Each and every Friday — I outline a few of the articles and/or books that I have read over the last week or two that are worth taking a look at.
In the landscape of our digital age, a remarkable shift has taken place: the rise of deep learning. In the past, machines were meticulously crafted, with their behavior strictly defined by rules, written one line of code at a time. However, a slow transformation began to emerge. Engineers started creating machines capable of learning tasks through their own experiences, navigating through vast seas of digital information beyond human comprehension. These machines processed data, learned, adapted, and evolved in ways previously unimaginable.
The outcome? A new breed of machine – powerful, capable, yet also cloaked in a layer of mystery and unpredictability. No longer were these machines simply tools under human control. They morphed into learning entities, growing and changing in ways that echo the complexity and unpredictability of life itself. Welcome to the era of deep learning – a world where technology learns, evolves, and even surprises us.
A New Kind of Machine
Step into the shoes of Frank Rosenblatt, who in the 1950s made an essential contribution to the development of artificial intelligence. His creation, the Perceptron, was the first neural network to learn and adapt on its own. Though its design was simple by today’s standards, it set a precedent that paved the way for the modern era of machine learning.
Now, shift your perspective to Geoffrey Hinton, often dubbed the “godfather of deep learning”. His work in the 1980s and 1990s on backpropagation was a landmark achievement, helping to shape the field of artificial intelligence. Imagine creating a method for computers to understand complex patterns, thereby enhancing their capability to learn and understand the world around them.
Meanwhile, Li Deng is pushing the frontiers of artificial intelligence at Microsoft Research, where he is applying deep learning techniques to advance speech recognition. Picture a world where machines not only comprehend human speech but respond intelligibly. Deng’s innovative work in this field creates a paradigm shift in how we interact with technology.
But let’s not forgot Andrew Ng, co-founder of Coursera, who is democratizing education in artificial intelligence. His courses make complex concepts accessible to the masses. Furthermore, his research into deep learning and neural networks provides a robust foundation for many current applications of artificial intelligence. Imagine a world where artificial intelligence education is as commonplace as learning a second language.
Finally, consider Alan Eustace, who, during his tenure at Google, significantly enhanced the company’s search algorithms. His work integrates machine learning techniques, vastly improving the platform’s ability to retrieve and understand information. This innovative approach pushes the boundaries of what artificial intelligence can achieve in information processing.
The intertwining narratives of these pioneers illustrate how varied the development of artificial intelligence has been. Rosenblatt’s Perceptron set the stage, Hinton’s backpropagation techniques advanced the field, Deng’s application of artificial intelligence to speech recognition transformed human-machine interactions, Ng’s democratization of artificial intelligence education broadened access to the field, and Eustace’s work in information retrieval pushed the envelope of what artificial intelligence could achieve.
As a leader, it’s your job to appreciate the myriad paths that development can take. You must be open to innovation and constantly on the lookout for new applications and methods. This adaptability is what drove Hinton to revolutionize the field with his backpropagation techniques, Deng to transform human-machine interaction with his work on speech recognition, and Eustace to redefine the capabilities of artificial intelligence in information processing. Embrace change and harness the lessons from these pioneers to ensure you’re maximizing the potential of artificial intelligence within your sphere of influence.
Go here to get a copy of this great book: https://a.co/d/7OTRDln
The other day, I returned home from a short trip, and immediately unpacked and washed my clothes, putting everything away. It felt nice.
The next morning, I was feeling a bit unsettled. So I started cleaning. I cleaned in the kitchen, outside in the yard, swept the garage. I felt so good.
I’ve come to realize that cleaning, organizing, decluttering … for me, it’s a form of self-care. It helps me feel settled, makes me feel like I’m taking care of my life.
Yes, cleaning and organizing can be overwhelming, and is often avoided. But it doesn’t have to be. Take a small corner to tidy up, and let yourself just enjoy the cleaning. Get lost in it.
Feel the niceness of making things nicer.
Yes, there’s always more to do. But that’s a disempowering way to think about it. Why does it matter that there will always be more to do? That just means there’s more self-care available, always. Just do a small portion right now, and enjoy it. A good analogy is that there will always be more tea to drink … but I only need to focus on this single cup of tea, and enjoy it fully.
As you clean, you might feel things getting cleaner. As you organize, you might feel the progression of settledness of things. As you declutter, you might feel the slight liberation with everything you toss out.
