AI: A Snapshot of Progress, Peril and Promise

These are extraordinary times in the world of AI…

“The best way to predict the future is to invent it.”

Alan Kay

In just decades we have gone from rigid systems that play chess to flexible algorithms that debate philosophy. AI is now embedded across industries, powering everything from web search to medical diagnosis. The pace of innovation continues to accelerate.

We have an opportunity to develop AI that assists human potential and creativity, while avoiding misuse. But progress requires actively shaping development through ethics and policy.

I want to start by grounding us in the history and context of AI…

To begin, we need to travel back to the origins of AI in the 1950s, when scientists first started exploring whether computers could be made to “think” intelligently like humans.

In those early days, they took an approach called “symbolic AI” – hard coding human logic and rules of reasoning into programs. Chess was an early challenge. But these systems were incredibly brittle. They could only operate within narrow constraints programmed by creators.

So the hype around AI quickly faded when it failed to live up to its promise. This period even became known as the “AI Winter” given the lack of progress.

But then in the 90s and 2000s, we saw the rise of “machine learning” – training algorithms on data to infer their own rules rather than having humans explicitly program them. This enabled AI systems to learn from experience and handle more complexity.

Machine learning allowed AI to finally conquer challenges like speech recognition, computer vision and language translation that had long eluded symbolic AI.

As this learning approach took off, we saw great commercial success stories emerge from Silicon Valley giants like Google, Facebook and Amazon, who had massive datasets and computing power to fuel ever-more sophisticated models.

And that brings us to today – the Deep Learning era. Models known as neural networks that contain millions or even billions of interconnected parameters have proven especially adept at finding subtle patterns in vast seas of data.

With deep learning, a new kind of AI emerged – one powered by raw computational brute force rather than the elegant reasoning of human programmers. These systems could teach themselves to recognize cats in videos, detect cancer in medical scans or beat the world’s top players at the most complex games like Go and Poker.

The mastery of narrow tasks through deep learning has led to a Cambrian explosion of commercial AI applications in recent years. AI now underpins everything from web search to smartphone assistants to recommendations on Netflix and e-commerce. It has become deeply embedded across industries from finance to healthcare to transportation.

But despite its successes, this data-hungry approach has severe limitations. Deep learning models still struggle with flexibility, common sense and goals. They excel at pattern recognition but lack deeper understanding of the world.

This brings us to the next phase of AI – where we are today. Scientists realized that just throwing more data and computing power at deep learning algorithms would not magically make them generally intelligent like humans.

To achieve this kind of broadly capable AI, we need to return to some of the original goals of replicating functions of the human mind, like reasoning, planning, creativity and empathy. This resurrection of symbolic AI is leading to new hybrid systems that combine learning approaches with knowledge and reasoning.

This leading edge work is concentrated in startups and university labs, not the tech giants … yet. They are building a new generation of AI that can understand language, generate novel ideas, plan for the future and work collaboratively with humans.

Unlike previous eras, this frontier of AI research is unfolding publicly on the internet, accelerated by open source tools like GPT-3. The entire world is participating in real-time: building, sharing and critiquing these models.

So in just a few short decades, we have made immense progress, from rigid systems that could only play chess to flexible algorithms that can debate philosophy…and everything in between. The unprecedented pace of innovation continues to accelerate thanks to abundant data, unlimited storage and cloud computing.

We are truly in an age of wonder with regards to artificial intelligence. But where do we go next and what does it mean for humanity? That is the trillion dollar question.

Before we look to the future, I think we have all become numb to the sheer marvel of what AI has achieved due to the incremental nature of progress and media hype cycles. We take for granted that we can ask a question in plain English and receive a coherent response from our phone. Just sit with that for a moment…it’s incredible if you really reflect on it.

The theorists of the 1950s would be stunned and overjoyed to see AI working at the scale it does today – imperfect as it still may be. I want us all to take a second to just appreciate how far we have come in such a short timeframe.

Alright, let’s keep going. We still have a lot of ground to cover!

These rapid advances can seem mystifying but are the result of steady, quantifiable improvements by researchers pursuing clearly-defined goals.

AI systems today remain constrained compared to humans. They lack intentionality, social awareness or shared goals with people. Their skills are narrow, brittle and limited.

So while we should thoughtfully steward these technologies, doomsday scenarios of AI turning against humanity are not an imminent concern. We must avoid both unbounded optimism and fatalistic anxiety.

Instead, stay balanced and pragmatic. Recognize that AI progress depends on human choices, not autonomous forces of nature. We must guide development towards empowerment rather than disempowerment.

Now, where might AI be heading in the decades ahead? The long-term trajectory remains uncertain. Rather than questionable forecasts, I find it helpful to consider multiple scenarios that bound speculation.

For instance, progress could stall for decades in a new “AI winter”…

Or we may successfully develop advanced AI that catalyzes tremendous prosperity…

Or reckless competition could concentrate power among a few corporations and nations…

These visions are not exhaustive but meant to stimulate imagination about how this could unfold based on human decisions, not technology alone.

So I urge you to take an active role in shaping our collective trajectory. We need all voices at the table wrestling with the hard questions. No easy answers exist, but that is a call to participation, not cynicism.

With openness and good faith, I am optimistic about our ability to forge solutions that serve us all. But it will require compromise, trust and moral courage.

Now, here are 3 simple but powerful steps you can take to start your AI journey:

STEP 1: Get access to AI models through free trial APIs like AnthropicCohere and AI21 Studio. Experiment with basic prompts to see capabilities firsthand.

STEP 2: Try no-code AI tools like Teachable Machine from Google. Visual interactives make core concepts intuitive.

STEP 3: Join an AI community like the AI Exchange to connect with over 1,000 like-minded AI enthusiasts. Rachel Woods has created a thriving ecosystem for new learners and experts to grow together.

Start small but start today. AI is too important to watch from the sidelines. With some fundamental literacy, you can help steer these technologies towards empowerment.

So get involved however you can. We need people from all backgrounds shaping AI through policy, ethics, creativity and grassroots projects. Our tools are waiting for your dreams to guide them.

Become part of the mission to democratize AI for the benefit of humanity. Join passionate builders who believe technology should serve all people.

“Alone we can do so little; together we can do so much.”

Helen Keller

The next chapter of AI is ours to write if we approach it with wisdom, ethics and courage. Progress will require rolling up our sleeves and shaping development through action.

So let’s work together to author an inspiring AI story we can proudly call our own. A future where AI assists human potential rather than replaces it.

The best is yet to come, my friends. Thank you!

October 12, 2023

Marcus Dickinson

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