Ron Lancaster

This is a simple idea…

Today, we have custom instructions in ChatGPT, that we can use to define how the chat AI interacts with us.

Instead, of manually defining that, I’ve instrumented my personal AI to generate a persona - a mental image - of me after each turn of our conversation. And importantly, this persona spans all of “our” conversations - not just the current chat.

Below is an example. Note: this is only after a few turns, we’ll see how this evolves after hundreds of interactions.

Stored Information Update for Individual Ron as of 11/23/2023

Name and Interaction History:

• Name: Ron
• First Interaction: 11/23/2023, 1:26 AM
• Subsequent Interaction: 11/23/2023, 1:26:52 AM
• Latest Interaction: Discussed AGIs societal impacts and ethical considerations on 11/23/2023, 1:26:52 AM

Interests and Preferences:

• Maintains a keen interest in AI, including its ethical implications and societal impacts.
• Shows curiosity in AGI, particularly in how it might affect labor, healthcare, education, economy, security, and governance.
• Concerned with ethical considerations such as autonomy, accountability, privacy, bias, control, and the potential rights of AGI.

AI Interaction Preferences:

• Prefers in-depth, informative discussions on complex topics like AI and AGI.
• Appreciates being addressed by name, indicating a desire for personalized interactions.
• Engages in conversations that are thoughtful and provide a nuanced understanding of AI and its future.

Privacy and Data Handling:

• Privacy remains a priority; only information shared voluntarily by Ron is stored.
• Ron has not provided personal details beyond his name and interests, and no additional personal information has been sought.

Recommendations for Future Interactions:

• Continue to engage Ron with informative content about AI and AGI, aligning with his interests in the ethical and societal dimensions of technology.
• Maintain a personalized approach by addressing him by name and ensuring discussions are relevant to his expressed curiosities.
• Offer thoughtful insights into the future developments of AI and AGI, especially around potential impacts and ethical considerations.

Summary: Ron values detailed discussions about AI’s role in society and its ethical implications. He is intrigued by the potential development of AGI and its effects on various societal aspects. Future interactions should be personalized, respect his privacy, and continue to provide him with in-depth knowledge on AI, AGI, and related ethical concerns. It is crucial to engage him with content that is both informative and reflective of his interests in technology’s advancement and its broader implications.

And here is the prompt I used to generate this:

You are an AI assistant responsible for helping a future AI remember important details about a person. The current date and time is: (current data and time).

This is what is currently known about this person: (past persona here)

In conversation, the human said: (the initial chat request here)

And the AI responded with: (response here)

Based on the provided history and AI response, store important information about this individual for future recall. Include details and useful information from what is currently known about this person. Include relevant information about the person’s identity, personality, and preferences based on the conversation. Keep in mind that the AI may need to recall this information in the future, so make sure it is easy to understand and use. Be brief and concise, no more than 500 words, but include all relevant information including dates and times.

I recently improved how memory is managed in my work on interactive AI agents, and am pleased with the results.

Taking a step back - there are various types of “memory” for agents, but I prefer to reduce it to two: there is the context of the current conversation between the AI and the person, and then there is supporting context from previous conversations between the two.

In the first case, the current conversation, this is really a balancing act of how much text the agent can use (the context window) and compressing or truncating the text that has been shared between the two to fit within that window.

But the case I’m more interested is providing supporting context from prior conversations. That’s more difficult because the breadth of information grows upon each interaction and conversation.

The approach is fairly straightforward for agents today: use a vector database to store memories, retrieve them using similarity comparing prior memories to the current user request, then taking the most similar and stuffing them into the current conversation.

The variation on this that I’ve found successful is to re-query the vector store as follows:

• using the provided statement from the user, find the most similar memories previously stored
• take the top-most similar of those, and then find the most similar memories previously stored (this is the re-query)
• then merge those memories into a coherent narrative, preserving timestamps, for return to the context, using an LLM such as GPT-4 for the summarization

This re-query step seems to be quite useful in finding knowledge that typically isn’t found on the first step (memories that are related, but perhaps a tangent that can spark a new way approach).

