A Conversation With Einstein’s Brain
Can Sales Algorithms Bring True Understanding?
An algorithm is best described as a) A computer language b) A step by step procedure for solving a problem c) A branch of mathematics d) All of the above.
The virtue of algorithms lies in their performance ie. the results they deliver.
In the mid 1980’s Philosopher John Searle posited an opposing theory to Turing Test — to test machines ability to exhibit intelligent behaviour- about how an almost infinite number of interactions need to happen between machines to come to real conclusions, without the true ‘understanding’ of inputs relating to an event ( when done in isolation).
Searle’s Chinese Room theory — which was already represented by Roger Schank’s ‘computer program’ , presented the quandary of a burnt hamburger patty versus a Non Burnt on. The scenario was then put forward — in Chinese symbols — through an opening to the room, and Schank’s machine (versus Searles manual operation of instructions) is programmed in English . Searle’s conclusion —
‘was that the mere successful Algorithm does not in itself imply that that any understanding has taken place’.
We’ve come a long way since and now with the ever improvement of programmed neural networks- programs to act like the brain, and Google is now also releasing an AI assistant with Voice recognition together Google Lens, that has the ability to incorporate image recognition over text ; and analyse those — which might make the above example a moot point in case.
If you look at the growth of MarTech from 2011 -17, it may prove a point and allude to the ongoing challenge of ‘really understanding’ where Sales & Marketing is going, yet CMO/CRO/ Sales Manager still report — they want better Leads, more Opportunities and better Conversion.
Merely following a set of sequenced steps doesn’t mean it’s the right sequence — as is the case when trading on the stock market with smart algo’s that triggers certain actions when the price of another stock hits a certain point, trading you out of, and into another position- to rebalance your portfolio.
A retailers example of the above (which adds complexity plus more data sets) that brings up an interesting point, which the guys of JTBD theory posits are:
· ‘what steps brought a potential customer to each point in their decision making process?
· what was the variance of responses?
· around what intrinsic motivations were those decisions made…?
· in order to achieve what?
Basic Algorithms
=IF Formula in Excel — which I learned when I was sixteen — but never really felt that passionately about, keeps reappearing into my life again and again.
I do think I’ve wasted more than a week in total in Excel, struggling to get formulas to do what I need to — something which obviously is NOT my forte ;)
What I have noticed is that the questioning is part of strategy and Sales specifically in Customer Development Projects & Lead Qualification…to test and validate a certain hypothesis and try to understand the customers better — for example test their Customer readiness ( how far they are in the buyers journey)
Questions like -
‘Do you currently struggle to deliver on this?
Can you elaborate?
‘‘=IF money wasn’t a real concern, THEN what would you consider an ideal solution? ‘’
More recently SaaS and other Software negates any coding, and just Filtering is a given.
In LinkedIn Sales Navigator if I was looking for a set of potential new clients, with key words and some other criteria, as part of my Customer Development process, so I can send them a mailer with a survey to learn what to do next, what they liked most AND who in their network I should talk to about X/Y/ or Z, then LinkedIn’s algorithm would follow a sequence of queries that would get a result set for me for example a 1000 new connections.
I could then Filter those further to get to a result set that exemplifies and have certain keywords in their profiles that are talking to pain points around the service my business is offering.
LinkedIn even goes further and suggest which of those to ‘Save as Leads’, after I have saved a subset. You can go even further like Glen Cathey does in his hour-long talk on the intricacies of boolean searches.
A few great examples which illustrate how Algorithms are deployed in a few different industries are found in SAP’s Digitalist platform here.
‘Mental activity is simply the carrying out of some well-defined sequence of operations, frequently referred to as an Algorithm’ — Sir Roger Penrose, The Emperors New Mind
I’d like to extend that further and remark on the most excellent piece of brain literature between Mr. Tortoise and Achilles; their conversation about Einstein’s Brain, from The Mind’s I by Douglas Hofstadter et al. Mr. Tortoise explains how ..
“…thought occurs when a series of connected neurons fire in succession…not necessarily like a string of dominoes falling down one after another — it may be more like several neurons at a time tending to trigger another few, and so forth.”
He goes on to explain is how the signals dissipate dependant on the strength of the firing of cells ( caused by stimuli/inputs) and resistances encountered.
What makes this reading so fascinating is to try to understand the complexity of how AI will have to simulate and improve our abilities to draw inferences and replicate the brain’s ability to cross-pollinate thought patterns.
— —
I’m trying to help companies to get better insights to construct Ideal Customers Profiles — and then curate content and build out their LinkedIn Profile so they can connect with the right people.
(In future — hopefully have a tool to use to apply what Einstein intended Humans to do and have more meaningful convers(ations.)
Please connect with me if you want to know more how we can improve your CEO’s/Managers profile to compliment your Sales efforts with targetted, high quality and — still human — Algorithms.
Ps.
I’m putting a side bet that Mark Benioff and his team came across this Conversation between Mr T. and Achilles, and has a massive poster on the wall of their Salesforce’s AI/ Einstein’s Headquarters.
