didn’t think that he’d ever seen “such a large improvement in any [natural-language-processing] task.”

The results are pretty astonishing: the researchers provided several examples, showing the original article, a human-generated summary, and a summary generated by their own model, and in each case, the summaries are considerably shorter than the original text, but contain the essentials in a readable form. Despite their advances, there’s still considerable work to be done in this field: MIT Technology Review spoke with Kristian Hammond, a professor at Northwestern University, who noted that the advance “shows the limits of relying purely on statistical machine learning,” but that it’s a step in the right direction.