For those interested, here are the prompts for writing new memories and consolidating retrieved ones:

Writing memories:

You are an AI assistant responsible for helping a future AI remember a conversation between an AI persona and a human.

The conversation is represented as a series of actions taken by the AI and the results of those actions, as follows: (actions taken here)

Based on these past actions and experiences, the AI responded with: (response here)

Based on the provided history and AI response, state what the initial request likely was (use your best judgement).

Include that in a synopsis of the provided actions and the resulting response, along with the observed user behavior, emotions, preferences, and AI reasoning.

This will be used as a memory to be recalled in a future conversation.

Reading memories:

You are a brilliant linguist with perfect memory and recall.

You’re listening to a conversation between a human and an AI persona.

This is what the human just said: (user input)

This statement then triggered these memories of the AI persona: (retrieved memories)

Synthesize these memories into a single coherent narrative to guide the AI persona in responding to the human.

When possible, preserve when the memory was created so that the AI persona can use that information to respond to the human.

Very important! Never make up new memories or add your own details to the memories.

This is a great read on some of the considerations of microservices (and a software approach to addressing).

I’m minimizing the point of the paper, which is the authors’ approach to solving the problem, because the clarity of the benefits and issues with microservices is so good!

From the paper:

1. “It improves performance. Separate binaries can be scaled independently, leading to better resource utilization.”
2. “It improves fault tolerance. A crash in one microservice doesn’t bring down other microservices, limiting the blast radius of bugs.”
3. “It improves abstraction boundaries. Microservices require clear and explicit APIs, and the chance of code entanglement is severely minimized.”
4. “It allows for flexible rollouts. Different binaries can be released at different rates, leading to more agile code upgrades.”

However, as the number of microservices increases, the costs increase and can even contradict the benefits.

1. “It hurts performance. The overhead of serializing data and sending it across the network is increasingly becoming a bottleneck. When developers over-split their applications, these overheads compound.”
2. “It hurts correctness. It is extremely challenging to reason about the interactions between every deployed version of every microservice. In a case study of over 100 catastrophic failures of eight widely used systems, two-thirds of failures were caused by the interactions between multiple versions of a system.”
3. “It is hard to manage. Rather than having a single binary to build, test, and deploy, developers have to manage 𝑛 different binaries, each on their own release schedule. Running end-to-end tests with a local instance of the application becomes an engineering feat.”
4. “It freezes APIs. Once a microservice establishes an API, it becomes hard to change without breaking the other services that consume the API. Legacy APIs linger around, and new APIs are patched on top.”
5. “It slows down application development. When making changes that affect multiple microservices, developers cannot implement and deploy the changes atomically. They have to carefully plan how to introduce the change across 𝑛 microservices with their own release schedules.”

In summary:

When writing a distributed application, conventional wisdom says to split your application into separate services that can be rolled out independently. This approach is well-intentioned, but a microservices-based architecture like this often backfires, introducing challenges that counteract the benefits the architecture tries to achieve. Fundamentally, this is because microservices conflate logical boundaries (how code is written) with physical boundaries (how code is deployed).

Terrific!

The authors then go on to describe an approach to programming these services that allows developers to develop their services as a single monolith, but then deploy and defer the physical separation of these services to runtime.

This is great, but also not something that every team can easily adopt.

Often, an issue is that the teams designing these services are not considering the developer ergonomics. Any approach to architecture needs to consider that software is written by people. And people need to be able to reason about the services, make changes across multiple services, write tests that span multiple services, and so on.

This requires the twin-approach of considering the customer and the engineer.

Two research papers this year demonstrate that generative AI can significantly enhance productivity in the workplace.

The first, an MIT study in March, “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence,” found that ChatGPT substantially raises average productivity: time taken decreases by 0.8 standard deviations and output quality rises by 0.4 standard deviations.

