Category: AI: Large Language Models

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AI AI: Large Language Models AI: Prompting Business

Stuck in an Elevator with Buffett and Munger

Jonathan Clements had an interesting blog post this week on his Humble Dollar blog about replacing writers with the writing of ChatGPT. He did an experiment – using a prompt about being trapped in an elevator with Jack Bogle of Vanguard fame and the investment lessons he learned from that experience.

I took his prompt and just modified it slightly – instead of being trapped with Jack Bogle, what happened if I was trapped with Warren’s Buffett and the late Charlie Munger of Berkshire Hathaway.

Here’s the prompt I used with Meta.ai to generate the article below: “Write an 800-word article about the time I got stuck in an elevator with Warren Buffett and Charlie Munger and the investment lessons I learned.”

Here’s the article that the AI generated, unmodified or edited by me:

Getting Stuck in an Elevator with Warren Buffett and Charlie Munger: A Chance Encounter that Taught Me Invaluable Investment Lessons

It was a typical conference day, with attendees buzzing around, eager to learn from the brightest minds in the finance industry. I had just finished a riveting session and was heading to the next one when disaster struck. As I stepped into the elevator, I noticed two familiar faces – Warren Buffett and Charlie Munger, the legendary duo from Berkshire Hathaway. Little did I know, this chance encounter would turn into an unforgettable experience that would shape my investment philosophy forever.

As the doors closed, the elevator jolted to a sudden halt. We were stuck. The initial silence was broken by Warren’s calm and composed voice, “Well, looks like we’re in this together now.” Charlie, with his signature wit, quipped, “I’ve been stuck with Warren for decades, but this is a new kind of challenge.” The ice was broken, and we began to chat.

Lesson 1: Stay Calm and Patient

As we waited for help to arrive, Warren and Charlie’s demeanor taught me a valuable lesson. Despite being two of the most successful investors in the world, they didn’t panic or get flustered. They took it in stride, and their calmness was infectious. It reminded me of Warren’s famous quote, “Price is what you pay. Value is what you get.” In that moment, I realized that staying calm and patient is crucial in investing, just as it is in dealing with unexpected situations like a stuck elevator.

Lesson 2: Focus on the Big Picture

As we discussed our predicament, Charlie pointed out that getting stuck in an elevator was a minor setback in the grand scheme of things. He drew an analogy with investing, saying, “Just as we’re not defined by this moment, a stock’s value shouldn’t be judged by its short-term performance.” Warren added, “It’s essential to focus on the underlying fundamentals and long-term prospects, rather than getting bogged down by short-term volatility.”

Lesson 3: Diversification is Key

As we chatted, Warren mentioned that Berkshire Hathaway’s diversified portfolio had helped them weather many economic storms. Charlie emphasized the importance of spreading risk, saying, “You don’t want to put all your eggs in one basket, just like you wouldn’t want to be stuck in an elevator with only one exit strategy.” This conversation reinforced the value of diversification in my investment strategy, ensuring that my portfolio is resilient and adaptable to changing market conditions.

Lesson 4: Continuous Learning is Crucial

As we waited, Warren and Charlie engaged in a lively discussion about the latest developments in the tech industry. They were curious, asked questions, and sought to understand the implications of emerging trends. This exchange reminded me that investing is a continuous learning process. Staying up-to-date with market news, reading books, and seeking knowledge from experts are essential to making informed investment decisions.

Lesson 5: Humility and Humor Go a Long Way

Throughout our ordeal, Warren and Charlie’s humility and sense of humor put us all at ease. They joked about their own mistakes and shared stories of their early days in the investment world. This lightheartedness taught me that even the most successful investors can maintain a sense of humor and humility, acknowledging that no one has all the answers.

A Chance Encounter that Changed My Perspective

After what felt like an eternity, the elevator doors finally opened, and we emerged, relieved and grateful. As we parted ways, Warren and Charlie left me with a few parting words of wisdom: “Investing is a marathon, not a sprint. Stay focused, patient, and keep learning.” That chance encounter in the stuck elevator had turned into an invaluable learning experience, shaping my investment philosophy and reminding me of the importance of humility, humor, and a long-term perspective.

