Choosing The Right AI, Where's The Smart Money Going And Win $1,000,000

More gems from the world of digital transformation and no apologies for the emphasis on what I've found recently on AI. The title I hope says it all and the $1,000,000 prize is real for anyone working on next generation AI beyond LLMs and on the path to AGI (Artificial General Intelligence). You thought GenAI was smart - check out the conversations below.

Meanwhile, I'm talking to a lot of very smart boutique and independent consultants who are struggling to differentiate in their 'go to market' and value propositions. The temptation always seems to go broad and list no end of experience and capabilities which firmly positions them as "Jack of all trades..." I should know, I've been there!

To help with this, increasingly, I find myself referring them to the concept of Jobs To Be Done pioneered by Tony Ulwick. You may have heard the term 'fall in love with the problem, not the solution" - well, Tony's work will help to determine who your customers are , what problems they are experiencing and how you can engage with them and solve their problems for them - your service proposition - it brings clarity.

If you need help with this get in touch I can help point you in the direction of people and resources that can help.

Meanwhile, check out Jobs-to-be-Done: A Framework for Customer Needs it will help !

As for the rest on this newsletter - enjoy, there's some good stuff here .. it'll help :)

Tim

Choosing The Right AI For The Job

AI is a tool with many nuances and like all tools, choosing the right one for the job is paramount to getting the job done. These 2 articles recently shared by IMD and the Dean of Big Data Bill Schmarzo will help you make the right choice.

The right AI for the job: Generative vs. legacy – when to use each

GenAI vs Legacy AI... Generative AI is certainly a hot topic in tech, but that doesn't mean it's the right tool for every task. In this IbyIMD article, Achim Plueckebaum and Michael Wade share a guide to help you decide when generative AI is most useful and when it should be avoided and perhaps a rules based AI should be used instead.

Please share your experiences in the comments >>

Read the Article >>

Synergy of Generative, Analytical, Causal, and Autonomous AI

A post from the Dean of Big Data Bill Schmarzo who says

"Lots of confusion about AI and Generative AI. We tend to get sloppy when defining AI and want to make AI = Generative AI. It doesn't."

His article in Data Science Central begins:

The current fascination with Generative AI (GenAI) – especially as manifested by OpenAI’s ChatGPT – has raised public awareness of Artificial Intelligence (AI) and its ability to create new sources of customer, product, service, and operational value. Leveraging GenAI tools and Large Language Models (LLMs) to generate new textual, graphical, video, and audio content is astounding.

However, let’s not forget about the predictive, understandable, and continuously learning legs of AI – analytical AI, which focuses on pattern recognition and prediction; causal AI, which seeks to identify and understand cause-and-effect relationships; and autonomous AI, which aims to operate independently and make real-time decisions. In the ever-evolving landscape of artificial intelligence (AI), four distinct but equally transformative branches have emerged: Generative AI, Analytical AI, Causal AI, and Autonomous AI........

Read the Article >> and Join the Conversation >>

More Insights on AI Business Models, Competitive Advantage and Where The Smart Money Is Going And Why

Funding Ventures Through The GenAI Age With James Currier

James Currier, co-founder of NFX and veteran venture capitalist, joins Simone Cicero for a gripping conversation the Boundaryless Conversations podcast.

In it they go through the current and future states of Venture Capital and startups, the role and impacts that AI will have in these ecosystems, and the incumbents' seemingly dominating position in this market.?

You'll hear James explain the NFX’s investment strategy and break down what it means to be living in a world of AI omnipresence. He covers how AI impacts investment size and deals and shares key ideas about integrating startup solutions into the customer’s workflow as a key defensibility - and thus value - driver.T

Tune in for fascinating and deep insights into the future of ventures.

Key Highlights:

???AI is not a revolutionary new platform but a powerful addition to existing technologies, driving productivity and creativity.

???If incumbents leverage their established infrastructures and resources, they have a significant upper hand in the AI race.

???Incumbents will likely capture a much greater share of the AI-driven market than they did during the mobile revolution.

???True defensibility of a startup lies in embedding products into customer workflows and creating network effects rather than relying on intellectual property.

???The future of venture capital is shifting towards smaller and more frequent exits rather than billion-dollar unicorns, challenging traditional investment models.

???A government’s increased involvement in tech industries could potentially stifle innovation, leading to slower progress and bureaucratic challenges for startups.

???Looking beyond the glamour of being a startup founder or a VC is realizing that it involves a significant emotional and financial sacrifice.?

Listen to Podcast >>

Pattern Breakers: How to find a breakthrough startup idea | Mike Maples, Jr. (Partner at Floodgate)

Thanks for Donald Hawthorne who alerted me to this excellent interview from Lenny's Podcast by Lenny Rachitsky who says:

Mike Maples, Jr is one of the most successful startup investors in history. He's worked with more early-stage startups than almost anyone alive, and with his fund, Floodgate, helped pioneer seed-stage investing as a category. He's been on the Forbes Midas List eight times and has made early bets on transformative companies like Twitter, Lyft, Twitch, and Okta. In his new book (coming out this Tuesday!), Pattern Breakers: Why Some Start-Ups Change the Future, he shares the three common elements he's uncovered that separate startups (and founders) that break through and change the world from those that don’t. This research is rooted in his decades of notes, decks, and founder relationships, and is unlike anything I've seen elsewhere.

In our conversation, Mike shares:

?? The three elements of breakthrough startup ideas

?? The importance of founder disagreeableness

?? Why you need to both think and act differently

?? How to avoid the “comparison trap” and “conformity trap”

?? How to apply pattern-breaking principles within large companies

?? Mike’s one piece of advice for founders

?? Much moreListen now

??- YouTube: https://lnkd.in/gPjw8RXk- Spotify: https://lnkd.in/gAUbgGxz- Apple: https://lnkd.in/g6t367uY

Join the Conversation >>

And while we are on the topic AI

Francois Chollet - LLMs won’t lead to AGI - $1,000,000 Prize to find true solution

Thanks to Oliver Molander for sharing this fascinating conversation between Dwarkesh Patel and Francois Chollet (AI researcher at Google, creator of Keras and the ARC LLM Benchmark) who says that current LLM architectures won't take us toward something that could be labeled "AGI" and calls current LLMs an offramp in the journey to AGI. He says current systems are barely intelligent.

Also Oliver shares that in May, Meta AI chief Yann LeCun advised students looking to work in the AI ecosystem, to not work on LLMs:

“If you are a student interested in building the next generation of AI systems, don’t work on LLMs."“I'm working on the next generation AI systems myself, not on LLMs,” said LeCun. According to LeCun, no top scientists at Meta are working on LLMs anymore; it's been passed on to the 'marketing' team.

The heat is on! ??

00:00:00 – The ARC benchmark

00:11:53 – Why LLMs struggle with ARC

00:19:43 – Skill vs intelligence

00:28:38 – Do we need “AGI” to automate most jobs?

00:49:11 – Future of AI progress: deep learning + program synthesis

01:01:23 – How Mike Knoop got nerd-sniped by ARC

01:09:20 – Million $ ARC Prize

01:11:16 – Resisting benchmark saturation

01:18:51 – ARC scores on frontier vs open source models

01:27:02 – Possible solutions to ARC Prize

Watch The Interview >>

Jobs-to-be-Done: A Framework for Customer Needs

"The problem with innovation isn't that customers don't know their needs. The problem is that companies can't agree on what a "need" even is! Outcome-Driven Innovation brings order to the often random process of innovation." Tony Ulwick

Read The Article

Thanks for reading!

Tim