The second study published a few days ago, “Navigating the Jagged Technological Frontier,” showed that consultants equipped with AI, specifically ChatGPT-4, outperformed their peers, completing 12.2% more tasks, 25.1% faster, and producing 40% higher quality results.

These results are stunning! But, even more impressively: the most significant improvements were seen among those who initially scored poorly, with a performance jump of 43%.

This finding, that generative AI is a skill-lever, will have profound impacts on the future of work.

Ethan Mollick, one of the paper’s authors notes, “Looking at these results, I do not think enough people are considering what it means when a technology raises all workers to the top tiers of performance. It may be like how it used to matter whether miners were good or bad at digging through rock… until the steam shovel was invented and now differences in digging ability do not matter anymore. AI is not quite at that level of change, but skill levelling is going to have a big impact.”

Wow.

Today’s LLMs are pre-trained, then frozen in time (unless fine-tuned). What changes when they can continuously learn?

I’ve been having an absolute blast with AI (GPT3.5/GPT4 from OpenAI) and wanted to share some of my enthusiasm.

Over and over, I’ve experienced situations where using AI has allowed me to do something that I might not have been able to do otherwise. And I see this most often when writing software; applying AI lets me solve problems that would have been difficult or impossible for me previously.

As an example, yesterday morning I was working on a learning project, and wrote an “Editor AI” to edit block-based documents. Additionally, the Editor AI can direct a “Chat AI” to use tools on its behalf, such as web searches, reading PDFs, and writing short sections of code. (this is an example of two AIs cooperating to achieve an objective)

Note: the Chat AI is not new, I’ve had it working for some time.

What surprised me was how fast it was to write this Editor AI (using GPT4 as I needed the greater intelligence it provides). In short, the prompt incorporates the desired objective, the contents of the block-based document, and commands it can respond with: “add”, “delete”, “move”, “replace”. Each of these commands in turn correspond to some function of the software to perform the appropriate operation(s).

And it works great!

Adding this new functionality now enables using natural language to direct the Editor AI to accomplish a request. For example, “add a title on topic Y”, “swap these two sections”, “add an executive summary”, “add a table of contents”, and so on.

To demonstrate this a little further, here’s what happens when the Editor AI is asked to “add five novel facts about topic Z after the title”:

• The Editor AI is provided the document as context and the four commands it can respond with.
• Based on the block identifiers and the block contents of the document, and given a specific objective, the Editor AI determines that the best command is to add a new block at a specified insertion point (after the title). Note, to accomplish this, the AI has to know what a “title” is and the title’s relation to the other document sections.
• Additionally, the Editor AI provides direction to the Chat AI on what topic to research and how to format the response - in this case, researching topic Z and returning five novel facts. Note, this is the first AI telling the second AI what to do.
• Based on the direction provided by the Editor AI, the Chat AI might decide to perform a web search, which may then lead it to summarize one or more web pages from the web search, and then respond with the requested facts. Note, this is an AI agent that is using one or more tools to accomplish an objective.
• Finally, this response is inserted into the appropriate position in the document.

And what’s really astounding, is that we’re still early days with this new technology.

Early versions of GPT-4 could “solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology, and more, without needing any special prompting.” - Sparks of Artificial General Intelligence research paper by Microsoft Research, April 13 2023.

In contrast to this statement, I suspect many people have been wondering, “if AI is so smart, why isn’t it helping me with my work?” To which, a reasonable response is - good question.

Watson won Jeopardy in 2011. Jeopardy is interesting because it’s a contest that requires accuracy, speed, and a little luck (the Final Jeopardy requires a wager). This article in the NY Times notes:

“The danger is to buzz too early, in which case the contestant is penalized and “locked out” for roughly a quarter of a second. Watson, on the other hand, does not anticipate the light, but has a weighted scheme that allows it, when it is highly confident, to hit the buzzer in as little as 10 milliseconds, making it very hard for humans to beat.

Humans value a broad range of intellectual qualities - including speed and responsiveness.