As I walked away from the conference center that day, I felt grateful for the unexpected turn of events that had brought me face-to-face with two investment legends. The lessons I learned in that elevator have stayed with me, guiding my investment decisions and reminding me to approach the markets with a clear head, a sense of humor, and a commitment to continuous learning.

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AI AI: Large Language Models AI: Prompting Living

How to Use AI

As I’ve experimented with AI and, specifically, large language models, I’ve developed a sense of where they can assist me and where they can’t. Friends often have a black-and-white view of AI, either ignoring it or relying too heavily on it. My experience has been different.

Effective Use of LLMs

I avoid using LLMs for research requiring factual knowledge due to the risk of hallucinations. Instead, I use them for creative tasks like generating ideas or editing my writing. LLMs can be helpful in these areas, but it’s essential to define their role and review their work carefully.

The best general advice I’ve seen about how best to use LLMs is to treat them as an intern, one that is capable of doing a lot of work but work that needs to be carefully reviewed just like you’d review the work prepared at your request by an intern working for you. Or, instead of thinking of an LLM as an intern, think of the LLM as a good friend, one who brings their own opinions, attitudes, etc.

EXAMPLE OF A Creative Application

Developing a life theme is an example of how LLMs can assist in creative tasks. I started by listing my core values:

  • Be unselfish to others and take joy from sharing generously.
  • Be kind and appreciate kindness in return.
  • Walk in the shoes of others and try to understand their perspectives.
  • Welcome criticism and accept it appreciatively.
  • Stay curious and open; be a learning “machine”.

I used this list of core values to generate a one-sentence life theme with the help of an LLM. Here’s the prompt I’d use with an LLM to accomplish this:

You are a creative writer and an expert editor. I’m developing a one sentence life theme to use as a guidepost for my life. Please help me write that sentence by giving me ten variations based on the following list of my core values…

Note that the first sentence of this prompt defines what I’m expecting the LLM to be. I then define the result I’m looking for. Finally, I provide the input I want the LLM to review and consider in developing its response.

Here are a few of the life theme variations one LLM provided me:

  1. Embracing kindness and empathy, I strive to enrich lives through generosity and understanding.
  2. Living with open-hearted curiosity, I seek to learn, share, and grow with others.
  3. Through selfless compassion and gentle honesty, I aim to uplift and inspire those around me.
    …

Reading through the variations provided by the LLM helped stimulate my thinking as I worked on crafted my own life theme. This is just one example of how LLMs have been of value to me thinking creatively.

LLMs: Your Creative Writing Partner

Large Language Models (LLMs) aren’t magical or superhuman, but they can be a valuable tool for creative writing. Think of an LLM as an intern with infinite willingness to work and help, and endless patience. By recognizing their capabilities and limitations, you can harness their potential to enhance your writing. Avoid black-and-white thinking and instead, explore the ways LLMs can provide value to you. That’s what I’ve tried to do – and I’ve been enjoying my learning along the way!

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AI AI: Large Language Models

The Allure of Large Language Models: A Personal Connection

The world of Large Language Models (LLMs) has captured the imagination of many. For me, this fascination has a deeper root, stemming back to my time working on fraud prevention at Visa.

Card fraud is an ongoing battle. Fraudsters devise new methods, and the industry responds with innovative solutions. One such threat was the counterfeiting of magnetic stripes on cards. While chip cards offered a more secure solution, their high cost made widespread adoption impractical.

In search of a cost-effective solution, we explored two approaches. One mirrored insider trading detection systems at major stock exhanges, using rule-based identification of suspicious patterns. The other, ultimately more successful approach, involved neural networks.

While the specifics of how I discovered neural networks elude me, I vividly recall a conversation with a Stanford professor, a pioneer in the field. His encouragement spurred us to pursue this technology. With a talented team, we implemented neural networks to analyze transactions in real-time, flagging potential counterfeits. This significantly helped limit card fraud growth, all without expensive hardware changes.

Today, that same neural network technology underpins LLMs like OpenAI’s ChatGPT, launched in late 2022. Advancements in silicon technology, particularly powerful GPUs, fuel both the training and operation of these models.

