You're invited to:

Playing
Business

An Exclusive
                                           ON_Discourse
                                                                 Members-Only Dinner
           (and After Party)

June 22nd, 2023

Le Maschou, Cannes

Dinner: 7:00 pm- 10:00 pm

After Party: 10:00 pm- 2:00 am

During Cannes LIONS, ON_Discourse will host four days of programming in partnership with Stagwell’s Sport Beach. The week culminates with a private dinner and after-party hosted at Le Maschou. RSVP memberships@ondiscourse.com

Special

Guests

Kyle Martino

Former Professional Soccer Player

Mack Hollins

NFL Athlete

DeShone Kizer

Former NFL QB

Spencer Dinwiddie

NBA Athlete and Entrepreneur

Vanita Krouch

‘23 NFL ProBowl NFC Offensive Coordinator

Diana Flores

Mexican Flag Football Team Quarterback and Captain

James Worthy

TV Commentator and former NBA Athlete

Conrad Anker

American Rock Climber, Mountaineer, and Author

Performance by

World Renowned

DJ and

Entrepreneur

MICK

What?

ON_Discourse is a new membership media company focused on the business of technology, raising the level of discourse with expert-driven perspectives.

We provide member-only content for those that crave substance and closed-door events where you can ditch the small talk.

During Cannes Lions, Stagwell’s Sport Beach will bring together creatives, brands, marketers, athletes, coaches and leagues to discuss the future of fandom, and celebrate the impact sport has on shaping global culture.

Why
Attend?

We are Surrounded
by Fake-experts
_______Lacking Depth
in their Thinking

Ideas in our Industry are
_______Trapped within
Conventional
Boundaries

The People
_in our Industry
Often Think
_the Same

Unintended Consequences
_in Tech Lead to
Costly Mistakes
_in Business

Can I
Join?

The ‘Playing Business’ dinner is exclusively for ON_Discourse members. Premier members receive an invite to the dinner, the daily closed-door sessions, and will receive complimentary helicopter flight from Cannes to Nice with our partners BLADE. For more information email memberships@ondiscourse.com

Before starting,
we need your eyes on this...

I hereby apply for membership with ON_Discourse. I agree to be bound by the rules of membership.
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The

Open
Source

AI
Revolution

Dylan Patel

Semiconductor Analyst

In the late 20th century, the technology world witnessed a seismic shift as open-source Linux rose to prominence, challenging the dominance of proprietary operating systems from the era’s tech giants. Today, we are on the cusp of a similar revolution in the realm of AI, as open-source language models gain ground on their closed-source counterparts, such as those developed by Google and OpenAI.

In the 1990s, the UNIX ecosystem was dominated by proprietary solutions from major players like Sun Microsystems, IBM, and HP. These companies had developed sophisticated, high-performance systems tailored to the needs of their customers, and they maintained tight control over the source code and licensing. However, Linux, an open-source operating system created by Linus Torvalds, started gaining traction, ultimately disrupting the market.

The Linux revolution was propelled by three key factors: rapid community-driven innovation, cost-effectiveness, and adaptability. By embracing a decentralized development model built off the x86 personal computer, Linux empowered developers worldwide to contribute to its growth. This allowed it to evolve more quickly than its rivals and adapt to a diverse range of applications. Furthermore, Linux’s open-source nature made it significantly more cost-effective than proprietary alternatives, which relied on expensive licensing fees.

Fast-forward to the present day, and we are witnessing a similar upheaval in the AI landscape. The past two months have seen open-source AI projects such as EleutherAI GPT, Stanford Alpaca, Berkeley Koala, and Vicuna GPT, make rapid strides, closing the gap with closed-source solutions from giants like Google and OpenAI. Open-source AI models have become more customizable, more private, and pound-for-pound more capable than their proprietary counterparts. Their adoption has been fueled by the advent of powerful foundation models like Meta’s LLaMA, which was leaked to the public and triggered a wave of innovation.