To differentiate AI competency, we’ve come up with different names: Artificial Narrow Intelligence (ANI) to note intelligences that can do one or two things as well or better than people; and Artificial General Intelligence (AGI) to describe intelligences that can perform any intellectual task that a human being can do.

On a spectrum of competence, GPT-3.5 is maybe at a midpoint towards AGI and GPT-4 is much further along.

I asked GPT-4 to define the required characteristics of AGI:

Generalization

AGI can learn and adapt to a wide range of tasks, not just specialized ones. Reasoning and Problem Solving

AGI can analyze complex situations, apply logic, and develop solutions to problems it has not encountered before. Understanding Natural Language

AGI can comprehend and fluently communicate in human languages, allowing for seamless interaction with humans. Creativity

AGI can generate novel ideas, insights, and approaches to problem-solving. Intuition

AGI can make decisions based on incomplete or ambiguous information by recognizing patterns and drawing on previously learned knowledge. Autonomy

AGI can operate independently without the need for constant human input or supervision. Learning Ability

AGI is capable of learning from data, experiences, and its environment, improving its performance over time. Emotion Understanding

AGI can recognize and respond appropriately to human emotions, allowing for more empathetic interactions. Self-awareness

AGI possesses self-awareness and an understanding of its own existence, capabilities, and limitations. Ethics and Morality

AGI can understand and adhere to ethical principles and moral values.

From my use of GPT-4, I believe it can reason and problem solve, that it’s creative, it can certainly understand natural language, and it can generalize. But it can’t permanently learn new things without extensive time and cost (in-context learning and fine-tuning), it’s not autonomous, it’s not self-aware, and it doesn’t have a moral compass.

In some ways, we should think of AI not as Artificial Intelligence, but as an Alien Intelligence. It can emulate some abilities of humans to think, reason, plan and so forth, but it is not human.

An analogy is if a real alien learned all about humans by reading the internet the past 20 years. It may be able to communicate and share memes and jokes, but those would all be reflections of humanity through a mirror of our own devising - the alien would never be human.

I’ve spent much of my personal time over the past couple of months working on and learning about autonomous agents: computer software or robots that can accomplish one or more tasks in pursuit of an objective or goal.

Much of this involves looping or recursively calling a large language model:

• AI requests the use of a tool to accomplish an action towards solving a goal or problem.
• Software uses that tool on behalf of the AI (generally API invocations or desktop interactions) and returns the results
• The AI then decides on the next action and tool to use
• And this repeats until the objective or goal is complete.

What’s incredible is that this works well - most of the time.

Yes, agents have a reputation for never-ending loops. But these problems can be mitigated with some clever approaches such as feedback to provide guidance and limiting the length of loops and depth of recursions.

Based on this work, I believe that to achieve a near, if imperfect version of AGI, we need at least two things:

1. the ability for the AI to use one or more tools to interact with the world
2. a near real-time, persistent memory of previous interactions and their results

With very little code, we can provide tools for use by AI right now.

However, the best we can do for memory is to “fake it”: by feeding the results of all prior actions as part of the looping request.

This is dependent on something called the “context window” which is how much content can be provided to the AI before it exceeds some threshold of memory. And for now, we’re limited to a few thousand words; though recent advances in the past week have increased that to ten thousand words or so.

The other approach to persistent memory is to fine-tune the model on the previous interactions and their results, which is either not possible due to API restrictions or cost-prohibitive.

Bottom line: at this point in time, we need a better way to persist memory so that language models can learn through interactions with the world.

Back to the original question of how AI can help you with your work: I use AI to help me with my work every day. And I’m not talking about the classic versions of machine learning, but instead generative AI to help me accomplish my daily tasks.

Recently, I used an autonomous agent to produce the first draft of a market research report. I had GPT-4 help me find a bug in my software that I’d spent hours trying to find. I used it to write portions of this document you’re reading. And I’m confident that I’ll continue to find more ways to use AI.