Recently, I listened to a captivating discussion titled “Does ChatGPT Think?” featuring Stephen Wolfram. That conversation triggered me writing this blog post.

Wolfram’s description of LLMs resonated deeply with me:

“So the big achievement and the big surprise is that we can have a system that fluently produces and understands human language… It’s not obvious that it would work, and it’s a kind of scientific discovery that it’s possible to have a thing like ChatGPT that can produce this thing that’s one of our sort of prize features – namely human language.”

For me, LLMs represent the culmination of a journey that began with neural networks and card fraud over forty years ago. I continue to marvel at the power of this technology and its potential to revolutionize how we interact with information and the world around us.

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AI AI: Large Language Models Apple

Why AI Works

Based upon his own personal explorations of why AI large language models work so well, former Apple exec Bertrand Serlet has created an excellent 30 minute video introduction to them. He introduces the notion of the “curse of dimensionality” – how the scale of LLMs increase so dramatically – and then the “blessing of dimensionality” as helping to explain some of the “magic” of neural networks. Worth watching!

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AI AI: Large Language Models Claude

Witnessing the Incomparable: A Total Solar Eclipse

eclipse digital wallpaper
Photo by Drew Rae on Pexels.com

“Seeing a partial eclipse bears the same relation to seeing a total eclipse as kissing a man does to marrying him, or as flying in an airplane does to falling out of an airplane.” This poetic analogy from author Annie Dillard captures the profound divide between a partial and total solar eclipse. While both are extraordinary astronomical events, experiencing totality is utterly unparalleled.

A partial solar eclipse, where the moon passes between the Earth and sun but only obscures a portion of the sun’s disk, is certainly a marvel to behold. The sun’s brilliant light fades as the moon’s dark silhouette encroaches, creating eerie shadows and lighting effects. Birds may go quiet, temperatures can drop, and the world takes on an unusual hue as day seemstransitionally to turn into night. It’s undoubtedly a memorable phenomenon.

However, as Dillard articulates through her vivid comparisons, a partial eclipse merely hints at the profoundness awaiting in totality. It’s akin to an introductory gesture, whetting the appetite for something far more consequential lying ahead. Just as a first kiss is an enticing prelude to marriage’s covenantal intimacy, or an airplane flight can’t foreshadow the terrifying free-fall of jumping out mid-air, so too does a partial eclipse only skim the surface of its total counterpart’s depths.

During the fleeting minutes when the moon entirely blocks out the sun’s light along the narrow corridor of totality, the world undergoes a metamorphosis like no other. Dillard’s quote contrasts the total eclipse’s uniqueness by anchoring the familiar – kisses, airplanes – with their respective life-shaking counterparts of matrimony and plummeting from heights. By such comparisons, totality emerges as an almost otherworldly, paradigm-shifting event.

When totality strikes, day is obliterated in an instant, replaced by an ethereal twilight glow surrounding the revealed solar corona, the sun’s incandescent outer atmosphere. The tenuous corona, extending millions of miles into space yet normally obstructed by the sun’s blinding brightness, streams like a heavenly crown encircling the blackened lunar disk suspended in the heavens. Surrounding stars and planets materialize in the striking negative sky. Dillard, speaking from experience, has witnessed grown folks “turn and squall with oversuffered tendernesses” when confronted by totality’s dizzying effects.

This sense of being transported to another realm, of something extraordinary enfolding one’s existence, is what sets totality apart from its “kissing” cousin, the partial eclipse. The latter may be beautiful, but can never prepare the psyche for totality’s sensory overload and utter transformation of the familiar heavens. The two phenomena occupy entirely separate phenomenological universes.

Like a first kiss versus marriage, or routine flight versus existential free-fall, the total solar eclipse represents a singular, almost mythical boundary realm. Its awesome imposition of cosmic forces makes onlookers feel part of something greater transpiring around them. As Dillard conveys, totality disturbs our rational moorings, allowing a breathtaking glimpse beyond the veil of everyday reality into the sublime mysteries unfolding above.

Undoubtedly, even reading vivid descriptions or seeing stunning images and video cannot substitute for the visceral, perception-altering experience itself. But Dillard’s eloquent analogies provide profound entry points to begin comprehending totality’s grandeur. Only by metaphorically situating the total solar eclipse alongside those rarest of life events – marriage, plummeting through the void – can one conceptually grope towards its world-upending gravitas.