And of course, we can extend this self-care of cleaning and organization into every part of our lives — today I worked on organizing my finances. I’ve been fixing little things around the house. This morning I deleted a bunch of apps on my phone, and turned off a lot of notifications, to simplify my phone experience. I also unsubscribed from a bunch of newsletters and started clearing out my email inbox.
You can think of taking a task from your task list as a form of this self-care. One item at a time, taking care of your life.
It can be overwhelming and dreaded … or it can be nourishing and lovely. It’s a choice, and I choose to feel the care that I bring to every sweep of the broom or rake.
Becoming wealthy is cringe.
Who wants to be on the Forbes list and drive a wank on wheels? Not me. The hidden desire of 99% of wealthy people is to have time freedom.
To do things out of love, not obligation.
To control their time and do as they please. So to get there one must understand how money works.
Across my career in banking and networking with wealthy people on the internet, here are the 9 money lessons they taught me (should be mandatory for all schools to teach them).
Skills = Money (but there’s a big problem)
I met Dan Koe online a few years back.
He mixes wealth with spirituality and philosophy which I’ve never seen done before. This idea from him left me speechless:
Most people aren’t increasing their skill level, they are increasing the amount of time they spend at their current skill level.
Read that line a few times. Does it sound like many of the people you work with? It’s why “experience” is so damn overrated. Beginner-level skills repeated for the length of a career won’t make you wealthy.
People go crazy over working hard, but as the cliché goes, you have to work smarter not harder. To get wealthy you have to constantly upgrade your skills, and stack new skills on top.
Focus on skill acquisition
The path to monetization isn’t what you think
I see it with creators all the time.
As soon as they have a tiny bit of success online they want to cash the check and retire. So they spam people with ads and multi-level marketing scams.
@boredelonmusk tweets memes for a living. You’d think memes couldn’t make money, but his account is valued at $20M. He makes millions of dollars a year from it.
Wait, what? How?
The social media account gives him early access to startup investing opportunities. Imagine investing in Stripe when they had two employees. That’s the sort of deal he gets access to.
Wealthy people make money from their network. Wealthy people use social media to attract opportunities to their email inboxes on auto-pilot.
Stop short-changing yourself by trying to charge fees for everything.
Go here to finish reading: https://medium.com/swlh/ive-interviewed-100s-of-wealthy-people-here-are-9-money-lessons-they-taught-me-541848a0437f
When responding to questions about AI replacing humans in certain roles, most “experts” claim that AI will replace some jobs but will be a much more valuable tool for augmenting human intelligence and ability. What if they are wrong?
In all of the hype associated with this latest technology wave, a significant trend is occurring across industries that could significantly change the impact of AI — the retirement of the knowledge worker.
We need not look further than the last wave of intelligent technology — the “Internet of Things” (IoT) to see the impact.
What past waves of intelligent technology tell us
The term “Internet of Things” was coined in 1999 by computer scientist Kevin Ashton. While working at Procter & Gamble, Ashton proposed putting radio-frequency identification (RFID) chips on products to track them through a supply chain.
“Machines talking to machines” started rolling out in early to mid-2010, making their way into manufacturing, precision agriculture, complex information networks and for consumers in a new wave of wearables.
Now, having about a decade of experience in how IoT has impacted certain industries and markets, perhaps it can give us some interesting insights into the future of AI.
Cisco launched the “Tomorrow Starts Here” IoT campaign in 2010, at a time when communication networks were transitioning from hardware “stacks” to software development networks (SDN).
The change meant that for carriers to expand their bandwidth, they no longer needed to “rip and replace” hardware. They only needed to upgrade the software. This transition began the era of machines monitoring their performance and communicating with each other, with the promise of one day producing self-healing networks.
Over this same period, network engineers who ushered in the transition from analog to digital began retiring. These experienced knowledge workers are often replaced by technicians who understand the monitoring tools but not necessarily how the network works.
Networks have grown in complexity over the last dozen years to include cellular, with the number of connections growing exponentially. To help manage this complexity, numerous monitoring tools have been developed and implemented.
Go here to finish reading: https://martech.org/artificial-intelligence-human-intelligence-success/
Hope you enjoy these articles and books. Have a great rest of your Friday and amazing weekend