The Linux saga offers important lessons for the AI community, as the similarities between the rise of Linux and the current open-source AI renaissance are striking. Just as Linux thrived on rapid community-driven innovation built off the backs of the x86 PC, open-source AI benefits from a global pool of developers and researchers who build upon each other’s work in a collaborative manner off the backs of gaming GPUs. This results in a breadth-first exploration of the solution space that far outpaces the capabilities of closed-source organizations.

Another parallel is the cost-effectiveness of open-source AI. Techniques such as low-rank adaptation (LoRA) have made it possible to fine-tune models at a fraction of the cost and time previously required. This has lowered the barrier to entry for AI experimentation, allowing individuals with powerful laptops to participate, driving further innovation.

Moreover, open-source AI models are highly adaptable. The same factors that make them cost-effective also make them easy to iterate upon and customize for specific use cases. This flexibility enables open-source AI to cater to niche markets and stay abreast of the latest developments in the field, much like Linux did with diverse applications.

The implications of this open-source AI revolution are profound, especially for closed-source organizations like Google and OpenAI. As the quality gap between proprietary and open-source models continues to shrink, customers will increasingly opt for free, unrestricted alternatives. The experience of proprietary UNIX-based systems in the face of Linux’s rise serves as a stark reminder of the perils of ignoring this trend. In fact, with image generation bots, OpenAI’s Dall-E and Google’s various closed models are barely a point of discussion as the world flocked to open Stable Diffusion models.

To avoid being left behind, closed-source AI organizations must adapt their strategies. Embracing the open-source ecosystem, collaborating with the community, and facilitating third-party integrations are crucial steps. By doing so, these organizations can position themselves as leaders in the AI space, shaping the narrative on cutting-edge ideas and technologies. Companies like Replit, MosaicML, Together.xyz, and Cerebras are doing just that, releasing open-source models, but offering services, finetuning, or operations as a service instead.

“ The implications of this open-source AI revolution are profound, especially for closed-source organizations like Google and OpenAI. As the quality gap between proprietary and open-source models continues to shrink, customers will increasingly opt for free, unrestricted alternatives.”

The flip side of the argument is that this is only possible for a certain model size. There are many emergent behaviors that have only been witnessed on the largest models. While open-source AIs that are an order of magnitude smaller than GPT-3 have already surpassed GPT-3’s quality, this does not necessarily apply to models of the scale of GPT-4 and beyond. With continued scaling in sequence length, parameter count, and training data set sizes, it is possible the gap between open-source and closed-source widens again.

Furthermore, while models are free to use, services that are built on top will still require significant investments. Google, Microsoft, and Meta are able to build these closed-source services for use in people’s everyday lives due to the moat of their platforms. Lastly, the cost of inference is a significant barrier given most consumer devices do not have the horsepower required for models larger than 7 billion parameters (GPT-3 is 175 billion parameters, GPT-4 is over 1 trillion), and it is possible that only the largest organizations can afford to scale their model out to billions of users.

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We’d love to know

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The Open Source AI Revolution

Before starting,
we need your eyes on this...

I hereby apply for membership with ON_Discourse. I agree to be bound by the rules of membership.
All information shall be treated in confidence.
Membership fees are subject to applicable taxes and services fees.
While we are exclusive, we are not exclusionary. We do not discriminate on the basis of race, ethnicity, gender expression, age, sexual orientation, religion, or disability, but we are committed to ensuring that our members are aligned with our values.

I have read and agree to the above

Accept

Please make selection above

The

Shift from

Knowledge

Work

to

Direction

Work

Toby Daniels

Co founder ON_Discourse

or

Sorry, Not Everyone

Can Be a Director

Dan Gardner

Founder & Exec Chair of Code and Theory & Founder, ON_Discourse

The worst piece of advice you could give today to a college freshman hoping to work in tech is to tell them to major in computer science, math, or engineering. Same for coding, which is about to go from being a surefire way into the industry to immaterial.

Automation has been replacing manual labor for decades and now artificial intelligence is ready to take over the bulk of knowledge work. We are on the precipice of a great shift that will drastically change which workers will be the most valuable recruits.