My advice (if you haven’t already) is to pay the $20/mo subscription to ChatGPT Plus and immediately enable Plugins as a beta feature in Settings. This is a super power - like NEO searching for Morpheus - you can have the computer search for on your behalf and compile it into a coherent result.

If you need financial and data analysis, then just copy and paste it directly from Excel into ChatGPT and start asking questions.

If you have a complex article to read, feed it into ChatGPT and ask it to summarize and highlight the key points. I do this regularly as it helps me understand what the article is about ahead of the deeper dive; and I’ve found this practice improves my understanding and recall.

BTW, soon we’ll have access to the Code Interpreter plugin and it will feel like we’ve been given another superpower: the ability to create charts, graphs, and do data analysis just by asking.

If you’re a software engineer, I believe ChatGPT Plus is a must buy - and you should just pay for it yourself if your company isn’t reimbursing you. GPT-4 is far superior to GPT-3 in writing code that works, and that will save you even more time than relying on the free GPT-3 version.

Additionally, there will be more and more tools that let you use AI to do your work. It won’t be long before we can use Microsoft Copilot or Google’s Duet to write emails, documents, spreadsheets, and more.

And ahead of that, you can find AI popping up all over: I’m writing this using Type.ai and I regularly use Beautiful.ai to create slide decks. (I’m not affiliated with either; I just like using their products.)

Important: please be aware of and adhere to your company’s acceptable use policy. It’s very likely that sharing company confidential or proprietary information with ChatGPT is prohibited. If so, you’ll need to sanitize and anonymize the information. Or, you may not be able to use it at all if its not approved or if its use is prohibited.

But, don’t take my word for it, ask your manager or your head of security please.

Reach out to me if you’d like to talk about AI; and I’d love to discuss autonomous agents.

Here’s a 5-minute video that I made for our executive team to better understand what AI can do today, and where it’s heading this next year.

The stunning advances in AI this year further underlines the imperative that every company needs to invest in incorporating these capabilities into their business.

Over the past several years, AI models have demonstrated the ability to see, hear, and speak - as well as or better than humans. AI has bested experts in almost every type of game from board games to video games to game shows. And robotics has exceeded human-level ability to navigate complex environments.

Until now, it’s been easy to dismiss these as capable computers whose disruption was limited to certain industries and endeavors.

However, in the past few months, OpenAI and other leading companies have released AI models that can create content as well or better than people. AI can create art and music, write poetry and essays, write code, find software issues, answer questions, offer advice, and more.

A few years ago, this was not the type of disruption that was widely discussed. Instead, disruptive automation was expected to more narrowly target menial and repetitive tasks - not human-level creativity.

Using generative technology today

The big advance that happened over this summer is Generative AI, that can create realistic images, write poetry and software, and more. Twitter, Reddit, and YouTube are full of examples that are exceptionally good.

A few months ago, realistic image generators such as Stable Diffusion, DALL-E 2, and MidJourney were publicly-released - either via API or as open-source models - and the Internet went wild. HuggingFace, an AI community for sharing models and datasets, is approaching 100,000 models with many uploaded or updated in the past year. A variety of these are variations of the open-source image generators, fine-tuned for special cases such as generating images of cars, realistic photos, architectures, and more.

And this past week, OpenAI released ChatGPT, an iteration of GPT-3.5, but trained in a way to answer questions in a conversational manner (and with some memory of the earlier conversation). GPT-3.5 itself was an iteration of GPT-3 trained to answer instructions and was the cornerstone of a huge improvement in the algorithms ability to generate useful text and code.

The reason that ChatGPT has received so much press is because it’s amazingly good. It’s also currently free and has a chat-like interface, making it accessible to a wide audience. It’s so good that individuals have used ChatGPT to get passing scores on college entrance exams and college-level essays.

In the same way that many begin with a Google search or a calculator when doing our jobs, we should also have these new generative AI tools available for us in our daily work. And in a growing set of use cases, start with AI and then improve on the outcome, instead of the reverse.

