Web/Tech

  • Get Yourself Optimized with Stephan Spencer

    I was recently on a podcast with Stephan Spencer where we talked about the future of AI – of course – but also about personal development, mindsets, and the hidden opportunities created by the byproducts of your strategies and business models. 

    It was a nice talk, and I hope you enjoy it and find it helpful.

     

    via Get Yourself Optimized

    The whole video is worth a watch, but the idea of strategic byproducts is a simple but powerful one. Essentially, while you're working on your core business, or operating your core business, you'll often realize that you have created other capabilities or outcomes of that business that can become a complementary business or platform in-and-of-itself. Instead of just being the exhaust of your business, they can become a valuable resource and the path to something new and potentially bigger and better than the original business. 

    That conversation starts around the 18-minute mark and picks back up around the 38-minute mark. 

    Stephan Spencer does an excellent job of that, not only in his businesses but with his podcast. What could simply be a video he records with the interviewee becomes audio, a transcript with highlights, a timeline of topics, and a checklist of action items that he (or you) could personally take from the interview. 

    He's already shot the podcast – so why not capitalize on the "exhaust" of it as well. 

  • Creating Your Artificial Intelligence Methodology

    We often talk about Artificial Intelligence's applications – meaning, what we use it for – but we often forget to talk about a more crucial question:

    How do we use AI effectively?

    Many people misuse AI.  They think they can simply plug in a dataset, press a button, and poof!  Magically, an edge appears.

    Most commonly, people lack the infrastructure (or the data literacy) to properly handle even the most basic algorithms and operations. And even before that – they haven't even properly assessed whether AI is needed in their business. Remember, AI is a tool, not the goal. 

    Even though this is the golden age of AI … we are just at the beginning.  Awareness leads to focus, which leads to experimentation, which leads to finer distinctions, which leads to wisdom.

    Do you remember Maslow's Hierarchy of Needs?  Ultimately, self-actualization is the goal … but before you can focus on that, you need food, water, shelter, etc.

    In other words, you most likely have to crawl before you can walk, and you have to be able to survive before you can thrive. 

    Artificial Intelligence and Data Science follow a similar model. Here it is:

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    Monica Rogati via hackernoon

    First, there's data collection. Do you have the right dataset? Is it complete?

    Then, data flow. How is the data going to move through your systems? 

    Once your data is accessible and manageable you can begin to explore and transform it. 

    Exploring and transforming is a crucial stage that's often neglected.

    One of the biggest challenges we had to overcome at Capitalogix was handling real-time market data.

    The data stream from exchanges isn't perfect.

    Consequently, using real-time market data as an input for AI is challenging.  We have to identify, fix, and re-publish bad ticks or missing ticks as quickly as possible.  Think of this like trying to drink muddy stream water (without a filtration process, it isn't always safe).

    Once your data is clean, you can then define which metrics you care about, how they all rank in the grand scheme of things … and then begin to train your data. 

    Compared to just plugging in a data set, there are a lot more steps; but, the results are worth it. 

    That's the foundation to allow you to start model creation and optimization.

    The point is, ultimately, it's more efficient and effective to spend the time on the infrastructure and methodology of your project (rather than to rush the process and get poor results).

    If you put garbage into a system, most likely you'll get garbage out. 

    Slower sometimes means faster.

    Onwards.

  • Here Are Some Links For Your Weekly Reading – October 10th, 2021

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    Here are some of the posts that caught my eye. Hope you find something interesting.

    Lighter Links:

     
     

     

    Trading Links:

  • How To Amplify Your Capabilities Like Elon Musk

    I recently shot a podcast with Mike Koenigs about taking your ideas and transforming them not just into products but into platforms. It was also featured on Forbes. 

    Many of the most valuable companies (like Tesla, Apple, and Amazon) leverage platforms to scale past their initial products and create profitable ecosystems. 

    The video is 50+ minutes – but covers the topic in great depth, and Mike adds a lot of significant distinctions. I think you will like it.

     

    via Capability Amplifier

    Since recording this podcast, I've continued to make finer distinctions. 

     

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    One such distinction, to help businesses plan around new technologies, was to ask two key questions. 

    1. What technologies that already exist are going to impact your industry the most in the next 3-5 years?
    2. What technologies that you expect to exist are going to impact your industry the most in the next 5-10 years?

    I ask these questions because adopting new technologies doesn't mean you have to invent something new. It can mean capitalizing on existing technologies and finding new ways to use them. Understanding what is "likely" lets you lean in the right direction and helps you visualize the most likely paths forward. 

    This helps you figure out where to spend focus, time, energy, and other resources.  Remember, it is easier to follow and leverage a trend, rather than to fight it.

