Episode Details

Behavioral Signals aims to “turn your conversational data into actionable insights for your business” via the automated cross-cultural detection of emotions in the audio of conversations. This is a super-new front in machine language recognition and usage one which has to date been rather simplistically approached but one which as we all know every day is essential to real human interaction – witness all the confusion that for example emails can cause which would not occur if we saw people’s body language and heard the tone of what they were saying as well as simply the content in Times New Roman. Indeed it is this deficiency of the latter which led to the invention of emoticons to crudely add back some disambiguation.

Rana Gujral, CEO of Behavioral Signals, is a super-experienced veteran entrepreneur and CEO in Silicon Valley and in this episode he draws the curtain back on if not the final frontier then certainly a might important new one of man-machine interfacing.

Alexa and Siri can do an amazing job today compared to their predecessors a decade or two back, yet neither of them have any sense that they are dealing with a human being – they simply detect “words in Times New Roman” as it were and have no concept that the person asking is a human being for whom words are at times a small part of the bandwidth.

In this show Rana covers one Case Study of a usage of this technology in FS which ably demonstrated that when one is dealing with real technological innovation the use case innovation is itself also truly radical and requires large amounts of initiative and imagination to think beyond the obvious – after all if people already detect emotion how could a computer supplement that?

Topics discussed include:

• mountain biking in San Francisco during lockdown
• Rana’s impressive career journey and interesting product journeys – a great reminder that the West Coast of the States has really led in deep innovation for decades
• California, tech, Texas and Elon Musk
• the challenges of extreme innovation
• huge unexplored potential use case domains
• how computer-voice innovation has developed in recent decades
• affect is relatively new given the tall order of getting computers to understand multiple speakers in multiple languages in the first place
• NLP and NLU
• in this quest tone of voic