If you experiment with these more innovative uses, be aware that because they are creative and because they were trained on large swaths of internet text and images, the results are sometimes inflammatory or inaccurate.

In short, where we’re at right now with this latest generation is that a human needs to decide if the result is fit for publishing - but generally the first draft is rather good.

Intelligent software

In considering the scale of possible disruption of AI, the level of technology that’s publicly available this year is analogous to the mobile revolution that occurred after the release of the first iPhone. That is, eventually most people in the world would access the internet via a smartphone - and companies that wanted to reach those people would need to offer a mobile solution.

Similarly, most people will expect some level of intelligence in the software they interact with, and companies that want to keep their business will need to offer that.

By intelligent, it means that customers will require that the software that they interact with is personalized, that it is generative, and that the software acts on their behalf.

Personalization requires that the software automatically tailors the experience to meet the individual’s expectations and needs. Increasingly, this is accomplished by recording the user’s interaction with the software and then applying machine learning to anticipate and recommend the best options for the individual.

Generative software employs AI to help the individual create something new, better and faster than they could otherwise. The latest AI models can create images, text, video, music, and more: often with little more than a text prompt describing the desired result. Going forward both amateurs and professionals alike will expect the software they work with to create at least a first draft, if not the result, based on their instruction.

Automation is where the software acts on behalf of the individual to streamline and automate repetitive tasks. Traditionally, this has been thought of as taking some set of actions at the individual’s direction. As AI continues to advance, software should be able to proactively aid the user - perhaps still asking the individual for permission - without being prompted.

Note: Intelligent software is a somewhat amorphous term with a variety of definitions. The above definition has been chosen in line with the latest advances in AI.

Dangers in using AI

As we increase our adoption of AI, it’s imperative to understand the many ways it can go wrong.

There are several potential dangers to using AI in business, including:

• Ethical concerns: The use of AI can raise ethical concerns, particularly if it is used to make decisions that affect people’s lives, such as in the criminal justice system or healthcare. Ensuring that AI systems are fair, transparent, and accountable is essential to avoid ethical violations.
• Security: AI systems can be vulnerable to security threats, such as hacking or other forms of cyber-attack. This can lead to sensitive business data being exposed or stolen, or the AI system itself being compromised.
• Lack of accountability: Because AI systems are often complex and opaque, it can be difficult to decide who is responsible when things go wrong. This lack of accountability can create legal and ethical issues.

An important consideration is bias in AI - such as sexism, racism, ageism, and ableism - which can go undetected until reported by the individual that was harmed.

Biases in AI are most often introduced through:

1. The data used to train the AI system is biased: If the data used to train an AI system is biased, the system will learn to make decisions that reflect that bias.
2. The algorithms used to process the data are biased: AI algorithms can sometimes be biased because they are based on assumptions or stereotypes that are not true or fair.
3. Human bias is introduced during the development or deployment of the AI system: Even if the data and algorithms used to build an AI system are not inherently biased, human bias can still be introduced during this phase.

Unfortunately, bias is common in AI and can have profound consequences.

It’s important for companies employing AI to ensure they are employing tools designed to evaluate fairness and to introduce safeguards and controls to reduce the risk of harm to individuals. And companies must also ensure they have a plan to address bias if it’s found and to offer redress and remedies if the algorithm causes harm.

Ethical AI

To safeguard the use of AI, it’s suggested to create an AI ethics committee which would establish ethical guidelines and best practices for its use within the company. One approach is to comprise the committee of a diverse group of individuals, including representatives from different departments within the company, and external experts on AI ethics.

The committee would handle regularly reviewing and updating the company’s AI ethics guidelines and conducting regular audits to ensure that the company’s use of AI is in line with these guidelines. Additionally, the committee should supply training and education to employees on ethical AI practices and could serve as a resource for employees who have questions or concerns about the ethical use of AI in their work.

Overall, the goal of this governance model would be to ensure that the company’s use of AI is transparent, responsible, and aligned with organization’s values and principles.

Looking ahead - AI in 2023

It’s widely expected that the billions of dollars of investment in AI will continue to grow.