    Since the beginning of time, humans have been confronted with disruptive new technologies.  While technologies continue to change, human nature has remained relatively stable. As a result, predicting human nature is often easier than predicting technology.

    So, rather than trying to predict what technologies will win, you can focus on which needs and capabilities are most likely to attract attention and resources. Innovation and technology will follow to satisfy the desire.  

    Knowing that, the question is what can you build that leverages your unique abilities and the likely path of your chosen market.

    It sounds simple, but it's a powerful distinction and potential differentiator between you and your competitors. 

  • Top Influencers (By Platform)

    When you ask children what they want to be, many likely say YouTuber, Influencer, or some other variant of that theme.

    Influence is a complicated thing. From an abstract perspective, it's the ability to affect someone else's behavior. A high schooler can influence their classmates. As entrepreneurs, we can influence our employees, our industry, and more. You can have immense influence over a small number of people or a little bit of influence over many people – both still count as "influence."

    But, in this case, many of the most popular influencers aren't famous for changing the world; they are celebrities or just famous for being famous.

    Below is a chart of the top 50 "influencers" by social media platform. 

     

    Top-50-Social-Media-Influencers-2via visualcapitalist

    In the digital age, it's worth acknowledging social reach as power. People with a large platform have the opportunity to exert enormous influence – and it's why you often see the spread of misinformation reach far, fast. 

    It would be interesting to see how many of these people use their platforms to be a beacon to their followers (rather than a beacon to attract followers).

    It would also be interesting to see how much (or little) engagement many of these "influencers" actually have with their followers (and how that level of engagement relates to the growth or decay of their followings). 

    While I assume that the readers of this post aren't in the business of being "Influencers,"  Most of us recognize the value of influence – and getting more of it.

    As a result, it is probably worth thinking about influence as an asset.  And now is time to think strategically about how to grow and use that asset better. 

  • US vs. The World

    Here is a chart that looks at the top 100 companies from the perspective of the U.S. vs the rest of the world. 

    Every year, PwC releases a list of the 100 biggest companies in the world by market cap. This year, Visual Capitalist put together a great visualization separating the companies into sector and country.

    Click To See the Full Image. 

    Screen Shot 2021-09-18 at 10.04.17 PMvia visualcapitalist

    The top 100 companies account for over $31.7 trillion in market cap. Unsurprisingly the U.S. takes the largest portion of the pie, but China continues to make headway. Though, the U.S. still accounts for 65% of the total market cap value of the top 100 companies. 

    A lot of the staying power of the U.S. (and the fading of much of Europe) can be attributed to Tech and Retail giants like Apple and Walmart. 

    I'll be interested to see how the numbers change as both Tech and Retail continue to grow as industries. Will other countries find a way to compete, or will the U.S. extend their lead?

  • Gartner’s 2021 Hype Cycle For Emerging Technologies

    Each year, I share an article about Gartner's Hype Cycle for Emerging Technologies. Here’s last year’s.

    It's one of the few reports that I make sure to track every year. It does a good job of explaining what technologies are reaching maturity, and which technologies are being supported by the cultural zeitgeist. 

    Technology has become cultural. It influences almost every aspect of everyday life.

    Identifying which technologies are making real waves (and will impact the world) can be a monumental task. Gartner's report is a great benchmark to compare reality against. 

    2021’s trends aren’t all that different from 2020 – but you can now find NFTs, digital humans, and physics-informed AI on the list. While there have been a lot of innovations, the industry movers have stayed the same – advanced AI and analytics, post-classical computing and communication, and the increasing ubiquity of technology (sensors, augmentation, IoT, etc.). 

    What's a "Hype Cycle"?

    As technology advances, it is human nature to get excited about the possibilities and to get disappointed when those expectations aren't met. 

    At its core, the Hype Cycle tells us where in the product's timeline we are, and how long it will take the technology to hit maturity. It attempts to tell us which technologies will survive the hype and have the potential to become a part of our daily life. 

    Gartner's Hype Cycle Report is a considered analysis of market excitement, maturity, and the benefit of various technologies.  It aggregates data and distills more than 2,000 technologies into a succinct and contextually understandable snapshot of where various emerging technologies sit in their hype cycle.

    Here are the five regions of Gartner's Hype Cycle framework:

    1. Innovation Trigger (potential technology breakthrough kicks off),
    2. Peak of Inflated Expectations (Success stories through early publicity),
    3. Trough of Disillusionment (waning interest),
    4. Slope of Enlightenment (2nd & 3rd generation products appear), and
    5. Plateau of Productivity (Mainstream adoption starts). 

