CoCounsel.ai’s Post

How can we be neurodiverse-affirming in everyday life? By changing our terminology! See a list of phrases to avoid, and alternatives you can use instead. Source: Play Spark, https://play-spark.com/

Applications of GenAI: Summarizing a document based on key topics of interest. The tool and the blog link is in the video description.

*Love this as a perspective, full-blog linked in Matt Klein's comment.* A thought-provoking read on the impact of algorithms, curators, and influencers on culture. As a politics student, we endlessly debated the challenges posed by echo chambers. Now, as a strategist, I always actively try to break the echo chamber my own algorithm creates. How can I hope to out-think if my input is limited by those around me or by what the same voices tell me I should think? Working across the Entertainment category, I've had countless conversations about algorithms, influencers, the growing demand amongst audiences for convenient discovery, and what lies ahead. It's refreshing to see both sides of the argument explored. If algorithms and curators/influencers are both 'bad', is there space for us to embrace an even more human approach? What do you think? #strategy #curators #creators #entertainment The Value Engineers

Is human evaluation scalable in LLM-powered systems? In our latest episode, Aaro and I explored the common practice of using domain experts to assess the outputs of LLM systems. This method, while reliable, comes with its drawbacks. It's no secret that having experts manually evaluate every output is costly and time-consuming. Every tweak to the system requires a fresh evaluation, driving up expenses and delaying progress. Is this the best we can do? The lack of scalability creates room for alternative solutions like LLM-as-judge, which you’ll hear us discussing soon. Dive deeper into the topic: https://lnkd.in/dWuySZR9

What is the Z-Transform? What does its formula tell us? What is the use of the Z-Transform? My article "Introduction to the Z-Transform: Understanding Discrete-Time Signals" answers these questions.

Notes: 1) You have a different voice for every person you talk to. 2) Outward voice vs Inward voice 3) Three mechanisms: Bone conduction, Mechanical filter, Biological filter 4) By listening a voice you can understand possible failures such as for: - Types of turbulences and non-linearity: Parkinson's disease - Breathiness: Heart disease - Changes of tempo: Depression #ted #tedtalks #tedconferences #voice #prediction

A great clarifying challenge: to insert the Old and New Testaments into an Artificial Intelligence to detect consistencies, contradictions, discrepancies, what is impossible to do so far, etc..... within all the books that compose them.   

Some of the inside story on our recent paper on a natural non-coding regulatory variant and an elegantly simple mechanism of parrot color variation: https://lnkd.in/eJ2ytAyY

Using Chronicle and Blackout together can be the dynamic duo that rescues you from tedious tasks like identifying and redacting original values. Discover the solution in this quick write-up! https://lnkd.in/dR35Vktz