For B2B Generative AI Apps, Is Less More? Andreessen Horowitz

In fact, the definition of the classic “Turing Test” for artificial intelligence is that a human should be unable to distinguish between a chat conversation with an AI versus a human. We’re now seeing generative AI models that can capture animation straight from a video. This is much more efficient, both because it removes the need for an expensive motion capture rig, and because it means you can capture animation from existing videos.

My firm and I are thrilled to back as many of them as we can, and we will stand alongside them and their work 100%. First, we have laws on the books to criminalize Yakov Livshits most of the bad things that anyone is going to do with AI. We can simply focus on preventing those crimes when we can, and prosecuting them when we cannot.

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They are paid to be doomers, and their statements should be processed appropriately. My response is that their position is non-scientific – What is the testable hypothesis? These questions go mainly unanswered apart from “You can’t prove it won’t happen!

As LLMs evolve to better understand and interact with multiple modalities, they’ll be able to use existing apps that rely on GUIs today, like the browser. They can also offer more engaging, connected, and comprehensive experiences to consumers, who will be able to engage outside of a chat interface. “A lot of great integration with multimodal models can make things a lot more engaging and connected to the user,” Shazeer points out.

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Storied AAA developer Activision Blizzard has built Blizzard Diffusion, a riff on image-generator Stable Diffusion, to help generate varied concept art for characters and outfits. Our view is that AI companions will soon become commonplace with agents entering our everyday social sphere. Simulation games provide a digital sandbox where we can interact with our favorite AI companions in fun and unpredictable ways. Long-term, the nature of sim games will likely change to reflect that these agents are not just toys, but potential friends, family, coworkers, advisors, and even lovers. The early discourse on the Generative AI Revolution in Games has largely focused on how AI tools can make game creators more efficient – enabling games to be built faster and at greater scale than before.

The end-goal in personalized gaming is to offer a unique gameplay experience for every player. For example, let’s start with character creation – a pillar of nearly all role-playing games (RPGs) from the original Dungeons & Dragons (D&D) tabletop game to Mihoyo’s Genshin Impact. Most RPGs allow players to choose from Yakov Livshits preset options to customize appearance, gender, class, etc. But what if you could go beyond presets to generating a unique character for each player and playthrough? Personalized character builders combining LLMs with text-to-image diffusion models like Stable Diffusion or Midjourney could make this possible.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Third, California is justifiably famous for our many thousands of cults, from EST to the Peoples Temple, from Heaven’s Gate to the Manson Family. Many, although not all, of these cults are harmless, and maybe even serve a purpose for alienated people who find homes in them. But some are very dangerous indeed, and cults have a notoriously hard time straddling the line that ultimately leads to violence and death. There is a whole profession of “AI safety expert”, “AI ethicist”, “AI risk researcher”.

• It’s worth noting that dynamically generated worlds on their own are not enough to make a good game, as evidenced by the critical reviews of No Man’s Sky which launched with over 18 quintillion procedurally generated planets.
• The substitution of ‘i’ for ‘e’ was originally a designer’s typo from our early days.
• With Visa+, Visa simplifies how merchants can receive payments; instead of having to integrate with three or four P2P providers, they can now (potentially) just integrate with one.
• We have not yet seen a suite such as Runway for games emerge yet, but we know it’s a space of active development.
• GPT-3 was 175 billion parameters, and the newly available GPT-4 is 170 trillion parameters.

And the reality, which is obvious to everyone in the Bay Area but probably not outside of it, is that “AI risk” has developed into a cult, which has suddenly emerged into the daylight of global press attention and the public conversation. This cult has pulled in not just fringe characters, but also some actual industry experts and a not small number of wealthy donors – including, until recently, Sam Bankman-Fried. The fear that technology of our own creation will rise up and destroy us is deeply coded into our culture. The Greeks expressed this fear in the Prometheus Myth – Prometheus brought the destructive power of fire, and more generally technology (“techne”), to man, for which Prometheus was condemned to perpetual torture by the gods. Later, Mary Shelley gave us moderns our own version of this myth in her novel Frankenstein, or, The Modern Prometheus, in which we develop the technology for eternal life, which then rises up and seeks to destroy us. And of course, no AI panic newspaper story is complete without a still image of a gleaming red-eyed killer robot from James Cameron’s Terminator films.

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Entire generations of AI scientists over the last 80 years were born, went to school, worked, and in many cases passed away without seeing the payoff that we are receiving now. You can learn how to build AI from thousands of free online courses, books, papers, and videos, and there are outstanding open source implementations proliferating by the day. The level of totalitarian oppression that would be required to arrest that would be so draconian – a world government monitoring and controlling all computers?

No, that’s not going to happen – and in fact AI, if allowed to develop and proliferate throughout the economy, may cause the most dramatic and sustained economic boom of all time, with correspondingly record job and wage growth – the exact opposite of the fear. We’ve been through two such technology-driven unemployment panic cycles in our recent past – the outsourcing panic of the 2000’s, and the automation panic of the 2010’s. This is a relatively recent doomer concern that branched off from and somewhat took over the “AI risk” movement that I described above.

After all, the most striking part of the Mind Game was how it shaped itself to Ender, not the world itself. Generative AI could dramatically advance the simulation genre by making agents more lifelike with emergent social behavior powered by large language models (LLMs). We think AI will completely reinvent the workflow and UI for software, as increasingly AI software acts as a system of prediction and execution, not just a system of record. The speed with which we already see this shift happening means that nimble companies who can attract AI talent and move fast with some distribution are well-positioned to win. One can also think about individual products as being subdivided into a product architecture that’s organized around tolerance to probabilistic outputs. Adaptive music has been a topic in game audio for more than two decades, going all the way back to Microsoft’s “DirectMusic” system for creating interactive music.

Products that tackle other forms of content are also becoming increasingly popular in high schools and colleges across the country—Tome and Beautiful.ai, for example, assist in creating presentations. Edtech has long struggled with the trade-off between effectiveness and scale. Build something that works for the masses, and you lose the personalization that engages the individual. Build something that perfectly meets the needs of an individual, and it’s too expensive to scale.

With this power, it’s possible to build large neural networks that can identify patterns and representations in highly complex domains. Together, increased context windows and retrieval will be invaluable for enterprise use cases like navigating large knowledge repositories or complex databases. Companies will be able to better leverage their proprietary data, like internal knowledge, historical customer support tickets, or financial results as inputs to LLMs without fine-tuning. Improving LLMs’ memory will lead to improved and deeply customized capabilities in areas like training, reporting, internal search, data analysis and business intelligence, and customer support. These resources provide a base understanding of fundamental ideas in machine learning and AI, from the basics of deep learning to university-level courses from AI experts.