    Understanding this hype cycle framework enables you to ask important questions like "How will these technologies impact my business?" and "Which technologies can I trust to stay relevant in 5 years?"

    That being said – it's worth acknowledging that the hype cycle can't predict which technologies will survive the trough of disillusionment and which ones will fade into obscurity. 

    What's exciting this year?

    Before I focus on this year, it's important to remember that in 2019 Gartner shifted towards introducing new technologies at the expense of technologies that would normally persist through multiple iterations of the cycle. This change is indicative of more innovation and more technologies being introduced than in the genesis of this report. Many of the technologies from the past couple of years (like Augmented Intelligence, 5G, biochips, the decentralized web, etc.) are represented within newer modalities or distinctions. 

    It's also worth noting the impact of the pandemic on the prevalent technologies. 

    For comparison, here's my article from 2019, and here's my article from 2015. Click on the chart below to see a larger version of this year's Hype Cycle.

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    via Gartner

    Last year, the key technologies were bucketed into 5 major trends – but this year Gartner focused on 3 major themes.

    • Engineering Trust represents technologies that create the infrastructure of trusted businesses. The emphasis is on security, reliability, and repeatability of practices. Change is hard, and so is the integration of new technologies into existing businesses. That’s why it’s important to do it right the first time to prevent technologies from being cost centers.  Sample technologies from this year’s hype cycle include real-time incident command centers, data fabric, and sovereign cloud. If I could include a technology not on the list – I’d heavily support the blockchain as an instrumental asset in this domain.
    • Accelerating Growth is the second theme, and it builds on top of “Engineering Trust”. Once you have a good business core you can focus on driving organizational and industrial growth. Last year, "composite architectures" was a trend that emphasized the shift to agile/responsive architectures and decentralization. This year, many of the technologies gaining attention are AI-driven tools that can be applied to improve and accelerate human-facing support. Think HR training, customer service, and onboarding. As a culture, we’ve become more comfortable with the ubiquity of AI and technology, and while there are still ethical and societal roadblocks, you can expect many new use-cases to show up sooner rather than later. Sample technologies from this year’s hype cycle include digital humans, industry cloud, and quantum machine learning.  To see more of my thoughts on Accelerating Growth check out my article on “Turning Thoughts Into Things”.
    • Sculpting Change is the third theme and closes off what I believe is a very strong thematic year from Gartner. The nexus of this theme is that change is disruptive and that many of the technologies we will gravitate toward will be attempting to create order from the chaos. This is especially important in the context of rapid innovation, societal changes, and Covid-19. The emphasis of these technologies is on generalized and reliable technologies that are less brittle and specific than our current uses. AI is already a massively exciting space, but many of the use cases are too specific to be useful. Sample technologies include physics-informed AI, composable applications, and influence engineering.

    If we compare this year’s list to last year, I think we’ve seen a massive increase in the maturity of “Digital Me”, the integration of technology with people in both reality and virtual reality. But, we’ve seen less progress on “Beyond Silicon” despite the massive chip shortage. It’s a space I’m hoping to see more improvement in, fast, to meet increasing demand.

    Of course, I’m always most interested in the intersection of AI and other spaces. Last year, many of the emerging trends were AI-centric, and this year it feels as if AI has become the underpinning of broader trends. In my opinion, this points towards the increasing maturity and adoption of AI. Models are becoming more generalized, and able to attack more problems. They're becoming integrated with human behavior and even with humans.

    As we reach new echelons of AI, it's likely that you'll see over-hype and short-term failures. As you reach for new heights, you often miss a rung on the ladder… but it doesn't mean you stop climbing. More importantly, it doesn't mean failure or even a lack of progress.  Challenges and practical realities act as force functions that forge better, stronger, more resilient, and adaptable solutions that do what you wanted (or something better).  It just takes longer than you initially wanted or hoped.

    To paraphrase a quote I have up on the wall in my office from Rudiger Dornbusch … Things often take longer to happen than you think they will, and then they happen faster than you thought they could. 

    Many of these technologies have been hyped for years – but the hype cycle is different than the adoption cycle. We often overestimate a year and underestimate 10. 

    Which technologies do you think will survive the hype?

  • Confirmation Bias 101

    Echo chambers and confirmation bias aren't new.

    Recently, however, it seems that we are increasingly presented with issues divided into polar opposite points of view, with little to no tolerance for disagreement. 

    Nonetheless, not all topics need to be debated or negotiated. 

    Sometimes, a fact is a fact.

    Hopefully, this video won't step on any toes – but if you're a "flat earther," I wouldn't watch. 

    Here's a clip from Behind The Curve (a documentary on the flat earth society) that I think perfectly shows confirmation bias. 