Knowledge workers who, at the turn of the century, were described as the most important workers within a modern, thriving organization, will be replaced by what we’re calling direction workers. The evolution in technology isn’t so much going to eliminate high-end human jobs, it’s going to change what high-end human jobs look like and require.

But the shifts in these sectors do not just show AI replacing human skills. They show a need for a new kind of human skill set. This is where the direction worker comes in.

For the last 60 years, knowledge work has been used to describe a kind of intellectual work that demands a high degree of specialization or training, and the ability to perform non-routine tasks like problem-solving, analysis, decision-making, and the creation of new information. Knowledge workers were the upper crust of all white-collar workers: financial analysts, architects, lawyers, data scientists, and engineers.

That was before.

Across many disciplines, knowledge work is already being replaced. In the financial sector, AI systems are able to analyze vast amounts of data and make sophisticated investment decisions. In healthcare, AI systems are able to diagnose medical conditions and recommend treatments with a high degree of accuracy. AI systems don’t take days off; they do not call in sick. They can work 24 hours a day.

But the shifts in these sectors do not just show AI replacing human skills. They show a need for a new kind of human skill set. This is where the direction worker comes in.

I use “direction” not so much to convey the management work of a director in a company but more to refer to the literal act of directing, as in instructing or conducting. It could just as easily be called “Instruction Work.”

The image of an orchestra conductor comes to mind, expertly guiding musicians and instruments to produce the right sound. The image of a NASCAR driver may even be more appropriate. The engine may be beautiful, but it won’t win the race without the expertise, the direction, of its driver.

In finance just as in healthcare, human workers are needed to provide direction to AI systems even as they are no longer required to crunch the data themselves. On the tail end, human workers also need to evaluate the results, use critical, lateral thinking, and offer follow-up instructions.

Ferenc Huszár, a machine learning professor at the University of Cambridge, tweeted last year that the current version of OpenAI’s large language model, ChatGPT, would be a good teaching tool in mathematics, precisely because its answers are sometimes wrong. “Give it a problem, it generates convincing-looking but potentially bullsh*t answer, ask the student if they are convinced by the response,” he wrote.

What Huszár is suggesting here to me is not just teaching students to simply produce an accurate answer, but to develop an ability to go past the appearance of a fact and, with a critical eye, evaluate whether it is indeed accurate. If it isn’t, that eye needs to figure out why not, edit the original question, and do it all over again.

As systems progress, there may be less need for correction and editing, but the need for direction will not disappear. Ever-improving technologies will only call for more excellent direction.

Where to find and how to train these direction workers then becomes the question.

I am not sold on telling young people to just go to business school. Sure, we need a good generation of leaders who understand how to manage this new landscape, but more than managers we need critical thinkers who can ask the right questions, look for blind spots, understand connections, and have the creativity and humility to rethink the problem at its end and its base.

Direction workers are likely going to be people who can juggle different skill sets all at once: dual majors in math and anthropology PhDs who have trained quantitatively and qualitatively, journalism majors who work with Python, law school graduates willing to engage with the practicalities of coding and ethics. In short, we are going to need what David Epstein called “generalists” in his best-selling book, Range.

Realizing that to be competitive in the marketplace in the next ten years is going to look totally different than it did in the last ten is not just something young professionals need to do. Those of us in business should also be paying attention: The biggest cost to businesses over the next decade will be hiring the wrong people with the wrong skill sets.

As Max Penk put it in a post on LinkedIn earlier this year:

“Good news: AI will not replace you. Bad news: a person using AI will.”

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Do you disagree or have a completely different perspective?
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Against AI "art"

Molly Crabapple

Artist and writer in New York City. Her work was nominated for three Emmys and is in the permanent collection of the Museum of Modern Art.

Like most illustrators, I’ve been horrified by the rise of AI image generators, like Dall-e, Midjourney, and Stable Diffusion. These systems are trained on billions of images obtained without the knowledge, compensation, or consent of their creators, and churn out passable-looking images in seconds, for pennies or for free. Since they are faster and cheaper than any human illustrator can be, these generators threaten to destroy the industry that I have devoted my life to. Worse, they are using our own stolen images against us. My work, like that of many other illustrators, is stored in the LAION-5B database, which billion-dollar corporation Stability AI uses to train their generator. Even more alarmingly, DALL-E, owned by multi-billion dollar Open AI, can create ersatz versions of my work if you type “drawn in the style of Molly Crabapple."