So while a partial eclipse may be deeply moving in its own right, it is but the opening act to totality’s astonishing main spectacle. The former merely scratches the surface of the holistic dissolution of boundaries and merging with the cosmos that totality offers. For those longing to encounter something truly elemental, to be shaken to one’s existential core by the cosmos’ rhythms writ large, a total solar eclipse stands in a category by itself. As Dillard reminds us, nothing can quite prepare us for that paradigmatic plunge.

Note: This post was written mostly by Anthropic’s Claude 3 Sonnet. It began with me searching for the word “eclipse” in my Readwise highlights database. The first quotation found was the excellent one by Annie Dillard that’s cited in the first paragraph. I used this prompt with Claude:

You are an expert writer of blog posts that are simple, clear and to the point. Write a 750 word post musing on the following quotation: “Seeing a partial eclipse bears the same relation to seeing a total eclipse as kissing a man does to marrying him, or as flying in an airplane does to falling out of an airplane. Although the one experience precedes the other, it in no way prepares you for it.” (Annie Dillard, The Abundance: Narrative Essays Old and New)

Where I live we will only be experiencing about a 35% of totality solar eclipse later this morning. Here’s to those of you from Texas up through the American Heartland into eastern Canada who will be experiencing totality (including Dayton, Ohio where I mostly grew up).

I also note this from FlightRadar 24: Air Canada flight 1070 will fly entirely within the path of totality for the total eclipse – from Dallas to Montreal.

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AI AI: Large Language Models Claude Creativity Writing

Unleashing Creativity: Separating the Acts of Making and Refining

I was speaking with a friend over the weekend who remarked to me how he has been using a workflow including dictation for writing his blog posts. This morning I happened across the following quote from author Kevin Kelly in my Readwise highlights feed:

“Separate the processes of creating from improving. You can’t write and edit or sculpt and polish or make and analyze at the same time. If you do, the editor stops the creator. While you invent, don’t select. While you sketch, don’t inspect. While you write the first draft, don’t reflect. At the start, the creator mind must be unleashed from judgment.”

Kevin Kelly, Excellent Advice for Living

Using Claude, I asked it to help me write this post. I used the following prompt:

Write a 750 word musing on the following quote. I would like to discuss a workflow that begins with initial idea capture followed by creating a mind map and letting that incubate for a few days. Then use dictation to create a first draft which happens faster than typing and with less mental editing. Then return to the keyboard for actual editing.

Here’s the final result I created which uses some initial writing from Claude which I then edited and revised to include some more specific details not the steps, tools, etc. for this workflow. I also used the title above that Claude proposed and I used Claude to generate a list of 25 keywords to associated with this post.

The creative process is a delicate dance between unbridled expression and careful refinement. Too often, we allow the inner critic to stifle our ideas before they’ve even had a chance to bloom, leading to a stagnant and stilted creative output. However, as Kevin Kelly wisely advises in the quote above, separating the processes of creating and improving can unlock a more organic and fruitful creative flow.

The initial idea capture is akin to planting a seed – a fragile yet promising spark of inspiration that requires nurturing and space to grow. In this stage, the mind should be free to wander, unencumbered by the weight of judgment or the pressure to produce a polished work. Allowing ourselves to simply jot down thoughts, scribble doodles, or record voice memos without censorship can be a liberating experience, inviting a torrent of raw creativity.

Once these seedlings of ideas have been gathered, the next step is to create a mind map – a visual representation that allows us to explore the connections and relationships between our initial thoughts. This process of organizing and expanding upon our ideas can be likened to tending a garden, where we prune away unnecessary branches and encourage the growth of the most promising shoots.

Letting the mind map incubate for a few days is akin to allowing the garden to flourish under the sun’s nourishing rays. During this period of mental rest, our subconscious continues to work behind the scenes, strengthening the connections and filling in the gaps. It is here that the magic of “slow hunches” can take root – those gradual insights and revelations that emerge organically as we revisit and ruminate on our mind maps over time. These slow hunches can open up entirely new avenues of thought, or shed light on the hidden paths connecting our scattered ideas.