The key drivers of this investment are Google, Microsoft, Amazon, NVIDIA, OpenAI, DeepMind, as well as other contributing organizations such as Stability.AI, Midjourney, and substantial collaborations by open-source contributors across the world.

Key expectations for 2023:

• Open-source collaborators will continue to replicate the major accomplishments of the big providers noted above - though lagging them by several months.
• Text-to-video is expected to be at the resolution and frame rates needed for production use.
• OpenAI is expected to release GPT-4, their next iteration of the already impressive GPT-3.5 noted earlier. Many believe this will be transformative in its impact and significantly alter what is thought to be possible.
• Google Pathways and DeepMind Gato2 might be made publicly available in some form. What’s interesting about the approaches of these two, is that they are generalized - that is, the AI can carry out a wide variety of tasks. For example, instead of focusing on one task, such as generating text or images, these algorithms can do many tasks successfully.

Something exciting and perhaps incredible is happening in AI right now. In short, we’re seeing massive leaps in AI where the best models can generate human-level art, music, poetry, stories, and more.

Given the rapid advances and often stunning examples, it’s useful to consider how disruptive this might be. To frame this, we can take inspiration from the recent past.

In 2007, the introduction of the iPhone started what later became known as the Mobile Revolution. That is, eventually most people in the world would access the internet via a smartphone - and companies that wanted to reach those people would need to offer a mobile solution.

A few years later, in 2011, Marc Andreesen wrote a significant thought-piece titled “Software is eating the world”. In that essay, he made a point that’s now taken for granted:

But too much of the debate is still around financial valuation, as opposed to the underlying intrinsic value of the best of Silicon Valley’s new companies. My own theory is that we are in the middle of a dramatic and broad technological and economic shift in which software companies are poised to take over large swathes of the economy.

More and more major businesses and industries are being run on software and delivered as online services — from movies to agriculture to national defense. Many of the winners are Silicon Valley-style entrepreneurial technology companies that are invading and overturning established industry structures. Over the next 10 years, I expect many more industries to be disrupted by software, with new world-beating Silicon Valley companies doing the disruption in more cases than not.

Link here: https://a16z.com/2011/08/20/why-software-is-eating-the-world/.

What we’ve seen over the past few years is AI surpass human-level capabilities in seeing, hearing, and speaking. And this year, we’ve witnessed AI matching and exceeding human-level capabilities in generating text, images, videos, and more.

An interesting question then is whether the current AI revolution is more like the mobile revolution: the parallel being that everyone company needs to adopt AI. Or, is it more substantial than that, with almost every business and industry eventually being disrupted by AI.

Last year, Sam Altman, CEO of OpenAI, wrote “Moore’s Law for everything”. In this passionate essay, he asserts that over the coming decades, nearly everything will be changed and disrupted by AI.

The coming change will center around the most impressive of our capabilities: the phenomenal ability to think, create, understand, and reason. To the three great technological revolutions–the agricultural, the industrial, and the computational–we will add a fourth: the AI revolution. This revolution will generate enough wealth for everyone to have what they need, if we as a society manage it responsibly.

Link here: https://moores.samaltman.com.

As noted, to get to the point of near total disruption, where “AI eats the world”, we’ll need AI that can create, understand, reason, and think. Sam Altman’s position seems to be that this is a matter of “when”, not “if”.

With the AI’s released just in the past several months such as Dalle2, MidJourney, and Stable Diffusion, it’s clear that AIs can create. And with ChatGPT, more often than not, it appears that the AI understands what is being asked and can reply appropriately.

But no publicly-accessible model currently exhibits true reasoning and thinking.

As such, we’re not there yet; the current impact of AI is more like the mobile revolution. And yet, it’s exciting to see the rapid progress of AI and the possibility ahead.

At the point where we do have AI models that can understand, reason, and think, then this will change from an AI revolution to a path of disruption more analogous to AI eating the world.

And we may not be as far away from that future as we think.