One might argue that the rise of AI image generators is only a problem for illustrators like me. But what about you, editor, art director, or publisher? Why should you forgo something so convenient? Sure, generative AI threatens mass unemployment for millions of people, far beyond the illustration field, but appeals to ethics and solidarity won’t stop you from using it any more than it stopped you from using Amazon or Uber or AirBnB. You need stronger stuff.

Well, here are some reasons why you should avoid using AI art generators, even if the future of illustrators does not concern you in the slightest.

Reason One:
Lawsuits

There are currently two major lawsuits against the corporations that make the major image generators.

In January, three artists launched a class action lawsuit against Stability AI (the company behind Stable Diffusion), Midjourney and DeviantArt, alleging mass copyright infringement.

In April, stock image company Getty launched another lawsuit against Stability AI for 1.8 trillion dollars for scraping their entire archive (the theft was so obvious that generators spat out images with Getty’s mangled watermark).

Many more lawsuits will undoubtedly follow – especially in the EU, where privacy restrictions are stricter than in the US. Why is this relevant to you?

The terms of service at many generators make users liable for copyright violations in the images they generate. In Midjourney’s words: “If you knowingly infringe someone else’s intellectual property and that costs us money, we’re going to come to find You and collect that money from You.”

Reason Two: Blowback

I am yet one of many who are strongly opposed to AI art generators. When I co-released an Open Letter calling for their restriction in publishing, thousands of people, from every continent signed – including MSNBC host Chris Hayes, actor John Cusack, and author Naomi Klein. This opposition extends from organized professional groups to unaffiliated art lovers, but it is passionate and it is growing.

One might argue that the rise of AI image generators is only a problem for illustrators like me. But what about you, editor, art director, or publisher?

Any use of AI-generated work is likely to inspire a loud and persistent backlash. Look at Amnesty International, which tried to use photorealistic images from Midjourney to illustrate an article commemorating the second anniversary of Colombia’s national strike. The criticism was so harsh they were forced to pull the images and issue an apology. Entities from Netflix Japan to Amsterdam’s Rijksmuseum to the Bradford Literary Festival have all faced fury for using AI-generated images. The point of the illustration is to make you look good. Why use something that will do the opposite?

Discover more

Discourse

Reason Three: Sameness

It’s been mere months since AI art generators exploded, but that’s already long enough for them to have developed certain stylistic tics. There’s the gelatinous smoothness of the skin. Profusion of fingers and teeth. Limbs that go nowhere. Above all, there’s the sameness. An image generator cannot create – it can only spit out pastiches of the art it stole; and if these generators become ubiquitous, the quantity of human art will be dwarfed by oceans of AI-excreted schlock, which the generators will train on and spit out versions of, in an algorithmically-enabled ouroboros of visual mediocrity.

If you use images from a generator, you are using images that look like the images posted by every other yahoo who uses a generator… including your competitors. You give up the very advantage that art is supposed to give you… the ability to be visually unique.

So there you have it – three unemotional, business-wise reasons why you, my hard-nosed reader, should stay away from AI image generators.

Having defined what I’m against, I now want to talk about what I’m for. Even more than an artist trying to save her field, I am what the digital theorist Douglas Rushkoff calls Team Human – in that I believe that technological developments must be aligned with human values, rather than treated as some sort of manifest destiny, to be imposed upon us regardless of the costs.

While generative AI might be coming for us illustrators, it is coming for you as well. Anyone who translates, writes, sings, codes, conceptualized, or creates is in danger of being chucked out of work by robots trained on their own bodies of work. This also goes for the people, like you, whose job it is to commission us.

We have one shot to resist Silicon Valley’s plagiarism bots. Let's take it.

or

or

Do you agree with this?
Do you disagree or have a completely different perspective?
We’d love to know

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