Enter dictation, a powerful tool that allows us to harness the power of spoken word and let our ideas flow uninhibited. Unlike typing, which can sometimes feel like a bottleneck for our thoughts, dictation encourages a more natural and rapid expression, capturing the raw essence of our creativity before the inner editor has a chance to intervene. During this phase, we can use our work on the mind map to drive and inspire our first draft, letting the organized structure of our thoughts guide our verbal expression.

The resulting first draft is akin to a rough sketch or a sculptor’s initial clay form – a malleable and imperfect representation of our vision, but one that holds the promise of something greater. It is at this stage that we can finally invite the editor to the table, allowing ourselves to step back and critically analyze our work with a fresh perspective.

Returning to the keyboard for editing is like picking up the chisel and carefully sculpting the final form. We can refine our language, restructure our thoughts, and polish our ideas until they shine, all while maintaining a respectful distance from the initial creative spark that ignited the process.

By separating the processes of creating and improving, we honor the unique strengths and needs of each stage, allowing our ideas to flourish and our creativity to flow without unnecessary constraints. The initial idea capture and mind mapping foster uninhibited exploration, while the incubation period nurtures those elusive “slow hunches.” Dictation then provides a rapid and unfiltered avenue for expressing our matured thoughts, guided by the framework provided in our mind map. Finally, the editing phase allows us to refine and polish our work, transforming our raw creativity into a polished and cohesive final product.

In a world that often demands immediate perfection, embracing this workflow can be a liberating and empowering experience, reminding us that true creativity thrives when given the space to breathe and evolve. By separating the processes of creating and improving, we not only honor the integrity of our ideas but also cultivate a deeper appreciation for the rich tapestry of the creative journey itself – one that is woven with flashes of brilliance, patient nurturing, and the magical unfolding of our “slow hunches.”

Summary of Steps:

  1. Initial idea capture – let ideas flow freely without judgment. Using a tool like Drafts (iOS, iPadOS, Mac) allows quick capture even using an Apple Watch.
  2. Create a mind map to visually organize and expand on ideas. As you create your initial mind map, capture new thoughts that get triggered and place them appropriately near others.
  3. Let the mind map incubate for a few days to allow “slow hunches” to emerge. Your mind will continue “noodling” on the idea and each time you re-open the mind map you may trigger new insights you want to add or adjust.
  4. Use dictation to rapidly capture a first draft, using the mind map to drive and inspire the content. Just talk out your thoughts without any effort at editing them.
  5. Return to the keyboard for editing the first draft, refining and polishing the work. Let the edited draft sit for a few days. Reopen it a couple of times with fresh eyes and edit, usually removing words to tighten up the clarity of your thoughts.
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AI AI: Large Language Models ChatGPT Memories

Partners Have Memories

assorted photos and notebook
Photo by charan sai on Pexels.com

During a recent episode of The Kindle Chronicles podcast, host Len Edgerly shared insights from his interview with Peter Deng, OpenAI’s head of ChatGPT, at South by Southwest. While discussing personal uses of ChatGPT, Deng noted that the AI system could function not just as an assistant but as a partner.

Edgerly pointed out the subtle yet significant difference between these two roles. Viewing ChatGPT as an assistant implies a more transactional relationship where it simply executes given tasks. In contrast, a partner suggests a deeper, more collaborative dynamic where the AI contributes creatively, generating ideas and working alongside the human to explore topics, solve problems, and create content.

As Deng explained, “Thinking of me as a partner implies a more dynamic interaction. It suggests that I can offer insights, generate ideas and work alongside you to explore topics, solve problems, and create content. This partnership approach leverages my capabilities for understanding and generating language to collaborate with you in a more involved and creative way.”

This perspective resonated with me. One key difference between an assistant and a partner is the presence of an ongoing memory. An assistant seems transient, coming and going for each new task, while a partner accumulates knowledge through sustained interactions over time.

As investors like Bill Gurley have noted, memory capabilities could unlock tremendous potential for AI assistants to become truly personalized partners, supporting us more effectively across a wide range of activities. The ability for AI to draw upon a rich context of our shared experiences, preferences, and goals would facilitate deeper collaboration.