     

    via Behind The Curve

    Start with the evidence and then form a conclusion. Doing that in reverse doesn't tend to work out as well.

    As a polite reminder, if a conspiracy relies on millions of people (as well as different countries and organizations) to all commit to the disinformation campaign … it's not likely true.

    As Occam's razor states, the simplest explanation is often the correct one. 

  • GPT-3: Boom or Bust?

    GPT-3 was released by OpenAI in 2020 – and was considered by many a huge jump in natural language processing. 

    GPT stands for Generative Pre-trained Transformer. It uses deep learning to generate text responses based on an input text. Even more simply, it's a bot that creates a quality of text so high that it can be difficult to tell whether it's written by a human or an AI.

    GPT-3 is 100x bigger than any previous language AI model and comes pre-trained on 45TB of training text (499 billion words). It cost at least 4.6 million US dollars (some estimated as high as $12 million) to train on GPUs. The resulting model has 175 billion parameters. On top of that, it can be tuned to your specific use after the fact. 

    1_C-KNWQC_wXh-Q2wc6VPK1gvia Towards Data Science

    Here are some interesting GPT-3 based tools: 

    • Frase – AI-Curated SEO Content 
    • Emerson – AI Chatbot
    • Viable – Customer Feedback Analytics Platform
    • Sapling– Customer Service

    Practically, GPT-3 was a huge milestone. It represents a huge jump in NLP's capabilities and a massive increase in scale. That being said, there was a frenzy in the community that may not match the results. To the general public, it felt like a discontinuity; like a big jump toward general intelligence.  

    To me, and to others I know in the space, GPT-3 represents a preview of what's to come. It's a reminder that Artificial General Intelligence (AGI) is coming and that we need to be thinking about the rules of engagement and ethics of AI before we get there. 

    Especially with Musk unveiling his intention to build 'friendly' robots this week. 

    On the scale of AI's potential, GPT-3 was a relatively small step. It's profoundly intelligent in many ways – but it's also inconsistent and not cognitively concrete enough.

    Take it from me, the fact that an algorithm can do something amazing isn't surprising to me anymore … but neither is the fact that an amazing algorithm can do stupid things more often than you'd suspect.  It is all part of the promise and the peril of exponential technologies.

    It's hard to measure the intelligence of tools like this because metrics like IQ don't work.  Really it comes down to utility.  Does it help you do things more efficiently, more effectively, or with more certainty? 

    For the most part, these tools are early. They show great promise, and they do a small subset set of things surprisingly well. If I think about them simply as a tool, a backstop, or a catalyst to get me moving when I'm stuck … the current set of tools is exciting.  On the other hand, if you compare current tools to your fantasy of artificial general intelligence, there are a lot of things to be improved upon. 

    Clearly, we are making progress. Soon, GPT-4 will take us further. In the meantime, enjoy the progress and imagine what you will do with the capabilities, prototypes, products, and platforms you predict will exist for you soon.

    Onwards. 

  • Camp Kotok 2021

    Each year I look forward to Camp Kotok, or as I like to call it Economists in Nature. It's basically 5 days of canoeing, fishing, and dining with economists, wealth managers, traders, investors and more. 

    One of few chances for people from these backgrounds to come together and talk about the world, big trends, investing, economics, politics, and more … in an open and safe forum. The event goes by the Chatham House Rule – which basically means you can share the information you receive, but not who said it. 

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    This year we talked about everything from China, digital currencies, the pandemic, and the state of markets. 

    Interestingly, for all the takeaways I could focus on, the main takeaway was uncertainty. 

    For all the intelligent and "in-the-know" people in the room, very few people had clear opinions of what was going to happen. There were too many variables at play, and while they posited a lot of potential paths, it feels like the general census was we're at a crossroads with many potential futures in front of us. 

    Despite the general uncertainty in the room, it wasn't fear-laden. The general mood was optimistic, and for the most part, everyone sees paths toward economic success post-COVID.

    With that said, when and what "post-COVID" means is another issue.

    One of the other key discussions that came up often was the new generation of workers and their changing relationship with work. It's plain to see the rate of quitting is higher, that wages are rising, and it's getting hard to fill minimum wage jobs. It's hard to get employees back in an office space, and many are willing to take pay cuts or switch to other companies to stay at home. 

    The long-term impact on our economy (and our culture) is yet to be seen.

    We live in interesting times. 

    As a bonus, here's an interview I shot at Camp Kotok in 2018 with Bob Eisenbeis,  Cumberland Advisors' Vice Chairman & Chief Monetary Economist.  Check it out.  

     

    Cumberland Advisors via YouTube