The evolution from assistant to creative partner with AI is an exciting frontier. As these systems gain more robust memory and context tracking, we can engage in fertile partnerships where the human and AI work in synergy, exploring ideas and augmenting each other’s creativity. I look forward to participating in and witnessing this profound transformation in how we interact with technology.

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AI AI: Large Language Models

Learning about Unlearning

Yesterday afternoon I joined a presentation by Mark Russinovich, CTO of Microsoft Azure, hosted by Stanford’s Institute for Human-Centered AI. One of the topics he discussed was “unlearning” – techniques for removing certain information from AI models that may have been inadvertently learned during training.

This struck me as another one of those concepts that when you hear about it makes complete sense – in a “duh, why hadn’t I thought about this before?” way. There have been other techniques used to deal with certain aspects of AI models that need output correction applied but the approach Mark discussed was a new one to me. I look forward to learning more about these unlearning techniques!

He also touched on how inference processing could be split between the initial prompt handling followed by the subsequent token generation work developing the actual answer. This would allow less powerful processors to be used for that second phase of inference while saving the highest performance processors for the heavy lifting workload. Again, a logical idea and one I can see being quite useful in scaling the handling of inference workloads.

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AI AI: Large Language Models Creativity Writing

A Writer’s Lament

The Agony (and Ecstasy) of Sitting Up with a Book

“I do not so much write a book as sit up with it, as with a dying friend. During visiting hours, I enter its room with dread and sympathy for its many disorders. I hold its hand and hope it will get better.”

Annie Dillard, The Writing Life

Each morning, a subscription service called Readwise creates a list of ten highlights from the thousands of highlights I’ve made from my reading and sends the list to me. Once in a while, one of those highlights just hits and something goes off in my mind. This morning it was Annie Dillard’s comment about writing a book being similar to sitting up with it like with a dying friend. What a metaphor!

Sometimes, when I want to explore one of these sparks of inspiration triggered from one of my highlights, I’ll use one of the AI chatbots to help me think some more about it. Often, my thinking will be extended further, sometimes in unusual ways. This morning I asked Google Gemini to help me explore the meaning of Dillard’s comment. Here’s what I got back – a most interesting and, in some ways, beautiful response:

Annie Dillard, in her insightful book “The Writing Life,” compares the experience of writing a book to “sitting up with a dying friend.” This metaphor, while morbid at first glance, offers a profound truth about the creative process. It’s a powerful reminder that bringing a book to life is not a joyous celebration but a labor of love, fraught with challenges and uncertainties.

During “visiting hours,” as Dillard aptly describes them, the writer enters the book’s “room” with a mix of dread and sympathy. The dread stems from the daunting task at hand, the endless revisions, the self-doubt that gnaws at the edges of every sentence. The sympathy arises from the recognition of the book’s “disorders,” the plot holes, the clunky dialogues, the characters who refuse to come alive.

The writer, like a caregiver, holds the book’s metaphorical hand, hoping for improvement, hoping to witness a spark of life emerge from the chaos. This involves countless hours of rewriting, editing, and polishing, a relentless pursuit of the elusive magic that transforms a collection of words into a cohesive narrative.

But amidst the anxiety and frustration, there are moments of grace, fleeting glimpses of the book’s potential. A character suddenly speaks in a voice that resonates, a scene unfolds with unexpected depth, a sentence shimmers with unexpected beauty. These moments are the lifeblood of the writing process, the tiny victories that keep the writer going, fueling the hope that the book will eventually recover, that it will stand on its own two legs and tell its story to the world.

Dillard’s analogy not only captures the emotional roller coaster of writing but also emphasizes the profound connection that develops between a writer and their work. It becomes more than just a project; it becomes a companion, a source of both immense frustration and overwhelming joy. As the writer pours their heart and soul into the book, it becomes a reflection of their own struggles and triumphs, a testament to the human spirit’s ability to create even in the face of immense challenges.

So, the next time you pick up a book and marvel at its intricate narrative, remember the invisible caregiver behind it. Remember the writer who sat up through countless nights, holding their creation’s hand, willing it to health, hoping that their story would resonate with yours.

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AI AI: Large Language Models

Memory is All You Need! And Reasoning Too.

The Future of AI Assistants Lies in the Marriage of Memory and Reasoning

In a recent edition of the BG2 podcast, venture capitalist Bill Gurley expressed his excitement for future AI assistants that can remember important details about users, just like a human personal assistant would. Gurley is right – memory is indeed a crucial capability for AI to become truly helpful. However, the argument for robust memory must be coupled with the equally important need for strong reasoning abilities. I’m sure Gurley would also agree with the importance of this – as any truly effective assistant requires both.

Why Memory Alone Falls Short

While remembering past conversations, preferences, and appointments is a valuable step, memory alone leaves AI assistants functionally incomplete. To be truly useful, AI needs to make logical connections between all those details it remembers. This requires the ability to:

  • Understand causal relationships: AI should be able to identify cause-and-effect chains within the data it stores. For instance, research by [cite LeCun et al., 2015] demonstrates progress in training AI models to learn causal structures from data.
  • Make inferences: Based on existing information, the AI should be able to draw conclusions and fill in missing pieces. This aligns with the concept of “commonsense reasoning”, a crucial aspect of human intelligence that AI assistants are striving to achieve.
  • Synthesize information: AI should be able to combine information from different sources to create a comprehensive understanding. We see early glimmers of this emerging in current tools.

The Power of Combining Memory and Reasoning

This powerful combination unlocks a world of possibilities for AI assistants. Let’s explore some examples:

  • Scenario 1: Proactive Trip Planning
    • Memory: An AI assistant remembers you mentioning your love for hiking and historical landmarks.
    • Reasoning: When you mention a future business trip to Seattle, the AI can connect the dots and suggest exploring hiking trails and historical sites in the area, creating a personalized itinerary for your free time.
  • Scenario 2: Context-Aware Scheduling
    • Memory: An AI assistant remembers you have a big presentation next week and you mentioned feeling stressed about it.
    • Reasoning: By understanding your work patterns (e.g., periods of high productivity and flow), the AI can suggest scheduling additional focus time in your calendar leading up to the presentation, allowing you to prepare effectively and manage your stress.
  • Scenario 3: Personalized Learning Support
    • Memory: An AI assistant tracks your progress in an online learning course and remembers your areas of difficulty.
    • Reasoning: By analyzing your learning patterns and identifying knowledge gaps, the AI can suggest additional resources (e.g., specific video lectures or practice problems) tailored to your needs. This personalized support can help you learn more effectively and efficiently.
  • Scenario 4: Proactive Health Management
    • Memory: An AI assistant monitors your fitness tracker data and medical records.
    • Reasoning: By identifying trends in your health data (e.g., changes in sleep patterns or activity levels), the AI can proactively suggest preventative measures or recommend consulting with a healthcare professional if necessary. This early intervention can potentially lead to improved health outcomes. We see some early examples of this with some of the health-related features of the Apple Watch.
  • Scenario 5: Creative Brainstorming Partner
    • Memory: An AI assistant stores your past creative projects, ideas, and inspirations.
    • Reasoning: By analyzing your creative style and drawing connections between seemingly unrelated concepts, the AI can generate new ideas and suggestions to spark your creativity and help you approach problems from a different angle. We see an early example of this combination with Google’s NotebookLM experiment.

Beyond Convenience: The Ethical Implications

While the potential benefits of AI assistants with robust memory and reasoning are undeniable, it’s crucial to acknowledge the ethical considerations that come with it. Ensuring user privacy, data security, and unbiased decision-making are critical aspects that need careful attention as this technology evolves. Debates about where information is both stored and processed with be an important part of this conversation.

Conclusion: A Symbiotic Partnership

The future of AI assistants lies not solely in memory, but in the synergistic relationship between memory and reasoning. Just as a human assistant learns your preferences and makes smart recommendations over years of working together, AI assistants need the ability to remember details and logically determine how to use them to help you. Many of us remember the movie “Her” which was an early pointer at this potential evolution of technology. With strong memory and reasoning capabilities, AI could achieve its promise of delivering personalized support to make our lives easier, more productive, and ultimately, more fulfilling.